U.S. GLOBAL CHANGE RESEARCH PROGRAM

CLIMATE SCIENCE SPECIAL REPORT (CSSR)

Public Comment Period

15 December 2016 – 3 February 2017

[revised end date]

Third-Order Draft (TOD)

COORDINATING LEAD AUTHORS

Donald Wuebbles

Office of Science & Technology Policy

Executive Office of the President

David Fahey

NOAA Earth System Research Lab

Kathleen Hibbard

NASA Headquarters

LEAD AUTHORS

Jeff Arnold, U.S. Army Corps of Engineers

Benjamin DeAngelo, U.S. Global Change Research Program

Sarah Doherty, University of Washington

David Easterling, NOAA National Centers for Environmental 

Information

James Edmonds, Pacific Northwest National Laboratory

Timothy Hall, NASA Goddard Institute for Space Studies

Katharine Hayhoe, Texas Tech University

Forrest Hoffman, Oak Ridge National Laboratory

Radley Horton, Columbia University

Deborah Huntzinger, Northern Arizona University

Libby Jewett, NOAA Ocean Acidification Program

Thomas Knutson, NOAA Geophysical Fluid Dynamics Lab

Robert Kopp, Rutgers University

James Kossin, NOAA National Centers for Environmental 

Information

Kenneth Kunkel, North Carolina State University

Allegra LeGrande, NASA Goddard Institute for Space Studies

L. Ruby Leung, Pacific Northwest National Laboratory

Wieslaw Maslowski, Naval Postgraduate School

Carl Mears, Remote Sensing Systems

Judith Perlwitz, NOAA Earth System Research Laboratory

Anastasia Romanou, Columbia University

Benjamin Sanderson, National Center for Atmospheric Research

William Sweet, NOAA National Ocean Service

Patrick Taylor, NASA Langley Research Center

Robert Trapp, University of Illinois at Urbana-Champaign

Russell Vose, NOAA National Centers for Environmental 

Information

Duane Waliser, NASA Jet Propulsion Laboratory

Chris Weaver, USEPA

Michael Wehner, Lawrence Berkeley National Laboratory

Tristram West, DOE Office of Science

CONTRIBUTING AUTHORS

Richard Alley, Penn State University

Shallin Busch, NOAA Ocean Acidification Program

Sarah Champion, North Carolina State University

Imke Durre, NOAA National Centers for Environmental

Information

Dwight Gledhill, NOAA Ocean Acidification Program

Justin Goldstein, U.S. Global Change Research Program

Lisa Levin, University of California – San Diego

Allan Rhoades, University of California – Davis

Paul Ullrich, University of California – Davis

Eugene Wahl, NOAA National Centers for Environmental 

Information

John Walsh, University of Alaska Fairbanks
December 2016 CSSR Public Comment Period

ii

U.S. GLOBAL CHANGE RESEARCH PROGRAM

CLIMATE SCIENCE SPECIAL REPORT (CSSR)

Third-Order Draft

Table of Contents

Front Matter

About This Report ..........................................................................................................................................................1

Guide to the Report ........................................................................................................................................................3

Executive Summary ......................................................................................................................................................11

Chapters

1. Our Globally Changing Climate ....................................................................................................................32

2. Physical Drivers of Climate Change .............................................................................................................85

3. Detection and Attribution of Climate Change .............................................................................................139

4. Climate Models, Scenarios, and Projections ...............................................................................................152

5. Large-Scale Circulation and Climate Variability ........................................................................................186

6. Temperature Changes in the United States .................................................................................................217

7. Precipitation Change in the United States ...................................................................................................252

8. Droughts, Floods, and Hydrology ...............................................................................................................281

9. Extreme Storms ...........................................................................................................................................308

10. Changes in Land Cover and Terrestrial Biogeochemistry ..........................................................................337

11. Arctic Changes and their Effects on Alaska and the Rest of the United States ..........................................370

12. Sea Level Rise .............................................................................................................................................411

13. Ocean Changes: Warming, Stratification, Circulation, Acidification, and Deoxygenation.........................452

14. Perspectives on Climate Change Mitigation ...............................................................................................481

15. Potential Surprises: Compound Extremes and Tipping Elements ...............................................................500

Appendices

A. Observational Datasets Used in Climate Studies ........................................................................................523

B. Weighting Strategy for the 4th National Climate Assessment ....................................................................529

C. Acronyms and Units.....................................................................................................................................539

D. Glossary..................................................................................................................................................... TBD
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1 About This Report

2 As a key input into the Fourth National Climate Assessment (NCA4), the U.S. Global Change 

3 Research Program (USGCRP) oversaw the production of this special, stand-alone report of the 

4 state of science relating to climate change and its physical impacts. The Climate Science Special 

5 Report (CSSR) serves several purposes for NCA4, including providing 1) an updated detailed 

6 analysis of the findings of how climate change is affecting weather and climate across the United 

7 States, 2) an executive summary that will be used as the basis for the science summary of NCA4,

8 and 3) foundational information and projections for climate change, including extremes, to 

9 improve “end-to-end” consistency in sectoral, regional, and resilience analyses for NCA4. This 

10 report allows NCA4 to focus more heavily on the human welfare, societal, and environmental 

11 elements of climate change, in particular with regard to observed and projected risks, impacts, 

12 adaptation options, regional analyses, and implications (such as avoided risks) of known 

13 mitigation actions.

14 Much of this report is intended for a scientific and technically savvy audience, though the 

15 Executive Summary is designed to be accessible to a broader audience.

16 Report Development, Review, and Approval Process

17 The National Oceanic and Atmospheric Administration (NOAA) served as the administrative 

lead agency for the preparation of this report. The Science Steering Committee (SSC1 18 ) comprises

19 representatives from three agencies (NOAA, NASA, and DOE) and the U.S. Global Change 

Research Program (USGCRP),2 20 and three Coordinating Lead Authors, all of whom were Federal 

21 employees during the development of this report. Following a public notice for author 

22 nominations, the SSC selected 30 Lead Authors, who are scientists representing Federal

23 agencies, national laboratories, universities, and the private sector. Contributing Authors were 

24 later chosen to provide special input on select areas of the assessment.

25 The Sustained National Climate Assessment 

26 The Climate Science Special Report has been developed as part of the USGCRP’s sustained 

27 National Climate Assessment (NCA) process. This process facilitates continuous and transparent 

28 participation of scientists and stakeholders across regions and sectors, enabling new information 

 1 The Science Steering Committee is a federal advisory committee that oversees the production of the CSSR.

2 The USGCRP is made up of 13 Federal departments and agencies that carry out research and support the Nation’s

response to global change. The USGCRP is overseen by the Subcommittee on Global Change Research (SGCR) of

the National Science and Technology Council’s Committee on Environment, Natural Resources, and Sustainability

(CENRS), which in turn is overseen by the White House Office of Science and Technology Policy (OSTP). The

agencies within USGCRP are the Department of Agriculture, the Department of Commerce (NOAA), the

Department of Defense, the Department of Energy, the Department of Health and Human Services, the Department

of the Interior, the Department of State, the Department of Transportation, the Environmental Protection Agency,

the National Aeronautics and Space Administration, the National Science Foundation, the Smithsonian Institution,

and the U.S. Agency for International Development.
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1 and insights to be assessed as they emerge. Relative to other analyses done under the sustained 

2 assessment process, the Climate Science Special Report provides a more comprehensive 

3 assessment of the science underlying the changes occurring in the Earth’s climate system, with a 

4 special focus on the United States.

5 Sources Used in this Report

6 The findings in this report are based on a large body of scientific, peer-reviewed research, as well 

7 as a number of other publicly available sources, including well-established and carefully 

8 evaluated observational and modeling datasets. The team of authors carefully reviewed these 

9 sources to ensure a reliable assessment of the state of scientific understanding. Each source of 

10 information was determined to meet the four parts of the IQA Guidance provided to authors: 1) 

11 utility, 2) transparency and traceability, 3) objectivity, and 4) integrity and security. Report 

12 authors assessed and synthesized information from peer-reviewed journal articles, technical re13 ports produced by federal agencies, scientific assessments (such as IPCC 2013), reports of the 

14 National Academy of Sciences and its associated National Research Council, and various 

15 regional climate impact assessments, conference proceedings, and government statistics (such as 

16 population census and energy usage).

17
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1 Guide to the Report

2 The following describes the format of the Climate Science Special Report and the overall 

3 structure and features of the chapters. 

4 Executive Summary 

5 The Executive Summary describes the major findings from the Climate Science Special Report. 

6 It summarizes the overall findings and includes some key figures and additional bullet points 

7 covering overarching and especially noteworthy conclusions. The Executive Summary and the 

8 majority of the Key Findings are written for the non-expert.

9 Chapters 

10 Key Findings and Traceable Accounts 

11 Each topical chapter includes Key Findings, which are based on the authors’ expert judgment of 

12 the synthesis of the assessed literature. Each Key Finding includes a confidence statement and, as 

13 appropriate, framing of key scientific uncertainties, so as to be better support assessment of 

14 climate-related risks. (See “Documenting Uncertainty” below).

15 Each Key Finding is also accompanied by a Traceable Account that documents the supporting 

16 evidence, process, and rationale the authors used in reaching these conclusions and provides 

17 additional information on sources of uncertainty through confidence and likelihood statements. 

18 The Traceable Accounts can be found at the end of each chapter.

19 Regional Analyses

20 Throughout the report, the regional analyses of climate changes for the United States are based 

21 on ten different regions as shown in Figure 1. There are differences from the regions used in the 

22 Third National Climate Assessment (Melillo et al. 2014): 1) the Great Plains are split into the 

23 Northern Great Plains and Southern Great Plains; and 2) The U.S. islands in the Caribbean are 

24 analyzed as a separate region apart from the Southeast.

25 Chapter Text 

26 Each chapter assesses the state of the science for a particular aspect of the changing climate. The 

27 first chapter gives a summary of the global changes occurring in the Earth’s climate system. This 

28 is followed in Chapter 2 by a summary of the scientific basis for climate change. Chapter 3 gives 

29 an overview of the processes used in the detection and attribution of climate change and 

30 associated studies using those techniques. Chapter 4 then discusses the scenarios for greenhouse 

31 gases and particles and the modeling tools used to study future projections. Chapters 5 through 9 

32 primarily focus on physical changes in climate occurring in the United States, including those 

33 projected to occur in the future. Chapter 10 provides a focus on land use change and associated 

34 feedbacks on climate. Chapter 11 addresses changes in Alaska in the Arctic, and how the latter 
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1 affects the United States. Chapters 12 and 13 discuss key issues connected with sea level rise and 

2 ocean changes, including ocean acidification, and their potential effects on the United States. 

3 Finally, Chapters 14 and 15 discuss some important perspectives on how mitigation activities 

4 could affect future changes in climate and provide perspectives on what surprises could be in 

5 store for the changing climate beyond the analyses already covered in the rest of the assessment. 

6 This report is designed to be an authoritative assessment of the science of climate change, with a 

7 focus on the United States, to serve as the foundation for efforts to assess climate-related risks 

8 and inform decision-making about responses. In accordance with this purpose, it does not

9 include an assessment of literature on climate change mitigation, adaptation, economic valuation, 

10 or societal responses, nor does it include policy recommendations.

11 Throughout the report, results are presented in American units as well as in the International 

12 System of Units.

13 Reference time periods for graphics

14 There are many different types of graphics in the Climate Science Special Report. Some of the 

15 graphs in this report illustrate historical changes and future trends in climate compared to some 

16 reference period, with the choice of this period determined by the purpose of the graph and the 

17 availability of data. Where graphs were generated for this report, they are mostly based on one of 

18 two reference periods. The 1901–1960 reference period is particularly used for graphs that 

19 illustrate past changes in climate conditions, whether in observations or in model simulations. 

20 This 60-year time period was also used for analyses in the Third National Climate Assessment 

21 (NCA3; Melillo et al. 2014). The beginning date was chosen because earlier historical 

22 observations are generally considered to be less reliable. Thus, these graphs are able to highlight 

23 the recent, more rapid changes relative to the early part of the century (the reference period) and

24 also reveal how well the climate models simulate these observed changes. In this report, this time 

25 period is used as the base period in most maps of observed trends and all time-varying, area26 weighted averages that show both observed and projected quantities.

27 The other commonly used reference period in this report is 1976–2005. The choice of a 30-year 

28 period is consistent with the World Meteorological Organization’s recommendation for climate 

29 statistics. This period is used for graphs that illustrate projected changes simulated by climate 

30 models. The purpose of these graphs is to show projected changes compared to a period that 

31 allows stakeholders and decision makers to base fundamental planning and decisions on average 

32 and extreme climate conditions in a non-stationary climate; thus, a recent available 30-year 

33 period was chosen (Arguez and Vose 2011). The year 2005 was chosen as an end date because 

34 the historical period simulated by the models used in this assessment ends in that year.

35 For future projections, 30-year periods are again used for consistency. Projections are centered

36 around 2030, 2050, and 2085 with an interval of plus and minus 15 years (for example, results 

37 for 2030 cover the period 2015–2045); Most model runs used here only project out to 2100 for 
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1 future scenarios, but where possible, results beyond 2100 are shown. Note that these time periods 

2 are different than those used in some of the graphics in NCA3. There are also exceptions for 

3 graphics that are based on existing publications.

4 For global results that may be dependent on findings from other assessments (such as those 

5 produced by the Intergovernmental Panel on Climate Change, or IPCC), and for other graphics 

6 that depend on specific published work, the use of other time periods was also allowed, but an 

7 attempt was made to keep them as similar to the selected periods as possible. For example, in the 

8 discussion of radiative forcing, the report uses the standard analyses from IPCC for the industrial 

9 era (1750 to 2011) (following IPCC 2013). And, of course, the paleoclimatic discussion of past 

10 climates goes back much further in time.

11 Model Results: Past Trends and Projected Futures

12 While the NCA3 included global modeling results from both the CMIP3 (Coupled Model 

13 Intercomparison Project, 3rd phase) models used in the 2007 international assessment (IPCC 

14 2007) and the CMIP5 (Coupled Model Intercomparison Project, 5th phase) models used in the 

15 more recent international assessment (IPCC 2013), the primary focus in this assessment is the 

16 global model results and associated downscaled products from CMIP5. The CMIP5 models and 

17 the associated downscaled products are discussed in Chapter 4.

18 Treatment of Uncertainties: Likelihoods, Confidence, and Risk Framing

19 Throughout this report’s assessment of the scientific understanding of climate change, the 

20 authors have assessed to the fullest extent possible the range of information in the scientific 

21 literature to arrive at a series of findings referred to as Key Findings. The approach used to 

22 represent the state of certainty in this understanding as represented in the Key Findings is done 

23 through two metrics:

24 • Confidence in the validity of a finding based on the type, amount, quality, strength, and 

25 consistency of evidence (such as mechanistic understanding, theory, data, models, and expert 

26 judgment); the skill, range, and consistency of model projections; and the degree of

27 agreement within the body of literature. 

28 • Likelihood, or probability of an effect or impact occurring, is based on measures of 

29 uncertainty expressed probabilistically (in other words, based on statistical analysis of 

30 observations or model results or on the authors’ expert judgment). 

31 The terminology used in the report associated with these metrics is shown in Figure 2. This 

32 language is based on that used in NCA3 (Melillo et al. 2014), the IPCC’s Fifth Assessment 

33 Report (IPCC 2013), and most recently the USGCRP Climate and Health assessment (USGCRP 

34 2016). Wherever used, the confidence and likelihood statements are italicized.
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1 Assessments of confidence in the Key Findings are based on the expert judgment of the author 

2 team. Authors provide supporting evidence for each of the chapter’s Key Findings in the 

3 Traceable Accounts. Confidence is expressed qualitatively and ranges from low confidence 

4 (inconclusive evidence or disagreement among experts) to very high confidence (strong evidence 

5 and high consensus) (see Figure 2). Confidence should not be interpreted probabilistically, as it 

6 is distinct from statistical likelihood.

7 In this report, likelihood is the chance of occurrence of an effect or impact based on measures of 

8 uncertainty expressed probabilistically (in other words, based on statistical analysis of 

9 observations or model results or on expert judgment). The authors used expert judgment based 

10 on the synthesis of the literature assessed to arrive at an estimation of the likelihood that a 

11 particular observed effect was related to human contributions to climate change or that a 

12 particular impact will occur within the range of possible outcomes. Where it is considered 

13 justified to report the likelihood of particular impacts within the range of possible outcomes, this 

14 report takes a plain-language approach to expressing the expert judgment of the chapter team, 

15 based on the best available evidence. For example, an outcome termed “likely” has at least a 

16 66% chance of occurring; an outcome termed “very likely,” at least a 90% chance. See Figure 2 

17 for a complete list of the likelihood terminology used in this report. 

18 Traceable Accounts for each Key Finding 1) document the process and rationale the authors used 

19 in reaching the conclusions in their Key Finding, 2) provide additional information to readers 

20 about the quality of the information used, 3) allow traceability to resources and data, and 4) 

21 describe the level of likelihood and confidence in the Key Finding. Thus, the Traceable Accounts 

22 represent a synthesis of the chapter author team’s judgment of the validity of findings, as 

23 determined through evaluation of evidence and agreement in the scientific literature. The 

24 Traceable Accounts also identify areas where data are limited or emerging. Each Traceable 

25 Account includes 1) a description of the evidence base, 2) major uncertainties, and 3) an 

26 assessment of confidence based on evidence.

27 All Key Findings include a description of confidence. Where it is considered scientifically 

28 justified to report the likelihood of particular impacts within the range of possible outcomes, Key 

29 Findings also include a likelihood designation. 

30 Confidence and likelihood levels are based on the expert assessment of the author team. They 

31 determined the appropriate level of confidence or likelihood by assessing the available literature, 

32 determining the quality and quantity of available evidence, and evaluating the level of agreement 

33 across different studies. Often, the underlying studies provided their own estimates of uncertainty 

34 and confidence intervals. When available, these confidence intervals were assessed by the 

35 authors in making their own expert judgments. For specific descriptions of the process by which 

36 the author team came to agreement on the Key Findings and the assessment of confidence and 

37 likelihood, see the Traceable Accounts in each chapter.
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1 In addition to the use of systematic language to convey confidence and likelihood information, 

2 this report attempts to highlight aspects of the science that are most relevant for supporting the 

3 assessment (for example, in the upcoming fourth National Climate Assessment) of key societal 

4 risks posed by climate change. This includes attention to the tails of the probability distribution 

5 of future climate change and its proximate impacts (for example, on sea level or temperature and 

6 precipitation extremes) and on defining plausible bounds for the magnitude of future changes, 

7 since many key risks are disproportionately determined by low-probability, high-consequence 

8 outcomes. Therefore, in addition to presenting the “most likely” or “best guess” range of 

9 projected future climate outcomes, where appropriate, this report also provides information on 

10 the outcomes lying outside this range which nevertheless cannot be ruled out, and may therefore 

11 be relevant for assessing overall risk. In some cases, this involves an evaluation of the full range 

12 of information contained in the ensemble of climate models used for this report, and in other 

13 cases will involve the consideration of additional lines of scientific evidence beyond the models.

14 Complementing this use of risk-focused language and presentation around specific scientific 

15 findings in the report, Chapter 15 provides an overview of potential surprises resulting from 

16 climate change, including tipping elements in the climate system and the compounding effects of 

17 multiple, interacting climate change impacts whose consequences may be much greater than the 

18 sum of the individual impacts. Chapter 15 also highlights critical knowledge gaps that determine 

19 the degree to which such high-risk tails and bounding scenarios can be precisely defined, 

20 including missing processes and feedbacks that make it more likely than not that climate models 

21 currently underestimate the potential for high-end changes, reinforcing the need to look beyond 

22 the central tendencies of model projections to meaningfully assess climate change risk.

23
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1

2 Figure 1. Map of the ten regions of the United States used throughout the Climate Science 

3 Special Report. Regions are similar to that used in the Third National Climate Assessment except 

4 that 1) the Great Plains are split into the Northern Great Plains and Southern Great Plains, and 2) 

5 the Caribbean islands have been split from the Southeast region. (Figure source: adapted from 

6 Melillo et al. 2014).

7

8
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1

2

3 Figure 2. Confidence levels and likelihood statements used in the report. (Figure source: adapted 

4 from USGCRP 2016 and IPCC 2013).

5

6
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1 Front Matter References

2 Arguez, A. and R.S. Vose, 2011: The definition of the standard WMO climate normal: The key 

3 to deriving alternative climate normals. Bulletin of the American Meteorological Society, 92,

4 699-704. http://dx.doi.org/10.1175/2010BAMS2955.1 

5 IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group 

6 I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. 

7 Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. 

8 Miller, Eds. Cambridge University Press, Cambridge. U.K, New York, NY, USA, 996 pp. 

9 www.ipcc.ch/publications_and_data/publications_ipcc_fourth_assessment_report_wg1_repor

10 t_the_physical_science_basis.htm

11 IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group 

12 I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

13 Cambridge University Press, Cambridge, UK and New York, NY, 1535 pp. 

14 http://dx.doi.org/10.1017/CBO9781107415324 www.climatechange2013.org

15 Melillo, J.M., T.C. Richmond, and G.W. Yohe, eds. Climate Change Impacts in the United 

16 States: The Third National Climate Assessment. 2014, U.S. Global Change Research 

17 Program: Washington, D.C. 842. http://dx.doi.org/10.7930/J0Z31WJ2. 

18 OMB, 2002: Guidelines for Ensuring and Maximizing the Quality, Objectivity, Utility, and 

19 Integrity of Information Disseminated by Federal Agencies; Republication. Federal Register,

20 67, 8452-8460. https://www.whitehouse.gov/sites/default/files/omb/fedreg/reproducible2.pdf

21 USGCRP, 2016: The Impacts of Climate Change on Human Health in the United States: A 

22 Scientific Assessment. Crimmins, A., J. Balbus, J.L. Gamble, C.B. Beard, J.E. Bell, D. 

23 Dodgen, R.J. Eisen, N. Fann, M.D. Hawkins, S.C. Herring, L. Jantarasami, D.M. Mills, S. 

24 Saha, M.C. Sarofim, J. Trtanj, and L. Ziska, Eds. U.S. Global Change Research Program, 

25 Washington, DC, 312 pp. http://dx.doi.org/10.7930/J0R49NQX 

26
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1 U.S. GLOBAL CHANGE RESEARCH PROGRAM

2 CLIMATE SCIENCE SPECIAL REPORT (CSSR)

3 Executive Summary

4 Introduction

5 New observations and new research have increased scientists’ understanding of past, current, 

6 and future climate change since the Third U.S. National Climate Assessment (NCA3) was

7 published in May 2014. This Climate Science Special Report (CSSR) is designed to capture 

8 that new information, build on the existing body of science, and summarize the current state 

9 of knowledge. 

10 Predicting how climate will change in future decades is a different scientific issue from 

11 predicting weather a few weeks from now. Weather is what is happening in the atmosphere in 

12 a given location at a particular time—temperature, humidity, winds, clouds, and precipitation. 

13 Climate consists of the patterns exhibited by the weather—the averages and extremes of the 

14 indicated weather phenomena and how those averages and extremes vary from month to 

15 month over the course of a typical year—as observed over a period of decades. One can 

16 sensibly speak of the climate of a specific location (for example, Chicago) or a region (for 

17 example, the Midwest). Climate change means that these weather patterns—the averages and 

18 extremes and their timing—are shifting in consistent directions from decade to decade.

19 The world has warmed (globally and annually averaged surface air temperature) by about 

20 1.6°F (0.9°C) over the last 150 years (1865–2015), and the spatial and temporal non21 uniformity of the warming has triggered many other changes to the Earth’s climate. Evidence 

22 for a changing climate abounds, from the top of the atmosphere to the depths of the oceans. 

23 Thousands of studies conducted by tens of thousands of scientists around the world have 

24 documented changes in surface, atmospheric, and oceanic temperatures; melting glaciers; 

25 disappearing snow cover; shrinking sea ice; rising sea level; and an increase in atmospheric 

26 water vapor. Many lines of evidence demonstrate that human activities, especially emissions 

27 of greenhouse (heat-trapping) gases, are primarily responsible for recent observed climate 

28 changes.

29 The last few years have also seen record-breaking, climate-related, weather extremes, as well 

30 as the warmest years on record for the globe. Periodically taking stock of the current state of 

31 knowledge about climate change and putting new weather extremes into context ensures that 

32 rigorous, scientifically based information is available to inform dialogue and decisions at 

33 every level. 

34 Most of this special report is intended for those who have a technical background in climate 

35 science and is also designed to provide input to the authors of the Fourth U.S. National 

36 Climate Assessment (NCA4). In this executive summary, green boxes present highlights of 

37 the main report followed by related bullet points and selected figures covering more scientific 
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1 details. The summary material on each topic presents the most salient points of chapter 

2 findings and therefore represents only a subset of the report contents. For more details, the 

3 reader is referred to the content of individual chapters. This report discusses climate trends 

4 and findings at several scales: global, nationwide for the United States, and according to ten 

5 specific U.S. regions (shown in Figure 1 in the Guide to the Report). A statement of scientific 

6 confidence also follows each bullet in the executive summary. The confidence scale is 

7 described in the Guide to the Report.

8

9
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1 Global and U.S. Temperatures Will Continue to Rise

2 Long-term temperature observations are among the most consistent and widespread evidence 

3 of a warming planet. Temperature (and, above all, its local averages and extremes) affects 

4 agricultural productivity, energy use, human health, infrastructure, natural ecosystems, and 

5 many other essential aspects of society and the natural environment. (Ch. 1)

6 Observed Global and U.S. Temperature

7

8

9

10

11 • Global annual average temperature, measured over both land and ocean, has increased by 

12 more than 1.6°F (0.9°C) from 1880 through 2015 (very high confidence). Longer-term 

13 climate records indicate that average temperatures in recent decades over much of the 

14 world have been much higher than at any time in at least the past 1700 years (high 

15 confidence). (Ch.1)

16 • Many lines of evidence demonstrate that human activities, especially emissions of 

17 greenhouse gases, are primarily responsible for observed climate changes in the industrial 

18 era. There are no alternative explanations, and no natural cycles are found in the 

19 observational record that can explain the observed changes in climate. (Very high 

20 confidence) (Ch.1)

21 • The likely range of the human contribution to the global mean temperature increase over 

22 the period 1951–2010 is 1.1° to 1.3°F (0.6° to 0.7°C), which is close to the observed 

23 warming of 1.2°F (0.65°C) over this period (high confidence). It is extremely likely that 

24 most of the global mean temperature increase since 1951 was caused by human influence 

25 on climate (high confidence). The estimated influence of natural forcing and internal 

26 variability on globally and annually averaged temperatures over that period is small (high 

27 confidence). (Ch. 3)

28 • Natural variability, including El Niño events and other recurring patterns of ocean–

29 atmosphere interactions, has important climate impacts on short time scales, but its 

30 influence is limited on global and regional climate trends over longer timescales (that is, a 

31 decade or more). (Very high confidence) (Ch. 1)

32 • The average annual temperature of the contiguous United States has increased by about 

33 1.2°F (0.7°C) between 1901 and 2015. Surface and satellite data both show rapid warming 

The global, long-term, and unambiguous warming trend has continued during 

recent years. Since the last National Climate Assessment was published, 2014 

became the warmest year on record up to that time; 2015 surpassed 2014 by a 

wide margin; and 2016 is expected to surpass 2015. Fifteen of the last 16 years 

are the warmest years on record for the globe. (Ch.1; Fig ES.1) 
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1 since the late 1970s, while paleo-temperature evidence shows that recent decades are the 

2 warmest in at least the past 1,500 years. (High confidence) (Ch. 6)

3 • For the contiguous United States, the largest temperature changes (from the average 

4 temperature in early 1900s compared to the average of the last 30 years) have occurred in 

5 the western United States, where average temperature increased by more than 1.5°F 

6 (0.8°C) across the Northwest and Southwest, and in the Northern Great Plains. (Very high 

7 confidence). (Ch. 6)

8

9 Figure ES.1 Global Temperatures Continue to Rise

10 Left: Global annual average temperature has increased by more than 1.6°F (0.9°C) for the period 

11 1986–2015 relative to 1901–1960. Red bars show temperatures above the long-term 1880–2015 

12 average, and blue bars indicate temperatures below the long-term average. Right: Surface temperature 

13 trends (change in °F) for the period 1986–2015 relative to 1901–1960. From Figures 1.2. and 1.3 in 

14 Ch. 1.

15 Projected Global and U.S. Temperature

16 • Global climate is projected to continue to change over this century and beyond. Even if 

17 humans immediately ceased emitting greenhouse gases into the atmosphere, existing

18 levels would commit the world to at least an additional 0.5°F (0.3°C) of warming over this 

19 century relative to today (high confidence). The magnitude of climate change beyond the 

20 next few decades depends primarily on the additional amount of greenhouse gases emitted 

21 globally, and on the sensitivity of Earth’s climate to those emissions (very high 

22 confidence). (Ch. 1, 4; Fig ES.2)

23

24

The average annual temperature of the contiguous United States is projected to 

continue to rise throughout the century. (Very high confidence). (Ch.6; Fig ES.3)
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1 • For the United States, near-term increases of at least 2.5°F (1.4°C) are projected over the 

2 next few decades even under significantly reduced future emissions, meaning that the 

3 temperatures of recent record-setting years will become relatively common in the near 

4 future. Increases will be much larger by late century (5.0°F [2.8°C] under a scenario with 

5 lower emissions and 8.7°F [4.8°C] under a scenario with higher emissions). (High 

6 confidence) (Ch.6; Fig ES.3)

7

8 ES.2 Figure ES.2. Greater Emissions Lead to Significantly More Warming

9 The two panels above show projected changes in annual carbon emissions in units of gigatons 

10 of carbon (GtC) per year (left) and temperature change that would result from the central 

11 estimate (lines) and the likely ranges (shaded areas) of climate sensitivity (right). See the main 

12 report for more details on these scenarios and implications. Based on Figure 4.1 in Chapter 4.
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1

2 Figure ES.3 Significantly More Warming Occurs Under Higher Greenhouse Gas 

3 Concentrations 

4 This figure shows the projected changes in annual average temperature for mid- and late-21st

5 century for various future pathways. Changes are the difference between the average for mid6 century (2036–2065; top), late-century (2071–2100; bottom), and the average for near-present 

7 (1976–2005). See Figure 6.7 in Chapter 6 for more details.

8
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1 Many Temperature and Precipitation Extremes Are Becoming More 

2 Common

3 The increases in extreme weather that accompany global climate change are having

4 significant, direct effects on the United States and the global economy and society. 

5 Temperature and precipitation extremes can affect water quality and availability, agricultural 

6 productivity, human health, vital infrastructure, iconic ecosystems and species, and the 

7 likelihood of disasters. Some extremes have already become more frequent, intense, or of 

8 longer duration, and many extremes are expected to continue to increase or worsen, 

9 presenting substantial challenges for built, agricultural, and natural systems. Some storm 

10 types such as hurricanes, tornadoes, and winter storms are also exhibiting changes that have 

11 been linked to climate change, although detailed understanding of these linkages is still 

12 insufficient in the current state of the science.

13

14

15

16

17 • Extremely cold days have become warmer since the early 1900s, and extremely warm 

18 days have become warmer since the early 1960s. In recent decades, extreme cold waves 

19 have become less common while extreme heat waves have become more common. (Very 

20 high confidence) (Ch. 6)

21 • Heavy precipitation events across the United States have increased in both intensity and 

22 frequency since 1901. There are important regional differences in trends, with the largest 

23 increases occurring in the northeastern United States (high confidence). The frequency 

24 and intensity of heavy precipitation events are projected to continue to increase over the 

25 century (high confidence). (Ch.7)

26 • The frequency and severity of landfalling “atmospheric rivers” on the U.S. West Coast 

27 (narrow streams of moisture that account for 30%–40% of precipitation and snowpack in 

28 the region and are associated with severe flooding events) are projected to increase as a 

29 result of increasing evaporation and resulting higher atmospheric water vapor that occurs 

30 with increasing temperature. (Medium confidence) (Ch.9)

31 • Recent droughts and associated heat waves have reached record intensities in some 

32 regions of the United States, but, by geographical scale and duration, the Dust Bowl era of 

33 the 1930s remains the benchmark drought and extreme heat event in the U.S. historical 

34 record. (Very high confidence) (Ch. 8)

The frequency and intensity of heavy precipitation and extreme heat events are 

increasing in most regions of the world and will very likely continue to rise in the 

future. Trends for some other types of extreme events, such as floods, droughts, 

and severe storms, vary by region. (Very high confidence) (Ch.1)
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1 • Reductions in western U.S. winter and spring snowpack are projected as the climate 

2 warms. Under higher-emissions scenarios, and assuming no change to current water3 resources management, chronic, long-lasting, hydrological drought is possible by end of 

4 century. (Very high confidence) (Ch. 8)

5 • For Atlantic and eastern North Pacific hurricanes and western North Pacific typhoons, 

6 increases are projected in precipitation rates (high confidence) and intensity (medium 

7 confidence). The frequency of the most intense storms is projected to increase in the 

8 Atlantic and western North Pacific (low confidence) and in the eastern North Pacific 

9 (medium confidence). (Ch. 9)

10

11 Figure ES.4: Extreme Precipitation Has Increased Across the United States

12 This figure shows the percentage difference between the 1901–1960 average and the 1981–

13 2015 average of the top 20% of the annual maximum daily precipitation values in each period

14 for events exceeding the threshold for a 5-year return period. The amount of precipitation 

15 falling in heavy events is greater across all regions in the entire contiguous United States. 

16 Based on figure 7.3 in Chapter 7.
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1

2 Figure ES.5 Extreme Cold Days Are Warming; Extreme Hot Days Dominated by 1930s 

3 Dust Bowl 

4 Observed changes in the coldest and warmest daily temperatures (in °F) of the year. Maps 

5 (top) depict changes at stations; changes are the difference between the average for present6 day (1986–2015) and the average for the first half of the last century (1901–1960). Time 

7 series (bottom) depict changes averaged over the contiguous United States. Figure 6.3 from 

8 Chapter 6.

9 ****BOX ES.1  

10 The Connected Climate System: Changes Halfway Across the World Are 

11 Affecting the United States

12 Weather conditions and the ways they vary across regions and over the course of the year are 

13 influenced, in the United States as elsewhere, by a combination of fixed and variable factors, 

14 including local conditions (such as topography and urban heat islands), global trends (such as 

15 human-caused warming), and global and regional circulation patterns, including cyclical and 

16 chaotic patterns of natural variability within the climate system. For example, during an El 

17 Niño year, winters across the southwestern United States are typically wetter than average, 

18 and global temperatures are warmer than average. During a La Niña year, conditions across 

19 the southwestern United States are typically dry, and there tends to be a cooling effect on 

20 global temperatures. 

21 El Niño is not the only repeating pattern of natural variability in the climate system. Other 

22 important patterns include the North Atlantic Oscillation (NAO)/Northern Annular Mode 
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1 (NAM) that particularly affects conditions on the U.S. East Coast, and the North Pacific 

2 Oscillation (NPO) and Pacific North American Pattern (PNA) that especially affect conditions 

3 in Alaska and the U.S. West Coast, all of which are closely linked to other atmospheric 

4 circulation phenomena like the position of the jet streams. The influences of human activities 

5 on the climate system are now so pervasive that the current and future behavior of these 

6 previous “natural” climate features can no longer be assumed to be independent of those 

7 human influences. (Ch.5)

8 Understanding the full scope of human impacts on climate requires a global focus because of 

9 the interconnected nature of the climate system. For example, the climate of the Arctic and the 

10 climate of the continental United States are strongly connected through atmospheric11 circulation patterns. While the Arctic may seem physically remote to those living in other 

12 regions of the planet, the climatic effects of perturbations to Arctic sea ice, land ice, surface 

13 temperature, snow cover, and permafrost affect the amount of warming, sea level change, 

14 carbon cycle impacts, and potentially even weather patterns in the lower 48 states. The Arctic 

15 is warming at a rate approximately twice as fast as the global average and, if it continues to 

16 warm at the same rate, Septembers will be nearly ice-free in the Arctic Ocean sometime 

17 between now and the 2040s. The important influence of Arctic climate change on Alaska is 

18 apparent; understanding the details of how climate change in the Arctic is affecting the 

19 climate in the continental United States is an area of active research. (Ch. 11)

20 Changes in the tropics can also impact the rest of the globe, including the United States. There 

21 is growing evidence that the tropics have expanded over the past several decades, with an 

22 associated shift towards the poles of the subtropical dry zones in each hemisphere. The exact 

23 causes of this shift in the latitude of dry zones, and its implications, are not yet clear, although 

24 the shift is associated with projected drying of the American Southwest over the rest of the 

25 century. (Ch. 5)
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1

2 Figure ES.6. Large-Scale Patterns of Natural Variability, Now Being Influenced by 

3 Human Activities, Affect U.S. Climate

4 Typical January–March weather anomalies and atmospheric circulation during moderate to 

5 strong (top) El Niño, and (bottom) La Niña. From Figure 5.2 in Chapter 5.

6 ** **END BOX ES.1 

7 Oceans Are Rising, Warming, and Becoming More Acidic 

8 Oceans occupy two thirds of the planet’s surface and host unique ecosystems and species, 

9 including those important for global commercial and subsistence fishing. Understanding 

10 climate impacts on the ocean and the ocean’s feedbacks to the climate system is critical for a 

11 comprehensive understanding of current and future changes in climate.

12

13

14 • Global mean sea level (GMSL) has risen by about 8–9 inches since 1880, with about 3 

15 inches of that rise occurring since 1990 (very high confidence). Human-caused climate 

More than 90% percent of the extra heat being trapped inside the climate system 

by human emissions is being absorbed by the ocean (very high confidence), and 

the rate of acidification by uptake of CO2 is faster than in at least the past 66 

million years (medium confidence). (Ch. 13)
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1 change has made a substantial contribution to GMSL rise since 1900 (high confidence), 

2 contributing to a rate of rise faster than during any comparable period for at least 2800 

3 years (medium confidence). (Ch. 12; Fig ES.7)

4 • Relative to the year 2000, GMSL is very likely to rise by 0.3–0.6 feet by 2030; 0.5–1.2 

5 feet by 2050; and 1–4 feet by 2100 (very high confidence in lower bounds of each of these 

6 predictions; medium confidence in upper bounds for 2030 and 2050; low confidence in 

7 upper bounds for 2100). (Ch.12)

8 • Differences in emissions trajectories over the next two decades (see Fig. ES.2) and beyond 

9 are estimated to have little effect on the projected amount of GMSL rise over the next few 

10 decades, but significantly affect how much more GMSL should be expected by the end of 

11 the century (high confidence). Emerging scientific results regarding ice-sheet stability 

12 suggests that, under a higher scenario, a GMSL rise exceeding 8 feet by 2100 cannot be 

13 ruled out. (Ch.12)

14 • In most projections, GMSL will continue to rise beyond 2100 and even beyond 2200. The 

15 concept of a “sea level rise commitment” refers to the long-term projected sea level rise 

16 were the planet’s temperature stabilized at a given level. The paleo sea level record 

17 suggests that even 2°C (3.6°F) of warming above preindustrial global temperature may 

18 represent a commitment to six or more feet of rise (high confidence). (Ch. 12)

19 • Relative sea level (RSL) rise in this century will vary along U.S. coastlines due to vertical 

20 land motion and changes in ocean circulation, as well as changes in Earth’s gravitational 

21 field and rotation from melting of land ice (very high confidence). For almost all future 

22 scenarios, RSL rise is likely to be greater than the global average in the U.S. Northeast and 

23 the western Gulf of Mexico. In intermediate and low scenarios, RSL rise is likely to be 

24 less than the global average in much of the Pacific Northwest and Alaska. For high 

25 scenarios, RSL rise is likely to be higher than the global average along all U.S. coastlines 

26 outside Alaska (high confidence). (Ch. 12)

27 • Annual occurrences of daily tidal flooding—exceeding local thresholds for minor impacts 

28 to infrastructure—have increased 5- to 10-fold since the 1960s in several U.S. coastal 

29 cities (very high confidence). The changes in flood frequency over time are greatest where 

30 elevation is lower, local RSL rise is higher, or extreme variability is less (very high 

31 confidence). Tidal flooding will continue increasing in depth and frequency in similar 

32 manners this century (very high confidence). (Ch.12; Fig. ES. 8)

33 • The world’s oceans are currently absorbing about a quarter of the carbon dioxide emitted 

34 to the atmosphere annually from human activities (very high confidence), making them 

35 more acidic with potential detrimental impacts to marine ecosystems.

36 • The rate of acidification is unparalleled in at least the past 66 million years (medium 

37 confidence). Acidification is regionally higher along U.S. coastal systems as a result of 
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1 changes in seasonal upwelling (for example, the Pacific Northwest and Alaska), changes 

2 in freshwater inputs (for example, the Gulf of Maine), and nutrient input (for example, in

3 urbanized estuaries) (medium confidence). (Ch. 11; Ch.13)

4 • Oxygen is essential to most life in the ocean, governing a host of biogeochemical and 

5 biological processes. Increasing sea surface temperatures, rising sea levels, and changing 

6 patterns of precipitation, winds, nutrients, and ocean circulation are all contributing to 

7 overall declining oxygen concentrations in ocean and coastal waters. Over the last half 

8 century, major oxygen losses have occurred in inland seas, estuaries, and in the coastal 

9 and open ocean. (High confidence) (Ch. 13)

10 • By 2100, global-average ocean-oxygen levels are projected to decrease from current 

11 levels by 2%–4% relative to current levels for a range of scenarios. Much larger losses are 

12 projected in some regions and in different water masses. Potential effects on ocean 

13 ecosystems could be significant, but are not well understood. (Ch. 13)

14

15 Figure ES.7 Recent Sea Level Rise Fastest for Over 2000 Years

16 The top panel shows observed and reconstructed mean sea level for the last 2500 years. The 

17 bottom panel shows projected mean sea level for six future scenarios, including a risk-based 

18 high scenario that assumes major ice melting on portions of Antarctica. Based on Figure 12.1 

19 in Chapter 12. See the main report for more details.
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1

2 Figure ES. 8 “Nuisance Flooding” Increases Across the United States

3 Annual occurrences of daily tidal flooding, also called sunny-day or nuisance flooding, have 

4 increased for some U.S. coastal cities. Examples shown above include Atlantic City, NJ;

5 Baltimore, MD; Charleston, SC; Port Isabel, TX; La Jolla, CA; and San Francisco, CA. Based 

6 on data in Figure 12.3, Chapter 12. 

7 Climate Change in Alaska and across the Arctic Continues to Outpace 

8 Global Climate Change

9 Residents of Alaska are on the front lines of climate change. Crumbling buildings, roads, 

10 bridges, and eroding shorelines are commonplace. Accelerated melting of multiyear sea ice 

11 cover, mass loss from the Greenland Ice Sheet, reduced snow cover, and permafrost thawing 

12 are stark examples of the rapid changes occurring in the Arctic. The climate system is 

13 connected (see Box ES.1), meaning that changes in the Arctic influence climate conditions 

14 outside the Artic.

15

16

17

18 • Rising Alaskan temperatures are causing permafrost to thaw and become more 

19 discontinuous; these changes lead to release of carbon dioxide and methane from the 

20 decomposition of previously frozen organic matter, adding to the global greenhouse gas 

21 forcing that is driving climate change. (High confidence) (Ch.11)

Alaska and Arctic surface and air temperatures are rising more than twice as 

fast as the global average. (Very high confidence) (Ch. 11)
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1 • Losses of Arctic sea ice and Greenland Ice Sheet mass are accelerating, and Alaskan 

2 mountain glaciers continue to steadily melt (very high confidence). Alaskan coastal sea3 ice loss rates exceed the Arctic average (very high confidence). Human activities have 

4 contributed to these reductions in sea and land ice (high confidence).

5 • Observed sea- and land-ice losses across the Arctic are occurring faster than earlier 

6 climate models predicted (very high confidence). Melting trends are expected to continue

7 with late summers becoming nearly ice-free for the Arctic Ocean by mid-century (very 

8 high confidence). (Ch.11)

9 • Atmospheric circulation patterns connect the climates of the Arctic and the continental 

10 United States. The midlatitude circulation influences Arctic climate change (medium-high 

11 confidence). In turn, Arctic warming may be influencing midlatitude circulation over the 

12 continental United States, affecting weather patterns (low-medium confidence). (Ch.11)

13
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1

2 Figure ES.9 Multiyear Sea Ice Has Declined Dramatically 

3 September sea ice extent and age (thickness) shown for 1984 (top) and 2016 (bottom), 

4 illustrating that significant reductions have occurred in sea ice extent and age. The bar graphs

5 in the lower right of each panel illustrate the sea ice area covered within each age category.

6 From Figure 11.1 in Chapter 11.

7 Limiting Globally Averaged Warming to 2°C (3.6°F) Will Require a Major 

8 Reduction in Emissions

9 Human activities are now the dominant cause of the observed changes in climate. For that 

10 reason, future climate projections are based on scenarios of how greenhouse gas emissions 

11 will continue to affect the climate over the remainder of this century and beyond. In 2016, 

12 significant steps were taken to limit future climate change in the form of three international 

13 agreements to reduce greenhouse-gas emissions: the Paris Agreement; an agreement to limit 

14 CO2 emissions from aircraft under the International Civil Aviation Organization; and an 
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1 agreement to phase down hydrofluorocarbon (HFC) emissions under the Montreal Protocol

2 (see Chapter 14 for more details on each). Despite the greenhouse-gas reductions planned

3 under these agreements, there is still uncertainty about emissions due to changing economic, 

4 political, and demographic factors. For that reason, this report quantifies possible climate 

5 changes for a broad set of plausible future scenarios through the end of the century. (Chs. 4, 

6 14)

7

8

9

10 • There will be a delay of decades or longer between significant actions that reduce CO2

11 emissions and reductions in atmospheric CO2 concentrations that contribute to surface 

12 warming. This delay—the result of the long lifetime of CO2 in the atmosphere and the

13 time delay in the response of the climate system to changes in the atmosphere—means 

14 that near-term changes in climate will be largely determined by past and present 

15 greenhouse gas emissions, modified by natural variability. (Very high confidence) (Ch. 

16 14)

17 • Limiting the global-mean temperature increase to 2°C (3.6°F) above preindustrial levels 

18 requires significant reductions in global CO2 emissions relative to present-day emission 

19 rates. Cumulative emissions would likely have to stay below 1,000 gigatons carbon (GtC)

20 for a 2°C objective, leaving about 400 GtC still to be emitted. Assuming future global 

21 emissions follow the RCP4.5 scenario (mid-low scenario in Fig ES.2), the total, 

22 cumulative emissions commensurate with the 2°C objective would likely be reached 

23 between 2051 and 2065, while under the RCP8.5 scenario (higher scenario in Fig ES.2), 

24 this point would likely be reached between 2043 and 2050. (High confidence). (Ch 14)

25 • If projected atmospheric CO2 concentrations do not remain sufficiently low to prevent 2°C 

26 warming, climate-intervention strategies such as CO2 removal or solar-radiation 

27 management could possibly offer additional means to limit or reduce temperature 

28 increases. Assessing the technical feasibility, costs, risks, co-benefits, and governance

29 challenges of these additional measures, which are as-yet unproven at scale, would be of 

30 value to decision makers. (Medium confidence) (Ch. 14)

31 • Atmospheric CO2 levels have now passed 400 ppm, last seen during the Pliocene, 

32 approximately 3 million years ago, when global mean temperatures were 3.6° to 6.3°F (2°

33 to 3.5°C) higher than preindustrial and sea levels were 66 ± 33 feet (20 ± 10 meters)

34 higher than today. (High confidence) (Ch. 4) 

35

Choices made today will determine the magnitude of climate change risks beyond 

the next few decades. (Chs. 4,14)
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1 • Continued growth in CO2 emissions over this century and beyond would lead to 

2 concentrations not experienced in many millions of years. Present-day emissions rates of 

3 nearly 10 GtC per year, however, suggests that there is no precise past climate analogue 

4 for this century any time in at least the last 66 million years. (Medium confidence).(Ch.4)

5 There is a Significant Possibility for Unanticipated Changes

6 Humanity is conducting an unprecedented experiment with the Earth’s climate system 

7 through emissions from large-scale fossil-fuel combustion, widespread deforestation, and 

8 other changes to the landscape. While scientists and policymakers rely on climate-model 

9 projections for a representative picture of the future Earth system under these conditions, 

10 there are still elements of the Earth system that models do not capture well. For this reason, 

11 there is significant potential for humankind’s planetary experiment to result in unanticipated 

12 surprises—and the further and faster the Earth’s climate system is changed, the greater the 

13 risk of such surprises.

14 There are at least two types of potential surprises: compound events, where multiple extreme 

15 climate events occur simultaneously or sequentially (creating greater overall impact), and 

16 critical threshold or tipping point events, where some critical threshold is crossed in the 

17 climate system (that can lead to large impacts). The probability of such surprises, as well as 

18 other more predictable but difficult-to-manage impacts, increases as the influence of human 

19 activities on the climate system increases. (Ch. 15)

20

21

22

23 • Self-reinforcing cycles, or positive feedbacks, in the climate system have the potential to 

24 substantially accelerate human-induced climate change and even shift the Earth’s climate 

25 system, in part or in whole, into new states that are very different from those experienced 

26 in the recent past—for example, ones with greatly diminished ice sheets or different large27 scale patterns of atmosphere or ocean circulation. Some feedbacks and potential state 

28 shifts can be modeled and their probability of occurrence quantified; others can be 

Unanticipated changes are possible throughout the next century as tipping points 

are crossed and/or multiple climate-related extreme events occur simultaneously.

(Ch. 15)

The observed acceleration in carbon emissions over the past 15–20 years is 

consistent with higher future scenarios (very high confidence); since 2014, 

growth rates have slowed as economic growth begins to uncouple from 

carbon emissions (medium confidence) but not yet at a rate that would 

stabilize climate at either the 1.5° or 2°C Paris objectives (high confidence).

(Ch. 4)
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1 modeled or identified but not quantified; and some are probably still unknown (very high 

2 confidence). (Ch. 2 and 15)

3 • The physical and socioeconomic impacts of a compound extreme event (such as 

4 simultaneous heat and drought, wildfires associated with hot and dry conditions, or 

5 flooding associated with high precipitation on top of snow or water-saturated ground) can 

6 be greater than the sum of those from individual extreme events (very high confidence). 

7 Few analyses consider the spatial or temporal correlation between extreme events. (Ch. 

8 15)

9 • Climate models are not yet able to include all of the processes that contribute to positive 

10 feedbacks, occurrence of extremes, and abrupt and/or irreversible changes. For this 

11 reason, future changes outside the range projected by current climate models cannot be 

12 ruled out (very high confidence), and climate models are more likely to underestimate than 

13 to overestimate the amount of future change (medium confidence). (Ch. 15)

14 ** **BOX ES.2*****

15 A Summary of What’s New Since NCA3

16 A more detailed summary of what’s new since the release of the Third National Climate 

17 Assessment (NCA3) can be found at the end of Chapter 1, including the most notable 

18 advances in scientific understanding, new or improved tools and approaches, and changing 

19 context such as global-policy developments.

20 New Understanding

21 Detection and attribution: Significant advances have been made in the attribution of the 

22 human influence on individual climate and weather extreme events since NCA3. (Chapters 3,

23 6, 7, 8).

24 Atmospheric circulation and extreme events: The extent to which atmospheric circulation in 

25 the midlatitudes is changing or is projected to change is a new important area of research; this 

26 is particularly important for understanding changing extreme-climate conditions (Chapters 5,

27 6, 7).

28 Increased understanding of specific types of extreme events: The effects of climate change on

29 specific types of extreme events in the United States is a key area where scientific 

30 understanding has advanced. (Chapter 9). 

31 The so-called global warming hiatus: Since NCA3, many studies have investigated causes for 

32 the temporary slowdown in the rate of increase in near-surface global mean temperature from 

33 2000 to 2013. This report provides a brief assessment of these studies. On the timescales 

34 relevant to human-induced climate change, the planet has continued to warm at a steady pace 
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1 as predicted by basic atmospheric physics and the well-documented buildup of heat-trapping 

2 gases in the atmosphere (Chapter 1).

3 Oceans and coastal waters: New research on ocean acidification, warming, and oxygen loss is 

4 included in this report. There is also growing evidence that the Atlantic meridional 

5 overturning circulation (AMOC), sometimes referred to as the ocean’s conveyor belt, has 

6 slowed (Chapter 13). 

7 Local sea-level-change projections: For the first time in the NCA process, sea level rise 

8 projections incorporate geographic variation based on factors such as local land subsidence, 

9 ocean currents, and changes in Earth’s gravitational field (Chapter 12). 

10 Accelerated ice-sheet loss and irreversibility: New observations from many different sources 

11 confirm that ice-sheet loss is accelerating (Chapters 1, 11, 12).

12 Slowing of the regrowth of Arctic sea ice extent: The annual Arctic sea ice-extent minimum 

13 for 2016 was the second lowest on record. In fall 2016, record-setting, slow ice regrowth may 

14 lead to record-low values in 2016–2017 winter ice volume as well (Chapter 11).

15 Potential surprises: Both large-scale state shifts in the climate system (sometimes 

16 called “tipping points”) and compound climate extremes (multiple simultaneous or sequential 

17 events) have the potential to generate unanticipated surprises. The discussion of these 

18 potential surprises in Chapter 15 marks the first extended treatment of this topic in an NCA 

19 report. (Chapter 15). 

20 Better Tools and Approaches

21 Spatial downscaling: Modeled projections of climate changes are now statistically 

22 downscaled to a finer spatial resolution, generating temperature and precipitation predictions 

23 on a 1/16 degree latitude/longitude grid for the contiguous United States. (Chapters 4, 6, 7).

24 Risk-based framing: Highlighting aspects of climate science most relevant to assessment of 

25 key societal risks is included more completely than in prior NCA assessments.

26 Model weighting: For the first time, maps and plots of climate projections will use weighted

27 averages of all available climate models. Individual model weights are based on their 1) 

28 historical performance relative to observations and 2) independence relative to other models. 

29 (Chapters 4, 6, 7). 

30 High-resolution global climate model simulations: As computing resources have grown, more 

31 realistic simulations of intense weather systems, including hurricanes, are now possible. Even 

32 with the limited number of high-resolution models currently available, confidence has

33 increased in projections of extreme weather (Chapter 9).

34
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1 Changing Global Context

2 The Paris Agreement: The COP21 Paris Agreement, which entered into force November 4, 

3 2016, provides a new framework for all nations to mitigate and adapt to climate change. The 

4 present document addresses the global climate implications of the agreement objectives

5 (Chapter 4, 14).

6  End Box ES.2** **
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1 1. Our Globally Changing Climate

2 KEY FINDINGS

3 1. The global climate continues to change rapidly compared to the pace of the natural 

4 changes in climate that have occurred throughout Earth’s history. Trends in globally5 averaged temperature, sea-level rise, upper-ocean heat content, land-based ice melt, and 

6 other climate variables provide consistent evidence of a warming planet. These observed 

7 trends are robust, and have been confirmed by independent research groups around the 

8 world. (Very high confidence)

9 2. The frequency and intensity of heavy precipitation and extreme heat events are increasing 

10 in most regions of the world. These trends are consistent with expected physical 

11 responses to a warming climate and with climate model studies, although models tend to

12 underestimate the observed trends. The frequency and intensity of such extreme events 

13 will very likely continue to rise in the future. Trends for some other types of extreme 

14 events, such as floods, droughts, and severe storms, have more regional characteristics. 

15 (Very high confidence)

16 3. Many lines of evidence demonstrate that human activities, especially emissions of 

17 greenhouse gases, are primarily responsible for the observed climate changes in the 

18 industrial era. There are no alternative explanations, and no natural cycles are found in 

19 the observational record that can explain the observed changes in climate. (Very high 

20 confidence)

21 4. Global climate is projected to continue to change over this century and beyond. The 

22 magnitude of climate change beyond the next few decades depends primarily on the 

23 amount of greenhouse (heat trapping) gases emitted globally and the sensitivity of Earth’s 

24 climate to those emissions. (Very high confidence)

25 5. Natural variability, including El Niño events and other recurring patterns of 

26 ocean−atmosphere interactions, have important, but limited influences on global and 

27 regional climate over timescales ranging from months to decades. (Very high confidence)

28 6. Longer-term climate records indicate that average temperatures in recent decades over 

29 much of the world have been much higher than at any time in the past 1700 years or 

30 more. (High confidence)

31 1.1. Introduction

32 Since the Third U.S. National Climate Assessment (NCA3) was published in May 2014, new 

33 observations along multiple lines of evidence have strengthened the conclusion that Earth’s 

34 climate is changing at a pace and in a pattern not explainable by natural influences. While this 
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1 report focuses especially on observed and projected future changes in the United States, it is 

2 important to understand those changes in the global context (this chapter). 

3 The world has warmed over the last 150 years, and that warming has triggered many other 

4 changes to the Earth’s climate. Evidence for a changing climate abounds, from the top of the 

5 atmosphere to the depths of the oceans. Thousands of studies conducted by tens of thousands of 

6 scientists around the world have documented changes in surface, atmospheric, and oceanic 

7 temperatures; melting glaciers; disappearing snow cover; shrinking sea ice; rising sea level; and 

8 an increase in atmospheric water vapor. Rainfall patterns and storms are changing and the 

9 occurrence of droughts is shifting.

10 Many lines of evidence demonstrate that human activities, especially emissions of greenhouse 

11 gases, are primarily responsible for the observed climate changes over the last 15 decades. There 

12 are no alternative explanations. There are no apparent natural cycles in the observational record 

13 that can explain the recent changes in climate (e.g., PAGES 2K 2013; Marcott et al. 2013). In 

14 addition, natural cycles within the Earth’s climate system can only redistribute heat; they cannot 

15 be responsible for the observed increase in the overall heat content of the climate system (Church 

16 et al. 2011). Internal variability, alternative explanations, or even unknown forcing factors cannot 

17 explain the majority of the observed changes in climate (Anderson et al. 2012). The science 

18 underlying this evidence, along with the observed and projected changes in climate, is discussed 

19 in later chapters, starting with the basis for a human influence on climate in Chapter 2.

20 Predicting how climate will change in future decades is a different scientific issue from 

21 predicting weather a few weeks from now. Local weather is short term and chaotic, and 

22 determined by the complicated movement and interaction of high-pressure and low-pressure 

23 systems in the atmosphere, and thus it is difficult to predict day-to-day changes beyond about 

24 two weeks into the future. Climate, on the other hand, is the statistics of weather--meaning not 

25 just mean values but also the prevalence and intensity of extremes--as observed over a period of 

26 decades. Climate emerges from the interaction, over time, of rapidly and quite unpredictably 

27 changing local weather and more slowly changing and more predictable regional and global 

28 influences, such as the distribution of heat in the oceans, the amount of energy reaching Earth 

29 from the sun, and the composition of the atmosphere.

30 Throughout this report, there are many new findings relative to those found in NCA3 and other

31 assessments of the science. Several of these are highlighted in a “What’s New” box at the end of 

32 this chapter.

33 1.2. The Globally Changing Climate

34 1.2.1. Indicators of a Globally Changing Climate

35 Highly diverse types of direct measurements made on land, sea, and in the atmosphere over 

36 many decades have allowed scientists to conclude with high confidence that global mean 
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1 temperature is increasing. Observational datasets for many other climate variables support the 

2 conclusion with high confidence that the global climate is changing. Figure 1.1 depicts several of 

3 the indicators that demonstrate trends consistent with a warming planet over the last century. 

4 Temperatures in the lower atmosphere and oceans have increased, as have near-surface humidity

5 and sea level. Basic physics tells us that a warmer atmosphere can hold more water vapor; this is 

6 exactly what is measured from satellite data. At the same time, a warmer world means higher 

7 evaporation rates and major changes to the hydrological cycle, including increases in the 

8 prevalence of torrential downpours. In addition, Arctic sea ice, mountain glaciers, and Northern 

9 Hemisphere spring snow cover have all decreased. The relatively small increase in Antarctic sea 

10 ice in the last 15 years appears to be best explained a being due to localized natural variability

11 (see e.g., Meehl et al. 2016). The vast majority of the glaciers in the world are losing mass at 

12 significant rates. The two largest ice sheets on our planet—Greenland and Antarctica—are 

13 shrinking. Five different observational datasets show the heat content of the oceans is increasing. 

14 Many other indicators of the changing climate have been determined from other observations –

15 for example, changes in the growing season and the allergy season (see e.g., 

16 https://www3.epa.gov/climatechange/science/indicators/; 

17 http://www.globalchange.gov/browse/indicators). In general, the indicators demonstrate 

18 continuing changes in climate since the publication of NCA3. As with temperature, independent

19 researchers have analyzed each of these indicators and come to the same conclusion: all of these 

20 changes paint a consistent and compelling picture of a warming planet.

21 [INSERT FIGURE 1.1 HERE:

22 Figure 1.1. Examples of the observations from many different indicators of a changing climate. 

23 Anomalies are relative to 1976–2005 averages for the indicated variables. (Figure source: 

24 updated from Melillo et al. 2014). [Figure source: (top) adapted from NCEI 2016, (bottom) 

25 NOAA NCEI / CICS-NC]

26 1.2.2. Trends in Global Temperatures

27 Global annual average temperature (as measured over both land and oceans) has increased by 

28 more than 1.6°F (0.9°C) for the period from 1986-2015 relative to 1901-1960 (Figure 1.2). 

29 Global-average temperatures are not expected to increase smoothly in response to the human 

30 warming influences, because the warming trend is superimposed on natural variability associated 

31 with, e.g., the El Nino / La Nina ocean-heat oscillations and the cooling effects of particles 

32 emitted by volcanic eruptions. Even so, of the 16 warmest years in the ‘instrumental record’--the 

33 period, starting in the late 1800s, when coverage of thermometer measurements became adequate 

34 to calculate an global-average temperature for each year--15 occurred in the period from 2001 to 

35 2015; and 2015 itself was the single warmest year in the entire instrumental record, eclipsing 

36 2014 by 0.16°C (0.29°F), four times greater than the difference between 2014 and the next 

37 warmest year, 2010 (from NOAA data: http://www.ncdc.noaa.gov/cag/). As of November 2016, 

38 it appears that 2016 will eclipse 2015. According to NOAA’s temperature analyses, 2015 and
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1 2014 were followed by 2010, 2013, 2005, 2009 and 1998 as the warmest years. Three of the four 

2 warmest years on record have occurred since the analyses through 2012 were reported in NCA3.

3 A strong El Nino contributed to 2015’s record warmth (Blunden and Arndt 2016). It’s instructive 

4 to note, however, that the then-record global temperature of 1998, to which the previous, even 

5 more powerful El Nino contributed, was much lower than that of 2015. This fact indicates that 

6 the human warming influence, not El Nino per se, is the dominant factor producing new record7 high temperatures. It must only be added that the El Nino / La Nina cycle itself can no longer be 

8 considered to be entirely ‘natural’; the human influence on Earth’s climate system is now so 

9 pervasive that we must assume that virtually all weather and climate phenomena are being 

10 affected in one way or another (Trenberth 2015). It is the complex interaction of natural sources 

11 of variability with the continuously growing human warming influence that is now shaping the 

12 Earth’s weather and climate.

13 Globally, the trend over the past 50 years far exceeds what can be accounted for by natural 

14 variability alone (IPCC 2013). That does not mean, of course, that natural sources of variability 

15 have become insignificant. They can be expected to continue to contribute a degree of 

16 “bumpiness” in the year-to-year global-average temperature trajectory, as well as influences on 

17 the average rate of warming that can last as much as a decade or so (Karl et al. 2015; Deser et al. 

18 2012). For example, some combination of those natural sources of variability--and, perhaps, 

19 short- to medium-term changes in relation between human-caused warming and cooling effects--

20 produced a much-discussed slowdown in the average pace of global warming in the early 2000s 

21 (see Box 1.1).” 

22 [INSERT FIGURE 1.2 HERE:

23 Figure 1.2. Top: Global annual average temperature (as measured over both land and oceans) 

24 has increased by more than 1.6°F (0.9°C) for the period from 1986-2015 relative to 1901-1960. 

25 Red bars show temperatures above the long-term 1880-2015 average, and blue bars indicate 

26 temperatures below the average over the entire period. While there is a clear long-term global 

27 warming trend, some years do not show a temperature increase relative to the previous year, and 

28 some years show greater changes than others. These year-to-year fluctuations in temperature are 

29 mainly due to natural sources of variability, such as the effects of El Niños, La Niñas, and 

30 volcanic eruptions. Based on the NCEI (NOAAGlobalTemp) data set 1880–2015 (updated from 

31 Vose et al. 2012). Bottom: Global average temperature averaged over decadal periods (1886–

32 1895, 1896–1905, ..., 1996–2005, 2006–2015). Horizontal label indicates midpoint year of 

33 decadal period. Every decade since 1966–1975 has been warmer than the previous decade.]

34 Warming during the first half of the 1900s occurred mostly in the Northern Hemisphere 

35 (Delworth and Knutson 2000). The last three decades have seen greater warming in response to 

36 accelerating increases in greenhouse gas concentrations, particularly at high northern latitudes, 

37 and over land as compared to the oceans (see Figure 1.3). In general, winter is warming faster 

38 than summer (especially in northern latitudes). Also, nights are warming faster than days 
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1 (Alexander et al. 2006, Davy et al. 2016). There is also some evidence of faster warming at 

2 higher elevations (Mountain Research Initiative 2015).

3 A few regions, such as the North Atlantic Ocean, have experienced cooling over the last century, 

4 though these areas have warmed over recent decades. Regional climate variability is important

5 (e.g., Hurrel and Deser 2009; Hoegh-Guldberg et al. 2014), but the effects of the increasing fresh 

6 water in the North Atlantic from melting of sea and land ice are also important (Rahmstorf et al. 

7 2015). Even in the absence of significant ice melt, we could expect the North Atlantic to warm 

8 more slowly given the larger heat capacity of the ocean, leading to land–ocean differences in 

9 warming. As a result, the climate for land areas often responds more rapidly than the ocean 

10 areas, even though the forcing driving a change in climate occurs equally over land and the 

11 oceans (IPCC 2013).

12 [INSERT FIGURE 1.3 HERE:

13 Figure 1.3. Surface temperature trends (change in °F) for the period 1986–2015 relative to 

14 1901–1960 from the National Centers for Environmental Information’s (NCEI) surface 

15 temperature product. The relatively coarse (5.0° x 5.0°) resolution of these maps does not capture 

16 the finer details associated with mountains, coastlines, and other small-scale effects. (Figure 

17 source: updated from Vose et al. 2012).]

18 Figure 1.4 shows the projected changes in globally averaged temperature for a range of future 

19 pathways that vary from assuming strong continued dependence on fossil fuels in energy and 

20 transportation systems over the 21st century (the high scenario is Representative Concentration 

21 Pathway 8.5, or RCP8.5) to assuming major emission-reduction actions (the very low scenario, 

22 RCP2.6). Chapter 4 (Projections) describes the future scenarios and the models of the Earth’s 

23 climate system being used to quantify the impact of human choices and natural variability on 

24 future climate. Figure 1.4 suggests that global surface temperature increases for the end of the 

25 21st century are very likely to exceed 1.5°C (2.7°F) relative to the 1850–1900 average for all 

26 projections except for RCP2.6 (IPCC 2013).

27 [INSERT FIGURE 1.4 HERE:

28 Figure 1.4. Multimodel simulated time series from 1950 to 2100 for the change in global annual 

29 mean surface temperature relative to 1986–2005 for a range of future scenarios that account for 

30 the uncertainty in future emissions from human activities [as analyzed with the 20+ models from 

31 around the world used in the most recent international assessment (IPCC 2013)]. The mean and 

32 associated uncertainties [1.64 standard deviations (5%−95%) across the distribution of individual 

33 models (shading)] based on the averaged over 2081–2100 are given for all of the RCP scenarios 

34 as colored vertical bars. The numbers of models used to calculate the multimodel means are 

35 indicated. (Figure source: adapted from Walsh et al. 2014).]

36 ----- START BOX 1.1 HERE -----

37
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1 Box 1.1. Was there a “Hiatus” in Global Warming?

2 Over the past decade, there have been numerous assertions about a ‘hiatus’ (which means 

3 ‘pause’) in global warming. These assertions are explored here in the context of long-term 

4 climate change.

5 Statements about the hiatus often take the form of “there has been no global warming over the 

6 past X years,” where X is typically less than two decades. For relatively short periods of time, 

7 linear fits to the global mean temperature series can show zero or even slightly negative trends as 

8 a result of natural variability in the climate system (see Figure 1.5). However, since 1980, all 

9 periods exceeding 18 years (satellite data) or 13 years (surface data) have positive trends (Santer 

10 et al. 2016). In other words, surface and tropospheric temperature records do not support the 

11 assertion that long-term global warming ceased (Lewandowsky et al. 2016), a conclusion further 

12 reinforced by recently updated and improved datasets (Karl et al. 2015; Mears and Wentz 2016; 

13 Richardson et al. 2016).

14 [INSERT FIGURE 1.5 HERE:

15 Figure 1.5. Panel A shows the annual mean temperature anomalies relative to a 1971–2000 

16 baseline for global mean surface temperature and global mean tropospheric temperature. A 

17 previous period of relatively slow-to-no warming (the “Big Hiatus”) is obvious from the mid18 1940s to the mid-1970s. Panel B shows the linear trend of 17-year overlapping periods (the 

19 maximum number of years historically for less than positive trends), plotted at the time of the 

20 center of the trend period. During the recent slowdown period, warming only ceased for two 

21 versions of the satellite data, and for a very narrow range of time periods. All 17-year trends are 

22 increasing rapidly as the effects of the 2015–2016 El Niño–Southern Oscillation (ENSO) event 

23 begin to affect the trends. Panel C shows the annual mean Pacific Decadal Oscillation (PDO) 

24 index. Temperature trends show a marked tendency to be lower during periods of generally 

25 negative PDO index, shown by the blue shading. (Figure source: adapted and updated from 

26 Trenberth 2015 and Santer et al. 2016; Panel B, © American Meteorological Society. Used with 

27 permission.)]

28 For the 15 years following the 1997–1998 ENSO event, the observed rate of warming was 

29 smaller than the underlying long-term increasing trend on 30-year climate time scales (Fyfe et al. 

30 2016). Variation in the rate of warming on this time scale is not unexpected and can be the result 

31 of long-term internal variability in the climate system, or short-term changes in climate forcings 

32 such as aerosols or solar irradiance. Temporary periods similar or larger in magnitude to the 

33 current slowdown have occurred earlier in the historical record; almost no increase occurred in 

34 the “Big Hiatus” occurred from the mid 1940s to the mid 1970s, which is understood to mostly 

35 be due to an increase in anthropogenic and volcanic aerosols during this period. Shorter-term 

36 slowdowns also occur after major volcanic eruptions, such as Pinatubo’s eruption in 1991. 

37 Temporary speedups have also occurred, most notably in the 1930s and early 1940s, and in the 

38 late 1970s and early 1980s. Comparable slowdown and speedup events are also present in 
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1 climate simulations of both historical and future climate, even without decadal scale fluctuations 

2 in forcing (Easterling and Wehner 2009), and thus the recent slowdown is not particularly 

3 surprising from a statistical point of view.

4 Even though the slowdown of the early 2000s is not unexpected on statistical grounds, it has 

5 been used as informal evidence to cast doubt on the accuracy of climate projections from CMIP5 

6 models, since the measured rate of warming in all surface and tropospheric temperature datasets 

7 from 2000–2015 was less than was expected given the results of the CMIP3 and CMIP5 

8 historical climate simulations (Fyfe et al. 2016; Santer et al. 2016). Thus it is important to 

9 explore a physical explanation of the recent slowdown and to identify the relative contributions 

10 of different factors.

11 A number of studies have investigated the role of natural modes of variability and how they 

12 affected the flow of energy in the climate system of the post-2000 period (Balmaseda et al. 2013; 

13 England et al. 2014; Meehl et al. 2011; Kosaka and Xie 2013). For the 2000-2013 time period, 

14 they find:

15 • In the Pacific Ocean, a number of interrelated features, including cooler than expected 

16 tropical ocean surface temperatures, stronger than normal trade winds, and a shift to the 

17 cool phase of the Pacific Decadal Oscillation (PDO) led to cooler than expected surface 

18 temperatures in the Eastern Tropical Pacific, a region that has been shown to have a 

19 strong influence on global-scale climate (Kosaka and Xie 2013). 

20 • For most of the world’s oceans, an excess amount of heat was transferred from the 

21 surface into the deeper ocean (Balmaseda et al. 2013; Chen and Tung 2014; Nieves et al. 

22 2015). The transfer of this heat to the deeper oceans removed heat from the atmosphere, 

23 causing a reduction in surface warming worldwide. 

24 • Other studies attributed part of the cause of the measurement/model discrepancy to 

25 natural fluctuations in radiative forcings, such as stratospheric water vapor, solar output,

26 or volcanic aerosols (add Solomon et al. 2010; Schmidt et al 2014; (Huber and Knutti 

27 2014; Ridley et al. 2014; Santer et al. 2014). 

28 When comparing model predictions with measurements, it is important to note that the CMIP5 

29 runs used predicted values (not actual values) of these factors for time periods after 2000. Thus 

30 for these forcings, the model inputs were often different than what happened in the real-world, 

31 causing spurious warming in the model output. It is very likely that the early 2000s slowdown 

32 was caused by a combination of these factors, with natural internal variability in the world’s 

33 oceans being the dominant factor (Trenberth 2015).

34 Although 2014 already set a new in globally averaged temperature record up to that time, in 

35 2015–2016, the situation changed dramatically. A switch of the PDO to the positive phase, 

36 combined with a strong El Niño event during the fall and winter of 2015–2016, led to months of 
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1 record-breaking globally averaged temperatures in both the surface and satellite temperature 

2 records (see Figure 1.5; Trenberth 2015). A plot of the trends in 17-year intervals shows a 

3 marked increase for trends ending over the past several years, suggesting that the slowdown may 

4 be over. 

5 On longer time scales, observed temperature changes are more consistent with model predictions 

6 and have been attributed to anthropogenic causes with high confidence (Bindoff et al. 2013). The 

7 pronounced globally averaged surface temperature record of 2015 appears to make recent 

8 observed temperature changes more consistent with model simulations—including with CMIP5 

9 projections that were (notably) developed in advance of occurrence of the 2015 observed 

10 anomalies (Figure 1.6). A second important point illustrated by Figure 1.6 is the broad overall 

11 agreement between observations and models on the century timescale, which is robust to the 

12 shorter-term variations in trends in the past decade or so. Continued global warming and the 

13 frequent setting of new high global mean temperature records or near-records is consistent with 

14 expectations based on model projections of continued anthropogenic forcing toward warmer 

15 global mean conditions.

16 [INSERT FIGURE 1.6 HERE:

Figure 1.6. Comparison of globally averaged temperature anomalies (o 17 F) from observations 

18 (through 2015) and the CMIP5 multimodel ensemble (through 2016), using the reference period 

19 1961–1990. The CMIP5 multimodel ensemble (black) is constructed from blended surface 

20 temperature and surface air temperature data from the models, masked where observations are 

21 not available in the HadCRUT4 dataset (Knutson et al. 2016; see also Richardson et al. 2016). 

22 The sources for the three observational indices are: HadCRUT4.5 (red): 

23 http://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/download.html; NOAA (green):

24 https://www.ncdc.noaa.gov/monitoring-references/faq/anomalies.php; and GISTEMP (blue): 

25 http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt (all downloaded on Oct. 3,

26 2016) (Figure source: adapted from Knutson et al. 2016).]

27 ----- END BOX 1.1 HERE -----

28 1.2.3. Trends in Global Precipitation

29 Annual averaged precipitation across global land areas exhibits a slight rise over the past century 

30 along with ongoing increases in atmospheric moisture levels (see Figure 1.7). Interannual and 

31 interdecadal variability is clearly found in all precipitation reconstructions, owing to factors such 

32 as the North Atlantic Oscillation (NAO) and ENSO—the latter accounting for record-low global 

33 totals in 2015 in several major analyses; note that precipitation reconstructions are updated 

34 operationally by NOAA NCEI on a monthly basis (Becker et al. 2013; Adler et al. 2003).

35 [INSERT FIGURE 1.7 HERE:

36 Figure 1.7. Surface annually-averaged precipitation trends (change in inches) for the period 

37 1986–2015 relative to 1901–1960. The relatively coarse (0.5° x 0.5°) resolution of these maps 
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1 does not capture the finer details associated with mountains, coastlines, and other small-scale 

2 effects. (Figure source: NOAA NCEI / CICS-NC).]

3 The hydrological cycle and the amount of global mean precipitation is primarily controlled by 

4 energy budget considerations (Allen and Ingram 2002). The amount of global mean precipitation 

5 also changes as a result of a mix of fast and slow atmospheric responses to the changing climate

6 (Collins et al. 2013). In the long term, increases in tropospheric radiative cooling due to CO2

7 increases must be balanced by increased latent heating, resulting in precipitation increases of 

8 approximately 1% to 3% per °C change (0.55% to 0.72% per °F) (IPCC 2013; Held and Soden 

9 2006). Changes in global atmospheric water vapor, on the other hand, are controlled by the 

10 Clausius-Clapeyron relationship (see Chapter 2: Science), increasing by about 6%–7% per °C of 

11 warming. Satellite observations of changes in precipitable water over ocean have been detected 

12 at about this rate and attributed to human changes in the atmosphere (Santer et al. 2007). Similar 

13 observed changes in land-based measurements have also been attributed to the changes in 

14 climate from greenhouse gases (Willet et al. 2010).

15 Earlier studies suggested a pattern from climate change of wet areas getting wetter and dry areas 

16 getting dryer (e.g., Greve et al. 2014). While this behavior appears to be valid over ocean areas, 

17 changes over land are more complicated. The wet versus dry pattern in observed precipitation 

18 has only been attributed in a zonal mean sense (Zhang et al. 2007; Marvel and Bonfils 2013) due 

19 to the large amount of spatial variation in precipitation changes as well as significant natural 

20 variability. The detected signal in zonal mean is largest in the Northern Hemisphere, with 

21 decreases in the sub-tropics and increases at high latitudes. As a result, changes in annual 

22 averaged Arctic precipitation have been detected and attributed to human activities (Min et al.

23 2008).

24 1.2.4. Global Trends in Extreme Weather Events

25 A change in the frequency, duration, and/or magnitude of extreme weather events is one of the 

26 most important consequences of a warming climate. In statistical terms, a small shift in the mean 

27 of a weather variable occurring in concert with a change in the shape of its probability 

28 distribution can cause a large increase or decrease in the probability of a value above or below an 

29 extreme threshold (Katz and Brown 1992). Examples include extreme high-temperature events 

30 and heavy precipitation events. Additionally, extreme events such as intense tropical cyclones, 

31 mid-latitude cyclones, and hail and tornadoes associated with thunderstorms, can occur as 

32 isolated events that are not generally studied in terms of extremes within a probability 

33 distribution. Detecting trends in the frequency and intensity of extreme weather events is 

34 challenging (Sardeshmukh et al. 2015). The most intense events are rare by definition, and 

35 observations may be incomplete and suffer from reporting biases. Further discussion on trends 

36 and projections of extreme events for the United States can be found in Chapter 9: Extreme 

37 Storms.
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1 Extreme Heat and Cold

2 The frequency of multiday heat waves and extreme high temperatures at both daytime and 

3 nighttime hours is increasing over the United States (Meehl et al. 2009) and over much of the 

4 global land areas (IPCC 2013). The land area experiencing daily highs above given thresholds 

5 (for example, 90° F) has been increasing since about 1998 (Seneviratne et al. 2014). At the same 

6 time, frequencies of cold waves and daytime and nighttime extremely low temperatures are 

7 decreasing over the United States and much of the Earth (IPCC 2013; Easterling et al. 2016).

8 The enhanced radiative forcing caused by greenhouse gases has a direct influence on heat 

9 extremes by shifting distributions of daily temperature (Min et al. 2013). Recent work indicates 

10 changes in atmospheric circulation may also play a significant role (See Chapter 5). For example, 

11 a recent study found that increasing anticyclonic circulations partially explain observed trends in

12 heat events over North America and Eurasia, among other effects (Horton et al. 2015). Although 

13 the subject of significant study still, the observed changes in circulation may also be the result of 

14 human influences on climate.

15 Extreme Precipitation

16 A robust consequence of a warming climate is an increase in atmospheric water vapor, which 

17 exacerbates precipitation events under similar meteorological conditions, meaning that when 

18 rainfall occurs, the amount of rain falling in that event tends to be greater. As a result, extreme 

19 precipitation events globally are becoming more frequent (IPCC 2013; Asadieh and Krakauer 

20 2015; Kunkel and Frankson 2015; Donat et al. 2016). On a global scale, the observational 

21 annual-maximum daily precipitation has increased by 8.5% over the last 110 years; global 

22 climate models also derive an increase in extreme precipitation globally but tend to 

23 underestimate the rate of the observed increase (Asadieh and Krakauer 2015; Donat et al. 2016). 

24 Extreme precipitation events are increasing globally in frequency over both wet and dry regions 

25 (Donat et al. 2016). Although more spatially heterogeneous than heat extremes, numerous 

26 studies have found increases in precipitation extremes on many regions using a variety of 

27 methods and threshold definitions (Kunkel et al. 2013), and those increases can be attributed to 

28 human-caused changes to the atmosphere (Min et al. 2011; Zhang et al., 2013). Finally, extreme 

29 precipitation associated with tropical cyclones (TCs) is expected to increase in the future 

30 (Knutson et al. 2015), but current trends are not clear (Kunkel et al. 2013).

31 The impact of extreme precipitation trends on flooding globally is complex because additional 

32 factors like soil moisture and changes in land cover are important (Berghuijs et al. 2016). 

33 Globally, there is low confidence in current river-flooding trends (Kundzewicz et al. 2014), but 

34 the magnitude and intensity of river flooding is projected to increase in the future (Arnell and 

35 Gosling 2014). More on flooding trends in the United States is in Chapter 8: Droughts, Floods, 

36 and Hydrology

37
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1 Tornadoes and Thunderstorms

2 Increasing air temperature and moisture increase the risk of extreme convection, and there is 

3 evidence for a global increase in severe thunderstorm conditions (Sander et al. 2013). Strong 

4 convection, along with wind shear, represents favorable conditions for tornadoes. Thus, there is 

5 reason to expect increased tornado frequency and intensity in a warming climate (Diffenbaugh et 

6 al. 2013). Inferring current changes in tornado activity is hampered by changes in reporting 

7 standards, and trends remain highly uncertain (Kunkel et al. 2013). 

8 Winter Storms

9 Winter storm tracks have shifted slightly northward in recent decades over the Northern 

10 Hemisphere (Bender et al. 2012). More generally, extra-tropical cyclone (ETC) activity is 

11 projected to change in complex ways under future climate scenarios, with increases in some 

12 regions and seasons and decreases in others. There was good general agreement on these points 

13 among CMIP5 climate models, although some models underestimated the current cyclone track 

14 density (Colle et al. 2013; Chang 2013). 

15 Enhanced Arctic warming (arctic amplification), due in part to sea ice loss, reduces lower 

16 tropospheric meridional temperature gradients, diminishing baroclinicity (a measure of how 

17 misaligned the gradient of pressure is from the gradient of air density)—an important energy 

18 source for ETCs. At the same time, upper-level meridional temperature gradients will increase, 

19 due to a warming upper tropical troposphere and a cooling high-latitude lower stratosphere. 

20 While both effects counteract each other with respect to a projected change in mid-latitude storm 

21 tracks, the simulations indicate that the magnitude of arctic amplification is a controlling factor

22 on circulation changes in the North Atlantic region (Barnes and Polvani 2015).

23 Tropical Cyclones

24 Detection of trends in past tropical cyclone activity is hampered by uncertainties in the data 

25 collected prior to the satellite era and by uncertainty in the relative contributions of natural 

26 variability and anthropogenic influences. Theoretical arguments and numerical modeling 

27 simulations support an expectation that radiative forcing by greenhouse gases and anthropogenic 

28 aerosols can affect tropical cyclone (TC) activity in a variety of ways, but robust formal 

29 detection and attribution for past observed changes has not yet been realized. Since the IPCC 

30 AR5 (2013), there is new evidence that the locations where TCs reach their peak intensity have 

31 migrated poleward in both the Northern and Southern Hemispheres, in concert with the 

32 independently measured expansion of the tropics (Kossin et al. 2014). In the western North 

33 Pacific, this migration has substantially changed the TC hazard exposure patterns in the region 

34 and appears to have occurred outside of the historically measured modes of regional natural 

35 variability (Kossin et al. 2016). 
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1 Whether global trends in high-intensity TCs are already observable is a topic of active debate. 

2 One study using the best-track data archive 1982–2009 found a significant positive global trend 

3 in lifetime maximum wind speed of high-intensity TCs, corroborating earlier work (Elsner et al.

4 2008; Kossin et al. 2013). When the same procedure is applied to a homogenized satellite record 

5 over the same period, the trends are no longer significant. However, the same study also 

6 demonstrated that the observed changes in the environment are unlikely to support a detectable 

7 trend over that period (Kossin et al. 2013). Other studies have suggested that aerosol pollution 

8 has masked the increase in TC intensity expected otherwise from greenhouse warming (Wang et 

9 al. 2014; Sobel et al. 2016).

10 TC intensities are expected to increase with warming, both on average and at the high end of the 

11 scale, as the range of achievable intensities expands, so that the most intense storms will exceed 

12 the intensity of any in the historical record (Sobel et al. 2016). Some studies have projected an 

13 overall increase in TC activity (Emanuel 2013). However studies with high-resolution models 

14 are giving a different result. For example, a high-resolution dynamical downscaling study of 

15 global TC activity under the RCP4.5 scenario projects an increased occurrence of the highest16 intensity category TCs (Saffir-Simpson Categories 4 and 5), along with a reduced overall TC 

17 frequency, though there are considerable basin-to-basin differences (Knutson et al. 2015).

18 Chapter 9 covers more on extreme storms affecting the United States.

19 1.2.5. Global Changes in Land Processes

20 Changes in land cover have had important effects on climate, while climate change also has 

21 important effects on land cover (IPCC 2013). In some case, there are changes in land cover that 

22 are both consequences of and influences on global climate change (e.g., declines in sea ice and 

23 snow cover, thawing permafrost). Other changes are currently mainly causes of climate change 

24 but in the future could become consequences (e.g., deforestation), while other changes are 

25 mainly consequences of climate change (e.g., effects of drought).

26 Northern Hemisphere snow cover extent has decreased, especially in spring, primarily due to 

27 earlier spring snowmelt (Kunkel et al. 2016), and this decrease since the 1970s is at least 

28 partially driven by anthropogenic influences (Rupp et al. 2013). Snow cover reductions, 

29 especially in the Arctic region in summer, have led to reduced seasonal albedo. 

30 While global-scale trends in drought are uncertain due to lack of direct observations, regional 

31 trends indicate increased frequency and intensity of drought in the Mediterranean (Sousa et al. 

32 2011; Hoerling et al. 2013) and West Africa (Dai 2013; Sheffield et al. 2012), and decreased 

33 frequency and intensity in central North America (Peterson et al. 2013) and northwest Australia 

34 (Dai 2013; Sheffield et al. 2012; Jones et al. 2009). 

35 Anthropogenic land-use changes, such as deforestation and growing cropland extent, have 

36 increased the global land surface albedo, and a small amount of cooling can be attributed to this 

37 albedo change. Effects of other land use changes, including modifications of surface roughness, 
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1 latent heat flux, river runoff, and irrigation, are difficult to quantify, but may offset the direct

2 land-use albedo changes (Bonan 2008; de Noblet-Ducoudré et al. 2012).

3 Globally, land-use change since 1750 has been typified by deforestation, driven by the growth in 

4 intensive farming and urban development. Global land-use change is estimated to have released 

5 190 ± 65 PgC (petagrams of carbon) through 2014 (Le Quéré et al. 2015). Over the same period,

6 cumulative fossil fuel and industrial emissions are estimated to have been 405 ± 20 PgC, yielding 

7 total anthropogenic emissions of 590 ± 70 PgC, of which cumulative land-use change emissions 

8 were about 32% (Le Quéré et al. 2015). Tropical deforestation is the dominant driver of land-use 

9 change emissions, estimated at 0.1–1.7 PgC per year. Global deforestation emissions of about 3 

10 PgC per year are compensated by around 2 PgC per year of forest regrowth in some regions, 

11 mainly from abandoned agricultural land (Houghton et al. 2012; Pan et al. 2011). 

12 Natural terrestrial ecosystems are gaining carbon through uptake of CO2 by enhanced 

13 photosynthesis due to higher CO2 levels, increased nitrogen deposition, and longer growing 

14 seasons in mid- and high latitudes. Anthropogenic atmospheric CO2 absorbed by land 

15 ecosystems is stored as organic matter in live biomass (leaves, stems, and roots), dead biomass 

16 (litter and woody debris), and soil carbon. 

17 Many studies have documented a lengthening growing (non-frozen) season, primarily due to the 

18 changing climate (Myneni et al. 1997; Parmesan and Yohe 2003; Menzel et al. 2006; Schwartz et 

19 al. 2006; Kim et al. 2012), and elevated CO2 is expected to further lengthen the growing season 

20 (Reyes-Fox et al. 2014). In addition, a recent study has shown an overall increase in greening of 

21 the Earth in vegetated regions (Zhu et al. 2016), while another has demonstrated evidence that 

22 the greening of Northern Hemisphere extratropical vegetation is attributable to anthropogenic 

23 forcings, particularly rising atmospheric greenhouse gas levels (Mao et al. 2016). However, 

24 observations (Finzi et al. 2006; Palmroth et al. 2006; Norby et al. 2010) and models (Sokolov et 

25 al. 2008; Thornton et al. 2009; Zaehle and Friend 2010) indicate that nutrient limitations and 

26 land availability will constrain future land carbon sinks.

27 Modifications to the water, carbon, and biogeochemical cycles on land result in both positive and 

28 negative feedbacks to temperature increases (Betts et al. 2007; Bonan 2008; Bernier et al. 2011). 

29 Snow and ice albedo feedbacks are positive, leading to increased temperatures with loss of snow 

30 and ice extent. While land ecosystems are expected to have a net positive feedback due to 

31 reduced natural sinks of CO2 in a warmer world, anthropogenically increased nitrogen deposition 

32 may reduce the magnitude of the net feedback (Churkina et al. 2009; Zaehle et al. 2010; 

33 Thornton et al. 2009). Increased temperature and reduced precipitation increase wildfire risk and 

34 susceptibility of terrestrial ecosystems to pests and disease, with resulting feedbacks on carbon 

35 storage. Increased temperature and precipitation, particularly at high latitudes, drives up soil 

36 decomposition, which leads to increased CO2 and CH4 emissions (Page et al. 2002; Ciais et al. 

37 2005; Chambers et al. 2007; Kurz et al. 2008; Clark et al. 2010; van der Werf et al. 2010; Lewis 

38 et al. 2011). While some of these feedbacks are well known, others are not so well quantified and 
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1 yet others remain unknown; the potential for surprise is discussed further in Chapter 15: Potential 

2 Surprises.

3 1.2.6. Global Changes in Sea Ice, Glaciers, and Land Ice

4 Since NCA3 (Melillo et al. 2014), there have been significant advances in the understanding of 

5 changes in the cryosphere. Observations continue to show that Arctic sea ice extent and 

6 thickness, Northern Hemisphere snow cover, and the volume of mountain glaciers and 

7 continental ice sheets are all decreasing (Stroeve et al. 2014a,b; Comiso and Hall 2014). In many 

8 cases, evidence suggests that the net loss of mass from the global cryosphere is accelerating 

9 (Rignot et al. 2011, 2014; Williams et al. 2014; Zemp et al. 2015; Seo et al. 2015; Harig and 

10 Simons 2016). See Chapter 11 for more details on the Arctic and Alaska beyond the short 

11 discussion in this chapter.

12 Arctic Sea Ice

13 Arctic sea ice is a key component of the global climate system and appears to be in rapid 

14 transition. For example, sea-ice areal extent, thickness, and volume have been in decline since at 

15 least 1979 (IPCC 2013; Stroeve et al. 2014a,b; Comiso and Hall 2014), and annually averaged

16 Arctic sea-ice extent has decreased since 1979 at a rate of 3.5%–4.1% per decade (IPCC 2013; 

17 https://nsidc.org/arcticseaicenews/). Reductions in Arctic sea ice are found in all months and are 

18 most rapid in summer and autumn (Stroeve et al. 2012b; Stroeve et al. 2014a; Comiso and Hall 

19 2014). October 2016 was the slowest growth rate in Arctic sea ice in history for that month.

20 Between 1979 and 2014, sea ice extent changes in March and September (the months of 

21 maximum and minimum extent) are −2.6% and −13.3% per decade, respectively (Perovich et al. 

22 2015). At the same time, the age distribution of sea ice has also become younger since 1988 

23 (Perovich et al. 2015). 

24 The rate of perennial and multiyear sea ice loss has been 11.5% ± 2.1% and 13.5% ± 2.5% per 

25 decade, respectively, at the time of minimum extent (IPCC 2013). The thickness of the Arctic sea 

26 ice during winter has decreased between 1.3 and 2.3 meters (4 to 7.5 feet) (IPCC 2013). The 

27 length of the sea ice melt season has also increased by at least five days per decade since 1979 

28 for much of the Arctic (Stroeve et al. 2014a; Parkinson 2014). Lastly, current generation climate 

29 models still exhibit difficulties in simulating changes in Arctic sea ice characteristics, simulating 

30 weaker reductions in sea ice volume and extent than observed (IPCC 2013; Stroeve et al. 2012a; 

31 Stroeve et al. 2014b; Zhang and Knutson 2013). See Chapter 11 for further discussion of the 

32 implications of changes in the Arctic. 

33 Antarctic Sea Ice Extent

34 The area of sea ice around Antarctica has increased between 1979 and 2012 by 1.2% to 1.8% per 

35 decade (IPCC 2013), much smaller than the decrease in total sea ice area found in the Arctic 

36 summer. Strong regional differences in the sea ice growth rates around Antarctica are found, but 
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1 most (about 75%) of the sea ice area has expanded over the last 30 years (Zunz et al. 2013; IPCC 

2 2013). Changes in wind patterns, ice–ocean feedbacks, and changes in freshwater flux have been 

3 investigated as contributing to the Antarctic sea ice growth, and there is still scientific debate 

4 around the physical cause (Zunz et al. 2013; Eisenman et al. 2014; Pauling et al. 2016). Scientific

5 progress on understanding the observed changes in Antarctic sea ice extent is stymied by the 

6 short observational record; complex interactions between the sea ice, ocean, atmosphere, and 

7 Antarctic Ice Sheet; and large interannual variability. The most recent scientific evidence ties the 

8 increase in Antarctic sea ice extent to the negative phase of the Interdecadal Pacific Oscillation 

9 (IPO) climate variability pattern. The negative phase (1999–present) of the IPO resulted in cooler 

10 tropical Pacific sea surface temperatures, a slower warming trend, and a deepening of the 

11 Amundsen Sea Low near Antarctica, which contributed to regional circulation changes in the 

12 Ross Sea region and an expansion of sea ice (Meehl et al. 2016).

13 Continental Ice Sheets and Mountain Glaciers

14 Since the NCA3 (Mellilo et al. 2014), the Gravity Recovery and Climate Experiment (GRACE) 

15 constellation of satellites (e.g., Velicogna and Wahr 2013) has continued to provide a record of 

16 gravimetric measurements of land ice changes, advancing knowledge of recent mass loss to the 

17 global cryosphere. These measurements indicate that mass loss from the Antarctic Ice Sheet 

18 (AIS), Greenland Ice Sheet (GrIS), and mountain glaciers around the world continues. 

19 The annual average net mass change from AIS is –92 ± 10 Gt per year since 2003 (Harig and 

20 Simons 2016). Strong spatial variations are found in mass loss; gains are found in the East 

21 Antarctic Ice Sheet (EAIS), and significant losses are found in the West Antarctic Ice Sheet 

22 (WAIS). Multiple data sources indicate that losses from WAIS outpace the EAIS gains (Rignot 

23 et al. 2014; Joughin et al. 2014; Williams et al. 2014; Harig and Simons 2015; Seo et al. 2015; 

24 Harig and Simons 2016). The WAIS ice shelves are undergoing rapid change due to ocean 

25 warming in this region from increased oceanic heat transport (Jenkins et al. 2010; Feldmann and 

26 Levermann 2015) contributing to the increase in flow rate of discharge glaciers. 

27 Recent evidence has found that the grounding line retreat of glaciers in the Amundsen Sea sector 

28 has crossed a threshold and this sector is expected to eventually disintegrate entirely, with the 

29 potential to destabilize the entire WAIS (Rignot et al. 2014; Joughin et al. 2014; Feldmann and 

30 Levermann 2015). As a result, the evidence suggests an eventual committed global sea level rise 

31 from this disintegration could be at least 1.2 meters (about 4 feet) and possibly up to 3 meters

32 (about 10 feet). The timescale over which the melt will occur is thought to be several centuries. 

33 However, recent analyses suggest that this could happen faster than previously thought, with a 

34 potential for an additional one or more feet of sea level rise during this century (DeConto and 

35 Pollard 2016; see Chapter 12: Sea Level Rise for further details). The potential for unanticipated 

36 rapid ice sheet melt and/or disintegration is discussed further in Chapter 15: Potential Surprises.
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1 Average annual mass loss from GrIS between January 2003 and May 2013 was 244 ± 6 Gt per 

2 year (Harig and Simons 2016), an increase from 215 Gt per year for the period from 2002 to

3 2011 (IPCC 2013). Major GrIS melting events have been observed in recent years associated 

4 with increased surface air temperatures in response to variability in the atmospheric circulation 

5 (IPCC 2013; Lim et al. 2016). GrIS is rapidly losing mass at its edges and slightly gaining in its 

6 interior and has been the largest land ice contributor to global sea level rise over the last decade 

7 (Harig and Simons 2012; Jacob et al. 2012). The surface area of the Greenland Ice Sheet 

8 experiencing melt has increased significantly since 1980 (Tedesco et al. 2011; Fettweis et al. 

9 2011; Tedesco et al. 2015). The Greenland surface melt recorded in 2012, where melt occurred 

10 over 98.6% of the ice sheet surface area on a single day in July, remains unprecedented (Nghiem 

11 et al. 2012; Tedesco et al. 2013). GRACE data indicate that the Greenland mass loss between 

12 April 2012 and April 2013 was 562 Gt—more than double the average annual rate found over 

13 recent decades.

14 The annually averaged ice mass from 37 global reference glaciers has decreased every year since 

15 1984, and the rate of global glacier melt is accelerating (Pelto 2015; Zemp et al. 2015). This 

16 mountain glacier melt is contributing to sea level rise and will continue to contribute through the 

17 21st Century (Mengel et al. 2016). Some of the greatest glacier mass losses are occurring in 

18 Alaska and the Pacific Northwest (IPCC 2013; Zemp et al. 2015). The current data also show 

19 strong imbalances in glaciers around the globe indicating additional ice loss even if climate were 

20 to stabilize (IPCC 2013; Zemp et al. 2015).

21 Arctic Snow Cover and Permafrost

22 Snow cover extent has decreased in the Northern Hemisphere, including over the United States; 

23 the decrease has been especially significant over the last decade (Derksen and Brown 2012). 

24 Observations indicate that between 1967 and 2012, Northern Hemisphere June snow cover 

25 extent has decreased by more than 50% (IPCC 2013). Reductions in May and June snow cover 

26 extent of 7.3% and 19.8% per decade, respectively, have occurred over the period from 1979 to 

27 2014, while trends in snow cover duration show regions of both earlier and later snow cover 

28 onset (Derksen et al. 2015).

29 Annual mean temperature and thickness of the active soil layer—the layer experiencing seasonal 

30 thaw—are critical permafrost characteristics for the concerns about potential emissions of carbon 

31 dioxide and methane from thawing permafrost. Permafrost temperatures have increased in most 

32 regions of the Arctic. The rate of permafrost warming varies regionally; however, greater 

33 warming is consistently found for colder permafrost than for warmer permafrost (IPCC 2013; 

34 Romanovsky et al. 2015). Decadal trends in the permafrost active layer show strong regional 

35 variability (Shiklomanov et al. 2012); however, the thickness of the active layer is increasing in 

36 most areas across the Arctic (IPCC 2013; Romanovsky et al. 2015). The potentially large 

37 contribution of carbon and methane emissions from permafrost and the continental shelf in the 

38 Arctic to overall warming is discussed further in Chapter 15: Potential Surprises.
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1 1.2.7. Global Changes in Sea Level

2 Statistical analyses of tide-gauge data indicate that global mean sea level has risen about 20–23 

3 cm (8–9 inches) since 1880, with a rise rate of approximately 1.2–1.5 mm/year from 1901–1990

4 (~0.5–0.6 inches per decade; Church and White 2011; Hay et al. 2015; also see Chapter 12: Sea 

5 Level Rise). However, since the early 1990s, both tide gauges and satellite altimeters have 

6 recorded a faster rate of sea level rise of about 3 mm/year (approximately 0.12 inches per year; 

7 Church and White 2011; Nerem et al. 2010; Hay et al. 2015), resulting in about 8 cm (about 3 

8 inches) of the global rise since the early 1990s. Nearly two-thirds of the sea level rise measured 

9 since 2005 has resulted from increases in ocean mass, primarily from land-based ice melt; the 

10 remaining one-third of the rise is in response to changes in density from increasing ocean 

11 temperatures (Merrifield et al. 2015).

12 Global sea level rise and its regional variability forced by climatic and ocean circulation patterns 

13 are contributing to significant increases in annual tidal-flood frequencies, which are measured by 

14 NOAA tide gauges and associated with minor infrastructure impacts; along some portions of the 

15 U.S. coast, frequency of the impacts from such events appear to be accelerating (Ezer and 

16 Atkinson 2014; Sweet and Park 2014; also see Chapter 12: Sea-Level Rise).

17 Future projections show that by 2100, global mean sea level is very likely to rise by 0.5–1.3 m

18 (1.6–4.3 feet) under RCP8.5, 0.35–0.95 m (1.1–3.1 feet) under RCP4.5, and 0.24–0.79 m (0.8–

19 2.6 feet) under RCP2.6 (see Chapter 4: Projections of Climate Change for a description of the 

20 scenarios) (Kopp et al. 2014). Sea level will not rise uniformly around the coasts of the United 

21 States and its oversea territories. Local sea level rise is likely to be greater than the global 

22 average along the U.S. Atlantic and Gulf Coasts and less than the global average in most of the 

23 Pacific Northwest. Emerging science suggests these projections may be underestimates, 

24 particularly for higher scenarios; a global mean sea level rise exceeding 2.4 m (8 feet) by 2100 

25 cannot be excluded (see Chapter 12: Sea Level Rise), and even higher amounts are possible as a 

26 result of marine ice sheet instability (see Chapter 15: Potential Surprises). We have updated the 

27 global sea level rise scenarios for 2100 of Parris et al. (2012) accordingly (Sweet et al., In Prep),

28 and also extended to year 2200 in Chapter 12: Sea-Level Rise. The scenarios are regionalized to 

29 better match the decision context needed for local risk framing purposes.

30 1.2.8. Recent Global Changes relative to Paleoclimates

31 Covering the last two millennia, referred to here as the “Common Era,” paleoclimate records 

32 provide a longer-term sample of the natural variability of modern climate, with a small overprint 

33 of human-forced climate change. The strongest drivers of climate in the last two thousand years 

34 have been volcanoes, land-use change (which has both albedo and greenhouse gas emissions 

35 effects), and emissions of greenhouse gases from fossil fuels and other human-related activities 

36 (Schmidt et al. 2011). Based on a number of proxies for temperature (for example, from tree 

37 rings, fossil pollen, corals, ocean and lake sediments, ice cores, etc.), temperature records are 
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1 available for the last 2000 years on hemispherical and continental scales (Figures 1.8 and 1.9) 

2 (Mann et al. 2008; PAGES 2K 2013). High-resolution temperature records for North America 

3 extend back less than half of this period, with temperatures in the early parts of the Common Era 

4 inferred from pollen archives. For this era, there is a general cooling trend, with a relatively rapid 

5 increase in temperature over the last 150–200 years (Figure 1.9, PAGES 2k 2013). For context, 

6 temperatures for 2015 are much higher than any period in the past 2000 years.

7 [INSERT FIGURE 1.8 HERE:

8 Figure 1.8. Changes in the temperature of the Northern Hemisphere from surface observations 

9 (in red) and from proxies (in black; uncertainty range represented by shading) relative to 1961–

10 1990 average temperature. These analyses suggest that current temperatures are higher than seen 

11 globally in at least the last 1700 years, and that the last decade (2006 to 2015) was the warmest 

12 decade on record. (Figure source: adapted and updated from Mann et al. 2008).]

13 [INSERT FIGURE 1.9 HERE:

14 Figure 1.9. Proxy temperatures reconstructions for the seven regions of the PAGES-2K 

15 Network. Temperature anomalies are relative to the 1961–1990 reference period. Gray lines 

16 around expected-value estimates indicate uncertainty ranges as defined by each regional group 

17 (see PAGE 2K 2013 and related Supplementary Information). Note that the changes in 

18 temperature over the last century tend to occur at a much faster rate than found in the previous 

19 time periods. (Figure source: adapted from PAGES 2k et al. 2013)]

20 Global temperatures of the magnitude observed recently (and projected for the rest of this 

21 century) were likely last observed during the Eemian period—the last interglacial—125,000 

22 years ago; at that time, global temperatures were, at their peak, about 1.8°–3.6°F (1°–2°C) 

23 warmer than preindustrial temperatures (Turney and Jones 2010). Coincident with these higher 

24 temperatures, sea levels were 6–9 meters (about 16–30 feet) higher than modern levels (Kopp et 

25 al. 2009; Dutton and Lambeck 2012). Modeling studies suggest that the Eemian period warming 

26 can be explained in part by increased solar insolation from orbital forcing as the Earth travels 

27 around the Sun (e.g., Kaspar et al. 2005). However, greenhouse gas concentrations were similar 

28 to preindustrial levels. Equilibrium climate with modern greenhouse gas concentrations (about 

29 400 ppm CO2) most recently occurred 3 million years ago during the Pliocene. During the 

30 warmest parts of this period, global temperatures were 5.4°–7.2°F (3°–4°C) higher than today,

31 and sea levels were 25 meters (about 82 feet) higher (Haywood et al. 2013).

32 ----- START BOX 1.2 HERE -----

33 Box 1.2: What’s New in This Report

34 This assessment reflects both advances in scientific understanding and approach since NCA3, as 

35 well as global policy developments. Highlights of what’s new include:
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1 Spatial downscaling: Projections of climate changes are downscaled to a finer resolution than the 

2 original global climate models using the Localized Constructed Analogs (LOCA) empirical 

3 statistical downscaling model. The downscaling generates temperature and precipitation on a 

4 1/16 degree latitude/longitude grid for the contiguous United States (Chapters 4,6,7).

5 Risk-based framing: Highlighting aspects of climate science most relevant to assessment of key 

6 societal risks is included more here than in prior NCA assessments. This approach allows for 

7 emphasis of possible outcomes that, while relatively unlikely to occur or characterized by high 

8 uncertainty, would be particularly consequential, and thus associated with large risks. 

9 Detection and attribution: Significant advances have been made in the attribution of the human 

10 influence on individual climate and weather extreme events since NCA3. This assessment 

11 contains extensive discussion of new and emerging findings in this area (Chapters 3,6,7,8).

12 Atmospheric circulation and extreme events: The extent to which atmospheric circulation in the 

13 mid latitudes is changing or is projected to change, possibly in ways not captured by current 

14 climate models, is a new important area of research. While still in its formative stages, this 

15 research is critically important because of the implications of such changes for climate extremes 

16 including extended cold air outbreaks, long-duration heat waves, and changes in storms and 

17 drought patterns (Chapters 5,6,7).

18 Increased understanding of specific types of extreme events: How climate change may affect 

19 specific types of extreme events in the United States is another key area where scientific 

20 understanding has advanced. For example, this report highlights how intense flooding associated 

21 with atmospheric rivers could increase dramatically as the atmosphere and oceans warm, or how 

22 tornadoes could be concentrated into a smaller number of high-impact days (Chapter 9).

23 Model weighting: For the first time, maps and plots of climate projections will not show a 

24 straight average of all available climate models. Rather, each model is given a weight based on 

25 their 1) historical performance relative to observations and 2) independence relative to other 

26 models. Although this is a more accurate way of representing model output, it does not 

27 significantly alter the results: the weighting produces very similar trends and spatial patterns to 

28 the equal-weighting-of-models approach used in prior assessments (Chapters 4,6,7).

29 High-resolution global climate model simulations: As computing resources have grown, 

30 multidecadal simulations of global climate models are now being conducted at horizontal 

31 resolutions on the order of 25 km (15 miles) that enable more realistic simulation of intense 

32 weather systems, including hurricanes. Even the limited number of high-resolution models 

33 currently available have increased confidence in projections of extreme weather (Chapter 9).

34 The so-called global warming hiatus: Since NCA3, many studies have investigated causes for

35 the temporary slowdown in the rate of increase in near-surface global mean temperature from 

36 2000 to 2013. The slowdown, which ended with the record warmth in 2014-2016, is understood 

37 to have been caused by a combination of internal variability, mostly in the heat exchange 

38 between the ocean and the atmosphere, and short-term variations in external forcing factors, both 
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1 human and natural. On longer time scales relevant to human-induced climate change, the planet 

2 continues to warm at a steady pace as predicted by basic atmospheric physics and the well3 documented increase in heat-trapping gases

4 Oceans and coastal waters: Concern over ocean acidification, warming, and oxygen loss is

5 increasing as scientific understanding of the severity of their impacts grows. Both acidification 

6 and oxygen decreases may be magnified in some U.S. coastal waters relative to the global 

7 average, raising the risk of serious ecological and economic consequences. There is also growing 

8 evidence that the Atlantic Meridional Circulation (AMOC), sometimes referred to as the ocean’s 

9 conveyor belt, has slowed down (Chapter 13). 

10 Local sea-level change projections: For the first time in the NCA process, sea-level rise 

11 projections incorporate geographic variation based on factors such as local land subsidence, 

12 ocean currents, and changes in Earth’s gravitational field (Chapter 12). 

13 Accelerated ice-sheet loss and irreversibility: New observations from many different sources 

14 confirm that ice-sheet loss is accelerating. Combined with simultaneous advances in the physical 

15 understanding of ice sheets, scientists are now concluding that up to 8.5 feet of global sea-level 

16 rise is possible by 2100 under a high-emissions scenario, up from 6.6 feet in NCA3 (Chapter 12).

17 Slowing in Arctic sea-ice area extent regrowth: The annual Arctic sea ice extent minimum for 

18 2016 was the second lowest on record. In fall 2016, record-setting slow ice regrowth put 2016–

19 2017 winter ice volume records in jeopardy as well (Chapter 11).

20 Potential surprises: Both large-scale state shifts in the climate system (sometimes called “tipping 

21 points”) and compound extremes have the potential to generate unanticipated surprises. The 

22 further the earth system departs from historical climate forcings, and the more the climate 

23 changes, the greater the potential for these surprises. For the first time in the NCA process we 

24 include an extended discussion of these potential surprises (Chapter 15). 

25 The Paris Agreement: The Paris Agreement, which entered into force November 4, 2016, 

26 provides a new framework for all nations to mitigate and adapt to climate change, and to 

27 periodically update and revisit their respective domestic commitments. The Agreement’s long28 term objective is to limit average global temperature change to well below 2°C (3.6°F) above 

29 preindustrial levels, with best efforts to limit it to(1.5°C (2.7°F), and this assessment addresses 

30 global climate implications of these objectives (Chapter 4, 14).

31 ----- END BOX 1.2 HERE -----

32
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1 TRACEABLE ACCOUNTS

2 Key Finding 1

3 The global climate continues to change rapidly compared to the pace of the natural changes in 

4 climate that have occurred throughout Earth’s history. Trends in globally-averaged temperature, 

5 sea-level rise, upper-ocean heat content, land-based ice melt, and other climate variables provide 

6 consistent evidence of a warming planet. These observed trends are robust, and have been 

7 confirmed by independent research groups around the world.

8 Description of evidence base

9 The Key Finding and supporting text summarize extensive evidence documented in the climate 

10 science literature. Similar to statements made in previous national (NCA3; Melillo et al. 2014) 

11 and international (IPCC 2013) assessments.

12 Evidence for changes in global climate arises from multiple analyses of data from in-situ, 

13 satellite, and other records undertaken by many groups over several decades. These observational 

14 datasets are used throughout this chapter and are discussed further in Appendix 1 (e.g., updates 

15 of prior uses of these datasets by Vose et al. 2012; Karl et al. 2015). Changes in the mean state 

16 have been accompanied by changes in the frequency and nature of extreme events (e.g., Kunkel 

17 and Frankson 2015; Donat et al. 2016). A substantial body of analysis comparing the observed 

18 changes to a broad range of climate simulations consistently points to the necessity of invoking 

19 human-caused changes to adequately explain the observed climate system behavior. The 

20 influence of human impacts on the climate system has also been observed in a number of 

21 individual climate variables (attribution studies are discussed in Chapter 3 and in other chapters). 

22 Major uncertainties 

23 Key remaining uncertainties relate to the precise magnitude and nature of changes at global, and 

24 particularly regional, scales, and especially for extreme events and our ability to simulate and 

25 attribute such changes using climate models. Innovative new approaches to climate data analysis, 

26 continued improvements in climate modeling, and instigation and maintenance of reference 

27 quality observation networks such as the U.S. Climate Reference Network 

28 (http://www.ncei.noaa.gov/crn/) all have the potential to reduce uncertainties. 

29 Assessment of confidence based on evidence and agreement, including short description of 

30 nature of evidence and level of agreement

31 x Very High 

32 ☐ High

33 ☐ Medium 

34 ☐ Low 

35 There is very high confidence that global climate is changing and this change is apparent across a 

36 wide range of observations, given the evidence base and remaining uncertainties. All 
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1 observational evidence is consistent with a warming climate since the late 1800s. There is very 

2 high confidence that the global climate change of the past 50 years is primarily due to human 

3 activities, given the evidence base and remaining uncertainties (IPCC 2013). Recent changes 

4 have been consistently attributed in large part to human factors across a very broad range of 

5 climate system characteristics.

6 Summary sentence or paragraph that integrates the above information

7 The key message and supporting text summarizes extensive evidence documented in the climate 

8 science peer-reviewed literature. The trends described in NCA3 have continued and our 

9 understanding of the observations related to climate and the ability to evaluate the many facets of 

10 the climate system have increased substantially. 

11

12 Key Finding 2

13 The frequency and intensity of heavy precipitation and extreme heat events are increasing in 

14 most regions of the world. These trends are consistent with expected physical responses to a 

15 warming climate and with climate model studies, although models tend to underestimate the 

16 observed trends. The frequency and intensity of such extreme events will very likely continue to 

17 rise in the future. Trends for some other types of extreme events, such as floods, droughts, and 

18 severe storms, have more regional characteristics.

19 Description of evidence base 

20 The Key Finding and supporting text summarizes extensive evidence documented in the climate 

21 science literature and are similar to statements made in previous national (NCA3; Melillo et al., 

22 2014) and international (IPCC 2013) assessments. The analyses of past trends and future 

23 projections in extreme events are also well substantiated through more recent peer review 

24 literature as well (Seneviratne et al. 2014; Easterling et al. 2016; Kunkel and Frankson 2015; 

25 Donat et al. 2016; Berghuijs et al. 2016; Arnell and Gosling 2014).

26 Major uncertainties 

27 Key remaining uncertainties relate to the precise magnitude and nature of changes at global, and 

28 particularly regional, scales, and especially for extreme events and our ability to simulate and 

29 attribute such changes using climate models. Innovative new approaches to climate data analysis, 

30 continued improvements in climate modeling, and instigation and maintenance of reference 

31 quality observation networks such as the U.S. Climate Reference Network 

32 (http://www.ncei.noaa.gov/crn/) all have the potential to reduce uncertainties. 

33 Assessment of confidence based on evidence and agreement, including short description of 

34 nature of evidence and level of agreement 

35 x Very High 

36 ☐ High

37 ☐ Medium 
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1 ☐ Low 

2 There is very high confidence, based on the observational evidence and physical understanding, 

3 that there are major trends in extreme events and significant projected changes for the future.

4 Summary sentence or paragraph that integrates the above information

5 The key message and supporting text summarizes extensive evidence documented in the climate 

6 science peer-reviewed literature. The trends for extreme events that were described in the NCA3 

7 and IPCC assessments have continued and our understanding of the data and ability to evaluate 

8 the many facets of the climate system have increased substantially.

9

10 Key Finding 3

11 Many lines of evidence demonstrate that human activities, especially emissions of greenhouse 

12 gases, are primarily responsible for the observed climate changes in the industrial era. There are 

13 no alternative explanations, and no natural cycles are found in the observational record that can 

14 explain the observed changes in climate.

15 Description of evidence base 

16 The Key Finding and supporting text summarizes extensive evidence documented in the climate 

17 science literature and are similar to statements made in previous national (NCA3; Melillo et al. 

18 2014) and international (IPCC 2013) assessments. The human effects on climate have been well 

19 documented through many papers in the peer reviewed scientific literature (e.g., see Chapters 2 

20 and 3 for more discussion of supporting evidence).

21 Major uncertainties 

22 Key remaining uncertainties relate to the precise magnitude and nature of changes at global, and 

23 particularly regional, scales, and especially for extreme events and our ability to simulate and 

24 attribute such changes using climate models. The exact effects from land use changes relative to 

25 the effects from greenhouse gas emissions needs to be better understood.

26 Assessment of confidence based on evidence and agreement, including short description of 

27 nature of evidence and level of agreement 

28 x Very High 

29 ☐ High

30 ☐ Medium 

31 ☐ Low 

32 There is very high confidence for a major human influence on climate.

33 Summary sentence or paragraph that integrates the above information

34 The key message and supporting text summarizes extensive evidence documented in the climate 

35 science peer-reviewed literature. The analyses described in the NCA3 and IPCC assessments 
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1 support our findings and new observations and modeling studies have further substantiated these 

2 conclusions.

3

4 Key Finding 4

5 Global climate is projected to continue to change over this century and beyond. The magnitude 

6 of climate change beyond the next few decades depends primarily on the amount of greenhouse 

7 (heat trapping) gases emitted globally and the sensitivity of Earth’s climate to those emissions.

8 Description of evidence base

9 The Key Finding and supporting text summarizes extensive evidence documented in the climate 

10 science literature and are similar to statements made in previous national (NCA3; Melillo et al. 

11 2014) and international (IPCC 2013) assessments. The projections for future climate have been 

12 well documented through many papers in the peer reviewed scientific literature (e.g., see Chapter 

13 4 for descriptions of the scenarios and the models used).

14 Major uncertainties

15 Key remaining uncertainties relate to the precise magnitude and nature of changes at global, and 

16 particularly regional, scales, and especially for extreme events and our ability to simulate and 

17 attribute such changes using climate models. Continued improvements in climate modeling to 

18 represent the physical processes affecting the Earth’s climate system are aimed at reducing 

19 uncertainties. Monitoring and observation programs also can help improve the understanding 

20 needed to reduce uncertainties.

21 Assessment of confidence based on evidence and agreement, including short description of 

22 nature of evidence and level of agreement 

23 x Very High 

24 ☐ High

25 ☐ Medium 

26 ☐ Low 

27 There is very high confidence for continued changes in climate.

28 Summary sentence or paragraph that integrates the above information

29 The key message and supporting text summarizes extensive evidence documented in the climate 

30 science peer-reviewed literature. The projections that were described in the NCA3 and IPCC 

31 assessments support our findings and new modeling studies have further substantiated these 

32 conclusions.

33

34
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1 Key Finding 5

2 Natural variability, including El Niño events and other recurring patterns of ocean−atmosphere 

3 interactions, have important, but limited influences on global and regional climate over 

4 timescales ranging from months to decades.

5 Description of evidence base 

6 The Key Finding and supporting text summarizes extensive evidence documented in the climate 

7 science literature and are similar to statements made in previous national (NCA3; Melillo et al. 

8 2014) and international (IPCC 2013) assessments. The role of natural variability in climate 

9 trends has been extensively discussed in the peer reviewed literature (e.g., Karl et al. 2015; 

10 Rahmstorf et al. 2015; Lewandowsky et al. 2016; Mears and Wentz 2016; Trenberth et al. 2014; 

11 Santer et al. 2016).

12 Major uncertainties 

13 Uncertainties still exist in the precise magnitude and nature of the full effects of individual ocean 

14 cycles and other aspects of natural variability on the climate system. Increased emphasis on 

15 monitoring should reduce this uncertainty significantly over the next few decades.

16 Assessment of confidence based on evidence and agreement, including short description of 

17 nature of evidence and level of agreement 

18 x Very High 

19 ☐ High

20 ☐ Medium 

21 ☐ Low 

22 There is very high confidence, affected to some degree by limitations in the observational record, 

23 that the role of natural variability on future climate change is limited.

24 Summary sentence or paragraph that integrates the above information

25 The key message and supporting text summarizes extensive evidence documented in the climate 

26 science peer-reviewed literature. There has been an extensive increase in the understanding of 

27 the role of natural variability on the climate system over the last few decades, including a 

28 number of new findings since NCA3.

29

30 Key Finding 6

31 Longer-term climate records indicate that average temperatures in recent decades over much of 

32 the world have been much higher than at any time in the past 1700 years or more.

33 Description of evidence base 

34 The Key Finding and supporting text summarizes extensive evidence documented in the climate 

35 science literature and are similar to statements made in previous national (NCA3; Melillo et al., 

36 2014) and international (IPCC 2013) assessments. There are many recent studies of the 
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1 paleocliamte leading to this conclusion including those cited in the report (e.g., Mann et al. 2008; 

2 PAGE 2K 2013).

3 Major uncertainties 

4 Despite the extensive increase in knowledge in the last few decades, there are still many 

5 uncertainties in understanding the hemispheric and global changes in climate over the Earth’s 

6 history, including that of the last few millennia. Additional research efforts in this direction can 

7 help reduce those uncertainties.

8 Assessment of confidence based on evidence and agreement, including short description of 

9 nature of evidence and level of agreement 

10 ☐ Very High 

11 x High

12 ☐ Medium 

13 ☐ Low 

14 There is high confidence for current temperatures to be higher than they have been in at least 

15 1700 years and perhaps much longer.

16 Summary sentence or paragraph that integrates the above information

17 The key message and supporting text summarizes extensive evidence documented in the climate 

18 science peer-reviewed literature. There has been an extensive increase in the understanding of 

19 past climates on our planet, including a number of new findings since NCA3.

20
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1 FIGURES

2

3 Figure 1.1. Examples of the observations from many different indicators of a changing climate. 

4 Anomalies are relative to 1976–2005 averages for the indicated variables. (Figure source: 

5 updated from Melillo et al. 2014).

6
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1

2 Figure 1.2. Top: Global annual average temperature (as measured over both land and oceans) 

3 has increased by more than 1.6°F (0.9°C) for the period from 1986-2015 relative to 1901-1960. 

4 Red bars show temperatures above the long-term 1880-2015 average, and blue bars indicate 

5 temperatures below the average over the entire period. While there is a clear long-term global 

6 warming trend, some years do not show a temperature increase relative to the previous year, and 

7 some years show greater changes than others. These year-to-year fluctuations in temperature are 

8 mainly due to natural sources of variability, such as the effects of El Niños, La Niñas, and 

9 volcanic eruptions. Based on the NCEI (NOAAGlobalTemp) data set 1901–2015 (updated from 

10 Vose et al. 2012). Bottom: Global average temperature averaged over decadal periods (1886–

11 1895, 1896–1905, ..., 1996–2005, 2006–2015). Horizontal label indicates mid-point year of 

12 decadal period. Every decade since 1966–1975 has been warmer than the previous decade.

13 (Figure source: (top) adapted from NCEI 2016, (bottom) NOAA NCEI / CICS-NC)
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1

2

3 Figure 1.3. Surface temperature trends (change in °F) for the period 1986–2015 relative to 

4 1901–1960 from the NOAA National Centers for Environmental Information’s (NCEI) surface 

5 temperature product. The relatively coarse (5.0° x 5.0°) resolution of these maps does not capture 

6 the finer details associated with mountains, coastlines, and other small-scale effects. (Figure 

7 source: updated from Vose et al. 2012).

8
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1

2 Figure 1.4. Multimodel simulated time series from 1950 to 2100 for the change in global annual 

3 mean surface temperature relative to 1986–2005 for a range of future emissions scenarios that 

4 account for the uncertainty in future emissions from human activities [as analyzed with the 20+ 

5 models from around the world used in the most recent international assessment (IPCC 2013)]. 

6 The mean and associated uncertainties [1.64 standard deviations (5%–95%) across the 

7 distribution of individual models (shading)] based on the averaged over 2081–2100 are given for 

8 all of the RCP scenarios as colored vertical bars. The numbers of models used to calculate the 

9 multimodel means are indicated. (Figure source: adapted from Walsh et al. 2014).
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1

2 Figure 1.5. Panel A shows the annual mean temperature anomalies relative to a 1971–2000 

3 baseline for global mean surface temperature and global mean tropospheric temperature. A 

4 previous period of relatively slow warming (the “Big Hiatus”) is obvious from the mid-1940s to 

5 the mid-1970s. Panel B shows the linear trend of 17-year overlapping periods (the maximum 

6 number of years historically for less than positive trends), plotted at the time of the center of the 

7 trend period. During the recent slowdown period, warming only ceased for two versions of the 

8 satellite data, and for a very narrow range of time periods. All 17-year trends are increasing 

9 rapidly as the effects of the 2015–2016 El Niño-Southern Oscillation (ENSO) event begin to 

10 affect the trends. Panel C shows the annual mean Pacific Decadal Oscillation (PDO) index. 

11 Temperature trends show a marked tendency to be lower during periods of generally negative 

12 PDO index, shown by the blue shading. (Figure source: adapted and updated from Trenberth 

13 2015 and Santer et al. 2016; Panel B, © American Meteorological Society. Used with 

14 permission.)
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1

Figure 1.6. Comparison of global mean temperature anomalies (o 2 F) from observations (through 

3 2015) and the CMIP5 multimodel ensemble (through 2016), using the reference period 1961-

4 1990. The CMIP5 multimodel ensemble (black) is constructed from blended surface temperature 

5 and surface air temperature data from the models, masked where observations are not available 

6 in the HadCRUT4 dataset (Knutson et al. 2016; see also Richardson et al. 2016). The sources for 

7 the three observational indices are: HadCRUT4.5 (red): 

8 http://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/download.html; NOAA (green):

9 https://www.ncdc.noaa.gov/monitoring-references/faq/anomalies.php; and GISTEMP (blue): 

10 http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt (all downloaded on Oct. 3, 

11 2016). (Figure source: adapted from Knutson et al. 2016)
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1

2

3

4 Figure 1.7. Surface annually-averaged precipitation trends (change in inches) for the period 

5 1986–2015 relative to 1901–1960. The relatively coarse (0.5° x 0.5°) resolution of these maps 

6 does not capture the finer details associated with mountains, coastlines, and other small-scale 

7 effects. (Figure source: NOAA NCEI / CICS-NC).

8
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1

2 Figure 1.8. Changes in the temperature of the northern hemisphere from surface observations (in 

3 red) and from proxies (in black; uncertainty range represented by shading) relative to 1961-1990 

4 average temperature. These analyses suggest that current temperatures are higher than seen 

5 globally in at least the last 1700 years, and that the last decade (2006 to 2015) was the warmest 

6 decade on record. (Figure source: adapted from Mann et al. 2008). 

7
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1

2 Figure 1.9. Proxy temperatures reconstructions for the seven regions of the PAGES 2K 

3 Network. Temperature anomalies are relative to the 1961–1990 reference period. Grey lines 

4 around expected-value estimates indicate uncertainty ranges as defined by each regional group 

5 (see PAGE 2K et al. 2013 and related Supplementary Information). Note that the changes in 

6 temperature over the last century tend to occur at a much faster rate than found in the previous 

7 time periods. (Figure source: adapted from PAGES 2k et al. 2013)
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1 REFERENCES

2 Adler, R.F., G.J. Huffman, A. Chang, R. Ferraro, P.-P. Xie, J. Janowiak, B. Rudolf, U. 

3 Schneider, S. Curtis, D. Bolvin, A. Gruber, J. Susskind, P. Arkin, and E. Nelkin, 2003: The 

4 version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis 

5 (1979–present). Journal of Hydrometeorology, 4, 1147-1167. http://dx.doi.org/10.1175/1525-

6 7541(2003)004<1147:TVGPCP>2.0.CO;2 

7 Alexander, L.V., X. Zhang, T.C. Peterson, J. Caesar, B. Gleason, A.M.G. Klein Tank, M. 

8 Haylock, D. Collins, B. Trewin, F. Rahimzadeh, A. Tagipour, K. Rupa Kumar, J. Revadekar, 

9 G. Griffiths, L. Vincent, D.B. Stephenson, J. Burn, E. Aguilar, M. Brunet, M. Taylor, M. 

10 New, P. Zhai, M. Rusticucci, and J.L. Vazquez-Aguirre, 2006: Global observed changes in 

11 daily climate extremes of temperature and precipitation. Journal of Geophysical Research,

12 111, 22. http://dx.doi.org/10.1029/2005JD006290 

13 Allen, M.R. and W.J. Ingram, 2002: Constraints on future changes in climate and the hydrologic 

14 cycle. Nature, 419, 224-232. http://dx.doi.org/10.1038/nature01092 

15 Anderson, B.T., J.R. Knight, M.A. Ringer, J.-H. Yoon, and A. Cherchi, 2012: Testing for the 

16 Possible Influence of Unknown Climate Forcings upon Global Temperature Increases from 

17 1950 to 2000. Journal of Climate, 25, 7163-7172. http://dx.doi.org/10.1175/jcli-d-11-00645.1 

18 Arnell, N.W. and S.N. Gosling, 2016: The impacts of climate change on river flood risk at the 

19 global scale. Climatic Change, 134, 387-401. http://dx.doi.org/10.1007/s10584-014-1084-5 

20 Asadieh, B. and N.Y. Krakauer, 2015: Global trends in extreme precipitation: climate models 

21 versus observations. Hydrology and Earth System Sciences, 19, 877-891.

22 http://dx.doi.org/10.5194/hess-19-877-2015 

23 Balmaseda, M.A., K.E. Trenberth, and E. Källén, 2013: Distinctive climate signals in reanalysis 

24 of global ocean heat content. Geophysical Research Letters, 40, 1754-1759.

25 http://dx.doi.org/10.1002/grl.50382 

26 Barnes, E.A. and L.M. Polvani, 2015: CMIP5 Projections of Arctic Amplification, of the North 

27 American/North Atlantic Circulation, and of Their Relationship. Journal of Climate, 28,

28 5254-5271. http://dx.doi.org/10.1175/JCLI-D-14-00589.1 

29 Becker, A., P. Finger, A. Meyer-Christoffer, B. Rudolf, K. Schamm, U. Schneider, and M. Ziese, 

30 2013: A description of the global land-surface precipitation data products of the Global 

31 Precipitation Climatology Centre with sample applications including centennial (trend) 

32 analysis from 1901–present. Earth System Science Data, 5, 71-99.

33 http://dx.doi.org/10.5194/essd-5-71-2013 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 1

68

1 Bender, F.A.-M., V. Ramanathan, and G. Tselioudis, 2012: Changes in extratropical storm track 

2 cloudiness 1983–2008: observational support for a poleward shift. Climate Dynamics, 38,

3 2037-2053. http://dx.doi.org/10.1007/s00382-011-1065-6 

4 Berghuijs, W.R., R.A. Woods, C.J. Hutton, and M. Sivapalan, 2016: Dominant flood generating 

5 mechanisms across the United States. Geophysical Research Letters, 43, 4382-4390.

6 http://dx.doi.org/10.1002/2016GL068070 

7 Bernier, P.Y., R.L. Desjardins, Y. Karimi-Zindashty, D. Worth, A. Beaudoin, Y. Luo, and S. 

8 Wang, 2011: Boreal lichen woodlands: A possible negative feedback to climate change in 

9 eastern North America. Agricultural and Forest Meteorology, 151, 521-528.

10 http://dx.doi.org/10.1016/j.agrformet.2010.12.013 

11 Betts, R.A., O. Boucher, M. Collins, P.M. Cox, P.D. Falloon, N. Gedney, D.L. Hemming, C. 

12 Huntingford, C.D. Jones, D.M.H. Sexton, and M.J. Webb, 2007: Projected increase in 

13 continental runoff due to plant responses to increasing carbon dioxide. Nature, 448, 1037-

14 1041. http://dx.doi.org/10.1038/nature06045 

15 Bindoff, N.L., P.A. Stott, K.M. AchutaRao, M.R. Allen, N. Gillett, D. Gutzler, K. Hansingo, G. 

16 Hegerl, Y. Hu, S. Jain, I.I. Mokhov, J. Overland, J. Perlwitz, R. Sebbari, and X. Zhang, 2013: 

17 Detection and Attribution of Climate Change: from Global to Regional. Climate Change 

18 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment 

19 Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. 

20 Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, 

21 Eds. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 

22 867–952. http://dx.doi.org/10.1017/CBO9781107415324.022 www.climatechange2013.org

23 Blunden, J. and D.S. Arndt, 2016: State of the Climate in 2015. Bulletin of the American 

24 Meteorological Society, 97, Si-S275.

25 http://dx.doi.org/10.1175/2016BAMSStateoftheClimate.1 

26 Bonan, G.B., 2008: Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits 

27 of Forests. Science, 320, 1444-1449. http://dx.doi.org/10.1126/science.1155121 

28 Chambers, J.Q., J.I. Fisher, H. Zeng, E.L. Chapman, D.B. Baker, and G.C. Hurtt, 2007: 

29 Hurricane Katrina's Carbon Footprint on U.S. Gulf Coast Forests. Science, 318, 1107-1107.

30 http://dx.doi.org/10.1126/science.1148913 

31 Chang, E.K.M., 2013: CMIP5 projection of significant reduction in extratropical cyclone activity 

32 over North America. Journal of Climate, 26, 9903-9922. http://dx.doi.org/10.1175/JCLI-D33 13-00209.1 

34 Chen, X. and K.-K. Tung, 2014: Varying planetary heat sink led to global-warming slowdown 

35 and acceleration. Science, 345, 897-903. http://dx.doi.org/10.1126/science.1254937 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 1

69

1 Church, J.A. and N.J. White, 2011: Sea-level rise from the late 19th to the early 21st century. 

2 Surveys in Geophysics, 32, 585-602. http://dx.doi.org/10.1007/s10712-011-9119-1 

3 Church, J.A., N.J. White, L.F. Konikow, C.M. Domingues, J.G. Cogley, E. Rignot, J.M.

4 Gregory, M.R. van den Broeke, A.J. Monaghan, and I. Velicogna, 2011: Revisiting the 

5 Earth’s sea-level and energy budgets from 1961 to 2008. Geophysical Research Letters, 38,

6 L18601. http://dx.doi.org/10.1029/2011GL048794 

7 Churkina, G., V. Brovkin, W. von Bloh, K. Trusilova, M. Jung, and F. Dentener, 2009: Synergy 

8 of rising nitrogen depositions and atmospheric CO2 on land carbon uptake moderately offsets 

9 global warming. Global Biogeochemical Cycles, 23, n/a-n/a.

10 http://dx.doi.org/10.1029/2008GB003291 

11 Ciais, P., M. Reichstein, N. Viovy, A. Granier, J. Ogee, V. Allard, M. Aubinet, N. Buchmann, C. 

12 Bernhofer, A. Carrara, F. Chevallier, N. De Noblet, A.D. Friend, P. Friedlingstein, T. 

13 Grunwald, B. Heinesch, P. Keronen, A. Knohl, G. Krinner, D. Loustau, G. Manca, G. 

14 Matteucci, F. Miglietta, J.M. Ourcival, D. Papale, K. Pilegaard, S. Rambal, G. Seufert, J.F. 

15 Soussana, M.J. Sanz, E.D. Schulze, T. Vesala, and R. Valentini, 2005: Europe-wide 

16 reduction in primary productivity caused by the heat and drought in 2003. Nature, 437, 529-

17 533. http://dx.doi.org/10.1038/nature03972 

18 Clark, D.B., D.A. Clark, and S.F. Oberbauer, 2010: Annual wood production in a tropical rain 

19 forest in NE Costa Rica linked to climatic variation but not to increasing CO2. Global 

20 Change Biology, 16, 747-759. http://dx.doi.org/10.1111/j.1365-2486.2009.02004.x 

21 Colle, B.A., Z. Zhang, K.A. Lombardo, E. Chang, P. Liu, and M. Zhang, 2013: Historical 

22 Evaluation and Future Prediction of Eastern North American and Western Atlantic 

23 Extratropical Cyclones in the CMIP5 Models during the Cool Season. Journal of Climate,

24 26, 6882-6903. http://dx.doi.org/10.1175/JCLI-D-12-00498.1 

25 Collins, M., R. Knutti, J. Arblaster, J.-L. Dufresne, T. Fichefet, P. Friedlingstein, X. Gao, W.J. 

26 Gutowski, T. Johns, G. Krinner, M. Shongwe, C. Tebaldi, A.J. Weaver, and M. Wehner, 

27 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. Climate 

28 Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth 

29 Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. 

30 Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and 

31 P.M. Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom and New 

32 York, NY, USA, 1029–1136. http://dx.doi.org/10.1017/CBO9781107415324.024 

33 www.climatechange2013.org

34 Comiso, J.C. and D.K. Hall, 2014: Climate trends in the Arctic as observed from space. Wiley 

35 Interdisciplinary Reviews: Climate Change, 5, 389-409. http://dx.doi.org/10.1002/wcc.277 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 1

70

1 Dai, A., 2013: Increasing drought under global warming in observations and models. Nature 

2 Climate Change, 3, 52-58. http://dx.doi.org/10.1038/nclimate1633 

3 Davy, R., I. Esau, A. Chernokulsky, S. Outten, and S. Zilitinkevich, 2016: Diurnal asymmetry to 

4 the observed global warming. International Journal of Climatology, n/a-n/a.

5 http://dx.doi.org/10.1002/joc.4688 

6 de Noblet-Ducoudré, N., J.-P. Boisier, A. Pitman, G.B. Bonan, V. Brovkin, F. Cruz, C. Delire, 

7 V. Gayler, B.J.J.M.v.d. Hurk, P.J. Lawrence, M.K.v.d. Molen, C. Müller, C.H. Reick, B.J. 

8 Strengers, and A. Voldoire, 2012: Determining Robust Impacts of Land-Use-Induced Land 

9 Cover Changes on Surface Climate over North America and Eurasia: Results from the First 

10 Set of LUCID Experiments. Journal of Climate, 25, 3261-3281.

11 http://dx.doi.org/10.1175/JCLI-D-11-00338.1 

12 DeConto, R.M. and D. Pollard, 2016: Contribution of Antarctica to past and future sea-level rise.

13 Nature, 531, 591-597. http://dx.doi.org/10.1038/nature17145 

14 Delworth, T.L. and T.R. Knutson, 2000: Simulation of Early 20th Century Global Warming. 

15 Science, 287, 2246-2250. http://dx.doi.org/10.1126/science.287.5461.2246 

16 Derksen, C. and R. Brown, 2012: Spring snow cover extent reductions in the 2008–2012 period 

17 exceeding climate model projections. Geophysical Research Letters, 39, L19504.

18 http://dx.doi.org/10.1029/2012gl053387 

19 Derksen, D., R. Brown, L. Mudryk, and K. Loujus, 2015: [The Arctice] Terrestrial snow cover 

20 [in “State of the Climate in 2014”]. Bulletin of the American Meteorological Society, 96,

21 S133-S135. http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1 

22 Deser, C., R. Knutti, S. Solomon, and A.S. Phillips, 2012: Communication of the role of natural 

23 variability in future North American climate. Nature Climate Change, 2, 775-779.

24 http://dx.doi.org/10.1038/nclimate1562 

25 http://www.nature.com/nclimate/journal/v2/n11/full/nclimate1562.html?WT.ec_id=NCLIM

26 ATE-201211

27 Diffenbaugh, N.S., M. Scherer, and R.J. Trapp, 2013: Robust increases in severe thunderstorm 

28 environments in response to greenhouse forcing. Proceedings of the National Academy of 

29 Sciences, 110, 16361-16366. http://dx.doi.org/10.1073/pnas.1307758110 

30 Donat, M.G., A.L. Lowry, L.V. Alexander, P.A. Ogorman, and N. Maher, 2016: More extreme 

31 precipitation in the world[rsquor]s dry and wet regions. Nature Climate Change, 6, 508-513.

32 http://dx.doi.org/10.1038/nclimate2941 

33 Dutton, A. and K. Lambeck, 2012: Ice Volume and Sea Level During the Last Interglacial. 

34 Science, 337, 216-219. http://dx.doi.org/10.1126/science.1205749 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 1

71

1 Easterling, D.R., K.E. Kunkel, M.F. Wehner, and L. Sun, 2016: Detection and attribution of 

2 climate extremes in the observed record. Weather and Climate Extremes, 11, 17-27.

3 http://dx.doi.org/10.1016/j.wace.2016.01.001 

4 Easterling, D.R. and M.F. Wehner, 2009: Is the climate warming or cooling? Geophysical 

5 Research Letters, 36, 3. http://dx.doi.org/10.1029/2009GL037810 

6 Eisenman, I., W.N. Meier, and J.R. Norris, 2014: A spurious jump in the satellite record: has 

7 Antarctic sea ice expansion been overestimated? The Cryosphere, 8, 1289-1296.

8 http://dx.doi.org/10.5194/tc-8-1289-2014 

9 Elsner, J.B., J.P. Kossin, and T.H. Jagger, 2008: The increasing intensity of the strongest tropical 

10 cyclones. Nature, 455, 92-95. http://dx.doi.org/10.1038/nature07234 

11 Emanuel, K.A., 2013: Downscaling CMIP5 climate models shows increased tropical cyclone 

12 activity over the 21st century. Proceedings of the National Academy of Sciences, 110, 12219-

13 12224. http://dx.doi.org/10.1073/pnas.1301293110 

14 England, M.H., S. McGregor, P. Spence, G.A. Meehl, A. Timmermann, W. Cai, A.S. Gupta, 

15 M.J. McPhaden, A. Purich, and A. Santoso, 2014: Recent intensification of wind-driven 

16 circulation in the Pacific and the ongoing warming hiatus. Nature Climate Change, 4, 222-

17 227. http://dx.doi.org/10.1038/nclimate2106 

18 Ezer, T. and L.P. Atkinson, 2014: Accelerated flooding along the U.S. East Coast: On the impact 

19 of sea-level rise, tides, storms, the Gulf Stream, and the North Atlantic Oscillations. Earth's 

20 Future, 2, 362-382. http://dx.doi.org/10.1002/2014EF000252 

21 Feldmann, J. and A. Levermann, 2015: Collapse of the West Antarctic Ice Sheet after local 

22 destabilization of the Amundsen Basin. Proceedings of the National Academy of Sciences,

23 112, 14191-14196. http://dx.doi.org/10.1073/pnas.1512482112 

24 Fettweis, X., M. Tedesco, M. van den Broeke, and J. Ettema, 2011: Melting trends over the 

25 Greenland ice sheet (1958–2009) from spaceborne microwave data and regional climate 

26 models. The Cryosphere, 5, 359-375. http://dx.doi.org/10.5194/tc-5-359-2011 

27 Finzi, A.C., D.J.P. Moore, E.H. DeLucia, J. Lichter, K.S. Hofmockel, R.B. Jackson, H.-S. Kim, 

28 R. Matamala, H.R. McCarthy, R. Oren, J.S. Pippen, and W.H. Schlesinger, 2006: Progressive 

29 nitrogen limitation of ecosystem processes under elevated CO2 in a warm-temperate forest. 

30 Ecology, 87, 15-25. http://dx.doi.org/10.1890/04-1748 

31 Fyfe, J.C., G.A. Meehl, M.H. England, M.E. Mann, B.D. Santer, G.M. Flato, E. Hawkins, N.P. 

32 Gillett, S.-P. Xie, Y. Kosaka, and N.C. Swart, 2016: Making sense of the early-2000s 

33 warming slowdown. Nature Climate Change, 6, 224-228.

34 http://dx.doi.org/10.1038/nclimate2938 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 1

72

1 Greve, P., B. Orlowsky, B. Mueller, J. Sheffield, M. Reichstein, and S.I. Seneviratne, 2014: 

2 Global assessment of trends in wetting and drying over land. Nature Geoscience, 7, 716-721.

3 http://dx.doi.org/10.1038/ngeo2247 

4 Harig, C. and F.J. Simons, 2012: Mapping Greenland’s mass loss in space and time. Proceedings 

5 of the National Academy of Sciences, 109, 19934-19937.

6 http://dx.doi.org/10.1073/pnas.1206785109 

7 Harig, C. and F.J. Simons, 2015: Accelerated West Antarctic ice mass loss continues to outpace 

8 East Antarctic gains. Earth and Planetary Science Letters, 415, 134-141.

9 http://dx.doi.org/10.1016/j.epsl.2015.01.029 

10 Harig, C. and F.J. Simons, 2016: Ice mass loss in Greenland, the Gulf of Alaska, and the 

11 Canadian Archipelago: Seasonal cycles and decadal trends. Geophysical Research Letters,

12 43, 3150-3159. http://dx.doi.org/10.1002/2016GL067759 

13 Hay, C.C., E. Morrow, R.E. Kopp, and J.X. Mitrovica, 2015: Probabilistic reanalysis of 

14 twentieth-century sea-level rise. Nature, 517, 481-484. http://dx.doi.org/10.1038/nature14093 

15 Haywood, A.M., D.J. Hill, A.M. Dolan, B.L. Otto-Bliesner, F. Bragg, W.L. Chan, M.A. 

16 Chandler, C. Contoux, H.J. Dowsett, A. Jost, Y. Kamae, G. Lohmann, D.J. Lunt, A. Abe17 Ouchi, S.J. Pickering, G. Ramstein, N.A. Rosenbloom, U. Salzmann, L. Sohl, C. Stepanek, 

18 H. Ueda, Q. Yan, and Z. Zhang, 2013: Large-scale features of Pliocene climate: results from 

19 the Pliocene Model Intercomparison Project. Climate of the Past, 9, 191-209.

20 http://dx.doi.org/10.5194/cp-9-191-2013 

21 Held, I.M. and B.J. Soden, 2006: Robust responses of the hydrological cycle to global warming. 

22 Journal of Climate, 19, 5686-5699. http://dx.doi.org/10.1175/jcli3990.1 

23 Hoegh-Guldberg, O., R. Cai, E.S. Poloczanska, P.G. Brewer, S. Sundby, K. Hilmi, V.J. Fabry, 

24 and S. Jung, 2014: The Ocean. Climate Change 2014: Impacts, Adaptation, and 

25 Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth 

26 Assessment Report of the Intergovernmental Panel of Climate Change. Barros, V.R., C.B. 

27 Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. 

28 Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, 

29 and L.L. White, Eds. Cambridge University Press, Cambridge, United Kingdom and New 

30 York, NY, USA, 1655-1731.

31 Hoerling, M., M. Chen, R. Dole, J. Eischeid, A. Kumar, J.W. Nielsen-Gammon, P. Pegion, J. 

32 Perlwitz, X.-W. Quan, and T. Zhang, 2013: Anatomy of an extreme event. Journal of 

33 Climate, 26, 2811–2832. http://dx.doi.org/10.1175/JCLI-D-12-00270.1 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 1

73

1 Horton, D.E., N.C. Johnson, D. Singh, D.L. Swain, B. Rajaratnam, and N.S. Diffenbaugh, 2015: 

2 Contribution of changes in atmospheric circulation patterns to extreme temperature trends. 

3 Nature, 522, 465-469. http://dx.doi.org/10.1038/nature14550 

4 Houghton, R.A., J.I. House, J. Pongratz, G.R. van der Werf, R.S. DeFries, M.C. Hansen, C. Le 

5 Quéré, and N. Ramankutty, 2012: Carbon emissions from land use and land-cover change. 

6 Biogeosciences, 9, 5125-5142. http://dx.doi.org/10.5194/bg-9-5125-2012 

7 Huber, M. and R. Knutti, 2014: Natural variability, radiative forcing and climate response in the 

8 recent hiatus reconciled. Nature Geoscience, 7, 651-656. http://dx.doi.org/10.1038/ngeo2228 

9 Hurrell, J.W. and C. Deser, 2009: North Atlantic climate variability: The role of the North 

10 Atlantic Oscillation. Journal of Marine Systems, 78, 28-41.

11 http://dx.doi.org/10.1016/j.jmarsys.2008.11.026 

12 IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group 

13 I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

14 Cambridge University Press, Cambridge, UK and New York, NY, 1535 pp.

15 http://dx.doi.org/10.1017/CBO9781107415324 www.climatechange2013.org

16 Jacob, T., J. Wahr, W.T. Pfeffer, and S. Swenson, 2012: Recent contributions of glaciers and ice 

17 caps to sea level rise. Nature, 482, 514-518. http://dx.doi.org/10.1038/nature10847 

18 Jenkins, A., P. Dutrieux, S.S. Jacobs, S.D. McPhail, J.R. Perrett, A.T. Webb, and D. White, 

19 2010: Observations beneath Pine Island Glacier in West Antarctica and implications for its 

20 retreat. Nature Geoscience, 3, 468-472. http://dx.doi.org/10.1038/ngeo890 

21 Jones, D.A., W. Wang, and R. Fawcett, 2009: High-quality spatial climate data-sets for 

22 Australia. Australian Meteorological and Oceanographic Journal, 58, 233-248.

23 Joughin, I., B.E. Smith, and B. Medley, 2014: Marine Ice Sheet Collapse Potentially Under Way 

24 for the Thwaites Glacier Basin, West Antarctica. Science, 344, 735-738.

25 http://dx.doi.org/10.1126/science.1249055 

26 Karl, T.R., A. Arguez, B. Huang, J.H. Lawrimore, J.R. McMahon, M.J. Menne, T.C. Peterson, 

27 R.S. Vose, and H.-M. Zhang, 2015: Possible artifacts of data biases in the recent global 

28 surface warming hiatus. Science, 348, 1469-1472. http://dx.doi.org/10.1126/science.aaa5632 

29 Kaspar, F., N. Kühl, U. Cubasch, and T. Litt, 2005: A model-data comparison of European 

30 temperatures in the Eemian interglacial. Geophysical Research Letters, 32, n/a-n/a.

31 http://dx.doi.org/10.1029/2005GL022456 

32 Katz, R.W. and B.G. Brown, 1992: Extreme events in a changing climate: Variability is more 

33 important than averages. Climatic Change, 21, 289-302.

34 http://dx.doi.org/10.1007/bf00139728 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 1

74

1 Kim, Y., J.S. Kimball, K. Zhang, and K.C. McDonald, 2012: Satellite detection of increasing 

2 Northern Hemisphere non-frozen seasons from 1979 to 2008: Implications for regional 

3 vegetation growth. Remote Sensing of Environment, 121, 472-487.

4 http://dx.doi.org/10.1016/j.rse.2012.02.014 

5 Knutson, T.R., J.J. Sirutis, M. Zhao, R.E. Tuleya, M. Bender, G.A. Vecchi, G. Villarini, and D. 

6 Chavas, 2015: Global Projections of Intense Tropical Cyclone Activity for the Late Twenty7 First Century from Dynamical Downscaling of CMIP5/RCP4.5 Scenarios. Journal of 

8 Climate, 28, 7203-7224. http://dx.doi.org/10.1175/JCLI-D-15-0129.1 

9 Knutson, T.R., R. Zhang, and L.W. Horowitz, 2016: Prospects for a prolonged slowdown in 

10 global warming in the early 21st century. Nature Communcations, 7, 13676.

11 http://dx.doi.org/10.1038/ncomms13676 

12 Kopp, R.E., R.M. Horton, C.M. Little, J.X. Mitrovica, M. Oppenheimer, D.J. Rasmussen, B.H. 

13 Strauss, and C. Tebaldi, 2014: Probabilistic 21st and 22nd century sea-level projections at a 

14 global network of tide-gauge sites. Earth's Future, 2, 383-406.

15 http://dx.doi.org/10.1002/2014EF000239 

16 Kopp, R.E., F.J. Simons, J.X. Mitrovica, A.C. Maloof, and M. Oppenheimer, 2009: Probabilistic 

17 assessment of sea level during the last interglacial stage. Nature, 462, 863-867.

18 http://dx.doi.org/10.1038/nature08686 

19 Kosaka, Y. and S.-P. Xie, 2013: Recent global-warming hiatus tied to equatorial Pacific surface 

20 cooling. Nature, 501, 403-407. http://dx.doi.org/10.1038/nature12534 

21 Kossin, J.P., K.A. Emanuel, and S.J. Camargo, 2016: Past and Projected Changes in Western 

22 North Pacific Tropical Cyclone Exposure. Journal of Climate, 29, 5725-5739.

23 http://dx.doi.org/10.1175/JCLI-D-16-0076.1 

24 Kossin, J.P., K.A. Emanuel, and G.A. Vecchi, 2014: The poleward migration of the location of 

25 tropical cyclone maximum intensity. Nature, 509, 349-352.

26 http://dx.doi.org/10.1038/nature13278 

27 Kossin, J.P., T.L. Olander, and K.R. Knapp, 2013: Trend analysis with a new global record of 

28 tropical cyclone intensity. Journal of Climate, 26, 9960-9976.

29 http://dx.doi.org/10.1175/JCLI-D-13-00262.1 

30 Kundzewicz, Z.W., S. Kanae, S.I. Seneviratne, J. Handmer, N. Nicholls, P. Peduzzi, R. Mechler, 

31 L.M. Bouwer, N. Arnell, K. Mach, R. Muir-Wood, G.R. Brakenridge, W. Kron, G. Benito, 

32 Y. Honda, K. Takahashi, and B. Sherstyukov, 2014: Flood risk and climate change: global 

33 and regional perspectives. Hydrological Sciences Journal, 59, 1-28.

34 http://dx.doi.org/10.1080/02626667.2013.857411 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 1

75

1 Kunkel, K.E. and R.M. Frankson, 2015: Global Land Surface Extremes of Precipitation: Data 

2 Limitations and Trends. Journal of Extreme Events, 02, 1550004.

3 http://dx.doi.org/10.1142/S2345737615500049 

4 Kunkel, K.E., T.R. Karl, H. Brooks, J. Kossin, J. Lawrimore, D. Arndt, L. Bosart, D. Changnon, 

5 S.L. Cutter, N. Doesken, K. Emanuel, P.Y. Groisman, R.W. Katz, T. Knutson, J. O’Brien, 

6 C.J. Paciorek, T.C. Peterson, K. Redmond, D. Robinson, J. Trapp, R. Vose, S. Weaver, M. 

7 Wehner, K. Wolter, and D. Wuebbles, 2013: Monitoring and understanding trends in extreme 

8 storms: State of knowledge. Bulletin of the American Meteorological Society, 94.

9 http://dx.doi.org/10.1175/BAMS-D-11-00262.1 

10 Kunkel, K.E., D.A. Robinson, S. Champion, X. Yin, T. Estilow, and R.M. Frankson, 2016: 

11 Trends and Extremes in Northern Hemisphere Snow Characteristics. Current Climate 

12 Change Reports, 2, 65-73. http://dx.doi.org/10.1007/s40641-016-0036-8 

13 Kurz, W.A., G. Stinson, G.J. Rampley, C.C. Dymond, and E.T. Neilson, 2008: Risk of natural 

14 disturbances makes future contribution of Canada's forests to the global carbon cycle highly 

15 uncertain. Proceedings of the National Academy of Sciences, 105, 1551-1555.

16 http://dx.doi.org/10.1073/pnas.0708133105 

17 Le Quéré, C., R. Moriarty, R.M. Andrew, J.G. Canadell, S. Sitch, J.I. Korsbakken, P. 

18 Friedlingstein, G.P. Peters, R.J. Andres, T.A. Boden, R.A. Houghton, J.I. House, R.F. 

19 Keeling, P. Tans, A. Arneth, D.C.E. Bakker, L. Barbero, L. Bopp, J. Chang, F. Chevallier, 

20 L.P. Chini, P. Ciais, M. Fader, R.A. Feely, T. Gkritzalis, I. Harris, J. Hauck, T. Ilyina, A.K. 

21 Jain, E. Kato, V. Kitidis, K. Klein Goldewijk, C. Koven, P. Landschützer, S.K. Lauvset, N. 

22 Lefèvre, A. Lenton, I.D. Lima, N. Metzl, F. Millero, D.R. Munro, A. Murata, J.E.M.S. Nabel, 

23 S. Nakaoka, Y. Nojiri, K. O'Brien, A. Olsen, T. Ono, F.F. Pérez, B. Pfeil, D. Pierrot, B. 

24 Poulter, G. Rehder, C. Rödenbeck, S. Saito, U. Schuster, J. Schwinger, R. Séférian, T. 

25 Steinhoff, B.D. Stocker, A.J. Sutton, T. Takahashi, B. Tilbrook, I.T. van der Laan-Luijkx, 

26 G.R. van der Werf, S. van Heuven, D. Vandemark, N. Viovy, A. Wiltshire, S. Zaehle, and N. 

27 Zeng, 2015: Global Carbon Budget 2015. Earth System Science Data, 7, 349-396.

28 http://dx.doi.org/10.5194/essd-7-349-2015 

29 Lewandowsky, S., J.S. Risbey, and N. Oreskes, 2016: The “Pause” in Global Warming: Turning 

30 a Routine Fluctuation into a Problem for Science. Bulletin of the American Meteorological 

31 Society, 97, 723-733. http://dx.doi.org/10.1175/BAMS-D-14-00106.1 

32 Lewis, S.L., P.M. Brando, O.L. Phillips, G.M.F. van der Heijden, and D. Nepstad, 2011: The 

33 2010 Amazon Drought. Science, 331, 554-554. http://dx.doi.org/10.1126/science.1200807 

34 Lim, Y.-K., D.S. Siegfried, M.J.N. Sophie, N.L. Jae, M.M. Andrea, I.C. Richard, Z. Bin, and V. 

35 Isabella, 2016: Atmospheric summer teleconnections and Greenland Ice Sheet surface mass 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 1

76

1 variations: insights from MERRA-2. Environmental Research Letters, 11, 024002.

2 http://dx.doi.org/10.1088/1748-9326/11/2/024002 

3 Mann, M.E., Z. Zhang, M.K. Hughes, R.S. Bradley, S.K. Miller, S. Rutherford, and F. Ni, 2008: 

4 Proxy-based reconstructions of hemispheric and global surface temperature variations over 

5 the past two millennia. Proceedings of the National Academy of Sciences, 105, 13252-13257.

6 http://dx.doi.org/10.1073/pnas.0805721105 

7 Mao, J., A. Ribes, B. Yan, X. Shi, P.E. Thornton, R. Seferian, P. Ciais, R.B. Myneni, H. 

8 Douville, S. Piao, Z. Zhu, R.E. Dickinson, Y. Dai, D.M. Ricciuto, M. Jin, F.M. Hoffman, B. 

9 Wang, M. Huang, and X. Lian, 2016: Human-induced greening of the northern extratropical 

10 land surface. Nature Climate Change, advance online publication.

11 http://dx.doi.org/10.1038/nclimate3056 

12 Marcott, S.A., J.D. Shakun, P.U. Clark, and A.C. Mix, 2013: A reconstruction of regional and 

13 global temperature for the past 11,300 years. Science, 339, 1198-1201.

14 http://dx.doi.org/10.1126/science.1228026 

15 Marvel, K. and C. Bonfils, 2013: Identifying external influences on global precipitation. 

16 Proceedings of the National Academy of Sciences, 110, 19301-19306.

17 http://dx.doi.org/10.1073/pnas.1314382110 

18 Mears, C.A. and F.J. Wentz, 2016: Sensitivity of satellite-derived tropospheric temperature 

19 trends to the diurnal cycle adjustment. Journal of Climate, 29, 3629-3646.

20 http://dx.doi.org/10.1175/JCLI-D-15-0744.1 

21 Meehl, G.A., J.M. Arblaster, C.M. Bitz, C.T.Y. Chung, and H. Teng, 2016: Antarctic sea-ice 

22 expansion between 2000 and 2014 driven by tropical Pacific decadal climate variability. 

23 Nature Geoscience, 9, 590-595. http://dx.doi.org/10.1038/ngeo2751 

24 Meehl, G.A., J.M. Arblaster, J.T. Fasullo, A. Hu, and K.E. Trenberth, 2011: Model-based 

25 evidence of deep-ocean heat uptake during surface-temperature hiatus periods. Nature 

26 Climate Change, 1, 360-364. http://dx.doi.org/10.1038/nclimate1229 

27 Meehl, G.A., C. Tebaldi, G. Walton, D. Easterling, and L. McDaniel, 2009: Relative increase of 

28 record high maximum temperatures compared to record low minimum temperatures in the 

29 US. Geophysical Research Letters, 36, L23701. http://dx.doi.org/10.1029/2009GL040736 

30 Melillo, J.M., T.C. Richmond, and G.W. Yohe, eds. Climate Change Impacts in the United 

31 States: The Third National Climate Assessment. 2014, U.S. Global Change Research 

32 Program: Washington, D.C. 842. http://dx.doi.org/10.7930/J0Z31WJ2. 

33 Mengel, M., A. Levermann, K. Frieler, A. Robinson, B. Marzeion, and R. Winkelmann, 2016: 

34 Future sea level rise constrained by observations and long-term commitment. Proceedings of 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 1

77

1 the National Academy of Sciences, 113, 2597-2602.

2 http://dx.doi.org/10.1073/pnas.1500515113 

3 Menzel, A., T.H. Sparks, N. Estrella, E. Koch, A. Aasa, R. Ahas, K. Alm-Kübler, P. Bissolli, 

4 O.G. Braslavská, A. Briede, F.M. Chmielewski, Z. Crepinsek, Y. Curnel, Å. Dahl, C. Defila, 

5 A. Donnelly, Y. Filella, K. Jatczak, F. Måge, A. Mestre, Ø. Nordli, J. Peñuelas, P. Pirinen, V. 

6 Remišvá, H. Scheifinger, M. Striz, A. Susnik, A.J.H. Van Vliet, F.-E. Wielgolaski, S. Zach, 

7 and A.N.A. Zust, 2006: European phenological response to climate change matches the 

8 warming pattern. Global Change Biology, 12, 1969-1976. http://dx.doi.org/10.1111/j.1365-

9 2486.2006.01193.x 

10 Merrifield, M.A., P. Thompson, E. Leuliette, G.T. Mitchum, D.P. Chambers, S. Jevrejeva, R.S. 

11 Nerem, M. Menéndez, W. Sweet, B. Hamlington, and J.J. Marra, 2015: [Global Oceans] Sea 

12 level variability and change [in “State of the Climate in 2014”]. Bulletin of the American 

13 Meteorological Society, 96, S82-S85.

14 http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1 

15 Min, S.K., X. Zhang, F.W. Zwiers, and G.C. Hegerl, 2011: Human contribution to more-intense 

16 precipitation extremes. Nature, 470, 378-381. http://dx.doi.org/10.1038/nature09763 

17 Min, S.-K., X. Zhang, and F. Zwiers, 2008: Human-Induced Arctic Moistening. Science, 320,

18 518-520. http://dx.doi.org/10.1126/science.1153468 

19 Min, S.-K., X. Zhang, F. Zwiers, H. Shiogama, Y.-S. Tung, and M. Wehner, 2013: Multimodel 

20 Detection and Attribution of Extreme Temperature Changes. Journal of Climate, 26, 7430-

21 7451. http://dx.doi.org/10.1175/JCLI-D-12-00551.1 

22 Mountain Research Initiative, 2015: Elevation-dependent warming in mountain regions of the 

23 world. Nature Clim. Change, 5, 424-430. http://dx.doi.org/10.1038/nclimate2563 

24 Myneni, R.B., C.D. Keeling, C.J. Tucker, G. Asrar, and R.R. Nemani, 1997: Increased plant 

25 growth in the northern high latitudes from 1981 to 1991. Nature, 386, 698-702.

26 http://dx.doi.org/10.1038/386698a0 

27 NCEI, 2016: Climate at a glance. www.ncdc.noaa.gov/cag/time28 series/global/globe/land_ocean/ytd/12/1880-2015

29 Nerem, R.S., D.P. Chambers, C. Choe, and G.T. Mitchum, 2010: Estimating mean sea level 

30 change from the TOPEX and Jason altimeter missions. Marine Geodesy, 33, 435-446.

31 http://dx.doi.org/10.1080/01490419.2010.491031 

32 Nghiem, S.V., D.K. Hall, T.L. Mote, M. Tedesco, M.R. Albert, K. Keegan, C.A. Shuman, N.E. 

33 DiGirolamo, and G. Neumann, 2012: The extreme melt across the Greenland ice sheet in 

34 2012. Geophysical Research Letters, 39, L20502. http://dx.doi.org/10.1029/2012GL053611 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 1

78

1 Nieves, V., J.K. Willis, and W.C. Patzert, 2015: Recent hiatus caused by decadal shift in Indo2 Pacific heating. Science, 349, 532-535. http://dx.doi.org/10.1126/science.aaa4521 

3 Norby, R.J., J.M. Warren, C.M. Iversen, B.E. Medlyn, and R.E. McMurtrie, 2010: CO2 

4 enhancement of forest productivity constrained by limited nitrogen availability. Proceedings 

5 of the National Academy of Sciences, 107, 19368-19373.

6 http://dx.doi.org/10.1073/pnas.1006463107 

7 Page, S.E., F. Siegert, J.O. Rieley, H.-D.V. Boehm, A. Jaya, and S. Limin, 2002: The amount of 

8 carbon released from peat and forest fires in Indonesia during 1997. Nature, 420, 61-65.

9 http://dx.doi.org/10.1038/nature01131 

10 PAGES 2K, 2013: Continental-scale temperature variability during the past two millennia. 

11 Nature Geoscience, 6, 339-346. http://dx.doi.org/10.1038/ngeo1797 

12 Palmroth, S., R. Oren, H.R. McCarthy, K.H. Johnsen, A.C. Finzi, J.R. Butnor, M.G. Ryan, and 

13 W.H. Schlesinger, 2006: Aboveground sink strength in forests controls the allocation of 

14 carbon below ground and its [CO2]-induced enhancement. Proceedings of the National 

15 Academy of Sciences, 103, 19362-19367. http://dx.doi.org/10.1073/pnas.0609492103 

16 Pan, Y., R.A. Birdsey, J. Fang, R. Houghton, P.E. Kauppi, W.A. Kurz, O.L. Phillips, A. 

17 Shvidenko, S.L. Lewis, J.G. Canadell, P. Ciais, R.B. Jackson, S.W. Pacala, A.D. McGuire, S. 

18 Piao, A. Rautiainen, S. Sitch, and D. Hayes, 2011: A large and persistent carbon sink in the 

19 world’s forests. Science, 333, 988-93. http://dx.doi.org/10.1126/science.1201609 

20 http://www.lter.uaf.edu/pdf/1545_Pan_Birdsey_2011.pdf

21 Parkinson, C.L., 2014: Spatially mapped reductions in the length of the Arctic sea ice season. 

22 Geophysical Research Letters, 41, 4316-4322. http://dx.doi.org/10.1002/2014GL060434 

23 Parmesan, C. and G. Yohe, 2003: A globally coherent fingerprint of climate change impacts 

24 across natural systems. Nature, 421, 37-42. http://dx.doi.org/10.1038/nature01286 

25 Parris, A., P. Bromirski, V. Burkett, D. Cayan, M. Culver, J. Hall, R. Horton, K. Knuuti, R. 

26 Moss, J. Obeysekera, A. Sallenger, and J. Weiss, 2012: Global Sea Level Rise Scenarios for 

27 the United States National Climate Assessment. NOAA Tech Memo OAR CPO-1. 37 pp. 

28 National Oceanic and Atmospheric Administration, Silver Spring, MD. 

29 http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf

30 Pauling, A.G., C.M. Bitz, I.J. Smith, and P.J. Langhorne, 2016: The Response of the Southern 

31 Ocean and Antarctic Sea Ice to Freshwater from Ice Shelves in an Earth System Model. 

32 Journal of Climate, 29, 1655-1672. http://dx.doi.org/10.1175/JCLI-D-15-0501.1 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 1

79

1 Pelto, M.S., 2015: [Global Climate] Alpine glaciers [in “State of the Climate in 2014”]. Bulletin 

2 of the American Meteorological Society, 96, S19-S20.

3 http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1 

4 Perovich, D., S. Gerlnad, S. Hendricks, W. Meier, M. Nicolaus, and M. Tschudi, 2015: [The 

5 Arctic] Sea ice cover [in “State of the Climate in 2014”]. Bulletin of the American 

6 Meteorological Society, 96, S145-S146.

7 http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1 

8 Peterson, T.C., R.R. Heim, R. Hirsch, D.P. Kaiser, H. Brooks, N.S. Diffenbaugh, R.M. Dole, J.P. 

9 Giovannettone, K. Guirguis, T.R. Karl, R.W. Katz, K. Kunkel, D. Lettenmaier, G.J. McCabe, 

10 C.J. Paciorek, K.R. Ryberg, S. Schubert, V.B.S. Silva, B.C. Stewart, A.V. Vecchia, G. 

11 Villarini, R.S. Vose, J. Walsh, M. Wehner, D. Wolock, K. Wolter, C.A. Woodhouse, and D. 

12 Wuebbles, 2013: Monitoring and understanding changes in heat waves, cold waves, floods 

13 and droughts in the United States: State of knowledge. Bulletin of the American 

14 Meteorological Society, 94, 821-834. http://dx.doi.org/10.1175/BAMS-D-12-00066.1 

15 Rahmstorf, S., J.E. Box, G. Feulner, M.E. Mann, A. Robinson, S. Rutherford, and E.J. 

16 Schaffernicht, 2015: Exceptional twentieth-century slowdown in Atlantic Ocean overturning 

17 circulation. Nature Climate Change, 5, 475-480. http://dx.doi.org/10.1038/nclimate2554 

18 Reyes-Fox, M., H. Steltzer, M.J. Trlica, G.S. McMaster, A.A. Andales, D.R. LeCain, and J.A. 

19 Morgan, 2014: Elevated CO2 further lengthens growing season under warming conditions. 

20 Nature, 510, 259-262. http://dx.doi.org/10.1038/nature13207 

21 Richardson, M., K. Cowtan, E. Hawkins, and M.B. Stolpe, 2016: Reconciled climate response 

22 estimates from climate models and the energy budget of Earth. Nature Climate Change, 6,

23 931-935. http://dx.doi.org/10.1038/nclimate3066 

24 Ridley, D.A., S. Solomon, J.E. Barnes, V.D. Burlakov, T. Deshler, S.I. Dolgii, A.B. Herber, T. 

25 Nagai, R.R. Neely, A.V. Nevzorov, C. Ritter, T. Sakai, B.D. Santer, M. Sato, A. Schmidt, O. 

26 Uchino, and J.P. Vernier, 2014: Total volcanic stratospheric aerosol optical depths and 

27 implications for global climate change. Geophysical Research Letters, 41, 7763-7769.

28 http://dx.doi.org/10.1002/2014GL061541 

29 Rignot, E., J. Mouginot, M. Morlighem, H. Seroussi, and B. Scheuchl, 2014: Widespread, rapid 

30 grounding line retreat of Pine Island, Thwaites, Smith, and Kohler glaciers, West Antarctica, 

31 from 1992 to 2011. Geophysical Research Letters, 41, 3502-3509.

32 http://dx.doi.org/10.1002/2014GL060140 

33 Rignot, E., I. Velicogna, M.R. van den Broeke, A. Monaghan, and J.T.M. Lenaerts, 2011: 

34 Acceleration of the contribution of the Greenland and Antarctic ice sheets to sea level rise. 

35 Geophysical Research Letters, 38, L05503. http://dx.doi.org/10.1029/2011GL046583 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 1

80

1 Romanovsky, V.E., S.L. Smith, H.H. Christiansen, N.I. Shiklomanov, D.A. Streletskiy, D.S. 

2 Drozdov, G.V. Malkova, N.G. Oberman, A.L. Kholodov, and S.S. Marchenko, 2015: [The 

3 Arctic] Terrestrial permafrost [in “State of the Climate in 2014”]. Bulletin of the American 

4 Meteorological Society, 96, S139-S141.

5 http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1 

6 Rupp, D.E., P.W. Mote, N.L. Bindoff, P.A. Stott, and D.A. Robinson, 2013: Detection and 

7 Attribution of Observed Changes in Northern Hemisphere Spring Snow Cover. Journal of 

8 Climate, 26, 6904-6914. http://dx.doi.org/10.1175/JCLI-D-12-00563.1 

9 Sander, J., J.F. Eichner, E. Faust, and M. Steuer, 2013: Rising Variability in Thunderstorm10 Related U.S. Losses as a Reflection of Changes in Large-Scale Thunderstorm Forcing. 

11 Weather, Climate, and Society, 5, 317-331. http://dx.doi.org/10.1175/WCAS-D-12-00023.1 

12 Santer, B.D., C. Bonfils, J.F. Painter, M.D. Zelinka, C. Mears, S. Solomon, G.A. Schmidt, J.C. 

13 Fyfe, J.N.S. Cole, L. Nazarenko, K.E. Taylor, and F.J. Wentz, 2014: Volcanic contribution to 

14 decadal changes in tropospheric temperature. Nature Geoscience, 7, 185-189.

15 http://dx.doi.org/10.1038/ngeo2098 

16 Santer, B.D., C. Mears, F.J. Wentz, K.E. Taylor, P.J. Gleckler, T.M.L. Wigley, T.P. Barnett, J.S. 

17 Boyle, W. Brüggemann, N.P. Gillett, S.A. Klein, G.A. Meehl, T. Nozawa, D.W. Pierce, P.A. 

18 Stott, W.M. Washington, and M.F. Wehner, 2007: Identification of human-induced changes 

19 in atmospheric moisture content. Proceedings of the National Academy of Sciences, 104,

20 15248-15253. http://dx.doi.org/10.1073/pnas.0702872104 

21 http://sa.indiaenvironmentportal.org.in/files/file/PNAS-2007-Santer-15248-53.pdf

22 Santer, B.D., S. Solomon, G. Pallotta, C. Mears, S. Po-Chedley, Q. Fu, F. Wentz, C.-Z. Zou, J. 

23 Painter, I. Cvijanovic, and C. Bonfils, Comparing tropospheric warming in climate models 

24 and satellite data. Journal of Climate, 0, null. http://dx.doi.org/10.1175/JCLI-D-16-0333.1 

25 Sardeshmukh, P.D., G.P. Compo, and C. Penland, 2015: Need for Caution in Interpreting 

26 Extreme Weather Statistics. Journal of Climate, 28, 9166-9187.

27 http://dx.doi.org/10.1175/JCLI-D-15-0020.1 

28 Schmidt, G.A., J.H. Jungclaus, C.M. Ammann, E. Bard, P. Braconnot, T.J. Crowley, G. 

29 Delaygue, F. Joos, N.A. Krivova, R. Muscheler, B.L. Otto-Bliesner, J. Pongratz, D.T. 

30 Shindell, S.K. Solanki, F. Steinhilber, and L.E.A. Vieira, 2011: Climate forcing 

31 reconstructions for use in PMIP simulations of the last millennium (v1.0). Geoscientific 

32 Model Development, 4, 33-45. http://dx.doi.org/10.5194/gmd-4-33-2011 

33 Schmidt, G.A., D.T. Shindell, and K. Tsigaridis, 2014: Reconciling warming trends. Nature 

34 Geoscience, 7, 158-160. http://dx.doi.org/10.1038/ngeo2105 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 1

81

1 Schwartz, M.D., R. Ahas, and A. Aasa, 2006: Onset of spring starting earlier across the Northern 

2 Hemisphere. Global Change Biology, 12, 343-351. http://dx.doi.org/10.1111/j.1365-

3 2486.2005.01097.x 

4 Seneviratne, S.I., M.G. Donat, B. Mueller, and L.V. Alexander, 2014: No pause in the increase 

5 of hot temperature extremes. Nature Climate Change, 4, 161-163.

6 http://dx.doi.org/10.1038/nclimate2145 

7 Seo, K.-W., C.R. Wilson, T. Scambos, B.-M. Kim, D.E. Waliser, B. Tian, B.-H. Kim, and J. 

8 Eom, 2015: Surface mass balance contributions to acceleration of Antarctic ice mass loss 

9 during 2003–2013. Journal of Geophysical Research: Solid Earth, 120, 3617-3627.

10 http://dx.doi.org/10.1002/2014JB011755 

11 Sheffield, J., E.F. Wood, and M.L. Roderick, 2012: Little change in global drought over the past 

12 60 years. Nature, 491, 435-438. http://dx.doi.org/10.1038/nature11575 

13 Shiklomanov, N.E., D.A. Streletskiy, and F.E. Nelson. Northern Hemisphere component of the 

14 global Circumpolar Active Layer Monitory (CALM) program. in Proceedings of the 10th 

15 International Conference on Permafrost. 2012. Salekhard, Russia.

16 Sobel, A.H., S.J. Camargo, T.M. Hall, C.-Y. Lee, M.K. Tippett, and A.A. Wing, 2016: Human 

17 influence on tropical cyclone intensity. Science, 353, 242-246.

18 http://dx.doi.org/10.1126/science.aaf6574 

19 Sokolov, A.P., D.W. Kicklighter, J.M. Melillo, B.S. Felzer, C.A. Schlosser, and T.W. Cronin, 

20 2008: Consequences of Considering Carbon–Nitrogen Interactions on the Feedbacks between 

21 Climate and the Terrestrial Carbon Cycle. Journal of Climate, 21, 3776-3796.

22 http://dx.doi.org/10.1175/2008JCLI2038.1 

23 Solomon, S., K.H. Rosenlof, R.W. Portmann, J.S. Daniel, S.M. Davis, T.J. Sanford, and G.-K. 

24 Plattner, 2010: Contributions of Stratospheric Water Vapor to Decadal Changes in the Rate 

25 of Global Warming. Science, 327, 1219-1223. http://dx.doi.org/10.1126/science.1182488 

26 Sousa, P.M., R.M. Trigo, P. Aizpurua, R. Nieto, L. Gimeno, and R. Garcia-Herrera, 2011: 

27 Trends and extremes of drought indices throughout the 20th century in the Mediterranean. 

28 Natural Hazards and Earth System Sciences, 11, 33-51. http://dx.doi.org/10.5194/nhess-11-

29 33-2011 

30 Stroeve, J., A. Barrett, M. Serreze, and A. Schweiger, 2014: Using records from submarine, 

31 aircraft and satellites to evaluate climate model simulations of Arctic sea ice thickness. The 

32 Cryosphere, 8, 1839-1854. http://dx.doi.org/10.5194/tc-8-1839-2014 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 1

82

1 Stroeve, J.C., V. Kattsov, A. Barrett, M. Serreze, T. Pavlova, M. Holland, and W.N. Meier, 

2 2012: Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations. Geophysical 

3 Research Letters, 39, L16502. http://dx.doi.org/10.1029/2012GL052676 

4 Stroeve, J.C., T. Markus, L. Boisvert, J. Miller, and A. Barrett, 2014: Changes in Arctic melt 

5 season and implications for sea ice loss. Geophysical Research Letters, 41, 1216-1225.

6 http://dx.doi.org/10.1002/2013GL058951 

7 Stroeve, J.C., M.C. Serreze, M.M. Holland, J.E. Kay, J. Malanik, and A.P. Barrett, 2012: The 

8 Arctic’s rapidly shrinking sea ice cover: A research synthesis. Climatic Change, 110, 1005-

9 1027. http://dx.doi.org/10.1007/s10584-011-0101-1 

10 Sweet, W.V., R.E. Kopp, C. Weaver, J. Obeysekera, R. Horton, E.R. Thieler, and C. Zervas, In 

11 review: Global and Regional Sea Level Rise Scenarios for the United States. NOAA.

12 Sweet, W.V. and J. Park, 2014: From the extreme to the mean: Acceleration and tipping points 

13 of coastal inundation from sea level rise. Earth's Future, 2, 579-600.

14 http://dx.doi.org/10.1002/2014EF000272 

15 Tedesco, M., E. Box, J. Cappelen, R.S. Fausto, X. Fettweis, K. Hansen, T. Mote, C.J.P.P. 

16 Smeets, D.V. As, R.S.W.V.d. Wal, and J. Wahr, 2015: [The Arctic] Greenland ice sheet [in 

17 “State of the Climate in 2014”]. Bulletin of the American Meteorological Society, 96, S137-

18 S139. http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1 

19 Tedesco, M., X. Fettweis, M.R.v.d. Broeke, R.S.W.v.d. Wal, C.J.P.P. Smeets, W.J.v.d. Berg, 

20 M.C. Serreze, and J.E. Box, 2011: The role of albedo and accumulation in the 2010 melting 

21 record in Greenland. Environmental Research Letters, 6, 014005.

22 http://dx.doi.org/10.1088/1748-9326/6/1/014005 

23 Tedesco, M., X. Fettweis, T. Mote, J. Wahr, P. Alexander, J.E. Box, and B. Wouters, 2013: 

24 Evidence and analysis of 2012 Greenland records from spaceborne observations, a regional 

25 climate model and reanalysis data. The Cryosphere, 7, 615-630. http://dx.doi.org/10.5194/tc26 7-615-2013 

27 Thornton, P.E., S.C. Doney, K. Lindsay, J.K. Moore, N. Mahowald, J.T. Randerson, I. Fung, J.F. 

28 Lamarque, J.J. Feddema, and Y.H. Lee, 2009: Carbon-nitrogen interactions regulate climate29 carbon cycle feedbacks: results from an atmosphere-ocean general circulation model. 

30 Biogeosciences, 6, 2099-2120. http://dx.doi.org/10.5194/bg-6-2099-2009 

31 Trenberth, K.E., 2015: Has there been a hiatus? Science, 349, 691-692.

32 http://dx.doi.org/10.1126/science.aac9225 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 1

83

1 Trenberth, K.E., A. Dai, G. van der Schrier, P.D. Jones, J. Barichivich, K.R. Briffa, and J. 

2 Sheffield, 2014: Global warming and changes in drought. Nature Climate Change, 4, 17-22.

3 http://dx.doi.org/10.1038/nclimate2067 

4 Turney, C.S.M. and R.T. Jones, 2010: Does the Agulhas Current amplify global temperatures 

5 during super-interglacials? Journal of Quaternary Science, 25, 839-843.

6 http://dx.doi.org/10.1002/jqs.1423 

7 van der Werf, G.R., J.T. Randerson, L. Giglio, G.J. Collatz, M. Mu, P.S. Kasibhatla, D.C. 

8 Morton, R.S. DeFries, Y. Jin, and T.T. van Leeuwen, 2010: Global fire emissions and the 

9 contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). 

10 Atmospheric Chemistry and Physics, 10, 11707-11735. http://dx.doi.org/10.5194/acp-10-

11 11707-2010 

12 Velicogna, I. and J. Wahr, 2013: Time-variable gravity observations of ice sheet mass balance: 

13 Precision and limitations of the GRACE satellite data. Geophysical Research Letters, 40,

14 3055-3063. http://dx.doi.org/10.1002/grl.50527 

15 Vose, R.S., D. Arndt, V.F. Banzon, D.R. Easterling, B. Gleason, B. Huang, E. Kearns, J.H. 

16 Lawrimore, M.J. Menne, T.C. Peterson, R.W. Reynolds, T.M. Smith, C.N. Williams, and 

17 D.L. Wuertz, 2012: NOAA’s Merged Land-Ocean Surface Temperature Analysis. Bulletin of 

18 the American Meteorological Society, 93, 1677-1685. http://dx.doi.org/10.1175/BAMS-D19 11-00241.1 

20 Walsh, J., D. Wuebbles, K. Hayhoe, J. Kossin, K. Kunkel, G. Stephens, P. Thorne, R. Vose, M. 

21 Wehner, J. Willis, D. Anderson, S. Doney, R. Feely, P. Hennon, V. Kharin, T. Knutson, F. 

22 Landerer, T. Lenton, J. Kennedy, and R. Somerville, 2014: Ch. 2: Our changing climate. 

23 Climate Change Impacts in the United States: The Third National Climate Assessment. 

24 Melillo, J.M., T.C. Richmond, and G.W. Yohe, Eds. U.S. Global Change Research Program, 

25 Washington, D.C., 19-67. http://dx.doi.org/10.7930/J0KW5CXT 

26 Wang, C., L. Zhang, S.-K. Lee, L. Wu, and C.R. Mechoso, 2014: A global perspective on 

27 CMIP5 climate model biases. Nature Climate Change, 4, 201-205.

28 http://dx.doi.org/10.1038/nclimate2118 

29 Willett, K.M., D.J. Philip, W.T. Peter, and P.G. Nathan, 2010: A comparison of large scale 

30 changes in surface humidity over land in observations and CMIP3 general circulation 

31 models. Environmental Research Letters, 5, 025210. http://dx.doi.org/10.1088/1748-

32 9326/5/2/025210 

33 Williams, S.D.P., P. Moore, M.A. King, and P.L. Whitehouse, 2014: Revisiting GRACE 

34 Antarctic ice mass trends and accelerations considering autocorrelation. Earth and Planetary 

35 Science Letters, 385, 12-21. http://dx.doi.org/10.1016/j.epsl.2013.10.016 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 1

84

1 Zaehle, S., P. Friedlingstein, and A.D. Friend, 2010: Terrestrial nitrogen feedbacks may 

2 accelerate future climate change. Geophysical Research Letters, 37, L01401.

3 http://dx.doi.org/10.1029/2009GL041345 

4 Zaehle, S. and A.D. Friend, 2010: Carbon and nitrogen cycle dynamics in the O-CN land surface 

5 model: 1. Model description, site-scale evaluation, and sensitivity to parameter estimates. 

6 Global Biogeochemical Cycles, 24, n/a-n/a. http://dx.doi.org/10.1029/2009GB003521 

7 Zemp, M., H. Frey, I. Gärtner-Roer, S.U. Nussbaumer, M. Hoelzle, F. Paul, W. Haeberli, F. 

8 Denzinger, A.P. Ahlstrøm, B. Anderson, S. Bajracharya, C. Baroni, L.N. Braun, B.E. 

9 Cáceres, G. Casassa, G. Cobos, L.R. Dávila, H. Delgado Granados, M.N. Demuth, L. 

10 Espizua, A. Fischer, K. Fujita, B. Gadek, A. Ghazanfar, J.O. Hagen, P. Holmlund, N. Karimi, 

11 Z. Li, M. Pelto, P. Pitte, V.V. Popovnin, C.A. Portocarrero, R. Prinz, C.V. Sangewar, I. 

12 Severskiy, O. Sigurðsson, A. Soruco, R. Usubaliev, and C. Vincent, 2015: Historically 

13 unprecedented global glacier decline in the early 21st century. Journal of Glaciology, 61,

14 745-762. http://dx.doi.org/10.3189/2015JoG15J017 

15 Zhang, R. and T.R. Knutson, 2013: The role of global climate change in the extreme low

16 summer Arctic sea ice extent in 2012 [in "Explaining Extremes of 2012 from a Climate 

17 Perspective"]. Bulletin of the American Meteorological Society, 94, S23-S26.

18 http://dx.doi.org/10.1175/BAMS-D-13-00085.1 

19 Zhang, X., H. Wan, F.W. Zwiers, G.C. Hegerl, and S.-K. Min, 2013: Attributing intensification 

20 of precipitation extremes to human influence. Geophysical Research Letters, 40, 5252-5257.

21 http://dx.doi.org/10.1002/grl.51010 

22 Zhang, X., F.W. Zwiers, G.C. Hegerl, F.H. Lambert, N.P. Gillett, S. Solomon, P.A. Stott, and T. 

23 Nozawa, 2007: Detection of human influence on twentieth-century precipitation trends. 

24 Nature, 448, 461-465. http://dx.doi.org/10.1038/nature06025 

25 Zhu, Z., S. Piao, R.B. Myneni, M. Huang, Z. Zeng, J.G. Canadell, P. Ciais, S. Sitch, P. 

26 Friedlingstein, A. Arneth, C. Cao, L. Cheng, E. Kato, C. Koven, Y. Li, X. Lian, Y. Liu, R. 

27 Liu, J. Mao, Y. Pan, S. Peng, J. Penuelas, B. Poulter, T.A.M. Pugh, B.D. Stocker, N. Viovy, 

28 X. Wang, Y. Wang, Z. Xiao, H. Yang, S. Zaehle, and N. Zeng, 2016: Greening of the Earth 

29 and its drivers. Nature Climate Change, 6, 791-795. http://dx.doi.org/10.1038/nclimate3004 

30 Zunz, V., H. Goosse, and F. Massonnet, 2013: How does internal variability influence the ability 

31 of CMIP5 models to reproduce the recent trend in Southern Ocean sea ice extent? The 

32 Cryosphere, 7, 451-468. http://dx.doi.org/10.5194/tc-7-451-2013
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1 2. Physical Drivers of Climate Change 

2 Key Findings

3 1. Human activities continue to significantly affect Earth’s climate by altering factors that 

4 change its radiative balance (known as a radiative forcing). These factors include greenhouse 

5 gases, small airborne particles (aerosols), and the reflectivity of the Earth’s surface. In the 

6 industrial era, human activities have been and remain the dominant cause of climate warming 

7 and have far exceeded the relatively small net increase due to natural factors, which include 

8 changes in energy from the sun and the cooling effect of volcanic eruptions. (Very high 

9 confidence)

10 2. Aerosols caused by human activity play a profound and complex role in the climate system 

11 through direct radiative effects and indirect effects on cloud formation and properties. The 

12 combined forcing of aerosol–radiation and aerosol–cloud interactions is negative over the 

13 industrial era, substantially offsetting a substantial part of greenhouse gas forcing, which is 

14 currently the predominant human contribution (high confidence). The magnitude of this 

15 offset has declined in recent decades due to a decreasing trend in net aerosol forcing.

16 (Medium to high confidence)

17 3. The climate system includes a number of positive and negative feedback processes that can 

18 either strengthen (positive feedback) or weaken (negative feedback) the system’s responses

19 to human and natural influences. These feedbacks operate on a range of timescales from very 

20 short (essentially instantaneous) to very long (centuries). While there are large uncertainties

21 associated with some of these feedbacks, the net feedback effect over the industrial era has 

22 been positive (amplifying warming) and will continue to be positive in coming decades. 

23 (High confidence)

24

25 2.1 Earth’s Energy Balance and the Greenhouse Effect

26 The temperature of the Earth system is determined by the amounts of incoming (short27 wavelength) and outgoing (both short- and long-wavelength) radiation. In the modern era, the 

28 magnitudes of these flows are accurately determined from satellite measurements. Figure 2.1

29 shows that about a third of incoming, short-wavelength energy from the sun is reflected back to 

30 space and the remainder absorbed by the Earth system. The fraction of sunlight scattered back to 

31 space is determined by the reflectivity (albedo) of land surfaces (including snow and ice),

32 oceans, and clouds and particles in the atmosphere. The amount and albedo of clouds, snow

33 cover, and ice cover are particularly strong determinants of the amount of sunlight reflected back 

34 to space because their albedos are much higher than that of land and oceans. 
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1 In addition to reflected sunlight, the Earth loses energy through infrared (long-wavelength) 

2 radiation from the surface and atmosphere. Greenhouse gases in the atmosphere absorb some of 

3 this radiation, much of which is re-radiated back towards the surface (Figure 2.1) where it is 

4 absorbed, further heating the Earth; the remainder is emitted to space. The naturally occurring 

5 greenhouse gases in Earth’s atmosphere--principally water vapor and carbon dioxide--keep the 

6 near-surface air temperature about 33°C (60°F) warmer than it would be in their absence.

7 Geothermal heat from the Earth’s interior, direct heating from energy production, and frictional 

8 heating through tidal flows also contribute to the amount of energy available for heating the 

9 Earth’s surface and atmosphere, but their contribution is an extremely small fraction (<0.1%) of 

10 that due to net solar (shortwave) and infrared (longwave) radiation. (e.g., see Davies and Davies 

11 2010; Flanner 2009; Munk and Wunsch 1998 for estimates of these forcings).

12 [INSERT FIGURE 2.1 HERE:

13 Figure 2.1: Global mean energy budget of the Earth under present-day climate conditions. 

Numbers state magnitudes of the individual energy fluxes in watts per square meter (W/m2 14 ) 

15 averaged over Earth’s surface, adjusted within their uncertainty ranges to balance the energy 

16 budgets of the atmosphere and the surface. Numbers in parentheses attached to the energy fluxes 

17 cover the range of values in line with observational constraints. These constraints are largely 

18 provided by satellite-based observations, which have directly measured solar and infrared fluxes 

19 at the top of the atmosphere over nearly the whole globe since 1984 (Barkstrom 1984; Smith et 

20 al. 1994). More advanced satellite-based measurements focusing on the role of clouds in Earth’s 

21 radiative fluxes, have been available since 1998 (Wielicki et al. 1995, 1996). (Figure source: 

22 IPCC 2013; © IPCC, used with permission).]

23 Thus, Earth’s equilibrium temperature is controlled by a short list of factors: incoming sunlight, 

24 absorbed and reflected sunlight, emitted infrared radiation, and infrared radiation absorbed in the 

25 atmosphere, primarily by greenhouse gases. Changes in these factors affect Earth’s radiative 

26 balance and therefore its climate, including but not limited to the average, near-surface air 

27 temperature. Anthropogenic activities have changed the Earth’s radiative balance and its albedo

28 by adding greenhouse gases, particles (aerosols), and aircraft contrails to the atmosphere, and

29 through land-use changes.

30 Changes in the radiative balance produce changes in temperature, precipitation, and other climate 

31 variables through a complex set of physical processes, many of which are coupled (Figure 2.2). 

32 In the following sections, the principal components of the framework shown in Figure 2.2 are 

33 described. Climate models are structured to represent these processes; climate models, and their 

34 components and associated uncertainties, are discussed in more detail in Chapter 4: Projections.

35 [INSERT FIGURE 2.2 HERE:

36 Figure 2.2 Simplified conceptual modeling framework for the climate system as implemented in 

37 many climate models (Chapter 4). Modeling components include forcing agents, feedback 

38 processes, carbon uptake processes and radiative forcing and balance. The lines indicate physical 
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87

1 interconnections (solid lines) and feedback pathways (dashed lines). Principal changes (blue 

2 boxes) lead to climate impacts (red box) and feedbacks. (Figure source: adapted from Knutti and 

3 Rugenstein 2015).]

4 The processes and feedbacks connecting changes in Earth’s radiative balance to a climate 

5 response (Figure 2.2) operate on a large range of timescales. Reaching an equilibrium 

6 temperature distribution in response to anthropogenic activities takes decades or longer because 

7 the Earth system—in particular the oceans and cryosphere—are slow to respond due to their 

8 large thermal masses and the long timescale of circulation between the ocean surface and the 

9 deep ocean. Of the substantial energy gained in the combined ocean–atmosphere system over the 

10 previous four decades, over 90% of it has gone into ocean warming (Rhein et al. 2014; see Box 

11 3.1 Fig 1). Even at equilibrium, internal variability in the Earth’s climate system causes limited

12 annual to decadal-scale variations in regional temperatures and other climate parameters that do 

13 not contribute to long-term trends. For example, it is likely that natural variability has led to 

14 between −0.1°C (−0.18°F) and 0.1°C (0.18°F) changes in surface temperatures from 1951 to 

15 2010; by comparison, anthropogenic greenhouse gases have likely contributed between 0.5°C 

16 (0.9°F) and 1.3°C (2.3°F) to observed surface warming over this same period (Bindoff et al. 

17 2013). Due to these longer timescale responses and natural variability, changes in Earth’s 

18 radiative balance are not realized immediately as changes in climate, and even in equilibrium 

19 there will always be variability around mean trends.

20 2.2 Radiative Forcing (RF) and Effective Radiative Forcing (ERF)

21 Radiative forcing (RF) is widely used to quantify a radiative imbalance in Earth’s atmosphere 

22 resulting from either natural changes or anthropogenic activities. It is expressed as a change in 

net radiative flux (W/m2 23 ) at the tropopause or top of the atmosphere over the industrial era

24 (Myhre et al. 2013). RF serves as a metric to compare present, past, or future perturbations to the 

25 climate system (e.g. Boer and Yu 2003; Gillett et al. 2004; Matthews et al. 2004; Meehl et al. 

26 2004; Jones et al. 2007; Mahajan et al. 2013; Shiogama et al. 2013). The equilibrium surface 

27 temperature response (DT) to a forcing (RF) is given by DT = l RF where l is the climate 

28 sensitivity factor (Knutti and Hegerl 2008; Flato et al. 2013). For clarity and consistency, RF 

29 calculations require that a time period be defined over which the forcing occurs. Here, this period 

30 is the industrial era, defined as beginning in 1750 and extending to 2011, unless otherwise noted.

31 The 2011 end date is that adopted by the CMIP5 calculations, which are the basis of RF 

32 evaluations by the Intergovernmental Panel on Climate Change (IPCC; Myhre et al. 2013). In 

33 practice, the calculation of RF over a given period is defined in several ways based on where it is 

34 evaluated (tropopause or top of the atmosphere) and on assumptions concerning, for example, 

35 whether the surface or stratospheric temperature is allowed to respond (Myhre et al. 2013). In 

36 this report, we follow the IPCC recommendation that the RF caused by a forcing agent be 

37 evaluated as the net radiative flux change at the tropopause after stratospheric temperatures have 

38 adjusted to a new equilibrium while assuming all other variables (for example, temperatures and 
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1 cloud cover) are held fixed (Myhre et al. 2013). A change that results in a net increase in the 

2 downward flux at the tropopause constitutes a positive RF, normally resulting in a warming of 

3 the surface and/or atmosphere, and potentially changes in other climate parameters. Conversely, 

4 a change that yields an increase in the net upward flux constitutes a negative RF, leading to a 

5 cooling of the surface and/or atmosphere, and potentially changes in other climate parameters.

6 A refinement of the RF concept introduced in the latest IPCC assessment (IPCC 2013) is the use 

7 of effective radiative forcing (ERF). ERF for a climate driver is defined is its RF plus all rapid

8 adjustment(s) to that RF (Myhre et al. 2013). These rapid adjustments occur on timescales much 

9 shorter than, for example, the response of ocean temperatures. For an important subset of climate 

10 drivers, ERF is more reliably correlated with the climate response to the forcing than is RF; as 

11 such, it is an increasingly used metric when discussing forcing. For atmospheric components,

12 ERF includes rapid adjustments due to direct warming of the troposphere, which produces

13 horizontal temperature variations, variations in the vertical lapse rate, and changes in clouds and 

14 vegetation, and it includes the microphysical effects of aerosols on cloud lifetime. Not included 

15 in ERF are climate responses driven by surface air temperature changes. For aerosols in surface 

16 snow, ERF includes the effects of direct warming of the snowpack by particulate absorption (for 

17 example, snow-grain size changes). The largest differences between RF and ERF occur for 

18 forcing by light-absorbing aerosols because of their influence on clouds and snow. Changes in 

19 these climate parameters can be quantified in terms of their impact on radiative fluxes (for 

20 example, albedo). For example, black carbon (BC) aerosol in the atmosphere absorbs sunlight, 

21 producing a positive RF. In addition, this absorption warms the atmosphere; on net this response 

22 is expected to increase cloud cover and therefore increase planetary albedo (the “semi-direct 

23 effect”). This “rapid response” lowers the ERF of atmospheric BC by approximately 15% 

24 relative to its RF from direct absorption alone (Bond et al. 2013). For BC deposited on snow, the 

25 ERF is a factor of three higher than the RF because of the positive feedbacks of reducing snow 

26 albedo and increasing snow melt (e.g., Flanner et al. 2009; Bond et al. 2013). For most non27 aerosol climate drivers the differences are small.

28 2.3 Drivers of Climate Change over the Industrial Era

29 Climate drivers of significance over the industrial era include both those associated with 

30 anthropogenic activity and those of natural origin. The only significant natural climate drivers in 

31 the industrial era are changes in solar irradiance and volcanic eruptions. Natural emissions and 

32 sinks of greenhouse gases and aerosols have varied over the industrial era but have not 

33 contributed significantly to RF. Other known drivers of natural origin that operate on longer 

34 timescales are changes in Earth’s orbit (that is, the Milankovich cycles), asteroids, changes in 

35 atmospheric CO2 via chemical weathering of rock, and potentially cosmic rays. Anthropogenic

36 drivers can be divided into a number of categories, including well-mixed greenhouse gases 

37 (WMGHGs), short-lived climate forcers (SLCFs, which include methane, some 

38 hydrofluorocarbons [HFCs], ozone, and aerosols), contrails, and changes in albedo (for example,
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1 land-use changes). Some WMGHGs are also considered SLCFs (for example, methane). Figure 

2 2.3 summarizes RF and/or ERF for the principal climate drivers in the industrial era. Each is 

3 described briefly in the following.

4 [INSERT FIGURE 2.3 HERE:

5 Figure 2.3 Bar chart for RF (hatched) and ERF (solid) for the period 1750–2011, where the total 

6 ERF is derived from IPCC. Uncertainties (5% to 95% confidence range) are given for RF (dotted 

7 lines) and ERF (solid lines). Volcanic forcing is not shown because this forcing is negligible over 

8 the industrial era. (Figure source: IPCC 2013© IPCC, used with permission).]

9 2.3.1 Natural Drivers

10 SOLAR IRRADIANCE

11 Solar irradiance changes directly impact the climate system because the irradiance is its primary 

12 source of energy (Lean 1997). At the century scale, the largest variations in total solar irradiance

13 (TSI) are associated with the 11-year solar cycle (Frölich and Lean 2004; Gray et al. 2010), 

14 direct observations of which have been available since 1978 (Kopp 2014) though proxy 

15 indicators of solar cycles are available back to the early 1600s (Kopp et al. 2016). Although the 

variations in TSI amount to only 0.1% of the sun’s total output of about 1360 W/m

2 16 (Kopp and 

17 Lean 2011), variations in irradiance at specific wavelengths can be much larger (tens of percent). 

18 Solar spectral irradiance (SSI) is most variable at near-ultraviolet (UV) and shorter wavelengths 

19 (Floyd et al. 2003), which are also the most important in driving changes in ozone (Ermolli et al. 

20 2013; Bolduc et al. 2015). Variations in TSI and SSI can thus induce important changes in the 

21 stratosphere and troposphere, both through direct heating and through changes in stratospheric 

22 ozone that in turn further affect heating rates and both stratospheric and tropospheric circulation 

23 (Gray et al. 2010; Lockwood 2012, Seppälä et al. 2014). Further, the relationships between 

24 changes in TSI and changes in atmospheric composition, heating, and dynamics are complex. 

25 Changes in UV irradiance can be out of phase with changes in TSI with mixed consequences, for 

26 example, for the net production and destruction of stratospheric ozone (Ball et al. 2016). As a 

27 result, changes in TSI are not directly correlated with the resulting radiative flux changes

28 (Ermolli et al. 2013; Xu and Powell 2013; Gao et al. 2015).

IPCC has provided an estimate of the TSI RF of 0.05 W/m2 (range: 0.0 to 0.10 W/m2 29 ) (Myhre et 

30 al. 2013). This forcing does not account for radiative flux changes resulting from SSI-driven 

31 changes in stratospheric ozone. Understanding of the link between changes in SSI, stratospheric 

32 ozone, heating rates, and circulation changes has recently improved using, in particular, satellite 

33 data starting in 2002 that provide SSI measurements through the UV (Ermolli et al. 2013) along 

34 with a series of chemistry–climate modeling studies (Swartz et al. 2012; Chiodo et al. 2014; 

35 Dhomse et al. 2013; Ermolli et al. 2013; Bolduc et al. 2015). At the regional scale, circulation 

36 changes driven by SSI variations may be significant for some locations and seasons, but this is 

37 not yet sufficiently understood to quantify (Lockwood 2012). Despite remaining uncertainties, 
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1 there is very high confidence that solar radiance-induced changes in RF are small relative to RF

2 from anthropogenic greenhouse gases over the industrial era (Myhre et al. 2013) (Figure 2.3). On 

3 millennial timescales, changes in solar output are expected to have influenced climate but there 

4 is uncertainty in extending the TSI and SSI records back in time.

5 VOLCANOES

6 Explosive volcanic eruptions inject sulfur dioxide (SO2) and ash into the stratosphere, which 

7 leads to significant short-term climate effects (Myhre et al. 2013, and references therein). SO2

8 oxidizes to form sulfuric acid (H2SO4) which condenses, forming new particles or adding mass 

9 to preexisting particles, thereby substantially enhancing the attenuation of sunlight transmitted 

10 through the stratosphere (that is, increasing the aerosol optical depth). These aerosols increase 

11 the Earth’s albedo by scattering sunlight back to space, creating a negative RF that cools the 

12 planet (Andronova et al. 1999; Robock 2000). The RF persists for the lifetime of aerosol in the 

13 stratosphere, which is a few years, far exceeding that in the troposphere (about a week). Volcanic

14 RF is integrated by the ocean, resulting in ocean cooling and associated changes in ocean

15 circulation patterns that last for decades after major eruptions (for example, Mt. Tambora in 

16 1815) (Stenchikov et al. 2009; Otterå et al. 2010; Zanchettin et al. 2012; Zhang et al. 2013). In 

17 addition to the direct RF, volcanic aerosol heats the stratosphere, altering circulation patterns and

18 destroying ozone, which further changes heating and circulation. The resulting impacts on 

19 advective heat transport can be larger than the temperature impacts of the direct forcing (Robock 

20 2000). Aerosol from both explosive and non-explosive eruptions also affects the troposphere 

21 through changes in diffuse radiation and through aerosol–cloud interactions, both through the 

22 initial emissions and later when volcanic aerosol eventually sediments out of the stratosphere. It 

23 has been proposed that major eruptions might “fertilize” the ocean with sufficient iron to affect 

24 phyotoplankton production and therefore the ocean CO2 sink, though this is a new area of 

25 research (Langmann 2014). Volcanoes also emit CO2 and water vapor, although in small 

26 quantities relative to other emissions. Annual CO2 emissions from volcanoes are conservatively 

27 estimated at <1% that from anthropogenic activities (Gerlach 2011). The magnitude of volcanic

28 effects on climate depend on the number and strengths of eruptions, the latitude of injection and, 

29 for ocean temperature and circulation impacts, the timing of the eruption relative to ocean 

30 temperature and circulation patterns (Zanchettin et al. 2012; Zhang et al. 2013).

31 Volcanic eruptions are the largest natural forcings within the industrial era and in the last 

millennium caused multiyear transient episodes of negative RF of up to several W/m

2 32 (Figure 

33 2.5). The RF of the last major volcanic eruption, Mt. Pinatubo in 1991, decayed to negligible 

34 values later in the 1990s, with the temperature signal lasting about twice as long due to the 

35 effects of changes in ocean heat uptake (Stenchikov et al. 2009). The present day volcanic RF 

evaluated for periods since 2000 yields values of about −0.1 W/m

2 36 or less due to several small 

37 non-explosive eruptions. A net volcanic RF has been omitted from the drivers of climate change 

38 in the industrial era in Figure 2.3 because the episodic short-term nature of volcanic RF is not 

39 comparable with the other climate drivers, which produce long-term effects. While future 
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1 explosive volcanic eruptions have the potential to again alter Earth’s climate for periods of 

2 several years, predictions of occurrence, intensity, and location remain elusive.

3 2.3.2 Anthropogenic Drivers

4 PRINCIPAL WELL-MIXED GREENHOUSE GASES (WMGHGs)

5 The principal WMGHGs are carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O).

6 These gases have modest-to-small regional variabilities and, with atmospheric lifetimes of a 

7 decade or more, are circulated and mixed around the globe to yield small inter-hemispheric 

8 gradients. The atmospheric abundances and associated radiative forcings of WMGHGs have

9 increased substantially over the industrial era (Figures 2.4–2.6). Contributions from natural 

10 sources of these constituents are accounted for in these industrial-era RF calculations.

11 [INSERT FIGURE 2.4 HERE:

12 Figure 2.4 Atmospheric concentrations of carbon dioxide, methane, and nitrous oxide over the 

13 last 10,000 years (large panels) and since 1750 (inset panels). Measurements are shown from ice 

14 cores (symbols with different colors for different studies) and atmospheric samples (red lines). 

15 The corresponding radiative forcings are shown on the right-hand axes of the large panels. The 

16 concentrations of these gases have continued to increase in the 2000 to 2016 period 

17 (http://www.esrl.noaa.gov/gmd/ccgg/aggi.html). (Figure source: IPCC 2007© IPCC, used with 

18 permission)]

19

20 [INSERT FIGURE 2.5 HERE:

21 Figure 2.5 (a) Radiative forcing (RF) from the major WMGHGs and groups of halocarbons 

22 (Others) from 1850 to 2011; (b) the data in (a) with a logarithmic scale; (c) RFs from the minor 

23 WMGHGs from 1850 to 2011 (logarithmic scale); (d) rate of change in forcing from the major 

24 WMGHGs and halocarbons from 1850 to 2011. (Figure source: IPCC 2013; © IPCC, used with 

25 permission).]

26 [INSERT FIGURE 2.6 HERE:

27 Figure 2.6 Effective radiative forcing changes across the industrial era for anthropogenic and 

28 natural forcing mechanisms. Also shown are the sum of all forcings (Total) and the sum of 

29 anthropogenic forcings (Total Anthropogenic). Bars with the forcing and uncertainty ranges (5%

30 to 95% confidence range) at present are given in the right part of the figure. For aerosol, the ERF 

31 due to aerosol–radiation interaction and total aerosol ERF are shown. The uncertainty ranges are 

32 for present (2011 versus 1750) and are given in Table 8.6. For aerosols, only the uncertainty in 

33 the total aerosol ERF is given. For several of the forcing agents the relative uncertainty may be 

34 larger for certain time periods compared to present. See IPCC AR5 Supplementary Material 

35 Table 8.SM.8 for further information on the forcing time evolutions. Forcing numbers provided 

in Annex II of this report. The total anthropogenic forcing was 0.57 (0.29 to 0.85) W/m

2 36 in 1950, 

1.25 (0.64 to 1.86) W/m2 in 1980 and 2.29 (1.13 to 3.33) W/m2 37 in 2011. (Figure source: IPCC 

38 2013; © IPCC, used with permission).]
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1 CO2 has substantial global sources and sinks (Figure 2.7). CO2 emission sources have grown in 

2 the industrial era primarily from fossil fuel combustion (that is, coal, gas, oil), cement 

3 manufacturing, and land-use change from activities such as deforestation Ciais et al. 2013).

4 Processes that remove emitted CO2 from the atmosphere include uptake in the oceans, residual 

5 land uptake, and ultimately rock weathering, thereby yielding an atmospheric lifetime of many 

6 decades to millennia, far greater than any other major GHG. Seasonal variations in CO2

7 atmospheric concentrations occur in response to transpiration in the biosphere, and to a lesser 

8 degree due to seasonal variations in anthropogenic emissions. In addition to fossil fuel reserves, 

9 there are large natural reservoirs of carbon in the oceans, in vegetation and soils, and in 

10 permafrost. In the industrial era, the CO2 atmospheric growth rate has been exponential (Figure 

11 2.4), with the increase in atmospheric CO2 approximately twice the ocean uptake. Over the last 

12 50 years or more, CO2 has shown the largest annual concentration and RF increases among all 

13 GHGs (Figures 2.4 and 2.5). The global average CO2 concentration has increased by 40% over 

14 the industrial era, increasing from 278 parts per million (ppm) in 1750 to 390 ppm in 2011 (Ciais 

15 et al. 2013); it now exceeds 400 ppm (2016) (http://www.esrl.noaa.gov/gmd/ccgg/trends/). CO2

16 has been chosen as the reference in defining the global warming potential (GWP) of other GHGs 

17 and climate agents. The GWP of a GHG is the integrated RF over a specified time period (for 

18 example, 100 years) from the emission of a given mass of the GHG divided by the integrated RF

19 from the same mass emission of CO2. 

20 [INSERT FIGURE 2.7 HERE:

Figure 2.7 | CO2 sources and sinks (PgC/yr

1 21 ) over the industrial era (1750–2011).The 

22 partitioning of atmospheric emissions among the atmosphere, land, and ocean is shown as 

23 equivalent negative emissions in the lower panel; of these, the land and ocean terms are true 

24 sinks of atmospheric CO2. The top panel shows an expanded view of emissions from fossil fuels 

25 and cement manufacturing. The atmospheric CO2 growth rate is derived from atmospheric 

26 observations and ice core data. The ocean CO2 sink is derived from a combination of models and 

27 observations. The land sink is the residual of the other terms in a balanced CO2 budget, and 

28 represents the sink of anthropogenic CO2 in natural land ecosystems. These terms only represent 

29 changes since 1750 and do not include natural CO2 fluxes (for example, from weathering and 

30 outgassing from lakes and rivers). (Figure source: Ciais et al. 2014; © IPCC, used with 

31 permission)]

32 Methane concentrations and RF have also grown substantially in the industrial era (Figures 2.4 

33 and 2.5). Methane is a much more potent greenhouse gas than CO2 but it has a shorter 

34 atmospheric lifetime of about 12 years. Methane also has indirect climate effects through 

35 induced changes in CO2, stratospheric water vapor and ozone (Lelieveld and Crutzen 1992). The

36 100-year GWP of methane is high (28, direct; 34 including indirect), and its 20-year GWP is 

37 even higher (84; 86) (Myhre et al. 2013 Table 8.7). With a current global value near 1840 parts 

38 per billion by volume (ppb), methane concentrations have increased by a factor of about 2.5 over 
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1 the industrial era. Variability in the methane annual growth rate over the past several decades has 

2 been larger than for CO2 and N2O, and occasionally negative for short periods.

3 Methane has a variety of natural and anthropogenic sources estimated to total 556 ± 56 Tg CH4

4 per year in 2011, with the anthropogenic fraction estimated to be about 60% (Ciais et al. 2013).

5 The methane budget is complicated by the variety of natural and anthropogenic sources and sinks

6 that influence its atmospheric concentration. These include the global abundance of the hydroxyl 

7 radical (OH), which controls the methane atmospheric lifetime; changes in large-scale 

8 anthropogenic activities such as mining, natural gas extraction, animal husbandry, and 

9 agricultural practices; and natural wetland emissions. The remaining uncertainty in the cause(s) 

10 of the approximately 20-year negative trend in the methane annual growth rate starting in the 

11 mid-1980s reflects the budget complexity (IPCC 2013).

12 Growth in nitrous oxide concentrations and RF over the industrial era are smaller than for CO2

13 and methane (Figures 2.4 and 2.5). Nitrous oxide is emitted in the nitrogen cycle in natural 

14 ecosystems and has a variety of anthropogenic sources, including the use of synthetic fertilizers 

15 in agriculture, motor vehicle exhaust, and some manufacturing processes. The current global 

16 value near 330 ppb reflects steady growth over the industrial era with average increases in recent 

17 decades of 0.75 ppb per year (Ciais et al. 2013) (Figure 2.4). Fertilization in global food 

18 production is estimated to be responsible for 80% of the growth rate. Anthropogenic sources 

19 account for approximately 40% of the annual N2O emissions of 17.9 (8.1 to 30.7) TgN. Nitrous 

20 oxide has an atmospheric lifetime of about 120 years and GWP of 265 (direct; Myhre et al. 2013

21 Table 8.7). The primary sink of nitrous oxide is photochemical destruction in the stratosphere, 

22 which produces nitrogen oxides (NOx) that catalytically destroy ozone (e.g. Skiba and Rees 

23 2014). Small indirect climate effects, such as the response of stratospheric ozone, are generally 

24 not included in the nitrous oxide RF.

25 Nitrous oxide is a component of the larger global budget of total N comprising N2O, ammonia 

26 (NH3), and reactive nitrogen (NOx). Significant uncertainties are associated with balancing this 

27 budget over oceans and land while accounting for deposition and emission processes (Ciais et al. 

28 2013). Furthermore, changes in climate parameters such as temperature, moisture, and CO2

29 concentrations are expected to affect the N2O budget in the future, and perhaps atmospheric 

30 concentrations.

31 OTHER WELL-MIXED GREENHOUSE GASES

32 Other WMGHGs primarily include several categories of synthetic gases, including 

33 chlorofluorocarbons (CFCs), halons, hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons 

34 (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6). These gases entered the

35 atmosphere as early as the mid-20th century, beginning with the expanded use of CFCs as 

36 refrigerants and in other applications. The rapid growth of CFCs declined beginning in the 1990s

37 with their regulation as ozone-depleting substances under the United Nations Montreal Protocol
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1 (Figure 2.4). All of these gases are greenhouse gases covering a wide range of GWPs, 

2 atmospheric concentrations, and trends. PFCs, SF6, and HFCs are in the basket of gases covered 

3 under the United Nations Framework Convention on Climate Change. The United States has 

4 joined other countries in proposing that HFCs be controlled as a WMGHG under the Montreal 

5 Protocol because of their large projected future abundances (http://m.state.gov/mc70621.htm). In 

6 October 2016, the Montreal Protocol adopted an amendment to phase down global HFC 

7 production and consumption, avoiding emissions of an estimated 105 Gt CO2-eq by 2100

8 (http://ozone.unep.org/sites/ozone/files/pdfs/FAQs_Kigali-Amendment.pdf). CFCs, HCFCs, 

9 HFCs, halons and a few other gases comprise atmospheric halocarbons. The atmospheric growth 

10 rates of some halocarbon concentrations are large (for example, SF6 and HFC-134a), although 

11 their RF contributions remain small (Figure 2.4).

12 WATER VAPOR

13 Water vapor in the atmosphere acts as a powerful natural GHG, significantly increasing the 

14 Earth’s equilibrium temperature. In the stratosphere, water vapor abundances are controlled by 

15 transport from the troposphere and from oxidation of methane. Increases in methane from 

16 anthropogenic activities therefore increase stratospheric water vapor, producing a positive RF

17 (e.g. Solomon et al. 2010; Hegglin et al. 2014). Other less-important anthropogenic sources of 

18 stratospheric water vapor are hydrogen oxidation (le Texier et al. 1988), aircraft exhaust 

19 (Rosenlof et al. 2001; Morris et al. 2003), and explosive volcanic eruptions (Löffler et al. 2016).

20 In the troposphere, changes in troposphere water vapor are considered a feedback in the climate 

21 system (see 2.6.1 and Figure 2.2). As GHGs warm the atmosphere, tropospheric water vapor 

22 concentrations increase, thereby amplifying the warming effect (Held and Soden 2000).

23 OZONE

24 Ozone is a naturally occurring GHG. Ozone changes in the troposphere and stratosphere in 

25 response to anthropogenic and natural emissions. The changes generally have substantial spatial 

26 and temporal variability due to the nature of the production, loss, and transport processes 

27 controlling ozone abundances. Ozone RF calculations are complex because ozone naturally 

28 occurs in both the troposphere and stratosphere and has a lifetime that varies by atmospheric 

29 region. In the global troposphere, photochemical ozone formation is increased by emissions of 

30 methane, NOx, carbon monoxide (CO), and non-methane volatile organic compounds (VOCs), 

31 yielding a positive RF near and downwind of these precursor source emissions (e.g., Dentener et 

32 al. 2005). Stratospheric ozone is photochemically destroyed in reactions involving halogen 

33 species chlorine and bromine. Halogens are released in the stratosphere from the decomposition 

34 of synthetic halocarbons emitted at the surface (WMO 2014). Stratospheric ozone depletion, 

35 which is most notable in the polar regions, yields a net negative RF (Myhre et al. 2013).

36
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1 AEROSOLS

2 Atmospheric aerosols are perhaps the most complex and are the most uncertain component of 

3 forcing due to anthropogenic activities (Myhre et al. 2013). Aerosols have diverse natural and 

4 anthropogenic sources, and emissions from these sources can interact in non-linear ways

5 (Boucher et al. 2013). Aerosol types are categorized by composition; namely, sulfate, black 

6 carbon, organic aerosols, nitrate, dust, and sea salt. Individual particles generally include a mix 

7 of these components due to both chemical and physical transformations of aerosols and aerosol 

8 precursor gases following emission. Aerosol tropospheric lifetimes are days to weeks due to the 

9 general hydroscopic nature of primary and secondary particles and the ubiquity of cloud and 

10 precipitation systems in the troposphere. Particles which act as cloud condensation nuclei (CCN) 

11 or which are scavenged by cloud droplets are removed from the troposphere in precipitation. The 

12 heterogeneity of aerosol sources and locations combined with short aerosol lifetimes leads to the 

13 high spatial and temporal variabilities observed in global aerosol distributions and forcings.

14 Aerosols from anthropogenic activities influence RF in three primary ways: through the aerosol–

15 radiation interaction (RFari), the aerosol–cloud interaction (RFaci), and the albedo change from 

16 absorbing-aerosol deposition on snow and ice (Boucher et al. 2013). RFari is also known as the 

17 aerosol “direct effect,” involving absorption and scattering of longwave and shortwave radiation.

18 RFaci is also known as the cloud albedo “indirect effect” from changes in cloud particle number.

19 Global net RFari and RFaci are negative (Myhre et al. 2013), although light-absorbing aerosol 

20 components (for example, BC) have positive RF (Bond et al. 2013). The complexity of aerosol 

21 forcing increases with the use of ERF. EFRaci incorporates the rapid adjustment from the semi22 direct effect of absorbing aerosol (that is, the cloud response to atmospheric heating) and 

23 includes cloud lifetime effects (for example, glaciation and thermodynamic effects) (Boucher et 

24 al. 2013). Light-absorbing aerosols also affect climate when present in surface snow, by lowering 

25 surface albedo (e.g. Flanner et al. 2009). There is very high confidence that the RF from snow 

26 and ice albedo is positive; as noted above, the ERF of this forcing is significantly higher than its 

27 RF (Bond et al. 2013). Aerosol RF and ERF calculations and uncertainties continue to improve, 

28 as noted by IPCC (Boucher et al. 2013), as aerosol observations become more available and 

29 aerosol model skill improves.

30 LAND SURFACE

31 Land-cover changes (LCC) due to anthropogenic activities in the industrial era have changed the 

32 land surface brightness. There is strong evidence that these changes have increased Earth’s 

33 surface albedo, creating a globally averaged net-negative RF (Myhre et al. 2013). In specific

34 regions, however, LCC has produced a positive RF by lowering surface albedo (for example 

35 through afforestation and pasture abandonment). In addition to the direct radiative forcing 

36 through albedo changes, land-cover changes also have indirect effects on climate, such as

37 altering the hydrologic and carbon cycles and altering dust emissions. These effects are generally 

38 not included in the LCC RF calculations, and the sign of their forcing may be opposite that of the 
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1 LCC albedo forcing. Some of these responses, such as alteration of the carbon cycle, constitute 

2 climate feedbacks (Figure 2.2), as discussed more extensively in Chapter 10 (Changes in Land 

3 Cover and Terrestrial Biogeochemistry). The principal global terms in LCC are deforestation and 

4 afforestation. The increased use of satellite observations to quantify LCC has lowered recent 

5 estimates of the negative LCC RF (e.g., Ju and Masek 2016). In areas with significant irrigation,

6 surface temperatures and precipitation are affected by a change in energy partitioning from 

7 sensible to latent heating. Direct RF due to irrigation is generally small and can be positive or 

8 negative, depending on the balance of long-wave (surface cooling or increases in water vapor) 

9 and short-wave (increased cloudiness) effects (Cook et al. 2015).

10 CONTRAILS

11 Persistent line-shaped (linear) contrails are formed in the wake of jet-engine aircraft operating in 

12 the mid to upper troposphere. Persistent contrail formation begins in the expanding exhaust 

13 plume on ambient or aircraft-induced aerosol and requires ambient ice-supersaturated conditions.

14 As contrails spread and drift with the local winds after formation, they lose their linear feature 

15 while creating additional contrail cirrus cloudiness that is indistinguishable from background 

16 cloudiness. Contrails and contrail cirrus are additional forms of cirrus cloudiness, which interact 

17 with solar and thermal radiation to provide a global net positive RF and, thus, are visible 

18 evidence of an anthropogenic contribution to climate change (Burkhardt and Kärcher 2012).

19 2.4 Industrial-era Changes in Radiative Forcing Agents

20 The best estimates of present day RFs and ERFs from principal anthropogenic and natural 

21 climate drivers are shown in Figure 2.3 and in Table 2.1. The past changes in the industrial era 

22 leading up to present day RF are shown for anthropogenic gases in Figure 2.5 and for all climate 

23 drivers in Figure 2.6. The combined figures have several striking features. First, the sum of ERFs 

24 from CO2 and non-CO2 GHGs, tropospheric ozone, stratospheric water, contrails, and BC on 

25 snow shows a gradual, monotonic increase since 1750, with an accelerated trend in the past 50 

26 years. The sum of aerosol effects, stratospheric ozone depletion, and land use show a gradual, 

27 monotonic decrease until near the end of the 20th century, followed by decades with no further 

28 decrease. Volcanic RFs reveal their episodic, short-lived characteristics along with large values 

29 that at times dominate the total RF.

30 Changes in total solar irradiance over the industrial era are dominated by the 11-year solar cycle 

31 and other short-term variations (Figure 2.6). Radiative forcing due to changes in solar irradiance 

are estimated at 0.05 (0.0 – 0.1) W/m

2 32 between 1745 and 2005 (Myhre et al. 2013). 

33 Inconsistencies among models, which all rely on proxies of solar irradiance to fit the industrial 

34 era, lead to the large relative uncertainty in solar RF.

35 The atmospheric concentrations of CO2, CH4, and N2O are higher now than they have been in the 

36 past 800,000 years (Masson-Delmotte et al. 2014). All have increased monotonically over the 

37 industrial era, and are now 40%, 250%, and 20%, respectively, above their preindustrial 
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1 concentrations as reflected in the RF time series in Figure 2.5. Tropospheric ozone has increased 

2 in response to growth in precursor emissions in the industrial era. Synthetic GHG emissions have 

3 grown rapidly beginning in the mid-20th century, with many bringing halogens to the 

4 stratosphere and causing ozone depletion in subsequent decades. Aerosol RF effects are a sum 

5 over aerosol–radiation and aerosol–cloud interactions, which increased in the industrial era due 

6 to increased emissions of aerosol and aerosol precursors. These global trends average across 

7 disparate trends in concentrations at the regional scale, and to a lesser degree temporal trends in 

8 aerosol composition.

9 2.5 The Complex Relationship between Concentrations, Forcing, and 

10 Climate Response

11 Emissions, concentrations, forcing, and climate change metrics are often discussed at the global, 

12 annual-average scale. However, all vary both geographically and seasonally with the 

13 consequence that the associated patterns of concentration, forcing, and climate change do not 

14 strictly map to each other. In particular, feedbacks (Section 2.6) either amplify or dampen the 

15 direct effects of radiative forcing, as well as affecting the geographic and temporal patterns of 

16 climate response. As such, feedbacks are one reason that forcing and the climate change caused 

17 by that forcing are not linearly related.

18 The RF patterns of short-lived climate drivers with inhomogeneous source distributions, such as 

19 aerosols, ozone, contrails, and LCC, are leading examples of highly inhomogeneous forcings.

20 Spatial variability in aerosol emissions is enhanced by factors associated with meteorology (for 

21 example, precipitation, temperature, and transport) and chemical transformation or formation 

22 (for example, primary to secondary aerosol formation). These factors highlight the additional

23 inhomogeneity that exists, in general, in the temporal dimension. Even for relatively uniformly

24 distributed species (for example, WMGHGs), RF patterns are less homogenous than their 

25 concentrations. The RF of a uniform CO2 distribution, for example, is highly latitude and 

26 humidity dependent. With the added complexity and variability of regional forcings, the global 

27 mean RFs are known with more confidence than the regional RF patterns.

28 Quantifying the relationship between spatial RF patterns and regional and global responses is 

29 difficult because it requires distinguishing forcing responses from the inherent internal variability 

30 of the climate system, which acts on a range of time scales. In addition, studies have shown that 

31 the spatial pattern and timing of climate responses are not always well correlated with the spatial 

32 pattern and timing of radiative forcing, since adjustments within the climate system can 

33 determine much of the response (e.g., Shindell and Faluvegi 2009; Crook and Forster 2011; 

34 Knutti and Rugenstein 2015). The ability to test the accuracy of modeled responses to forcing 

35 patterns is limited by the sparsity of long-term observational records of regional climate 

36 variables. As a result there is very low confidence in our understanding of the qualitative and 

37 quantitative forcing–response relationship at the regional scale. However there is medium to high 

38 confidence in some more robust features, such as aerosol effects altering the location of the Inter
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1 Tropical Convergence Zone (ITCZ) and the positive feedback to reductions snow and ice albedo 

2 changes at high latitudes (Boucher et al. 2013; Myhre et al. 2013).

3 2.6 Climate-forcing Feedbacks 

4 Climate sensitivity is determined by the magnitude of the imposed forcings (ERFs) and by the 

5 climate responses to those forcings (Figure 2.2). All feedbacks can be quantified themselves as 

6 forcings, since each acts by affecting the Earth’s albedo or its greenhouse effect. The responses 

7 to radiative forcing that constitute climate feedbacks are the largest source of uncertainty in 

8 climate sensitivity (Flato et al. 2013); namely, the response of clouds, the carbon cycle and, to a 

9 lesser extent, land and sea ice to surface temperature and precipitation changes driven by ERFs.

10 These feedbacks operate on a range of time scales, and some may not be realized for decades or

11 centuries. Near-term and long-term feedbacks are described in the following sections.

12 2.6.1 Near-term Feedbacks

13 PLANCK FEEDBACK

14 When the temperatures of Earth’s surface and atmosphere increase in response to RF, more 

15 infrared radiation is emitted into the lower atmosphere; this serves to restore radiative balance at 

16 the tropopause. This radiative feedback, defined as the Planck feedback, only partially offsets the 

17 positive RF while triggering other feedbacks that affect radiative balance. The Planck feedback 

magnitude is −3.20 ± 0.04 W/m

2 18 per 1°C warming and is the strongest and primary stabilizing 

19 feedback in the climate system (Vial et al. 2013).

20 WATER VAPOR AND LAPSE RATE FEEDBACKS

21 Warmer air holds more moisture (water vapor) than cooler air—about 7% more per degree 

22 Celsius—as dictated by the Clausius-Clapeyron relationship (Allen and Igram 2002). Thus, as

23 global temperatures increase, the total amount of water vapor in the atmosphere increases, 

adding further to greenhouse warming—a positive feedback, adding approximately 1.6 W/m

2 24 per 

25 1°C of warming (Flato et al. 2013, Table 9.5). The water vapor feedback is responsible for more 

26 than doubling the direct climate warming from CO2 emissions alone (Bony et al. 2006; Soden 

27 and Held 2006; Vial et al. 2013). Observations confirm that global tropospheric water vapor has 

28 increased commensurate with measured warming (IPCC 2013, FAQ 3.2 and Figure 1a).

29 Interannual variations and trends in stratospheric water vapor, while influenced by tropospheric 

30 abundances, are controlled largely by tropopause temperatures and dynamical processes (Dessler 

31 et al. 2014). Increases in tropospheric water vapor have a larger warming effect in the upper 

32 troposphere (where it is cooler) than in the lower troposphere, thereby decreasing the rate at 

33 which temperatures decrease with altitude (the lapse rate). Warmer temperatures aloft increase 

34 outgoing infrared radiation—a negative feedback. Water vapor and lapse rate feedback strengths 

are 1.71 ± 0.13 W/m2 per 1°C warming and −0.66 ± 0.17 W/m2 35 per 1°C warming, respectively 

36 (Vial et al. 2013). These values remain largely unchanged between recent IPCC assessments
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1 (IPCC 2007; 2013). Recent advances in both observations and models have increased confidence 

2 that the net effect of the water vapor and lapse rate feedbacks is a significant positive RF (Flato 

3 et al. 2013).

4 CLOUD FEEDBACKS

5 Increases in cloudiness have two direct impacts on radiative fluxes: increased scattering of

6 sunlight, which increases Earth’s albedo (the shortwave cloud radiative effect, SWCRE), and 

7 increased trapping of infrared radiation (the longwave cloud radiative effect, LWCRE), which 

8 warms the surface. Decreases in cloudiness have the opposite effects. The SWCRE has a larger 

9 effect on local albedo when clouds are over dark surfaces (for example, oceans) than when over 

10 higher albedo surfaces, such as sea ice and deserts. For clouds globally, the SWCRE is about −50 

W/m

2 and the LWCRE about +30 W/m2 11 , yielding a net cooling influence (Loeb et al. 2009; Sohn 

12 et al. 2010). The relative magnitudes of the SWCRE and LWCRE vary with cloud type as well 

13 as with location. Low-altitude, thick clouds (for example, stratus and stratocumulus) have a net 

14 cooling, whereas high-altitude, thin clouds (for example, cirrus) have a net warming (e.g. 

15 Hartmann et al. 1992; Chen et al. 2000). Therefore, increases in low clouds that result from RF 

16 are a negative feedback to forcing, while increases in high clouds are a positive feedback. Cloud 

17 feedbacks to RF have the potential to be significant because the potential magnitudes of cloud 

18 effects are large compared with global RF (see Section 2.4). Cloud feedbacks also influence 

19 natural variability within the climate system and may amplify atmospheric circulation patterns 

20 and the El Niño–Southern Oscillation (Rädel et al. 2016). The net effect of cloud feedbacks is 

estimated to be positive over the industrial era, with a value of +0.27 ± 0.42 W/m

2 21 per 1°C 

22 warming (Vial et al. 2013). The net cloud feedback can be broken into components, where the 

LW cloud feedback is positive (+0.24 ± 0.26 W/m

2 23 per 1°C warming) and the SW feedback is 

near-zero (+0.14 ± 0.40 W/m

2 24 per 1°C warming; Vial et al. 2013), though the two do not add 

25 linearly. The value of the SW cloud feedbacks shows a significant sensitivity to computation 

26 methodology (Taylor et al. 2011; Vial et al. 2013; Klocke et al. 2013). Uncertainty in cloud

27 feedbacks remains the largest source of inter-model differences in calculated climate sensitivity

28 (Vial et al. 2013; Boucher et al. 2013).

29 SNOW, ICE, AND SURFACE ALBEDO

30 Snow and ice are highly reflective of solar radiation relative to land surfaces and the ocean. Loss 

31 of snow cover, glaciers, ice sheets, and sea ice resulting from climate warming lowers Earth’s 

32 surface albedo, which increases absorbed solar radiation and leads to further warming as well as 

33 changes in turbulent heat fluxes at the surface (Sejas et al. 2014). For ice sheets (for example, on 

34 Antarctica and Greenland [Ch. 11: Arctic Changes]), the positive radiative feedback is further 

35 amplified by dynamical feedbacks on ice sheet mass loss. Specifically, continental ice shelves 

36 limit the discharge rates of ice sheets into the ocean; melting of ice shelves results in an 

37 acceleration of the discharge rate and appears to be a positive feedback on the ice stream flow 

38 rate and total mass loss (e.g. Holland et al. 2008; Schoof 2010; Rignot et al. 2010; Joughin et al. 
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1 2012). Feedbacks related to ice sheet dynamics occur on longer timescales than other 

2 feedbacks—many centuries or longer. Significant ice sheet melt can also lead to changes in 

3 freshwater input to the oceans, which in turn can affect ocean temperatures and circulation, 

4 ocean–atmosphere heat exchange and moisture fluxes, and atmospheric circulation (Masson5 Delmotte et al. 2014). 

6 The complete contribution of ice sheet feedbacks on timescales of millennia are not generally 

7 included in CMIP5 climate simulations. These slow feedbacks are also not thought to change in 

8 proportion to global mean surface temperature change, implying that the climate sensitivity 

9 changes with time, making it difficult to fully understand climate sensitivity considering only the 

10 industrial age. This also implies a high likelihood for tipping points, as discussed further in 

11 Chapter 15.

12 The surface-albedo feedback is important to interannual variations in sea ice as well as to long13 term climate change. While there is a significant range in estimates of the snow-albedo feedback,

14 it is assessed as positive (Hall and Qu 2006; Fernandes et al. 2009; Vial et al. 2013), with a best 

estimate of 0.27 ± 0.06 W/m

2 15 per 1°C of warming globally. This feedback acts only where snow 

16 and ice are present and, thus, is most effective in polar regions (Winton 2006; Taylor et al. 

17 2011). However, there is evidence that the presence of a polar surface-albedo feedback 

18 influences the tropical climate as well (Hall 2004).

19 Changes in sea ice can also influence Arctic cloudiness. Recent work indicates that Arctic clouds 

20 have responded to sea ice loss in fall but not summer (Kay and Gettelman 2009; Kay et al. 2011; 

21 Taylor et al. 2015; Kay and L’Ecuyer 2013; Pistone et al. 2014). This has important implications 

22 for future climate change, as an increase in summer clouds could offset a portion of the 

23 amplifying surface albedo feedback, slowing down the rate of Arctic warming.

24 ATMOSPHERIC COMPOSITION

25 Climate change can alter the atmospheric abundance and distribution of some radiatively active 

26 species by changing natural emissions, atmospheric photochemical reaction rates, atmospheric 

27 lifetimes, transport patterns, or deposition rates. These changes in turn alter the associated ERFs,

28 leading to further climate changes (Liao et al. 2009; Unger et al. 2009; Raes et al. 2010). 

29 Important examples include climate-driven changes in temperature and precipitation that affect 

30 1) natural sources of NOx from soils and lightning and VOC sources from vegetation, all of 

31 which affect ozone abundances (Raes et al. 2010); 2) regional aridity, which influences surface 

32 dust sources as well as susceptibility to wildfires; and 3) surface winds, which control the 

33 emission of dust from the land surface and the emissions of sea salt and dimethyl sulfide—a 

34 natural precursor to sulfate aerosol—from the ocean surface. 

35 Feedbacks through changes in composition occur through a variety of processes. Climate-driven 

36 ecosystem changes that alter the carbon cycle potentially impact atmospheric CO2 and CH4

37 abundances (Section 2.6.2). Atmospheric aerosols affect clouds and precipitation rates, which in 
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1 turn alter aerosol removal rates, lifetimes, and atmospheric abundances. Longwave radiative 

2 feedbacks and climate-driven circulation changes also alter stratospheric ozone abundance 

3 (Nowack et al. 2015). Investigation of these and other chemistry–climate interactions is an active 

4 area of research (e.g., John et al. 2012; Pacifico et al. 2012; Morgenstern et al. 2013; Holmes et 

5 al. 2013; Naik et al. 2013, Voulgarakis et al. 2013; Isaksen et al. 2014; Dietmuller et al. 2014; 

6 Banerjee et al. 2014). While understanding of key processes is improving, atmospheric chemistry 

7 feedbacks are absent or limited in many global climate modeling studies used to project future 

8 climate, though this is rapidly changing (https://cmip.ucar.edu/aer-chem-mip). For some 

9 chemistry–climate feedbacks involving shorter-lived constituents, the net effects may be near10 zero at the global scale while significant at local to regional scales (e.g. Raes et al. 2010; Han et 

11 al. 2013).

12 2.6.2 Long-term Feedbacks

13 TERRESTRIAL ECOSYSTEMS AND CLIMATE CHANGE FEEDBACKS

14 The cycling of carbon through the climate system is an important long-term climate feedback 

15 that affects atmospheric CO2 concentrations. Atmospheric CO2 concentrations are determined by 

16 emissions from burning fossil fuels, wildfires, and permafrost thaw balanced against CO2 uptake 

17 by the oceans and terrestrial biosphere (Ciais et al. 2013; Le Quéré et al. 2016) (Figure 2.2). 

18 About two-thirds of anthropogenic CO2 is taken up by the terrestrial environment and the oceans, 

19 through photosynthesis and through direct diffusion into ocean surface waters, respectively. The 

20 ability of the land to continue uptake of CO2 is uncertain and depends on land-use management 

21 through mitigation and/or policy and urbanization and ocean acidification processes (see 

22 Chapters 10 and 13). Altered uptake rates will affect atmospheric CO2 abundances, forcing, and 

23 rates of climate change. Such changes are expected to evolve on the decadal and longer time24 scale, though abrupt changes are possible.

25 Significant uncertainty exists in quantification of carbon cycle feedbacks. Differences in the 

26 assumed characteristics of the land carbon-cycle processes are the primary cause of the inter27 model spread in modeling the present-day carbon cycle and a leading source of uncertainty.

28 Significant uncertainties also exist in ocean carbon-cycle changes in future climate scenarios. 

29 Basic principles of carbon cycle dynamics in terrestrial ecosystems suggest that increased 

30 atmospheric CO2 concentrations can directly enhance plant growth rates and, therefore, increase 

31 carbon uptake (the “CO2 fertilization” effect), nominally sequestering much of the added carbon

32 from fossil-fuel combustion (e.g., Wenzel et al. 2016). However, this effect is variable; 

33 sometimes plants acclimate so that higher CO2 concentrations no longer enhance growth (e.g.,

34 Franks et al. 2013). In addition, CO2 fertilization is often offset by other factors limiting plant 

35 growth, such as water and or nutrient availability, temperature, and incoming solar radiation that 

36 can be modified by changes in vegetation structure. Large-scale plant mortality through fire, soil 

37 moisture drought, and/or temperature changes also impact successional processes that contribute 
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1 to reestablishment and revegetation (or not) of disturbed ecosystems, altering the amount and 

2 distribution of plants available to uptake CO2.

3 Climate-induced changes in the horizontal (for example, landscape to biome) and vertical (soils 

4 to canopy) structure of terrestrial ecosystems also alter the physical surface roughness and 

5 albedo, as well as biogeochemical (carbon, nitrogen) cycles and biophysical evapotranspiration 

6 and water demand. Combined, these responses constitute climate feedbacks by altering surface 

7 albedo and atmospheric GHG abundances. Drivers of these changes in terrestrial ecosystems

8 include changes in the biophysical growing season, altered seasonality, wildfire patterns, and 

9 multiple other interacting factors (Chapter 10). 

10 Determination of accurate future CO2 stabilization scenarios depends on accounting for the 

11 significant role that the land biosphere plays in the global carbon cycle and feedbacks between 

12 climate change and the terrestrial carbon cycle (Hibbard et al. 2007). Earth System Models 

13 (ESMs) are increasing representation of terrestrial carbon cycle processes, including plant 

14 photosynthesis, plant and soil respiration and decomposition as well as CO2 fertilization, with the 

15 latter based on the assumption that increased atmospheric CO2 concentrations provide more 

16 substrate for photosynthesis and productivity. Recent advances in ESMs are beginning to 

17 account for other important factors such as nutrient limitations (Thornton et al. 2007; Brzostek et 

18 al. 2014; Wieder et al. 2015). ESMs that do include carbon-cycle feedbacks appear, on average, 

19 to overestimate terrestrial CO2 uptake under the present-day climate (Anav et al. 2013; Smith et 

20 al. 2016) and underestimate nutrient limitations to CO2 fertilization (Wieder et al. 2015). The 

21 sign of the land carbon-cycle feedback through 2100 remains unclear in the newest generation of 

22 ESMs (Friedlingstein et al. 2006, 2014; Wieder et al. 2015). Eleven CMIP5 ESMs forced with 

23 the same CO2 emissions scenario—one consistent with RCP8.5 concentrations—produce a range 

24 of 795 to 1145 ppm for atmospheric CO2 concentration in 2100. The majority of the ESMs (7 out 

25 of 11) simulated a CO2 concentration larger (by 44 ppm on average) than their equivalent non26 interactive carbon cycle counterpart (Friedlingstein et al. 2014). This difference in CO2 equates 

27 to about 0.2°C more warming by 2100. The inclusion of carbon-cycle feedbacks does not alter 

28 the lower-end estimate of climate sensitivity, but in most climate models it pushes the upper 

29 bound higher (Friedlingstein et al. 2014).

30 OCEAN CHEMISTRY, ECOSYSTEM, AND CIRCULATION CHANGES 

31 The ocean plays a critical role in regulating climate change by controlling the amount of 

32 greenhouse gases (including CO2, water vapor, and nitrous oxide) and heat that remain in the 

33 atmosphere. The ocean also absorbs most of the net energy increase in the climate system from 

34 anthropogenic RF. This additional heat is stored predominantly (about 60%) in the upper 700 

35 meters of the ocean (Johnson et al. 2016 and see Ch. 12: Sea Level Rise and Ch. 13: Ocean 

36 Acidification).
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1 Marine ecosystems take up CO2 from the atmosphere in the same way that plants do on land. 

2 About half of the global net primary production (NPP) is by marine plants (approximately 50 ± 

3 28 PgC/year; Falkowski et al. 2004; Carr et al. 2006; Chavez et al. 2011). Phytoplankton NPP 

4 supports the biological pump, which transports 2–12 PgC/year of organic carbon to the deep sea

5 (Doney 2010; Passow and Carlson 2012), where it is sequestered away from the atmospheric 

6 pool of carbon for 200–1500 years. Estimates of future changes in phytoplankton distributions 

7 and uptake of CO2 vary significantly.

8 Remote sensing of sea surface temperature and chlorophyll as well as model simulations and 

9 sediment records suggest that global phytoplankton NPP may have increased over the last 

10 century as a consequence of decadal-scale natural climate variability such as the El Niño–

11 Southern Oscillation, which promotes nutrient enrichment of the euphotic zone through vertical 

12 mixing and upwelling (Bidigare et al. 2009; Chavez et al. 2011; Zhai et al. 2013). In contrast, 

13 other analyses of chlorophyll distributions suggest that annual phytoplankton NPP in the global 

14 ocean has declined by more than 6% over the last three decades, mostly attributed to diatom 

15 changes (Gregg et al. 2003; Rousseaux and Gregg 2015). In contrast, other analyses suggest that 

16 phytoplankton NPP has decreased by about 1% per year over the last 100 years (Behrenfeld et al. 

17 2006; Boyce et al. 2010; Capotondi et al. 2012). These results are consistent with model 

18 simulations indicating that both NPP and the biological pump have decreased by 6.6% and 8%, 

19 respectively, over the last five decades (Laufkötter et al. 2015), trends that are expected to 

20 continue through the end of this century (Steinacher et al. 2010). Consistent with this result,

21 carbon cycle feedbacks in the ocean were positive across the suite of CMIP5 models.

22 In addition to being an important carbon sink, the ocean dominates the hydrological cycle, since

23 most of the surface evaporation and rainfall occurs over the ocean (Trenberth et al. 2007; 

24 Schanze et al. 2010). The rate of evaporation, and thus the water vapor feedback, depends on 

25 surface wind stress and ocean temperature. Climate warming from radiative forcing also is 

26 associated with intensification of the water cycle (Ch. 7: Precipitation Changes). Over decadal 

27 timescales the surface ocean salinity has increased in areas of high salinity, such as the 

28 subtropical gyres, and decreased in areas of low salinity, such as the Warm Pool region (Durack 

29 and Wijfels 2010; Good et al. 2013). This increase in stratification in select regions and mixing 

30 in other regions leads to altered patterns of ocean circulation, which impacts uptake of 

31 anthropogenic heat and CO2.

32 Increased ocean temperatures also affect ice sheet melt, particularly for the Antarctic Ice Sheet 

33 where basal sea ice melting is important relative to surface melting due to colder surface 

34 temperatures (Rignot and Thomas 2002). For the Greenland Ice Sheet, submarine melting at 

35 tidewater margins is also contributing to volume loss (van Den Broeke et al. 2009). In turn, 

36 changes in ice sheet melt rates change cold and fresh water inputs, altering ocean stratification. 

37 This affects ocean circulation and the ability of the ocean to absorb more greenhouse gases and 

38 heat (Enderlin and Hamilton 2014). Enhanced sea ice export to lower latitudes gives rise to local 

39 salinity anomalies (such as the Great Salinity Anomaly; Gelderloos et al. 2012) and therefore to 
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1 changes in ocean circulation and air–sea exchanges of momentum, heat, and freshwater, which in 

2 turn affect the atmospheric distribution of heat and greenhouse gases.

3 Additionally, as the ocean warms and freshens it becomes more stratified, inhibiting surface 

4 mixing, high-latitude convection, and deep water formation, thereby weakening the Meridional 

5 Overturning Circulation (MOC), the global ocean’s conveyor belt (Kostov et al. 2014; Andrews 

6 et al. 2012; see also Ch. 13: Ocean Acidification). Reduced deep water formation and slower 

7 overturning are associated with decreased heat and carbon sequestration at greater depths. 

8 Sporadic observations in the 1980s, 90s, and 2000s have led to the conclusion that there already 

9 is a slowdown (Lherminier et al. 2007). Other observational studies have not found any 

10 significant slowdown (Lumpkin et al., 2008). Recent continuous observations of MOC in the 

11 North Atlantic show that there are no detectible trends since 2004 (Cunningham and Marsh

12 2010). However, a recent analysis (Rahmstorf et al. 2015) finds that there has been an 

13 approximately 10% reduction in the strength of the overturning circulation over the 20th and 

14 early 21st Centuries. Future projections show that the strength of MOC will significantly 

15 decrease as the ocean warms and freshens and as upwelling in the Southern Ocean weakens due 

16 to storm track moving poleward (Rahmstorf et al. 2015; see also Ch. 13: Ocean Acidification). 

17 Such a slowdown of the ocean currents will impact the rate at which the ocean will absorb CO2

18 and heat from the atmosphere. 

19 PERMAFROST AND HYDRATES

20 Permafrost and methane hydrates contain large stores of carbon in the form of organic materials, 

21 mostly at northern high latitudes. With warming, this organic material can thaw, making 

22 previously frozen organic matter available for microbial decomposition, releasing CO2 and 

23 methane to the atmosphere, providing additional radiative forcing and accelerating warming.

24 This process defines the permafrost-carbon feedback. Combined data and modeling studies 

25 suggest that the permafrost-carbon feedback is very likely positive (Schaefer et al. 2014; Koven 

26 et al. 2015a; Schuur et al. 2015). This feedback was not included in the IPCC projections but is 

27 an active area of research. Accounting for permafrost-carbon release reduces the amount of 

28 emissions allowable from anthropogenic sources if future GHG mitigation targets are to be met 

29 (González-Eguino and Neumann 2016). 

30 The permafrost-carbon feedback strength indicates a 120 ± 85 Gt release of carbon from 

31 permafrost by 2100, corresponding to a global temperature increase of +0.94° ± 0.68°F (+0.52° ±

32 0.38°C) (Schaefer et al. 2014). A key feature of the permafrost feedback is that, once initiated, it 

33 continues for an extended period because emissions from decomposition occur slowly over 

34 decades and longer. In the coming few decades, enhanced plant growth at high latitudes and its 

35 associated CO2 sink (Friedlingstein et al. 2006) are expected to partially offset the increased 

36 emissions from permafrost thaw (Schaefer et al. 2014; Schuur et al. 2015); thereafter, 

37 decomposition will dominate uptake. Recent evidence indicates that permafrost thaw is occurring 

38 faster than expected; poorly understood deep-soil carbon decomposition and ice wedge processes 
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1 likely contribute (Koven et al. 2015b; Liljedahl et al. 2016). Chapter 11 includes a more detailed 

2 discussion of permafrost and methane hydrates in the Arctic. Future changes in permafrost 

3 emissions and the potential for even greater emissions from methane hydrates in the continental 

4 shelf are discussed further in Chapter 15.

5
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1 TRACEABLE ACCOUNTS

2 Key Finding 1

3 Human activities continue to significantly affect Earth’s climate by altering factors that change 

4 its radiative balance (known as a radiative forcing). These factors include greenhouse gases, 

5 small airborne particles (aerosols), and the reflectivity of the Earth’s surface. In the industrial 

6 era, human activities have been and remain the dominant cause of climate warming and have far 

7 exceeded the relatively small net increase due to natural factors, which include changes in energy 

8 from the sun and the cooling effect of volcanic eruptions.

9 Description of evidence base

10 The Key Finding and supporting text summarizes extensive evidence documented in the climate 

11 science literature, including in previous national (NCA3; Melillo et al. 2014) and international 

12 (IPCC 2013) assessments. The assertion that Earth’s climate is controlled by its radiative balance 

13 is a well-established physical property of the planet. Quantification of the changes in Earth’s 

14 radiative balance come from a combination of observations and calculations. Satellite data are 

15 used directly to observe changes in Earth’s outgoing visible and infrared radiation. Since 2002, 

16 observations of incoming sunlight include both total solar irradiance and solar spectral irradiance 

17 (Ermolli et al. 2013). Extensive in situ and remote sensing data are used to measure the 

18 concentrations of radiative forcing agents (greenhouse gases and aerosols) and changes in land 

19 cover, as well as the relevant properties of these agents (for example, aerosol microphysical and 

20 optical properties). Concentrations of long-lived greenhouse gases in particular are well21 quantified through a limited number of observations because of their relatively high spatial 

22 homogeneity. Calculations of radiative forcing by greenhouse gases and aerosols are supported 

23 by observations of radiative fluxes from the surface, from airborne research platforms and from 

24 satellites. Both direct observations and modeling studies support the assertion that while 

25 volcanoes can have significant effects on climate over periods ranging from a couple of years 

26 (more moderate eruptions) to decades (very large eruptions), over the industrial era radiative 

27 forcing by volcanoes has been episodic and has not contributed significantly to forcing trends. 

28 Observations indicate a positive but small increase in solar input over the industrial era. 

29 Relatively higher variations in solar input at shorter (UV) wavelengths may be leading to indirect 

30 changes in Earth’s radiative balance through their impact on ozone concentrations that are larger 

31 than the radiative impact of changes in total solar irradiance, but these changes are also small in 

32 comparison to anthropogenic greenhouse gas and aerosol forcing.

33 Major uncertainties

34 The largest source of uncertainty regarding changes in the Earth’s radiative balance over the 

35 industrial era is quantifying forcing by aerosols. This has been a consistent finding across 

36 previous assessments (e.g., IPCC 2007; IPCC 2013). See discussion of major uncertainties 

37 associated with aerosol forcing in the Traceable Accounts for Key Finding 2 below. 
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1 Recent work has highlighted the potentially larger role of variations in UV solar irradiance, 

2 versus total solar irradiance, in solar forcing. However, this increase in solar forcing uncertainty 

3 is not sufficiently large to reduce confidence that anthropogenic activities dominate industrial4 era forcing.

5 Assessment of confidence based on evidence and agreement, including short description of 

6 nature of evidence and level of agreement 

7 x Very High 

8 ☐ High 

9 ☐ Medium 

10 ☐ Low

11 There is very high confidence that anthropogenic radiative forcing exceeds natural forcing over 

12 the industrial era. While there remain large uncertainties in aerosol radiative forcing in particular, 

13 natural forcing through solar irradiance changes and volcanic activity has been, with very high 

14 confidence, small over the industrial era relative to anthropogenic forcing. Estimates of 

15 anthropogenic industrial-era forcing have become larger and more positive with time: from the 

16 AR4 estimate (IPCC 2007) of anthropogenic forcing for the industrial era up to 2005 to the AR5 

17 estimate (IPCC 2013) of forcing up to 2011, ERF increased by 43%. This is due to an increase in 

18 positive radiative forcing from greenhouse gas concentrations and improved understanding of 

19 forcing by aerosols that led to a reduction in the estimates of their negative forcing (IPCC 2013).

20 Summary sentence or paragraph that integrates the above information

21 This key finding is consistent with that in IPCC AR4 (IPCC 2007) and IPCC AR5 (IPCC 2013); 

22 namely, anthropogenic radiative forcing is positive (climate warming) and substantially larger 

23 than natural forcing from variations in solar input and volcanic emissions. Confidence in this 

24 finding has increased from AR4 to AR5, as anthropogenic greenhouse-gas forcings have 

25 continued to increase, whereas solar forcing remains small and volcanic forcing near-zero over 

26 decadal timescales.

27 Key Finding 2

28 Aerosols caused by human activity play a profound and complex role in the climate system 

29 through direct radiative effects and indirect effects on cloud formation and properties. The 

30 combined forcing of aerosol–radiation and aerosol–cloud interactions is negative over the 

31 industrial era, substantially offsetting a substantial part of greenhouse gas forcing, which is 

32 currently the predominant human contribution. The magnitude of this offset has declined in 

33 recent decades due to a decreasing trend in net aerosol forcing.

34
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1 Description of evidence base

2 The Key Finding and supporting text summarize extensive evidence documented in the climate 

3 science literature, including in previous national (NCA3; Melillo et al. 2014) and international 

4 (IPCC 2013) assessments. Fundamental physics dictates that aerosols suspended in the 

5 atmosphere will scatter sunlight, and thereby reduce incoming solar radiation. Extensive in situ 

6 and remote sensing data are used to measure emission of aerosols and aerosol precursors from 

7 specific source types, the concentrations of aerosols in the atmosphere, aerosol microphysical 

8 and optical properties, and, via remote sensing, their direct impacts on radiative fluxes. Model 

9 calculations of aerosol forcing are constrained by these observations.

10 In addition to their direct impact on radiative fluxes, aerosols also act as cloud condensation 

11 nuclei. Multiple observational and modeling studies have concluded that increasing the number 

12 of aerosols in the atmosphere increases cloud albedo and lifetime, adding to the negative forcing 

13 (aerosol “indirect effects”) (e.g., Twohy 2005; Lohmann and Feichter 2005; Quaas et al. 2009; 

14 Rosenfeld et al. 2014). Particles that absorb sunlight increase atmospheric heating; if they are 

15 sufficiently dark, the net effect of scattering plus absorption can be a positive top-of-atmosphere 

16 radiative forcing. However, only a few very specific types of aerosols (for example, from diesel 

17 engines) are sufficiently dark that they have a positive radiative forcing (Bond et al. 2013). 

18 Modeling studies, combined with observational input, have investigated the thermodynamic 

19 response to aerosol absorption in the atmosphere (the “semi-direct effects”). Depending on 

20 aerosol location relative to the clouds and other factors the resulting changes in cloud properties 

21 can have a positive or negative effect on net downward radiative flux. The best estimate is that 

22 the semi-direct effect of aerosols is negative, offsetting approximately 15% of the positive 

23 radiative forcing by absorbing aerosols (specifically, black carbon) (Bond et al. 2013). 

24 Major uncertainties

25 Aerosol–cloud interactions in particular are the largest source of uncertainty in both aerosol and 

26 total anthropogenic radiative forcing. These include the microphysical effects of aerosols on 

27 clouds (the “indirect effects”) and changes in clouds that result from the rapid response to 

28 absorption of sunlight by aerosols (the “semi-direct effects”). This has been a consistent finding 

29 of previous assessments (e.g., IPCC 2007; IPCC 2013). Aerosols affect the Earth’s albedo by 

30 directly interacting with solar radiation (scattering and absorbing sunlight) and by affecting cloud 

31 properties (albedo and lifetime). Aerosol cloud effects are, in particular, the most significant 

32 single source of uncertainty in anthropogenic ERF. This is due to poor understanding of how 

33 both natural and anthropogenic aerosol emissions have changed and how changing aerosol 

34 concentrations and composition affect cloud properties (albedo and lifetime) (Boucher et al. 

35 2013; Carslaw et al. 2013). From a theoretical standpoint, aerosol–cloud these interactions are 

36 complex, and using observations to isolate the effects of aerosols on clouds is complicated by the 

37 fact that other factors (for example, the thermodynamic state of the atmosphere) also control 

38 cloud properties. Further, changes in aerosol properties and the atmospheric thermodynamic state 

39 are often correlated and interact in non-linear ways (Stevens and Feingold 2009).
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1 While the indirect effects lead to negative forcing with high confidence, the semi-direct effects 

2 are uncertain in both sign and magnitude, but are assessed to be likely negative.

3 Assessment of confidence based on evidence and agreement, including short description of 

4 nature of evidence and level of agreement 

5 ☐ Very High 

6 x High 

7 ☐ Medium 

8 ☐ Low 

9 There is very high confidence that aerosol radiative forcing is negative on a global, annually

10 averaged basis, medium confidence in the magnitude of the aerosol radiative forcing (RF), high 

11 confidence that aerosol effective radiative forcing (ERF) is also, on average, negative, and low to 

12 medium confidence in the magnitude of aerosol effective radiative forcing (ERF). Lower 

13 confidence in the magnitude of the aerosol ERF is due to large uncertainties in the effects of 

14 aerosols on clouds. Combined, we assess a high level of confidence that aerosol forcing is net15 negative and sufficiently large to be substantially offsetting positive greenhouse gas forcing.

16 Improvements in emissions estimates, observations (from both surface-based networks and 

17 satellites), and modeling capability give medium to high confidence in the finding that aerosol 

18 forcing trends are decreasing in recent decades.

19 Summary sentence or paragraph that integrates the above information

20 This key finding parallels the findings of IPCC AR5 (Myhre et al. 2013) that aerosols on net 

21 constitute a negative radiative forcing. While significant uncertainty remains in the quantification 

22 of aerosol ERF, we assess with high confidence that aerosols offset about half of the positive 

23 forcing by anthropogenic CO2 and about a third of the forcing by all well-mixed anthropogenic 

24 greenhouse gases. The fraction of greenhouse gas forcing that is offset by aerosols has been 

25 decreasing over recent decades, as aerosol forcing has leveled off while greenhouse gas forcing 

26 continues to increase.

27 Key Finding 3

28 The climate system includes a number of positive and negative feedback processes that can 

29 either strengthen (positive feedback) or weaken (negative feedback) the system’s responses to 

30 human and natural influences. These feedbacks operate on a range of timescales from very short 

31 (essentially instantaneous) to very long (centuries). While there are large uncertainties associated 

32 with some of these feedbacks, the net feedback effect over the industrial era has been positive 

33 (amplifying warming) and will continue to be positive in coming decades.

34
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1 Description of evidence base

2 Fundamental physics dictates that the Planck feedback only partially offsets warming by 

3 increasing emitted infrared radiation. The largest feedback, the water vapor feedback, is again 

4 dictated by fundamental physics and with very high confidence is positive, approximately 

5 doubling the direct warming due to CO2 emissions alone. The lapse rate feedback is, also with 

6 very high confidence, negative, but only partially offsets the water vapor feedback, with the two 

7 linked by the fact that both are driven by increases in atmospheric water vapor with warming. 

8 Estimates of this feedback strength have changed little across recent assessments (IPCC 2007; 

9 IPCC 2013). The snow and ice albedo feedback is also definitively positive in sign, with the 

10 magnitude of the feedback dependent in part on timescale of interest. Assessment of its strength 

11 has also not significantly changed since IPCC (2007). Cloud feedbacks can be either positive or 

12 negative, depending on the sign of the change in clouds with warming (increase or decrease) and 

13 the type of cloud that changes (low or high clouds). Recent international assessments (IPCC 

14 2007; IPCC 2013) and a separate assessment specifically of feedbacks (Vial et al. 2013) all give 

15 best estimates of cloud feedbacks as positive on net, with uncertainty bounds allowing for a 

16 small negative feedback. Feedbacks via changes in atmospheric chemistry are an active area of 

17 research. They are not well-quantified, but are expected to be small relative to water-vapor-plus18 lapse-rate, snow, and cloud feedbacks at the global scale. Carbon cycle feedbacks through 

19 changes in the land biosphere are currently of uncertain direction but uncertainties are 

20 asymmetric: they might be small and negative but could also be large and positive. Recent best 

21 estimates of ocean carbon cycle feedbacks are that they are positive, with significant uncertainty 

22 that includes allowance of a negative feedback for present-day CO2 levels (Laufkötter et al. 

23 2015; Steinacher et al. 2010). The thaw of permafrost with climate warming also has the 

24 potential to release large stores of carbon. While this source of CO2 is currently likely small, the 

25 permafrost-carbon feedback is very likely positive, and as discussed in Chapter 15, could be a 

26 large positive feedback in longer term. Thus, while negative feedback processes exist the 

27 preponderance of evidence is that positive feedback processes dominate.

28 Major uncertainties

29 Cloud feedbacks carry the largest uncertainty of all the feedbacks, particularly on the decadal to 

30 century time-scale. This results from the fact cloud feedbacks can be either positive or negative, 

31 depending not only on the direction of change (more or less cloud) but also on the type of cloud 

32 affected and, to a lesser degree, the location of the cloud. 

33 Assessment of confidence based on evidence and agreement, including short description of 

34 nature of evidence and level of agreement 

35 ☐ Very High 

36 x High 

37 ☐ Medium 
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1 ☐ Low 

2 There is high confidence that the net effect of all feedback processes in the climate system are 

3 positive, i.e. reinforce warming. This is based on consistency across multiple assessments, 

4 including IPCC AR5 (IPCC 2013 and references therein) of the magnitude of, in particular, the 

5 largest feedbacks in the climate system, two of which (water vapor feedback and snow/ice 

6 albedo feedback) are definitively positive in sign. While significant increases in low cloud cover 

7 with climate warming would be a large negative feedback to warming, modeling and 

8 observational studies do not support the idea of increases, on average, in low clouds with climate 

9 warming.

10 Summary sentence or paragraph that integrates the above information

11 The net effect of all identified feedbacks to forcing is, by best current estimates, positive and 

12 therefore reinforces climate warming. The various feedback processes operate on different 

13 timescales with, in particular, carbon cycle and snow and ice albedo feedbacks operating on 

14 longer timelines than water vapor, lapse rate, cloud, and atmospheric composition feedbacks.

15
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1 TABLES

Table 2.1. Global mean RF and ERF values in 2011 for the industrial era a 2

Climate forcing agent Radiative forcing (W/m2) Effective radiative 

forcing (Wm2) b

Well-mixed greenhouse gases 

(CO2, CH4, N2O, and halocarbons) 

+2.83 (2.54 to 3.12) +2.83 (2.26 to 3.40)

Tropospheric ozone +0.40 (0.20 to 0.60)

Stratospheric ozone −0.05 (-0.15 to +0.05)

Stratospheric water vapor from 

CH4

+0.07 (+0.02 to +0.12)

Aerosol–radiation interactions −0.35 (−0.85 to +0.15) −0.45 (−0.95 to +0.05)

Aerosol–cloud interactions Not estimated −0.45 (−1.2 to 0.0)

Surface albedo (land use) −0.15 (−0.25 to −0.05)

Surface albedo (black carbon 

aerosol on snow and ice) 

+0.04 (+0.02 to +0.09)

Contrails +0.01 (+0.005 to +0.03)

Combined contrails and contrailinduced cirrus Not estimated +0.05 (0.02 to 0.15)

Total anthropogenic Not estimated +2.3 (1.1 to 3.3)

Solar irradiance +0.05 (0.0 to +0.10)

a 3 From IPCC (Myhre et al. 2013)

b 4 RF is a good estimate of ERF for most forcing agents except black carbon on snow and ice and 

5 aerosol–cloud interactions.

6

7
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1 FIGURES

2

3 Figure 2.1: Global mean energy budget of the Earth under present-day climate conditions. 

Numbers state magnitudes of the individual energy fluxes in watts per square meter (W/m2 4 ) 

5 averaged over Earth’s surface, adjusted within their uncertainty ranges to balance the energy 

6 budgets of the atmosphere and the surface. Numbers in parentheses attached to the energy fluxes 

7 cover the range of values in line with observational constraints. These constraints are largely 

8 provided by satellite-based observations, which have directly measured solar and infrared fluxes 

9 at the top of the atmosphere over nearly the whole globe since 1984 (Barkstrom, 1984; Smith et 

10 al., 1994). More advanced satellite-based measurements focusing on the role of clouds in Earth’s 

11 radiative fluxes have been available since 1998 (Wielicki et al., 1995 & 1996). (Figure source: 

12 Hartmann et al. 2013; © IPCC, used with permission).

13
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1

2 Figure 2.2: Simplified conceptual modeling framework for the climate system as implemented 

3 in many climate models (Chapter 4). Modeling components include forcing agents, feedback 

4 processes, carbon uptake processes and radiative forcing and balance. The lines indicate physical 

5 interconnections (solid lines) and feedback pathways (dashed lines). Principal changes (blue 

6 boxes) lead to climate impacts (red box) and feedbacks. (Figure source: adapted from Knutti and 

7 Rugenstein 2015).

8
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1

2 Figure 2.3: Bar chart for RF (hatched) and ERF (solid) for the period 1750–2011, where the 

3 total ERF is derived from IPCC. Uncertainties (5% to 95% confidence range) are given for RF 

4 (dotted lines) and ERF (solid lines). Volcanic forcing is not shown because this forcing is 

5 negligible over the industrial era. (Figure source: Myhre et al. 2013; © IPCC, used with 

6 permission).

7
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1

2

3 Figure 2.4: Atmospheric concentrations of carbon dioxide, methane and nitrous oxide over the 

4 last 10,000 years (large panels) and since 1750 (inset panels). Measurements are shown from ice 

5 cores (symbols with different colors for different studies) and atmospheric samples (red lines). 

6 The corresponding radiative forcings are shown on the right hand axes of the large. The 

7 concentrations of these gases have continued to increase in the 2000 to 2016 period 

8 (http://www.esrl.noaa.gov/gmd/ccgg/aggi.html). (Figure source: IPCC 2007; © IPCC, used with

9 permission)
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1

2 Figure 2.5: (a) Radiative forcing (RF) from the major WMGHGs and groups of halocarbons

3 (Others) from 1850 to 2011; (b) the data in (a) with a logarithmic scale; (c) RFs from the minor 

4 WMGHGs from 1850 to 2011 (logarithmic scale); (d) rate of change in forcing from the major 

5 WMGHGs and halocarbons from 1850 to 2011. (Figure source: Myhre et al. 2013; © IPCC, used 

6 with permission).
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1

2 Figure 2.6: Effective radiative forcing changes across the industrial era for anthropogenic and 

3 natural forcing mechanisms. Also shown are the sum of all forcings (Total) and the sum of 

4 anthropogenic forcings (Total Anthropogenic). Bars with the forcing and uncertainty ranges (5 to 

5 95% confidence range) at present are given in the right part of the figure. For aerosol the ERF 

6 due to aerosol–radiation interaction and total aerosol ERF are shown. The uncertainty ranges are 

7 for present (2011 versus 1750) and are given in Table 8.6. For aerosols, only the uncertainty in 

8 the total aerosol ERF is given. For several of the forcing agents the relative uncertainty may be 

9 larger for certain time periods compared to present. See IPCC AR5 Supplementary Material 

10 Table 8.SM.8 for further information on the forcing time evolutions. Forcing numbers provided 

in Annex II of this report. The total anthropogenic forcing was 0.57 (0.29 to 0.85) W m–2 11 in 

1950, 1.25 (0.64 to 1.86) W/m2 in 1980 and 2.29 (1.13 to 3.33) W/m2 12 in 2011. (Figure source: 

13 Myhre et al. 2013; © IPCC, used with permission).

14
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1

Figure 2.7: CO2 sources and sinks (PgC/yr

1 2 ) over the industrial era (1750–2011). The 

3 partitioning of atmospheric emissions among the atmosphere, land and ocean is shown as 

4 equivalent negative emissions in the lower panel; of these, the land and ocean terms are true 

5 sinks of atmospheric CO2. The top panel shows an expanded view of emissions from fossil fuels 

6 and cement manufacturing. The atmospheric CO2 growth rate is derived from atmospheric 

7 observations and ice core data. The ocean CO2 sink is derived from a combination of models and 

8 observations. The land sink is the residual of the other terms in a balanced CO2 budget, and 

9 represents the sink of anthropogenic CO2 in natural land ecosystems. These terms only represent 

10 changes since 1750 and do not include natural CO2 fluxes (for example, from weathering and 

11 outgassing from lakes and rivers). (Figure source: Ciais et al. 2013; © IPCC, used with 

12 permission)
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1 REFERENCES

2 Allen, M.R. and W.J. Ingram, 2002: Constraints on future changes in climate and the hydrologic 

3 cycle. Nature, 419, 224-232. http://dx.doi.org/10.1038/nature01092 

4 Anav, A., P. Friedlingstein, M. Kidston, L. Bopp, P. Ciais, P. Cox, C. Jones, M. Jung, R. 

5 Myneni, and Z. Zhu, 2013: Evaluating the Land and Ocean Components of the Global 

6 Carbon Cycle in the CMIP5 Earth System Models. Journal of Climate, 26, 6801-6843.

7 http://dx.doi.org/10.1175/jcli-d-12-00417.1 

8 Andrews, T., J.M. Gregory, M.J. Webb, and K.E. Taylor, 2012: Forcing, feedbacks and climate 

9 sensitivity in CMIP5 coupled atmosphere-ocean climate models. Geophysical Research 

10 Letters, 39, L09712. http://dx.doi.org/10.1029/2012GL051607 

11 Andronova, N.G., E.V. Rozanov, F. Yang, M.E. Schlesinger, and G.L. Stenchikov, 1999: 

12 Radiative forcing by volcanic aerosols from 1850 to 1994. Journal of Geophysical Research: 

13 Atmospheres, 104, 16807-16826. http://dx.doi.org/10.1029/1999JD900165 

14 Ball, W.T., J.D. Haigh, E.V. Rozanov, A. Kuchar, T. Sukhodolov, F. Tummon, A.V. Shapiro, 

15 and W. Schmutz, 2016: High solar cycle spectral variations inconsistent with stratospheric 

16 ozone observations. Nature Geoscience, 9, 206-209. http://dx.doi.org/10.1038/ngeo2640 

17 Banerjee, A., A.T. Archibald, A.C. Maycock, P. Telford, N.L. Abraham, X. Yang, P. Braesicke, 

18 and J.A. Pyle, 2014: Lightning NO<sub>x</sub>, a key chemistry–climate interaction: 

19 impacts of future climate change and consequences for tropospheric oxidising capacity. 

20 Atmospheric Chemistry and Physics, 14, 9871-9881. http://dx.doi.org/10.5194/acp-14-9871-

21 2014 

22 Barkstrom, B.R., 1984: The Earth Radiation Budget Experiment (ERBE). Bulletin of the 

23 American Meteorological Society, 65, 1170-1185. http://dx.doi.org/10.1175/1520-

24 0477(1984)065<1170:terbe>2.0.co;2 

25 Behrenfeld, M.J., R.T. O’Malley, D.A. Siegel, C.R. McClain, J.L. Sarmiento, G.C. Feldman, 

26 A.J. Milligan, P.G. Falkowski, R.M. Letelier, and E.S. Boss, 2006: Climate-driven trends in 

27 contemporary ocean productivity. Nature, 444, 752-755.

28 http://dx.doi.org/10.1038/nature05317 

29 Bidigare, R.R., F. Chai, M.R. Landry, R. Lukas, C.C.S. Hannides, S.J. Christensen, D.M. Karl, 

30 L. Shi, and Y. Chao, 2009: Subtropical ocean ecosystem structure changes forced by North 

31 Pacific climate variations. Journal of Plankton Research, 31, 1131-1139.

32 http://dx.doi.org/10.1093/plankt/fbp064 

33 plankt.oxfordjournals.org/content/31/10/1131.abstract
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 2

121

1 Bindoff, N.L., P.A. Stott, K.M. AchutaRao, M.R. Allen, N. Gillett, D. Gutzler, K. Hansingo, G. 

2 Hegerl, Y. Hu, S. Jain, I.I. Mokhov, J. Overland, J. Perlwitz, R. Sebbari, and X. Zhang, 2013: 

3 Detection and Attribution of Climate Change: from Global to Regional. Climate Change 

4 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment 

5 Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. 

6 Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, 

7 Eds. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 

8 867–952. http://dx.doi.org/10.1017/CBO9781107415324.022 www.climatechange2013.org

9 Boer, G. and B. Yu, 2003: Climate sensitivity and response. Climate Dynamics, 20, 415-429.

10 http://dx.doi.org/10.1007/s00382-002-0283-3 

11 Bolduc, C., M.S. Bourqui, and P. Charbonneau, 2015: A comparison of stratospheric 

12 photochemical response to different reconstructions of solar ultraviolet radiative variability. 

13 Journal of Atmospheric and Solar-Terrestrial Physics, 132, 22-32.

14 http://dx.doi.org/10.1016/j.jastp.2015.06.008 

15 Bond, T.C., S.J. Doherty, D.W. Fahey, P.M. Forster, T. Berntsen, B.J. DeAngelo, M.G. Flanner, 

16 S. Ghan, B. Kärcher, D. Koch, S. Kinne, Y. Kondo, P.K. Quinn, M.C. Sarofim, M.G. 

17 Schultz, M. Schulz, C. Venkataraman, H. Zhang, S. Zhang, N. Bellouin, S.K. Guttikunda, 

18 P.K. Hopke, M.Z. Jacobson, J.W. Kaiser, Z. Klimont, U. Lohmann, J.P. Schwarz, D. 

19 Shindell, T. Storelvmo, S.G. Warren, and C.S. Zender, 2013: Bounding the role of black 

20 carbon in the climate system: A scientific assessment. Journal of Geophysical Research: 

21 Atmospheres, 118, 5380-5552. http://dx.doi.org/10.1002/jgrd.50171 

22 Bony, S., R. Colman, V.M. Kattsov, R.P. Allan, C.S. Bretherton, J.-L. Dufresne, A. Hall, S. 

23 Hallegatte, M.M. Holland, W. Ingram, D.A. Randall, B.J. Soden, G. Tselioudis, and M.J. 

24 Webb, 2006: How Well Do We Understand and Evaluate Climate Change Feedback 

25 Processes? Journal of Climate, 19, 3445-3482. http://dx.doi.org/10.1175/JCLI3819.1 

26 Boucher, O., D. Randall, P. Artaxo, C. Bretherton, G. Feingold, P. Forster, V.-M. Kerminen, Y. 

27 Kondo, H. Liao, U. Lohmann, P. Rasch, S.K. Satheesh, S. Sherwood, B. Stevens, and X.Y. 

28 Zhang, 2013: Clouds and Aerosols. Climate Change 2013: The Physical Science Basis. 

29 Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental 

30 Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. 

31 Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. Cambridge University Press, 

32 Cambridge, United Kingdom and New York, NY, USA, 571–658. 

33 http://dx.doi.org/10.1017/CBO9781107415324.016 www.climatechange2013.org

34 Boyce, D.G., M.R. Lewis, and B. Worm, 2010: Global phytoplankton decline over the past 

35 century. Nature, 466, 591-596. http://dx.doi.org/10.1038/nature09268 

36 www.nature.com/nature/journal/v466/n7306/pdf/nature09268.pdf
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 2

122

1 Brzostek, E.R., J.B. Fisher, and R.P. Phillips, 2014: Modeling the carbon cost of plant nitrogen 

2 acquisition: Mycorrhizal trade-offs and multipath resistance uptake improve predictions of 

3 retranslocation. Journal of Geophysical Research: Biogeosciences, 119, 1684-1697.

4 http://dx.doi.org/10.1002/2014JG002660 

5 Burkhardt, U. and B. Kärcher, 2011: Global radiative forcing from contrail cirrus. Nature 

6 Climate Change, 1, 54-58. http://dx.doi.org/10.1038/nclimate1068 

7 Capotondi, A., M.A. Alexander, N.A. Bond, E.N. Curchitser, and J.D. Scott, 2012: Enhanced 

8 upper ocean stratification with climate change in the CMIP3 models. Journal of Geophysical 

9 Research: Oceans, 117, n/a-n/a. http://dx.doi.org/10.1029/2011JC007409 

10 Carr, M.-E., M.A.M. Friedrichs, M. Schmeltz, M. Noguchi Aita, D. Antoine, K.R. Arrigo, I. 

11 Asanuma, O. Aumont, R. Barber, M. Behrenfeld, R. Bidigare, E.T. Buitenhuis, J. Campbell, 

12 A. Ciotti, H. Dierssen, M. Dowell, J. Dunne, W. Esaias, B. Gentili, W. Gregg, S. Groom, N. 

13 Hoepffner, J. Ishizaka, T. Kameda, C. Le Quéré, S. Lohrenz, J. Marra, F. Mélin, K. Moore, 

14 A. Morel, T.E. Reddy, J. Ryan, M. Scardi, T. Smyth, K. Turpie, G. Tilstone, K. Waters, and 

15 Y. Yamanaka, 2006: A comparison of global estimates of marine primary production from 

16 ocean color. Deep Sea Research Part II: Topical Studies in Oceanography, 53, 741-770.

17 http://dx.doi.org/10.1016/j.dsr2.2006.01.028 

18 www.sciencedirect.com/science/article/pii/S0967064506000555

19 Carslaw, K.S., L.A. Lee, C.L. Reddington, K.J. Pringle, A. Rap, P.M. Forster, G.W. Mann, D.V. 

20 Spracklen, M.T. Woodhouse, L.A. Regayre, and J.R. Pierce, 2013: Large contribution of 

21 natural aerosols to uncertainty in indirect forcing. Nature, 503, 67-71.

22 http://dx.doi.org/10.1038/nature12674 

23 Chavez, F.P., M. Messié, and J.T. Pennington, 2011: Marine primary production in relation to 

24 climate variability and change. Annual Review of Marine Science, 3, 227-260.

25 http://dx.doi.org/10.1146/annurev.marine.010908.163917 

26 Chen, T., W.B. Rossow, and Y. Zhang, 2000: Radiative Effects of Cloud-Type Variations. 

27 Journal of Climate, 13, 264-286. http://dx.doi.org/10.1175/1520-

28 0442(2000)013<0264:reoctv>2.0.co;2 

29 Chiodo, G., D.R. Marsh, R. Garcia-Herrera, N. Calvo, and J.A. García, 2014: On the detection of 

30 the solar signal in the tropical stratosphere. Atmospheric Chemistry and Physics, 14, 5251-

31 5269. http://dx.doi.org/10.5194/acp-14-5251-2014 

32 Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, R. DeFries, J. 

33 Galloway, M. Heimann, C. Jones, C. Le Quéré, R.B. Myneni, S. Piao, and P. Thornton, 

34 2013: Carbon and Other Biogeochemical Cycles. Climate Change 2013: The Physical 

35 Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 2

123

1 Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. 

2 Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. 

3 Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 465–

4 570. http://dx.doi.org/10.1017/CBO9781107415324.015 www.climatechange2013.org

5 Cook, B.I., S.P. Shukla, M.J. Puma, and L.S. Nazarenko, 2015: Irrigation as an historical climate 

6 forcing. Climate Dynamics, 44, 1715-1730. http://dx.doi.org/10.1007/s00382-014-2204-7 

7 Crook, J.A. and P.M. Forster, 2011: A balance between radiative forcing and climate feedback in 

8 the modeled 20th century temperature response. Journal of Geophysical Research: 

9 Atmospheres, 116, n/a-n/a. http://dx.doi.org/10.1029/2011JD015924 

10 Cunningham, S.A. and R. Marsh, 2010: Observing and modeling changes in the Atlantic MOC. 

11 Wiley Interdisciplinary Reviews: Climate Change, 1, 180-191.

12 http://dx.doi.org/10.1002/wcc.22 

13 Davies, J.H. and D.R. Davies, 2010: Earth's surface heat flux. Solid Earth, 1, 5-24.

14 http://dx.doi.org/10.5194/se-1-5-2010 www.solid-earth.net/1/5/2010/

15 Dentener, F., D. Stevenson, J. Cofala, R. Mechler, M. Amann, P. Bergamaschi, F. Raes, and R. 

16 Derwent, 2005: The impact of air pollutant and methane emission controls on tropospheric 

17 ozone and radiative forcing: CTM calculations for the period 1990-2030. Atmospheric 

18 Chemistry and Physics, 5, 1731-1755. http://dx.doi.org/10.5194/acp-5-1731-2005 

19 www.atmos-chem-phys.net/5/1731/2005/

20 Dessler, A.E., M.R. Schoeberl, T. Wang, S.M. Davis, K.H. Rosenlof, and J.P. Vernier, 2014: 

21 Variations of stratospheric water vapor over the past three decades. Journal of Geophysical 

22 Research: Atmospheres, 119, 12,588-12,598. http://dx.doi.org/10.1002/2014JD021712 

23 Dhomse, S.S., M.P. Chipperfield, W. Feng, W.T. Ball, Y.C. Unruh, J.D. Haigh, N.A. Krivova, 

24 S.K. Solanki, and A.K. Smith, 2013: Stratospheric O<sub>3</sub> changes during 

25 2001–2010: the small role of solar flux variations in a chemical transport model. 

26 Atmospheric Chemistry and Physics, 13, 10113-10123. http://dx.doi.org/10.5194/acp-13-

27 10113-2013 

28 Dietmüller, S., M. Ponater, and R. Sausen, 2014: Interactive ozone induces a negative feedback 

29 in CO2-driven climate change simulations. Journal of Geophysical Research: Atmospheres,

30 119, 1796-1805. http://dx.doi.org/10.1002/2013JD020575 

31 Doney, S.C., 2010: The growing human footprint on coastal and open-ocean biogeochemistry. 

32 Science, 328, 1512-6. http://dx.doi.org/10.1126/science.1185198 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 2

124

1 Durack, P.J. and S.E. Wijffels, 2010: Fifty-Year Trends in Global Ocean Salinities and Their 

2 Relationship to Broad-Scale Warming. Journal of Climate, 23, 4342-4362.

3 http://dx.doi.org/10.1175/2010jcli3377.1 

4 Enderlin, E.M. and G.S. Hamilton, 2014: Estimates of iceberg submarine melting from high5 resolution digital elevation models: application to Sermilik Fjord, East Greenland. Journal of 

6 Glaciology, 60, 1084-1092. http://dx.doi.org/10.3189/2014JoG14J085 

7 Ermolli, I., K. Matthes, T. Dudok de Wit, N.A. Krivova, K. Tourpali, M. Weber, Y.C. Unruh, L. 

8 Gray, U. Langematz, P. Pilewskie, E. Rozanov, W. Schmutz, A. Shapiro, S.K. Solanki, and 

9 T.N. Woods, 2013: Recent variability of the solar spectral irradiance and its impact on 

10 climate modelling. Atmospheric Chemistry and Physics, 13, 3945-3977.

11 http://dx.doi.org/10.5194/acp-13-3945-2013 

12 Falkowski, P.G., M.E. Katz, A.H. Knoll, A. Quigg, J.A. Raven, O. Schofield, and F.J.R. Taylor, 

13 2004: The Evolution of Modern Eukaryotic Phytoplankton. Science, 305, 354-360.

14 http://dx.doi.org/10.1126/science.1095964 

15 Fernandes, R., H. Zhao, X. Wang, J. Key, X. Qu, and A. Hall, 2009: Controls on Northern 

16 Hemisphere snow albedo feedback quantified using satellite Earth observations. Geophysical 

17 Research Letters, 36, L21702. http://dx.doi.org/10.1029/2009GL040057 

18 Flanner, M.G., 2009: Integrating anthropogenic heat flux with global climate models. 

19 Geophysical Research Letters, 36, L02801. http://dx.doi.org/10.1029/2008gl036465 

20 Flanner, M.G., C.S. Zender, P.G. Hess, N.M. Mahowald, T.H. Painter, V. Ramanathan, and P.J. 

21 Rasch, 2009: Springtime warming and reduced snow cover from carbonaceous particles. 

22 Atmospheric Chemistry and Physics, 9, 2481-2497. http://dx.doi.org/10.5194/acp-9-2481-

23 2009 www.atmos-chem-phys.net/9/2481/2009/

24 Flato, G., J. Marotzke, B. Abiodun, P. Braconnot, S.C. Chou, W. Collins, P. Cox, F. Driouech, S. 

25 Emori, V. Eyring, C. Forest, P. Gleckler, E. Guilyardi, C. Jakob, V. Kattsov, C. Reason, and 

26 M. Rummukainen, 2013: Evaluation of Climate Models. Climate Change 2013: The Physical 

27 Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the 

28 Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. 

29 Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. 

30 Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 741–

31 866. http://dx.doi.org/10.1017/CBO9781107415324.020 www.climatechange2013.org

32 Floyd, L.E., J.W. Cook, L.C. Herring, and P.C. Crane, 2003: SUSIM'S 11-year observational 

33 record of the solar UV irradiance. Advances in Space Research, 31, 2111-2120.

34 http://dx.doi.org/10.1016/S0273-1177(03)00148-0 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 2

125

1 Franks, P.J., M.A. Adams, J.S. Amthor, M.M. Barbour, J.A. Berry, D.S. Ellsworth, G.D. 

2 Farquhar, O. Ghannoum, J. Lloyd, N. McDowell, R.J. Norby, D.T. Tissue, and S. von 

3 Caemmerer, 2013: Sensitivity of plants to changing atmospheric CO2 concentration: from 

4 the geological past to the next century. New Phytologist, 197, 1077-1094.

5 http://dx.doi.org/10.1111/nph.12104 

6 Friedlingstein, P., P. Cox, R. Betts, L. Bopp, W.v. Bloh, V. Brovkin, P. Cadule, S. Doney, M. 

7 Eby, I. Fung, G. Bala, J. John, C. Jones, F. Joos, T. Kato, M. Kawamiya, W. Knorr, K. 

8 Lindsay, H.D. Matthews, T. Raddatz, P. Rayner, C. Reick, E. Roeckner, K.-G. Schnitzler, R. 

9 Schnur, K. Strassmann, A.J. Weaver, C. Yoshikawa, and N. Zeng, 2006: Climate–Carbon 

10 Cycle Feedback Analysis: Results from the C4MIP Model Intercomparison. Journal of 

11 Climate, 19, 3337-3353. http://dx.doi.org/10.1175/JCLI3800.1 

12 Friedlingstein, P., M. Meinshausen, V.K. Arora, C.D. Jones, A. Anav, S.K. Liddicoat, and R. 

13 Knutti, 2014: Uncertainties in CMIP5 Climate Projections due to Carbon Cycle Feedbacks. 

14 Journal of Climate, 27, 511-526. http://dx.doi.org/10.1175/JCLI-D-12-00579.1 

15 Fröhlich, C. and J. Lean, 2004: Solar radiative output and its variability: evidence and 

16 mechanisms. The Astronomy and Astrophysics Review, 12, 273-320.

17 http://dx.doi.org/10.1007/s00159-004-0024-1 

18 Gao, F.-L., L.-R. Tao, G.-M. Cui, J.-L. Xu, and T.-C. Hua, 2015: The influence of solar spectral 

19 variations on global radiative balance. Advances in Space Research, 55, 682-687.

20 http://dx.doi.org/10.1016/j.asr.2014.10.028 

21 Gelderloos, R., F. Straneo, and C.A. Katsman, 2012: Mechanisms behind the Temporary 

22 Shutdown of Deep Convection in the Labrador Sea: Lessons from the Great Salinity 

23 Anomaly Years 1968–71. Journal of Climate, 25, 6743-6755. http://dx.doi.org/10.1175/jcli24 d-11-00549.1 

25 Gerlach, T., 2011: Volcanic versus anthropogenic carbon dioxide. Eos, Transactions of the 

26 American Geophysical Union, 92, 201-202. http://dx.doi.org/10.1029/2011EO240001 

27 Gillett, N.P., M.F. Wehner, S.F.B. Tett, and A.J. Weaver, 2004: Testing the linearity of the 

28 response to combined greenhouse gas and sulfate aerosol forcing. Geophysical Research 

29 Letters, 31, L14201. http://dx.doi.org/10.1029/2004GL020111 

30 González-Eguino, M. and M.B. Neumann, 2016: Significant implications of permafrost thawing 

31 for climate change control. Climatic Change, 136, 381-388.

32 http://dx.doi.org/10.1007/s10584-016-1666-5 

33 Good, P., J.M. Gregory, J.A. Lowe, and T. Andrews, 2013: Abrupt CO2 experiments as tools for 

34 predicting and understanding CMIP5 representative concentration pathway projections. 

35 Climate Dynamics, 40, 1041-1053. http://dx.doi.org/10.1007/s00382-012-1410-4 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 2

126

1 Gray, L.J., J. Beer, M. Geller, J.D. Haigh, M. Lockwood, K. Matthes, U. Cubasch, D. Fleitmann, 

2 G. Harrison, L. Hood, J. Luterbacher, G.A. Meehl, D. Shindell, B. van Geel, and W. White, 

3 2010: Solar influences on climate. Reviews of Geophysics, 48, RG4001.

4 http://dx.doi.org/10.1029/2009RG000282 

5 Gregg, W.W., M.E. Conkright, P. Ginoux, J.E. O'Reilly, and N.W. Casey, 2003: Ocean primary 

6 production and climate: Global decadal changes. Geophysical Research Letters, 30, 1809.

7 http://dx.doi.org/10.1029/2003GL016889 

8 Hall, A., 2004: The Role of Surface Albedo Feedback in Climate. Journal of Climate, 17, 1550-

9 1568. http://dx.doi.org/10.1175/1520-0442(2004)017<1550:TROSAF>2.0.CO;2 

10 Hall, A. and X. Qu, 2006: Using the current seasonal cycle to constrain snow albedo feedback in 

11 future climate change. Geophysical Research Letters, 33, L03502.

12 http://dx.doi.org/10.1029/2005GL025127 

13 Han, Z., J. Li, W. Guo, Z. Xiong, and W. Zhang, 2013: A study of dust radiative feedback on 

14 dust cycle and meteorology over East Asia by a coupled regional climate-chemistry-aerosol 

15 model. Atmospheric Environment, 68, 54-63.

16 http://dx.doi.org/10.1016/j.atmosenv.2012.11.032 

17 Hartmann, D.L., M.E. Ockert-Bell, and M.L. Michelsen, 1992: The Effect of Cloud Type on 

18 Earth's Energy Balance: Global Analysis. Journal of Climate, 5, 1281-1304.

19 http://dx.doi.org/10.1175/1520-0442(1992)005<1281:teocto>2.0.co;2 

20 Hartmann, D.L., A.M.G. Klein Tank, M. Rusticucci, L.V. Alexander, S. Brönnimann, Y. 

21 Charabi, F.J. Dentener, E.J. Dlugokencky, D.R. Easterling, A. Kaplan, B.J. Soden, P.W. 

22 Thorne, M. Wild, and P.M. Zhai, 2013: Observations: Atmosphere and Surface. Climate 

23 Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth 

24 Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. 

25 Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and 

26 P.M. Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom and New 

27 York, NY, USA, 159–254. http://dx.doi.org/10.1017/CBO9781107415324.008 

28 www.climatechange2013.org

29 Hegglin, M.I., D.A. Plummer, T.G. Shepherd, J.F. Scinocca, J. Anderson, L. Froidevaux, B. 

30 Funke, D. Hurst, A. Rozanov, J. Urban, T. von Clarmann, K.A. Walker, H.J. Wang, S. 

31 Tegtmeier, and K. Weigel, 2014: Vertical structure of stratospheric water vapour trends 

32 derived from merged satellite data. Nature Geoscience, 7, 768-776.

33 http://dx.doi.org/10.1038/ngeo2236 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 2

127

1 Held, I.M. and K.M. Shell, 2012: Using Relative Humidity as a State Variable in Climate 

2 Feedback Analysis. Journal of Climate, 25, 2578-2582. http://dx.doi.org/10.1175/JCLI-D-11-

3 00721.1 

4 Held, I.M. and B.J. Soden, 2000: Water vapor feedback and global warming. Annual Review of 

5 Energy and the Environment, 25, 441-475.

6 http://dx.doi.org/10.1146/annurev.energy.25.1.441 

7 Hibbard, K.A., G.A. Meehl, P.M. Cox, and P. Friedlingstein, 2007: A strategy for climate change 

8 stabilization experiments. Eos, Transactions American Geophysical Union, 88, 217-221.

9 http://dx.doi.org/10.1029/2007EO200002 

10 Holland, D.M., R.H. Thomas, B. de Young, M.H. Ribergaard, and B. Lyberth, 2008: 

11 Acceleration of Jakobshavn Isbrae triggered by warm subsurface ocean waters. Nature 

12 Geoscience, 1, 659-664. http://dx.doi.org/10.1038/ngeo316 

13 Holmes, C.D., M.J. Prather, O.A. Søvde, and G. Myhre, 2013: Future methane, hydroxyl, and 

14 their uncertainties: key climate and emission parameters for future predictions. Atmospheric

15 Chemistry and Physics, 13, 285-302. http://dx.doi.org/10.5194/acp-13-285-2013 

16 IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group 

17 I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. 

18 Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. 

19 Miller, Eds. Cambridge University Press, Cambridge. U.K, New York, NY, USA, 996 pp.

20 www.ipcc.ch/publications_and_data/publications_ipcc_fourth_assessment_report_wg1_repor

21 t_the_physical_science_basis.htm

22 IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group 

23 I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

24 Cambridge University Press, Cambridge, UK and New York, NY, 1535 pp.

25 http://dx.doi.org/10.1017/CBO9781107415324 www.climatechange2013.org

26 Isaksen, I., T. Berntsen, S. Dalsøren, K. Eleftheratos, Y. Orsolini, B. Rognerud, F. Stordal, O. 

27 Søvde, C. Zerefos, and C. Holmes, 2014: Atmospheric Ozone and Methane in a Changing 

28 Climate. Atmosphere, 5, 518. http://dx.doi.org/10.3390/atmos5030518 www.mdpi.com/2073-

29 4433/5/3/518

30 John, J.G., A.M. Fiore, V. Naik, L.W. Horowitz, and J.P. Dunne, 2012: Climate versus emission 

31 drivers of methane lifetime against loss by tropospheric OH from 1860–2100. Atmospheric 

32 Chemistry and Physics, 12, 12021-12036. http://dx.doi.org/10.5194/acp-12-12021-2012 

33 Johnson, G.C., J.M. Lyman, T. Boyer, C.M. Domingues, M. Ishii, R. Killick, D. Monselesan, 

34 and S.E. Wijffels, 2016: [Global Oceans] Ocean heat content [in “State of the Climate in 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 2

128

1 2015”]. Bulletin of the American Meteorological Society, 97, S66-S70.

2 http://dx.doi.org/10.1175/2016BAMSStateoftheClimate.1 

3 Jones, A., J.M. Haywood, and O. Boucher, 2007: Aerosol forcing, climate response and climate 

4 sensitivity in the Hadley Centre climate model. Journal of Geophysical Research, 112,

5 D20211. http://dx.doi.org/10.1029/2007JD008688 

6 Joughin, I., R.B. Alley, and D.M. Holland, 2012: Ice-Sheet Response to Oceanic Forcing. 

7 Science, 338, 1172-1176. http://dx.doi.org/10.1126/science.1226481 

8 Ju, J. and J.G. Masek, 2016: The vegetation greenness trend in Canada and US Alaska from 

9 1984–2012 Landsat data. Remote Sensing of Environment, 176, 1-16.

10 http://dx.doi.org/10.1016/j.rse.2016.01.001 

11 Kay, J.E. and A. Gettelman, 2009: Cloud influence on and response to seasonal Arctic sea ice 

12 loss. Journal of Geophysical Research, 114, D18204.

13 http://dx.doi.org/10.1029/2009JD011773 

14 Kay, J.E. and T. L'Ecuyer, 2013: Observational constraints on Arctic Ocean clouds and radiative 

15 fluxes during the early 21st century. Journal of Geophysical Research: Atmospheres, 118,

16 7219-7236. http://dx.doi.org/10.1002/jgrd.50489 

17 Kay, J.E., K. Raeder, A. Gettelman, and J. Anderson, 2011: The Boundary Layer Response to 

18 Recent Arctic Sea Ice Loss and Implications for High-Latitude Climate Feedbacks. Journal 

19 of Climate, 24, 428-447. http://dx.doi.org/10.1175/2010JCLI3651.1 

20 Klocke, D., J. Quaas, and B. Stevens, 2013: Assessment of different metrics for physical climate 

21 feedbacks. Climate Dynamics, 41, 1173-1185. http://dx.doi.org/10.1007/s00382-013-1757-1 

22 Knutti, R. and G.C. Hegerl, 2008: The equilibrium sensitivity of the Earth’s temperature to 

23 radiation changes. Nature Geoscience, 1, 735-743. http://dx.doi.org/10.1038/ngeo337 

24 Knutti, R. and M.A.A. Rugenstein, 2015: Feedbacks, climate sensitivity and the limits of linear 

25 models. Philosophical Transactions of the Royal Society A: Mathematical, Physical and 

26 Engineering Sciences, 373. http://dx.doi.org/10.1098/rsta.2015.0146 

27 Kopp, G., 2014: An assessment of the solar irradiance record for climate studies. Journal of 

28 Space Weather and Space Climate, 4, A14. http://dx.doi.org/10.1051/swsc/2014012 

29 Kopp, G., N. Krivova, C.J. Wu, and J. Lean, 2016: The Impact of the Revised Sunspot Record 

30 on Solar Irradiance Reconstructions. Solar Physics, 1-15. http://dx.doi.org/10.1007/s11207-

31 016-0853-x 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 2

129

1 Kopp, G. and J.L. Lean, 2011: A new, lower value of total solar irradiance: Evidence and climate 

2 significance. Geophysical Research Letters, 38, L01706.

3 http://dx.doi.org/10.1029/2010GL045777 

4 Kostov, Y., K.C. Armour, and J. Marshall, 2014: Impact of the Atlantic meridional overturning 

5 circulation on ocean heat storage and transient climate change. Geophysical Research 

6 Letters, 41, 2108-2116. http://dx.doi.org/10.1002/2013GL058998 

7 Koven, C.D., D.M. Lawrence, and W.J. Riley, 2015: Permafrost carbon−climate feedback is 

8 sensitive to deep soil carbon decomposability but not deep soil nitrogen dynamics. 

9 Proceedings of the National Academy of Sciences, 112, 3752-3757.

10 http://dx.doi.org/10.1073/pnas.1415123112 

11 Koven, C.D., E.A.G. Schuur, C. Schädel, T.J. Bohn, E.J. Burke, G. Chen, X. Chen, P. Ciais, G. 

12 Grosse, J.W. Harden, D.J. Hayes, G. Hugelius, E.E. Jafarov, G. Krinner, P. Kuhry, D.M. 

13 Lawrence, A.H. MacDougall, S.S. Marchenko, A.D. McGuire, S.M. Natali, D.J. Nicolsky, D. 

14 Olefeldt, S. Peng, V.E. Romanovsky, K.M. Schaefer, J. Strauss, C.C. Treat, and M. Turetsky, 

15 2015: A simplified, data-constrained approach to estimate the permafrost carbon–climate 

16 feedback. Philosophical Transactions of the Royal Society A: Mathematical, Physical and 

17 Engineering Sciences, 373. http://dx.doi.org/10.1098/rsta.2014.0423 

18 Lambert, F.H. and P.C. Taylor, 2014: Regional variation of the tropical water vapor and lapse 

19 rate feedbacks. Geophysical Research Letters, 41, 7634-7641.

20 http://dx.doi.org/10.1002/2014GL061987 

21 Langmann, B., 2014: On the Role of Climate Forcing by Volcanic Sulphate and Volcanic Ash. 

22 Advances in Meteorology, 2014, 17. http://dx.doi.org/10.1155/2014/340123 

23 Laufkötter, C., M. Vogt, N. Gruber, M. Aita-Noguchi, O. Aumont, L. Bopp, E. Buitenhuis, S.C. 

24 Doney, J. Dunne, T. Hashioka, J. Hauck, T. Hirata, J. John, C. Le Quéré, I.D. Lima, H. 

25 Nakano, R. Seferian, I. Totterdell, M. Vichi, and C. Völker, 2015: Drivers and uncertainties 

26 of future global marine primary production in marine ecosystem models. Biogeosciences, 12,

27 6955-6984. http://dx.doi.org/10.5194/bg-12-6955-2015 

28 www.biogeosciences.net/12/6955/2015/

29 Le Quéré, C., R.M. Andrew, J.G. Canadell, S. Sitch, J.I. Korsbakken, G.P. Peters, A.C. Manning, 

30 T.A. Boden, P.P. Tans, R.A. Houghton, R.F. Keeling, S. Alin, O.D. Andrews, P. Anthoni, L. 

31 Barbero, L. Bopp, F. Chevallier, L.P. Chini, P. Ciais, K. Currie, C. Delire, S.C. Doney, P. 

32 Friedlingstein, T. Gkritzalis, I. Harris, J. Hauck, V. Haverd, M. Hoppema, K. Klein 

33 Goldewijk, A.K. Jain, E. Kato, A. Körtzinger, P. Landschützer, N. Lefèvre, A. Lenton, S. 

34 Lienert, D. Lombardozzi, J.R. Melton, N. Metzl, F. Millero, P.M.S. Monteiro, D.R. Munro, 

35 J.E.M.S. Nabel, S.I. Nakaoka, K. O'Brien, A. Olsen, A.M. Omar, T. Ono, D. Pierrot, B. 

36 Poulter, C. Rödenbeck, J. Salisbury, U. Schuster, J. Schwinger, R. Séférian, I. Skjelvan, B.D. 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 2

130

1 Stocker, A.J. Sutton, T. Takahashi, H. Tian, B. Tilbrook, I.T. van der Laan-Luijkx, G.R. van 

2 der Werf, N. Viovy, A.P. Walker, A.J. Wiltshire, and S. Zaehle, 2016: Global Carbon Budget 

3 2016. Earth System Science Data, 8, 605-649. http://dx.doi.org/10.5194/essd-8-605-2016 

4 www.earth-syst-sci-data.net/8/605/2016/

5 le Texier, H., S. Solomon, and R.R. Garcia, 1988: The role of molecular hydrogen and methane 

6 oxidation in the water vapour budget of the stratosphere. Quarterly Journal of the Royal 

7 Meteorological Society, 114, 281-295. http://dx.doi.org/10.1002/qj.49711448002 

8 Lean, J., 1997: The sun's variable radiation and its relevance for earth. Annual Review of 

9 Astronomy and Astrophysics, 35, 33-67. http://dx.doi.org/10.1146/annurev.astro.35.1.33 

10 Lelieveld, J. and P.J. Crutzen, 1992: Indirect chemical effects of methane on climate warming. 

11 Nature, 355, 339-342.

12 Lherminier, P., H. Mercier, C. Gourcuff, M. Alvarez, S. Bacon, and C. Kermabon, 2007: 

13 Transports across the 2002 Greenland-Portugal Ovide section and comparison with 1997. 

14 Journal of Geophysical Research: Oceans, 112, C07003.

15 http://dx.doi.org/10.1029/2006JC003716 

16 Liao, H., Y. Zhang, W.-T. Chen, F. Raes, and J.H. Seinfeld, 2009: Effect of chemistry-aerosol17 climate coupling on predictions of future climate and future levels of tropospheric ozone and 

18 aerosols. Journal of Geophysical Research: Atmospheres, 114, D10306.

19 http://dx.doi.org/10.1029/2008JD010984 

20 Liljedahl, A.K., J. Boike, R.P. Daanen, A.N. Fedorov, G.V. Frost, G. Grosse, L.D. Hinzman, Y. 

21 Iijma, J.C. Jorgenson, N. Matveyeva, M. Necsoiu, M.K. Raynolds, V.E. Romanovsky, J. 

22 Schulla, K.D. Tape, D.A. Walker, C.J. Wilson, H. Yabuki, and D. Zona, 2016: Pan-Arctic 

23 ice-wedge degradation in warming permafrost and its influence on tundra hydrology. Nature 

24 Geoscience, 9, 312-318. http://dx.doi.org/10.1038/ngeo2674 

25 Lockwood, M., 2012: Solar Influence on Global and Regional Climates. Surveys in Geophysics,

26 33, 503-534. http://dx.doi.org/10.1007/s10712-012-9181-3 

27 Loeb, N.G., B.A. Wielicki, D.R. Doelling, G.L. Smith, D.F. Keyes, S. Kato, N. Manalo-Smith, 

28 and T. Wong, 2009: Toward Optimal Closure of the Earth's Top-of-Atmosphere Radiation 

29 Budget. Journal of Climate, 22, 748-766. http://dx.doi.org/10.1175/2008JCLI2637.1 

30 Löffler, M., S. Brinkop, and P. Jöckel, 2016: Impact of major volcanic eruptions on stratospheric 

31 water vapour. Atmospheric Chemistry and Physics, 16, 6547-6562.

32 http://dx.doi.org/10.5194/acp-16-6547-2016 www.atmos-chem-phys.net/16/6547/2016/
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 2

131

1 Lohmann, U. and J. Feichter, 2005: Global indirect aerosol effects: a review. Atmospheric 

2 Chemistry and Physics, 5, 715-737. http://dx.doi.org/10.5194/acp-5-715-2005 www.atmos3 chem-phys.net/5/715/2005/

4 Lumpkin, R., K.G. Speer, and K.P. Koltermann, 2008: Transport across 48°N in the Atlantic 

5 Ocean. Journal of Physical Oceanography, 38, 733-752.

6 http://dx.doi.org/10.1175/2007jpo3636.1 

7 Mahajan, S., K.J. Evans, J.J. Hack, and J.E. Truesdale, 2013: Linearity of Climate Response to 

8 Increases in Black Carbon Aerosols. Journal of Climate, 26, 8223-8237.

9 http://dx.doi.org/10.1175/JCLI-D-12-00715.1 

10 Masson-Delmotte, V., M. Schulz, A. Abe-Ouchi, J. Beer, A. Ganopolski, J.F. González Rouco, 

11 E. Jansen, K. Lambeck, J. Luterbacher, T. Naish, T. Osborn, B. Otto-Bliesner, T. Quinn, R. 

12 Ramesh, M. Rojas, X. Shao, and A. Timmermann, 2013: Information from Paleoclimate 

13 Archives. Climate Change 2013: The Physical Science Basis. Contribution of Working 

14 Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. 

15 Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, 

16 V. Bex, and P.M. Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom 

17 and New York, NY, USA, 383–464. http://dx.doi.org/10.1017/CBO9781107415324.013 

18 www.climatechange2013.org

19 Matthews, H.D., A.J. Weaver, K.J. Meissner, N.P. Gillett, and M. Eby, 2004: Natural and 

20 anthropogenic climate change: incorporating historical land cover change, vegetation 

21 dynamics and the global carbon cycle. Climate Dynamics, 22, 461-479.

22 http://dx.doi.org/10.1007/s00382-004-0392-2 

23 Meehl, G.A., W.M. Washington, C.M. Ammann, J.M. Arblaster, T.M.L. Wigley, and C. Tebaldi, 

24 2004: Combinations of Natural and Anthropogenic Forcings in Twentieth-Century Climate. 

25 Journal of Climate, 17, 3721-3727. http://dx.doi.org/10.1175/1520-

26 0442(2004)017<3721:CONAAF>2.0.CO;2 

27 Melillo, J.M., T.C. Richmond, and G.W. Yohe, eds. Climate Change Impacts in the United 

28 States: The Third National Climate Assessment. 2014, U.S. Global Change Research 

29 Program: Washington, D.C. 842. http://dx.doi.org/10.7930/J0Z31WJ2. 

30 Morgenstern, O., G. Zeng, N. Luke Abraham, P.J. Telford, P. Braesicke, J.A. Pyle, S.C. 

31 Hardiman, F.M. O'Connor, and C.E. Johnson, 2013: Impacts of climate change, ozone 

32 recovery, and increasing methane on surface ozone and the tropospheric oxidizing capacity. 

33 Journal of Geophysical Research: Atmospheres, 118, 1028-1041.

34 http://dx.doi.org/10.1029/2012JD018382 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 2

132

1 Morris, G.A., J.E. Rosenfield, M.R. Schoeberl, and C.H. Jackman, 2003: Potential impact of 

2 subsonic and supersonic aircraft exhaust on water vapor in the lower stratosphere assessed 

3 via a trajectory model. Journal of Geophysical Research: Atmospheres, 108, 4103.

4 http://dx.doi.org/10.1029/2002JD002614 

5 Munk, W. and C. Wunsch, 1998: Abyssal recipes II: energetics of tidal and wind mixing. Deep 

6 Sea Research Part I: Oceanographic Research Papers, 45, 1977-2010.

7 http://dx.doi.org/10.1016/S0967-0637(98)00070-3 

8 www.sciencedirect.com/science/article/pii/S0967063798000703

9 Myhre, G., D. Shindell, F.-M. Bréon, W. Collins, J. Fuglestvedt, J. Huang, D. Koch, J.-F. 

10 Lamarque, D. Lee, B. Mendoza, T. Nakajima, A. Robock, G. Stephens, T. Takemura, and H. 

11 Zhang, 2013: Anthropogenic and Natural Radiative Forcing. Climate Change 2013: The 

12 Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of 

13 the Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. 

14 Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. 

15 Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 659–

16 740. http://dx.doi.org/10.1017/CBO9781107415324.018 www.climatechange2013.org

17 Naik, V., A. Voulgarakis, A.M. Fiore, L.W. Horowitz, J.F. Lamarque, M. Lin, M.J. Prather, P.J. 

18 Young, D. Bergmann, P.J. Cameron-Smith, I. Cionni, W.J. Collins, S.B. Dalsøren, R. 

19 Doherty, V. Eyring, G. Faluvegi, G.A. Folberth, B. Josse, Y.H. Lee, I.A. MacKenzie, T. 

20 Nagashima, T.P.C. van Noije, D.A. Plummer, M. Righi, S.T. Rumbold, R. Skeie, D.T. 

21 Shindell, D.S. Stevenson, S. Strode, K. Sudo, S. Szopa, and G. Zeng, 2013: Preindustrial to 

22 present-day changes in tropospheric hydroxyl radical and methane lifetime from the 

23 Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). 

24 Atmospheric Chemistry and Physics, 13, 5277-5298. http://dx.doi.org/10.5194/acp-13-5277-

25 2013 

26 Nowack, P.J., N. Luke Abraham, A.C. Maycock, P. Braesicke, J.M. Gregory, M.M. Joshi, A. 

27 Osprey, and J.A. Pyle, 2015: A large ozone-circulation feedback and its implications for 

28 global warming assessments. Nature Climate Change, 5, 41-45.

29 http://dx.doi.org/10.1038/nclimate2451 

30 Otterå, O.H., M. Bentsen, H. Drange, and L. Suo, 2010: External forcing as a metronome for 

31 Atlantic multidecadal variability. Nature Geoscience, 3, 688-694.

32 http://dx.doi.org/10.1038/ngeo955 

33 Pacifico, F., G.A. Folberth, C.D. Jones, S.P. Harrison, and W.J. Collins, 2012: Sensitivity of 

34 biogenic isoprene emissions to past, present, and future environmental conditions and 

35 implications for atmospheric chemistry. Journal of Geophysical Research, 117, D22302.

36 http://dx.doi.org/10.1029/2012JD018276 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 2

133

1 Passow, U. and C.A. Carlson, 2012: The biological pump in a high CO2 world. Marine Ecology 

2 Progress Series, 470, 249-271. www.int-res.com/abstracts/meps/v470/p249-271/

3 Pistone, K., I. Eisenman, and V. Ramanathan, 2014: Observational determination of albedo 

4 decrease caused by vanishing Arctic sea ice. Proceedings of the National Academy of 

5 Sciences, 111, 3322-3326. http://dx.doi.org/10.1073/pnas.1318201111 

6 Quaas, J., Y. Ming, S. Menon, T. Takemura, M. Wang, J.E. Penner, A. Gettelman, U. Lohmann, 

7 N. Bellouin, O. Boucher, A.M. Sayer, G.E. Thomas, A. McComiskey, G. Feingold, C. 

8 Hoose, J.E. Kristjánsson, X. Liu, Y. Balkanski, L.J. Donner, P.A. Ginoux, P. Stier, B. 

9 Grandey, J. Feichter, I. Sednev, S.E. Bauer, D. Koch, R.G. Grainger, Kirkev, aring, A. g, T. 

10 Iversen, Ø. Seland, R. Easter, S.J. Ghan, P.J. Rasch, H. Morrison, J.F. Lamarque, M.J. 

11 Iacono, S. Kinne, and M. Schulz, 2009: Aerosol indirect effects – general circulation model 

12 intercomparison and evaluation with satellite data. Atmospheric Chemistry and Physics, 9,

13 8697-8717. http://dx.doi.org/10.5194/acp-9-8697-2009 www.atmos-chem14 phys.net/9/8697/2009/

15 Rädel, G., T. Mauritsen, B. Stevens, D. Dommenget, D. Matei, K. Bellomo, and A. Clement, 

16 2016: Amplification of El Nino by cloud longwave coupling to atmospheric circulation. 

17 Nature Geoscience, 9, 106-110. http://dx.doi.org/10.1038/ngeo2630 

18 Raes, F., H. Liao, W.-T. Chen, and J.H. Seinfeld, 2010: Atmospheric chemistry-climate

19 feedbacks. Journal of Geophysical Research, 115, D12121.

20 http://dx.doi.org/10.1029/2009JD013300 

21 Rahmstorf, S., J.E. Box, G. Feulner, M.E. Mann, A. Robinson, S. Rutherford, and E.J. 

22 Schaffernicht, 2015: Exceptional twentieth-century slowdown in Atlantic Ocean overturning 

23 circulation. Nature Climate Change, 5, 475-480. http://dx.doi.org/10.1038/nclimate2554 

24 Rhein, M., S.R. Rintoul, S. Aoki, E. Campos, D. Chambers, R.A. Feely, S. Gulev, G.C. Johnson, 

25 S.A. Josey, A. Kostianoy, C. Mauritzen, D. Roemmich, L.D. Talley, and F. Wang, 2013: 

26 Observations: Ocean. Climate Change 2013: The Physical Science Basis. Contribution of 

27 Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate 

28 Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. 

29 Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. Cambridge University Press, Cambridge, 

30 United Kingdom and New York, NY, USA, 255–316. 

31 http://dx.doi.org/10.1017/CBO9781107415324.010 www.climatechange2013.org

32 Rignot, E., M. Koppes, and I. Velicogna, 2010: Rapid submarine melting of the calving faces of 

33 West Greenland glaciers. Nature Geoscience, 3, 187-191. http://dx.doi.org/10.1038/ngeo765 

34 Rignot, E. and R.H. Thomas, 2002: Mass Balance of Polar Ice Sheets. Science, 297, 1502-1506.

35 http://dx.doi.org/10.1126/science.1073888 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 2

134

1 Robock, A., 2000: Volcanic eruptions and climate. Reviews of Geophysics, 38, 191-219.

2 http://dx.doi.org/10.1029/1998RG000054 

3 Rosenfeld, D., M.O. Andreae, A. Asmi, M. Chin, G. de Leeuw, D.P. Donovan, R. Kahn, S. 

4 Kinne, N. Kivekäs, M. Kulmala, W. Lau, K.S. Schmidt, T. Suni, T. Wagner, M. Wild, and J. 

5 Quaas, 2014: Global observations of aerosol-cloud-precipitation-climate interactions. 

6 Reviews of Geophysics, 52, 750-808. http://dx.doi.org/10.1002/2013RG000441 

7 Rosenlof, K.H., S.J. Oltmans, D. Kley, J.M. Russell, E.W. Chiou, W.P. Chu, D.G. Johnson, K.K. 

8 Kelly, H.A. Michelsen, G.E. Nedoluha, E.E. Remsberg, G.C. Toon, and M.P. McCormick, 

9 2001: Stratospheric water vapor increases over the past half-century. Geophysical Research 

10 Letters, 28, 1195-1198. http://dx.doi.org/10.1029/2000GL012502 

11 Rousseaux, C.S. and W.W. Gregg, 2015: Recent decadal trends in global phytoplankton 

12 composition. Global Biogeochemical Cycles, 29, 1674-1688.

13 http://dx.doi.org/10.1002/2015GB005139 

14 Schaefer, K., H. Lantuit, E.R. Vladimir, E.A.G. Schuur, and R. Witt, 2014: The impact of the 

15 permafrost carbon feedback on global climate. Environmental Research Letters, 9, 085003.

16 http://dx.doi.org/10.1088/1748-9326/9/8/085003 

17 Schanze, J.J., R.W. Schmitt, and L.L. Yu, 2010: The global oceanic freshwater cycle: A state-of18 the-art quantification. Journal of Marine Research, 68, 569-595.

19 http://dx.doi.org/10.1357/002224010794657164 

20 Schoof, C., 2010: Ice-sheet acceleration driven by melt supply variability. Nature, 468, 803-806.

21 http://dx.doi.org/10.1038/nature09618 

22 Schuur, E.A.G., A.D. McGuire, C. Schadel, G. Grosse, J.W. Harden, D.J. Hayes, G. Hugelius, 

23 C.D. Koven, P. Kuhry, D.M. Lawrence, S.M. Natali, D. Olefeldt, V.E. Romanovsky, K. 

24 Schaefer, M.R. Turetsky, C.C. Treat, and J.E. Vonk, 2015: Climate change and the 

25 permafrost carbon feedback. Nature, 520, 171-179. http://dx.doi.org/10.1038/nature14338 

26 Sejas, S.A., M. Cai, A. Hu, G.A. Meehl, W. Washington, and P.C. Taylor, 2014: Individual 

27 Feedback Contributions to the Seasonality of Surface Warming. Journal of Climate, 27,

28 5653-5669. http://dx.doi.org/10.1175/JCLI-D-13-00658.1 

29 Seppälä, A., K. Matthes, C.E. Randall, and I.A. Mironova, 2014: What is the solar influence on 

30 climate? Overview of activities during CAWSES-II. Progress in Earth and Planetary 

31 Science, 1, 1-12. http://dx.doi.org/10.1186/s40645-014-0024-3 

32 Shindell, D. and G. Faluvegi, 2009: Climate response to regional radiative forcing during the 

33 twentieth century. Nature Geoscience, 2, 294-300. http://dx.doi.org/10.1038/ngeo473 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 2

135

1 Shiogama, H., D.A. Stone, T. Nagashima, T. Nozawa, and S. Emori, 2013: On the linear 

2 additivity of climate forcing-response relationships at global and continental scales. 

3 International Journal of Climatology, 33, 2542-2550. http://dx.doi.org/10.1002/joc.3607 

4 Skiba, U.M. and R.M. Rees, 2014: Nitrous oxide, climate change and agriculture. CAB Reviews,

5 9, 7. http://dx.doi.org/10.1079/PAVSNNR20149010 

6 Smith, G.L., B.R. Barkstrom, E.F. Harrison, R.B. Lee, and B.A. Wielicki, 1994: Radiation 

7 budget measurements for the eighties and nineties. Advances in Space Research, 14, 81-84.

8 http://dx.doi.org/10.1016/0273-1177(94)90351-4 

9 www.sciencedirect.com/science/article/pii/0273117794903514

10 Smith, W.K., S.C. Reed, C.C. Cleveland, A.P. Ballantyne, W.R.L. Anderegg, W.R. Wieder, Y.Y. 

11 Liu, and S.W. Running, 2016: Large divergence of satellite and Earth system model 

12 estimates of global terrestrial CO2 fertilization. Nature Climate Change, 6, 306-310.

13 http://dx.doi.org/10.1038/nclimate2879 

14 Soden, B.J. and I.M. Held, 2006: An Assessment of Climate Feedbacks in Coupled Ocean–

15 Atmosphere Models. Journal of Climate, 19, 3354-3360.

16 http://dx.doi.org/10.1175/JCLI3799.1 

17 Sohn, B.J., T. Nakajima, M. Satoh, and H.S. Jang, 2010: Impact of different definitions of clear18 sky flux on the determination of longwave cloud radiative forcing: NICAM simulation 

19 results. Atmospheric Chemistry and Physics, 10, 11641-11646. http://dx.doi.org/10.5194/acp20 10-11641-2010 

21 Solomon, S., K.H. Rosenlof, R.W. Portmann, J.S. Daniel, S.M. Davis, T.J. Sanford, and G.-K. 

22 Plattner, 2010: Contributions of Stratospheric Water Vapor to Decadal Changes in the Rate 

23 of Global Warming. Science, 327, 1219-1223. http://dx.doi.org/10.1126/science.1182488 

24 Steinacher, M., F. Joos, T.L. Frölicher, L. Bopp, P. Cadule, V. Cocco, S.C. Doney, M. Gehlen, 

25 K. Lindsay, and J.K. Moore, 2010: Projected 21st century decrease in marine productivity: A 

26 multi-model analysis. Biogeosciences, 7, 979-1005. http://dx.doi.org/10.5194/bg-7-979-2010 

27 Stenchikov, G., T.L. Delworth, V. Ramaswamy, R.J. Stouffer, A. Wittenberg, and F. Zeng, 2009: 

28 Volcanic signals in oceans. Journal of Geophysical Research, 114, D16104.

29 http://dx.doi.org/10.1029/2008JD011673 

30 Stevens, B. and G. Feingold, 2009: Untangling aerosol effects on clouds and precipitation in a 

31 buffered system. Nature, 461, 607-613.

32 Swartz, W.H., R.S. Stolarski, L.D. Oman, E.L. Fleming, and C.H. Jackman, 2012: Middle 

33 atmosphere response to different descriptions of the 11-yr solar cycle in spectral irradiance in 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 2

136

1 a chemistry-climate model. Atmospheric Chemistry and Physics, 12, 5937-5948.

2 http://dx.doi.org/10.5194/acp-12-5937-2012 

3 Taylor, P.C., R.G. Ellingson, and M. Cai, 2011: Geographical Distribution of Climate Feedbacks

4 in the NCAR CCSM3.0. Journal of Climate, 24, 2737-2753.

5 http://dx.doi.org/10.1175/2010JCLI3788.1 

6 Taylor, P.C., S. Kato, K.-M. Xu, and M. Cai, 2015: Covariance between Arctic sea ice and 

7 clouds within atmospheric state regimes at the satellite footprint level. Journal of 

8 Geophysical Research: Atmospheres, 120, 12656-12678.

9 http://dx.doi.org/10.1002/2015JD023520 

10 Thornton, P.E., J.-F. Lamarque, N.A. Rosenbloom, and N.M. Mahowald, 2007: Influence of 

11 carbon-nitrogen cycle coupling on land model response to CO2 fertilization and climate 

12 variability. Global Biogeochemical Cycles, 21, GB4018.

13 http://dx.doi.org/10.1029/2006GB002868 

14 Trenberth, K.E., P.D. Jones, P. Ambenje, R. Bojariu, D. Easterling, A.K. Tank, D. Parker, F. 

15 Rahimzadeh, J.A. Renwick, M. Rusticucci, B. Soden, and P. Zhai, 2007: Observations: 

16 Surface and atmospheric climate change. Climate Change 2007: The Physical Science Basis. 

17 Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental 

18 Panel on Climate Change. Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. 

19 Averyt, M. Tignor, and H.L. Miller, Eds. Cambridge University Press, Cambridge, United 

20 Kingdom and New York, NY, USA.

21 www.ipcc.ch/publications_and_data/ar4/wg1/en/ch3.html

22 Twohy, C.H., M.D. Petters, J.R. Snider, B. Stevens, W. Tahnk, M. Wetzel, L. Russell, and F. 

23 Burnet, 2005: Evaluation of the aerosol indirect effect in marine stratocumulus clouds: 

24 Droplet number, size, liquid water path, and radiative impact. Journal of Geophysical 

25 Research: Atmospheres, 110, D08203. http://dx.doi.org/10.1029/2004JD005116 

26 Unger, N., S. Menon, D.M. Koch, and D.T. Shindell, 2009: Impacts of aerosol-cloud interactions 

27 on past and future changes in tropospheric composition. Atmospheric Chemistry and Physics,

28 9, 4115-4129. http://dx.doi.org/10.5194/acp-9-4115-2009 www.atmos-chem29 phys.net/9/4115/2009/

30 van den Broeke, M., J. Bamber, J. Ettema, E. Rignot, E. Schrama, W.J. van de Berg, E. van 

31 Meijgaard, I. Velicogna, and B. Wouters, 2009: Partitioning Recent Greenland Mass Loss.

32 Science, 326, 984-986. http://dx.doi.org/10.1126/science.1178176 

33 Vial, J., J.-L. Dufresne, and S. Bony, 2013: On the interpretation of inter-model spread in CMIP5 

34 climate sensitivity estimates. Climate Dynamics, 41, 3339-3362.

35 http://dx.doi.org/10.1007/s00382-013-1725-9 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 2

137

1 Voulgarakis, A., V. Naik, J.F. Lamarque, D.T. Shindell, P.J. Young, M.J. Prather, O. Wild, R.D. 

2 Field, D. Bergmann, P. Cameron-Smith, I. Cionni, W.J. Collins, S.B. Dalsøren, R.M. 

3 Doherty, V. Eyring, G. Faluvegi, G.A. Folberth, L.W. Horowitz, B. Josse, I.A. MacKenzie, 

4 T. Nagashima, D.A. Plummer, M. Righi, S.T. Rumbold, D.S. Stevenson, S.A. Strode, K. 

5 Sudo, S. Szopa, and G. Zeng, 2013: Analysis of present day and future OH and methane 

6 lifetime in the ACCMIP simulations. Atmospheric Chemistry and Physics, 13, 2563-2587.

7 http://dx.doi.org/10.5194/acp-13-2563-2013 

8 Wenzel, S., P.M. Cox, V. Eyring, and P. Friedlingstein, 2016: Projected land photosynthesis 

9 constrained by changes in the seasonal cycle of atmospheric CO2. Nature, 538, 499-501.

10 http://dx.doi.org/10.1038/nature19772 

11 Wieder, W.R., C.C. Cleveland, W.K. Smith, and K. Todd-Brown, 2015: Future productivity and 

12 carbon storage limited by terrestrial nutrient availability. Nature Geoscience, 8, 441-444.

13 http://dx.doi.org/10.1038/ngeo2413 

14 Wielicki, B.A., B.R. Barkstrom, E.F. Harrison, R.B.L. III, G.L. Smith, and J.E. Cooper, 1996: 

15 Clouds and the Earth's Radiant Energy System (CERES): An Earth Observing System 

16 Experiment. Bulletin of the American Meteorological Society, 77, 853-868.

17 http://dx.doi.org/10.1175/1520-0477(1996)077<0853:catere>2.0.co;2 

18 Wielicki, B.A., E.F. Harrison, R.D. Cess, M.D. King, and D.A. Randall, 1995: Mission to Planet 

19 Earth: Role of Clouds and Radiation in Climate. Bulletin of the American Meteorological 

20 Society, 76, 2125-2153. http://dx.doi.org/10.1175/1520-

21 0477(1995)076<2125:mtpero>2.0.co;2 

22 Winton, M., 2006: Surface Albedo Feedback Estimates for the AR4 Climate Models. Journal of 

23 Climate, 19, 359-365. http://dx.doi.org/10.1175/JCLI3624.1 

24 WMO, 2014: Scientific Asssessment of Ozone Depletion: 2014. 416 pp. World Meteorological 

25 Organization Geneva, Switzerland. http://www.esrl.noaa.gov/csd/assessments/ozone/2014/

26 Xu, J. and A.M. Powell, 2013: What happened to surface temperature with sunspot activity in the 

27 past 130 years? Theoretical and Applied Climatology, 111, 609-622.

28 http://dx.doi.org/10.1007/s00704-012-0694-y 

29 Zanchettin, D., C. Timmreck, H.-F. Graf, A. Rubino, S. Lorenz, K. Lohmann, K. Krüger, and 

30 J.H. Jungclaus, 2012: Bi-decadal variability excited in the coupled ocean–atmosphere system 

31 by strong tropical volcanic eruptions. Climate Dynamics, 39, 419-444.

32 http://dx.doi.org/10.1007/s00382-011-1167-1 

33 Zhai, P.-W., Y. Hu, C.A. Hostetler, B. Cairns, R.A. Ferrare, K.D. Knobelspiesse, D.B. Josset, 

34 C.R. Trepte, P.L. Lucker, and J. Chowdhary, 2013: Uncertainty and interpretation of aerosol 

35 remote sensing due to vertical inhomogeneity. Journal of Quantitative Spectroscopy and 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 2

138

1 Radiative Transfer, 114, 91-100. http://dx.doi.org/10.1016/j.jqsrt.2012.08.006 

2 www.sciencedirect.com/science/article/pii/S0022407312003706

3 Zhang, D., R. Blender, and K. Fraedrich, 2013: Volcanoes and ENSO in millennium simulations: 

4 global impacts and regional reconstructions in East Asia. Theoretical and Applied 

5 Climatology, 111, 437-454. http://dx.doi.org/10.1007/s00704-012-0670-6
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 3

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1 3. Detection and Attribution of Climate Change

2 Key Findings

3 1. The likely range of the human contribution to the global mean temperature increase over the 

4 period 1951–2010 is 1.1° to 1.3°F (0.6° to 0.7°C), which is close to the observed warming of 

5 1.2°F (0.65°C) (high confidence). It is extremely likely that more than half of the global mean 

6 temperature increase since 1951 was caused by human influence on climate (high 

7 confidence). The estimated influence of natural forcing and internal variability on global 

8 temperatures over that period is minor (high confidence) 

9 3.1 Introduction

10 Detection and attribution of climate change involves assessing the causes of observed changes in 

11 the climate system through systematic comparison of climate models and observations using 

12 various statistical methods. Attributing an observed change or an event partly to a causal factor 

13 (such as anthropogenic climate forcing) normally requires that the change first be detectable 

14 (Hegerl et al. 2010). A detectable change is one in which an observed change is distinguishable 

15 from natural variability in some defined statistical sense, again without necessarily ascribing a 

16 cause. An attributable change refers to a change in which the relative contribution of causal 

17 factors has been evaluated along with an assignment of statistical confidence (e.g., Bindoff et al. 

18 2013; Hegerl et al. 2010).

19 More confident statements about attribution are underpinned by a thorough understanding of the 

20 physical processes involved. Since the release of the Intergovernmental Panel on Climate 

21 Change’s Fifth Assessment Report (IPCC AR5) and the Third National Climate Assessment 

22 (NCA3; Melillo et al. 2014), there have been some advances in the science of detection and 

23 attribution of climate change. The IPCC AR5 presented an assessment of detection and 

24 attribution research at the global to regional scale (Bindoff et al. 2013) which is briefly 

25 summarized here. An emerging area in the science of detection and attribution is the attribution 

26 of extreme weather and climate events (NAS 2016; Stott 2016; Easterling et al. 2016).

27 A growing number of climate change and extreme event attribution studies use a multi-step 

28 attribution (Hegerl et al. 2010) or attribution without detection approaches. These are methods

29 that attribute a climate change or a change in the likelihood of occurrence of an event to a causal 

30 factor without detecting a change in the phenomenon itself. Detection, for example, would mean 

31 demonstrating that a long-term trend or change in a phenomenon is highly unusual compared to 

32 natural variability. For the multi-step approach, the attribution may be based on a change in 

33 climate conditions that are closely related to a given type of event. As an example, some 

34 attribution statements for phenomena such as droughts or hurricane activity—where there are not 

35 necessarily detectable trends—are based on models and on detected changes in related variables

36 such as surface temperature, as well as an understanding of the relevant physical processes. 
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1 Possible anthropogenic influence on an extreme event can be assessed using a risk-based 

2 approach, which examines whether the odds of occurrence of a type of extreme event have 

3 changed, or through an ingredients-based or conditional attribution approach. In the latter case,

4 for example, an investigator may look for changes in occurrence of atmospheric circulation and 

5 weather patterns relevant to the extreme event, or at the impact of certain environmental changes 

6 (for example, greater atmospheric moisture) on the character of an extreme event (Trenberth et 

7 al. 2015; Shepherd 2016; Horton et al. 2016). An example of the conditional attribution

8 approach, as applied to Hurricane Sandy, assumes that the weather patterns in which the storm 

9 was embedded, and the storm itself, could have occurred in a preindustrial climate, and the event 

10 is re-simulated changing only some aspects of the large-scale environment (for example, sea 

11 surface temperatures, atmospheric temperatures and moisture) by an estimated anthropogenic 

12 climate change signal. One study using this approach found that anthropogenic climate change to 

13 date did not have a statistically significant influence on the intensity of Hurricane Sandy

14 (Lackmann 2015). 

15 There are reasons why attribution without detection statements can be appropriate, despite the 

16 lower confidence typically associated with such statements as compared to attribution statements 

17 that are supported by detection of a change in the phenomenon itself. The event may be so rare 

18 that a trend analysis for similar events is not practical. Including attribution without detection 

19 events in analysis of climate change impacts reduces the chances of a false negative, that is, 

20 incorrectly concluding that climate change had no influence on a given extreme events 

21 (Anderegg et al. 2014) in a case where it did have an influence. However, avoiding this type of 

22 error through attribution without detection comes at the risk of increasing the rate of false 

23 positives, where one incorrectly concludes that anthropogenic climate change had a certain type 

24 of influence on an extreme event when in fact it did not have such an influence. 

25 Review of Key Detection and Attribution Findings in IPCC AR5

26 Key attribution assessment results for global mean temperature are summarized in Figure 3.1

27 (from Bindoff et al. 2013), which shows assessed likely ranges and midpoint estimates for 

28 several factors contributing to increases in global mean temperature. According to Bindoff et al., 

29 it is extremely likely that anthropogenic forcings caused more than half of the warming for 

30 1951–2010, with a likely contribution range of 0.6° to 0.7°C (1.1°F to 1.3°F), compared with the 

31 observed warming of about 0.65°C (1.2°F). The estimated likely contribution ranges for natural 

32 forcing and internal variability were both much smaller (−0.1° to 0.1°C, or −0.2° to 0.2°F).

33 [INSERT FIGURE 3.1 HERE:

34 Figure 3.1: Attributable warming likely ranges (bar-whisker plots) and midpoint values (colored 

35 bars) for global mean temperature trends (degrees Celsius) over 1951–2010 from IPCC AR5 

36 (Bindoff et al. 2013). Observations are from HadCRUT4, along with observational uncertainty 

37 (5% to 95%) error bars (Morice et al. 2012). GHG refers to well-mixed greenhouse gases, OA to 

38 other anthropogenic forcings, NAT to natural forcings, and ANT to all anthropogenic forcings 
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1 combined. The ranges within which the true value is extremely likely to occur are broader than 

2 the likely ranges shown in the figure, which is why the term “more than half” is used to 

3 characterize the fraction of warming that is extremely likely due on anthropogenic influence, 

4 despite the fact that the midpoint of the likely range of the anthropogenic forcing contribution is 

5 close to the observed warming value. Likely ranges are broader for contributions from well6 mixed greenhouse gases or other anthropogenic forcings, assessed separately, than for the 

7 contributions from all anthropogenic forcings, as it is more difficult to quantitatively constrain 

8 the separate contributions of the various anthropogenic forcing agents. (Figure source: redrawn 

9 from Bindoff et al. 2013; © IPCC. Used with permission.)]

10 Likely or very likely attributable human contributions have also been reported by IPCC AR5 for 

11 warming over all continents except Antarctica, and, globally, changes in daily temperature 

12 extremes, ocean surface and subsurface temperature and salinity, and sea level pressure patterns; 

13 Arctic sea ice loss; northern hemispheric snow cover decrease; global mean sea level rise; and 

14 ocean acidification (Bindoff et al. 2013). IPCC AR5 also reported medium confidence in 

15 anthropogenic contributions to increased atmospheric specific humidity, zonal mean 

16 precipitation over northern hemisphere mid to high latitudes, and intensification or heavy

17 precipitation over land regions. IPCC AR5 had weaker attribution conclusions than IPCC AR4 

18 on some phenomena, including tropical cyclone and drought changes. The present assessment 

19 does not change any of the IPCC AR5 conclusions, although we make some additional 

20 attribution statements in the relevant chapters of this report regarding regional temperature, 

21 extreme precipitation, and flooding frequency increases over parts of the United States.

22 3.2 Extreme Event Attribution

23 Attribution of extreme weather events under a changing climate is an important aspect of climate 

24 science. The European heat wave of 2003 (Stott et al. 2004) and Australia’s extreme 

25 temperatures and heat indices of 2013 (e.g., Arblaster et al. 2014; King et al. 2014; Knutson et al. 

26 2014; Lewis and Karoly 2014; Perkins et al. 2014) are examples of extreme weather or climate 

27 events where relatively strong evidence for a human contribution to the event has been found for 

28 cases outside of the United States. The science of event attribution for weather and climate 

29 extremes over the United States has also significantly evolved since the NCA3. For example, 

30 following several extreme climate events, such as the 2011 Texas heat wave and drought or the 

31 recent/ongoing California drought, investigators have attempted to determine, using various 

32 methods discussed in this chapter, whether human-caused climate change contributed to the 

33 event. Several recent reports have extensively reviewed the topic of extreme event attribution

34 (NAS 2016; Easterling et al. 2016). While this topic cannot be comprehensively reviewed here, a 

35 few highlighted statements from the National Academy of Sciences study (NAS 2016) are given 

36 here:

37 • Event attribution is more reliable when based on sound physical principles, consistent 

38 evidence from observations, and numerical models that can replicate the event.
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1 • Confidence in attribution findings of anthropogenic influence is greatest for extreme 

2 events that are related to an aspect of temperature.

3 • Statements about attribution are sensitive to the way the questions are posed (that is, 

4 framing)

5 In addition, the National Academies noted that conclusions would be more robust in cases where 

6 observed changes in the event being examined are consistent with expectations from model7 based attribution studies. Typically, there is less confidence in such an attribution-without8 detection statement than one where a detectable anthropogenic influence (for example, a 

9 detectable and attributable long-term trend or increase in variability) on the phenomenon itself 

10 had also been demonstrated. An example would be stating that a change in the probability or 

11 magnitude of a heat wave in the southeastern United States was attributable to greenhouse gases 

12 when there is not a detectable trend in either long-term temperature or in temperature variability 

13 in the data in that region, as discussed below. No extreme weather event observed to date has 

14 been found to have zero probability of occurrence in a preindustrial climate according to climate 

15 model simulations. Therefore, the causes of attributed extreme events are a combination of 

16 natural variations in the climate system compounded (or alleviated) by the anthropogenic change 

17 to the climate system. Event attribution statements quantify the relative contribution of these 

18 human and natural causal factors.

19 As an example illustrating different methods of event attribution, for the 2011 Texas heat 

20 wave/meteorological drought, Hoerling et al. (2013) found that the event was primarily caused 

21 by antecedent and concurrent negative rainfall anomalies due mainly to natural variability and 

22 the La Niña conditions at the time of the event, but with a relatively small (not detected) 

23 warming contribution from anthropogenic forcing. The anthropogenic contribution nonetheless 

24 doubled the chances of reaching a new temperature record in 2011 compared to the 1981–2010 

25 reference period, according to their study. Rupp et al. (2012), meanwhile, concluded that extreme 

26 heat events in Texas were about 20 times more likely for 2008 La Niña conditions than similar 

27 conditions during the 1960s. This pair of studies illustrates how the framing of the attribution 

28 question can matter. The Hoerling et al. analysis focused more on what caused most of the 

29 magnitude of the anomalies, whereas Rupp et al. focused more on the changes in the probability 

30 of the event. Otto et al. (2012) show how such approaches can give seemingly conflicting results

31 yet have no fundamental contradiction. In this case, we conclude that there is medium confidence 

32 that anthropogenic forcing contributed to the Texas heat wave of 2011, both in terms of a small 

33 contribution to the anomaly magnitude and a significant increase in the probability of occurrence 

34 of the event. 

35 In this report, we do not assess all individual weather or climate extreme events for which an 

36 attributable anthropogenic climate change has been claimed in a published study, as there are 

37 now many such studies. A few selected individual United States studies are discussed in more 
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1 detail either in this chapter or in Chapters 6, 7, 8, and 9, which focus on particular weather and 

2 climate phenomena.

3 3.3 Updated Detection and Attribution Summaries 

4 In general, detection and attribution at regional scales are more challenging than at the global 

5 scale for a number of reasons. Regional changes typically have smaller signal-to-noise ratios 

6 than changes at global scales. Also, there is less spatial pattern information for distinguishing 

7 contributions from different forcings. Omitted forcings in climate models, such as land-use 

8 change, could be more important at regional scales, and simulated internal variability may be less 

9 reliable (Bindoff et al. 2013). 

10 In the various phenomena chapters of this report, updated detection attribution statements 

11 focusing on the United States region are presented. 

12
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1 TRACEABLE ACCOUNTS

2 Key Finding 1

3 The likely range of the human contribution to the global mean temperature increase over the 

4 period 1951–2010 is 1.1° to 1.3°F (0.6° to 0.7°C), which is close to the observed warming of 

5 1.2°F (0.65°C) (high confidence). It is extremely likely that more than half of the global mean 

6 temperature increase since 1951 was caused by human influence on climate (high confidence). 

7 The estimated influence of natural forcing and internal variability on global temperatures over 

8 that period is minor (high confidence) 

9 Description of evidence base

10 This Key Finding summarizes key detection and attribution evidence documented in the climate 

11 science literature and in the IPCC AR5 (Bindoff et al. 2013), and references therein. The Key 

12 Finding is essentially the same as the summary assessment of IPCC AR5. The attribution of 

13 temperature increases since 1951 is based on the detection and attribution analyses of Gillett et 

14 al. (2013), Jones et al. (2013), and consideration of Ribes and Terray (2013), Huber and Knutti 

15 (2011), Wigley and Santer (2013), and IPCC AR4 (Hegerl et al. 2007). The estimated potential 

16 influence of internal variability is based on Knutson et al. (2013) and Huber and Knutti (2011), 

17 with consideration of the above references. Moreover, simulated global temperature multidecadal 

18 variability is assessed to be adequate (Bindoff et al. 2013), with high confidence that models 

19 reproduce global and northern hemisphere temperature variability across a range of timescales 

20 (Flato et al. 2013). Further support for these assessments comes from paleoclimate data (Masson21 Delmotte et al. 2013) and physical understanding of the climate system (IPCC 2013). A more 

22 detailed traceable account is contained in Bindoff et al. (2013). Post IPCC AR5 supporting 

23 evidence includes additional analyses showing unusual nature of observed global warming 

24 compared to simulated internal climate variability (Knutson et al., in press) and recent 

25 occurrence of new record high global mean temperatures, consistent with model projections of 

26 continued warming on multidecadal scales (for example, Chapter 1). 

27 Major uncertainties 

28 The transient climate response (TCR) is defined as the global mean surface temperature change 

29 at the time of CO2 doubling in a 1%/year CO2 transient increase experiment. The TCR of the 

30 climate system to greenhouse gas increases remains uncertain, with ranges of 0.9° to 2.0°C (1.6°

31 to 3.6°F) and 0.9° to 2.5°C (1.6° to 4.5°F) in two recent assessments (Otto et al. 2013 and Lewis 

32 and Curry 2014, respectively). The climate system response to aerosol forcing (direct and 

33 indirect effects combined) remains highly uncertain (Myhre et al. 2013), because although more 

34 of the relevant processes are being in included in models, confidence in these representations 

35 remains low (Boucher et al. 2013). Therefore, there is considerable uncertainty in quantifying the 

36 attributable warming contributions of greenhouse gases and aerosols separately. There is 

37 uncertainty in the possible levels of internal climate variability, but current estimates (likely 

38 range of +/− 0.1°C, or 0.2°F, over 60 years) would have to be too low by more than a factor or
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1 two or three for the observed trend to be explainable by internal variability (e.g., Knutson et al. 

2 2013; Huber and Knutti 2011). 

3 Assessment of confidence based on evidence and agreement, including short description of 

4 nature of evidence and level of agreement 

5 x Very High 

6 x High

7 ☐ Medium 

8 ☐ Low

9 There is very high confidence that global temperature has been increasing and that anthropogenic 

10 forcings have played a major role in the increase observed over the past 60 years, with strong 

11 evidence from several studies using well-established detection and attribution techniques. There 

12 is high confidence that the role of internal variability is minor, as climate models simulate only a 

13 minor role and the models have been assessed as adequate for the purpose of estimating the 

14 potential role of internal variability.

15 If appropriate, estimate likelihood of impact or consequence, including short description of 

16 basis of estimate 

17 x Greater than 9 in 10 / Very Likely 

18 ☐ Greater than 2 in 3 / Likely

19 ☐ About 1 in 2 / As Likely as Not

20 ☐ Less than 1 in 3 / Unlikely 

21 ☐ Less than 1 in 10 / Very Unlikely

22 Summary sentence or paragraph that integrates the above information

23 Detection and attribution studies, climate models, observations, paleoclimate data, and physical 

24 understanding lead to high confidence (extremely likely) that more half of the observed global 

25 mean warming since 1951 was caused by humans, and high confidence that internal climate 

26 variability played only a minor role (and possibly even a negative contribution) in the observed 

27 warming. The key message and supporting text summarizes extensive evidence documented in 

28 the peer-reviewed detection and attribution literature, including in the IPCC AR5. 

29
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1 FIGURES

2

3 Figure 3.1: Attributable warming likely ranges (bar-whisker plots) and midpoint values (colored 

4 bars) for global mean temperature trends (degrees Celsius) over 1951–2010 from IPCC AR5 

5 (Bindoff et al. 2013). Observations are from HadCRUT4, along with observational uncertainty 

6 (5% to 95%) error bars (Morice et al. 2012). GHG refers to well-mixed greenhouse gases, OA to 

7 other anthropogenic forcings, NAT to natural forcings, and ANT to all anthropogenic forcings 

8 combined. The ranges within which the true value is extremely likely to occur are broader than 

9 the likely ranges shown in the figure, which is why the term “more than half” is used to 

10 characterize the fraction of warming that is extremely likely due on anthropogenic influence, 

11 despite the fact that the midpoint of the likely range of the anthropogenic forcing contribution is 

12 close to the observed warming value. Likely ranges are broader for contributions from well13 mixed greenhouse gases or other anthropogenic forcings, assessed separately, than for the 

14 contributions from all anthropogenic forcings, as it is more difficult to quantitatively constrain 

15 the separate contributions of the various anthropogenic forcing agents. (Figure source: redrawn 

16 from Bindoff et al. 2013; © IPCC. Used with permission.)

17
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1 REFERENCES

2 Anderegg, W.R.L., E.S. Callaway, M.T. Boykoff, G. Yohe, and T.y.L. Root, 2014: Awareness of 

3 both type 1 and 2 errors in climate science and assessment. Bulletin of the American 

4 Meteorological Society, 95, 1445-1451. http://dx.doi.org/10.1175/BAMS-D-13-00115.1 

5 Arblaster, J.M., E.-P. Lim, H.H. Hendon, B.C. Trewin, M.C. Wheeler, G. Liu, and K. Braganza, 

6 2014: Understanding Australia’s hottest September on record [in "Explaining Extremes of 

7 2013 from a Climate Perspective"]. Bulletin of the American Meteorological Society, 95,

8 S37-S41. http://dx.doi.org/10.1175/1520-0477-95.9.S1.1 

9 Bindoff, N.L., P.A. Stott, K.M. AchutaRao, M.R. Allen, N. Gillett, D. Gutzler, K. Hansingo, G. 

10 Hegerl, Y. Hu, S. Jain, I.I. Mokhov, J. Overland, J. Perlwitz, R. Sebbari, and X. Zhang, 2013: 

11 Detection and Attribution of Climate Change: from Global to Regional. Climate Change 

12 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment 

13 Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. 

14 Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, 

15 Eds. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 

16 867–952. http://dx.doi.org/10.1017/CBO9781107415324.022 www.climatechange2013.org

17 Boucher, O., D. Randall, P. Artaxo, C. Bretherton, G. Feingold, P. Forster, V.-M. Kerminen, Y. 

18 Kondo, H. Liao, U. Lohmann, P. Rasch, S.K. Satheesh, S. Sherwood, B. Stevens, and X.Y. 

19 Zhang, 2013: Clouds and Aerosols. Climate Change 2013: The Physical Science Basis. 

20 Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental 

21 Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. 

22 Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. Cambridge University Press, 

23 Cambridge, United Kingdom and New York, NY, USA, 571–658. 

24 http://dx.doi.org/10.1017/CBO9781107415324.016 www.climatechange2013.org

25 Easterling, D.R., K.E. Kunkel, M.F. Wehner, and L. Sun, 2016: Detection and attribution of 

26 climate extremes in the observed record. Weather and Climate Extremes, 11, 17-27. 

27 http://dx.doi.org/10.1016/j.wace.2016.01.001 

28 Flato, G., J. Marotzke, B. Abiodun, P. Braconnot, S.C. Chou, W. Collins, P. Cox, F. Driouech, S. 

29 Emori, V. Eyring, C. Forest, P. Gleckler, E. Guilyardi, C. Jakob, V. Kattsov, C. Reason, and 

30 M. Rummukainen, 2013: Evaluation of Climate Models. Climate Change 2013: The Physical 

31 Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the 

32 Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. 

33 Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. 

34 Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 741–

35 866. http://dx.doi.org/10.1017/CBO9781107415324.020 www.climatechange2013.org
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 3

148

1 Gillett, N.P., J.C. Fyfe, and D.E. Parker, 2013: Attribution of observed sea level pressure trends 

2 to greenhouse gas, aerosol, and ozone changes. Geophysical Research Letters, 40, 2302-

3 2306. http://dx.doi.org/10.1002/grl.50500 

4 Hegerl, G.C., O. Hoegh-Guldberg, G. Casassa, M.P. Hoerling, R.S. Kovats, C. Parmesan, D.W. 

5 Pierce, and P.A. Stott, 2010: Good Practice Guidance Paper on Detection and Attribution 

6 Related to Anthropogenic Climate Change. Meeting Report of the Intergovernmental 

7 Panel on Climate Change Expert Meeting on Detection and Attribution of 

8 Anthropogenic Climate Change. Stocker, T.F., C.B. Field, D. Qin, V. Barros, G.-K. Plattner, 

9 M. Tignor, P.M. Midgley, and K.L. Ebi, Eds. IPCC Working Group I Technical Support 

10 Unit, University of Bern, Bern, Switzerland, 1-8. http://www.ipcc.ch/pdf/supporting11 material/ipcc_good_practice_guidance_paper_anthropogenic.pdf

12 Hegerl, G.C., F.W. Zwiers, P. Braconnot, N.P. Gillett, Y. Luo, J.A.M. Orsini, N. Nicholls, J.E. 

13 Penner, and P.A. Stott, 2007: Understanding and attributing climate change. Climate Change 

14 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth 

15 Assessment Report of the Intergovernmental Panel on Climate Change. Solomon, S., D. Qin, 

16 M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. Miller, Eds. 

17 Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 663-

18 745. http://www.ipcc.ch/publications_and_data/ar4/wg1/en/ch9.html

19 Hoerling, M., M. Chen, R. Dole, J. Eischeid, A. Kumar, J.W. Nielsen-Gammon, P. Pegion, J. 

20 Perlwitz, X.-W. Quan, and T. Zhang, 2013: Anatomy of an extreme event. Journal of 

21 Climate, 26, 2811–2832. http://dx.doi.org/10.1175/JCLI-D-12-00270.1 

22 Horton, R.M., J.S. Mankin, C. Lesk, E. Coffel, and C. Raymond, 2016: A Review of Recent 

23 Advances in Research on Extreme Heat Events. Current Climate Change Reports, 1-18. 

24 http://dx.doi.org/10.1007/s40641-016-0042-x 

25 Huber, M. and R. Knutti, 2012: Anthropogenic and natural warming inferred from changes in 

26 Earth/'s energy balance. Nature Geoscience, 5, 31-36. http://dx.doi.org/10.1038/ngeo1327 

27 IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group 

28 I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

29 Cambridge University Press, Cambridge, UK and New York, NY, 1535 pp. 

30 http://dx.doi.org/10.1017/CBO9781107415324 www.climatechange2013.org

31 Jones, G.S., P.A. Stott, and N. Christidis, 2013: Attribution of observed historical near surface 

32 temperature variations to anthropogenic and natural causes using CMIP5 simulations. 

33 Journal of Geophysical Research, 118, 4001-4024. http://dx.doi.org/10.1002/jgrd.50239 

34 King, A.D., D.J. Karoly, M.G. Donat, and L.V. Alexander, 2014: Climate change turns 

35 Australia’s 2013 Big Dry into a year of record-breaking heat [in "Explaining Extremes of 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 3

149

1 2013 from a Climate Perspective"]. Bulletin of the American Meteorological Society, 95,

2 S41-S45. http://dx.doi.org/10.1175/1520-0477-95.9.S1.1 

3 Knutson, T.R., F. Zeng, and A.T. Wittenberg, 2013: Multimodel Assessment of Regional 

4 Surface Temperature Trends: CMIP3 and CMIP5 Twentieth-Century Simulations. Journal of 

5 Climate, 26, 8709-8743. http://dx.doi.org/10.1175/JCLI-D-12-00567.1 

6 Knutson, T.R., F. Zeng, and A.T. Wittenberg, 2014: Multimodel assessment of extreme annual7 mean warm anomalies during 2013 over regions of Australia and the western tropical Pacific 

8 [in "Explaining Extremes of 2013 from a Climate Perspective"]. Bulletin of the American 

9 Meteorological Society, 95, S26-S30. http://dx.doi.org/10.1175/1520-0477-95.9.S1.1 

10 Knutson, T.R., R. Zhang, and L.W. Horowitz, 2016: Prospects for a prolonged slowdown in 

11 global warming in the early 21st century. Nature Communcations, 7, 13676. 

12 http://dx.doi.org/10.1038/ncomms13676 

13 Lackmann, G.M., 2015: Hurricane Sandy before 1900 and after 2100. Bulletin of the American 

14 Meteorological Society, 96, 547-560. http://dx.doi.org/10.1175/BAMS-D-14-00123.1 

15 Lewis, N. and J.A. Curry, 2015: The implications for climate sensitivity of AR5 forcing and heat 

16 uptake estimates. Climate Dynamics, 45, 1009-1023. http://dx.doi.org/10.1007/s00382-014-

17 2342-y 

18 Lewis, S. and D.J. Karoly, 2014: The role of anthropogenic forcing in the record 2013 Australia19 wide annual and spring temperatures [in "Explaining Extremes of 2013 from a Climate 

20 Perspective"]. Bulletin of the American Meteorological Society, 95, S31-S33. 

21 http://dx.doi.org/10.1175/1520-0477-95.9.S1.1 

22 Masson-Delmotte, V., M. Schulz, A. Abe-Ouchi, J. Beer, A. Ganopolski, J.F. González Rouco, 

23 E. Jansen, K. Lambeck, J. Luterbacher, T. Naish, T. Osborn, B. Otto-Bliesner, T. Quinn, R. 

24 Ramesh, M. Rojas, X. Shao, and A. Timmermann, 2013: Information from Paleoclimate 

25 Archives. Climate Change 2013: The Physical Science Basis. Contribution of Working 

26 Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. 

27 Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, 

28 V. Bex, and P.M. Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom 

29 and New York, NY, USA, 383–464. http://dx.doi.org/10.1017/CBO9781107415324.013 

30 www.climatechange2013.org

31 Melillo, J.M., T.C. Richmond, and G.W. Yohe, eds. Climate Change Impacts in the United 

32 States: The Third National Climate Assessment. 2014, U.S. Global Change Research 

33 Program: Washington, D.C. 842. http://dx.doi.org/10.7930/J0Z31WJ2. 

34 Morice, C.P., J.J. Kennedy, N.A. Rayner, and P.D. Jones, 2012: Quantifying uncertainties in 

35 global and regional temperature change using an ensemble of observational estimates: The 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 3

150

1 HadCRUT4 dataset. Journal of Geophysical Research, 117, D08101. 

2 http://dx.doi.org/10.1029/2011JD017187 

3 Myhre, G., D. Shindell, F.-M. Bréon, W. Collins, J. Fuglestvedt, J. Huang, D. Koch, J.-F. 

4 Lamarque, D. Lee, B. Mendoza, T. Nakajima, A. Robock, G. Stephens, T. Takemura, and H. 

5 Zhang, 2013: Anthropogenic and Natural Radiative Forcing. Climate Change 2013: The 

6 Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of 

7 the Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. 

8 Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. 

9 Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 659–

10 740. http://dx.doi.org/10.1017/CBO9781107415324.018 www.climatechange2013.org

11 National Academies of Sciences, E., and Medicine, 2016: Attribution of Extreme Weather Events 

12 in the Context of Climate Change. The National Academies Press, Washington, DC, 186 pp. 

13 http://dx.doi.org/10.17226/21852 

14 Otto, A., F.E.L. Otto, O. Boucher, J. Church, G. Hegerl, P.M. Forster, N.P. Gillett, J. Gregory, 

15 G.C. Johnson, R. Knutti, N. Lewis, U. Lohmann, J. Marotzke, G. Myhre, D. Shindell, B. 

16 Stevens, and M.R. Allen, 2013: Energy budget constraints on climate response. Nature 

17 Geoscience, 6, 415-416. http://dx.doi.org/10.1038/ngeo1836 

18 Otto, F.E.L., N. Massey, G.J. van Oldenborgh, R.G. Jones, and M.R. Allen, 2012: Reconciling 

19 two approaches to attribution of the 2010 Russian heat wave. Geophysical Research Letters,

20 39, L04702. http://dx.doi.org/10.1029/2011GL050422 

21 Perkins, S.E., S.C. Lewis, A.D. King, and L.V. Alexander, 2014: Increased simulated risk of the 

22 hot Australian summer of 2012/13 due to anthropogenic activity as measured by heat wave 

23 frequency and intensity [in "Explaining Extremes of 2013 from a Climate Perspective"]. 

24 Bulletin of the American Meteorological Society, 95, S34-S37. 

25 http://dx.doi.org/10.1175/1520-0477-95.9.S1.1 

26 Perlwitz, J., M. Hoerling, and R. Dole, 2015: Arctic Tropospheric Warming: Causes and 

27 Linkages to Lower Latitudes. Journal of Climate, 28, 2154-2167. 

28 http://dx.doi.org/10.1175/JCLI-D-14-00095.1 

29 Pierce, D.W., T.P. Barnett, B.D. Santer, and P.J. Gleckler, 2009: Selecting global climate models 

30 for regional climate change studies. Proceedings of the National Academy of Sciences, 106,

31 8441-8446. http://dx.doi.org/10.1073/pnas.0900094106 

32 Ribes, A. and L. Terray, 2013: Application of regularised optimal fingerprinting to attribution. 

33 Part II: application to global near-surface temperature. Climate Dynamics, 41, 2837-2853. 

34 http://dx.doi.org/10.1007/s00382-013-1736-6 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 3

151

1 Rupp, D.E., P.W. Mote, N. Massey, C.J. Rye, R. Jones, and M.R. Allen, 2012: Did human 

2 influence on climate make the 2011 Texas drought more probable? [in Explaining extreme 

3 events of 2011 from a climate perspective]. Bulletin of the American Meteorological Society,

4 93, 1052-1054. http://dx.doi.org/10.1175/BAMS-D-12-00021.1 

5 Shepherd, T.G., 2016: A Common Framework for Approaches to Extreme Event Attribution. 

6 Current Climate Change Reports, 2, 28-38. http://dx.doi.org/10.1007/s40641-016-0033-y 

7 Stott, P., 2016: How climate change affects extreme weather events. Science, 352, 1517-1518. 

8 http://dx.doi.org/10.1126/science.aaf7271 

9 Stott, P.A., D.A. Stone, and M.R. Allen, 2004: Human contribution to the European heatwave of 

10 2003. Nature, 432, 610-614. http://dx.doi.org/10.1038/nature03089 

11 Trenberth, K.E., J.T. Fasullo, and T.G. Shepherd, 2015: Attribution of climate extreme events. 

12 Nature Climate Change, 5, 725-730. http://dx.doi.org/10.1038/nclimate2657 

13 Walsh, J., D. Wuebbles, K. Hayhoe, J. Kossin, K. Kunkel, G. Stephens, P. Thorne, R. Vose, M. 

14 Wehner, J. Willis, D. Anderson, S. Doney, R. Feely, P. Hennon, V. Kharin, T. Knutson, F. 

15 Landerer, T. Lenton, J. Kennedy, and R. Somerville, 2014: Ch. 2: Our changing climate. 

16 Climate Change Impacts in the United States: The Third National Climate Assessment. 

17 Melillo, J.M., T.C. Richmond, and G.W. Yohe, Eds. U.S. Global Change Research Program, 

18 Washington, D.C., 19-67. http://dx.doi.org/10.7930/J0KW5CXT 

19 Wigley, T.M.L. and B.D. Santer, 2013: A probabilistic quantification of the anthropogenic 

20 component of twentieth century global warming. Climate Dynamics, 40, 1087-1102. 

21 http://dx.doi.org/10.1007/s00382-012-1585-8
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1 4. Climate Models, Scenarios, and Projections

2 KEY FINDINGS

3 1. Merely maintaining present-day levels of greenhouse (heat-trapping) gases in the 

4 atmosphere would commit the world to at least an additional 0.3°C (0.5°F) of warming 

5 over this century relative to today (high confidence). Projections over the next three 

6 decades differ modestly, primarily due to uncertainties in natural sources of variability. 

7 Past mid-century, the amount of climate change depends primarily on future emissions 

8 and the sensitivity of the climate system to those emissions.

9 2. Atmospheric carbon dioxide (CO2) levels have now passed 400 ppm, a concentration last 

10 seen about 3 million years ago, when average temperature and sea level were 

11 significantly higher than today. Continued growth in CO2 emissions over this century and 

12 beyond would lead to concentrations not experienced in tens to hundreds of millions of 

13 years. The rapid present-day emissions rate of nearly 10 GtC per year, however, suggests

14 that there is no precise past climate analogue for this century any time in at least the last 

15 66 million years. (Medium confidence)

16 3. The observed acceleration in carbon emissions over the past 15–20 years is consistent 

17 with higher future scenarios (very high confidence). Since 2014, growth rates have 

18 slowed as economic growth begins to uncouple from carbon emissions (medium 

19 confidence) but not yet at a rate that, were it to continue, would limit atmospheric 

20 temperature increase to the 2009 Copenhagen goal of 2°C (3.6°F), let alone the 1.5°C

21 (2.7°F) target of the 2015 Paris Agreement (high confidence).

22 4. Combining output from global climate models and dynamical and statistical downscaling 

23 models using advanced averaging, weighting, and pattern scaling approaches can result in 

24 more relevant and robust future projections. These techniques also allow the scientific 

25 community to provide better guidance on the use of climate projections for quantifying 

26 regional-scale impacts (medium to high confidence).

27

28 4.1. The Human Role in Future Climate

29 The Earth’s climate, past and future, is not static; it changes in response to both natural and 

30 anthropogenic drivers (see Ch. 2: Scientific Basis). Since the industrial era, human emissions of 

31 carbon dioxide (CO2), methane (CH4), and other greenhouse gases now overwhelm the influence 

32 of natural drivers on the external forcing of the Earth’s climate (see Ch. 3: Detection and 

33 Attribution). For this reason, projections of changes in Earth’s climate over this century and 

34 beyond focus primarily on its response to emissions of greenhouse gases, particulates, and other 

35 radiatively-active species from human activities.
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1 Climate change and ocean acidification (see Ch. 13: Ocean Acidification) are already occurring 

2 due to the buildup of atmospheric CO2 in the industrial era (Hartmann et al. 2013; Rhein et al.

3 2013). If atmospheric levels of greenhouse gases were frozen at current levels, temperature 

4 would continue to increase by an estimated 0.3°C (0.54°F) over this century (Collins et al. 2013). 

5 However, climate change over this century and beyond is primarily a function of future 

6 emissions and the response of the climate system to those emissions (see Ch. 2: Scientific Basis). 

7 For that reason, climate projections are not predictions; instead, they consist of a range of 

8 plausible scenarios or pathways that can be expressed in terms of population, energy sources, 

9 technology, emissions, atmospheric concentrations, radiative forcing, and/or global temperature 

10 change. For a given scenario, it is possible to estimate the range in potential climate change—as 

11 determined by climate sensitivity, which is the response of global temperature to a natural or 

12 anthropogenic forcing (see Ch. 2: Scientific Basis)—that would result at the global and regional 

13 scale (Collins et al. 2013). 

14 Over the past 15–20 years, growth rates in carbon emissions from human activities of 3%–4% 

15 per year largely tracked with those projected under higher scenarios, in large part to growing 

16 contributions from developing economies (Raupach et al. 2007; Le Quéré et al. 2009). Since 

17 2014, however, growth rates have flattened, a trend cautiously attributed to declining coal use in 

18 China, despite large uncertainties in emissions reporting (Jackson et al. 2016; Korsbakken et al. 

19 2016). Carbon emissions and economic growth may be beginning to decouple, as global 

20 economies led by China and the United States phase out coal and begin the transition to 

21 renewable, non-carbon energy (IEA 2016; Green and Stern 2016). In the 2015 Paris Agreement, 

22 signatories agree to “holding the increase in the global average temperature to well below 2°C 

23 (3.6oF) above preindustrial levels and pursuing efforts to limit the temperature increase to 1.5°C 

24 (2.7oF) above preindustrial levels” (UNFCCC 2015). To stabilize climate, however, it is not 

25 enough to halt the growth in annual carbon emissions; global net carbon emissions would

26 eventually need to reach zero (Collins et al. 2013) and most recent economic scenarios require 

negative emissions for a greater than 50% chance of limiting warming below 2°C (3.6o 27 F) (Smith 

28 et al. 2016; see also Ch. 14 Mitigation for a discussion of negative emission technologies).

29 4.2. Future Scenarios

30 4.2.1. Representative Concentration Pathways

31 Over the last 25 years, the climate modeling community has based its simulations on standard 

32 sets of scenarios that correspond with possible future emissions of greenhouse gases, aerosols, 

33 and other species. Developed by the integrated assessment modeling community, these sets of 

34 standard scenarios have become more comprehensive with each new generation: the IS92 

35 emission scenarios of the 1990s (Leggett et al. 1992); after 2000, the Special Report on Emission 

36 Scenarios (SRES; Nakicenovic et al. 2000); and today, the Representative Concentration 

37 Pathways (RCPs; Moss et al. 2010). 
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1 The SRES scenarios began with a storyline that lays out a consistent picture of demographics, 

2 international trade, flow of information and technology; these assumptions are then fed through 

3 socioeconomic and Integrated Assessment Models (IAMs) to derive emissions. In turn, 

4 emissions were used as input to carbon cycle or earth system models to calculate resulting 

5 atmospheric concentrations and radiative forcing. In contrast, RCP scenarios are tied to one 

6 value: the change in radiative forcing at the tropopause by 2100. The four RCPs are numbered 

7 according to specific changes in radiative forcing at the tropopause from preindustrial conditions 

to 2100: +2.6, +4.5, +6.0 and +8.5 watts per square meter (W/m2 8 ). From this value, it is possible 

9 to work backwards to derive a range of emissions trajectories and corresponding policies and 

10 technological strategies that would achieve the same ultimate impact on radiative forcing.

11 Although there are multiple emissions pathways that would lead to the same radiative forcing 

12 target, an associated pathway of annual carbon dioxide and other anthropogenic emissions of 

13 greenhouse gases, aerosols, air pollutants, and other short-lived species has been identified for 

14 each RCP to use as input to future climate model simulations (e.g., Riahi et al. 2011; Cubasch et 

15 al. 2013). In addition, RCPs provide climate modelers with gridded trajectories of land use and 

16 land cover. Using the RCPs as input, climate models produce trajectories of future climate 

17 change including global and regional changes in temperature, precipitation, and other physical 

18 characteristics of the climate system (Collins et al. 2013; Kirtman et al. 2013; see also Ch. 6-7).

19 Within the RCP family, individual scenarios have no likelihood attached to them. Higher20 numbered scenarios correspond to higher emissions, and a larger and more rapid global 

21 temperature change (Figure 4.1); the range of values covered by the scenarios was chosen to 

22 reflect the then-current range in the open literature. Since the choice of scenario constrains the 

23 magnitudes of future changes, most assessments (including this one; see Ch. 6: Temperature 

24 Change) quantify the impacts under a range of future scenarios that reflect the uncertainty in the 

25 consequences of human choices over the coming century.

26 The higher RCP8.5 scenario corresponds to a future where carbon emissions continue to rise as a 

27 result of fossil fuel use, albeit with significant declines in emission growth rates over the second 

28 half of the century (Figure 4.1) and modest improvements in energy intensity and technological 

29 change (Riahi et al. 2011). Atmospheric carbon dioxide levels rise from current-day levels of 400 

30 up to 936 parts per million (ppm) and global temperature increases by 3° to 5.5°C (5.4° to 9.9°F)

31 by 2100 relative to the 1986–2005 average. RCP8.5 reflects the upper range of the open 

32 literature on emissions, but is not intended to serve as an upper limit on possible emissions nor as 

33 a business as usual or reference scenario for the other three scenarios.

34 Projections based on SRES scenarios, such as those used in the Second and Third National 

35 Climate Assessments (NCA2 and NCA3; Karl et al. 2009; Melillo et al. 2014), are not 

36 necessarily incompatible with new RCP-based ones; RCP8.5 is similar to SRES A1fi, RCP6.0 is 

37 similar to SRES A1B, and RCP4.5 is similar to SRES B1. While none of the SRES scenarios 
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1 included a scenario with explicit policies and measures to limit climate forcing, however, the

2 three lower RCP scenarios (2.6, 4.5, and 6.0) are climate-policy scenarios.

3 Under the RCP4.5 and 2.6 scenarios, for example, atmospheric CO2 levels remain below 550 and 

4 450 ppm by 2100, respectively. The RCP2.6 scenario is much lower than any SRES scenario 

5 because it includes the option of using policies to achieve net negative carbon dioxide emissions 

6 before the end of the century, while SRES scenarios do not. The lower the atmospheric 

7 concentrations of CO2, the greater the chance that projected global temperature change will 

8 remain below 2°C (3.6°F) relative to preindustrial levels, consistent with the Paris Agreement. 

9 Under RCP4.5, global temperature change is more likely than not to exceed 2°C (3.6°F) 

10 (https://tntcat.iiasa.ac.at/RcpDb/dsd?Action=htmlpage&page=compare; Collins et al. 2013),

11 whereas under RCP2.6 it is likely to remain below 2°C (Sanderson et al. 2016; Collins et al. 

2013). RCPs do not consider climate forcing in the range of 2.0 W/m2 12 , a level consistent with 

13 limiting global mean surface temperature change to 1.5°C (2.7°F); it is estimated that a 66% 

14 chance of achieving this target would require net zero greenhouse gas emissions by 2050—or 

15 2060, if global temperature is permitted to temporarily exceed 1.5°C for up to 50 years 

16 (Sanderson et al. 2016).

17 [INSERT FIGURE 4.1 HERE:

18 Figure 4.1: The climate projections used in this report are based on the 2010 Representative 

19 Concentration Pathways (RCP, right). They are largely consistent with scenarios used in 

20 previous assessments, the 2000 Special Report on Emission Scenarios (SRES, left). This figure 

21 compares SRES and RCP annual carbon emissions (top), carbon dioxide equivalent levels in the 

22 atmosphere (middle), and temperature change that would result from the central estimate (lines) 

23 and the likely range (shaded areas) of climate sensitivity (bottom). (Data from CMIP3 and 

24 CMIP5). (Figure source: Walsh et al. 2014)]

25 4.2.2. Shared Socioeconomic Pathways

26 Shared Socioeconomic Pathways (SSPs) are a set of socioeconomic scenarios that include

27 assumptions regarding demographics, urbanization, economic growth and technology 

28 development. These scenarios were designed to meet the needs of the impacts, adaptation, and 

29 vulnerability (IAV) communities, enabling them to explore the socioeconomic challenges to 

30 emissions mitigation and adaptation to climate change (O’Neill et al. 2014). Five SSP scenarios 

31 have been developed: SSP1 (“Sustainability”; low challenges to mitigation and adaptation), 

32 SSP2 (“Middle of the Road”; middle challenges to mitigation and adaptation), SSP3 (“Regional 

33 Rivalry”; high challenges to mitigation and adaptation), SSP4 (“Inequality”; low challenges to 

34 mitigation, high challenges to adaptation), and SSP5 (“Fossil-fueled Development”; high 

35 challenges to mitigation, low challenges to adaptation). Each of the scenarios has an underlying 

36 SSP narrative, as well as a consistent quantification of demographic, urbanization, economic 

37 growth and technology development assumptions. 
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1 To allow IAV researchers to couple alternative socioeconomic scenarios with the climate 

2 scenarios developed using RCPs, SSP-driven scenarios have been constrained using emissions 

3 limitations policies consistent with the underlying SSP story lines to create new scenarios with 

4 climate forcing that matches RCP values, but a range of five alternative socioeconomic 

5 underpinnings. Only SSP5 produces a reference scenario that matches RCP8.5; the other SSPs 

have no-climate-policy reference scenarios with climate forcing below 8.5 W/m2 6 . Similarly, the 

7 nature of SSP3 makes it impossible for that scenario to produce a climate forcing as low as 2.6 

W/m2 8 . While new research is under way to explore scenarios that limit climate forcing to 2.0 

W/m2 9 , neither the RCPs nor the SSPs have produced scenarios in that range.

10 4.2.3. Global Mean Temperature Scenarios and Pattern Scaling 

11 Approaches

12 RCP scenarios and their associated SSPs provide the input for the global climate model 

13 simulations described in section 4.3 below. The output from these simulations is typically 

14 summarized over a range of future climatological time periods (for example, temperature change 

15 in 2040–2079 or 2070–2099 relative to 1980–2009). The time-slice approach has the advantage 

16 of developing projections for a given time horizon. It has the disadvantage, however, of 

17 including a broad range of uncertainty regarding what may occur over a given time frame, due to 

18 both scenario uncertainty and climate sensitivity. This uncertainty increases, the further out in 

19 time the projections go. A scenario-based approach is also increasingly disconnected with the 

20 framing of many climate targets, including the Paris Agreement, that are expressed in terms of 

21 global mean temperature rather than a given scenario, pathway, or time frame. This is one reason 

22 why the Paris Agreement requested that the IPCC provide a special report on the impacts of a 

23 1.5°C (2.7°F) world.

24 Global mean temperature (GMT) scenarios provide a way to connect model-based projections to 

25 climate targets, using pre-existing RCP or SRES-based climate model simulations. Traditional 

26 RCP or SRES-based simulations can be transformed into GMT scenarios by calculating the 

27 projected changes and resulting impacts that would occur under a transient warming of 1°, 2°, or 

28 3°C (1.8°, 3.6°, or 5.4°F) or more. The climatological time slice in each individual model 

29 simulation that corresponds to a given increase in global mean temperature can then be extracted. 

30 This increase can be defined relative to the desired baseline such as preindustrial, for example, or 

31 a more recent time period such as 1976–2005 (Figure 4.2). 

32 Many physical changes and impacts have been shown to scale with GMT, including shifts in 

33 average precipitation, extreme heat, runoff, drought risk, wildfire, temperature-related crop yield 

34 changes, and even risk of coral bleaching (e.g., NRC 2011; Collins et al. 2013; Frieler et al.

35 2013; Swain and Hayhoe 2015) and this approach has been found to reduce the multimodel 

36 spread of future projections (Herger et al. 2015; Swain and Hayhoe 2015). By quantifying 

37 projected changes for a given amount of warming, regardless of when it may be reached, this 

38 approach de-emphasizes the uncertainty due to both scenarios and climate sensitivity. Instead, 
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1 GMT scenarios highlight other aspects of scientific uncertainty regarding the response of the 

2 Earth’s climate system (which can be large, particularly at the regional scale) to human-induced 

3 change when a given global warming threshold or target is achieved. GMT scenarios are less 

4 useful for impacts such as species migration, however, that are more dependent on the rate than 

5 the magnitude of change.

6 Pattern scaling techniques (Mitchell 2003) are based on a similar assumption, namely that large7 scale patterns of regional change will scale with the amount of forcing. These techniques can be 

8 used to quantify regional change for scenarios that are not readily available in preexisting 

9 databases of global climate model simulations (as described in section 4.3.1 below), including 

10 changes in both mean and extremes (e.g., Fix et al. 2016). A comprehensive assessment both 

11 confirms and constrains the validity of applying pattern scaling to quantify climate response to a 

12 range of forcings (Tebaldi and Arblaster 2014). As the world moves towards quantifiable climate 

13 targets, it is expected that these pattern scaling frames or GMT scenarios will become more 

14 commonly used.

15 [INSERT FIGURE 4.2 HERE:

16 Figure 4.2: Global mean surface temperature anomalies (°C) relative to 1976–2005 for four RCP 

scenarios, 2.6 (green), 4.5 (yellow), 6.0 (orange), and 8.5 (red), calculated in 0.5o 17 C increments. 

18 Each line represents an individual simulation from the CMIP5 archive; every RCP-based 

19 simulation with annual or monthly temperature outputs available was used here. (Figure source:

20 adapted from Swain and Hayhoe 2015)]

21 4.2.4. Cumulative Carbon Emissions

22 The SRES, RCP, and global mean temperature scenarios described above all contain a 

23 component of time: how much will climate change, and by when? Ultimately, however, the 

24 magnitude of human-induced climate change depends less on the year-to-year emissions than it 

25 does on the net amount of carbon, or cumulative carbon, produced. To date, human activities,

26 including burning fossil fuels and deforestation, have emitted more than 600 gigatons of carbon

27 (GtC) into the atmosphere since preindustrial times. Unless substantial amounts are removed 

28 from the atmosphere via carbon sequestration, this amount has already committed the world to at 

29 least an additional 0.3°C (0.5°F) of warming over this century, relative to today.

30 In order to meet the ambitious 1.5°C (2.7°F) target in the Paris Agreement, only 150 GtC more 

31 of carbon can be emitted globally. To meet the higher 2°C (3.6°F) target, approximately 400 GtC 

32 more can be emitted. At current emission rates of just under 10 GtC per year, that would permit 

33 just 15 years for the lower target and around 40 more years of carbon emissions under the higher 

34 target. Under the RCP4.5 pathway, cumulative emissions totaling 1000 GtC, consistent with a 

35 2°C (3.6°F) target, would likely be reached between 2051 and 2065, while under the RCP8.5 

36 pathway, this level would likely be reached between 2043 and 2050. For the lower 1.5°C (2.7°F) 

37 target, the cumulative carbon limit of 750 GtC would likely be reached following the RCP4.5 
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1 pathway sometime between 2028 and 2041, and, following the RCP8.5 pathway, between 2026 

2 and 2036. When non-CO2 greenhouse gases such as methane and nitrous oxide (whose warming 

3 potentials differ over time, relative to CO2) are included, exactly when a given temperature 

4 threshold would be exceeded becomes even more uncertain.

5 The cumulative carbon emissions that would allow the world to meet a given global temperature 

6 target can also be compared to known fossil fuel reserves to calculate how much of their carbon 

7 would have to “stay in the ground” to meet these targets, in the absence of widespread carbon 

8 capture and storage (see Ch. 14). It is estimated that to meet the 2°C (3.6°F) target, two thirds of 

9 known global fossil fuel reserves would need to remain in the ground (McGlade and Ekins 

10 2015). Accounting for the differing carbon content of various types of fuels, in order to meet the 

11 2°C target one third of oil reserves, half of gas reserves, and over 80% of coal reserves would 

12 need to remain unused, as well as any new unconventional, undeveloped, or undiscovered 

13 resources (McGlade and Ekins 2015).

14 4.2.5. Paleoclimate Analogues for Long-Term Equilibrium Change

15 Most CMIP5 simulations project transient changes in climate through 2100; a few simulations 

16 extend to 2200, 2300 or beyond. The long-term impact of human activities on the carbon cycle 

17 and the Earth’s climate, however, can only be assessed by considering changes that occur over 

18 multiple centuries and even millennia, after net human emissions have reached zero, atmospheric 

19 carbon dioxide levels have stabilized, and the carbon cycle has re-balanced (NRC 2011).

20 In the past, there have been several extended periods of “hothouse” climates where carbon 

21 dioxide concentrations and/or global mean temperatures were similar to preindustrial, current, or 

22 plausible future levels. These periods are sometimes referenced as analogues, albeit imperfect 

23 and incomplete, of future climate (e.g., Crowley 1990). 

24

25 The last interglacial period, approximately 125,000 years ago, is known as the Eemian. During 

26 that time, CO2 levels were similar to preindustrial, around 280 ppm (Schneider et al. 2013). 

27 Global mean temperature was approximately 1° to 2°C (1.8° to 3.6°F) higher than preindustrial 

28 levels (Lunt et al. 2012; Otto-Bleisner et al. 2013), the poles were significantly warmer (NEEM 

29 2013; Jouzel et al. 2007), and sea level was 6 to 9 meters (20 to 30 feet) higher than today (Fig. 

30 4.3; Kopp et al. 2009). During the Pliocene, approximately 3 million years ago, long-term CO2

31 levels were similar to today’s, around 400 ppm (Seki et al. 2010) – although those concentrations 

32 were sustained over long periods of time, whereas ours are increasing rapidly. Global mean 

33 temperature in the Pliocene was approximately 2° to 3.5°C (3.6° to 6.3°F) above preindustrial, 

34 and sea level was somewhere between 20 ± 10 meters (66 ± 33 feet) higher than today (Fig. 4.3; 

35 Haywood et al. 2013; Dutton et al. 2015; Miller et al. 2012). 

36

37 Under the higher RCP8.5 scenario, CO2 concentrations are projected to exceed 900 ppm before

38 2100. During the Eocene, 35 to 55 million years ago, CO2 levels were between 680 and 1260 
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1 ppm, or two and a half to four and a half times above preindustrial levels (Jagniecki et al. 2015). 

2 Using Eocene conditions as an analogue, this suggests that if the CO2 concentrations projected to 

3 occur under the RCP8.5 scenario by 2100 were sustained over long periods of time, they would 

4 result in global temperatures approximately 5° to 8°C (9° to 14°F) above preindustrial levels 

5 (Royer 2014). During the Eocene, there were no permanent land-based ice sheets; Antarctic 

6 glaciation did not begin until approximately 34 million years ago (Pagani et al. 2011). 

7 Calibrating sea level rise models against these and other past climate conditions suggests that, 

8 under the RCP8.5 scenario, Antarctica could contribute 1 meter of sea level rise by 2100 and 15 

9 meters by 2500 (DeConto and Pollard 2016). If atmospheric CO2 were sustained at levels 

10 approximately two to three times above preindustrial for tens of thousands of years, it’s 

11 estimated that Greenland and Antarctic ice sheets could melt entirely (Gasson et al. 2014),

12 resulting in approximately 65 meters (215 feet) of sea level rise relative to present-day (Vaughn 

13 et al. 2013).

14 An analog for the rapid pace of change occurring today is the relatively abrupt warming of 5° to 

15 8°C (9° to 14°F) that occurred during the Paleocene-Eocene Thermal Maximum (PETM), 

16 approximately 55–56 million years ago (Bowen et al. 2015; Kirtland Turner et al. 2014; Penman 

17 et al. 2014; Crowley et al. 1990). However, new analyses reveal that this carbon was released 

18 over some 4000 years or so, and the rate of maximum sustained carbon release during that period 

19 was less than 1.1 GtC per year (Zeebe et al. 2016). In comparison, industrial era emissions have 

20 occurred over a few centuries, at rates now approaching 10 GtC per year. This suggests that there 

21 is no real past analogue any time in the last 66 million years that could help to constrain 

22 projections of future climate (Zeebe et al. 2016; Crowley et al. 1990). 

23 [INSERT FIGURE 4.3 HERE:

24 Figure 4.3: Putting present-day global mean temperature, CO2 concentrations, and sea level into 

25 context, this figure summarizes what is known about the range in peak global mean temperature, 

26 atmospheric CO2, maximum global mean sea level (GMSL), and source(s) of meltwater over 

27 three periods in the past with CO2 levels similar to pre-industrial levels (around 270 ppm) or 

28 today (around 400 ppm). Light blue shading indicates uncertainty of GMSL maximum. Red pie 

29 charts over Greenland and Antarctica denote fraction, not location, of ice retreat. (Figure source: 

30 Dutton et al. 2015)]

31 4.3. Modeling Tools

32 4.3.1. Global Climate Models

33 Climate scientists use a wide range of observational and computational tools to understand the 

34 complexity of the Earth’s climate system and to study how that system responds to external 

35 forces, including human activities. Computational tools include models that simulate different 

36 components of the climate system, including the atmosphere, ocean, land, and sea ice (see Ch. 2: 

37 Scientific Basis). The most sophisticated computational tools used by climate scientists are 
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1 global climate models, or GCMs (previously referred to as “general circulation models” when 

2 they included only the physics needed to simulate the general circulation of the atmosphere and 

3 oceans). Models that include an interactive carbon cycle and/or biogeochemistry component are 

4 sometimes also referred to as Earth System Models (ESMs).

5 Global climate models are mathematical models originally built on fundamental equations of 

6 physics that include the conservation of energy, mass, and momentum, and how these are 

7 exchanged among different components of the climate system. Using these fundamental 

8 relationships, the models generate many important features that are evident in the Earth’s climate 

9 system: the jet stream that circles the globe 30,000 feet above the Earth’s surface; the Gulf 

10 Stream and other ocean currents that transport heat from the tropics to the poles; and even 

11 hurricanes in the Atlantic Ocean and typhoons in the Pacific Ocean when the models are run at a 

12 fine enough spatial resolution. 

13 [INSERT FIGURE 4.4 HERE:

14 Figure 4.4: As climate modeling has evolved over the last 120 years, increasing amounts of 

15 physical science have been incorporated into the models. This figure shows the evolution from 

16 simple energy balance models through atmosphere–ocean general circulation models to today’s 

17 earth system models.]

18 In addition to expanding the number of processes in the models and improving the treatment of 

19 existing processes, the average horizontal spatial resolution of GCMs has increased over time, as 

20 computers become more powerful, and with each successive version of the World Climate 

21 Research Programme’s (WCRP’s) Coupled Model Intercomparison Project (CMIP). CMIP5 

22 provides output from over 50 GCMs with spatial resolutions ranging from about 50 to 300 km 

23 (30 to 200 miles) per horizontal size, and variable vertical resolution on the order of hundreds of 

24 meters in the troposphere or lower atmosphere. Versions 3 and 5 are currently available, and 

25 Version 6 is underway [Note: we will update CMIP6 progress in subsequent drafts]. These 

26 simulations provide output from a large ensemble of different climate models and future 

27 scenarios to quantify future climate change at global, continental, and broad regional scales.

28 GCMs are constantly being expanded to include more physics, chemistry, and, increasingly, even 

29 the biology and biogeochemistry at work in the climate system (Figure 4.4). However, these 

30 models build on previous generations and are not independent from each other. Many share both 

31 ideas and model components or code, complicating the interpretation of multi-model ensembles 

32 that often are assumed to be independent (Knutti et al. 2013; Sanderson et al. 2015). This is one 

33 of the key pieces of information going into the weighting approach used in this report (see 

34 Weighting Appendix). And even with new experimental high-resolution simulations at 25 km 

35 (15 miles) per horizontal grid cell, there are still important fine-scale processes occurring at 

36 regional to local scales that GCMs are unable to simulate. 
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1 To translate global projections into the higher-resolution information often required for impact 

2 assessment, climate impact studies often use the statistical or dynamical downscaling methods 

3 discussed above. Regional climate models can directly simulate the response of regional climate 

4 processes to global change, while statistical models can remove biases in simulations relative to 

5 observations. While some new approaches are combining dynamical and statistical methods into 

6 a hybrid framework, most assessments still tend to rely on one or the other type of downscaling, 

7 where the choice is based on the needs of the assessment. 

8 4.3.2. Regional Climate Models

9 Dynamical downscaling models are often referred to as regional climate models (RCMs), since 

10 they include many of the same physical processes that make up a global climate model, but 

11 simulate these processes at higher resolution over smaller rectangular regions, such as the 

12 western or eastern United States. Regional climate modeling can improve understanding of 

13 regional climate change by modeling areas with complex terrain, such as coastlines or 

14 mountains. They can also incorporate changes in land use, land cover, or hydrology into local 

15 climate at spatial scales relevant to planning and decision-making at the regional level.

16 RCMs are computationally intensive because of the higher resolution, but provide a broad range 

17 of output variables that resolve regional climate features important for assessing climate impacts. 

18 The size of individual grid cells can be as fine as 1 to 2 km (0.6 to 1.2 miles) per horizontal side 

19 in some studies, but more commonly range from about 10 to 50 km (6 to 30 miles). Despite the 

20 differences in resolution, RCMs are still subject to many of the same types of uncertainty as 

21 GCMs, such as not fully resolving physical processes that occur at even smaller scales than the 

22 model is able to resolve. One additional source of uncertainty unique to RCMs arises from the 

23 fact that at their boundaries RCMs require output from GCMs to provide large-scale circulation 

24 such as winds, temperature, and moisture.

25 The North America Coordinated Regional Climate Downscaling Experiment (CORDEX; Note: 

26 in progress, will need to be updated in subsequent drafts) is currently generating a set of high27 resolution RCM simulations for North America at spatial resolutions ranging from 10 to 50 km 

28 (6 to 30 miles) with 3-hour outputs for more than 60 different surface and upper-air variables. 

29 Currently-available simulations from the North American Regional Climate Change Assessment 

30 Program are useful for examining certain impacts over North America but, as they are based on 

31 simulations from four CMIP3 GCMS for a single mid-high SRES scenario, do not encompass 

32 the full range of uncertainty in future projections due to both human activities and climate 

33 sensitivity, as represented by the range of CMIP5 GCMs and RCP scenarios.

34 If the study is a sensitivity analysis, where using one or two future simulations is not a limitation, 

35 or if it requires many climate variables as input, then regional climate modeling may be more 

36 appropriate than statistical modeling. Kotamarthi et al. (2016) provides a full discussion of the 
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1 issues surrounding selecting and applying dynamical and statistical downscaling methods to

2 assess climate impacts.

3 [INSERT FIGURE 4.5 HERE:

4 Figure 4.5: Global climate models typically operate at coarser horizontal spatial scales, while 

5 regional climate models have much finer resolutions. This figure compares annual average 

6 precipitation for the historical period 1979–2008 using (a) a resolution of 25 km or 15 miles with 

7 (b) a resolution of 250 km or 150 miles, to illustrate the importance of spatial scale in resolving 

8 key topographical features, particularly along the coasts and in mountainous areas. In this case, 

9 both simulations are by the GFDL HIRAM model, an experimental high-resolution model.

10 (Figure source: adapted from Dixon et al. 2016; © American Meteorological Society, used with 

11 permission)]

12 4.3.3. Empirical Statistical Downscaling Models

13 Empirical statistical downscaling models (ESDMs) combine GCM output with historical 

14 observations to translate large-scale predictors or patterns into high-resolution projections at the 

15 scale of observations. The observations used in an ESDM can range from individual weather 

16 stations to gridded datasets. As output, they can generate a range of products, from large grids to 

17 analyses optimized for a specific location, variable, or decision-context. The statistical 

18 techniques are even more varied, from simple difference or delta approaches (subtracting 

19 historical simulated values from future values, and adding the resulting delta to historical 

20 observations, as used in the First National Climate Assessment) to complex clustering and neural 

21 network techniques that can rival dynamical downscaling in their demand for computational 

22 resources (see review by Kotamarthi et al. 2016). 

23 Statistical models are generally flexible and less computationally demanding than RCMs. A 

24 number of databases provide statistically downscaled projections for a continuous period from 

25 1960 to 2100 using many global models and a range of higher and lower future scenarios. 

26 ESDMs are also effective at removing biases in historical simulated values, leading to a good 

27 match between the average (multidecadal) statistics of observed and statistically downscaled 

28 climate at the spatial scale and over the historical period of the observational data used to train 

29 the statistical model. With the exception of methods that simultaneously downscale multiple 

30 variables, however, bias correction will remove the physical interdependence between variables. 

31 ESDMs are also limited in that they require observational data as input; the longer and more 

32 complete the record, the greater the confidence that the ESDM is being trained on a 

33 representative sample of climatic conditions for that location. Application of ESDMs to remote 

34 locations with sparse temporal and/or spatial records is challenging though in many cases 

35 reanalysis (Brands et al. 2012) or even monthly satellite data (Thrasher et al. 2013) can be used 

36 in lieu of in situ observations. Lack of data availability can also limit their use in applications 

37 that require more variables than temperature and precipitation. 
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1 Finally, statistical models are based on the key assumption that the relationship between large2 scale weather systems and local climate or the spatial pattern of surface climate will remain 

3 stationary over the time horizon of the projections. This assumption may not hold if climate 

4 change alters local feedback processes that affect these relationships; initial analyses have 

5 demonstrated that the assumption of stationarity can vary significantly by ESDM method, by 

6 quantile, and by the time scale (daily or monthly) of the GCM input (Dixon et al. 2016). 

7 ESDMs are best suited for analyses that require a broad range of future projections of standard, 

8 near-surface variables such as temperature and precipitation, at the scale of observations that 

9 may already be used for planning purposes. If the study needs to resolve the full range of 

10 projected changes under multiple models and scenarios or is more constrained by practical 

11 resources, then statistical downscaling may be more appropriate than dynamical downscaling. 

12 However, even within statistical downscaling, selecting an appropriate method for any given 

13 study depends on the questions being asked; these issues are discussed in greater detail by 

14 Kotamarthi et al. (2016).

15 4.3.4. Averaging, Weighting, and Selection of Global Models

16 Individual climate model simulations using the same inputs can differ from each other over 

17 several years to several decades. These differences are the result of normal, natural variability as 

18 well as the different ways models characterize various small-scale processes. Although decadal 

19 predictability is an active research area, the timing of natural variations is largely unpredictable 

20 beyond several seasons. For this reason, multimodel simulations are generally averaged (as the 

21 last stage in any analysis before preparing, for example, figures showing projected changes in 

22 annual or seasonal temperature or precipitation; see Ch. 6 and 7) to remove the effects of 

23 randomly occurring natural variations from long-term trends and make it easier to discern the 

24 impact of external drivers, both human and natural, on the Earth’s climate. The effect of 

25 averaging on the systematic errors depends on the extent to which models have similar errors or 

26 offsetting errors. For that reason, on time series plots, we also show a range of outcomes across 

27 GCMs, quantify the risks inherent to a given scenario.

28 Previous assessments have used a simple average to calculate the multimodel ensemble. Such 

29 approach implicitly assumes each climate model is independent from the others and of equal 

30 ability. Neither of these assumptions, however, are completely valid. As noted previously, some 

31 models share many components with other models in the CMIP5 archive, whereas others have 

32 been developed largely in isolation (Knutti et al. 2013; Sanderson et al. 2015). Also, some 

33 models are more successful than others: at replicating observed climate and trends over the past 

34 century; at simulating the large-scale dynamical features responsible for creating or affecting the 

35 average climate conditions over a certain region, such as the Arctic or the Caribbean (e.g., Wang 

36 et al. 2007, 2014; Ryu and Hayhoe 2014); or at simulating past climates with very different states 

37 than present day (Braconnot et al. 2012). Evaluation of models’ success often depends on the 

38 variable or metric being considered in the analysis, with some models performing better than 
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1 others for certain regions or variables. However, all future simulations agree that both global and 

2 regional temperatures will increase over this century in response to increasing emissions of 

3 greenhouse gases from human activities.

4 For the first time in an official U.S. Global Change Research Program report, this assessment 

5 uses model weighting to refine future climate change projections (see Appendix B: Model 

6 Weighting). The weighting approach takes into account the interdependence of individual 

7 climate models and their relative abilities in simulating North American climate. Understanding 

8 of model history, together with the fingerprints of particular model biases, has been used to 

9 identify model pairs that are not independent. In this report, model independence and selected 

10 global and North American model quality metrics are considered in order to determine the 

11 weighting parameters (Sanderson et al. in prep).

12 Sensitivity studies in the implementation of the weighting scheme show that global-scale 

13 temperature response is not significantly constrained by the weighting strategy, although there 

14 are small regional differences in significance. The choice of metric used to evaluate models has 

15 very little effect on the independence weighting, and some moderate influence on the skill 

16 weighting if only a small number of variables are used to assess model quality. Because a large 

17 number of variables are combined to produce a comprehensive “skill metric,” the metric is not 

18 highly sensitive to any single variable.

19 4.4. Uncertainty in Future Projections

20 The magnitude of future climate change depends on human choices (see Section 4.2), natural 

21 variability, and scientific uncertainty (Hawkins and Sutton 2009, 2011; Deser et al. 2012). 

22 Scientific uncertainty in turn encompasses multiple factors. The first is parametric uncertainty—

23 the ability of GCMs to simulate processes that occur on spatial or temporal scales smaller than 

24 they can resolve. The second is structural uncertainty—whether GCMs include and accurately 

25 represent all the important physical processes occurring on scales they can resolve. Structural 

26 uncertainty can arise because a process is not yet recognized—such as “tipping points” or 

27 mechanisms of abrupt change, as discussed in Ch.15, Potential Surprises—or because it is known 

28 but is not yet understood well enough to be modeled accurately—such as dynamical mechanisms 

29 that are important to melting ice sheets. The third is climate sensitivity—a measure of the 

30 response of the planet to increasing levels of CO2, formally defined as the equilibrium 

31 temperature change resulting from a doubling of CO2 levels in the atmosphere relative to 

32 preindustrial levels. Various lines of evidence constrain the likely value of climate sensitivity. 

33 These include historical warming (in the instrumental record, as well as events in the paleo34 climate record, such as the transition from the Last Glacial Maximum to today), and combining 

35 analysis of aspects of present-day climate with physical modeling of the climate system to 

36 constrain possible feedbacks such as how clouds might change in a warmer world (Knutti and 

37 Hegerl 2008). Combining this evidence, climate sensitivity is likely to lie between 2°C and 4.5°C 

38 (3.6°F and 8.1°F; IPCC 2013b).
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1 Which of these sources of uncertainty—human, natural, and scientific—is most important 

2 depends on the time frame and the variable considered. For temperature, it is clear that 

3 increasing greenhouse gas emissions from human activities will drive increases in global and 

4 most regional temperatures and that these rising temperatures will increase with the magnitude of 

5 future emissions, particularly past mid-century (Hawkins and Sutton 2009). Uncertainty in 

6 projected temperature change is generally smaller than uncertainty in projected changes in 

7 precipitation or other aspects of climate. For precipitation, the processes that form precipitation 

8 happen below the scale of the models, requiring a significant amount of parameterization. For 

9 that reason, scientific uncertainty tends to dominate in precipitation projections throughout the 

10 entire century (Hawkins and Sutton 2011).

11 Over the next few decades, the greater part of the range or uncertainty in projected global and 

12 regional change is the result of a combination of natural variability (mostly related to uncertainty 

13 in specifying the initial conditions of the state of the ocean) and scientific limitations in our 

14 ability to model and understand the Earth’s climate system (Figure 4.6). Differences in forcing 

15 scenarios, shown in orange in Figure 4.6, represent the scenarios, or human uncertainty. Over the 

16 short term, these differences are relatively small. As time progresses, however, differences in 

17 emissions become larger and the delayed ocean response to these differences begins to be 

18 realized. By about 2030, the human source of uncertainty becomes increasingly important in 

19 determining the magnitude and patterns of future change. Even though natural variability will 

20 continue to occur, most of the difference between present and future climates will be determined 

21 by choices that society makes today and over the next few decades. The further out in time we 

22 look, the greater the influence of these differences in human choices are on the differences in 

23 magnitude of future change.

24 [INSERT FIGURE 4.6 HERE:

25 Figure 4.6: The fraction of total variance in decadal mean surface air temperature predictions 

26 explained by the three components of total uncertainty is shown for (a) Alaska, (b) Hawai’i, and 

27 (c) the lower 48 states. Orange regions represent human or scenario uncertainty, blue regions 

28 represent model uncertainty, and green regions represent the internal variability component. As 

29 the size of the region is reduced, the relative importance of internal variability increases. In 

30 interpreting this figure, it is important to remember that it shows the fractional sources of 

31 uncertainty. Total uncertainty increases as time progresses. (Figure source: adapted from

32 Hawkins and Sutton 2009)]

33
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1 TRACEABLE ACCOUNTS

2 Key Finding 1

3 Merely maintaining present-day levels of greenhouse (heat-trapping) gases in the atmosphere 

4 would commit the world to at least an additional 0.3°C (0.5°F) of warming over this century 

5 relative to today (high confidence). Projections over the next three decades differ modestly, 

6 primarily due to uncertainties in natural sources of variability. Past mid-century, the amount of 

7 climate change depends primarily on future emissions and the sensitivity of the climate system to 

8 those emissions. 

9 Description of evidence base 

10 The basic physics underlying the impact of human emissions on global climate, and the role of 

11 climate sensitivity in moderating the impact of those emissions on global temperature, has been 

12 documented since the 1800s in a series of peer-reviewed journal articles that is summarized in a 

13 collection titled, “The Warming Papers: The Scientific Foundation for the Climate Change 

14 Forecast” (Archer and Pierrehumbert 2011). 

15 IPCC AR5 WG1 SPM states “Total radiative forcing is positive, and has led to an uptake of 

16 energy by the climate system. The largest contribution to total radiative forcing is caused by the 

17 increase in the atmospheric concentration of CO2 since 1750” (C, page 13) and “Observational 

18 and model studies of temperature change, climate feedbacks and changes in the Earth’s energy 

19 budget together provide confidence in the magnitude of global warming in response to past and 

20 future forcing.” (IPCC 2013b, D.2, page 16)

21 The estimate of committed warming at constant atmospheric concentrations is based on IPCC 

22 AR5 WG1, Collins et al. 2013.

23 Analysis of the sources of uncertainty in near-term versus long-term projections have been made 

24 by Hawkins & Sutton (2009, 2011) and Deser et al. (2012).

25 Major uncertainties 

26 In the statement, virtually none. In future emissions and climate sensitivity, there are significant 

27 uncertainties as reflected by the focus of this Key Message.

28

29 Assessment of confidence based on evidence and agreement, including short description of 

30 nature of evidence and level of agreement 

31 X Certain (100%)

32 ☐ Very High

33 X High

34 ☐Medium 

35 ☐Low

36 The first statement regarding additional warming has high confidence in the amount of warming; 

37 the second is virtually certain, as understanding of the radiative properties of greenhouse gases
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1 and the existence of both positive and negative feedbacks in the climate system is basic physics, 

2 dating to the 19th century.

3 Summary sentence or paragraph that integrates the above information

4 The key finding is based on basic physics that has been well established for decades to centuries 

5 and is referenced in every IPCC report from FAR to AR5.

6

7 Key Finding 2

8 Atmospheric carbon dioxide (CO2) levels have now passed 400 ppm, a concentration last seen 

9 about 3 million years ago, when average temperature and sea level were significantly higher than 

10 today. Continued growth in CO2 emissions over this century and beyond would lead to 

11 concentrations not experienced in tens to hundreds of millions of years. The rapid present-day 

12 emissions rate of nearly 10 GtC per year, however, suggests that there is no precise past climate 

13 analogue for this century any time in at least the last 66 million years. (Medium confidence)

14 Description of evidence base 

15 The Key Finding is based on a large body of research including Crowley (1990), Schneider et al. 

16 (2013), Lunt et al. (2012), Otto-Bleisner et al. (2013), NEEM (2013), Jouzel et al. (2007), Dutton 

17 et al. (2015), Seki et al. (2010), Haywood et al. (2013), Miller et al. (2012), Royer (2014), 

18 Bowen et al. (2015), Kirtland Turner et al. (2014), Penman et al. (2014), Zeebe et al. (2016), and 

19 summarized in NRC (2011) and Masson-Delmotte et al. (2013).

20 Major uncertainties 

21 The largest uncertainty is the measurement of past sea level, given the contributions of not only 

22 changes in land ice mass, but also in solid earth, mantle, isostatic adjustments, etc. that occur on 

23 timescales of millions of years. This uncertainty increases the further back in time we go; 

24 however, the signal (and forcing) size is also much greater. There are also associated 

25 uncertainties in precise quantification of past global mean temperature and carbon dioxide levels. 

26 There is uncertainty in the age models used to determine rates of change and coincidence of 

27 response at shorter, sub-millennial timescales.

28 Assessment of confidence based on evidence and agreement, including short description of 

29 nature of evidence and level of agreement 

30 ☐Very High

31 ☐High

32 X Medium 

33 ☐Low

34 Medium confidence in the likelihood statement that past global mean temperature and sea level 

35 rise were higher with similar or higher CO2 concentrations is based on Masson-Delmotte et al. 

36 (2013) in IPCC AR5. Medium confidence that no precise analog exists in 66 million years is 
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1 based on Zeebe et al. (2016) as well as the larger body of literature summarized in Masson2 Delmotte et al. (2013).

3 Summary sentence or paragraph that integrates the above information

4 The key finding is based on a vast body of literature that summarizes the results of observations, 

5 paleoclimate analyses, and paleoclimate modeling over the past 50 years and more.

6

7 Key Finding 3

8 The observed acceleration in carbon emissions over the past 15–20 years is consistent with 

9 higher future scenarios (very high confidence). Since 2014, growth rates have slowed as 

10 economic growth begins to uncouple from carbon emissions (medium confidence) but not yet at a 

11 rate that, were it to continue, would limit atmospheric temperature increase to the 2009 

12 Copenhagen goal of 2°C (3.6°F), let alone the 1.5°C (2.7°F) target of the 2015 Paris Agreement 

13 (high confidence).

14 Description of Evidence Base

15 Observed emissions for 2014 and 2015 and estimated emissions for 2016 suggest a decrease in 

16 the growth rate and possibly even emissions of carbon; this shift is attributed primarily to 

17 decreased coal use in China although with significant uncertainty as noted in the references in 

18 the text.

19 All credible climate models assessed in Chapter 9 of the IPCC WG1 AR5 (IPCC 2013a) from the 

20 simplest to the most complex respond with elevated global mean temperature, the simplest 

21 indicator of climate change, when greenhouse gases increase. It follows then that an emissions 

22 pathway that tracks or exceeds RCP8.5 would lead to larger amounts of climate change.

23 The evidence that actual emission rates track or exceed the RCP8.5 scenario are as follows. The 

24 actual emission of CO2 from fossil fuel consumption and concrete manufacture over the period 

25 2005–2014 is 90.11 Pg (Le Quéré et al. 2015) The RCP8.5 emissions over the same period 

26 assuming linear trends between years in the specification is 89.01 Pg.

27 Actual emissions: http://www.globalcarbonproject.org/carbonbudget/15/data.htm and Le Quere

28 et al. (2015).

29 RCP8.5 emissions

30 http://tntcat.iiasa.ac.at:8787/RcpDb/dsd?Action=htmlpage&page=compare

31 The actual numbers (red is estimated).

32
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RCP8.5 Actual difference

2005 7.971 8.076 0.105

2006 8.162 8.363 0.201

2007 8.353 8.532 0.179

2008 8.544 8.74 0.196

2009 8.735 8.7 -0.035

2010 8.9256 9.14 0.2144

2011 9.18716 9.449 0.26184

2012 9.44832 9.575025506 0.126705506

2013 9.70948 9.735033958 0.025553958

2014 9.97064 9.795211382 -0.175428618

total 89.0062 90.10527085 1.099070845

1

2 Major Uncertainties

3 None

4 Assessment of confidence based on evidence and agreement, including short description of 

5 nature of evidence and level of agreement

6 ☐Certain (100%)

7 X Very High

8 X High

9 X Medium 

10 ☐Low

11 Very high confidence in increasing emissions over the last 20 years and high confidence in the 

fact that recent emission trends will not be sufficient to avoid 2o 12 C. Medium confidence in recent 

13 findings that the growth rate is slowing and/or emissions are plateauing soon. Climate change 

14 scales with the amount of anthropogenic greenhouse gas in the atmosphere. If emissions exceed 

15 RCP8.5, the likely range of changes temperatures and climate variables will be larger than 

16 projected.

17 Summary sentence or paragraph that integrates the above information

18 The key finding is based on basic physics relating emissions to concentrations, radiative forcing, 

19 and resulting change in global mean temperature as well as on IEA data on national emissions as 

20 reported in the peer-reviewed literature.

21

22
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1 Key Finding 4

2 Combining output from global climate models and dynamical and statistical downscaling models 

3 using advanced averaging, weighting, and pattern scaling approaches can result in more relevant 

4 and robust future projections. These techniques also allow the scientific community to provide 

5 better guidance on the use of climate projections for quantifying regional-scale impacts (medium 

6 to high confidence).

7 Description of evidence base 

8 The contribution of weighting and pattern scaling to improving the robustness of multimodel 

9 ensemble projections is described and quantified by a large body of literature as summarized in 

10 the text. The state of the art of dynamical and statistical downscaling and the scientific 

11 community’s ability to provide guidance regarding the application of climate projections to 

12 regional impact assessments is summarized in Kotamarthi et al. (2016). This peer-reviewed DOD 

13 SERDP report documents new advances in testing and evaluating empirical statistical 

14 downscaling methods. This is the best available reference at this time, as downscaling receives 

15 only cursory mention in IPCC AR5 and—despite proposals for a report on this topic—has yet to 

16 be the focus of an NAS report.

17 Major uncertainties 

18 Regional climate models are subject to the same structural and parametric uncertainties as global 

19 models, as well as the uncertainty due to incorporating boundary conditions. The primary source 

20 of error in application of empirical statistical downscaling methods is inappropriate application, 

21 followed by stationarity.

22 Assessment of confidence based on evidence and agreement, including short description of 

23 nature of evidence and level of agreement 

24 ☐Very High

25 ☐High

26 X Medium 

27 ☐Low

28 Advanced weighting techniques have significantly improved over previous Bayesian approaches; 

29 confidence in their ability to improve the robustness of multi-model ensembles, while currently 

30 rated as medium, is likely to grow in coming years. Downscaling has evolved significantly over 

31 the last decade and is now broadly viewed as a robust source for high-resolution climate 

32 projections that can be used as input to regional impact assessments.

33 Summary sentence or paragraph that integrates the above information

34 Scientific understanding of climate projections, downscaling, multi-model ensembles, and 

35 weighting has evolved significantly over the last decades to the extent that appropriate methods 

36 are now broadly viewed as robust sources for climate projections that can be used as input to 

37 regional impact assessments.

38
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1 FIGURES

2

3

4 Figure 4.1: The climate projections used in this report are based on the 2010 Representative 

5 Concentration Pathways (RCP, right). They are largely consistent with scenarios used in 

6 previous assessments, the 2000 Special Report on Emission Scenarios (SRES, left). This figure 

7 compares SRES and RCP annual carbon emissions (top), carbon dioxide equivalent levels in the 

8 atmosphere (middle), and temperature change that would result from the central estimate (lines) 

9 and the likely range (shaded areas) of climate sensitivity (bottom). (Data from CMIP3 and 

10 CMIP5). (Figure source: Walsh et al. 2014)
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1

2

3

4 Figure 4.2: Global mean surface temperature anomalies (°C) relative to 1976–2005 for four RCP 

scenarios, 2.6 (green), 4.5 (yellow), 6.0 (orange), and 8.5 (red), calculated in 0.5o 5 C increments. 

6 Each line represents an individual simulation from the CMIP5 archive; every RCP-based CMIP5 

7 simulation with annual or monthly temperature outputs available was used here. (Figure source: 

8 adapted from Swain and Hayhoe 2015)

9
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1

2

3

4 Figure 4.3: Putting present-day global mean temperature, CO2 concentrations, and sea level into 

5 context, this figure summarizes what is known about the range in peak global mean temperature, 

6 atmospheric CO2, maximum global mean sea level (GMSL), and source(s) of meltwater over 

7 three periods in the past with CO2 levels similar to pre-industrial levels (around 270 ppm) or 

8 today (around 400 ppm). Light blue shading indicates uncertainty of GMSL maximum. Red pie 

9 charts over Greenland and Antarctica denote fraction, not location, of ice retreat. (Figure source: 

10 Dutton et al. 2015)

11
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1

2

3

4

5

6 Figure 4.4: As climate modeling has evolved over the last 120 years, increasing amounts of 

7 physical science have been incorporated into the models. This figure shows the evolution from 

8 simple energy balance models through atmosphere–ocean general circulation models to today’s 

9 earth system models.

10
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1

2

3 Figure 4.5: Global climate models typically operate at coarser horizontal spatial scales, while 

4 regional climate models have much finer resolutions. This figure compares annual average 

5 precipitation for the historical period 1979–2008 using (a) a resolution of 25 km or 15 miles with 

6 (b) a resolution of 250 km or 150 miles, to illustrate the importance of spatial scale in resolving 

7 key topographical features, particularly along the coasts and in mountainous areas. In this case, 

8 both simulations are by the GFDL HIRAM model, an experimental high-resolution model. 

9 (Figure source: adapted from Dixon et al. 2016; © American Meteorological Society, used with 

10 permission)

11
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1

2 Figure 4.6: The fraction of total 

3 variance in decadal mean surface air 

4 temperature predictions explained by 

5 the three components of total 

6 uncertainty is shown for (a) Alaska, (b) 

7 Hawai’I, and (c) the lower 48 states 

8 (bottom). Orange regions represent 

9 human or scenario uncertainty, blue 

10 regions represent model uncertainty, 

11 and green regions represent the internal 

12 variability component. As the size of 

13 the region is reduced, the relative 

14 importance of internal variability 

15 increases. In interpreting this figure, it 

16 is important to remember that it shows 

17 the fractional sources of uncertainty. 

18 Total uncertainty increases as time 

19 progresses. (Figure source: adapted 

20 from Hawkins and Sutton 2009)

21

22
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1 REFERENCES

2 Archer, D. and R. Pierrehumbert, eds. The Warming Papers: The Scientific Foundation for the 

3 Climate Change Forecast. 2011, Wiley-Blackwell: Oxford, UK. 432. 

4 Bowen, G.J., B.J. Maibauer, M.J. Kraus, U. Rohl, T. Westerhold, A. Steimke, P.D. Gingerich, 

5 S.L. Wing, and W.C. Clyde, 2015: Two massive, rapid releases of carbon during the onset of 

6 the Palaeocene-Eocene thermal maximum. Nature Geosci, 8, 44-47.

7 http://dx.doi.org/10.1038/ngeo2316 

8 Braconnot, P., S.P. Harrison, M. Kageyama, P.J. Bartlein, V. Masson-Delmotte, A. Abe-Ouchi, 

9 B. Otto-Bliesner, and Y. Zhao, 2012: Evaluation of climate models using palaeoclimatic 

10 data. Nature Clim. Change, 2, 417-424. http://dx.doi.org/10.1038/nclimate1456 

11 Brands, S., J.M. Gutiérrez, S. Herrera, and A.S. Cofiño, 2012: On the Use of Reanalysis Data for 

12 Downscaling. Journal of Climate, 25, 2517-2526. http://dx.doi.org/10.1175/jcli-d-11-00251.1 

13 Collins, M., R. Knutti, J. Arblaster, J.-L. Dufresne, T. Fichefet, P. Friedlingstein, X. Gao, W.J. 

14 Gutowski, T. Johns, G. Krinner, M. Shongwe, C. Tebaldi, A.J. Weaver, and M. Wehner, 

15 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. Climate 

16 Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth 

17 Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. 

18 Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and 

19 P.M. Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom and New 

20 York, NY, USA, 1029–1136. http://dx.doi.org/10.1017/CBO9781107415324.024 

21 www.climatechange2013.org

22 Crowley, T.J., 1990: Are There Any Satisfactory Geologic Analogs for a Future Greenhouse 

23 Warming? Journal of Climate, 3, 1282-1292. http://dx.doi.org/10.1175/1520-

24 0442(1990)003<1282:atasga>2.0.co;2 

25 Cubasch, U., D. Wuebbles, D. Chen, M.C. Facchini, D. Frame, N. Mahowald, and J.-G. Winther, 

26 2013: Introduction. Climate Change 2013: The Physical Science Basis. Contribution of 

27 Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate 

28 Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. 

29 Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. Cambridge University Press, Cambridge, 

30 United Kingdom and New York, NY, USA, 119–158. 

31 http://dx.doi.org/10.1017/CBO9781107415324.007 www.climatechange2013.org

32 DeConto, R.M. and D. Pollard, 2016: Contribution of Antarctica to past and future sea-level rise. 

33 Nature, 531, 591-597. http://dx.doi.org/10.1038/nature17145
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 4

178

1 Deser, C., A. Phillips, V. Bourdette, and H. Teng, 2012: Uncertainty in climate change 

2 projections: The role of internal variability. Climate Dynamics, 38, 527-546.

3 http://dx.doi.org/10.1007/s00382-010-0977-x 

4 Dixon, K.W., J.R. Lanzante, M.J. Nath, K. Hayhoe, A. Stoner, A. Radhakrishnan, V. Balaji, and 

5 C.F. Gaitán, 2016: Evaluating the stationarity assumption in statistically downscaled climate 

6 projections: is past performance an indicator of future results? Climatic Change, 135, 395-

7 408. http://dx.doi.org/10.1007/s10584-016-1598-0 

8 Dutton, A., A.E. Carlson, A.J. Long, G.A. Milne, P.U. Clark, R. DeConto, B.P. Horton, S. 

9 Rahmstorf, and M.E. Raymo, 2015: Sea-level rise due to polar ice-sheet mass loss during 

10 past warm periods. Science, 349. http://dx.doi.org/10.1126/science.aaa4019 

11 Fix, M.J., D. Cooley, S.R. Sain, and C. Tebaldi, 2016: A comparison of U.S. precipitation 

12 extremes under RCP8.5 and RCP4.5 with an application of pattern scaling. Climatic Change,

13 1-13. http://dx.doi.org/10.1007/s10584-016-1656-7 

14 Frieler, K., M. Meinshausen, A. Golly, M. Mengel, K. Lebek, S.D. Donner, and O. Hoegh15 Guldberg, 2013: Limiting global warming to 2°C is unlikely to save most coral reefs. Nature 

16 Climate Change, 3, 165-170. http://dx.doi.org/10.1038/nclimate1674 

17 Gasson, E., D.J. Lunt, R. DeConto, A. Goldner, M. Heinemann, M. Huber, A.N. LeGrande, D. 

18 Pollard, N. Sagoo, M. Siddall, A. Winguth, and P.J. Valdes, 2014: Uncertainties in the 

19 modelled CO<sub>2</sub> threshold for Antarctic glaciation. Climate of the Past, 10, 451-

20 466. http://dx.doi.org/10.5194/cp-10-451-2014 http://www.clim-past.net/10/451/2014/

21 Green, F. and N. Stern, 2016: China's changing economy: implications for its carbon dioxide 

22 emissions. Climate Policy, 1-15. http://dx.doi.org/10.1080/14693062.2016.1156515 

23 Hartmann, D.L., A.M.G. Klein Tank, M. Rusticucci, L.V. Alexander, S. Brönnimann, Y. 

24 Charabi, F.J. Dentener, E.J. Dlugokencky, D.R. Easterling, A. Kaplan, B.J. Soden, P.W. 

25 Thorne, M. Wild, and P.M. Zhai, 2013: Observations: Atmosphere and Surface. Climate 

26 Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth 

27 Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. 

28 Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and 

29 P.M. Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom and New 

30 York, NY, USA, 159–254. http://dx.doi.org/10.1017/CBO9781107415324.008 

31 www.climatechange2013.org

32 Hawkins, E. and R. Sutton, 2009: The potential to narrow uncertainty in regional climate 

33 predictions. Bulletin of the American Meteorological Society, 90, 1095-1107.

34 http://dx.doi.org/10.1175/2009BAMS2607.1 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 4

179

1 Hawkins, E. and R. Sutton, 2011: The potential to narrow uncertainty in projections of regional 

2 precipitation change. Climate Dynamics, 37, 407-418. http://dx.doi.org/10.1007/s00382-010-

3 0810-6 

4 Haywood, A.M., D.J. Hill, A.M. Dolan, B.L. Otto-Bliesner, F. Bragg, W.L. Chan, M.A. 

5 Chandler, C. Contoux, H.J. Dowsett, A. Jost, Y. Kamae, G. Lohmann, D.J. Lunt, A. Abe6 Ouchi, S.J. Pickering, G. Ramstein, N.A. Rosenbloom, U. Salzmann, L. Sohl, C. Stepanek, 

7 H. Ueda, Q. Yan, and Z. Zhang, 2013: Large-scale features of Pliocene climate: results from 

8 the Pliocene Model Intercomparison Project. Climate of the Past, 9, 191-209.

9 http://dx.doi.org/10.5194/cp-9-191-2013 

10 Herger, N., B.M. Sanderson, and R. Knutti, 2015: Improved pattern scaling approaches for the 

11 use in climate impact studies. Geophysical Research Letters, 42, 3486-3494.

12 http://dx.doi.org/10.1002/2015GL063569 

13 IEA, 2016: Decoupling of global emissions and economic growth confirmed. International 

14 Energy Agency, March 16. 

15 https://www.iea.org/newsroomandevents/pressreleases/2016/march/decoupling-of-global16 emissions-and-economic-growth-confirmed.html

17 IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group 

18 I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

19 Cambridge University Press, Cambridge, UK and New York, NY, 1535 pp.

20 http://dx.doi.org/10.1017/CBO9781107415324 www.climatechange2013.org

21 IPCC, 2013: Summary for policymakers. Climate Change 2013: The Physical Science Basis. 

22 Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental 

23 Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. 

24 Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. Cambridge University Press, 

25 Cambridge, United Kingdom and New York, NY, USA, 1–30. 

26 http://dx.doi.org/10.1017/CBO9781107415324.004 

27 Jackson, R.B., J.G. Canadell, C. Le Quere, R.M. Andrew, J.I. Korsbakken, G.P. Peters, and N. 

28 Nakicenovic, 2016: Reaching peak emissions. Nature Climate Change, 6, 7-10.

29 http://dx.doi.org/10.1038/nclimate2892 

30 Jagniecki, E.A., T.K. Lowenstein, D.M. Jenkins, and R.V. Demicco, 2015: Eocene atmospheric 

31 CO2 from the nahcolite proxy. Geology. http://dx.doi.org/10.1130/g36886.1 

32 http://geology.gsapubs.org/content/early/2015/10/23/G36886.1.abstract

33 Jouzel, J., V. Masson-Delmotte, O. Cattani, G. Dreyfus, S. Falourd, G. Hoffmann, B. Minster, J. 

34 Nouet, J.M. Barnola, J. Chappellaz, H. Fischer, J.C. Gallet, S. Johnsen, M. Leuenberger, L. 

35 Loulergue, D. Luethi, H. Oerter, F. Parrenin, G. Raisbeck, D. Raynaud, A. Schilt, J. 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 4

180

1 Schwander, E. Selmo, R. Souchez, R. Spahni, B. Stauffer, J.P. Steffensen, B. Stenni, T.F. 

2 Stocker, J.L. Tison, M. Werner, and E.W. Wolff, 2007: Orbital and Millennial Antarctic 

3 Climate Variability over the Past 800,000 Years. Science, 317, 793-796.

4 http://dx.doi.org/10.1126/science.1141038 

5 Karl, T.R., J.M. Melillo, and T.C. Peterson, 2009: Global Climate Change Impacts in the United 

6 States. 189 pp., New York. http://downloads.globalchange.gov/usimpacts/pdfs/climate7 impacts-report.pdf

8 Kirtland Turner, S., P.F. Sexton, C.D. Charles, and R.D. Norris, 2014: Persistence of carbon 

9 release events through the peak of early Eocene global warmth. Nature Geosci, 7, 748-751.

10 http://dx.doi.org/10.1038/ngeo2240 

11 Kirtman, B., S.B. Power, J.A. Adedoyin, G.J. Boer, R. Bojariu, I. Camilloni, F.J. Doblas-Reyes, 

12 A.M. Fiore, M. Kimoto, G.A. Meehl, M. Prather, A. Sarr, C. Schär, R. Sutton, G.J. 

13 van Oldenborgh, G. Vecchi, and H.J. Wang, 2013: Near-term climate change: Projections 

14 and predictability. Climate Change 2013: The Physical Science Basis. Contribution of 

15 Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate 

16 Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. 

17 Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. Cambridge University Press, Cambridge, 

18 UK and New York, NY, USA, 953–1028. 

19 http://dx.doi.org/10.1017/CBO9781107415324.023 

20 Knutti, R. and G.C. Hegerl, 2008: The equilibrium sensitivity of the Earth’s temperature to 

21 radiation changes. Nature Geoscience, 1, 735-743. http://dx.doi.org/10.1038/ngeo337 

22 Knutti, R., D. Masson, and A. Gettelman, 2013: Climate model genealogy: Generation CMIP5 

23 and how we got there. Geophysical Research Letters, 40, 1194-1199.

24 http://dx.doi.org/10.1002/grl.50256 

25 Kopp, R.E., F.J. Simons, J.X. Mitrovica, A.C. Maloof, and M. Oppenheimer, 2009: Probabilistic 

26 assessment of sea level during the last interglacial stage. Nature, 462, 863-867.

27 http://dx.doi.org/10.1038/nature08686 

28 Korsbakken, J.I., G.P. Peters, and R.M. Andrew, 2016: Uncertainties around reductions in 

29 China[rsquor]s coal use and CO2 emissions. Nature Climate Change, 6, 687-690.

30 http://dx.doi.org/10.1038/nclimate2963 

31 Kotamarthi, R., L. Mearns, K. Hayhoe, C. Castro, and D. Wuebbles, 2016: Use of Climate 

32 Information for Decision-Making and Impact Research. 55 pp. U.S. Department of Defense, 

33 Strategic Environment Research and Development Program Report.

34 Le Quéré, C., R. Moriarty, R.M. Andrew, J.G. Canadell, S. Sitch, J.I. Korsbakken, P. 

35 Friedlingstein, G.P. Peters, R.J. Andres, T.A. Boden, R.A. Houghton, J.I. House, R.F. 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 4

181

1 Keeling, P. Tans, A. Arneth, D.C.E. Bakker, L. Barbero, L. Bopp, J. Chang, F. Chevallier, 

2 L.P. Chini, P. Ciais, M. Fader, R.A. Feely, T. Gkritzalis, I. Harris, J. Hauck, T. Ilyina, A.K. 

3 Jain, E. Kato, V. Kitidis, K. Klein Goldewijk, C. Koven, P. Landschützer, S.K. Lauvset, N. 

4 Lefèvre, A. Lenton, I.D. Lima, N. Metzl, F. Millero, D.R. Munro, A. Murata, J.E.M.S. Nabel, 

5 S. Nakaoka, Y. Nojiri, K. O'Brien, A. Olsen, T. Ono, F.F. Pérez, B. Pfeil, D. Pierrot, B. 

6 Poulter, G. Rehder, C. Rödenbeck, S. Saito, U. Schuster, J. Schwinger, R. Séférian, T. 

7 Steinhoff, B.D. Stocker, A.J. Sutton, T. Takahashi, B. Tilbrook, I.T. van der Laan-Luijkx, 

8 G.R. van der Werf, S. van Heuven, D. Vandemark, N. Viovy, A. Wiltshire, S. Zaehle, and N. 

9 Zeng, 2015: Global Carbon Budget 2015. Earth System Science Data, 7, 349-396.

10 http://dx.doi.org/10.5194/essd-7-349-2015 

11 Le Quéré, C., M.R. Raupach, J.G. Canadell, G. Marland, L. Bopp, P. Ciais, T.J. Conway, S.C. 

12 Doney, R.A. Feely, P. Foster, P. Friedlingstein, K. Gurney, R.A. Houghton, J.I. House, C. 

13 Huntingford, P.E. Levy, M.R. Lomas, J. Majkut, N. Metzl, J.P. Ometto, G.P. Peters, I.C. 

14 Prentice, J.T. Randerson, S.W. Running, J.L. Sarmiento, U. Schuster, S. Sitch, T. Takahashi, 

15 N. Viovy, G.R. van der Werf, and F.I. Woodward, 2009: Trends in the sources and sinks of 

16 carbon dioxide. Nature Geoscience, 2, 831-836. http://dx.doi.org/10.1038/ngeo689 

17 Leggett, J., W.J. Pepper, R.J. Swart, J. Edmonds, L.G.M. Filho, I. Mintzer, M.X. Wang, and J. 

18 Watson, 1992: Emissions Scenarios for the IPCC: an Update. Climate Change 1992: The 

19 Supplementary Report to the IPCC Scientific Assessment. Houghton, J.T., B.A. Callander, 

20 and S.K. Varney, Eds. Cambridge University Press, Cambridge, United Kingdom, New 

21 York, NY, USA, and Victoria, Australia, 73-95.

22 Lunt, D.J., T. Dunkley Jones, M. Heinemann, M. Huber, A. LeGrande, A. Winguth, C. Loptson, 

23 J. Marotzke, C.D. Roberts, J. Tindall, P. Valdes, and C. Winguth, 2012: A model–data 

24 comparison for a multi-model ensemble of early Eocene atmosphere–ocean simulations: 

25 EoMIP. Climate of the Past, 8, 1717-1736. http://dx.doi.org/10.5194/cp-8-1717-2012 

26 Masson-Delmotte, V., M. Schulz, A. Abe-Ouchi, J. Beer, A. Ganopolski, J.F. González Rouco, 

27 E. Jansen, K. Lambeck, J. Luterbacher, T. Naish, T. Osborn, B. Otto-Bliesner, T. Quinn, R. 

28 Ramesh, M. Rojas, X. Shao, and A. Timmermann, 2013: Information from Paleoclimate 

29 Archives. Climate Change 2013: The Physical Science Basis. Contribution of Working 

30 Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. 

31 Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, 

32 V. Bex, and P.M. Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom 

33 and New York, NY, USA, 383–464. http://dx.doi.org/10.1017/CBO9781107415324.013 

34 www.climatechange2013.org

35 McGlade, C. and P. Ekins, 2015: The geographical distribution of fossil fuels unused when 

36 limiting global warming to 2 [deg]C. Nature, 517, 187-190.

37 http://dx.doi.org/10.1038/nature14016 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 4

182

1 Melillo, J.M., T.C. Richmond, and G.W. Yohe, eds. Climate Change Impacts in the United 

2 States: The Third National Climate Assessment. 2014, U.S. Global Change Research 

3 Program: Washington, D.C. 842. http://dx.doi.org/10.7930/J0Z31WJ2. 

4 Miller, K.G., J.D. Wright, J.V. Browning, A. Kulpecz, M. Kominz, T.R. Naish, B.S. Cramer, Y. 

5 Rosenthal, W.R. Peltier, and S. Sosdian, 2012: High tide of the warm Pliocene: Implications 

6 of global sea level for Antarctic deglaciation. Geology. http://dx.doi.org/10.1130/g32869.1 

7 Mitchell, T.D., 2003: Pattern Scaling: An Examination of the Accuracy of the Technique for 

8 Describing Future Climates. Climatic Change, 60, 217-242.

9 http://dx.doi.org/10.1023/a:1026035305597 

10 Moss, R.H., J.A. Edmonds, K.A. Hibbard, M.R. Manning, S.K. Rose, D.P. van Vuuren, T.R. 

11 Carter, S. Emori, M. Kainuma, T. Kram, G.A. Meehl, J.F.B. Mitchell, N. Nakicenovic, K. 

12 Riahi, S.J. Smith, R.J. Stouffer, A.M. Thomson, J.P. Weyant, and T.J. Wilbanks, 2010: The 

13 next generation of scenarios for climate change research and assessment. Nature, 463, 747-

14 756. http://dx.doi.org/10.1038/nature08823 

15 Nakicenovic, N., J. Alcamo, G. Davis, B.d. Vries, J. Fenhann, S. Gaffin, K. Gregory, A. Grübler, 

16 T.Y. Jung, T. Kram, E.L.L. Rovere, L. Michaelis, S. Mori, T. Morita, W. Pepper, H. Pitcher, 

17 L. Price, K. Riahi, A. Roehrl, H.-H. Rogner, A. Sankovski, M. Schlesinger, P. Shukla, S. 

18 Smith, R. Swart, S.v. Rooijen, N. Victor, and Z. Dadi, 2000: IPCC Special Report on 

19 Emissions Scenarios. Cambridge University Press, 

20 NEEM, 2013: Eemian interglacial reconstructed from a Greenland folded ice core. Nature, 493,

21 489-494. http://dx.doi.org/10.1038/nature11789 

22 NRC, 2011: Climate Stabilization Targets: Emissions, Concentrations, and Impacts over 

23 Decades to Millennia. National Research Council. The National Academies Press, 

24 Washington, D.C., 298 pp. http://dx.doi.org/10.17226/12877 

25 O’Neill, B.C., E. Kriegler, K. Riahi, K.L. Ebi, S. Hallegatte, T.R. Carter, R. Mathur, and D.P. 

26 van Vuuren, 2014: A new scenario framework for climate change research: The concept of 

27 shared socioeconomic pathways. Climatic Change, 122, 387-400.

28 http://dx.doi.org/10.1007/s10584-013-0905-2 

29 Otto-Bliesner, B.L., N. Rosenbloom, E.J. Stone, N.P. McKay, D.J. Lunt, E.C. Brady, and J.T. 

30 Overpeck, 2013: How warm was the last interglacial? New model–data comparisons. 

31 Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering 

32 Sciences, 371. http://dx.doi.org/10.1098/rsta.2013.0097 

33 Pagani, M., M. Huber, Z. Liu, S.M. Bohaty, J. Henderiks, W. Sijp, S. Krishnan, and R.M. 

34 DeConto, 2011: The Role of Carbon Dioxide During the Onset of Antarctic Glaciation. 

35 Science, 334, 1261-1264. http://dx.doi.org/10.1126/science.1203909 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 4

183

1 Penman, D.E., B. Hönisch, R.E. Zeebe, E. Thomas, and J.C. Zachos, 2014: Rapid and sustained 

2 surface ocean acidification during the Paleocene-Eocene Thermal Maximum. 

3 Paleoceanography, 29, 357-369. http://dx.doi.org/10.1002/2014PA002621 

4 Raupach, M.R., G. Marland, P. Ciais, C. Le Quéré, J.G. Canadell, G. Klepper, and C.B. Field, 

5 2007: Global and regional drivers of accelerating CO2 emissions. Proceedings of the 

6 National Academy of Sciences, 104, 10288-10293.

7 http://dx.doi.org/10.1073/pnas.0700609104 

8 Rhein, M., S.R. Rintoul, S. Aoki, E. Campos, D. Chambers, R.A. Feely, S. Gulev, G.C. Johnson, 

9 S.A. Josey, A. Kostianoy, C. Mauritzen, D. Roemmich, L.D. Talley, and F. Wang, 2013: 

10 Observations: Ocean. Climate Change 2013: The Physical Science Basis. Contribution of 

11 Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate 

12 Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. 

13 Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. Cambridge University Press, Cambridge, 

14 United Kingdom and New York, NY, USA, 255–316. 

15 http://dx.doi.org/10.1017/CBO9781107415324.010 www.climatechange2013.org

16 Riahi, K., S. Rao, V. Krey, C. Cho, V. Chirkov, G. Fischer, G. Kindermann, N. Nakicenovic, and 

17 P. Rafaj, 2011: RCP 8.5—A scenario of comparatively high greenhouse gas emissions. 

18 Climatic Change, 109, 33-57. http://dx.doi.org/10.1007/s10584-011-0149-y 

19 Royer, D.L., 2014: 6.11 - Atmospheric CO2 and O2 During the Phanerozoic: Tools, Patterns, 

20 and Impacts A2 - Holland, Heinrich D. Treatise on Geochemistry (Second Edition). 

21 Turekian, K.K., Ed. Elsevier, Oxford, 251-267. http://dx.doi.org/10.1016/B978-0-08-095975-

22 7.01311-5 http://www.sciencedirect.com/science/article/pii/B9780080959757013115

23 Ryu, J.-H. and K. Hayhoe, 2013: Understanding the sources of Caribbean precipitation biases in 

24 CMIP3 and CMIP5 simulations. Climate Dynamics, 1-20. http://dx.doi.org/10.1007/s00382-

25 013-1801-1 

26 Sanderson, B.M., R. Knutti, and P. Caldwell, 2015: A Representative Democracy to Reduce 

27 Interdependency in a Multimodel Ensemble. Journal of Climate, 28, 5171-5194.

28 http://dx.doi.org/10.1175/JCLI-D-14-00362.1 

29 Sanderson, B.M., B.C. O'Neill, and C. Tebaldi, 2016: What would it take to achieve the Paris 

30 temperature targets? Geophysical Research Letters, 43, 7133-7142.

31 http://dx.doi.org/10.1002/2016GL069563 

32 Sanderson, B.M., M. Wehner, and R. Knutti, 2016: Skill and Independence weighting for multi33 model Assessment. Geoscientific Model Development Submitted.

34 Schneider, R., J. Schmitt, P. Köhler, F. Joos, and H. Fischer, 2013: A reconstruction of 

35 atmospheric carbon dioxide and its stable carbon isotopic composition from the penultimate 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 4

184

1 glacial maximum to the last glacial inception. Clim. Past, 9, 2507-2523.

2 http://dx.doi.org/10.5194/cp-9-2507-2013 http://www.clim-past.net/9/2507/2013/

3 Seki, O., G.L. Foster, D.N. Schmidt, A. Mackensen, K. Kawamura, and R.D. Pancost, 2010: 

4 Alkenone and boron-based Pliocene pCO2 records. Earth and Planetary Science Letters,

5 292, 201-211. http://dx.doi.org/10.1016/j.epsl.2010.01.037 

6 http://www.sciencedirect.com/science/article/pii/S0012821X10000816

7 Smith, P., S.J. Davis, F. Creutzig, S. Fuss, J. Minx, B. Gabrielle, E. Kato, R.B. Jackson, A. 

8 Cowie, E. Kriegler, D.P. van Vuuren, J. Rogelj, P. Ciais, J. Milne, J.G. Canadell, D. 

9 McCollum, G. Peters, R. Andrew, V. Krey, G. Shrestha, P. Friedlingstein, T. Gasser, A. 

10 Grubler, W.K. Heidug, M. Jonas, C.D. Jones, F. Kraxner, E. Littleton, J. Lowe, J.R. Moreira, 

11 N. Nakicenovic, M. Obersteiner, A. Patwardhan, M. Rogner, E. Rubin, A. Sharifi, A. 

12 Torvanger, Y. Yamagata, J. Edmonds, and C. Yongsung, 2015: Biophysical and economic 

13 limits to negative CO2 emissions. Nature Climate Change, 6, 42-50. 

14 http://dx.doi.org/10.1038/nclimate2870 

15 Swain, S. and K. Hayhoe, 2015: CMIP5 projected changes in spring and summer drought and 

16 wet conditions over North America. Climate Dynamics, 44, 2737-2750.

17 http://dx.doi.org/10.1007/s00382-014-2255-9 

18 Tebaldi, C. and J.M. Arblaster, 2014: Pattern scaling: Its strengths and limitations, and an update 

19 on the latest model simulations. Climatic Change, 122, 459-471.

20 http://dx.doi.org/10.1007/s10584-013-1032-9 

21 Thrasher, B., J. Xiong, W. Wang, F. Melton, A. Michaelis, and R. Nemani, 2013: Downscaled 

22 Climate Projections Suitable for Resource Management. Eos, Transactions of the American 

23 Geophysical Union, 94, 321-323. http://dx.doi.org/10.1002/2013EO370002 

24 UNFCCC, 2015: Paris Agreement. United Nations Framework Convention on Climate Change, 

25 [Bonn, Germany].

26 Vaughan, D.G., J.C. Comiso, I. Allison, J. Carrasco, G. Kaser, R. Kwok, P. Mote, T. Murray, F. 

27 Paul, J. Ren, E. Rignot, O. Solomina, K. Steffen, and T. Zhang, 2013: Observations: 

28 Cryosphere. Climate Change 2013: The Physical Science Basis. Contribution of Working 

29 Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. 

30 Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, 

31 V. Bex, and P.M. Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom 

32 and New York, NY, USA, 317–382. http://dx.doi.org/10.1017/CBO9781107415324.012 

33 www.climatechange2013.org

34 Walsh, J., D. Wuebbles, K. Hayhoe, J. Kossin, K. Kunkel, G. Stephens, P. Thorne, R. Vose, M. 

35 Wehner, J. Willis, D. Anderson, S. Doney, R. Feely, P. Hennon, V. Kharin, T. Knutson, F. 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 4

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1 Landerer, T. Lenton, J. Kennedy, and R. Somerville, 2014: Ch. 2: Our changing climate. 

2 Climate Change Impacts in the United States: The Third National Climate Assessment. 

3 Melillo, J.M., T.C. Richmond, and G.W. Yohe, Eds. U.S. Global Change Research Program, 

4 Washington, D.C., 19-67. http://dx.doi.org/10.7930/J0KW5CXT 

5 Wang, C., L. Zhang, S.-K. Lee, L. Wu, and C.R. Mechoso, 2014: A global perspective on 

6 CMIP5 climate model biases. Nature Climate Change, 4, 201-205.

7 http://dx.doi.org/10.1038/nclimate2118 

8 Wang, M., J.E. Overland, V. Kattsov, J.E. Walsh, X. Zhang, and T. Pavlova, 2007: Intrinsic 

9 versus Forced Variation in Coupled Climate Model Simulations over the Arctic during the 

10 Twentieth Century. Journal of Climate, 20, 1093-1107. http://dx.doi.org/10.1175/JCLI4043.1 

11 Zeebe, R.E., A. Ridgwell, and J.C. Zachos, 2016: Anthropogenic carbon release rate 

12 unprecedented during the past 66 million years. Nature Geoscience, 9, 325-329.

13 http://dx.doi.org/10.1038/ngeo2681
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1 5. Large-Scale Circulation and Climate Variability

2 KEY FINDINGS

3 1. Under increased greenhouse gas concentrations, the tropics are likely to expand with an 

4 accompanying poleward shift of the subtropical dry zones and midlatitude jets in each 

5 hemisphere (medium to high confidence). While it is likely that tropics have expanded 

6 since 1979 (medium confidence), uncertainties remain regarding the attribution of these

7 changes to human activities.

8 2. Recurring patterns of variability in large-scale atmospheric circulation (such as the North 

9 Atlantic Oscillation and Northern Annular Mode) and the atmosphere–ocean system 

10 (such as El Niño–Southern Oscillation) cause year-to-year variations in U.S. temperatures

11 and precipitation (high confidence). Changes in the occurrence of these patterns or their 

12 properties have contributed to recent U.S. temperature and precipitation trends (medium 

13 confidence) although uncertainties remain about the size of the role of human influences

14 in these changes.

15 3. Increasing temperatures and atmospheric specific humidity are already having important 

16 influences on extremes (high confidence). It is still unclear, however, to what extent 

17 increasing temperatures and humidity have influenced and will influence persistent 

18 circulation patterns, which in turn influence these extremes.

19

20 5.1.Introduction

21 The causes of regional climate trends cannot be understood without considering the impact of 

22 changes in large-scale atmospheric circulation and an assessment of the role of internally

23 generated climate variability. There are contributions to regional climate trends from changes in 

24 large-scale latitudinal circulation, which is generally organized into three cells in each 

25 hemisphere—Hadley Cell, Ferrell Cell and Polar Cell—and which determines the location of 

26 subtropical dry zones and midlatitude jet streams. These circulation cells are expected to shift 

27 poleward during warmer periods (Frierson et al. 2007; Sun et al. 2013; Vallis et al. 2015), which 

28 could result in poleward shifts in precipitation patterns affecting natural ecosystems, agriculture, 

29 and water resources (Seidel et al. 2008; Feng and Fu 2013).

30 In addition, regional climate can be strongly affected by non-local response to recurring patterns

31 (or modes) of variability of the atmospheric circulation or the coupled atmosphere–ocean system. 

32 These modes of variability represent preferred spatial patterns and their temporal variation and 

33 account for gross features in variance and for teleconnections. Modes of variability are often 

34 described as a product of a spatial climate pattern and an associated climate index time series that 

35 are identified based on statistical methods like Principle Component Analysis (PC analysis),
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1 which is also called Empirical Orthogonal Function Analysis (EOF analysis), and cluster 

2 analysis.

3 [INSERT FIGURE 5.1 HERE:

4 Figure 5.1: (Top) Plan and (bottom) cross section schematic view representation of the general 

5 circulation of the atmosphere. Three main circulations exist between the equator and poles due to

6 solar heating and the earth’s rotation. 

7 Hadley cell (1) – Low-latitude air moves toward the equator. Due to solar heating, air near the 

8 equator rises vertically and moves poleward in the upper atmosphere. 

9 Ferrel cell (2) – A midlatitude mean atmospheric circulation cell. In this cell, the air flows 

10 poleward and eastward near the surface and equatorward and westward at higher levels.

11 Polar cell (3) – Air rises, diverges, and travels toward the poles. Once over the poles, the air 

12 sinks, forming the polar highs. At the surface, air diverges outward from the polar highs. Surface 

13 winds in the polar cell are easterly (polar easterlies).

14 A high pressure band is located at about 30° N/S latitude, leading to dry/hot weather due to 

15 descending air motion (subtropical dry zones are indicated in orange in the schematic views).

16 Expanding tropics (indicted by orange arrows) are associated with a poleward shift of the 

17 subtropical dry zones. A low pressure band is found at 50°–60° N/S, with rainy and stormy 

18 weather in relation to the polar jet stream bands of strong westerly wind in the upper levels of the 

19 atmosphere. (Figure source: adapted from NWS 2016)]

20 On intra-seasonal to interannual time scales, the climate of the United States is strongly affected

21 by modes of atmospheric circulation variability like the North Atlantic Oscillation 

22 (NAO)/Northern Annular Mode (NAM), North Pacific Oscillation (NPO), and Pacific North 

23 American Pattern (PNA). They are closely linked to other atmospheric circulation phenomena 

24 like blocking and quasi-stationary wave patterns and jet streams. On an interannual time scale, 

25 coupled atmosphere–ocean phenomena like El Niño–Southern Oscillation (ENSO) have a 

26 prominent effect. On longer time scales, U.S. climate anomalies are linked to slow variations of 

27 sea surface temperature related to the Pacific Decadal Oscillation (PDO) and the Atlantic 

28 Multidecadal Oscillation (AMO). 

29 In general, the influences of human activities on the climate system are now so widespread that 

30 the current and future behavior of these previous ‘natural’ climate features can no longer be 

31 assumed independent of those human influences. Climate response to external forcing appears to 

32 project strongly onto these existing recurring modes of variability, although the regional 

33 temperature and precipitation impacts of these modes can be modified due to a changed 

34 background climate. However, modes of internal variability of the climate system also contribute 

35 to observed decadal and multidecadal temperature and precipitation trends on local scales,

36 masking possible systematic changes due to an anthropogenic influence. Recent studies point out 

37 though, that there are still large uncertainties in our understanding of the impact of human38 induced climate change on atmospheric circulation, and the predictability of large-scale 

39 circulation changes might be limited (Shepherd 2014; Vallis et al. 2015).
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1 5.2 Modes of Variability: Past and Projected Changes

2 5.2.1 Width of the Tropics and Global Circulation 

3 Sea level pressure gives an indication of surface changes in atmospheric circulation.

4 Contributions of greenhouse gas, ozone, and aerosol changes on the seasonal and geographical 

5 patterns of trends in global sea level pressure over 1951−2011 are detectable (Gillett et al. 2013).

6 On regional scales and particularly at higher latitudes, internal variability has been found to play 

7 a large role in uncertainties of future sea level pressure projections (Deser et al. 2012).

8 Evidence continues to mount for an expansion of the tropics over the past several decades, with a 

9 poleward expansion of the Hadley cell and an associated poleward shift of the subtropical dry 

10 zones in each hemisphere, although the rate of expansion is uncertain and depends on the metrics 

11 used (Birner et al. 2014; Brönnimann et al. 2015; Davis and Birner 2013; Feng and Fu 2013; 

12 Garfinkel et al. 2015; Karnauskas and Ummenhofer 2014; Lucas et al. 2014; Quan et al. 2014; 

13 Reichler 2016). While the roles of stratospheric ozone depletion in the Southern Hemisphere 

14 (Waugh et al. 2015) and anthropogenic aerosols in the Northern Hemisphere (Allen et al. 2012; 

15 Kovilakam and Mahajan 2015) have been implicated as contributors in the expansion, there is 

16 uncertainty in the relative contributions of natural and anthropogenic factors, and natural 

17 variability may be dominating (Adam et al. 2014; Allen et al. 2014; Garfinkel et al. 2015).

18 Most of the previous work on tropical expansion to date has focused on zonally-averaged 

19 changes. There are only a few recent studies that diagnose regional characteristics of tropical 

20 expansion. The findings depend on analysis methods and datasets. For example, a northward 

21 expansion of the tropics in most regions of the Northern Hemisphere, including the Eastern 

22 Pacific with impact on drying in the American Southwest, is found based on diagnosing outgoing 

23 longwave radiation (Chen et al. 2014). However, other studies do not find a significant poleward 

24 expansion of the tropics over the Eastern Pacific and North America (Schwendike et al. 2015; 

25 Lucas and Nguyen 2015). Thus, the implications of the recent widening of the tropics for the 

26 climate of the United States and thus observed drying of the Southwest (Feng and Fu 2013; Prein 

27 et al. 2016) are not clear.

28 Due to human-induced greenhouse gas increases, the Hadley cell is likely to widen in the future 

29 with an accompanying poleward shift in the subtropical dry zones and midlatitude jets (Collins et 

30 al. 2013; Barnes and Polvani 2013; Scheff and Frierson 2012a; Scheff and Frierson 2012b; Vallis 

31 et al. 2015; Feng and Fu 2013). Large uncertainties remain in projected changes in non-zonal to 

32 regional circulation components and related changes in precipitation patterns (Simpson et al. 

33 2014; Barnes and Polvani 2013; Shepherd 2014; Simpson et al. 2016). Uncertainties in projected 

34 changes in midlatitude jets are also related to projected rate of arctic amplification and changes

35 in the stratospheric polar vortex. Both factors could shift the mid-latitude jet equatorward

36 especially in the North Atlantic region (Barnes and Polvani 2015; Scaife et al. 2012; Karpechko 

37 and Manzini 2012).
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1 5.2.2 El Niño-Southern Oscillation

2 El Niño–Southern Oscillation (ENSO) is a main source of climate variability, with a 2–7 year 

3 timescale, originating from coupled ocean–atmosphere interactions in the tropical Pacific and 

4 affecting weather patterns over many parts of the globe through atmospheric teleconnections. It 

5 strongly affects precipitation and temperature in the United States (Figure 5.2) (Halpert and 

6 Ropelewski 1992; Hoerling et al. 2001; Kiladis and Diaz 1989; Ropelewski and Halpert 1987).

7 [INSERT FIGURE 5.2 HERE:

8 Figure 5.2: El Niño- and La Niña-related winter features over North America. Shown are typical 

9 January to March weather anomalies and atmospheric circulation during moderate to strong El 

10 Niño and La Niña conditions: (top) During El Niño, there is a tendency for a strong jet stream 

11 and storm track across the southern part of the United States. The southern tier of Alaska and the 

12 U.S. Pacific Northwest tend to be warmer than average, whereas the southern tier of U.S. states 

13 tends to be cooler and wetter than average. During La Niña, there is a tendency of a very wave14 like jet stream flow over the United States and Canada, with colder and stormier than average 

15 conditions across the North, and warmer and less stormy conditions across the South. (Figure 

16 source: adapted from Lindsey 2016)]

17 El Niño teleconnections are modulated by the location of maximum anomalous tropical Pacific 

18 sea surface temperatures (SST). Eastern Pacific (EP) El Niño events affect winter temperatures 

19 primarily over the Great Lakes, Northeast, and Southwest, while Central Pacific (CP) events 

20 influence temperatures primarily over the northwestern and southeastern United States (Yu et al. 

21 2012). The CP El Niño also enhances the drying effect, but weakens the wetting effect, typically 

22 produced by traditional EP El Niño events on the United States winter precipitation (Yu and Zou 

23 2013). It is not clear whether observed decadal-scale modulations of ENSO properties, including 

24 an increase in ENSO amplitude (Li et al. 2011) and an increase in frequency of CP El Niño 

25 events (Lee and McPhaden 2010; Yeh et al. 2009), are due to internal variability or 

26 anthropogenic forcing. There are at least a couple of reasons for this uncertainty. First, 

27 comprehensive observations that allow investigation of ENSO-related coupled atmosphere–

28 ocean feedbacks go back only to the late 1970s (Christensen et al. 2013). Second, unforced

29 global climate model simulations show that decadal to centennial modulations of ENSO can be 

30 generated without any change in external forcing (Capatondi et al. 2015). 

31 While there is high confidence that ENSO will remain a preferred mode of natural climate 

32 variability in the 21st century, only low confidence is indicated for specific projected changes in 

33 ENSO variability (Christensen et al. 2013). This low confidence is the result of the fact that 

34 models do not agree on the projected changes in El Niño intensity or on changes in the zonal 

35 gradient of tropical Pacific sea surface temperatures. Recent studies suggest a near doubling in 

36 frequency of occurrence of both extreme El Niño and La Niña events due to human-induced 

37 greenhouse gas increases for the 21st century relative to the 20th century, as determined by a 

38 subset of CMIP model simulations (Cai et al. 2014, 2015a,b). 
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1 There is robust evidence of an eastward shift of ENSO-induced teleconnection patterns due to 

2 greenhouse gas-induced climate change (Kug et al. 2010; Meehl and Teng 2007; Stevenson 

3 2012; Zhou et al. 2014). However, the impact of this shift on ENSO-induced climate anomalies

4 in the United States is not well understood (Seager et al. 2012; Zhou et al. 2014).

5 5.2.3 Extra-tropical Modes of Variability and Phenomena 

6 NORTH ATLANTIC OSCILLATION AND NORTHERN ANNULAR MODE

7 The North Atlantic Oscillation (NAO), the leading recurring mode of variability in the extra8 tropical North Atlantic region, describes an opposite variation in sea level pressure between the 

9 Atlantic subtropical high and the Iceland/Arctic low. Variations in the NAO are accompanied by 

10 changes in the location and intensity of the Atlantic midlatitude storm track and blocking activity 

11 that affect climate over the North Atlantic and surrounding continents. A negative NAO phase is 

12 related to anomalously cold conditions and an enhanced number of cold outbreaks in the eastern 

13 United States, while a strong positive phase of the NAO tends to be associated with above14 normal temperatures in this region (Hurrell and Deser 2009; Thompson and Wallace 2001). The 

15 positive phase of the NAO is associated with increased precipitation frequency and positive daily 

16 rainfall anomalies, including extreme daily precipitation anomalies in the northeastern United 

17 States (Durkee et al. 2008; Archambault et al. 2008).

18 The Northern Annular Mode/Arctic Oscillation (NAM/AO) is closely related to the NAO. It 

19 describes a pressure seesaw between mid and high latitudes on a hemispheric scale and thus 

20 includes a third anomaly center over the North Pacific Ocean (Thompson and Wallace 1998;

21 Thompson and Wallace 2000). The time series of the NAO and NAM/AO are highly correlated,

22 with persistent NAO and NAM/AO events being indistinguishable (Deser 2000; Feldstein and 

23 Franzke 2006).

24 The wintertime NAO/NAM index exhibits pronounced variability on multidecadal time scales,

25 with an increase from the 1960s to the 1990s, a shift to a more negative phase since the 1990s 

26 due to a series of winters like 2009–2010 and 2010–2011 (which had exceptionally low index 

27 values), and a return to more positive values after 2011 (Bindoff et al. 2013). Decadal scale 

28 temperature trends in the eastern United States, including occurrence of cold outbreaks during 

29 recent years, are linked to these changes in the NAO/NAM (Hurrell 1995; Cohen and Barlow 

30 2005; Overland and Wang 2015; Overland et al. 2015).

31 The CMIP5 models on average simulate a progressive shift of the NAO/NAM towards its 

32 positive phase due to human-induced climate change (Gillett and Fyfe 2013). However, the 

33 spread between model simulations is larger than the projected multimodel increase, and shifts 

34 between preferred periods of positive and negative NAO phase will continue to occur similar to 

35 those observed in the past (Deser et al. 2012; Christensen et al. 2013).
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1 The NAO’s influence on the ocean occurs through changes in heat content, gyre circulations, 

2 mixed layer depth, salinity, high-latitude deep water formation, and sea ice cover (Hurrell and 

3 Deser 2009). Climate model simulations show that multidecadal variation in the NAO induce 

4 multidecadal variations in the Atlantic meridional overturning circulation and poleward ocean

5 heat transport in the Atlantic that is extending to the Arctic. It has been suggested that these 

6 variations have contributed to the observed rapid loss of Arctic sea ice and Northern Hemisphere 

7 warming, especially in the late 1990s and early 2000s, and thus enhanced the long-term trends in 

8 Arctic sea ice loss and hemispheric warming that are mainly caused by anthropogenic forcing

9 (Delworth et al. 2016).

10 NORTH PACIFIC OSCILLATION/WEST PACIFIC OSCILLATION

11 The North Pacific Oscillation (NPO) is the leading mode of variability in the extratropical North 

12 Pacific region and is characterized by a north-south seesaw in sea level pressure. NPO effects on 

13 U.S. hydroclimate and marginal ice zone extent in the Arctic seas have been reported (Linkin 

14 and Nigam 2008). However, 21st century climate projections suggest no major changes in the 

15 NPO (Furtado et al. 2011).

16 PACIFIC/NORTH AMERICAN PATTERN 

17 The Pacific/North American (PNA) pattern is the leading recurring mode of internal atmospheric 

18 variability over the North Pacific and the North American continent, especially during the cold 

19 season. It describes a quadripole pattern of mid-tropospheric height anomalies, with anomalies of 

20 similar sign located over the subtropical northeastern Pacific and northwestern North America 

21 and of the opposite sign centered over the Gulf of Alaska and the southeastern United States. The 

22 PNA pattern is associated with strong fluctuations in the strength and location of the East Asian 

23 jet stream. The positive phase of the PNA pattern is associated with above-average temperatures 

24 over the western and northwestern United States, and below-average temperatures across the 

25 south-central and southeastern United States, including enhanced occurrence of extreme cold 

26 temperatures (Leathers et al. 1991; Loikith and Broccoli 2012; Ning and Bradley 2016).

27 Significant negative correlation between the PNA and winter precipitation over the Ohio River 

28 Valley has been documented (Leathers et al. 1991; Coleman and Rogers 2003; Ning and Bradley

29 2016).

30 The PNA is related to ENSO events (Nigam 2003) and also serves as a bridge linking ENSO and 

31 NAO variability (Li and Lau 2012). A single model sensitivity study suggests that the PNA 

32 mode of atmospheric internal variability remains largely unchanged in pattern in a warmer 

33 climate (Zhou et al. 2014). 

34 BLOCKING AND QUASI-STATIONARY WAVES

35 Anomalous atmospheric flow patterns in the extratropics that remain in place for an extended 

36 period of time (for example, blocking and quasi-stationary Rossby waves)—and thus affect a 
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1 region with similar weather conditions like rain or clear sky for several days to weeks—can lead 

2 to flooding, drought, heat waves, and cold waves (Grotjahn et al. 2016; Whan et al. 2016; 

3 Petoukhov et al. 2013). Specifically, blocking describes large-scale, persistent high pressure 

4 systems that interrupt the typical westerly flow, while planetary waves (Rossby waves) describe 

5 large meandering of the atmospheric jet stream. 

6 A persistent pattern of high pressure in the circulation off the west coast of the United States has 

7 been associated with the California drought (Ch.8; Swain et al. 2014; Seager et al. 2015).

8 Blocking in the Alaskan region, which is enhanced during La Niña winters (Figure 5.2) 

9 (Renwick and Wallace 1996), is associated with higher temperatures in western Alaska but a 

10 shift to lower mean and extreme surface temperatures from the Yukon southward to the southern 

11 Plains (Carrera et al. 2004). The anomalously cold winters of 2009–2010 and 2010–2011 in the 

12 United States are linked to the blocked (or negative) phase of the NAO (Guirguis et al. 2011). 

13 Stationary Rossby wave patterns may have contributed to the North American temperature 

14 extremes during summers like 2011 (Wang et al. 2014). It has been suggested that arctic 

15 amplification has already led to weakened westerly winds and hence more slowly moving and 

16 amplified wave patterns and enhanced occurrence of blocking (Francis and Vavrus 2012; Francis 

17 et al. 2016; Ch. 11: Arctic Changes).

18 While a study based on a homogenized/extended Greenland Blocking Index (GBI) identified a 

19 significant increase in the frequency of blocking events over Greenland since 1981 in all seasons 

20 as well as in the annual mean (Hanna et al. 2016), a series of other studies did not find a 

21 significant increase in the frequency of blocking, based on various blocking metrics in specific 

22 regions and seasons and on various reanalysis products (Barnes 2013; Barnes et al. 2014). 

23 Various metrics have been applied to diagnose recent changes in the amplitude of midlatitude 

24 planetary waves (Francis and Vavrus 2012; Screen and Simmonds 2013) that differ in their 

25 conclusions. While a metric based on the maximum latitude of selected 500 mb geopotential 

26 height (Z500) isopleths exhibits a statistical significant increase (Francis and Vavrus 2012), a 

27 metric based on midlatitude meridional wave amplitude at 500 mb does not show significant 

28 changes (Screen and Simmonds 2013).

29 A decrease of blocking frequency with climate change is found in CMIP3, CMIP5, and higher30 resolution models (Christensen et al. 2013; Hoskins and Woollings 2015, Kennedy et al. 2016). 

31 However, CMIP5 models still underestimate observed blocking activity in the North Atlantic 

32 sector while they tend to overestimate activity in the North Pacific, although with a large

33 intermodel spread (Christensen et al. 2013). Climate models robustly project a change in 

34 Northern Hemisphere winter quasi-stationary wave fields that are linked to a wetting of the 

35 North American West Coast (Brandefelt and Körnich 2008; Haarsma and Selten 2012; Simpson 

36 et al. 2014), due to a strengthening of the zonal mean westerlies in the subtropical upper 

37 troposphere. However, most of the climate models are found to overestimate the climate change 

38 related response because of biases in the representation of relevant waves (Simpson et al. 2016). 
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1 Therefore, there is low confidence in projected changes in atmospheric blocking and wintertime 

2 quasi-stationary waves.

3 5.2.4 Modes of Variability on Decadal to Multidecadal Time Scales

4 PACIFC DECADAL OSCILLATION (PDO)/INTERDECADAL PACIFIC 

5 OSCILLATION (IPO)

6 The Pacific Decadal Oscillation (PDO) is the leading year-round pattern of monthly North 

7 Pacific sea surface temperature variability, with a characteristic time scale of 40 to 60 years. 

8 Interdecadal Pacific Oscillation (IPO) refers to the same phenomenon based on Pacific-wide sea 

9 surface temperatures. PDO/IPO represents not a single phenomenon but rather a combination of 

10 processes that span the tropics and extratropics, including both remote tropical forcing and local 

11 North Pacific atmosphere–ocean interactions (Newman et al. 2016). Consequently, PDO-related 

12 impacts on temperature and precipitation of the United States are very similar to phenomena on 

13 interannual time scales like ENSO and variations in the strength of the Aleutian low (North 

14 Pacific Index, NPI), as shown in Figure 5.3. A PDO-related impact on Alaska temperatures is 

15 also apparent (Hartmann and Wendler 2005; McAfee 2014).

16 [INSERT FIGURE 5.3 HERE:

17 Cold season relationship between climate indices and U.S. precipitation and temperature 

18 anomalies determined from U.S. climate division data (Vose et al. 2014), for the years 1901–

19 2014. November–March mean U.S. precipitation anomalies correlated with (a) the PDO index, 

20 (b) the ENSO index, and (c) the North Pacific Index. November–March U.S. temperature 

21 anomalies correlated with (d) the PDO index, (e) the ENSO index, and (f) the NPI. Decadal 

22 impacts related to the Pacific Decadal Oscillation (PDO) on temperature and precipitation of the 

23 United States are very similar to the impact of phenomena on interannual time scales like ENSO 

24 and variations in the strength of the Aleutian low characterized by the North Pacific Index (NPI).

25 (Figure source: Newman et al. 2016; © American Meteorological Society, used with 

26 permission)]

27 Studies on future changes in the PDO/IPO are available based on CMIP3 models. It is found that 

28 most of these models do not exhibit significant changes in spatial and temporal characteristics in 

29 the PDO/IPO (Furtado et al. 2011), while some models suggest that the PDO/IPO becomes 

30 weaker and more frequent by the end of the 21st century (Lapp et al. 2012). Future emission 

31 changes have been suggested to also impact the PDO/IPO (Allen and Ajoku 2016). Therefore, 

32 there is only low confidence in projected future changes in the PDO/IPO.

33 ATLANTIC MULTI-DECADAL OSCILLATION (AMO)

34 The Atlantic Multi-Decadal Oscillation (AMO) is one of the principal features of multidecadal 

35 variability in the instrumental climate record, with a coherent pattern of 50- to 70-year variability 

36 in surface temperature centered on the North Atlantic Ocean. Cool AMO phases occurred in the 
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1 1900s–1920s and 1960s–1980s, while a warm phase occurred in the 1930s–1950s and has been 

2 observed since the mid-1990s. During AMO warm periods, less than normal precipitation is 

3 found in most of the United States, including the most severe 20th century droughts in the 1930s 

4 and 1950s (Enfield et al. 2001; Seager et al. 2008; Feng et al. 2011). It is suggested that the 

5 warm phase of the AMO strengthens the North Atlantic tropical cyclone activity (Goldenberg et 

6 al. 2001; Chylek and Lesins 2008; Zhang and Delworth 2009).

7 Long-lived Atlantic multidecadal variability is found in long control simulations carried out with 

8 climate models (Menary et al. 2012), and CMIP3 models do not show any fundamental change 

9 in the characteristics of the AMO in the 21st century as compared to the 20th century or 

10 preindustrial climate (Ting et al. 2011).

11 INTERNALLY-GENERATED VERSUS EXTERNALLY-FORCED DECADAL 

12 CLIMATE VARIABILITY

13 Several studies suggest that climate patterns in response to natural forcings (such as volcanic 

14 aerosols) and anthropogenic forcings (such as aerosols and greenhouse gases) project onto 

15 AMO- and PDO/IPO-related climate variability patterns (Boo et al. 2015; Booth et al. 2012; 

16 Evan et al. 2009; Mann and Emanuel 2006; Meehl et al. 2013). For example, historical aerosol 

17 cooling combined with global ocean warming due to increasing greenhouse gases could explain

18 a large fraction of Atlantic multidecadal variability (Booth et al. 2012). Changes in aerosols are 

19 also found to coincide with PDO-like variability in North Pacific sea surface temperatures (Boo 

20 et al. 2015). Furthermore, it has been determined that periods with near zero warming trends of 

21 global mean temperature and periods of accelerated temperatures result from the interplay 

22 between internally generated PDO/IPO-like cooling and warming in the tropical Pacific Ocean 

23 and greenhouse gas-induced ocean warming (Meehl et al. 2013). These findings have

24 implications for the attribution of causes of trends in global and regional mean temperatures, 

25 width of the tropics, droughts, and tropical cyclones (Ch. 1, 3, 8, 9 and Section 5.2.1). For 

26 example, studies that assign an entirely natural forcing component to regional patterns that 

27 resemble PDO/IPO may underestimate the role of human forcing, while studies that did not 

28 account for the impact of the PDO/IPO may overestimate the role of human-induced forcing

29 (Abatzoglou et al. 2014a; Abatzoglou et al. 2014b; Johnstone and Mantua 2014a; Johnstone and 

30 Mantua 2014b). Furthermore, it is likely that PDO/IPO and AMO-like variability will continue 

31 to occur in the future, modulating anthropogenic forcing and its climate impacts on the United

32 States and globally.

33 5.3. Quantifying the Role of Internal Variability on Past and Future U.S. 

34 Climate Trends

35 The role of internal variability in masking trends is substantially increased on regional and local 

36 scales relative to the global scale, and in the extratropics relative to the tropics (Ch. 4: 

37 Projections). Approaches are developed to better quantify the externally forced and internally 
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1 driven contributions to observed and future climate trends and variability and further separate 

2 these contributions into thermodynamically and dynamically driven factors (Wallace et al. 2015).

3 Specifically, large “initial condition” climate model ensembles with 30 ensemble members and 

4 more (Deser et al. 2012; Deser et al. 2014; Wettstein and Deser 2014) and long control runs 

5 (Thompson et al. 2015) have been shown to be useful tools to characterize uncertainties in 

6 climate change projections at local/regional scales.

7 North American temperature and precipitation trends on timescales of up to a few decades are 

8 strongly affected by intrinsic atmospheric circulation variability (Deser et al. 2014; Wallace et al. 

9 2015; Deser et al. 2016). For example, it is estimated that internal circulation trends account for 

10 approximately one-third of the observed wintertime warming over North America during the past 

11 50 years. In a few areas, such as the central Rocky Mountains and far western Alaska, internal 

12 dynamics have offset the warming trend by 10%–30% (Deser et al. 2016). Natural climate 

13 variability superimposed upon forced climate change will result in a large range of possible

14 trends for surface air temperature and precipitation in the United States over the next 50 years

15 (Figure 5.4) (Deser et al. 2014).

16 [INSERT FIGURE 5.4 HERE:

17 Figure 5.4: (left) Total 2010–60 winter trends decomposed into (center) internal and (right) 

18 forced components for two contrasting CCSM3 ensemble members (runs 29 and 6) for (a) SAT 

[color shading; °F (51 years)

−1 19 ] and SLP (contours) and (b) precipitation [color shading; inches 

per day (51 years)

−1

] and SLP (contours). SLP contour interval is 1 hPa (51 years)

−1 20 , with solid 

21 (dashed) contours for positive (negative) values; the zero contour is thickened. The same climate 

22 model (CCSM3) simulates a large range of possible trends in North American climate over the 

23 2010–2060 period because of the influence of internal climate variability superposed upon forced 

24 climate trends. (Figure source: adapted from Deser et al. 2014; © American Meteorological 

25 Society, used with permission)]

26 Climate models are evaluated with respect to their proper simulation of internal decadal 

27 variability. Comparing observed and simulated variability estimates at time scales longer than 10 

28 years suggest that models tend to overestimate the internal variability in the northern 

29 extratropics, including over the continental United States, but underestimate it over much of the 

30 tropics and subtropical ocean regions (Deser et al. 2012; Knutson et al. 2013). Such biases affect 

31 signal-to-noise estimates of regional scale climate change response and thus assessment of 

32 internally driven contributions to regional/local trends.

33
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1 TRACEABLE ACCOUNTS

2 Key Finding 1

3 Under increased greenhouse gas concentrations, the tropics are likely to expand with an 

4 accompanying poleward shift of the subtropical dry zones and midlatitude jets in each 

5 hemisphere (medium to high confidence). While it is likely that tropics have expanded since 1979 

6 (medium confidence), uncertainties remain regarding the attribution of these changes to human 

7 activities.

8 Description of evidence base 

9 The Key Finding is supported by statements of the previous international IPCC AR5 assessment 

10 (IPCC 2013). Further evidence of an impact of greenhouse gas increases on the widening of the 

11 tropical belt and poleward shifts of the mid-latitude jets is provided by the diagnosis of CMIP5 

12 simulations (Vallis et al. 2015, Barnes and Polvani 2013). Recent studies on estimates of changes 

13 in the width of the tropics provide additional evidence that the tropics has widened since 1979 

14 (Birner et al. 2014; Davis and Birner 2013; Feng and Fu 2013; Garfinkel et al. 2015; Karnauskas 

15 and Ummenhofer 2014; Lucas et al. 2014; Quan et al. 2014; Reichler 2016). Recent studies 

16 provide new evidence on the significance of internal variability on recent changes in the tropical 

17 width (Adam et al. 2014; Allen et al. 2014; Garfinkel et al. 2015). These studies are discussed in 

18 the text.

19 Major uncertainties 

20 The rate of observed expansion of tropics is uncertain and depends on the metrics used. 

21 Uncertainties also result from the utilizing of reanalysis to determine trends and limited 

22 observational records. There are major uncertainty in the estimates of the relative contribution of 

23 anthropogenic factors and internal variability to recent trends. Uncertainties in modeling future 

24 changes in global circulation arises from the presentation of stratosphere as well as simulated 

25 Arctic amplification.

26 Assessment of confidence based on evidence and agreement, including short description of 

27 nature of evidence and level of agreement 

28 ☐Very High 

29 x High

30 x Medium 

31 x Low

32 There is high confidence that increased greenhouse gases cause a poleward expansion of the 

33 Hadley circulation. This is based on the agreement of a large number of studies utilizing 

34 modeling of different complexity and theoretical considerations. There is only medium 

35 confidence in future changes of mid-latitude jets specifically in the Northern Hemisphere due to 

36 the potential impact of other factors (Arctic amplification, stratospheric circulation change) that 

37 can push the mid-latitude jets equatorward. Uncertainties in the causes of recent trends result 
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1 from uncertainties in the magnitude of observed widening and a possibly large contribution of 

2 internal variability. 

3 If appropriate, estimate likelihood of impact or consequence, including short description of 

4 basis of estimate 

5 ☐ Greater than 9 in 10 / Very Likely 

6 x Greater than 2 in 3 / Likely

7 ☐ About 1 in 2 / As Likely as Not

8 ☐ Less than 1 in 3 / Unlikely 

9 ☐ Less than 1 in 10 / Very Unlikely

10 Estimate is based on the assessment of a large number of studies that diagnose past changes in 

11 global circulation and the impact of increased greenhouse gas concentration on tropical width 

12 and mid-latitude jets. A poleward shift of global circulation results in poleward expansion of 

13 tropical dry zones and mid-latitude circulation patterns that affect natural ecosystems, 

14 agriculture, and water resources.

15 Summary sentence or paragraph that integrates the above information

16 This Key Finding is supported by a large amount of observational and modeling evidence 

17 documented in the climate science peer-reviewed literature. Compared to the previous 

18 international assessment (IPCC AR5) the confidence is increased for an observed poleward shift 

19 of circulation features since 1979 due to additional observational studies. Uncertainties regarding 

20 the attribution of the observed tropical widening results from both uncertainties in the magnitude 

21 of observed trends and the contribution of internal variability.

22

23 Key Finding 2

24 Recurring patterns of variability in large-scale atmospheric circulation (such as the North 

25 Atlantic Oscillation and Northern Annular Mode) and the atmosphere–ocean system (such as El 

26 Niño–Southern Oscillation) cause year-to-year variations in U.S. temperatures and precipitation 

27 (high confidence). Changes in the occurrence of these patterns or their properties have 

28 contributed to recent U.S. temperature and precipitation trends (medium confidence) although 

29 uncertainties remain about the size of the role of human influences in these changes.

30 Description of evidence base 

31 The Key Finding is supported by multiple studies as described in the text that diagnose recurring 

32 patterns of variability and their changes, as well as their impact on temperature and precipitation 

33 of the United States. These included studies on changes in the Northern Atlantic 

34 Oscillation/Northern Annular Mode (Hurrell 1995; Cohen and Barlow 2005, Overland and Wang 

35 2015; Overland et al. 2015) as well as studies on the observed decadal modification of ENSO 

36 (Yu et al. 2012; Yu and Zou 2013; Li et al. 2011; Lee and McPhaden 2010; Yeh et al. 2009;

37 Capantoni et al. 2013). The uncertainties in the attribution of changes in these preferred patterns 
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1 of variability results from limited observational records and the findings from long climate 

2 simulations showing that decadal to multi-decadal variations of El Nino–Southern Oscillation 

3 and Northern Annular Mode can be generated without any change in external forcing (Capantoni 

4 et al. 2013; Deser et al. 2012; IPCC 2013). These studies are discussed in the text.

5 Major uncertainties 

6 A key uncertainty is related to limited observational records and our capability to proper simulate 

7 climate variability on decadal to multidecadal time scale, as well as properly simulate modes of 

8 climate variability including El Nino–Southern Oscillation and Northern Hemisphere Annular 

9 Mode–North Atlantic Oscillation.

10 Assessment of confidence based on evidence and agreement, including short description of 

11 nature of evidence and level of agreement 

12 ☐ Very High 

13 x High

14 x Medium 

15 ☐ Low

16 There is high confidence that preferred modes of variability affect U.S. temperature on year-to17 year time scale and medium confidence on their impact on decadal time scales based on a large 

18 number of studies that diagnose observational data records and long simulations with climate 

19 models for a various of preferred modes of variability.

20 If appropriate, estimate likelihood of impact or consequence, including short description of 

21 basis of estimate 

22 ☐ Greater than 9 in 10 / Very Likely 

23 ☐ Greater than 2 in 3 / Likely

24 ☐ About 1 in 2 / As Likely as Not

25 ☐ Less than 1 in 3 / Unlikely 

26 ☐ Less than 1 in 10 / Very Unlikely

27 Summary sentence or paragraph that integrates the above information

28 The Key Finding is supported by multiple studies that diagnose recurring patterns of variability 

29 and their changes, as well as their impact on temperature and precipitation of the United States.

30 The causes of these changes are uncertain due to the limited observational record.

31

32 Key Finding 3

33 Increasing temperatures and atmospheric specific humidity are already having important 

34 influences on extremes (high confidence). It is still unclear, however, to what extent increasing 

35 temperatures and humidity have influenced and will influence persistent circulation patterns, 

36 which in turn influence these extremes.
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1 Description of evidence base 

2 The Key Finding integrates assessment from Chapters 5 and 6, regarding the impact of 

3 increasing temperatures and atmospheric specific humidity on extremes with the assessment of 

4 this chapter on circulation changes. The key finding on the low confidence the impact of 

5 increasing temperatures and humidity on quasi-persistent circulation patterns supported by 

6 statements of the previous international assessment (IPCC 2013) and recent studies on changes 

7 in atmospheric blocking and stationary waves in observation and climate models (Barnes 2013; 

8 Barnes et al. 2014, Francis and Vavrus 2012; Screen and Simmonds 2013; Simpson et al. 2016;

9 Kennedy et al. 2016), and theoretical considerations (Hoskins and Woollings 2015). These 

10 studies are discussed in the text.

11 Major uncertainties 

12 Key uncertainties result from the lack of climate models to properly simulate quasi-stationary 

13 circulation patterns like atmospheric blocking and quasi-stationary waves and limited records of 

14 observations. 

15 Assessment of confidence based on evidence and agreement, including short description of 

16 nature of evidence and level of agreement 

17 ☐ Very High 

18 x High

19 ☐ Medium 

20 x Low

21 Low confidence in the impact of changes of temperature and specific humidity on circulation 

22 patterns results from the lack of detectability of robust trends of changes in persistent circulation 

23 patterns in observational records as well as model biases that limit the confidence in projected 

24 trends. 

25 If appropriate, estimate likelihood of impact or consequence, including short description of 

26 basis of estimate 

27 ☐ Greater than 9 in 10 / Very Likely 

28 ☐ Greater than 2 in 3 / Likely

29 ☐ About 1 in 2 / As Likely as Not

30 ☐ Less than 1 in 3 / Unlikely 

31 ☐ Less than 1 in 10 / Very Unlikely

32 Summary sentence or paragraph that integrates the above information

33 Our confidence is low on the impact of changes in observed and future changes in temperature 

34 and specific humidity on persistent circulation patterns that could affect extremes. Uncertainty is 

35 large because of lack of robust detectability of past trends in these persistent circulation patterns 

36 and model biases in simulating these patterns that limit our confidence in simulated changes. 
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1 FIGURES

2

3 Figure 5.1:(Top) Plan and (bottom) cross section schematic view representation of the general 

4 circulation of the atmosphere. Three main circulations exist between the equator and poles due to 

5 solar heating and the earth’s rotation. 

6 Hadley cell (1) – Low-latitude air moves toward the equator. Due to solar heating, air near the 

7 equator rises vertically and moves poleward in the upper atmosphere. 

8 Ferrel cell (2) – A midlatitude mean atmospheric circulation cell. In this cell, the air flows 

9 poleward and eastward near the surface and equatorward and westward at higher levels.
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1 Polar cell (3) – Air rises, diverges, and travels toward the poles. Once over the poles, the air 

2 sinks, forming the polar highs. At the surface, air diverges outward from the polar highs. Surface 

3 winds in the polar cell are easterly (polar easterlies).

4 A high pressure band is located at about 30° N/S latitude, leading to dry/hot weather due to 

5 descending air motion (subtropical dry zones are indicated in orange in the schematic views).

6 Expanding tropics (indicted by orange arrows) are associated with a poleward shift of the 

7 subtropical dry zones. A low pressure band is found at 50°–60° N/S, with rainy and stormy 

8 weather in relation to the polar jet stream bands of strong westerly wind in the upper levels of the 

9 atmosphere. (Figure source: adapted from NWS 2016)

10
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1

2 Figure 5.2: El Niño- and La Niña-related winter features over North America. Shown are typical 

3 January to March weather anomalies and atmospheric circulation during moderate to strong El 

4 Niño and La Niña conditions: (top) During El Niño, there is a tendency for a strong jet stream 

5 and storm track across the southern part of the United States. The southern tier of Alaska and the 

6 U.S. Pacific Northwest tend to be warmer than average, whereas the southern tier of U.S. states 

7 tends to be cooler and wetter than average. During La Niña, there is a tendency of a very wave8 like jet stream flow over the United States and Canada, with colder and stormier than average 

9 conditions across the North, and warmer and less stormy conditions across the South. (Figure 

10 source: adapted from Lindsey 2016)
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1

2 Figure 5.3: Cold season relationship between climate indices and U.S. precipitation and 

3 temperature anomalies determined from U.S. climate division data (Vose et al. 2014), for the 

4 years 1901–2014. November–March mean U.S. precipitation anomalies correlated with (a) the 

5 PDO index, (b) the ENSO index, and (c) the North Pacific Index. November-March U.S. 

6 temperature anomalies correlated with (d) the PDO index, (e) the ENSO index, and (f) the NPI.

7 Decadal impacts related to the Pacific Decadal Oscillation (PDO) on temperature and 

8 precipitation of the United States are very similar to the impact of phenomena on interannual 

9 time scales like ENSO and variations in the strength of the Aleutian low characterized by the 

10 North Pacific Index (NPI). (Figure source: Newman et al. 2016; © American Meteorological 

11 Society, used with permission)
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1

2 Figure 5.4: (left) Total 2010–2060 winter trends decomposed into (center) internal and (right) 

3 forced components for two contrasting CCSM3 ensemble members (runs 29 and 6) for (a) SAT 

[color shading; °F (51 years)

-1 4 ] and SLP (contours) and (b) precipitation [color shading; inches

per day (51 years) -1] and SLP (contours). SLP contour interval is 1 hPa (51 years) -1 5 , with solid 

6 (dashed) contours for positive (negative) values; the zero contour is thickened. The same climate 

7 model (CCSM3) simulates a large range of possible trends in North American climate over the 

8 2010–2060 period because of the influence of internal climate variability superposed upon forced 

9 climate trends. (Figure source: adapted from Deser et al. 2014; © American Meteorological 

10 Society, used with permission.)

11
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1 REFERENCES

2 Abatzoglou, J.T., D.E. Rupp, and P.W. Mote, 2014: Seasonal Climate Variability and Change in 

3 the Pacific Northwest of the United States. Journal of Climate, 27, 2125-2142.

4 http://dx.doi.org/10.1175/jcli-d-13-00218.1 

5 Abatzoglou, J.T., D.E. Rupp, and P.W. Mote, 2014: Questionable evidence of natural warming 

6 of the northwestern United States. Proceedings of the National Academy of Sciences, 111,

7 E5605-E5606. http://dx.doi.org/10.1073/pnas.1421311112 

8 http://www.pnas.org/content/111/52/E5605.short

9 Adam, O., T. Schneider, and N. Harnik, 2014: Role of changes in mean temperatures versus 

10 temperature gradients in the recent widening of the Hadley circulation. Journal of Climate,

11 27, 7450-7461. http://dx.doi.org/10.1175/JCLI-D-14-00140.1 

12 Allen, R.J. and O. Ajoku, 2016: Future aerosol reductions and widening of the northern tropical 

13 belt. Journal of Geophysical Research: Atmospheres, 121, 6765-6786.

14 http://dx.doi.org/10.1002/2016JD024803 

15 Allen, R.J., J.R. Norris, and M. Kovilakam, 2014: Influence of anthropogenic aerosols and the 

16 Pacific decadal oscillation on tropical belt width. Nature Geoscience, 7, 270-274.

17 http://dx.doi.org/10.1038/ngeo2091 

18 Allen, R.J., S.C. Sherwood, J.R. Norris, and C.S. Zender, 2012: Recent Northern Hemisphere 

19 tropical expansion primarily driven by black carbon and tropospheric ozone. Nature, 485,

20 350-354. http://dx.doi.org/10.1038/nature11097 

21 Archambault, H.M., L.F. Bosart, D. Keyser, and A.R. Aiyyer, 2008: Influence of large-scale 

22 flow regimes on cool-season precipitation in the northeastern United States. Monthly 

23 Weather Review, 136, 2945-2963. http://dx.doi.org/10.1175/2007MWR2308.1 

24 Barnes, E.A., 2013: Revisiting the evidence linking Arctic amplification to extreme weather in 

25 midlatitudes. Geophysical Research Letters, 40, 4734-4739.

26 http://dx.doi.org/10.1002/grl.50880 

27 Barnes, E.A., E. Dunn-Sigouin, G. Masato, and T. Woollings, 2014: Exploring recent trends in 

28 Northern Hemisphere blocking. Geophysical Research Letters, 41, 638-644.

29 http://dx.doi.org/10.1002/2013GL058745 

30 Barnes, E.A. and L. Polvani, 2013: Response of the Midlatitude Jets, and of Their Variability, to 

31 Increased Greenhouse Gases in the CMIP5 Models. Journal of Climate, 26, 7117-7135.

32 http://dx.doi.org/10.1175/JCLI-D-12-00536.1 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 5

206

1 Bindoff, N.L., P.A. Stott, K.M. AchutaRao, M.R. Allen, N. Gillett, D. Gutzler, K. Hansingo, G. 

2 Hegerl, Y. Hu, S. Jain, I.I. Mokhov, J. Overland, J. Perlwitz, R. Sebbari, and X. Zhang, 2013: 

3 Detection and Attribution of Climate Change: from Global to Regional. Climate Change 

4 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment 

5 Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. 

6 Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, 

7 Eds. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 

8 867–952. http://dx.doi.org/10.1017/CBO9781107415324.022 www.climatechange2013.org

9 Birner, T., S.M. Davis, and D.J. Seidel, 2014: The changing width of Earth’s tropical belt. 

10 Physcis Today, 67, 38-44. http://dx.doi.org/10.1063/PT.3.2620 

11 Boo, K.-O., B.B.B. Booth, Y.-H. Byun, J. Lee, C. Cho, S. Shim, and K.-T. Kim, 2015: Influence 

12 of aerosols in multidecadal SST variability simulations over the North Pacific. Journal of 

13 Geophysical Research: Atmospheres, 120, 517-531. http://dx.doi.org/10.1002/2014JD021933 

14 Booth, B.B.B., N.J. Dunstone, P.R. Halloran, T. Andrews, and N. Bellouin, 2012: Aerosols 

15 implicated as a prime driver of twentieth-century North Atlantic climate variability. Nature,

16 484, 228-232. http://dx.doi.org/10.1038/nature10946 

17 Brandefelt, J. and H. Körnich, 2008: Northern Hemisphere Stationary Waves in Future Climate 

18 Projections. Journal of Climate, 21, 6341-6353. http://dx.doi.org/10.1175/2008JCLI2373.1 

19 Brönnimann, S., A.M. Fischer, E. Rozanov, P. Poli, G.P. Compo, and P.D. Sardeshmukh, 2015: 

20 Southward shift of the northern tropical belt from 1945 to 1980. Nature Geoscience, 8, 969-

21 974. http://dx.doi.org/10.1038/ngeo2568 

22 Cai, W., S. Borlace, M. Lengaigne, P. van Rensch, M. Collins, G. Vecchi, A. Timmermann, A. 

23 Santoso, M.J. McPhaden, L. Wu, M.H. England, G. Wang, E. Guilyardi, and F.-F. Jin, 2014: 

24 Increasing frequency of extreme El Nino events due to greenhouse warming. Nature Climate 

25 Change, 4, 111-116. http://dx.doi.org/10.1038/nclimate2100 

26 Cai, W., A. Santoso, G. Wang, S.-W. Yeh, S.-I. An, K.M. Cobb, M. Collins, E. Guilyardi, F.-F. 

27 Jin, J.-S. Kug, M. Lengaigne, M.J. McPhaden, K. Takahashi, A. Timmermann, G. Vecchi, 

28 M. Watanabe, and L. Wu, 2015: ENSO and greenhouse warming. Nature Climate Change, 5,

29 849-859. http://dx.doi.org/10.1038/nclimate2743 

30 Cai, W., G. Wang, A. Santoso, M.J. McPhaden, L. Wu, F.-F. Jin, A. Timmermann, M. Collins, 

31 G. Vecchi, M. Lengaigne, M.H. England, D. Dommenget, K. Takahashi, and E. Guilyardi, 

32 2015: Increased frequency of extreme La Nina events under greenhouse warming. Nature 

33 Climate Change, 5, 132-137. http://dx.doi.org/10.1038/nclimate2492 

34 Capotondi, A., A.T. Wittenberg, M. Newman, E.D. Lorenzo, J.-Y. Yu, P. Braconnot, J. Cole, B. 

35 Dewitte, B. Giese, E. Guilyardi, F.-F. Jin, K. Karnauskas, B. Kirtman, T. Lee, N. Schneider, 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 5

207

1 Y. Xue, and S.-W. Yeh, 2015: Understanding ENSO Diversity. Bulletin of the American 

2 Meteorological Society, 96, 921-938. http://dx.doi.org/10.1175/BAMS-D-13-00117.1 

3 Carrera, M.L., R.W. Higgins, and V.E. Kousky, 2004: Downstream Weather Impacts Associated 

4 with Atmospheric Blocking over the Northeast Pacific. Journal of Climate, 17, 4823-4839.

5 http://dx.doi.org/10.1175/JCLI-3237.1 

6 Chen, S., K. Wei, W. Chen, and L. Song, 2014: Regional changes in the annual mean Hadley 

7 circulation in recent decades. Journal of Geophysical Research: Atmospheres, 119, 7815-

8 7832. http://dx.doi.org/10.1002/2014JD021540 

9 Christensen, J.H., K. Krishna Kumar, E. Aldrian, S.-I. An, I.F.A. Cavalcanti, M. de Castro, W. 

10 Dong, P. Goswami, A. Hall, J.K. Kanyanga, A. Kitoh, J. Kossin, N.-C. Lau, J. Renwick, D.B. 

11 Stephenson, S.-P. Xie, and T. Zhou, 2013: Climate Phenomena and their Relevance for 

12 Future Regional Climate Change. Climate Change 2013: The Physical Science Basis. 

13 Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental 

14 Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. 

15 Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. Cambridge University Press, 

16 Cambridge, United Kingdom and New York, NY, USA, 1217–1308. 

17 http://dx.doi.org/10.1017/CBO9781107415324.028 www.climatechange2013.org

18 Chylek, P. and G. Lesins, 2008: Multidecadal variability of Atlantic hurricane activity: 1851–

19 2007. Journal of Geophysical Research, 113, D22106.

20 http://dx.doi.org/10.1029/2008JD010036 

21 Cohen, J. and M. Barlow, 2005: The NAO, the AO, and Global Warming: How Closely Related? 

22 Journal of Climate, 18, 4498-4513. http://dx.doi.org/10.1175/jcli3530.1 

23 Coleman, J.S.M. and J.C. Rogers, 2003: Ohio River Valley Winter Moisture Conditions 

24 Associated with the Pacific–North American Teleconnection Pattern. Journal of Climate, 16,

25 969-981. http://dx.doi.org/10.1175/1520-0442(2003)016<0969:ORVWMC>2.0.CO;2 

26 Collins, M., R. Knutti, J. Arblaster, J.-L. Dufresne, T. Fichefet, P. Friedlingstein, X. Gao, W.J. 

27 Gutowski, T. Johns, G. Krinner, M. Shongwe, C. Tebaldi, A.J. Weaver, and M. Wehner, 

28 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. Climate 

29 Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth 

30 Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. 

31 Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and 

32 P.M. Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom and New 

33 York, NY, USA, 1029–1136. http://dx.doi.org/10.1017/CBO9781107415324.024 

34 www.climatechange2013.org
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 5

208

1 Davis, N.A. and T. Birner, 2013: Seasonal to multidecadal variability of the width of the tropical 

2 belt. Journal of Geophysical Research: Atmospheres, 118, 7773-7787.

3 http://dx.doi.org/10.1002/jgrd.50610 

4 Delworth, T.L., F. Zeng, G.A. Vecchi, X. Yang, L. Zhang, and R. Zhang, 2016: The North 

5 Atlantic Oscillation as a driver of rapid climate change in the Northern Hemisphere. Nature 

6 Geoscience, 9, 509-512. http://dx.doi.org/10.1038/ngeo2738 

7 Deser, C., 2000: On the teleconnectivity of the “Arctic Oscillation”. Geophysical Research 

8 Letters, 27, 779-782. http://dx.doi.org/10.1029/1999GL010945 

9 Deser, C., A. Phillips, V. Bourdette, and H. Teng, 2012: Uncertainty in climate change 

10 projections: The role of internal variability. Climate Dynamics, 38, 527-546.

11 http://dx.doi.org/10.1007/s00382-010-0977-x 

12 Deser, C., A.S. Phillips, M.A. Alexander, and B.V. Smoliak, 2014: Projecting North American 

13 Climate over the Next 50 Years: Uncertainty due to Internal Variability. Journal of Climate,

14 27, 2271-2296. http://dx.doi.org/10.1175/JCLI-D-13-00451.1 

15 Deser, C., L. Terray, and A.S. Phillips, 2016: Forced and Internal Components of Winter Air 

16 Temperature Trends over North America during the past 50 Years: Mechanisms and 

17 Implications. Journal of Climate, 29, 2237-2258. http://dx.doi.org/10.1175/JCLI-D-15-

18 0304.1 

19 Durkee, J.D., J.D. Frye, C.M. Fuhrmann, M.C. Lacke, H.G. Jeong, and T.L. Mote, 2008: Effects 

20 of the North Atlantic Oscillation on precipitation-type frequency and distribution in the 

21 eastern United States. Theoretical and Applied Climatology, 94, 51-65.

22 http://dx.doi.org/10.1007/s00704-007-0345-x 

23 Enfield, D.B., A.M. Mestas-Nuñez, and P.J. Trimble, 2001: The Atlantic Multidecadal 

24 Oscillation and its relation to rainfall and river flows in the continental U.S. Geophysical 

25 Research Letters, 28, 2077-2080. http://dx.doi.org/10.1029/2000GL012745 

26 Evan, A.T., D.J. Vimont, A.K. Heidinger, J.P. Kossin, and R. Bennartz, 2009: The role of 

27 aerosols in the evolution of tropical North Atlantic Ocean temperature anomalies. Science,

28 324, 778-781. http://dx.doi.org/10.1126/science.1167404 

29 Feldstein, S.B. and C. Franzke, 2006: Are the North Atlantic Oscillation and the Northern 

30 Annular Mode Distinguishable? Journal of the Atmospheric Sciences, 63, 2915-2930.

31 http://dx.doi.org/10.1175/JAS3798.1 

32 Feng, S. and Q. Fu, 2013: Expansion of global drylands under a warming climate. Atmospheric 

33 Chemistry and Physics, 13, 10081-10094. http://dx.doi.org/10.5194/acp-13-10081-2013 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 5

209

1 Feng, S., Q. Hu, and R.J. Oglesby, 2011: Influence of Atlantic sea surface temperatures on 

2 persistent drought in North America. Climate Dynamics, 37, 569-586.

3 http://dx.doi.org/10.1007/s00382-010-0835-x 

4 Francis, J.A. and S.J. Vavrus, 2012: Evidence linking Arctic amplification to extreme weather in 

5 mid-latitudes. Geophysical Research Letters, 39, L06801.

6 http://dx.doi.org/10.1029/2012GL051000 

7 Francis, J.A., S.J. Vavrus, and J. Cohen, 2016: Amplified Arctic Warming and Mid-Latitude 

8 Weather: Emerging ConnectionsWiREs. WIRES, Submitted.

9 Frierson, D.M.W., J. Lu, and G. Chen, 2007: Width of the Hadley cell in simple and 

10 comprehensive general circulation models. Geophysical Research Letters, 34, n/a-n/a.

11 http://dx.doi.org/10.1029/2007GL031115 

12 Furtado, J.C., E.D. Lorenzo, N. Schneider, and N.A. Bond, 2011: North Pacific Decadal 

13 Variability and Climate Change in the IPCC AR4 Models. Journal of Climate, 24, 3049-

14 3067. http://dx.doi.org/10.1175/2010JCLI3584.1 

15 Garfinkel, C.I., D.W. Waugh, and L.M. Polvani, 2015: Recent Hadley cell expansion: The role 

16 of internal atmospheric variability in reconciling modeled and observed trends. Geophysical 

17 Research Letters, 42, 10,824-10,831. http://dx.doi.org/10.1002/2015GL066942 

18 Gillett, N.P. and J.C. Fyfe, 2013: Annular mode changes in the CMIP5 simulations. Geophysical 

19 Research Letters, 40, 1189-1193. http://dx.doi.org/10.1002/grl.50249 

20 Gillett, N.P., J.C. Fyfe, and D.E. Parker, 2013: Attribution of observed sea level pressure trends 

21 to greenhouse gas, aerosol, and ozone changes. Geophysical Research Letters, 40, 2302-

22 2306. http://dx.doi.org/10.1002/grl.50500 

23 Goldenberg, S.B., C.W. Landsea, A.M. Mestas-Nuñez, and W.M. Gray, 2001: The Recent 

24 Increase in Atlantic Hurricane Activity: Causes and Implications. Science, 293, 474-479.

25 http://dx.doi.org/10.1126/science.1060040 

26 Grotjahn, R., R. Black, R. Leung, M.F. Wehner, M. Barlow, M. Bosilovich, A. Gershunov, W.J. 

27 Gutowski, J.R. Gyakum, R.W. Katz, Y.-Y. Lee, Y.-K. Lim, and Prabhat, 2016: North 

28 American extreme temperature events and related large scale meteorological patterns: a 

29 review of statistical methods, dynamics, modeling, and trends. Climate Dynamics, 46, 1151-

30 1184. http://dx.doi.org/10.1007/s00382-015-2638-6 

31 Guirguis, K., A. Gershunov, R. Schwartz, and S. Bennett, 2011: Recent warm and cold daily 

32 winter temperature extremes in the Northern Hemisphere. Geophysical Research Letters, 38,

33 n/a-n/a. http://dx.doi.org/10.1029/2011GL048762 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 5

210

1 Haarsma, R.J. and F. Selten, 2012: Anthropogenic changes in the Walker circulation and their 

2 impact on the extra-tropical planetary wave structure in the Northern Hemisphere. Climate 

3 Dynamics, 39, 1781-1799. http://dx.doi.org/10.1007/s00382-012-1308-1 

4 Halpert, M.S. and C.F. Ropelewski, 1992: Surface Temperature Patterns Associated with the 

5 Southern Oscillation. Journal of Climate, 5, 577-593. http://dx.doi.org/10.1175/1520-

6 0442(1992)005<0577:STPAWT>2.0.CO;2 

7 Hanna, E., T.E. Cropper, R.J. Hall, and J. Cappelen, 2016: Greenland Blocking Index 1851–

8 2015: a regional climate change signal. International Journal of Climatology, n/a-n/a.

9 http://dx.doi.org/10.1002/joc.4673 

10 Hartmann, B. and G. Wendler, 2005: The Significance of the 1976 Pacific Climate Shift in the 

11 Climatology of Alaska. Journal of Climate, 18, 4824-4839.

12 http://dx.doi.org/10.1175/JCLI3532.1 

13 Hoerling, M.P., A. Kumar, and T. Xu, 2001: Robustness of the Nonlinear Climate Response to 

14 ENSO’s Extreme Phases. Journal of Climate, 14, 1277-1293. http://dx.doi.org/10.1175/1520-

15 0442(2001)014<1277:ROTNCR>2.0.CO;2 

16 Hoskins, B. and T. Woollings, 2015: Persistent Extratropical Regimes and Climate Extremes. 

17 Current Climate Change Reports, 1, 115-124. http://dx.doi.org/10.1007/s40641-015-0020-8 

18 Hurrell, J.W., 1995: Decadal Trends in the North Atlantic Oscillation: Regional Temperatures 

19 and Precipitation. Science, 269, 676-679. http://dx.doi.org/10.1126/science.269.5224.676 

20 Hurrell, J.W. and C. Deser, 2009: North Atlantic climate variability: The role of the North 

21 Atlantic Oscillation. Journal of Marine Systems, 78, 28-41.

22 http://dx.doi.org/10.1016/j.jmarsys.2008.11.026 

23 IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group 

24 I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

25 Cambridge University Press, Cambridge, UK and New York, NY, 1535 pp.

26 http://dx.doi.org/10.1017/CBO9781107415324 www.climatechange2013.org

27 Johnstone, J.A. and N.J. Mantua, 2014: Atmospheric controls on northeast Pacific temperature 

28 variability and change, 1900–2012. Proceedings of the National Academy of Sciences, 111,

29 14360-14365. http://dx.doi.org/10.1073/pnas.1318371111 

30 http://www.pnas.org/content/111/40/14360.abstract

31 Johnstone, J.A. and N.J. Mantua, 2014: Reply to Abatzoglou et al.: Atmospheric controls on 

32 northwest United States air temperatures, 1948–2012. Proceedings of the National Academy 

33 of Sciences, 111, E5607-E5608. http://dx.doi.org/10.1073/pnas.1421618112 

34 http://www.pnas.org/content/111/52/E5607.short
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 5

211

1 Karnauskas, K.B. and C.C. Ummenhofer, 2014: On the dynamics of the Hadley circulation and 

2 subtropical drying. Climate Dynamics, 42, 2259-2269. http://dx.doi.org/10.1007/s00382-014-

3 2129-1 

4 Karpechko, A.Y. and E. Manzini, 2012: Stratospheric influence on tropospheric climate change 

5 in the Northern Hemisphere. Journal of Geophysical Research: Atmospheres, 117, n/a-n/a.

6 http://dx.doi.org/10.1029/2011JD017036 

7 Kiladis, G.N. and H.F. Diaz, 1989: Global Climatic Anomalies Associated with Extremes in the 

8 Southern Oscillation. Journal of Climate, 2, 1069-1090. http://dx.doi.org/10.1175/1520-

9 0442(1989)002<1069:GCAAWE>2.0.CO;2

10 Kennedy, D., T. Parker, T. Woollings, B. Harvey, and L. Shaffrey, 2016: The response of high11 impact blocking weather systems to climate change, Geophys. Res. Lett., 43, 7250–7258, 

12 http://dx.doi.org/10.1002/2016GL069725

13 Knutson, T.R., F. Zeng, and A.T. Wittenberg, 2013: Multimodel Assessment of Regional 

14 Surface Temperature Trends: CMIP3 and CMIP5 Twentieth-Century Simulations. Journal of 

15 Climate, 26, 8709-8743. http://dx.doi.org/10.1175/JCLI-D-12-00567.1 

16 Kovilakam, M. and S. Mahajan, 2015: Black carbon aerosol-induced Northern Hemisphere 

17 tropical expansion. Geophysical Research Letters, 42, 4964-4972.

18 http://dx.doi.org/10.1002/2015GL064559 

19 Kug, J.-S., S.-I. An, Y.-G. Ham, and I.-S. Kang, 2010: Changes in El Niño and La Niña 

20 teleconnections over North Pacific–America in the global warming simulations. Theoretical 

21 and Applied Climatology, 100, 275-282. http://dx.doi.org/10.1007/s00704-009-0183-0 

22 Lapp, S.L., J.-M. St. Jacques, E.M. Barrow, and D.J. Sauchyn, 2012: GCM projections for the 

23 Pacific Decadal Oscillation under greenhouse forcing for the early 21st century. International 

24 Journal of Climatology, 32, 1423-1442. http://dx.doi.org/10.1002/joc.2364 

25 Leathers, D.J., B. Yarnal, and M.A. Palecki, 1991: The Pacific/North American Teleconnection 

26 Pattern and United States Climate. Part I: Regional Temperature and Precipitation 

27 Associations. Journal of Climate, 4, 517-528. http://dx.doi.org/10.1175/1520-

28 0442(1991)004<0517:TPATPA>2.0.CO;2 

29 Lee, T. and M.J. McPhaden, 2010: Increasing intensity of El Niño in the central-equatorial 

30 Pacific. Geophysical Research Letters, 37, n/a-n/a. http://dx.doi.org/10.1029/2010GL044007 

31 Li, J., S.-P. Xie, E.R. Cook, G. Huang, R. D'Arrigo, F. Liu, J. Ma, and X.-T. Zheng, 2011: 

32 Interdecadal modulation of El Nino amplitude during the past millennium. Nature Climate 

33 Change, 1, 114-118. http://dx.doi.org/10.1038/nclimate1086 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 5

212

1 Li, Y. and N.-C. Lau, 2012: Impact of ENSO on the Atmospheric Variability over the North 

2 Atlantic in Late Winter—Role of Transient Eddies. Journal of Climate, 25, 320-342.

3 http://dx.doi.org/10.1175/JCLI-D-11-00037.1 

4 Lindsey, R., 2016: How El Niño and La Niña affect the winter jet stream and U.S. climate. 

5 Climate.gov. https://www.climate.gov/news-features/featured-images/how-el-ni%C3%B1o6 and-la-ni%C3%B1a-affect-winter-jet-stream-and-us-climate

7 Linkin, M.E. and S. Nigam, 2008: The North Pacific Oscillation–West Pacific Teleconnection 

8 Pattern: Mature-Phase Structure and Winter Impacts. Journal of Climate, 21, 1979-1997.

9 http://dx.doi.org/10.1175/2007JCLI2048.1 

10 Loikith, P.C. and A.J. Broccoli, 2012: Characteristics of Observed Atmospheric Circulation 

11 Patterns Associated with Temperature Extremes over North America. Journal of Climate, 25,

12 7266-7281. http://dx.doi.org/10.1175/JCLI-D-11-00709.1 

13 Lucas, C. and H. Nguyen, 2015: Regional characteristics of tropical expansion and the role of 

14 climate variability. Journal of Geophysical Research: Atmospheres, 120, 6809-6824.

15 http://dx.doi.org/10.1002/2015JD023130 

16 Lucas, C., B. Timbal, and H. Nguyen, 2014: The expanding tropics: a critical assessment of the

17 observational and modeling studies. Wiley Interdisciplinary Reviews: Climate Change, 5, 89-

18 112. http://dx.doi.org/10.1002/wcc.251 

19 Mann, M.E. and K.A. Emanuel, 2006: Atlantic hurricane trends linked to climate change. Eos, 

20 Transactions of the American Geophysical Union, 87, 233-244.

21 http://dx.doi.org/10.1029/2006EO240001 

22 McAfee, S.A., 2014: Consistency and the Lack Thereof in Pacific Decadal Oscillation Impacts 

23 on North American Winter Climate. Journal of Climate, 27, 7410-7431.

24 http://dx.doi.org/10.1175/JCLI-D-14-00143.1 

25 Meehl, G.A., A. Hu, J.M. Arblaster, J. Fasullo, and K.E. Trenberth, 2013: Externally Forced and 

26 Internally Generated Decadal Climate Variability Associated with the Interdecadal Pacific 

27 Oscillation. Journal of Climate, 26, 7298-7310. http://dx.doi.org/10.1175/JCLI-D-12-

28 00548.1 

29 Meehl, G.A. and H. Teng, 2007: Multi-model changes in El Niño teleconnections over North 

30 America in a future warmer climate. Climate Dynamics, 29, 779-790.

31 http://dx.doi.org/10.1007/s00382-007-0268-3 

32 Menary, M.B., W. Park, K. Lohmann, M. Vellinga, M.D. Palmer, M. Latif, and J.H. Jungclaus, 

33 2012: A multimodel comparison of centennial Atlantic meridional overturning circulation 

34 variability. Climate Dynamics, 38, 2377-2388. http://dx.doi.org/10.1007/s00382-011-1172-4 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 5

213

1 Newman, M., M.A. Alexander, T.R. Ault, K.M. Cobb, C. Deser, E.D. Lorenzo, N.J. Mantua, 

2 A.J. Miller, S. Minobe, H. Nakamura, N. Schneider, D.J. Vimont, A.S. Phillips, J.D. Scott, 

3 and C.A. Smith, 2016: The Pacific Decadal Oscillation, Revisited. Journal of Climate, 29,

4 4399-4427. http://dx.doi.org/10.1175/JCLI-D-15-0508.1 

5 Nigam, S., 2003: Teleconnections. Encyclopedia of Atmospheric Sciences. Holton, J.R., Ed. 

6 Academic Press, 2243-2269.

7 Ning, L. and R.S. Bradley, 2016: NAO and PNA influences on winter temperature and 

8 precipitation over the eastern United States in CMIP5 GCMs. Climate Dynamics, 46, 1257-

9 1276. http://dx.doi.org/10.1007/s00382-015-2643-9 

10 Overland, J., J.A. Francis, R. Hall, E. Hanna, S.-J. Kim, and T. Vihma, 2015: The Melting Arctic 

11 and Midlatitude Weather Patterns: Are They Connected? Journal of Climate, 28, 7917-7932.

12 http://dx.doi.org/10.1175/JCLI-D-14-00822.1 

13 Overland, J.E. and M. Wang, 2015: Increased Variability in the Early Winter Subarctic North 

14 American Atmospheric Circulation. Journal of Climate, 28, 7297-7305.

15 http://dx.doi.org/10.1175/jcli-d-15-0395.1 

16 Petoukhov, V., S. Rahmstorf, S. Petri, and H.J. Schellnhuber, 2013: Quasiresonant amplification 

17 of planetary waves and recent Northern Hemisphere weather extremes. Proceedings of the 

18 National Academy of Sciences, 110, 5336-5341. http://dx.doi.org/10.1073/pnas.1222000110 

19 Prein, A.F., G.J. Holland, R.M. Rasmussen, M.P. Clark, and M.R. Tye, 2016: Running dry: The 

20 U.S. Southwest's drift into a drier climate state. Geophysical Research Letters, 43, 1272-

21 1279. http://dx.doi.org/10.1002/2015GL066727 

22 Quan, X.-W., M.P. Hoerling, J. Perlwitz, H.F. Diaz, and T. Xu, 2014: How Fast Are the Tropics 

23 Expanding? Journal of Climate, 27, 1999-2013. http://dx.doi.org/10.1175/JCLI-D-13-

24 00287.1 

25 Reichler, T., 2016: Chapter 6 - Poleward Expansion of the Atmospheric Circulation Climate 

26 Change (Second Edition). Letcher, T.M., Ed. Elsevier, Boston, 79-104.

27 http://dx.doi.org/10.1016/B978-0-444-63524-2.00006-3 

28 Renwick, J.A. and J.M. Wallace, 1996: Relationships between North Pacific Wintertime 

29 Blocking, El Niño, and the PNA Pattern. Monthly Weather Review, 124, 2071-2076.

30 http://dx.doi.org/10.1175/1520-0493(1996)124<2071:RBNPWB>2.0.CO;2 

31 Ropelewski, C.F. and M.S. Halpert, 1987: Global and Regional Scale Precipitation Patterns 

32 Associated with the El Niño/Southern Oscillation. Monthly Weather Review, 115, 1606-1626.

33 http://dx.doi.org/10.1175/1520-0493(1987)115<1606:GARSPP>2.0.CO;2 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 5

214

1 Scaife, A.A., T. Spangehl, D.R. Fereday, U. Cubasch, U. Langematz, H. Akiyoshi, S. Bekki, P. 

2 Braesicke, N. Butchart, M.P. Chipperfield, A. Gettelman, S.C. Hardiman, M. Michou, E. 

3 Rozanov, and T.G. Shepherd, 2012: Climate change projections and stratosphere–

4 troposphere interaction. Climate Dynamics, 38, 2089-2097.

5 http://dx.doi.org/10.1007/s00382-011-1080-7 

6 Scheff, J. and D. Frierson, 2012: Twenty-First-Century Multimodel Subtropical Precipitation 

7 Declines Are Mostly Midlatitude Shifts. Journal of Climate, 25, 4330-4347.

8 http://dx.doi.org/10.1175/JCLI-D-11-00393.1 

9 Scheff, J. and D.M.W. Frierson, 2012: Robust future precipitation declines in CMIP5 largely 

10 reflect the poleward expansion of model subtropical dry zones. Geophysical Research 

11 Letters, 39, L18704. http://dx.doi.org/10.1029/2012GL052910 

12 Schwendike, J., G.J. Berry, M.J. Reeder, C. Jakob, P. Govekar, and R. Wardle, 2015: Trends in 

13 the local Hadley and local Walker circulations. Journal of Geophysical Research: 

14 Atmospheres, 120, 7599-7618. http://dx.doi.org/10.1002/2014JD022652 

15 Screen, J.A. and I. Simmonds, 2013: Exploring links between Arctic amplification and mid16 latitude weather. Geophysical Research Letters, 40, 959-964.

17 http://dx.doi.org/10.1002/grl.50174 

18 Seager, R., M. Hoerling, S. Schubert, H. Wang, B. Lyon, A. Kumar, J. Nakamura, and N. 

19 Henderson, 2015: Causes of the 2011–14 California Drought. Journal of Climate, 28, 6997-

20 7024. http://dx.doi.org/10.1175/JCLI-D-14-00860.1 

21 Seager, R., Y. Kushnir, M. Ting, M. Cane, N. Naik, and J. Miller, 2008: Would Advance 

22 Knowledge of 1930s SSTs Have Allowed Prediction of the Dust Bowl Drought? Journal of 

23 Climate, 21, 3261-3281. http://dx.doi.org/10.1175/2007JCLI2134.1 

24 Seager, R., N. Naik, and L. Vogel, 2012: Does Global Warming Cause Intensified Interannual 

25 Hydroclimate Variability? Journal of Climate, 25, 3355-3372.

26 http://dx.doi.org/10.1175/JCLI-D-11-00363.1 

27 Seidel, D.J., Q. Fu, W.J. Randel, and T.J. Reichler, 2008: Widening of the tropical belt in a 

28 changing climate. Nature Geosci, 1, 21-24. http://dx.doi.org/10.1038/ngeo.2007.38 

29 Shepherd, T.G., 2014: Atmospheric circulation as a source of uncertainty in climate change 

30 projections. Nature Geoscience, 7, 703-708. http://dx.doi.org/10.1038/ngeo2253 

31 Simpson, I.R., R. Seager, M. Ting, and T.A. Shaw, 2016: Causes of change in Northern 

32 Hemisphere winter meridional winds and regional hydroclimate. Nature Climate Change, 6,

33 65-70. http://dx.doi.org/10.1038/nclimate2783 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 5

215

1 Simpson, I.R., T.A. Shaw, and R. Seager, 2014: A Diagnosis of the Seasonally and 

2 Longitudinally Varying Midlatitude Circulation Response to Global Warming. Journal of the 

3 Atmospheric Sciences, 71, 2489-2515. http://dx.doi.org/10.1175/JAS-D-13-0325.1 

4 Stevenson, S.L., 2012: Significant changes to ENSO strength and impacts in the twenty-first 

5 century: Results from CMIP5. Geophysical Research Letters, 39, L17703.

6 http://dx.doi.org/10.1029/2012GL052759 

7 Sun, Y., G. Ramstein, C. Contoux, and T. Zhou, 2013: A comparative study of large-scale 

8 atmospheric circulation in the context of a future scenario (RCP4.5) and past warmth (mid9 Pliocene). Climate of the Past, 9, 1613-1627. http://dx.doi.org/10.5194/cp-9-1613-2013 

10 Swain, D., M. Tsiang, M. Haughen, D. Singh, A. Charland, B. Rajarthan, and N.S. Diffenbaugh, 

11 2014: The extraordinary California drought of 2013/14: Character, context and the role of 

12 climate change [in "Explaining Extremes of 2013 from a Climate Perspective"]. Bulletin of 

13 the American Meteorological Society, 95, S3-S6. http://dx.doi.org/10.1175/1520-0477-

14 95.9.S1.1 

15 Thompson, D.W.J., E.A. Barnes, C. Deser, W.E. Foust, and A.S. Phillips, 2015: Quantifying the 

16 Role of Internal Climate Variability in Future Climate Trends. Journal of Climate, 28, 6443-

17 6456. http://dx.doi.org/10.1175/JCLI-D-14-00830.1 

18 Thompson, D.W.J. and J.M. Wallace, 1998: The Arctic oscillation signature in the wintertime 

19 geopotential height and temperature fields. Geophysical Research Letters, 25, 1297-1300.

20 http://dx.doi.org/10.1029/98GL00950 

21 Thompson, D.W.J. and J.M. Wallace, 2000: Annular Modes in the Extratropical Circulation. Part 

22 I: Month-to-Month Variability. Journal of Climate, 13, 1000-1016.

23 http://dx.doi.org/10.1175/1520-0442(2000)013<1000:AMITEC>2.0.CO;2 

24 Thompson, D.W.J. and J.M. Wallace, 2001: Regional Climate Impacts of the Northern 

25 Hemisphere Annular Mode. Science, 293, 85-89. http://dx.doi.org/10.1126/science.1058958 

26 Ting, M., Y. Kushnir, R. Seager, and C. Li, 2011: Robust features of Atlantic multi-decadal 

27 variability and its climate impacts. Geophysical Research Letters, 38, L17705.

28 http://dx.doi.org/10.1029/2011GL048712 

29 Vallis, G.K., P. Zurita-Gotor, C. Cairns, and J. Kidston, 2015: Response of the large-scale 

30 structure of the atmosphere to global warming. Quarterly Journal of the Royal 

31 Meteorological Society, 141, 1479-1501. http://dx.doi.org/10.1002/qj.2456 

32 Vose, R.S., S. Applequist, M. Squires, I. Durre, M.J. Menne, C.N.W. Jr., C. Fenimore, K. 

33 Gleason, and D. Arndt, 2014: Improved Historical Temperature and Precipitation Time 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 5

216

1 Series for U.S. Climate Divisions. Journal of Applied Meteorology and Climatology, 53,

2 1232-1251. http://dx.doi.org/10.1175/JAMC-D-13-0248.1 

3 Wallace, J.M., C. Deser, B.V. Smoliak, and A.S. Phillips, 2015: Attribution of Climate Change 

4 in the Presence of Internal Variability. Climate Change: Multidecadal and Beyond. WORLD 

5 SCIENTIFIC, 1-29. http://dx.doi.org/10.1142/9789814579933_0001 

6 Wang, H., S. Schubert, R. Koster, Y.-G. Ham, and M. Suarez, 2014: On the Role of SST Forcing 

7 in the 2011 and 2012 Extreme U.S. Heat and Drought: A Study in Contrasts. Journal of 

8 Hydrometeorology, 15, 1255-1273. http://dx.doi.org/10.1175/JHM-D-13-069.1 

9 Waugh, D.W., C.I. Garfinkel, and L.M. Polvani, 2015: Drivers of the Recent Tropical Expansion 

10 in the Southern Hemisphere: Changing SSTs or Ozone Depletion? Journal of Climate, 28,

11 6581-6586. http://dx.doi.org/10.1175/JCLI-D-15-0138.1 

12 Wettstein, J.J. and C. Deser, 2014: Internal Variability in Projections of Twenty-First-Century 

13 Arctic Sea Ice Loss: Role of the Large-Scale Atmospheric Circulation. Journal of Climate,

14 27, 527-550. http://dx.doi.org/10.1175/JCLI-D-12-00839.1 

15 Whan, K., F. Zwiers, and J. Sillmann, 2016: The Influence of Atmospheric Blocking on Extreme 

16 Winter Minimum Temperatures in North America. Journal of Climate, 29, 4361-4381.

17 http://dx.doi.org/10.1175/JCLI-D-15-0493.1 

18 Yeh, S.-W., J.-S. Kug, B. Dewitte, M.-H. Kwon, B.P. Kirtman, and F.-F. Jin, 2009: El Nino in a 

19 changing climate. Nature, 461, 511-514. http://dx.doi.org/10.1038/nature08316 

20 Yu, J.-Y. and Y. Zou, 2013: The enhanced drying effect of Central-Pacific El Niño on US 

21 winter. Environmental Research Letters, 8, 014019. http://dx.doi.org/10.1088/1748-

22 9326/8/1/014019 

23 Yu, J.-Y., Y. Zou, S.T. Kim, and T. Lee, 2012: The changing impact of El Niño on US winter 

24 temperatures. Geophysical Research Letters, 39, L15702.

25 http://dx.doi.org/10.1029/2012GL052483 

26 Zhang, R. and T.L. Delworth, 2009: A new method for attributing climate variations over the 

27 Atlantic Hurricane Basin's main development region. Geophysical Research Letters, 36,

28 L06701. http://dx.doi.org/10.1029/2009GL037260 

29 Zhou, Z.-Q., S.-P. Xie, X.-T. Zheng, Q. Liu, and H. Wang, 2014: Global Warming–Induced 

30 Changes in El Niño Teleconnections over the North Pacific and North America. Journal of 

31 Climate, 27, 9050-9064. http://dx.doi.org/10.1175/JCLI-D-14-00254.1
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 6

217

1 6. Temperature Changes in the United States

2 KEY FINDINGS

3 1. The annual-average, near-surface air temperature over the contiguous United States has 

4 increased by about 1.2°F (0.7°C) between 1901 and 2015. Surface and satellite data both 

5 show rapid warming since the late 1970s, while paleo-temperature evidence shows that 

6 recent decades have been the warmest in at least the past 1,500 years. (Extremely likely, 

7 High confidence)

8 2. Accompanying the rise is average temperatures, there have been – as is to be expected –

9 increases in extreme temperature events in most parts of the United States. Since the 

10 early 1900s, the temperature of extremely cold days has increased throughout the 

11 contiguous United States, and the temperature of extremely warm days has increased 

12 across much of the West. In recent decades, intense cold waves have become less 

13 common while intense heat waves have become more common. (Extremely likely, Very 

14 high confidence)

15 3. The average annual temperature of the contiguous United States is projected to rise 

16 throughout the century. Increases of at least 2.5°F (1.4°C) are projected over the next few 

17 decades, meaning that recent record-setting years will be relatively “common” in the near 

18 future. Increases of 5.0°–7.5°F (2.8°–4.8°C) are projected by late century depending upon 

19 the level of future emissions. (Extremely likely, Very high confidence)

20 4. Extreme temperatures are projected to increase even more than average temperatures.

21 The temperatures of extremely cold days and extremely warm days are both projected to 

22 increase. Cold waves are projected to become less intense while heat waves will become 

23 more intense. (Extremely likely, Very high confidence)

24 Introduction

25 Temperature is among the most important climatic elements used in decision-making. For 

26 example, builders and insurers use temperature data for planning and risk management. Energy 

27 companies and regulators use temperature data to predict demand and set utility rates. Farmers 

28 use temperature data to select crop types and determine planting times.

29 Temperature is also a key indicator of climate change: recent increases are apparent over the 

30 land, ocean, and troposphere, and substantial changes are expected for this century. This chapter 

31 summarizes the major observed and projected changes in near-surface air temperature over the 

32 United States, emphasizing new data sets and model projections since NCA3. Changes are 

33 depicted using a spectrum of observations, including surface weather stations, moored ocean 

34 buoys, polar-orbiting satellites, and temperature-sensitive proxies. Projections are based on 

35 global models and downscaled products from CMIP5 (Coupled Model Intercomparison Project 
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1 Phase 5) using a suite of Representative Concentration Pathways (RCPs; see Chapter 4 for more 

2 on RCPs and future scenarios).

3 6.1 Historical Changes

4 6.1.1. Average Temperatures

5 Changes in temperature are described using a suite of observational datasets. As in the Third 

6 National Climate Assessment (NCA3), the primary dataset for the contiguous United States is 

7 nClimGrid (Vose et al. 2014), but new datasets are now available to address changes in Alaska, 

8 Hawai‘i, and the Caribbean (Vose et al. submitted). Along U.S. coastlines, changes in sea surface 

9 temperatures are quantified using a new reconstruction (Huang et al. 2015), which now forms the 

10 ocean component of the NOAA Global Temperature dataset (Vose et al. 2012). Changes in 

11 middle tropospheric temperature are assessed using several recently improved satellite datasets.

12 Average annual temperature across the United States increased by about 0.7°C (1.2°F) between 

13 1901 and 2015, very slightly less than reported in NCA3 (Table 6.1). This difference stems from

14 the use of different time periods to represent present-day climate in each report. In particular, 

15 NCA3 defined present-day as the average of 1991-2012, which was slightly warmer than the 

16 1986-2015 period used here. (The reference period in both assessments is 1901-1960.)

17 [INSERT TABLE 6.1 HERE:

18 Table 6.1. Observed changes in average annual temperature (°F) for each NCA region. Changes 

19 are the difference between the average for present-day (1986–2015) and the average for the first 

20 half of the last century (1901–1960)].

21 Each NCA region experienced a net warming through 2015 (Table 6.1). The largest changes

22 were in the western United States, where average temperature increased by more than 0.8°C 

23 (1.50°F) in Alaska, the Northwest, the Southwest, and also in the Northern Great Plains. As 

24 noted in NCA3, the Southeast had the least warming, driven by a combination of natural 

25 variations and human influences. Across all regions, average minimum temperature increased at 

26 a slightly higher rate than average maximum temperature, with the Midwest having the largest 

27 discrepancy. This differential rate of warming resulted in a continuing decrease in the diurnal 

28 temperature range that is consistent with other parts of the globe (Thorne et al. 2016). Average 

29 sea surface temperature also increased along all regional coastlines (see Figure 1.3), though 

30 changes were generally smaller than over land owing to the higher heat capacity of water. 

31 Increases were largest in Alaska (greater than 0.6°C [1.0°F]) while increases were smallest (less 

32 than 0.3°C [0.5°F]) in coastal areas of the Southeast.

33 More than 97% of the land surface of the United States had an increase in average temperature 

34 from 1900 to 2015 (Figure 6.1). In contrast, only small (and somewhat dispersed) parts of the 

35 Southeast and Southern Great Plains experienced cooling. From a seasonal perspective, warming 

36 was greatest and most widespread in winter, with increases of over 0.8°C (1.5°F) in most areas. 
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1 In summer, warming was less extensive (mainly along the East Coast and in the western third of 

2 the Nation), while cooling was evident in parts of the Southeast, Midwest, and Great Plains.

3 [INSERT FIGURE 6.1 HERE:

4 Figure 6.1. Observed changes in annual, winter, and summer temperature (°F). Observed 

5 changes in annual, winter, and summer temperature (°F). Changes are the difference between the 

6 average for present-day (1986–2015) and the average for the first half of the last century (1901–

7 1960 for the contiguous United States, 1925–1960 for Alaska and Hawai‘i). (Figure source: 

8 NOAA/NCEI)].

9 There has been a rapid increase in the average temperature of the contiguous United States over 

10 the past several decades. There is general consistency on this point between the surface 

11 thermometer record from NOAA (Vose et al. 2014) and the middle tropospheric satellite records 

12 from Remote Sensing Systems (RSS; Mears and Wentz 2016), NOAA’s Center for Satellite 

13 Applications and Research (STAR; Zou and Li 2014), and the University of Alabama in 

14 Huntsville (UAH; Christy et al. 2011). In particular, for the period 1979–2015, the rate of 

15 warming in the surface record was 0.256°C (0.460°F) per decade, versus trends of 0.223°C 

16 (0.401°F), 0.210°C (0.378°F), and 0.130 °C (0.234°F) per decade for RSS version 4, STAR 

17 version 3, and UAH version 6, respectively (after accounting for stratospheric influences). All 

18 trends are statistically significant. For the contiguous United States, the year 2015 was the 

19 second-warmest on record at the surface and the warmest on record for the middle troposphere. 

20 Generally speaking, surface and satellite records do not have identical trends because they do not 

21 represent the same physical quantity; surface measurements are made using thermometers in 

22 shelters about 1.5 meters (4–5 feet) above the ground whereas satellite measurements are mass23 weighted averages of microwave emissions from deep atmospheric layers. The UAH record 

24 likely has a lower trend because it differs from the other satellite products in the treatment of 

25 target temperatures from the NOAA-9 satellite as well as in the correction for diurnal drift (Po26 Chedley et al. 2015). 

27 Recent paleo-temperature evidence confirms the unusual character of wide-scale warming during 

28 the past few decades as determined from the instrumental record. The most important new 

29 paleoclimate study since NCA3 showed that for each of the seven continental regions, the 

30 reconstructed area-weighted average temperature for 1971–2000 was higher than for any other 

31 time in nearly 1,400 years (PAGES 2K 2013), although with significant uncertainty around the 

32 central estimate that leads to this conclusion. Recent (up to 2006) 30-year smoothed temperatures 

33 across temperate North America (including most of the continental United States) are similarly 

34 reconstructed as the warmest over the past 1,500 years (Trouet et al. 2013) (Figure 6.3). Unlike 

35 the PAGES 2k seven-continent result mentioned above, this conclusion for North America is 

36 robust in relation to the estimated uncertainty range. Reconstruction data since 1500 for western 

37 temperate North America show the same conclusion at the annual time scale for 1986–2005. 

38 This time period and the running 20-year periods thereafter are warmer than all possible 
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1 continuous 20-year sequences in a 1,000-member statistical reconstruction ensemble (Wahl and 

2 Smerdon 2012).

3 [INSERT FIGURE 6.2 HERE:

4 Figure 6.2. Pollen-based temperature reconstruction for temperate North America. Pollen-based 

5 temperature reconstruction for temperate North America. The blue curve depicts the pollen6 based reconstruction of 30-year averages (as anomalies from 1904 to 1980) for the temperate 

7 region (30°–55°N, 7°5–130°W). The red curve shows the corresponding tree ring-based decadal 

8 average reconstruction, which was smoothed and used to calibrate the lower-frequency pollen9 based estimate. Light (medium) blue zones indicate 2 standard error (1 standard error) 

10 uncertainty estimations associated with each 30-year value. The black curve shows comparably 

11 smoothed instrumental temperature values up to 1980. The dashed black line represents the 

12 average temperature anomaly of comparably smoothed instrumental data for the period 2000–

13 2006. (Figure source: NOAA/NCEI)]

14 6.1.2. Temperature Extremes

15 Shifts in temperature extremes are examined using a suite of societally relevant climate change 

16 indices (Zhang et al. 2011) derived from long-term observations of daily surface temperature 

17 (Menne et al. 2012). The coldest and warmest temperatures of the year are of particular 

18 relevance given their widespread use in engineering, agricultural, and other sectoral applications 

19 (for example, extreme annual design conditions by the American Society of Heating, 

20 Refrigeration, and Air Conditioning; plant hardiness zones by the U.S. Department of 

21 Agriculture). Cold and warm spells (that is, extended periods of below- or above-normal 

22 temperature) are likewise of great importance because of their numerous societal and 

23 environmental impacts, which span from human health to plant phenology. Changes are 

24 considered for a spectrum of event frequencies and intensities, ranging from the typical annual 

25 extreme to the 1-in-10 year event (an extreme that only has a 10% chance of occurrence in any 

26 given year). Generally speaking, changes in many extremes have been larger than changes in 

27 average temperature.

28 The coldest daily temperature of the year increased at most locations in the contiguous United 

29 States through 2015 (Figure 6.3). All regions experienced net increases (Table 6.2), with the

30 largest rises in the Northern Great Plains and the Northwest (roughly 2.8°C [5.0°F]), and the 

31 smallest in the Southeast (about 0.6°C [1.0°F]). In general, there were increases throughout the 

32 period of record, with a slight acceleration in the past few decades (Figure 6.3). The temperature 

33 of extremely warm days (1-in-10 year events) generally exhibited the same pattern of increases

34 as the coldest daily temperature of the year. Consistent with these increases, the number of cool 

35 nights per year (those with a minimum temperature below the 10th percentile) declined in all 

36 regions, with much of the West having decreases of roughly two weeks.

37
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1 [INSERT TABLE 6.2 HERE:

2 Table 6.2. Observed changes in temperature extremes (°F) for each NCA region. Changes are 

3 the difference between the average for present-day (1986–2015) and the average for the first half 

4 of the last century (1901–1960).].

5 [INSERT FIGURE 6.3 HERE:

6 Figure 6.3. Observed changes in the coldest and warmest daily temperatures (°F) of the year. 

7 Maps (top) depict changes at stations; changes are the difference between the average for 

8 present-day (1986–2015) and the average for the first half of the last century (1901–1960). Time 

9 series (bottom) depict changes over the contiguous United States. (Figure source: 

10 NOAA/NCEI)].

11 The warmest daily temperature of the year generally increased at locations throughout the West

12 (Figure 6.3), as did the temperature of extremely warm days (1-in-10 year events) and the 

13 number of warm days per year (those with a maximum temperature above the 90th percentile). 

14 The largest regional increases were in the Southwest (Table 6.2). In contrast, there were 

15 decreases in maximum temperatures in almost all locations east of the Rocky Mountains; the 

16 decreases were actually larger for the 1-in-10 year events than for the warmest day of the year. 

17 The decreases in the eastern half of Nation (Figure 6.3) are generally tied to the hot summers in 

18 the 1930s Dust Bowl era, particularly across the Great Plains, where extreme agricultural 

19 drought and land mismanagement resulted in denuded landscapes, depleted soil moisture, and 

20 reduced evaporative cooling. Since the mid-1960s, however, there has been a slight increase in 

21 the warmest daily temperature of the year.

22 The frequency of cold spells (brief periods of below-normal temperatures) has steadily fallen 

23 across the contiguous United States during the past century (Figure 6.4). The frequency of 

24 intense cold waves (1-in-5 year events) peaked in the 1980s in all regions (including Alaska) and 

25 then reached record-low levels in the 2000s (Peterson et al. 2013). Nationally, the average 

26 temperature of extreme cold waves (5-day, 1-in-10 year events) was about 1.0 °C (1.8°F) warmer 

27 in the past three decades than in the first half of the 20th century, with increases in excess of 

28 1.7°C (3.0°F) in the Southwest, Northwest, and Northern Great Plains (Table 6.2).

29 [INSERT FIGURE 6.4 HERE:

30 Figure 6.4. Observed changes in cold and warm spells in the contiguous United States. The top 

31 panel depicts changes in the frequency of cold spells, the middle panel depicts changes in the 

32 frequency of warm spells, and the bottom panel depicts changes in the intensity of heat waves. 

33 (Figure source: NOAA/NCEI)]

34 Warm spells (brief periods of above-normal temperatures) increased in frequency until the mid35 1930s, became somewhat less common through the mid-1960s, and increased in frequency again 

36 thereafter (Figure 6.4). As with warm daily temperatures, the peak period for heat waves was the 

37 1930s in most regions except the West. Nationwide, the average temperature of extreme heat 
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1 waves (5-day, 1-in-10 year events) was about 0.8°C (1.5°F) warmer in the first half of the 20th

2 century than in the past three decades, with the Midwest having the largest regional difference 

3 (Table 6.2). The frequency of intense heat waves (1-in-5 year events) has generally increased 

4 since the 1960s in most regions except the Midwest and the Great Plains (Peterson et al. 2013;

5 Smith et al. 2013). Since the early 1980s (Figure 6.4), there is suggestive evidence of a slight 

6 increase in the intensity of heat waves nationwide (Russo et al. 2014) as well as an increase in 

7 the concurrence of droughts and heat waves (Mazdiyasni and AghaKouchak 2015). Recent warm 

8 spells have generally been longer in duration than those in the 1930s, as evidenced by the multi9 month heat waves in the Midwest in 2012.

10 6.2 Detection and Attribution

11 6.2.1 Average Temperatures

12 While a confident attribution of global temperature increases to anthropogenic forcing has been 

13 made (Bindoff et al. 2013), detection and attribution assessment statements for smaller regions 

14 are generally much weaker. Nevertheless, some detectable anthropogenic influences on average 

15 temperature have been reported for North America and parts of the United States (e.g., Christidis 

16 et al. 2010; Bonfils et al. 2008; Pierce et al. 2009). Figure 6.5 shows an example for 1901–2015

17 temperature trends, indicating a detectable anthropogenic warming since 1901 over the western 

18 and northern regions of the contiguous United States for the CMIP5 multi-model ensemble—a 

19 condition that was also met for most of the individual models (Knutson et al. 2013a). The 

20 Southeast stands out as the only region with no “detectable” warming since 1901; observed 

21 trends there were inconsistent with CMIP5 All Forcing historical runs (Knutson et al. 2013a). 

22 The cause of this “warming hole,” or lack of a long-term warming trend, remains uncertain, 

23 though it is likely a combination of natural and human causes. Some studies conclude that 

24 changes in anthropogenic aerosols have played a crucial role (e.g., Leibensperger et al. 2012a, b;

25 Yu et al. 2014), whereas other studies infer a possible large role for internal climate variability

26 (e.g., Meehl et al. 2012; Knutson et al. 2013a) as well as changes in land use (e.g., Goldstein et 

27 al. 2009; Xu et al. 2015). Notably, the Southeast has been warming rapidly since the early 1960s

28 (Walsh et al. 2014; Pan et al. 2013).

29 [INSERT FIGURE 6.5 HERE:

30 Figure 6.5. Detection and attribution assessment of trends in average annual temperature (°F).

31 Grid-box values indicate whether trends for 1901–2015 are detectable (that is, distinct from 

32 natural variability) and/or consistent with CMIP5 historical All-Forcing runs. If the grid-box 

33 trend is found to be both detectable and either consistent with or greater than the warming in the 

34 All-Forcing runs, then the grid box is assessed as having a detectable anthropogenic contribution 

35 to warming over the period. (Figure source: updated from Knutson et al. 2013; © American 

36 Meteorological Society, used with permission)]

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1 6.2.2 Temperature Extremes

2 IPCC AR5 (Bindoff et al. 2013) concluded that it is very likely that human influence has 

3 contributed to the observed changes in frequency and intensity of temperature extremes on the 

4 global scale since the mid-20th century. The combined influence of anthropogenic and natural 

5 forcings was also detectable over large subregions of North America (e.g., Zwiers et al. 2011;

6 Min et al. 2013). In general, however, results for the contiguous United States are not as 

7 compelling as for global land areas, in part because detection of changes in U.S. regional 

8 temperature extremes is affected by extreme temperature in the 1930s (Peterson et al. 2013). 

9 Table 6.3 summarizes available attribution statements for recent extreme U.S. temperature 

10 events. As an example, the recent record or near-record high March–May average temperatures 

11 occurring in 2012 over the eastern United States were attributed in part to external (natural plus 

12 anthropogenic) forcing (Knutson et al. 2013b); the century-scale trend response of temperature to 

13 external forcing is typically a close approximation to the anthropogenic forcing response alone. 

14 Another study found that although the extreme March 2012 warm anomalies over the United 

15 States were mostly due to natural variability, anthropogenic warming contributed to the severity

16 (Dole et al. 2014). Such statements reveal that both natural and anthropogenic factors influence 

17 the severity of extreme temperature events. Nearly every modern analysis of current extreme hot 

18 and cold events reveals some degree of attributable human influence.

19 [INSERT TABLE 6.3 HERE:

20 Table 6.3. Extreme temperature events in the United States for which attribution statements have 

21 been made. There are three possible attribution statements: “+” shows an attributable human22 induced increase in frequency or intensity, “−” shows an attributable human-induced decrease in 

23 frequency or intensity, “0” shows no attributable human contribution.]

24 6.3 Projected Changes

25 6.3.1 Average Temperatures

26 Temperature projections are based on global model results and associated downscaled products 

27 from CMIP5 using a suite of Representative Concentration Pathways (RCPs). In contrast to 

28 NCA3, model weighting is employed to refine projections of temperature for each RCP (Ch. 4: 

29 Projections; Appendix B: Model Weighting). Weighting parameters are based on model 

30 independence and model skill over North America for seasonal temperature and annual extremes

31 (Figure 6.5). Unless stated otherwise, all changes presented here represent the weighted multi32 model mean. The weighting scheme helps refine confidence and likelihood statements, but 

33 projections of U.S. surface air temperature remain very similar to those in NCA3. Generally 

34 speaking, extreme temperatures are projected to increase at a greater rate than changes in average 

35 temperatures (Collins et al. 2013).

36 [INSERT FIGURE 6.6 HERE:

37 Figure 6.6. Relative performance of the CMIP5 models used in this study in simulating observed 
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1 North American temperature indices. The first four rows depict performance for extremes while 

2 the next four rows depict performance for seasonal averages. The last row depicts the combined 

3 performance for all metrics. Models are ordered from left (best) to worst (right) based upon this 

4 combined metric. (Figure source: adapted from Sanderson et al., submitted 2016)]

5 The average annual temperature of the contiguous United States is projected to rise throughout 

6 the century. Near-term increases will be about 1.4°C (2.5°F) for RCP4.5 and 1.6°C (2.9°F) for 

7 RCP8.5; the similarity in warming reflects the similarity in greenhouse gas concentrations during 

8 this period (Figure 4.1). Notably, a 1.4°C (2.5°F) increase makes the near-term average roughly 

9 comparable to the hottest year in the historical record (2012). In other words, recent record10 breaking years could be “normal” by about 2030. By late-century, the RCPs diverge in a 

11 statistically significant sense, leading to very different rates of warming: approximately 2.8°C

12 (5.0°F) for RCP4.5 and 4.8°C (8.7°F) for RCP8.5. Unforced internal variations will continue to 

13 be evident in future U.S. temperatures, particularly in the near-term. Slightly larger increases are 

14 projected for summer than winter (except for Alaska), and average maximum temperature will 

15 rise slightly faster than average minimum temperature.

16 Warming is projected for all parts of the United States by mid- and late-century (Figure 6.7). The 

17 largest changes are in Alaska (5.5°C [10°F] or more by late-century under RCP8.5), in part due

18 to decreases in surface albedo as snow cover declines. In the contiguous United States, northern 

19 regions (the Northeast, Midwest, and Northern Great Plains) have slightly more warming than 

20 elsewhere, consistent with polar amplification (Table 6.4). The Southeast has slightly less 

21 warming because anthropogenic effects are partially offset by latent heat release from increases 

22 in evapotranspiration (as is already evident in the observed record). From a sub-regional 

23 perspective, less warming is projected along the coasts due to the moderating effects of the 

24 ocean, although the temperature increases are still substantial. In addition, anthropogenic 

25 warming at higher elevations may be underestimated because the resolution of the CMIP5 

26 models does not capture orography in detail, with important implications for future snowpack in 

27 the mountainous West.

28 [INSERT FIGURE 6.7 HERE:

29 Figure 6.7. Projected changes in average annual temperature (°F) for mid- and late-21st century. 

30 Changes are the difference between the average for mid-century (2036–2065; top) or late-century 

31 (2071–2100, bottom) and the average for near-present (1976–2005). (Figure source: CICS-NC / 

32 NOAA/NCEI).]

33 [INSERT TABLE 6.4 HERE:

34 Table 6.4. Projected changes in average annual temperature (°F) for each NCA region. Changes 

35 are the difference between the average in the future (either mid- or late-century) and the average 

36 for near-present (1976-2005).]

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1 6.3.2 Temperature Extremes

2 The coldest and warmest daily temperatures of the year are projected to increase substantially

3 over the coming decades (Figure 6.8). Under RCP8.5, increases in most areas exceed 2.8°C [5°F]

4 by mid-century (Fischer et al. 2013), rising to 5.5°C [10°F] or more by late-century (Sillmann et 

5 al. 2013). From a regional perspective, the coldest temperatures will increase the most in Alaska 

6 and in the northern half of the contiguous United States whereas the warmest temperatures will 

7 exhibit somewhat more uniform changes geographically (Table 6.5). Changes in “very rare”

8 temperature extremes are particularly dramatic under RCP8.5; by late century, current 1-in-20 

9 year maximums are projected to occur every year, while current 1-in-20 year minimums are not 

10 projected to occur at all over the contiguous United States (Wuebbles et al. 2014). Finally, there 

11 is a substantial projected decrease in the number of days with a minimum temperature below 

12 freezing, particularly in the West, and a marked increase in the number days with a maximum 

13 over 38°C [100°F], particularly in the Southwest, Great Plains, and Southeast (Figure 6.9).

14 [INSERT FIGURE 6.8 HERE:

15 Figure 6.8. Projected changes in the coldest and warmest daily temperatures (°F) of the year.

16 Changes are the difference between the average for mid-century (2036–2065) and the average 

17 for near-present (1976–2005) under RCP8.5. (Figure source: CICS-NC / NOAA/NCEI)]

18 [INSERT FIGURE 6.9 HERE:

19 Figure 6.9. Projected changes in the number of days per year with a minimum temperature 

20 below 32°F (left) and a maximum temperature above 100°F (right). Changes are the difference 

21 between the average for mid-century (2036–2065) and the average for near-present (1976–2005) 

22 under RCP8.5. (Figure source: CICS-NC / NOAA/NCEI)]

23 [INSERT TABLE 6.5 HERE:

24 Table 6.5. Projected changes in temperature extremes (°F) for each NCA region. Changes are 

25 the difference between the average for mid-century and the average for near-present (1976–

26 2005) under RCP8.5.]

27 The frequency and intensity of cold spells is projected to decrease while the frequency and 

28 intensity of warm spells is projected to increase throughout the century. The frequency of cold 

29 spells will decrease the most in Alaska and the least in the Northeast while the frequency of 

30 warm spells will increase in all regions, particularly the Southeast, Southwest, and Alaska. By 

31 mid-century, decreases in the frequency of cold spells are similar across RCPs whereas increases 

32 in the frequency of warm spells are about 50% greater in RCP8.5 than RCP4.5 (Sun et al. 2015).

33 The intensity of cold waves and heat waves is also projected to increase dramatically, 

34 particularly under RCP8.5. By mid-century, both extreme cold waves and extreme heat waves 

35 (5-day, 1-in-10 year events) are projected to have temperature increases of at least 6°C [11°F]

36 nationwide, with larger increases in northern regions (the Northeast, Midwest, Northern Great 

37 Plains, and Northwest; Table 6.5). Changes in land surface properties will play an important role 
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1 in the changes in warm spells. In the stagnant air conditions associated with prolonged heat 

2 waves, soils will dry out faster in a warmer climate. The reduction in evaporative cooling then 

3 compounds the average warming during the heat wave.

4
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1 TRACEABLE ACCOUNTS

2 Key Finding 1

3 The annual-average, near-surface air temperature over the contiguous United States has 

4 increased by about 1.2°F (0.7°C) between 1901 and 2015. Surface and satellite data both show 

5 rapid warming since the late 1970s, while paleo-temperature evidence shows that recent decades 

6 have been the warmest in at least the past 1,500 years. (Extremely likely, High confidence)

7 Description of Evidence Base

8 The key finding and supporting text summarize extensive evidence documented in the climate 

9 science literature. Similar statements about changes have also been made in other national 

10 assessments (such as NCA3) and in reports by the Climate Change Science Program (such as 

11 SAP1.1: Temperature trends in the lower atmosphere, and SAP 1.6: Global Climate Change 

12 Impacts in the United States). Statements about annual events and extremes are documented in 

13 the State of the Climate Reports by the Bulletin of the American Meteorological Society.

14 Evidence for changes in U.S. climate arises from multiple analyses of data from in-situ, satellite, 

15 and other records undertaken by many groups over several decades. The primary dataset for 

16 surface temperatures in the contiguous United States is nClimGrid (Vose et al. 2014), with other 

17 recently released datasets for Alaska and other areas. Changes in sea surface temperatures are 

18 derived from the NOAA Global Temperature dataset (Vose et al. 2012), which now uses the 

19 Extended Reconstructed Sea Surface Temperature Dataset version 4 (Huang et al. 2015). Several 

20 recently improved satellite datasets document changes in middle tropospheric temperatures 

21 (Mears and Wentz 2016; Zou and Li 2016; Christy et al. 2011). Longer-term changes are 

22 depicted using multiple paleo analyses (e.g., Wahl and Smerdon 2012, Truet et al. 2013).

23 Major Uncertainties

24 The primary uncertainties for surface data relate to historical changes in station location, 

25 temperature instrumentation, observing practice, and spatial sampling. Satellite records are 

26 similarly impacted by non-climatic changes such as orbital decay, diurnal sampling, and 

27 instrument calibration to target temperatures. Several uncertainties are inherent in temperature28 sensitive proxies, such as dating techniques and spatial sampling. 

29 Assessment of Confidence

30 Very High

31 Likelihood of Impact

32 Extremely Likely

33 Summary Sentence

34 There is very high confidence in observed changes in average temperature over the United States 

35 based upon the convergence of evidence from multiple data sources, analyses, and assessments.
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1 Key Finding 2

2 Accompanying the rise is average temperatures, there have been – as is to be expected –

3 increases in extreme temperature events in most parts of the United States. Since the early 1900s, 

4 the temperature of extremely cold days has increased throughout the contiguous United States, 

5 and the temperature of extremely warm days has increased across much of the West. In recent 

6 decades, intense cold waves have become less common while intense heat waves have become 

7 more common. (Extremely likely, Very high confidence)

8 Description of Evidence Base

9 The key finding and supporting text summarize extensive evidence documented in the climate 

10 science literature. Similar statements about changes have also been made in other national 

11 assessments (such as NCA3) and in reports by the Climate Change Science Program (such as 

12 SAP3.3: Weather and Climate Extremes in a Changing Climate) and the IPCC Special Report on 

13 Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation.

14 Statements about annual events and extremes are documented in the State of the Climate Reports 

15 and the Explaining Extreme Events Reports by the Bulletin of the American Meteorological 

16 Society.

17 Evidence for changes in U.S. climate arises from multiple analyses of in situ data and 

18 atmospheric reanalyses using widely published climate extremes indices. The primary source of 

19 in situ data is the Global Historical Climatology Network – Daily dataset (Menne et al. 2011), 

20 the largest collection of U.S. and global temperature data in the world. Climate extremes indices, 

21 comprehensively documented in Zhang et al. 2011, have been employed in numerous 

22 publications and assessments.

23 Major Uncertainties

24 The primary uncertainties for in situ data relate to historical changes in station location, 

25 temperature instrumentation, observing practice, and spatial sampling (particularly the precision 

26 of estimates of change in areas and periods with low station density, such as the intermountain 

27 West in the early 20th century).

28 Assessment of Confidence

29 Very High

30 Likelihood of Impact

31 Extremely Likely

32 Summary Sentence

33 There is very high confidence in observed changes in temperature extremes over the United 

34 States based upon the convergence of evidence from multiple data sources, analyses, and 

35 assessments.

36
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1 Key Finding 3

2 The average annual temperature of the contiguous United States is projected to rise throughout 

3 the century. Increases of at least 2.5°F (1.4°C) are projected over the next few decades, meaning 

4 that recent record-setting years will be relatively “common” in the near future. Increases of 5.0°–

5 7.5°F (2.8°–4.8°C) are projected by late century depending upon the level of future emissions. 

6 (Extremely likely, Very high confidence)

7 Description of Evidence Base

8 The key finding and supporting text summarize extensive evidence documented in the climate 

9 science literature. Similar statements about changes have also been made in other national 

10 assessments (such as NCA3) and in reports by the Climate Change Science Program (such as 

11 SAP 1.6: Global Climate Change Impacts in the United States). The basic physics underlying the 

12 impact of human emissions on global climate has also been documented in every IPCC 

13 assessment.

14 Major Uncertainties

15 Global climate models are subject to structural and parametric uncertainty, resulting in a range of 

16 estimates of future changes in average temperature. This is partially mitigated through the use of 

17 model weighting and pattern scaling. Furthermore, virtually every ensemble member of every 

18 model projection contains an increase in temperature by mid- and late-century. Empirical 

19 downscaling introduces additional uncertainty (e.g., with respect to stationarity). Projections will 

20 improve in the future along with improvements in model physics and resolution.

21 Assessment of Confidence

22 Very High

23 Likelihood of Impact

24 Extremely likely

25 Summary Sentence

26 There is high confidence in projected changes in average temperature over the United States 

27 based upon the convergence of evidence from multiple model simulations, analyses, and 

28 assessments.

29

30
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1 Key Finding 4

2 Extreme temperatures are projected to increase even more than average temperatures. The 

3 temperatures of extremely cold days and extremely warm days are both projected to increase. 

4 Cold waves are projected to become less intense while heat waves will become more intense. 

5 (Extremely likely, Very high confidence)

6 Description of Evidence Base

7 The key finding and supporting text summarize extensive evidence documented in the climate 

8 science literature. Similar statements about changes have also been made in other national 

9 assessments (such as NCA3) and in reports by the Climate Change Science Program (such as 

10 SAP 3,3: Weather and Climate Extremes in a Changing Climate). The basic physics underlying 

11 the impact of human emissions on global climate has also been documented in every IPCC 

12 assessment.

13 Major Uncertainties

14 Global climate models are subject to structural and parametric uncertainty, resulting in a range of 

15 estimates of future changes in temperature extremes. This is partially mitigated through the use 

16 of model weighting and pattern scaling. Furthermore, virtually every ensemble member of every 

17 model projection contains an increase in temperature by mid- and late-century. Empirical 

18 downscaling introduces additional uncertainty (e.g., with respect to stationarity). Projections will 

19 improve in the future along with improvements in model physics and resolution.

20 Assessment of Confidence

21 Very High

22 Likelihood of Impact

23 Extremely likely

24 Summary Sentence

25 There is high confidence in projected changes in temperature extremes over the United States 

26 based upon the convergence of evidence from multiple model simulations, analyses, and 

27 assessments.

28
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1 TABLES

2 Table 6.1. Observed changes in average annual temperature (°F) for each NCA region. Changes 

3 are the difference between the average for present-day (1986–2015) and the average for the first 

4 half of the last century (1901–1960).

NCA Region Average Annual 

Temperature

Average Annual 

Maximum

Temperature

Average Annual 

Minimum

Temperature

Contiguous U.S. 1.18 1.00 1.35

Northeast 1.37 1.09 1.65

Southeast 0.40 0.10 0.70

Midwest 1.18 0.71 1.66

Great Plains North 1.62 1.59 1.65

Great Plains South 0.70 0.50 0.90

Southwest 1.56 1.57 1.56

Northwest 1.51 1.48 1.52

Alaska 1.52 1.29 1.76

Hawaii 0.75 - -

5
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1 Table 6.2. Observed changes in temperature extremes ( ̊F) for each NCA region. Changes are 

2 the difference between the average for present-day (1986–2015) and the average for the first half 

3 of the last century (1901–1960).

NCA Region Coldest Day of 

the Year

Coldest 5-Day 

1-in-10 Year 

Event

Warmest Day 

of the Year

Warmest 5-Day

1-in-10 Year 

Event

Northeast 3.04 1.13 -0.99 -1.85

Southeast 1.13 0.43 -1.53 -1.64

Midwest 3.04 -1.32 -2.26 -4.12

Great Plains North 4.80 3.16 -1.16 -1.45

Great Plains South 3.44 1.55 -1.16 -1.10

Southwest 4.12 3.31 0.40 0.14

Northwest 5.00 3.53 -0.22 -0.85

4
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1 Table 6.3. Extreme temperature events in the United States for which attribution statements have 

2 been made. There are three possible attribution statements: “+” shows an attributable human3 induced increase in frequency or intensity, “−” shows an attributable human-induced decrease in 

4 frequency or intensity, “0” shows no attributable human contribution.

Study Period Region Type Statement

Rupp et al. 2012

Angelil et al. 2016

Spring/Summer

2011 Texas Hot

+

+

Hoerling et al. 2013 Summer 2011 Texas Hot +

Diffenbaugh and Scherer 2013

Angelil et al. 2016

July 2012 Northcentral and 

Northeast Hot

+

+

Cattiaux and Yiou 2013

Angelil et al. 2016

Spring 2012 East. Hot

0

+

Knutson et al. 2013

Angelil et al. 2016

Spring 2012 East Hot

+

+

Jeon et al 2016 Summer 2011 Texas/Oklahoma Hot +

Dole et al. 2014 March 2012 Upper Midwest Hot +

Seager et al. 2014 2011-2014 California Hot +

Wolter et al. 2015 Winter 2014 Midwest Cold −

Trenary et al. 2015 Winter 2014 East Cold 0

5

6
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1 Table 6.4. Projected changes in average annual temperature (°F) for each NCA region. Changes 

2 are the difference between the average in the future (either mid- or late-century) and the average 

3 for near-present (1976-2005).

NCA Region

RCP4.5

Mid-Century

(2036-2065)

RCP 8.5

Mid-Century

(2036-2065)

RCP 4.5

Late-Century

(2071-2100)

RCP 8.5

Late-Century

(2071-2100)

Contiguous U.S. 3.79 4.83 5.03 8.72

Northeast 3.98 5.09 5.27 9.11

Southeast 3.40 4.30 4.43 7.72

Midwest 4.21 5.29 5.57 9.49

Great Plains 

North 4.05 5.10 5.44 9.37

Great Plains 

South 3.62 4.61 4.78 8.44

Southwest 3.72 4.80 4.93 8.65

Northwest 3.66 4.67 4.99 8.51

4

5
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1 Table 6.5. Projected changes in temperature extremes ( ̊F) for each NCA region. Changes are the 

2 difference between the average for mid-century and the average for near-present (1976–2005)

3 under RCP8.5.

NCA Region Coldest Day of 

the Year

Coldest 5-Day 

1-in-10 Year 

Event

Warmest Day 

of the Year

Warmest 5-Day 

1-in-10 Year 

Event

Northeast 9.51 15.93 6.51 12.88

Southeast 4.97 8.84 5.79 11.09

Midwest 9.44 15.52 6.71 13.02

Great Plains North 8.01 12.01 6.48 12.00

Great Plains South 5.49 9.41 5.70 10.73

Southwest 6.13 10.20 5.85 11.17

Northwest 7.33 10.95 6.25 12.31

4
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1 FIGURES

2

3

4 Figure 6.1. Observed changes in annual, winter, and summer temperature (°F). Changes are the 

5 difference between the average for present-day (1986–2015) and the average for the first half of 

6 the last century (1901–1960 for the contiguous United States, 1925–1960 for Alaska and 

7 Hawai‘i). (Figure source: NOAA/NCEI)
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1

2 Figure 6.2. Pollen-based temperature reconstruction for temperate North America. The blue 

3 curve depicts the pollen-based reconstruction of 30-year averages (as anomalies from 1904 to 

4 1980) for the temperate region (30°–55°N, 7°5–130°W). The red curve shows the corresponding 

5 tree ring-based decadal average reconstruction, which was smoothed and used to calibrate the 

6 lower-frequency pollen-based estimate. Light (medium) blue zones indicate 2 standard error (1 

7 standard error) uncertainty estimations associated with each 30-year value. The black curve 

8 shows comparably smoothed instrumental temperature values up to 1980. The dashed black line 

9 represents the average temperature anomaly of comparably smoothed instrumental data for the 

10 period 2000–2006. (Figure source: NOAA/NCEI)
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1

2 Figure 6.3. Observed changes in the coldest and warmest daily temperatures (°F) of the year. 

3 Maps (top) depict changes at stations; changes are the difference between the average for 

4 present-day (1986–2015) and the average for the first half of the last century (1901–1960). Time 

5 series (bottom) depict changes over the contiguous United States. (Figure source: NOAA/NCEI)
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1

2

3 Figure 6.4. Observed changes in cold and warm spells in the contiguous United States. The top 

4 panel depicts changes in the frequency of cold spells, the middle panel depicts changes in the 

5 frequency of warm spells, and the bottom panel depicts changes in the intensity of heat waves.

6 (Figure source: NOAA/NCEI)
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1

2 Figure 6.5. Detection and attribution assessment of trends in average annual temperature (°F). 

3 Grid-box values indicate whether trends for 1901–2015 are detectable (that is, distinct from 

4 natural variability) and/or consistent with CMIP5 historical All-Forcing runs. If the grid-box

5 trend is found to be both detectable and either consistent with or greater than the warming in the 

6 All-Forcing runs, then the grid box is assessed as having a detectable anthropogenic contribution 

7 to warming over the period. (Figure source: updated from Knutson et al. 2013; © American 

8 Meteorological Society. Used with permission.)
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1

2

3 Figure 6.6. Relative performance of the CMIP5 models used in this study in simulating observed 

4 North American temperature indices. The first four rows depict performance for extremes while 

5 the next four rows depict performance for seasonal averages. The last row depicts the combined 

6 performance for all metrics. Models are ordered from left (best) to worst (right) based upon this 

7 combined metric. (Figure source: adapted from Sanderson et al., submitted 2016)

North America Temperature Skill

MPI_ESM_P

MPI_ESM_LR

MPI_ESM_MR

NorESM1_M

CESM1_CAM5

CCSM4

CESM1_BGC

BNU_ESM

CESM1_FASTCHEM

ACCESS1_3ACCESS1_0

bcc_csm1_1

GISS_E2_R_p2GISS_E2_R_p1

bcc_csm1_1_m

IPSL_CM5A_LR

CMCC_CMS

MIROC5

MRI_ESM1

GISS_E2_H_p2GISS_E2_H_p1

CNRM_CM5

MRI_CGCM3

GFDL_CM3

CMCC_CESM

IPSL_CM5B_LR

CSIRO_Mk3_6_0

HadGEM2_AO

CMCC_CM

HadGEM2_ES

IPSL_CM5A_MR

inmcm4

GFDL_ESM2G

HadCM3

MIROC4h

HadGEM2_CCGFDL_ESM2M

FGOALS_g2

MIROC_ESM_CHEM

MIROC_ESM

CanESM2

txx (hot days)

txn (cool days)

tnn (cold nights)

tnx (warm nights)

Mean TAS DJF

Mean TAS MAM

Mean TAS JJA

Mean TAS SON

Mean TAS ALL

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

Distance from obs (relative to CMIP mean)
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1

2

3 Figure 6.7. Projected changes in average annual temperature (°F) for mid- and late-21st century.

4 Changes are the difference between the average for mid-century (2036–2065; top) or late-century 

5 (2071–2100, bottom) and the average for near-present (1976–2005). (Figure source: CICS-NC / 

6 NOAA/NCEI)
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1

2 Figure 6.8. Projected changes in the coldest and warmest daily temperatures (°F) of the year.

3 Changes are the difference between the average for mid-century (2036–2065) and the average 

4 for near-present (1976–2005) under RCP8.5. (Figure source: CICS-NC / NOAA/NCEI)

5
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1

2 Figure 6.9. Projected changes in the number of days per year with a minimum temperature below 

3 32°F (left) and a maximum temperature above 100°F (right). Changes are the difference between

4 the average for mid-century (2036–2065) and the average for near-present (1976–2005) under 

5 RCP8.5. (Figure source: CICS-NC / NOAA/NCEI)

6
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1 REFERENCES

2 Angélil, O., D. Stone, M. Wehner, C.J. Paciorek, H. Krishnan, and W. Collins, 2016: An 

3 independent assessment of anthropogenic attribution statements for recent extreme 

4 temperature and rainfall events. Journal of Climate, in press, null.

5 http://dx.doi.org/10.1175/JCLI-D-16-0077.1 

6 Bindoff, N.L., P.A. Stott, K.M. AchutaRao, M.R. Allen, N. Gillett, D. Gutzler, K. Hansingo, G. 

7 Hegerl, Y. Hu, S. Jain, I.I. Mokhov, J. Overland, J. Perlwitz, R. Sebbari, and X. Zhang, 2013: 

8 Detection and Attribution of Climate Change: from Global to Regional. Climate Change 

9 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment 

10 Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. 

11 Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, 

12 Eds. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 

13 867–952. http://dx.doi.org/10.1017/CBO9781107415324.022 www.climatechange2013.org

14 Bonfils, C., P.B. Duffy, B.D. Santer, T.M.L. Wigley, D.B. Lobell, T.J. Phillips, and C. 

15 Doutriaux, 2008: Identification of external influences on temperatures in California. Climatic 

16 Change, 87, 43-55. http://dx.doi.org/10.1007/s10584-007-9374-9 

17 Cattiaux, J. and P. Yiou, 2013: U.S. heat waves of spring and summer 2012 from the flow 

18 analogue perspective [in "Explaining Extremes of 2012 from a Climate Perspective"]. 

19 Bulletin of the American Meteorological Society, 94, S10-S13.

20 http://dx.doi.org/10.1175/BAMS-D-13-00085.1 

21 Christidis, N., P.A. Stott, F.W. Zwiers, H. Shiogama, and T. Nozawa, 2010: Probabilistic 

22 estimates of recent changes in temperature: a multi-scale attribution analysis. Climate 

23 Dynamics, 34, 1139-1156. http://dx.doi.org/10.1007/s00382-009-0615-7 

24 Christy, J.R., R.W. Spencer, and W.B. Norris, 2011: The role of remote sensing in monitoring 

25 global bulk tropospheric temperatures. International Journal of Remote Sensing, 32, 671-

26 685. http://dx.doi.org/10.1080/01431161.2010.517803 

27 Collins, M., R. Knutti, J. Arblaster, J.-L. Dufresne, T. Fichefet, P. Friedlingstein, X. Gao, W.J. 

28 Gutowski, T. Johns, G. Krinner, M. Shongwe, C. Tebaldi, A.J. Weaver, and M. Wehner, 

29 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. Climate 

30 Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth 

31 Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. 

32 Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and 

33 P.M. Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom and New 

34 York, NY, USA, 1029–1136. http://dx.doi.org/10.1017/CBO9781107415324.024 

35 www.climatechange2013.org
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 6

246

1 Diffenbaugh, N.S. and M. Scherer, 2013: Likelihood of July 2012 U.S. temperatures in pre2 industrial and current forcing regimes [in "Explaining Extremes of 2012 from a Climate 

3 Perspective"]. Bulletin of the American Meteorological Society, 94, S6-S9.

4 http://dx.doi.org/10.1175/BAMS-D-13-00085.1 

5 Dole, R., M. Hoerling, A. Kumar, J. Eischeid, J. Perlwitz, X.-W. Quan, G. Kiladis, R. Webb, D. 

6 Murray, M. Chen, K. Wolter, and T. Zhang, 2014: The Making of an Extreme Event: Putting 

7 the Pieces Together. Bulletin of the American Meteorological Society, 95, 427-440.

8 http://dx.doi.org/10.1175/BAMS-D-12-00069.1 

9 Fischer, E.M., U. Beyerle, and R. Knutti, 2013: Robust spatially aggregated projections of 

10 climate extremes. Nature Climate Change, 3, 1033-1038.

11 http://dx.doi.org/10.1038/nclimate2051 

12 Goldstein, A.H., C.D. Koven, C.L. Heald, and I.Y. Fung, 2009: Biogenic carbon and 

13 anthropogenic pollutants combine to form a cooling haze over the southeastern United States. 

14 Proceedings of the National Academy of Sciences, 106, 8835-8840.

15 http://dx.doi.org/10.1073/pnas.0904128106 

16 http://www.pnas.org/content/106/22/8835.abstract

17 Hoerling, M., M. Chen, R. Dole, J. Eischeid, A. Kumar, J.W. Nielsen-Gammon, P. Pegion, J. 

18 Perlwitz, X.-W. Quan, and T. Zhang, 2013: Anatomy of an extreme event. Journal of 

19 Climate, 26, 2811–2832. http://dx.doi.org/10.1175/JCLI-D-12-00270.1 

20 Huang, B., V.F. Banzon, E. Freeman, J. Lawrimore, W. Liu, T.C. Peterson, T.M. Smith, P.W. 

21 Thorne, S.D. Woodruff, and H.-M. Zhang, 2015: Extended Reconstructed Sea Surface 

22 Temperature Version 4 (ERSST.v4). Part I: Upgrades and Intercomparisons. Journal of 

23 Climate, 28, 911-930. http://dx.doi.org/10.1175/JCLI-D-14-00006.1 

24 IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group 

25 I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

26 Cambridge University Press, Cambridge, UK and New York, NY, 1535 pp.

27 http://dx.doi.org/10.1017/CBO9781107415324 www.climatechange2013.org

28 Jeon, S., C.J. Paciorek, and M.F. Wehner, 2016: Quantile-based bias correction and uncertainty 

29 quantification of extreme event attribution statements. Weather and Climate Extremes, 12,

30 24-32. http://dx.doi.org/10.1016/j.wace.2016.02.001 

31 http://www.sciencedirect.com/science/article/pii/S2212094715300220

32 Jones, G.S., P.A. Stott, and N. Christidis, 2013: Attribution of observed historical near surface 

33 temperature variations to anthropogenic and natural causes using CMIP5 simulations. Journal 

34 of Geophysical Research, 118, 4001-4024. http://dx.doi.org/10.1002/jgrd.50239
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 6

247

1 Knutson, T.R., F. Zeng, and A.T. Wittenberg, 2013a: Multimodel Assessment of Regional 

2 Surface Temperature Trends: CMIP3 and CMIP5 Twentieth-Century Simulations. Journal of 

3 Climate, 26, 8709-8743. http://dx.doi.org/10.1175/JCLI-D-12-00567.1 

4 Knutson, T.R., F. Zeng, and A.T. Wittenberg, 2013b: The extreme March-May 2012 warm 

5 anomaly over the eastern United States: Global context and multimodel trend analysis [in 

6 "Explaining Extremes of 2012 from a Climate Perspective"]. Bulletin of the American 

7 Meteorological Society, 94, S13-S17. http://dx.doi.org/10.1175/BAMS-D-13-00085.1 

8 Leibensperger, E.M., L.J. Mickley, D.J. Jacob, W.T. Chen, J.H. Seinfeld, A. Nenes, P.J. Adams, 

9 D.G. Streets, N. Kumar, and D. Rind, 2012: Climatic effects of 1950-2050 changes in US 

10 anthropogenic aerosols - Part 1: Aerosol trends and radiative forcing. Atmospheric Chemistry 

11 and Physics 12, 3333-3348. http://dx.doi.org/10.5194/acp-12-3333-2012 

12 Leibensperger, E.M., L.J. Mickley, D.J. Jacob, W.T. Chen, J.H. Seinfeld, A. Nenes, P.J. Adams, 

13 D.G. Streets, N. Kumar, and D. Rind, 2012: Climatic effects of 1950–2050 changes in 

14 US anthropogenic aerosols – Part 2: Climate response. Atmospheric Chemistry and 

15 Physics, 12, 3349-3362. http://dx.doi.org/10.5194/acp-12-3349-2012 

16 Mazdiyasni, O. and A. AghaKouchak, 2015: Substantial increase in concurrent droughts and 

17 heatwaves in the United States. Proceedings of the National Academy of Sciences, 112,

18 11484-11489. http://dx.doi.org/10.1073/pnas.1422945112 

19 Mears, C.A. and F.J. Wentz, 2016?: Sensitivity of satellite-derived tropospheric temperature 

20 trends to the diurnal cycle adjustment. Journal of Climate, 29, 3629-3646.

21 http://dx.doi.org/10.1175/JCLI-D-15-0744.1 

22 Meehl, G.A., J.M. Arblaster, and G. Branstator, 2012: Mechanisms contributing to the warming 

23 hole and the consequent US east-west differential of heat extremes. Journal of Climate, 25,

24 6394-6408. http://dx.doi.org/10.1175/JCLI-D-11-00655.1 

25 Menne, M.J., I. Durre, R.S. Vose, B.E. Gleason, and T.G. Houston, 2012: An overview of the 

26 global historical climatology network-daily database. Journal of Atmospheric and Oceanic 

27 Technology, 29, 897-910. http://dx.doi.org/10.1175/JTECH-D-11-00103.1 

28 Min, S.-K., X. Zhang, F. Zwiers, H. Shiogama, Y.-S. Tung, and M. Wehner, 2013: Multimodel 

29 Detection and Attribution of Extreme Temperature Changes. Journal of Climate, 26, 7430-

30 7451. http://dx.doi.org/10.1175/JCLI-D-12-00551.1 

31 PAGES 2K, 2013: Continental-scale temperature variability during the past two millennia. 

32 Nature Geoscience, 6, 339-346. http://dx.doi.org/10.1038/ngeo1797 

33 Pan, Z., X. Liu, S. Kumar, Z. Gao, and J. Kinter, 2013: Intermodel Variability and Mechanism 

34 Attribution of Central and Southeastern U.S. Anomalous Cooling in the Twentieth Century 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 6

248

1 as Simulated by CMIP5 Models. Journal of Climate, 26, 6215-6237.

2 http://dx.doi.org/doi:10.1175/JCLI-D-12-00559.1 

3 http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-12-00559.1

4 Peterson, T.C., R.R. Heim, R. Hirsch, D.P. Kaiser, H. Brooks, N.S. Diffenbaugh, R.M. Dole, J.P. 

5 Giovannettone, K. Guirguis, T.R. Karl, R.W. Katz, K. Kunkel, D. Lettenmaier, G.J. McCabe, 

6 C.J. Paciorek, K.R. Ryberg, S. Schubert, V.B.S. Silva, B.C. Stewart, A.V. Vecchia, G. 

7 Villarini, R.S. Vose, J. Walsh, M. Wehner, D. Wolock, K. Wolter, C.A. Woodhouse, and D. 

8 Wuebbles, 2013: Monitoring and understanding changes in heat waves, cold waves, floods 

9 and droughts in the United States: State of knowledge. Bulletin of the American 

10 Meteorological Society, 94, 821-834. http://dx.doi.org/10.1175/BAMS-D-12-00066.1 

11 Pierce, D.W., T.P. Barnett, B.D. Santer, and P.J. Gleckler, 2009: Selecting global climate models 

12 for regional climate change studies. Proceedings of the National Academy of Sciences, 106,

13 8441-8446. http://dx.doi.org/10.1073/pnas.0900094106 

14 Po-Chedley, S., T.J. Thorsen, and Q. Fu, 2015: Removing diurnal cycle contamination in 

15 satellite-derived tropospheric temperatures: Understanding tropical tropospheric trend 

16 discrepancies. Journal of Climate, 28, 2274-2290. http://dx.doi.org/10.1175/JCLI-D-13-

17 00767.1 

18 Rupp, D.E., P.W. Mote, N. Massey, C.J. Rye, R. Jones, and M.R. Allen, 2012: Did human 

19 influence on climate make the 2011 Texas drought more probable? [in Explaining extreme 

20 events of 2011 from a climate perspective]. Bulletin of the American Meteorological Society,

21 93, 1052-1054. http://dx.doi.org/10.1175/BAMS-D-12-00021.1 

22 Russo, S., A. Dosio, R.G. Graversen, J. Sillmann, H. Carrao, M.B. Dunbar, A. Singleton, P. 

23 Montagna, P. Barbola, and J.V. Vogt, 2014: Magnitude of extreme heat waves in present 

24 climate and their projection in a warming world. Journal of Geophysical Research: 

25 Atmospheres, 119, 12,500-12,512. http://dx.doi.org/10.1002/2014JD022098

26 Seager, R., M. Hoerling, D.S. Siegfried, h. Wang, B. Lyon, A. Kumar, J. Nakamura, and N. 

27 Henderson, 2014: Causes and predictability of the 2011-14 California drought. 40 pp. NOAA 

28 Drought Task Force Narrative 

29 Team. http://docs.lib.noaa.gov/noaa_documents/OAR/CPO/MAPP/california_drought_2011-

30 2014.pdf

31 Sillmann, J., V.V. Kharin, F.W. Zwiers, X. Zhang, and D. Bronaugh, 2013: Climate extremes 

32 indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections. Journal of 

33 Geophysical Research: Atmospheres, 118, 2473-2493. http://dx.doi.org/10.1002/jgrd.50188 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 6

249

1 Smith, T.T., B.F. Zaitchik, and J.M. Gohlke, 2013: Heat waves in the United States: Definitions, 

2 patterns and trends. Climatic Change, 118, 811-825. http://dx.doi.org/10.1007/s10584-012-

3 0659-2 

4 Sun, L., K.E. Kunkel, L.E. Stevens, A. Buddenberg, J.G. Dobson, and D.R. Easterling, 2015: 

5 Regional Surface Climate Conditions in CMIP3 and CMIP5 for the United States: 

6 Differences, Similarities, and Implications for the U.S. National Climate Assessment. NOAA 

7 Technical Report NESDIS 144, 111 pp. National Oceanic and Atmospheric Administration, 

8 National Environmental Satellite, Data, and Information Service. 

9 http://www.nesdis.noaa.gov/technical_reports/NOAA_NESDIS_Technical_Report_144.pdf

10 Thorne, P.W., M.G. Donat, R.J.H. Dunn, C.N. Williams, L.V. Alexander, J. Caesar, I. Durre, I. 

11 Harris, Z. Hausfather, P.D. Jones, M.J. Menne, R. Rohde, R.S. Vose, R. Davy, A.M.G. 

12 Klein-Tank, J.H. Lawrimore, T.C. Peterson, and J.J. Rennie, 2016: Reassessing changes in 

13 diurnal temperature range: Intercomparison and evaluation of existing global data set 

14 estimates. Journal of Geophysical Research: Atmospheres, 121, 5138-5158.

15 http://dx.doi.org/10.1002/2015JD024584 

16 Trenary, L., T. DelSole, B. Doty, and M.K. Tippett, 2015: Was the Cold Eastern Us Winter of 

17 2014 Due to Increased Variability? Bulletin of the American Meteorological Society, 96,

18 S15-S19. http://dx.doi.org/10.1175/bams-d-15-00138.1 

19 Trouet, V., H.F. Diaz, E.R. Wahl, A.E. Viau, R. Graham, N. Graham, and E.R. Cook, 2013: A 

20 1500-year reconstruction of annual mean temperature for temperate North America on 

21 decadal-to-multidecadal time scales. Environmental Research Letters, 8, 024008.

22 http://dx.doi.org/10.1088/1748-9326/8/2/024008 

23 Vose, R.S., S. Applequist, M. Squires, I. Durre, M.J. Menne, C.N. Williams, C. Fenimore, K. 

24 Gleason, and D. Arndt, 2016: Improved historical temperature and precipitation time series 

25 for Alaska climate divisions. Journal of Applied Meteorology and Climatology Submitted.

26 Vose, R.S., S. Applequist, M. Squires, I. Durre, M.J. Menne, C.N.W. Jr., C. Fenimore, K. 

27 Gleason, and D. Arndt, 2014: Improved Historical Temperature and Precipitation Time 

28 Series for U.S. Climate Divisions. Journal of Applied Meteorology and Climatology, 53,

29 1232-1251. http://dx.doi.org/10.1175/JAMC-D-13-0248.1 

30 Vose, R.S., D. Arndt, V.F. Banzon, D.R. Easterling, B. Gleason, B. Huang, E. Kearns, J.H. 

31 Lawrimore, M.J. Menne, T.C. Peterson, R.W. Reynolds, T.M. Smith, C.N. Williams, and 

32 D.L. Wuertz, 2012: NOAA’s Merged Land-Ocean Surface Temperature Analysis. Bulletin of 

33 the American Meteorological Society, 93, 1677-1685. http://dx.doi.org/10.1175/BAMS-D34 11-00241.1 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 6

250

1 Wahl, E.R. and J.E. Smerdon, 2012: Comparative performance of paleoclimate field and index 

2 reconstructions derived from climate proxies and noise-only predictors. Geophysical 

3 Research Letters, 39, L06703. http://dx.doi.org/10.1029/2012GL051086 

4 Walsh, J., D. Wuebbles, K. Hayhoe, J. Kossin, K. Kunkel, G. Stephens, P. Thorne, R. Vose, M. 

5 Wehner, J. Willis, D. Anderson, S. Doney, R. Feely, P. Hennon, V. Kharin, T. Knutson, F. 

6 Landerer, T. Lenton, J. Kennedy, and R. Somerville, 2014: Ch. 2: Our changing climate. 

7 Climate Change Impacts in the United States: The Third National Climate Assessment. 

8 Melillo, J.M., T.C. Richmond, and G.W. Yohe, Eds. U.S. Global Change Research Program, 

9 Washington, D.C., 19-67. http://dx.doi.org/10.7930/J0KW5CXT 

10 Wolter, K., J.K. Eischeid, X.-W. Quan, T.N. Chase, M. Hoerling, R.M. Dole, G.J.V. 

11 Oldenborgh, and J.E. Walsh, 2015: How Unusual was the Cold Winter of 2013/14 in the 

12 Upper Midwest? Bulletin of the American Meteorological Society, 96, S10-S14.

13 http://dx.doi.org/10.1175/bams-d-15-00126.1 

14 Wuebbles, D., G. Meehl, K. Hayhoe, T.R. Karl, K. Kunkel, B. Santer, M. Wehner, B. Colle, 

15 E.M. Fischer, R. Fu, A. Goodman, E. Janssen, V. Kharin, H. Lee, W. Li, L.N. Long, S.C. 

16 Olsen, Z. Pan, A. Seth, J. Sheffield, and L. Sun, 2014: CMIP5 Climate Model Analyses: 

17 Climate Extremes in the United States. Bulletin of the American Meteorological Society, 95,

18 571-583. http://dx.doi.org/10.1175/BAMS-D-12-00172.1 

19 Xu, L., H. Guo, C.M. Boyd, M. Klein, A. Bougiatioti, K.M. Cerully, J.R. Hite, G. Isaacman20 VanWertz, N.M. Kreisberg, C. Knote, K. Olson, A. Koss, A.H. Goldstein, S.V. Hering, J. de 

21 Gouw, K. Baumann, S.-H. Lee, A. Nenes, R.J. Weber, and N.L. Ng, 2015: Effects of 

22 anthropogenic emissions on aerosol formation from isoprene and monoterpenes in the 

23 southeastern United States. Proceedings of the National Academy of Sciences, 112, 37-42.

24 http://dx.doi.org/10.1073/pnas.1417609112 http://www.pnas.org/content/112/1/37.abstract

25 Yu, S., K. Alapaty, R. Mathur, J. Pleim, Y. Zhang, C. Nolte, B. Eder, K. Foley, and T. 

26 Nagashima, 2014: Attribution of the United States “warming hole”: Aerosol indirect effect 

27 and precipitable water vapor. Scientific Reports, 4, 6929. http://dx.doi.org/10.1038/srep06929 

28 Zhang, X., L. Alexander, G.C. Hegerl, P. Jones, A.K. Tank, T.C. Peterson, B. Trewin, and F.W. 

29 Zwiers, 2011: Indices for monitoring changes in extremes based on daily temperature and 

30 precipitation data. Wiley Interdisciplinary Reviews: Climate Change, 2, 851-870.

31 http://dx.doi.org/10.1002/wcc.147 

32 Zou, C.-Z. and J. Li, 2014: NOAA MSU Mean Layer Temperature. 35 pp. 

33 NOAA/NESDIS/STAR. 

34 http://www.star.nesdis.noaa.gov/smcd/emb/mscat/documents/MSU_TCDR_CATBD_Zou_Li

35 .pdf
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 6

251

1 Zwiers, F.W., X.B. Zhang, and Y. Feng, 2011: Anthropogenic Influence on Long Return Period 

2 Daily Temperature Extremes at Regional Scales. Journal of Climate, 24, 881-892.

3 http://dx.doi.org/10.1175/2010jcli3908.1
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1 7. Precipitation Change in the United States

2 KEY FINDINGS

3 1. There are sizeable regional and seasonal differences in precipitation changes since 1901.

4 Annual precipitation has decreased in much of the West, Southwest and Southeast, and 

5 increased in most of the Northern and Southern Plains, Midwest and Northeast. A 

6 national average increase of 4% in annual precipitation since 1901 is mostly a result of 

7 large increases in the fall season. (Medium confidence)

8 2. Heavy precipitation events across the United States have increased in both intensity and 

9 frequency since 1901. There are important regional differences in trends, with the largest 

10 increases occurring in the northeastern United States. (High confidence)

11 3. The frequency and intensity of heavy precipitation events are projected to continue to 

12 increase over the 21st century (high confidence). However, there are regional and 

13 seasonal differences in projected changes in total precipitation with the northern United 

14 States, including Alaska getting wetter in the winter and spring, and parts of the 

15 southwest United States getting drier in the winter and spring (medium confidence).

16 4. Northern Hemisphere spring snow cover extent, North America maximum snow depth, 

17 and extreme snowfall years in the southern and western United States. have all declined 

18 while extreme snowfall years in parts of the northern United States. have increased 

19 (medium confidence). Projections indicate large declines in snowpack in the western 

20 United States and shifts to more precipitation falling as rain than snow in the cold season 

21 in many parts of the central and eastern United States (high confidence).

22 Introduction

23 Changes in precipitation are one of the most important potential outcomes of a warming world,

24 because precipitation is integral to the very nature of society and ecosystems. These systems 

25 have developed and adapted to the past envelope of precipitation variations. Any large changes 

26 beyond the historical envelope may have profound societal and ecological impacts.

27 Historical variations in precipitation, as observed from both instrumental and proxy records,

28 establish the context around which future projected changes can be interpreted because it is 

29 within that context that systems have evolved. Long-term station observations from core climate 

30 networks serve as a primary source to establish observed changes in both means and extremes.

31 Proxy records, which are used to reconstruct past climate conditions, are varied and include 

32 sources such as tree ring and ice core data. Projected changes are examined using the Coupled 

33 Model Intercomparison Project Phase 5 (CMIP5) suite of model simulations. They establish the 

34 likelihood of distinct regional and seasonal patterns of change.
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1 7.1 Historical Changes

2 7.1.1 Mean Changes

3 Annual precipitation averaged across the United States has increased approximately 4% over the 

4 1901–2015 period, slightly less than the 5% increase reported in the Third National Climate 

5 Assessment (NCA3) over the 1901–2012 period (Walsh et al. 2014). This slight decrease appears 

6 to be the result of the recent lingering droughts in the western and southwestern United States

7 (NOAA 2016a; Barnston and Lyon 2016). The current meteorological drought in California 

8 began in late 2011 (Seager et al. 2015; NOAA 2016b). Further, there continue to be important 

9 regional and seasonal differences in precipitation changes (Figure 7.1). Seasonally, national 

10 increases are largest in the fall, while little change is observed for winter. Regional differences 

11 are apparent, as the Northeast, Midwest, and Great Plains have had increases while parts of the 

12 Southwest and Southeast have had decreases. The year 2015 was the third wettest on record, just 

13 behind 1973 and 1983 (all of which were years marked by El Niño events). Interannual 

14 variability is substantial, as evidenced by large multiyear meteorological and agricultural 

15 droughts in the 1930s and 1950s.

16 [INSERT FIGURE 7.1 HERE:

17 Figure 7.1: Annual and seasonal changes in precipitation over the contiguous United States.

18 Changes are the average for present-day (1986-2015) minus the average for the first half of the 

19 last century (1901-1960 for the contiguous United States, 1925-1960 for Alaska and Hawaii)

20 divided by the average for the first half of the century. (Figure source: Panel 1: adapted from 

21 Peterson et al. 2013, © American Meteorological Society. Used with permission; Panels 2-5:

22 NOAA NCEI, data source: nCLIMDiv)].

23 Trends differ markedly across the seasons, as do regional patterns of increases and decreases.

24 Fall exhibits the largest (10%) and most widespread increase, exceeding 15% in much of the 

25 Northern Great Plains, Southeast, and Northeast. Winter has the smallest increase (2%), with 

26 drying over most of the western United States as well as parts of the Southeast. Spring and 

27 summer have comparable increases (about 3.5%) but substantially different patterns. In spring, 

28 the northern half of the contiguous United States has become wetter and the southern half has 

29 become drier. In summer, there is a mixture of increases and decreases across the Nation. Alaska 

30 shows little change in annual precipitation (+1.5%), however in all seasons central Alaska shows 

31 declines, and the panhandle shows increases. Hawai‘i shows a decline of more than 15% in 

32 annual precipitation.

33 7.1.2 Snow

34 Changes in snow cover extent (SCE) in the Northern Hemisphere exhibit a strong seasonal 

35 dependence. There has been little change since the 1960s (when the first satellite records became 

36 available) in the winter, while fall SCE has increased. However, spring SCE has declined, due in 
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1 part to higher temperatures that shorten the time snow spends on the ground in the spring. This 

2 tendency is highlighted by the recent occurrences of both unusually high and unusually low 

3 monthly (October–June) SCE values, including top 5 highest and top 5 lowest values in the 48 

4 years of data. From 2010 onward, 7 of the 45 highest monthly SCE values occurred, all in the 

5 fall or winter (mostly in November and December), while 9 of the 10 lowest May and June 

6 values occurred. This reflects the trend toward earlier spring snowmelt, particularly at high 

7 latitudes, while little trend is noted in extreme fall SCE (Kunkel et al. 2016). The seasonal 

8 maximum snow depth has decreased and shifted to an earlier date over North America since 

9 1951 (Kunkel et al. 2016). There has been a decrease in the frequency of large snowfall years

10 (years exceeding the 90th percentile) in the southern United States and the U.S. Pacific 

11 Northwest and an increase in the frequency of large snowfall years in the northern United States

12 (Kluver and Leathers 2015). In the snow belts of the Great Lakes, lake effect snowfall has 

13 increased overall since the early 20th Century for Lakes Superior, Michigan-Huron, and Erie 

14 (Kunkel et al. 2010). However, individual studies for Lakes Michigan (Bard and Kristovich 

15 2012) and Ontario (Harnett et al. 2014) indicate that this increase has not been continuous. In 

16 both cases, upward trends were observed till the 1970s/early 1980s. However, since then lake 

17 effect snowfall has decreased in these regions. 

18 7.1.3 Observed changes in U.S. seasonal extreme precipitation.

19 Extreme precipitation events occur when the air is nearly completely saturated. Hence, extreme 

20 precipitation events are generally observed to increase in intensity by about 6% to 7% for each 

21 degree Celsius of temperature increase, as dictated by the Clausius-Clapeyron relation. Figure

22 7.2 shows the observed change in the 20-year return value of the seasonal maximum 5-day 

23 precipitation totals (rx5day) over the period 1948 to 2015. A mix of increases and decreases in 

24 individual weather stations is observed. However, well over two-thirds of the stations exhibit 

25 statistically significant increases, consistent with theoretical expectations and the observed 

26 changes in atmospheric moisture content.

27 [INSERT FIGURE 7.2 HERE:

28 Figure 7.2: Observed changes in the 20-year return value of the seasonal daily precipitation totals 

29 over the period 1948 to 2015 using data from the Global Historical Climatology Network 

30 (GHCN) dataset. (Figure source: adapted from Kunkel et al. 2013; © American Meteorological 

31 Society. Used with permission.)]

32 Another metric of extreme precipitation, the annual maximum daily precipitation total, was 

33 calculated for the period 1901–2015. Those events exceeding a 5-year return value (essentially 

34 the top 20% of all annual maximum values) were averaged for 1986–2015 and 1901–1960. The 

35 difference between these two periods (Figure 7.3) indicates substantial increases over the eastern 

36 United States, particularly the northeast United States. The increases are much smaller over the 

37 western United States, with the southwest and northwest United States showing little increase. 
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1 [INSERT FIGURE 7.3 HERE:

2 Figure 7.3: Percentage difference between 1901–1960 average and 1981–2015 average of top 

3 20% (events exceeding the threshold for a 5-year return period) of annual maximum daily 

4 precipitation values in each period using 930 U.S. stations from the Global Historical 

5 Climatology Network (GHCN). The percentages are first calculated for individual stations, then 

6 averaged over 2° latitude by 2° longitude grid boxes, and finally averaged over each NCA4 

7 region. (Figure source: CICS-NC / NOAA NCEI)]

8 Figure 7.4 shows an update of a U.S. index of extreme precipitation from NCA3. This is the 

9 number of 2-day precipitation events exceeding the threshold for a 5-year recurrence, calculated 

10 over the period of 1896–2015. The number of events has been well above average for the last 

11 three decades. The slight drop from 2006–2010 to 2011–2015 reflects a below average number 

12 during the widespread severe meteorological drought year of 2012, while the other years in this 

13 pentad were well above average. The index value for 2015 was 80% above the 1901–1960 

14 reference period average and the third highest value in the 120 years of record (after 1998 and 

15 2008).

16 [INSERT FIGURE 7.4 HERE:

17 Figure 7.4: Index of the number of 2-day precipitation events exceeding the station-specific 

18 threshold for a 5-year recurrence interval. The annual values are averaged over 5-year periods,

19 with the pentad label indicating the ending year of the period. Annual time series of the number 

20 of events are first calculated at individual stations. Next, the grid box time series are calculated 

21 as the average of all stations in the grid box. Finally, a national time series is calculated as the 

22 average of the grid box time series. Data source: GHCN-Daily. (Figure source: CICS-NC / 

23 NOAA NCEI)]

24 7.1.4 Extratropical Cyclones and Precipitation

25 A large percentage of the extreme precipitation events in the United States are caused by 

26 extratropical cyclones (ETCs) and their associated fronts (Kunkel et al. 2012). In the northern 

27 United States, this is the case even in the summer when a sizeable fraction of extreme events 

28 occurs. The number of strong ETCs over North America in the summer has decreased since 1979 

29 by more than 35% (Chang et al. 2016), and overall ETC activity has decreased over this same 

30 time period. Most climate models simulate little change over this same historical period, but they 

31 project a decrease in summer ETC activity during the remainder of the 21st century (Chang et al. 

32 2016). This suggests that in the future there may be fewer opportunities in the summer for 

33 extreme precipitation, although increases in water vapor are likely to overcompensate for any 

34 decreases in ETCs by increasing the likelihood that an ETC will produce excessive rainfall 

35 amounts. An idealized set of climate simulations (Pfahl et al. 2015) suggests that substantial 

36 projected warming will lead to a decrease in the number of ETCs but an increase in the intensity 

37 of the strongest ETCs. Thus, the most extreme precipitation events associated with ETCs may be 

38 even greater in the future.
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1 7.1.5 Detection and Attribution 

2 TRENDS

3 Detectability of trends (compared to internal variability) for a number of precipitation metrics 

4 over the continental United States has been examined, however, trends identified for the United 

5 States regions have not been clearly attributed to anthropogenic forcing (Anderson et al. 2015; 

6 Easterling et al. 2016). One study concluded that increasing precipitation trends in some north7 central U.S. regions and the extreme annual anomalies there in 2013 were at least partly 

8 attributable to the combination of anthropogenic and natural forcing (Knutson et al. 2014).

9 At the global scale there is medium confidence that anthropogenic forcing has contributed to 

10 global-scale intensification of heavy precipitation over land regions with sufficient data coverage 

11 (Bindoff et al. 2013). Global changes in extreme precipitation have been attributed to 

12 anthropogenically forced climate change (Min et al. 2011, 2013), including annual maximum 1-

13 day and 5-day accumulated precipitation over northern hemisphere land regions and (relevant to 

14 this report) over the North American continent (Zhang et al. 2013). Although the United States 

15 was not separately assessed, the parts of North America with sufficient data for their analysis 

16 included the continental United States, and parts of southern Canada, Mexico and Central 

17 America. Since the covered region was, predominantly over the United States, their 

18 detection/attribution findings are applicable to the continental United States.

19 Analyses of precipitation extreme changes over the U.S. by region (20-year return values of 

20 seasonal daily precipitation over 1948–2015, Figure 7.2) show statistically significant increases

21 consistent with theoretical expectations and previous analyses (Westra et al. 2013). Further, a 

22 significant increase in the area affected by precipitation extremes over North America has also 

23 been detected (Dittus et al. 2015). Extreme rainfall from U.S. landfalling tropical cyclones has 

24 been higher in recent years (1994–2008) than the long-term historical average, even accounting 

25 for temporal changes in storm frequency (Kunkel et al. 2010). 

26 Based on current evidence it is concluded that detectable but not attributable increases in mean 

27 precipitation have occurred over parts of the central United States. Formal detection-attribution 

28 studies indicate a human contribution to extreme precipitation increases over the continental 

29 United States, but confidence is low based on those studies alone due to the short observational 

30 period, high natural variability, and model uncertainty. 

31 In summary, based on available studies, it is concluded that for the continental United States 

32 there is high confidence in the detection of extreme precipitation increases, while there is low 

33 confidence in attributing the extreme precipitation changes purely to anthropogenic forcing.

34 There is stronger evidence for a human contribution (medium confidence) when taking into 

35 account process-based understanding (increased water vapor in a warmer atmosphere), evidence

36 from weather and climate models, and trends in other parts of the world.
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1 EVENT ATTRIBUTION

2 A number of recent heavy precipitation events have been examined to determine the degree to 

3 which their occurrence and severity can be attributed to human-induced climate change. Table 

4 7.1 summarizes available attribution statements for recent extreme U.S. precipitation events.

5 Seasonal and annual precipitation extremes occurring in the north-central and eastern U.S.

6 regions in 2013 were examined for evidence of an anthropogenic influence on their occurrence

7 (Knutson et al. 2014). Increasing trends in annual precipitation were detected in the northern tier 

8 of states, March–May precipitation in the upper Midwest, and June–August precipitation in the 

9 eastern United States since 1900. These trends are attributed to some kind of external forcing 

10 (anthropogenic + natural) but could not be directly attributed to anthropogenic forcing alone.

11 However, based on this analysis it is concluded that the probability of these kinds of extremes 

12 has been made more likely by anthropogenic forcing. 

13 The human influence on individual storms has been investigated with conflicting results. In 

14 particular, Hoerling et al. (2014) find that despite the expected human-induced increase in 

15 available moisture, the GEOS-5 model produces fewer extreme storms in Colorado during the 

16 fall season and attribute that behavior to changes in the large-scale circulation. However, Pall et 

17 al. (2016) find that such coarse models cannot produce the observed magnitude of precipitation

18 due to resolution constraints. Based on a highly conditional set of hindcast simulations imposing 

19 the large-scale meteorology and a substantial increase in both the probability and magnitude of 

20 the observed precipitation accumulation magnitudes in that particular meteorological situation, 

21 their study could not address the question of whether such situations have become more or less 

22 probable. Extreme precipitation event attribution is inherently limited by the rarity of the 

23 necessary meteorological conditions and the limited number of model simulations that can be 

24 performed to examine rare events. This remains an open and active area of research. However, 

25 based on these two studies, the anthropogenic contribution to the 2013 Colorado heavy rainfall26 flood event is unclear.

27 [INSERT TABLE 7.1 HERE:

28 Table 7.1: A list of U.S. extreme precipitation events for which attribution statements have been 

29 made. In the last column, “+” indicates that an attributable human-induced increase in frequency 

30 and/or magnitude was found, “-“ indicates that an attributable human-induced decrease in 

31 frequency and/or magnitude was found, “0” indicates no attributable human contribution was 

32 identified. As in tables 6.1 and 8.2, several of the events were originally examined in the Bulletin

33 of the American Meteorological Society’s (BAMS) State of the Climate Reports and reexamined 

34 by Angelil et al. (2016). In these cases, both attribution statements are listed with the original 

35 authors first. Source: M. Wehner.]

36
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1 7.2 Projections

2 Changes in precipitation in a warmer climate are governed by many factors. Although energy 

3 constraints can be used to understand global changes in precipitation, projecting regional 

4 changes is much more difficult because of uncertainty in projecting changes in the large-scale 

5 circulation that plays important roles in the formation of clouds and precipitation (Shepherd 

6 2014). For the contiguous United States (CONUS), future changes in seasonal average 

7 precipitation will include a mix of increases, decreases, or little change, depending on location 

8 and season (Figure 7.6). High-latitude regions are generally projected to become wetter while the 

9 subtropical zone is projected to become drier. As the CONUS lies between these two regions, 

10 there is significant uncertainty about the sign and magnitude of future anthropogenic changes to 

11 seasonal precipitation in much of the region, particularly in the middle latitudes of the nation. 

12 However, because the physical mechanisms controlling extreme precipitation differ from those 

13 controlling seasonal average precipitation (Section 7.1.4), in particular atmospheric water vapor 

14 will increase with increasing temperatures, confidence is high that projected future precipitation 

15 extremes will increase in frequency and intensity throughout the CONUS.

16 Global climate models used to project precipitation changes exhibit varying degrees of fidelity in 

17 capturing the observed climatology and seasonal variations of precipitation across the United 

18 States. Global or regional climate models with higher horizontal resolution generally achieve 

19 better skill than the CMIP5 models in capturing the spatial patterns and magnitude of winter 

20 precipitation in the western and southeastern United States (e.g., Mearns et al. 2012; Wehner 

21 2013; Bacmeister et al. 2014; Wehner et al. 2014), leading to improved simulations of snowpack 

22 and runoff (e.g., Rauscher et al. 2008; Rasmussen et al. 2011). Simulation of present and future 

23 summer precipitation remains a significant challenge, as current convective parameterizations 

24 fail to properly represent the statistics of mesoscale convective systems (Klein et al. 2012). As a 

25 result, high-resolution models that still require the parameterization of deep convection exhibit 

26 mixed results (Wehner et al. 2014; Sakaguchi et al. 2015). Advances in computing technology 

27 are beginning to enable regional climate modeling at the higher resolutions (1–4 km), permitting 

28 the direct simulation of convective clouds systems (e.g., Ban et al. 2014) and eliminating the 

29 need for this class of parameterization. However, projections from such models are not yet ready 

30 for inclusion in this report.

31 Important progress has been made by the climate modeling community in providing multimodel 

32 ensembles such as CMIP5 (Taylor et al. 2012) and NARCCAP (Mearns et al. 2012) to 

33 characterize projection uncertainty arising from model differences, and large ensemble 

34 simulations such as CESM-LE (Kay et al. 2015) to characterize uncertainty inherent in the 

35 climate system due to internal variability. 

36 Projections in this report from the CMIP5 climate model database is based both on model 

37 independence and a multivariate measure of skill over North America as described in section 

38 4.4.2. The model skill metrics in simulating seasonal average and extreme precipitation are 
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1 shown in figure 7.5. The extreme precipitation index is the seasonal maximum pentad total,

2 defined as rx5day in the Expert Team on Climate Change Detection Indices (see climdex.org).

3 [INSERT FIGURE 7.5 HERE:

4 Figure 7.5: Relative performance of the CMIP5 models used in this study in simulating 

5 observed North American precipitation indices. Performance in simulating seasonal maxima 

6 pentad precipitation is shown in the top four rows. The next four rows show performance in 

7 replicating seasonal average precipitation (winter, spring, summer, fall). The bottom row is a 

8 combination of all eight precipitation performance metrics. Models are ordered from left (best) to 

9 right (worst) as determined by this combined metric. (Figure source: adapted from Sanderson et 

10 al., 2016]

11 7.2.1 Future Changes in U.S. Seasonal Mean Precipitation.

12 In the United States, projected changes in seasonal mean precipitation span the range from 

13 profound decreases to profound increases. And in many regions and seasons, projected changes 

14 in precipitation are not large compared to natural variations. The general pattern of change is 

15 clear and consistent with theoretical expectations. Figure 7.6 shows the weighted CMIP5 multi16 model average seasonal change at the end of the century compared to the present under the 

17 RCP8.5 scenario (see Ch. 4: Projections for discussion of RCPs). In this figure, changes 

18 projected with high confidence to be larger than natural variations are stippled. Regions where 

19 future changes are projected with high confidence to be smaller than natural variations are 

20 hashed. In winter and spring, the northern part of the country is projected to become wetter as the 

21 global climate warms. In the early to middle parts of this century, this will likely be manifested 

22 as increases in snowfall (O’Gorman 2014). Later on, as temperature continues to increase, it will 

23 be too warm to snow in many current snow-producing situations, and precipitation will mostly 

24 be rainfall. In the southwestern United States, precipitation will decrease in the spring but the 

25 changes are only a little larger than natural variations. Many other regions of the country will not 

26 experience significant changes in average precipitation. This is also the case over most of the 

27 country in the summer and fall.

28 [INSERT FIGURE 7.6 HERE:

29 Figure 7.6: CMIP5 weighted multi-model seasonal average precipitation percent change in the 

30 2070–2100 period relative to the 1976–2005 average under the RCP8.5 pathway. Stippling 

31 indicates that changes are assessed to be large compared to natural variations. Hashing indicates 

32 that changes are assessed to be small compared to natural variations. Blank regions (if any) are 

33 where projections are assessed to be inconclusive. Data source: World Climate Research 

34 Program's (WCRP's) Coupled Model Intercomparison Project. (Figure source: NOAA NCEI)].

35 This pattern of projected precipitation change arises because of changes in locally available 

36 water vapor and weather system shifts. In the northern part of the continent, increases in water 

37 vapor, together with changes in circulation that are the result of expansion of the Hadley cell,
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1 bring more moisture to these latitudes while maintaining or increasing the frequency of 

2 precipitation-producing weather systems. This change in the Hadley circulation also causes the 

3 subtropics to be drier in warmer climates as well as moving the mean storm track northward and 

4 away from the subtropics, decreasing the frequency of precipitation-producing systems. The 

5 combination of these two factors results in precipitation decreases in the southwestern United 

6 States, Mexico, and the Caribbean (Collins et al. 2013).

7 PROJECTED CHANGES IN SNOW

8 The Third National Climate Assessment (Georgakakos et al. 2014) projected reductions in 

9 annual snowpack of up to 40% in the western United States based on the SRES A2 emissions 

10 scenario in the CMIP3 suite of climate model projections. Recent research using the CMIP5 suite 

11 of climate model projections forced with the RCP8.5 scenario and statistically downscaled for 

12 the western United States continues to show the expected declines in various snow metrics,

13 including snow water equivalent, the number of extreme snowfall events, and number of 

14 snowfall days (Lute et al. 2015). A northward shift in the rain–snow transition zone in the central 

15 and eastern United States was found using statistically downscaled CMIP5 simulations forced 

16 with RCP8.5. By the end of the 21st century, large areas that are currently snow-dominated in 

17 the cold season are expected to be rainfall dominated (Ning and Bradley 2015). 

18 7.2.2 Extremes

19 HEAVY PRECIPITATION EVENTS

20 Studies project that the observed increase in heavy precipitation events will continue in the future

21 (e.g. Janssen et al. 2014, 2016). Similar to observed changes, increases are expected in all 

22 regions, even those regions where total precipitation is projected to decline, such as the 

23 southwestern United States. Under the RCP8.5 scenario the number of extreme events 

24 (exceeding a 5-year return period) increases by 2 to 3 times the historical average in every region 

25 (Figure 7.7) by the end of the 21st century, with the largest increases in the Northeast. Under the 

26 RCP4.5 scenario, increases are 50%–100%. Research shows that there is strong evidence, both 

27 from the observed record and modeling studies, that increased water vapor resulting from higher 

28 temperatures is the primary cause of the increases (Kunkel et al. 2013a,b; Wehner 2013). 

29 However, additional effects on extreme precipitation due to changes in dynamical processes are 

30 poorly understood.

31 [INSERT FIGURE 7.7 HERE:

32 Figure 7.7. Regional extreme precipitation event frequency for RCP4.5 (green) and RCP8.5 

33 (blue) for a 2-day duration and 5-year return. Calculated for 2006–2100 but decadal anomalies 

34 begin in 2011. Error bars are ±1 standard deviation. (Figure source: Janssen et al. 2014)]

35 Projections of changes in the 20-year return period amount for daily precipitation (Figure 7.8) 

36 using LOCA downscaled data also show large percentage increases for both the middle and late 
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21st 1 century. The lower emissions projections (RCP4.5) show increases of around 10% for mid2 century, and up to 14% for the late century projections. The higher emissions projections show 

3 even large increases for both mid-century and the late century projections. No region in either 

4 emissions scenario shows a decline in heavy precipitation.

5 [INSERT FIGURE 7.8 HERE:

6 Figure 7.8: Projected change in the 20-year return period amount for daily precipitation for mid7 and late-21st century for RCP4.5 and RCP8.5 emissions scenarios using LOCA downscaled data. 

8 Figure source: CICS-NC / NOAA NCEI)]

9 HURRICANE PRECIPITATION

10 For precipitation from hurricanes, several studies have projected increases of precipitation rates 

11 over ocean regions (Knutson et al. 2010), including for the Atlantic basin in particular (Knutson 

12 et al. 2013). The primary physical mechanism for this increase is the enhanced water vapor 

13 content in the warmer atmosphere, which enhances moisture convergence into the storm for a 

14 given circulation strength, although a more intense circulation can also contribute (Wang et al. 

15 2015). In a set of idealized forcing experiments, this effect was partly offset by differences in 

16 warming rates at the surface and at altitude (Villarini et al. 2014). Regional model projections of 

17 precipitation from landfalling tropical cyclones over the United States, based on downscaling of 

18 CM3 and CMIP5 model climate changes, suggest that the 21st century CMIP5-based projected

19 occurrence frequency of post-landfall tropical cyclones over the United States showed little 

20 change compared to present day, as the reduced frequency of tropical cyclones over the Atlantic 

21 domain was mostly offset by a greater landfalling fraction. CM3-based projections showed a 

22 reduced occurrence frequency over U.S. land. The average tropical cyclone rainfall rates within 

23 500 km (about 311 miles) of the storm center increased by 8% to 17% in the simulations, which 

24 was at least as much as expected from the water vapor content increase factor alone.

25
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1 TRACEABLE ACCOUNTS

2 Key Message 1

3 There are sizeable regional and seasonal differences in precipitation changes since 1901. Annual 

4 precipitation has decreased in much of the West, Southwest and Southeast, and increased in most 

5 of the Northern and Southern Plains, Midwest and Northeast. A national average increase of 4% 

6 in annual precipitation since 1901 is mostly a result of large increases in the fall season. (Medium 

7 confidence)

8 Description of evidence base

9 The key message and supporting text summarizes extensive evidence documented in the climate 

10 science peer-reviewed literature. Evidence of long-term changes in precipitation is based on 

11 analysis of daily precipitation observations from the U.S. Cooperative Observer Network 

12 (http://www.nws.noaa.gov/om/coop/) and shown in Figure 7.1. Published work (refs) and Figure

13 7.1 show important regional and seasonal differences in U.S. precipitation change since 1901.

14 New Information and remaining uncertainties

15 The main key issues that relates to uncertainty is the sensitivity of observed precipitation trends 

16 to the spatial distribution of observing stations, and to historical changes in station location, rain 

17 gauges, and observing practices. These issues are mitigated, somewhat, by new methods to 

18 produce spatial grids (Vose et al. 2014) through time.

19 Assessment of confidence based on evidence

20 Based on the evidence and understanding of the issues leading to uncertainties, confidence is 

21 high that average annual precipitation has increased in the U.S. Furthermore, confidence is also 

22 high, that the important regional and seasonal differences in changes documented in the text and 

23 in Figure 7.1 are robust.

24

25 Key Message 2

26 Heavy precipitation events across the United States have increased in both intensity and 

27 frequency since 1901. There are important regional differences in trends, with the largest 

28 increases occurring in the northeastern United States. (High confidence)

29 Description of evidence base

30 The key message and supporting text summarizes extensive evidence documented in the climate 

31 science peer-reviewed literature. Evidence of long-term changes in precipitation is based on 

32 analysis of daily precipitation observations from the U.S. Cooperative Observer Network 

33 (http://www.nws.noaa.gov/om/coop/) and shown in Figures 7.2, 7.3 and 7.4.

34 New Information and remaining uncertaintites

35 The main key issues that relates to uncertainty is the sensitivity of observed precipitation trends 

36 to the spatial distribution of observing stations, and to historical changes in station location, rain 
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1 gauges, and observing practices. These issues are mitigated, somewhat, by methods used to 

2 produce spatial grids through gridbox averaging.

3 Assessment of confidence based on evidence

4 Based on the evidence and understanding of the issues leading to uncertainties, confidence is 

5 high that heavy precipitation events have increased in the U.S. Furthermore, confidence is also 

6 high, that the important regional and seasonal differences in changes documented in the text and 

7 in Figures 7.2, 7.3, and 7.4 are robust.

8

9 Key Message 3

10 The frequency and intensity of heavy precipitation events are projected to continue to increase 

11 over the 21st century (high confidence). However, there are regional and seasonal differences in 

12 projected changes in total precipitation with the northern United States, including Alaska getting 

13 wetter in the winter and spring, and parts of the southwest United States getting drier in the 

14 winter and spring (medium confidence).

15 Description of evidence base

16 Evidence of future change in precipitation is based on climate model projections and our 

17 understanding of the climate system’s response to increasing greenhouse gases and on regional 

18 mechanisms behind the projected changes.

19 New information and remaining uncertainties

20 A key issue is how well climate models simulate precipitation, which is one of the more 

21 challenging aspects of weather and climate simulation. In particular, comparisons of model 

22 projections for total precipitation (from both CMIP3 and CMIP5, see Sun et al. 2015) by NCA3 

23 region show a spread of responses in some regions (e.g. southwest) such that they are opposite 

24 from the ensemble average response. The continental United States is positioned in the transition 

25 zone between expected drying in the sub-tropics and wetting in the mid- and higher-latitudes. 

26 There are some differences in the location of this transition between CMIP3 and CMIP5 models 

27 and thus there remains uncertainty in the exact location of the transition zone.

28 Assessment of confidence based on evidence

29 Based on evidence from climate model simulations and our fundamental understanding of the 

30 relationship of water vapor to temperature, confidence is high that extreme precipitation will 

31 increase in all regions of the United States. However, based on the evidence and understanding 

32 of the issues leading to uncertainties, confidence is medium that that more total precipitation is 

33 projected for the northern U.S. and less for the Southwest.

34

35
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1 Key Message 4

2 Northern Hemisphere spring snow cover extent, North America maximum snow depth, and 

3 extreme snowfall years in the southern and western United States. have all declined while 

4 extreme snowfall years in parts of the northern United States. have increased (medium 

5 confidence). Projections indicate large declines in snowpack in the western United States and 

6 shifts to more precipitation falling as rain than snow in the cold season in many parts of the 

7 central and eastern United States (high confidence).

8 Description of evidence base

9 Evidence of historical changes in snow cover extent and reduction in extreme snowfall years is 

10 consistent with our understanding of the climate system’s response to increasing greenhouse 

11 gases.

12 Furthermore, climate model continue to consistently show future declines in snowpack in the 

13 western United States. Recent model projections for the eastern United States also confirm a

14 future shift from snowfall to rainfall during the cold season in colder portions of the central and 

15 eastern United States.

16 New Information and remaining uncertainties

17 The main key issues that relates to uncertainty is the sensitivity of observed snow changes to the 

18 spatial distribution of observing stations, and to historical changes in station location, rain 

19 gauges, and observing practices, particularly for snow. Another key issue is the ability of climate 

20 models to simulate precipitation, particularly snow. Future changes in the frequency and 

21 intensity of meteorological systems causing heavy snow are less certain than temperature

22 changes.

23 Assessment of confidence based on evidence

24 Given the evidence base and uncertainties confidence is medium, that snow cover extent has 

25 declined in the United States and medium that extreme snowfall years have declined in recent 

26 years. Confidence is high that western United States snowpack will decline in the future, and 

27 confidence is medium that a shift from snow domination to rain domination will occur in the 

28 parts of the central and eastern United States cited in the text.

29
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1 TABLE

2 Table 7.1: A list of U.S. extreme precipitation events for which attribution statements have been 

3 made. In the last column, “+” indicates that an attributable human-induced increase in frequency 

4 and/or magnitude was found, “-“ indicates that an attributable human-induced decrease in 

5 frequency and/or magnitude was found, “0” indicates no attributable human contribution was 

6 identified. As in tables 6.2 and 8.2, several of the events were originally examined in the BAMS 

7 State of the Climate Reports and reexamined by Angelil et al. (2016) In these cases, both 

8 attribution statements are listed with the original authors first. Source: M. Wehner.

Authors Event year and 

duration

region type Attribution 

statement

Knutson et al. 2014 / 

Angelil et al. 2016

ANN 2013 U.S. Northern 

Tier

Wet +/0

Knutson et al. 2014 / 

Angelil et al. 2016

MAM 2013 U.S. Upper 

Midwest

Wet +/+

Knutson et al. 2014 / 

Angelil et al. 2016

JJA 2013 Eastern U.S. 

Region

Wet +/-

Edwards et al. 2014 October 4-5, 2013 South Dakota blizzard 0

Hoerling et al. 2014 September 10-14, 

2013

Colorado Wet 0

Pall et al. 2016 September 10-14, 

2013

Colorado Wet +

9

10
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1 FIGURES

2

3 Figure 7.1: Annual and seasonal changes in precipitation over the contiguous United States.

4 Changes are the average for present-day (1986-2015) minus the average for the first half of the 

5 last century (1901-1960 for the contiguous United States, 1925-1960 for Alaska and Hawaii) 

6 divided by the average for the first half of the century. (Figure source: Panel 1: adapted from 

7 Peterson et al. 2013, © American Meteorological Society. Used with permission; Panels 2-5: 

8 NOAA NCEI, data source: nCLIMDiv)
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1

2 Figure 7.2: Observed changes in the 20-year return value of the seasonal daily precipitation 

3 totals over the period 1948 to 2015 using data from the Global Historical Climatology Network 

4 (GHCN) dataset. (Figure source: adapted from Kunkel et al. 2013; © American Meteorological 

5 Society. Used with permission.)

6
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1

2 Figure 7.3: Percentage difference between 1901–1960 average and 1981–2015 average of top 

3 20% (events exceeding the threshold for a 5-year return period) of annual maximum daily 

4 precipitation values in each period using 930 U.S. stations from the Global Historical 

5 Climatology Network (GHCN). The percentages are first calculated for individual stations, then 

6 averaged over 2° latitude by 2° longitude grid boxes, and finally averaged over each NCA4 

7 region. (Figure source: CICS-NC / NOAA NCEI)

8
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1

2 Figure 7.4: Index of the number of 2-day precipitation events exceeding the station-specific 

3 threshold for a 5-year recurrence interval. The annual values are averaged over 5-year periods, 

4 with the pentad label indicating the ending year of the period. Annual time series of the number 

5 of events are first calculated at individual stations. Next, the grid box time series are calculated 

6 as the average of all stations in the grid box. Finally, a national time series is calculated as the 

7 average of the grid box time series. Data source: GHCN-Daily. (Figure source: CICS-NC / 

8 NOAA NCEI)

9
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1

2 Figure 7.5: Relative performance of the CMIP5 models used in this study in simulating observed 

3 North American precipitation indices. Performance in simulating seasonal maxima pentad 

4 precipitation is shown in the top four rows. The next four rows show performance in replicating 

5 seasonal average precipitation (winter, spring, summer, fall). The bottom row is a combination of 

6 all eight precipitation performance metrics. Models are ordered from left (best) to right (worst) 

7 as determined by this combined metric. (Figure source: adapted from Sanderson et al., 2016)

8

North America Precipitation Skill

HadCM3

HadGEM2_CC

MIROC5

HadGEM2_ES

CanESM2

CESM1_CAM5

HadGEM2_AO

CCSM4

CESM1_FASTCHEM

MPI_ESM_LR

CSIRO_Mk3_6_0

bcc_csm1_1

MPI_ESM_P

CESM1_BGC

NorESM1_M

GFDL_ESM2G

CMCC_CM

ACCESS1_0

CMCC_CMS

IPSL_CM5A_LR

FGOALS_g2

GFDL_ESM2M

MPI_ESM_MR

IPSL_CM5A_MR

GFDL_CM3

MIROC4h

bcc_csm1_1_m

CNRM_CM5

CMCC_CESM

ACCESS1_3

GISS_E2_R_p1

inmcm4

MIROC_ESM_CHEM

MRI_CGCM3

MIROC_ESM

GISS_E2_R_p2

MRI_ESM1

IPSL_CM5B_LR

GISS_E2_H_p1GISS_E2_H_p2

BNU_ESM

Rx5day DJF

Rx5day MAM

Rx5day JJA

Rx5day SON

Rx5day ALL

Mean Precip DJF

Mean Precip MAM

Mean Precip JJA

Mean Precip SON

Mean Precip ALL

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

Distance from obs (relative to CMIP mean)
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1

2 Figure 7.6: CMIP5 weighted multi-model seasonal average precipitation percent change in the 

3 2070–2100 period relative to the 1976–2005 average under the RCP8.5 pathway. Stippling 

4 indicates that changes are assessed to be large compared to natural variations. Hashing indicates 

5 that changes are assessed to be small compared to natural variations. Blank regions (if any) are 

6 where projections are assessed to be inconclusive. Data source: World Climate Research 

7 Program's (WCRP's) Coupled Model Intercomparison Project. Figure source: NOAA NCEI.

8
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1

2 Figure 7.7: Regional extreme precipitation event frequency for RCP4.5 (green) and RCP8.5 

3 (blue) for a 2-day duration and 5-year return. Calculated for 2006–2100 but decadal anomalies 

4 begin in 2011. Error bars are ±1 standard deviation. (Figure source: Janssen et al. 2014)

5
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1

2 Figure 7.8: Projected change in the 20-year return period amount for daily precipitation for mid3 and late-21st century for RCP4.5 and RCP8.5 emissions scenarios using LOCA downscaled data. 

4 Figure source: CICS-NC / NOAA NCEI)
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1 REFERENCES

2 Anderson, B.T., D.J. Gianotti, and G.D. Salvucci, 2015: Detectability of historical trends in 

3 station-based precipitation characteristics over the continental United States. Journal of 

4 Geophysical Research: Atmospheres, 120, 4842-4859.

5 http://dx.doi.org/10.1002/2014JD022960 

6 Angélil, O., D. Stone, M. Wehner, C.J. Paciorek, H. Krishnan, and W. Collins, 2016: An 

7 independent assessment of anthropogenic attribution statements for recent extreme 

8 temperature and rainfall events. Journal of Climate, in press, null.

9 http://dx.doi.org/10.1175/JCLI-D-16-0077.1 

10 Bacmeister, J.T., M.F. Wehner, R.B. Neale, A. Gettelman, C. Hannay, P.H. Lauritzen, J.M. 

11 Caron, and J.E. Truesdale, 2014: Exploratory high-resolution climate simulations using the 

12 Community Atmosphere Model (CAM). Journal of Climate, 27, 3073-3099.

13 http://dx.doi.org/10.1175/JCLI-D-13-00387.1 

14 Ban, N., J. Schmidli, and C. Schär, 2014: Evaluation of the convection-resolving regional 

15 climate modeling approach in decade-long simulations. Journal of Geophysical Research: 

16 Atmospheres, 119, 7889-7907. http://dx.doi.org/10.1002/2014JD021478 

17 Bard, L. and D.A.R. Kristovich, 2012: Trend Reversal in Lake Michigan Contribution to 

18 Snowfall. Journal of Applied Meteorology and Climatology, 51, 2038-2046.

19 http://dx.doi.org/10.1175/jamc-d-12-064.1 

20 http://journals.ametsoc.org/doi/abs/10.1175/JAMC-D-12-064.1

21 Barnston, A.G. and B. Lyon, 2016: Does the NMME Capture a Recent Decadal Shift toward 

22 Increasing Drought Occurrence in the Southwestern United States? Journal of Climate, 29,

23 561-581. http://dx.doi.org/10.1175/JCLI-D-15-0311.1 

24 Bindoff, N.L., P.A. Stott, K.M. AchutaRao, M.R. Allen, N. Gillett, D. Gutzler, K. Hansingo, G. 

25 Hegerl, Y. Hu, S. Jain, I.I. Mokhov, J. Overland, J. Perlwitz, R. Sebbari, and X. Zhang, 2013: 

26 Detection and Attribution of Climate Change: from Global to Regional. Climate Change 

27 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment 

28 Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. 

29 Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, 

30 Eds. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 

31 867–952. http://dx.doi.org/10.1017/CBO9781107415324.022 www.climatechange2013.org

32 Chang, E.K.M., C.-G. Ma, C. Zheng, and A.M.W. Yau, 2016: Observed and projected decrease 

33 in Northern Hemisphere extratropical cyclone activity in summer and its impacts on 

34 maximum temperature. Geophysical Research Letters, 43, 2200-2208.

35 http://dx.doi.org/10.1002/2016GL068172 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 7

275

1 Collins, M., R. Knutti, J. Arblaster, J.-L. Dufresne, T. Fichefet, P. Friedlingstein, X. Gao, W.J. 

2 Gutowski, T. Johns, G. Krinner, M. Shongwe, C. Tebaldi, A.J. Weaver, and M. Wehner, 

3 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. Climate 

4 Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth 

5 Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. 

6 Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and 

7 P.M. Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom and New 

8 York, NY, USA, 1029–1136. http://dx.doi.org/10.1017/CBO9781107415324.024 

9 www.climatechange2013.org

10 Dittus, A.J., D.J. Karoly, S.C. Lewis, and L.V. Alexander, 2015: A Multiregion Assessment of 

11 Observed Changes in the Areal Extent of Temperature and Precipitation Extremes. Journal 

12 of Climate, 28, 9206-9220. http://dx.doi.org/10.1175/JCLI-D-14-00753.1 

13 Easterling, D.R., K.E. Kunkel, M.F. Wehner, and L. Sun, 2016: Detection and attribution of 

14 climate extremes in the observed record. Weather and Climate Extremes, 11, 17-27.

15 http://dx.doi.org/10.1016/j.wace.2016.01.001 

16 Edwards, L.M., M. Bunkers, J.T. Abatzoglou, D.P. Todey, and L.E. Parker, 2014: October 2013 

17 Blizzard in Western South Dakota [in "Explaining Extremes of 2013 from a Climate 

18 Perspective"]. Bulletin of the American Meteorological Society, 95, S23-S26.

19 http://dx.doi.org/10.1175/1520-0477-95.9.S1.1 

20 Georgakakos, A., P. Fleming, M. Dettinger, C. Peters-Lidard, T.C. Richmond, K. Reckhow, K. 

21 White, and D. Yates, 2014: Ch. 3: Water resources. Climate Change Impacts in the United 

22 States: The Third National Climate Assessment. Melillo, J.M., T.C. Richmond, and G.W. 

23 Yohe, Eds. U.S. Global Change Research Program, Washington, D.C., 69-112. 

24 http://dx.doi.org/10.7930/J0G44N6T 

25 Hartnett, J.J., J.M. Collins, M.A. Baxter, and D.P. Chambers, 2014: Spatiotemporal Snowfall 

26 Trends in Central New York. Journal of Applied Meteorology and Climatology, 53, 2685-

27 2697. http://dx.doi.org/10.1175/jamc-d-14-0084.1 

28 http://journals.ametsoc.org/doi/abs/10.1175/JAMC-D-14-0084.1

29 Hoerling, M., K. Wolter, J. Perlwitz, X. Quan, J. Eischeid, H. Want, S. Schubert, H. Diaz, and R. 

30 Dole, 2014: Northeast Colorado extreme rains interpreted in a climate change context [in 

31 "Explaining Extremes of 2013 from a Climate Perspective"]. Bulletin of the American 

32 Meteorological Society, 95, S15-S18. http://dx.doi.org/10.1175/1520-0477-95.9.S1.1 

33 Janssen, E., R.L. Sriver, D.J. Wuebbles, and K.E. Kunkel, 2016: Seasonal and regional variations 

34 in extreme precipitation event frequency using CMIP5. Geophysical Research Letters, 43,

35 5385-5393. http://dx.doi.org/10.1002/2016GL069151 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 7

276

1 Janssen, E., D.J. Wuebbles, K.E. Kunkel, S.C. Olsen, and A. Goodman, 2014: Observational2 and model-based trends and projections of extreme precipitation over the contiguous United 

3 States. Earth's Future, 2, 99-113. http://dx.doi.org/10.1002/2013EF000185 

4 Kay, J.E., C. Deser, A. Phillips, A. Mai, C. Hannay, G. Strand, J.M. Arblaster, S.C. Bates, G. 

5 Danabasoglu, J. Edwards, M. Holland, P. Kushner, J.-F. Lamarque, D. Lawrence, K. 

6 Lindsay, A. Middleton, E. Munoz, R. Neale, K. Oleson, L. Polvani, and M. Vertenstein, 

7 2015: The Community Earth System Model (CESM) Large Ensemble Project: A Community 

8 Resource for Studying Climate Change in the Presence of Internal Climate Variability. 

9 Bulletin of the American Meteorological Society, 96, 1333-1349.

10 http://dx.doi.org/10.1175/BAMS-D-13-00255.1 

11 Klein, S.A., X. Jiang, J. Boyle, S. Malyshev, and S. Xie, 2006: Diagnosis of the summertime 

12 warm and dry bias over the U.S. Southern Great Plains in the GFDL climate model using a 

13 weather forecasting approach. Geophysical Research Letters, 33, n/a-n/a.

14 http://dx.doi.org/10.1029/2006GL027567 http://dx.doi.org/10.1029/2006GL027567

15 Kluver, D. and D. Leathers, 2015: Regionalization of snowfall frequency and trends 

16 over the contiguous United States. International Journal of Climatology, 35, 4348-4358.

17 http://dx.doi.org/10.1002/joc.4292 

18 Knutson, T.R., J.L. McBride, J. Chan, K. Emanuel, G. Holland, C. Landsea, I. Held, J.P. Kossin, 

19 A.K. Srivastava, and M. Sugi, 2010: Tropical cyclones and climate change. Nature 

20 Geoscience, 3, 157-163. http://dx.doi.org/10.1038/ngeo779 

21 Knutson, T.R., J.J. Sirutis, G.A. Vecchi, S. Garner, M. Zhao, H.-S. Kim, M. Bender, R.E. 

22 Tuleya, I.M. Held, and G. Villarini, 2013: Dynamical downscaling projections of twenty23 first-century Atlantic hurricane activity: CMIP3 and CMIP5 model-based scenarios. Journal 

24 of Climate, 27, 6591-6617. http://dx.doi.org/10.1175/jcli-d-12-00539.1 

25 Knutson, T.R., F. Zeng, and A.T. Wittenberg, 2014: Seasonal and annual mean precipitation 

26 extremes occurring during 2013: A U.S. focused analysis [in "Explaining Extremes of 2013 

27 from a Climate Perspective"]. Bulletin of the American Meteorological Society, 95, S19-S23.

28 http://dx.doi.org/10.1175/1520-0477-95.9.S1.1 

29 Kunkel, K.E., D.R. Easterling, D.A. Kristovich, B. Gleason, L. Stoecker, and R. Smith, 2012: 

30 Meteorological causes of the secular variations in observed extreme precipitation events for 

31 the conterminous United States. Journal of Hydrometeorology, 13, 1131-1141.

32 http://dx.doi.org/10.1175/JHM-D-11-0108.1 

33 Kunkel, K.E., D.R. Easterling, D.A.R. Kristovich, B. Gleason, L. Stoecker, and R. Smith, 2010: 

34 Recent increases in U.S. heavy precipitation associated with tropical cyclones. Geophysical 

35 Research Letters, 37, L24706. http://dx.doi.org/10.1029/2010GL045164 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 7

277

1 Kunkel, K.E., L. Ensor, M. Palecki, D. Easterling, D. Robinson, K.G. Hubbard, and K. 

2 Redmond, 2009: A new look at lake-effect snowfall trends in the Laurentian Great Lakes 

3 using a temporally homogeneous data set. Journal of Great Lakes Research, 35, 23-29.

4 http://dx.doi.org/10.1016/j.jglr.2008.11.003 

5 http://www.bioone.org/doi/pdf/10.1016/j.jglr.2008.11.003

6 Kunkel, K.E., T.R. Karl, H. Brooks, J. Kossin, J. Lawrimore, D. Arndt, L. Bosart, D. Changnon, 

7 S.L. Cutter, N. Doesken, K. Emanuel, P.Y. Groisman, R.W. Katz, T. Knutson, J. O’Brien, 

8 C.J. Paciorek, T.C. Peterson, K. Redmond, D. Robinson, J. Trapp, R. Vose, S. Weaver, M. 

9 Wehner, K. Wolter, and D. Wuebbles, 2013: Monitoring and understanding trends in extreme 

10 storms: State of knowledge. Bulletin of the American Meteorological Society, 94.

11 http://dx.doi.org/10.1175/BAMS-D-11-00262.1 

12 Kunkel, K.E., T.R. Karl, D.R. Easterling, K. Redmond, J. Young, X. Yin, and P. Hennon, 2013: 

13 Probable maximum precipitation and climate change. Geophysical Research Letters, 40,

14 1402-1408. http://dx.doi.org/10.1002/grl.50334 

15 Kunkel, K.E., D.A. Robinson, S. Champion, X. Yin, T. Estilow, and R.M. Frankson, 2016: 

16 Trends and Extremes in Northern Hemisphere Snow Characteristics. Current Climate 

17 Change Reports, 2, 65-73. http://dx.doi.org/10.1007/s40641-016-0036-8 

18 Lute, A.C., J.T. Abatzoglou, and K.C. Hegewisch, 2015: Projected changes in snowfall extremes 

19 and interannual variability of snowfall in the western United States. Water Resources 

20 Research, 51, 960-972. http://dx.doi.org/10.1002/2014WR016267 

21 Mearns, L.O., R. Arritt, S. Biner, M.S. Bukovsky, S. Stain, S. Sain, D. Caya, J. J. Correia, D. 

22 Flory, W. Gutowski, E.S. Takle, R. Jones, R. Leung, W. Moufouma-Okia, L. McDaniel, 

23 A.M.B. Nunes, Y. Qian, J. Roads, L. Sloan, and M. Snyder, 2012: The North American 

24 regional climate change assessment program: Overview of phase I results. Bulletin of the 

25 American Meteorological Society, 93, 1337-1362. http://dx.doi.org/10.1175/BAMS-D-11-

26 00223.1 

27 Min, S.K., X. Zhang, F.W. Zwiers, and G.C. Hegerl, 2011: Human contribution to more-intense 

28 precipitation extremes. Nature, 470, 378-381. http://dx.doi.org/10.1038/nature09763 

29 Min, S.-K., X. Zhang, F. Zwiers, H. Shiogama, Y.-S. Tung, and M. Wehner, 2013: Multimodel 

30 Detection and Attribution of Extreme Temperature Changes. Journal of Climate, 26, 7430-

31 7451. http://dx.doi.org/10.1175/JCLI-D-12-00551.1 

32 Ning, L. and R.S. Bradley, 2015: Snow occurrence changes over the central and eastern United 

33 States under future warming scenarios. Scientific Reports, 5, 17073.

34 http://dx.doi.org/10.1038/srep17073 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 7

278

1 NOAA, 2016: Climate at a glance. http://www.ncdc.noaa.gov/cag/time2 series/us/107/0/pdsi/12/12/1895-

3 2016?base_prd=true&firstbaseyear=1901&lastbaseyear=2000

4 NOAA, 2016: Climate at a glance. http://www.ncdc.noaa.gov/cag/time5 series/us/4/0/pdsi/12/9/1895-2016?base_prd=true&firstbaseyear=

6 O'Gorman, P.A., 2014: Contrasting responses of mean and extreme snowfall to climate change. 

7 Nature, 512, 416-418. http://dx.doi.org/10.1038/nature13625 

8 Pall, P.C.M.P., M.F. Wehner, D.A. Stone, C.J. Paciorek, and W.D. Collins, 2016: Diagnosing 

9 anthropogenic contributions to heavy Colorado rainfall in September 2013. Weather and 

10 Climate Extremes, in review.

11 Peterson, T.C., R.R. Heim, R. Hirsch, D.P. Kaiser, H. Brooks, N.S. Diffenbaugh, R.M. Dole, J.P. 

12 Giovannettone, K. Guirguis, T.R. Karl, R.W. Katz, K. Kunkel, D. Lettenmaier, G.J. McCabe, 

13 C.J. Paciorek, K.R. Ryberg, S. Schubert, V.B.S. Silva, B.C. Stewart, A.V. Vecchia, G. 

14 Villarini, R.S. Vose, J. Walsh, M. Wehner, D. Wolock, K. Wolter, C.A. Woodhouse, and D. 

15 Wuebbles, 2013: Monitoring and understanding changes in heat waves, cold waves, floods 

16 and droughts in the United States: State of knowledge. Bulletin of the American 

17 Meteorological Society, 94, 821-834. http://dx.doi.org/10.1175/BAMS-D-12-00066.1 

18 Pfahl, S., P.A. O’Gorman, and M.S. Singh, 2015: Extratropical Cyclones in Idealized 

19 Simulations of Changed Climates. Journal of Climate, 28, 9373-9392.

20 http://dx.doi.org/10.1175/JCLI-D-14-00816.1 

21 Rasmussen, R., C. Liu, K. Ikeda, D. Gochis, D. Yates, F. Chen, M. Tewari, M. Barlage, J. 

22 Dudhia, W. Yu, K. Miller, K. Arsenault, V. Grubišić, G. Thompson, and E. Gutmann, 2011: 

23 High-Resolution Coupled Climate Runoff Simulations of Seasonal Snowfall over Colorado: 

24 A Process Study of Current and Warmer Climate. Journal of Climate, 24, 3015-3048.

25 http://dx.doi.org/10.1175/2010JCLI3985.1 

26 Rauscher, S.A., J.S. Pal, N.S. Diffenbaugh, and M.M. Benedetti, 2008: Future changes in 

27 snowmelt-driven runoff timing over the western US. Geophysical Research Letters, 35,

28 L16703. http://dx.doi.org/10.1029/2008GL034424 

29 Sakaguchi, K., L.R. Leung, C. Zhao, Q. Yang, J. Lu, S. Hagos, S.A. Rauscher, L. Dong, T.D. 

30 Ringler, and P.H. Lauritzen, 2015: Exploring a Multiresolution Approach Using AMIP 

31 Simulations. Journal of Climate, 28, 5549-5574. http://dx.doi.org/10.1175/JCLI-D-14-

32 00729.1 

33 Seager, R., M. Hoerling, S. Schubert, H. Wang, B. Lyon, A. Kumar, J. Nakamura, and N. 

34 Henderson, 2015: Causes of the 2011–14 California Drought. Journal of Climate, 28, 6997-

35 7024. http://dx.doi.org/10.1175/JCLI-D-14-00860.1 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 7

279

1 Shepherd, T.G., 2014: Atmospheric circulation as a source of uncertainty in climate change 

2 projections. Nature Geoscience, 7, 703-708. http://dx.doi.org/10.1038/ngeo2253 

3 Sun, L., K.E. Kunkel, L.E. Stevens, A. Buddenberg, J.G. Dobson, and D.R. Easterling, 2015: 

4 Regional Surface Climate Conditions in CMIP3 and CMIP5 for the United States: 

5 Differences, Similarities, and Implications for the U.S. National Climate Assessment. NOAA 

6 Technical Report NESDIS 144, 111 pp. National Oceanic and Atmospheric Administration, 

7 National Environmental Satellite, Data, and Information Service. 

8 http://www.nesdis.noaa.gov/technical_reports/NOAA_NESDIS_Technical_Report_144.pdf

9 Taylor, K.E., R.J. Stouffer, and G.A. Meehl, 2012: An overview of CMIP5 and the experiment 

10 design. Bulletin of the American Meteorological Society, 93, 485-498.

11 http://dx.doi.org/10.1175/BAMS-D-11-00094.1 

12 Villarini, G., D.A. Lavers, E. Scoccimarro, M. Zhao, M.F. Wehner, G.A. Vecchi, T.R. Knutson, 

13 and K.A. Reed, 2014: Sensitivity of Tropical Cyclone Rainfall to Idealized Global-Scale 

14 Forcings. Journal of Climate, 27, 4622-4641. http://dx.doi.org/10.1175/JCLI-D-13-00780.1 

15 Vose, R.S., S. Applequist, M. Squires, I. Durre, M.J. Menne, C.N.W. Jr., C. Fenimore, K. 

16 Gleason, and D. Arndt, 2014: Improved Historical Temperature and Precipitation Time 

17 Series for U.S. Climate Divisions. Journal of Applied Meteorology and Climatology, 53,

18 1232-1251. http://dx.doi.org/10.1175/JAMC-D-13-0248.1 

19 Walsh, J., D. Wuebbles, K. Hayhoe, J. Kossin, K. Kunkel, G. Stephens, P. Thorne, R. Vose, M. 

20 Wehner, J. Willis, D. Anderson, S. Doney, R. Feely, P. Hennon, V. Kharin, T. Knutson, F. 

21 Landerer, T. Lenton, J. Kennedy, and R. Somerville, 2014: Ch. 2: Our changing climate. 

22 Climate Change Impacts in the United States: The Third National Climate Assessment. 

23 Melillo, J.M., T.C. Richmond, and G.W. Yohe, Eds. U.S. Global Change Research Program, 

24 Washington, D.C., 19-67. http://dx.doi.org/10.7930/J0KW5CXT 

25 Wang and Kotamarthi, 2014: Missing from list.

26 Wang, C.-C., B.-X. Lin, C.-T. Chen, and S.-H. Lo, 2015: Quantifying the Effects of Long-Term 

27 Climate Change on Tropical Cyclone Rainfall Using a Cloud-Resolving Model: Examples of 

28 Two Landfall Typhoons in Taiwan. Journal of Climate, 28, 66-85.

29 http://dx.doi.org/10.1175/JCLI-D-14-00044.1 

30 Wehner, M.F., 2013: Very extreme seasonal precipitation in the NARCCAP ensemble: Model 

31 performance and projections. Climate Dynamics, 40, 59-80.

32 http://dx.doi.org/10.1007/s00382-012-1393-1 

33 Wehner, M.F., K.A. Reed, F. Li, Prabhat, J. Bacmeister, C.-T. Chen, C. Paciorek, P.J. Gleckler, 

34 K.R. Sperber, W.D. Collins, A. Gettelman, and C. Jablonowski, 2014: The effect of 

35 horizontal resolution on simulation quality in the Community Atmospheric Model, CAM5.1. 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 7

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1 Journal of Advances in Modeling Earth Systems, 6, 980-997.

2 http://dx.doi.org/10.1002/2013MS000276 

3 Westra, S., L.V. Alexander, and F.W. Zwiers, 2013: Global Increasing Trends in Annual 

4 Maximum Daily Precipitation. Journal of Climate, 26, 3904-3918.

5 http://dx.doi.org/10.1175/JCLI-D-12-00502.1 

6 Zhang, X., H. Wan, F.W. Zwiers, G.C. Hegerl, and S.-K. Min, 2013: Attributing intensification 

7 of precipitation extremes to human influence. Geophysical Research Letters, 40, 5252-5257.

8 http://dx.doi.org/10.1002/grl.51010
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1 8. Droughts, Floods, and Hydrology

2 KEY FINDINGS

3 1. Recent droughts and associated heat waves have reached record intensity in some regions of 

4 the United States, but, by geographical scale and duration, the Dust Bowl era of the 1930s 

5 remains the benchmark drought and extreme heat event in the historical record. (Very high 

6 confidence)

7 2. The human effect on recent major U.S. droughts is complicated. Little evidence is found for a 

8 human influence on observed precipitation deficits but much evidence is found for a human 

9 influence on surface soil moisture deficits due to increased evapotranspiration caused by 

10 higher temperatures. (High confidence)

11 3. Future decreases in surface soil moisture over most of the United States are likely as the 

12 climate warms. (High confidence)

13 4. Reductions in western U.S. winter and spring snowpack are projected as the climate warms. 

14 Under higher emissions scenarios, and assuming no change to current water-resources 

15 management, chronic, long-duration hydrological drought is increasingly possible by the end 

16 of this century. (Very high confidence)

17 5. Detectable increases in seasonal flood frequency have occurred in parts of the central United 

18 States. This is to be expected in the presence of the increase in extreme downpours known 

19 with high confidence to be linked to a warming atmosphere, but formal attribution 

20 approaches have not certified the connection of increased flooding to human influences. 

21 (Medium confidence)

22 8.1. Drought

23 The word “drought” brings to mind abnormally dry conditions. However, the meaning of “dry” 

24 can be ambiguous and lead to confusion in how drought is actually defined. Three different 

25 classes of droughts are defined by NOAA and describe a useful hierarchal set of water deficit

26 characterization, each with different impacts. “Meteorological drought” describes conditions of 

27 precipitation deficit. “Agricultural drought” describes conditions of soil moisture deficit.

28 “Hydrological drought” describes conditions of deficit in runoff (NOAA 2008). Clearly these 

29 three characterizations of drought are related but are also different descriptions of water scarcity 

30 with different target audiences. In particular, agricultural drought is of concern to producers of 

31 food while hydrological drought is of concern to water system managers. Soil moisture is a 

32 function of both precipitation and evapotranspiration. Because potential evapotranspiration 

33 increases with temperature, anthropogenic climate change generally results in drier soils and 

34 often less runoff in the long term. In fact, under the RCP8.5 scenario (see Ch. 4 for a description 

35 of the RCP scenarios) at the end of the 21st century, no region of the planet is projected to 
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1 experience significantly higher levels of annual average surface soil moisture due to the 

2 sensitivity of evapotranspiration to temperature, even though much higher precipitation is 

3 projected in some regions (Collins et al. 2013). Runoff, on the other hand, is projected to both 

4 increase and decrease, depending on location and season under the same conditions, illustrating 

5 the complex relationships between the various components of the hydrological system. Hence, it 

6 is vital to describe precisely the definition of drought in any public discussion to avoid confusion 

7 due to this complexity. 

8 8.1.1. Historical Context

9 The United States has experienced all three types of droughts in the past, always driven in at 

10 least some part by natural variations in seasonal and/or annual precipitation amounts. As the 

11 climate changes, we can expect that human activities will alter the effect of these natural 

12 variations. The “Dust Bowl” drought of the 1930s is still the most significant meteorological and 

13 agricultural drought experienced in the United States in terms of its geographic and temporal 

14 extent. However, even though it happened prior to most of the current global warming, human 

15 activities exacerbated the dryness of the soil by the farming practices of the time (Bennet et al.

16 1936). Tree ring archives reveal that such droughts (in the agricultural sense) have occurred 

17 periodically over the last 1,000 years (Cook et al. 2004). Long climate model simulations suggest 

18 that such droughts lasting several years to decades occur naturally in the southwestern United 

19 States (Coats et al. 2015). The IPCC AR5 (Bindoff et al. 2013) concluded “there is low 

20 confidence in detection and attribution of changes in (meteorological) drought over global land 

21 areas since the mid-20th century, owing to observational uncertainties and difficulties in 

22 distinguishing decadal-scale variability in drought from long-term trends.” As they noted, this 

23 was a weaker attribution statement than the IPCC AR4, which had concluded “that an increased 

24 risk of drought was more likely than not due to anthropogenic forcing during the second half of 

25 the 20th century.” The weaker statement in AR5 reflected additional studies with conflicting 

26 conclusions on global drought trends (e.g., Sheffield et al. 2012; Dai 2013). The western North 

27 America region was noted as a region where determining if observed recent droughts were 

28 unusual compared to natural variability was particularly difficult, due to evidence from 

29 paleoclimate proxies of cases of central U.S. droughts during the past 1,000 years that were 

30 longer and more intense than historical U.S. droughts (Masson-Delmotte et al. 2013). Future 

31 projections of the anthropogenic contribution to changes in drought risk and severity must be 

32 considered in the context of the significant role of natural variability.

33 8.1.2. Recent Major U.S. Droughts

34

35 Meteorological and agricultural drought

36 The United States has suffered a number of very significant droughts of all types since 2011.

37 Each of these droughts was a result of different persistent, large-scale meteorological patterns of 

38 mostly natural origins, with varying degrees of attributable human influence. Table 8.1
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1 summarizes available attribution statements for recent extreme U.S. meteorological and 

2 agricultural droughts. Statements about meteorological data are decidedly mixed, revealing the 

3 complexities in interpreting the low tail of the distribution of precipitation. Statements about 

4 agricultural drought consistently maintain a human influence if only surface soil moisture 

5 measures are considered. The single agricultural drought attribution study at root depth comes to 

6 the opposite conclusion. In all cases, these attribution statements are made without detection (see 

7 Section 3.2). The absence of moisture during the 2011 Texas/Oklahoma drought and heat wave

8 was found to be a naturally occurring event whose likelihood was enhanced by the La Niña state 

9 of the ocean, but the human interference in the climate system still doubled the chances of 

10 reaching such high temperatures (Hoerling et al. 2013). This study illustrates that the effect of 

11 human induced climate change is combined with natural variations and can compound or inhibit 

12 the realized severity of any given extreme weather event.

13 [INSERT TABLE 8.1 HERE:

14 Table 8.1: A list of U.S. droughts for which attribution statements have been made. In the last 

15 column, “+” indicates that an attributable human induced increase in frequency and/or magnitude 

16 was found, “− “ indicates that an attributable human induced decrease in frequency and/or 

17 magnitude was found, “0” indicates no attributable human contribution was identified. As in 

18 tables 6.2 and 7.1, several of the events were originally examined in the Bulletin of the American 

19 Meteorological Society’s (BAMS) State of the Climate Reports and reexamined by Angelil et al. 

20 (2016). In these cases, both attribution statements are listed with the original authors first. 

21 Source: M. Wehner.]

22 The Great Plains/Midwest drought of 2012 was the most severe summer meteorological drought 

23 in the observational record for that region (Hoerling et al. 2014). An unfortunate string of three 

24 different patterns of large-scale meteorology from May through August 2012 precluded the 

25 normal frequency of summer thunderstorms but was not predicted by the NOAA seasonal 

26 forecasts (Hoerling et al. 2014). Little influence of the global sea surface temperature (SST)

27 pattern on meteorological drought frequency has been found in model simulations (Hoerling et 

28 al. 2014). No evidence of a human contribution to the 2012 precipitation deficit in the Great 

29 Plains and Midwest is consistently found (Rupp et al. 2013; Hoerling et al. 2014; Angelil et al. 

30 2016). However, again an increase in the chances of the unusually high U.S. 2012 temperatures,

31 partly associated with resultant dry summer soil moisture anomalies, was attributed to the human 

32 interference to the climate system (Diffenbaugh and Scherer 2013), indicating the strong 

33 feedback between soil moisture and surface air temperature variability from both natural and 

34 anthropogenic causes during periods of low precipitation. One study found that most, but not all,

35 of the 2012 surface moisture deficit in the Great Plains was attributable to the precipitation 

36 deficit (Livneh and Hoerling 2016). That study also noted that Great Plains deep soil moisture 

37 was higher than normal in 2012 despite the surface drying due to wet conditions in prior years,

38 indicating the long timescales relevant below the surface (Livneh and Hoerling 2016).
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1 The current California drought, which began in 2011, is unusual in different respects. In this 

2 case, the precipitation deficit from 2011 to 2014 was a result of the “ridiculously resilient ridge”

3 of high pressure. This very stable high-pressure system steered storms towards the north, away 

4 from the highly engineered California water resource system (Swain et al. 2014; Seager et al. 

5 2014, 2015). The ridge itself was due to a slow-moving high sea surface temperature (SST) 

6 anomaly, referred to as “The Blob”—a result of an anomalous atmospheric circulation pattern

7 (Bond et al. 2015). A principal attribution question regarding the precipitation deficit concerns 

8 the causes of this SST anomaly. Observational records are not long enough and the anomaly was 

9 unusual enough that similarly long-lived structures have not been often seen before. Hence,

10 attribution statements, such as that about an anthropogenic increase in the frequency of 

11 geopotential height anomalies similar to 2012–2014 (e.g., Swain et al. 2014), are without 

12 associated detection (Ch. 3: Detection and Attribution). A secondary attribution question 

13 concerns the anthropogenic precipitation response in the presence of this SST anomaly. In 

14 attribution studies with a prescribed 2013 SST anomaly, a consistent human increase in the 

15 chances of very dry California conditions was found (Angelil et al. 2016).

16 As in 2012, anthropogenic climate change did increase the risk of the high temperatures in 

17 California (Seager et al. 2015; Diffenbaugh et al. 2015), further exacerbating the soil moisture 

18 deficit and the associated stress on irrigation systems. An anthropogenic contribution to 

19 commonly used measures of agricultural drought, including the Palmer Drought Severity Index 

20 (PDSI), was found in California (Diffenbaugh et al. 2015; Williams et al. 2015) and is consistent 

21 with previous projections of changes in PDSI (Dai et al. 2013; Wehner et al. 2011; Walsh et al. 

22 2014) and with an attribution study (Brown et al. 2008). Due to its simplicity, the PDSI has been 

23 criticized as being overly sensitive to higher temperatures and thus may exaggerate the human 

24 contribution to soil dryness (Milly and Dunne 2016). In fact, this study also finds that 

25 formulations of potential evaporation used in more complicated hydrologic models are similarly 

26 biased, undermining confidence in the magnitude but not the sign of projected surface soil 

27 moisture changes in a warmer climate. Seager et al. (2013) analyzed climate model output 

28 directly finding that precipitation minus evaporation in the southwest United States is projected 

29 to experience significant decreases in surface water availability leading to surface runoff 

30 decreases in California, Nevada, the Colorado River headwaters and Texas even in the near term. 

31 However, the Milly and Dunne criticisms also apply to most of the CMIP5 land surface model 

32 evapotranspiration formulations. Analysis of soil moisture at deeper levels reveals less sensitivity 

33 to temperature increases than to precipitation variations, which have increased over the 20th

34 century (Cheng et al. 2016). Nonetheless, the warming trend has led to declines in a number of 

35 indicators, including Sierra snow water equivalent, that are relevant to hydrological drought 

36 (Mao et al. 2015). Attribution of the California drought and heat wave remains an interesting and 

37 controversial research topic.

38 In summary, there has not been a formal identification of a human influence on past changes in 

39 United States meteorological drought through the analysis of precipitation trends. Some, but not 
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1 all, United States meteorological drought event attribution studies, largely in the “without 

2 detection” class, exhibit a human influence. Attribution of a human influence on past changes in 

3 U.S. agricultural drought are limited both by availability of soil moisture observations and a lack 

4 of sub-surface modeling studies. While a human influence on surface soil moisture trends has 

5 been identified with medium confidence, its relevance to agriculture may be exaggerated.

6 Runoff and hydrological drought

7 Several studies focused on the Colorado River basin in the United States using more 

8 sophisticated runoff models driven by the CMIP3 models (Christensen and Lettenmaier 2007; 

9 McCabe and Wolock 2007; Barnett and Pierce 2009; Barnett et al. 2008; Hoerling et al. 2009) 

10 showed that annual runoff reductions in a warmer climate occur through a combination of 

11 evapotranspiration increases and precipitation decreases, with the overall reduction in river flow 

12 exacerbated by human water demands on the basin’s supply.

13 8.1.2. Projections of Future Droughts and Runoff

14 The future changes in seasonal precipitation shown in Chapter 7: Precipitation Change (Figure 

15 7.6) would indicate that the western United States may experience chronic future precipitation 

16 deficits, particularly in the spring. Such deficits are not confidently projected in other portions of 

17 the country. However, future higher temperatures will very likely lead to greater frequencies and 

18 magnitudes of agricultural droughts throughout the continental United States as the resulting 

19 increases in evapotranspiration outpace projected precipitation increases (Collins et al. 2013). 

20 Figure 8.1 shows the weighted multimodel projection of the percent change in near-surface soil 

21 moisture at the end of the 21st century under the RCP8.5 scenario, indicating widespread drying 

22 over the entire continental United States. Previous National Climate Assessments (Karl et al. 

23 2009; Walsh et al. 2014) have discussed the implication of these future drier conditions in the 

24 context of the Palmer Drought Severity Index (PDSI), finding that the future normal condition 

25 would be considered drought at the present time, and that the incidence of “extreme drought” 

26 (PDSI < −4) would be significantly increased. Confidence that future soils will generally be drier 

27 at the surface is high, as the mechanisms leading to increased evapotranspiration in a warmer 

28 climate are elementary scientific facts. However, the land surface component models in the 

29 CMIP5 climate models vary greatly in their sophistication, causing the projected magnitude of 

30 both the average soil moisture decrease and the increased risk for agricultural drought to be less 

31 certain. The weighted projected seasonal decreases in surface soil moisture are generally towards 

32 drier conditions, even in regions and seasons where precipitation is projected to experience large 

33 increases (Figure 7.6) due to increases in the evapotranspiration associated with higher 

34 temperature. Drying is assessed to be large relative to natural variations in much of the CONUS 

35 region in the summer. Significant spring and fall drying is also projected in the mountainous 

36 western states, with potential implications for forest and wildfire risk. Also, the combination of 

37 significant summer and fall drying in the midwestern states has potential agricultural 

38 implications. The largest percent changes are projected in the Southwestern United States and are 
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1 consistent in magnitude with an earlier study of the Colorado River Basin using more 

2 sophisticated macroscale hydrological models (Christensen and Lettenmaier 2007). 

3 Despite the important usage of PDSI as an early warning indicator of U.S. drought (e.g., NOAA 

4 2016), its suitability as a measure of future agricultural drought in much warmer climates is 

5 questionable due to its simplified representation of the water cycle, resulting in overly 

6 pessimistic projections (Hoerling et al. 2012). Similarly, a direct CMIP5 multimodel projection 

7 of soil moisture such as in Figure 8.1 must be limited to the surface (defined as the top 10 cm of 

8 the soil), as the land surface component sub-models vary greatly in their representation of the 

9 total depth of the soil. A more relevant projection to agricultural drought would be the soil 

10 moisture at the root depth of typical U.S. crops, which is not generally available from the CMIP5 

11 models. Few of the CMIP5 land models have detailed ecological representations of 

12 evapotranspiration processes, causing the simulation of the soil moisture budget to be less 

13 constrained than reality (Williams and Torn 2015). Nonetheless, Figure 8.1 shows a projected 

14 drying of surface soil moisture across nearly all of the coterminous United States in all seasons 

15 even in regions and seasons where precipitation is projected to increase.

16 Changes in average total seasonal runoff—including surface streamflow and groundwater—

17 differ significantly between the mountainous western United States, Alaska and the rest of the 

18 Nation. Figure 8.2 shows the projected end of the 21st century CMIP5 multimodel weighted 

19 average percent changes in near-total runoff under the RCP8.5 scenario, revealing increased 

20 runoff in Alaska and Northern Canada during winter due to the change from snow to rain in the 

21 warmer climate. Projected winter increases are assessed as large (Appendix B) in a small region

22 of the Rockies as well. For the rest of the contiguous United States, the weighted projection for

23 average total runoff is generally to be decreased as a result of increased evapotranspiration. 

24 However, these decreases are assessed to be small compared to natural variations in all seasons 

25 (Appendix B).

26 Reduced contiguous U.S. snowfall accumulations in much warmer future climates are virtually 

27 certain as frozen precipitation is replaced by rain regardless of the projected changes in total 

28 precipitation amounts discussed in Chapter 7 (Figure 7.6). Widespread reductions in mean 

29 snowfall across North America are projected by the CMIP5 models (O’Gorman 2014). Together 

30 with earlier snowmelt at altitudes high enough for snow, disruptions in western U.S. water 

31 delivery systems are expected to lead to more frequent hydrological drought conditions (Barnett 

32 et al. 2008; Pierce et al. 2008; Barnett and Pierce 2009; Cayan et al. 2010). The elevation of 

33 mountains as represented in the CMIP5 models is too low, due to resolution constraints, to 

34 adequately represent the effects of future temperature on snowpacks. However, increased model 

35 resolution has been demonstrated to have important impacts on future projections of snowpack 

36 change in warmer climates and is enabled by recent advances in high performance computing 

37 (Kapnick and Delworth 2013). Figure 8.3 and Table 8.2 show a projection of changes in western 

38 U.S. mountain winter (December, January, and February) hydrology obtained from a different 

39 high-resolution atmospheric model at the middle and end of the 21st century under the RCP8.5 
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1 scenario. These projections indicate dramatic reductions in all aspects of snow (Rhoades et al. in 

2 review) and are similar to a previous statistically downscaled projections (Cayan et al. 2013; 

3 Klos et al. 2014). Given the larger projected increases in temperature at high altitudes compared 

4 to adjacent lower altitudes (Pierce and Cayan 2012) and the resulting changes in both snowpack 

5 depth and melt timing in very warm future scenarios, and assuming no change to water-resource 

6 management, several important western U.S. snowpack reservoirs effectively disappear by 2100

7 in this dynamical projection, resulting in chronic, long-lasting hydrological drought. This 

8 dramatic statement is also supported by a multi-model statistical downscaling of the CMIP5 

9 RCP8.5 ensemble that finds large areal reductions in snow dominated regions of the western 

10 United States by mid-century and complete elimination of snow-dominated regions in certain 

11 watersheds (Klos et al. 2014).

12 [INSERT FIGURE 8.1 HERE:

13 Figure 8.1. Projected end of the 21st century weighted CMIP5 multimodel average percent 

14 changes in near surface seasonal soil moisture (mrsos) under the RCP8.5 scenario. Stippling 

15 indicates that changes are assessed to be large compared to natural variations. Hashing indicates

16 that changes are assessed to be small compared to natural variations. Blank regions (if any) are 

17 where projections are assessed to be inconclusive (Appendix B).]

18 [INSERT FIGURE 8.2 HERE:

19 Figure 8.2. Projected end of the 21st century weighted CMIP5 multimodel average percent 

20 changes in total seasonal runoff (mrro) under the RCP8.5 emissions scenario. Stippling indicates 

21 that changes are assessed to be large compared to natural variations. Hashing indicates that 

22 changes are assessed to be small compared to natural variations. Blank regions (if any) are where 

23 projections are assessed to be inconclusive (Appendix B).]

24 [INSERT FIGURE 8.3 HERE:

25 Figure 8.3. Projected changes in winter (DJF) Snow Water Equivalent at the middle and end of 

26 this century under the RCP8.5 scenario from a high-resolution version of the Community 

27 Atmospheric Model, CAM5 (Rhoades et al. 2016), Figure source: Lawrence Berkeley National 

28 Laboratory.] 

29 8.2. Floods

30 Flooding damage in the United States can come from flash floods of smaller rivers and creeks, 

31 prolonged flooding along major rivers, and coastal flooding from storm surge and the confluence 

32 of coastal storms and inland riverine flooding from the same precipitation event (Ch. 12: Sea 

33 Level Rise). Flash flooding is associated with extreme precipitation somewhere along the river 

34 which may occur upstream of the regions at risk. Flooding of major rivers in the United States 

35 usually occurs in the late winter or spring and can result from an unusually heavy seasonal 

36 snowfall followed by a “rain on snow” event or from a rapid onset of higher temperatures that 

37 leads to rapid snow melting within the river basin. Changes in streamflow rates depend on many 
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1 factors, both human and natural, in addition to climate change. Deforestation, urbanization, and 

2 changes in agricultural practices can all play a role in past and future changes in flood statistics.

3 Projection of future changes is thus a multivariate problem (Walsh et al. 2014).

4 Trends in extreme high values of streamflow are mixed across the United States, as reported in 

5 the Third National Climate Assessment (Walsh et al. 2014). Recent analysis of annual maximum 

6 streamflow shows statistically significant trends only in the upper Mississippi River valley 

7 (increasing) and in the Northwest (decreasing) (McCabe and Wolock 2014). This is seemingly in 

8 contrast to the much more widespread increasing trends in extreme precipitation over much of 

9 the eastern and northern United States. As noted above, floods are poorly explained by 

10 precipitation characteristics alone; the relevant mechanisms are more complex, involving 

11 processes that are seasonally and geographically variable, including the seasonal cycles of soil 

12 moisture content and snowfall/snowmelt (Berghuijs et al. 2016). The northeast United States is 

13 an interesting example. Strong increasing trends in extreme precipitation have been observed and 

14 appear to be ubiquitous across this region (Walsh et al. 2014; Frei et al. 2015). Trends in

15 maximum streamflow are less dramatic and less spatially coherent (McCabe and Wolock 2014; 

16 Frei et al. 2015), although one study found mostly increasing trends (Armstrong et al. 2014) in 

17 that region, somewhat at odds with other studies. This apparent disparity is caused by the 

18 seasonality of the two phenomena. Extreme precipitation events are larger in the warm season 

19 when soil moisture and seasonal streamflow levels are low and less favorable for flooding. By 

20 contrast, high streamflow events are larger in the cold season when soil moisture is high and 

21 snowmelt and frozen ground can enhance runoff (Frei et al. 2015). A future projection study 

22 based on coupling an ensemble of regional climate model output to a hydrology model (Najafi 

23 and Moradkhani 2015) finds that the magnitude of very extreme runoff (which can lead to 

24 flooding) is decreased in most of the summer months in Washington State, Oregon, Idaho and 

25 western Montana but is substantially increased in the other seasons. Projected increases in 

26 extreme runoff from the coast to the Cascades are particularly large in the fall and winter.

27 Thus, apparent disparities between extreme precipitation and flood trends are partially explained 

28 by the complex seasonal and geographic processes that affect flooding that go beyond simple 

29 precipitation characteristics. This presents a challenge for attribution studies and it has been 

30 suggested that additional scientific rigor is needed in flood attribution studies (Merz et al. 2012).

31 The IPCC WG1 AR5 (Bindoff et al. 2013) did not attribute changes in flooding to anthropogenic 

32 influence nor report detectable changes in flooding magnitude or frequency. Analysis of 200 

33 U.S. stream gauges indicates both areas of increasing and decreasing flooding magnitude (Hirsch 

34 and Ryberg 2012) but does not provide robust evidence that these trends are detectible or 

35 attributable to human influences. Significant increases in flood frequency have been detected in 

36 about one-third of stream gauge stations examined for the central United States, with a much 

37 stronger signal of change than is found for flood magnitude in these gauges (Mallakpour and 

38 Villarini 2015). Although both temperature and precipitation increases were influencing the 
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1 flooding changes, no attribution of these changes to anthropogenic forcing has been claimed 

2 (Mallakpour and Villarini 2015).

3 The nature of the proxy archives complicates the reconstruction of past flood events in a gridded 

4 fashion as has been done with droughts. However, reconstructions of past river outflows do exist. 

5 For instance, it has been suggested that the mid-20th century river allocations for the Colorado 

6 River were made during one of the wettest periods of the past five centuries (Woodhouse et al.

7 2006). For the eastern United States, the Mississippi River has undergone century-scale 

8 variability in flood frequency—perhaps linked to the moisture availability in the central United 

9 States and the temperature structure of the Atlantic Ocean (Munoz et al. 2015). 

10 No studies have clearly attributed long-term changes in observed flooding of major rivers in the 

11 United States to anthropogenic forcing. We conclude that there is medium confidence that 

12 detectable, though not attributable, increases in seasonal flood frequency have occurred in parts 

13 of the central United States. 

14 Studies of localized extreme flooding events are extremely limited, are confined to changes in 

15 the locally responsible precipitation event, and do not include detailed analyses of the events’

16 hydrology. Gochis et al. (2015) describes the massive floods of 2013 along the Colorado front 

17 range, estimating that the record rainfall exceeds 1,000-year return values in some regions. 

18 Hoerling et al. (2014) analyzed the 2013 northeastern Colorado heavy multiday precipitation 

19 event and resulting flood finding little evidence of an anthropogenic influence on its occurrence.

20 However, Pall et al. (2016) challenge their methodology with a more constrained study and find 

21 that the thermodynamic response of precipitation in this event due to anthropogenic forcing was 

22 substantially increased. The Pall et al. (2016) approach does not rule out that the likelihood of the 

23 extremely rare large-scale meteorological pattern responsible for the flood may have changed.

24 8.3 Wildfires

25 Recent decades have seen increased forest fire activity in the western United States and Alaska.

26 For the western United States, one study has estimated that human-caused climate change was 

27 responsible for nearly half of the total forest acreage burned by wildfires over 1984 to 2015 

28 (Abatzoglou and Williams 2016) while another study found an increased risk of fire in California 

29 due to human-caused climate change, based on a model assessment of the 2014 fire season 

30 (Yoon et al. 2015). For Alaska, one study found that human caused climate change had increased 

31 the risk of severe fire seasons like 2015 by 34%–60% (Partain et al., in review). In Abatzouglou 

32 and Williams, modeled increases in temperatures and vapor pressure deficits due to 

33 anthropogenic climate change caused increased fire potential by increasing the aridity of forest 

34 fuels during the fire season. None of the studies demonstrates that a long term increase in forest 

35 fire activity is highly unusual in comparison to natural variability, as they are generally inferring 

36 a human-caused climate change contribution to trends or probabilities based on model 

37 calculations. The degree of forestry management, which is greater in the western United States 
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1 than in Alaska, is a confounding factor that complicates attribution of changes to anthropogenic 

2 climate change. We conclude that there is medium confidence for a human-caused climate 

3 change contribution to increased forest fire activity in Alaska in recent decades, but low 

4 confidence for a detectable human climate change contribution in the western United States 

5 based on existing studies.

6
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1 TRACEABLE ACCOUNTS

2 Key Message 1

3 Recent droughts and associated heat waves have reached record intensity in some regions of the 

4 United States, but, by geographical scale and duration, the Dust Bowl era of the 1930s remains 

5 the benchmark drought and extreme heat event in the historical record. (Very high confidence)

6 Description of evidence base

7 Recent droughts are well characterized and described in the literature. The dust bowl is not as 

8 well documented, but available observational records support the key finding.

9 Major uncertainties

10 Record breaking temperatures are well documented with low uncertainty (Meehl et al 2009). The 

11 magnitude of the Dust Bowl relative to present times varies with location. Uncertainty in the key 

12 finding is affected by the quality of pre-WW2 observations but is relatively low.

13 Assessment of confidence based on evidence and agreement 

14 X Very High

15 ☐ High

16 ☐ Medium

17 ☐ Low

18 Precipitation is well observed in the United States leading to very high confidence.

19 Summary sentence or paragraph that integrates the above information

20 The key finding is a statement that recent U.S. droughts, while sometimes long and severe, are 

21 not unprecedented in the history of Earth’s hydrologic natural variation.

22

23 Key Message 2

24 The human effect on recent major U.S. droughts is complicated. Little evidence is found for a 

25 human influence on observed precipitation deficits but much evidence is found for a human 

26 influence on surface soil moisture deficits due to increased evapotranspiration caused by higher 

27 temperatures. (High confidence)

28 Description of evidence base

29 Observational records of meteorological drought are not long enough to detect statistically 

30 significant trends. Additionally, paleoclimatic evidence suggests that major droughts have 

31 occurred throughout the distant past. Surface soil moisture is not well observed throughout the 

32 CONUS but numerous event attribution studies attributes enhanced reduction of surface soil 

33 moisture during dry periods due to anthropogenic warming and enhanced evapotranspiration.

34
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1 Major uncertainties

2 Uncertainties stem from the length of precipitation observations and the lack of surface moisture 

3 observations.

4 Assessment of confidence based on evidence and agreement 

5 ☐ Very High

6 X High

7 ☐ Medium

8 ☐ Low

9 Summary sentence or paragraph that integrates the above information

10 The precipitation deficit portion of the key finding is a conservative statement reflecting the 

11 conflicting and limited event attribution literature on meteorological drought. The soil moisture 

12 portion of the key finding is limited to the surface and not the more relevant root depth and is 

13 supported by the studies cited in Chapter 8.

14

15 Key Message 3

16 Future decreases in surface soil moisture over most of the United States are likely as the climate 

17 warms. (High confidence)

18 Description of evidence base

19 First principles establish that evaporation is at least linearly dependent on temperatures and 

20 accounts for much of the surface moisture decrease as temperature increases. Plant transpiration 

21 for many non-desert species controls plant temperature and responds to increased temperature by 

22 opening stomata to release more water vapor. This water comes from the soil at root depth as the 

23 plant exhausts its stored water supply (very high confidence).

24 Major uncertainties

25 While both evaporation and transpiration changes are of the same sign as temperature increases, 

26 the relative importance of each as a function of depth is less well quantified. The amount of 

27 transpiration varies considerably among plant species and these are treated with widely varying 

28 of sophistication in the land surface components of contemporary climate models. Uncertainty in 

29 the sign of the anthropogenic change of root depth soil moisture is low in regions and seasons of 

30 projected precipitation decreases (Chapter 7). Uncertainty in the magnitude of the change in soil 

31 moisture at all depths and all regions and seasons is not low.

32 Assessment of confidence based on evidence and agreement 

33 ☐ Very High

34 x High
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1 ☐ Medium

2 ☐ Low

3 CMIP5 and regional models support the surface soil moisture key finding.

4 Summary sentence or paragraph that integrates the above information

5 In the northern United States, surface soil moisture (top 10 cm) is very likely to decrease as 

6 evaporation outpaces increases in precipitation. In the southwest, the combination of temperature 

7 increases and precipitation decreases causes surface soil moisture decreases to be virtually 

8 certain. In this region, decreases in soil moisture at the root depth is very likely.

9

10 Key Message 4

11 Reductions in western U.S. winter and spring snowpack are projected as the climate warms. 

12 Under higher emissions scenarios, and assuming no change to current water-resources 

13 management, chronic, long-duration hydrological drought is possible by the end of this century. 

14 (Very high confidence).

15 Description of evidence base

16 First principles tell us that as temperatures rise, minimum snow levels also must rise. Certain 

17 changes in western U.S. hydrology have already been reported in the papers following Barnett et 

18 al. (2008). The CMIP3/5 models project widespread warming with future increases in 

19 atmospheric GHG concentrations, although these are underestimated in the current generation of 

20 GCMs at the high altitudes of the western U.S. due to constraints on orographic representation at 

21 current GCM spatial resolutions.

22 CMIP5 models were not designed or constructed for direct projection of locally relevant 

23 snowpack amounts. However, a high-resolution climate model, selected for its ability to simulate 

24 Western U.S. snowpack amounts and extent, projects devastating changes in the hydrology of 

25 this region assuming constant water-resource management practices (Rhoades et al 2016). This 

26 conclusion is also supported by a statistical downscaling result shown in figure 3.1 of Walsh et 

27 al. 2014 and Cayan et al. 2013 and by the more recent statistical downscaling study of Klos et al. 

28 2014.

29 Major uncertainties

30 The major uncertainty is not so much “if” but rather “when” as changes to precipitation phase 

31 (rain or snow) are sensitive to temperature increases that in turn depends on GHG forcing 

32 changes. Also, changes to the lower elevation catchments will be realized prior to those at higher 

33 elevations that even at 25 km, is not adequately resolved. Uncertainty in the second statement 

34 also stems from the usage of one model. However, this simulation is a so-called “prescribed 

35 temperature” experiment with the usual uncertainties about climate sensitivity wired in by the 
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1 usage of one particular ocean temperature change. Uncertainty in the equator to pole differential 

2 ocean warming rate is also a factor.

3 Assessment of confidence based on evidence and agreement 

4 X Very High

5 ☐ High

6 ☐ Medium

7 ☐ Low

8 All CMIP5 models project large scale western U.S. warming as GHG forcing increases. 

9 Warming is underestimated in most of the western United States due to elevation deficiencies 

10 that are a consequence of coarse model resolution. Snow melts above 32°F.

11 Summary sentence or paragraph that integrates the above information

12 Warmer temperatures lead to less snow and more rain if total precipitation remains unchanged. 

13 Projected winter/spring precipitation changes are a mix of increases in northern states and 

14 decreases in the southwest. In the northern Rockies, snowpack is projected decrease even with a 

15 projected precipitation increase due to this phase change effect. This will lead to, at the very 

16 least, profound changes to the seasonal and sub-seasonal timing of the western U.S. hydrological 

17 cycle even where annual precipitation remains nearly unchanged.

18

19 Key Message 5

20 Detectable increases in seasonal flood frequency have occurred in parts of the central United 

21 States. This is to be expected in the presence of the increase in extreme downpours known with 

22 high confidence to be linked to a warming atmosphere, but formal attribution approaches have 

23 not certified the connection of increased flooding to human influences. (Medium confidence).

24 Description of evidence base

25 Observed increases are documented by Walsh et al. 2014 and other studies cited in the text. No 

26 attribution statements have been made.

27 Major uncertainties

28 Floods are highly variable both in space and time. The multi-variate nature of floods complicates 

29 detection and attribution.

30 Assessment of confidence based on evidence and agreement 

31 ☐ Very High

32 ☐ High

33 X Medium

34 ☐ Low
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1 Summary sentence or paragraph that integrates the above information

2 The key finding is a relatively weak statement reflecting the limited literature on the detection 

3 and attribution of anthropogenic changes in US flooding intensity, duration and frequency.

4

5

6 TABLES

7 Table 8.1: A list of U.S. droughts for which attribution statements have been made. In the last 

8 column, “+” indicates that an attributable human induced increase in frequency and/or magnitude 

9 was found, “−“ indicates that an attributable human induced decrease in frequency and/or 

10 magnitude was found, “0” indicates no attributable human contribution was identified. As in 

11 tables 6.2 and 7.1, several of the events were originally examined in the Bulletin of the American 

12 Meteorological Society’s (BAMS) State of the Climate Reports and reexamined by Angelil et al. 

13 (2016). In these cases, both attribution statements are listed with the original authors first. 

14 Source: M. Wehner.

15
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Authors Event Year and 

Duration

Region or State Type Attribution 

Statement

Rupp and Mote 2012 / 

Angelil et al. 2016

MAMJJA 

2011

Texas Meteorological +/+

Hoerling et al. 2013 2012 Texas Meteorological +

Rupp et al. 2013 / 

Angelil et al. 2016

MAMJJA 

2012

CO, NE, KS, OK, IA, 

MO, AR & IL

Meteorological 0/0

Rupp et al. 2013 / 

Angelil et al. 2016

MAM 2012 CO, NE, KS, OK, IA, 

MO, AR & IL

Meteorological 0/0

Rupp et al. 2013 / 

Angelil et al. 2016

JJA 2012 CO, NE, KS, OK, IA, 

MO, AR & IL

Meteorological 0/+

Hoerling et al. 2014 MJJA 2012 Great Plains/Midwest Meteorological 0

Swain et al. 2014 / 

Angelil et al. 2016

ANN 2013 California Meteorological +/+

Wang and Schubert 

2014 / Angelil et al. 

2016

JS 2013 California Meteorological 0/+

Knutson et al. 2014 / 

Angelil et al. 2016

ANN 2013 California Meteorological +/+

Knutson et al. 2014 / 

Angelil et al. 2016

MAM 2013 U.S. Southern Plains 

region

Meteorological +/+

Diffenbaugh et al. 2014 2012-2014 California Agricultural +

Seager et al. 2015 2012-2014 California Agricultural +

Cheng et al. 2016 2011-2015 California Agricultural -

1
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1 Table 8.2: Projected changes in western U.S. mountain range winter (DJF) snow-related 

2 hydrology variables at the middle and end of this century. Projections are for the RCP8.5 

3 scenario from a high-resolution version of the Community Atmospheric Model, CAM5 (Rhoades 

4 et al. 2016).

5

6
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1 FIGURES

2

3

4 Figure 8.1. Projected end of the 21st century weighted CMIP5 multimodel average percent 

5 changes in near surface seasonal soil moisture (mrsos) under the RCP8.5 scenario. Stippling 

6 indicates that changes are assessed to be large compared to natural variations. Hashing indicates 

7 that changes are assessed to be small compared to natural variations. Blank regions (if any) are 

8 where projections are assessed to be inconclusive (Appendix B).

9
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1

2 Figure 8.2. Projected end of the 21st century weighted CMIP5 multimodel average percent 

3 changes in total seasonal runoff (mrro) under the RCP8.5 scenario. Stippling indicates that 

4 changes are assessed to be large compared to natural variations. Hashing indicates that changes 

5 are assessed to be small compared to natural variations. Blank regions (if any) are where 

6 projections are assessed to be inconclusive (Appendix B).

7
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1

2

3 Figure 8.3. Projected changes in winter (DJF) Snow Water Equivalent at the middle and end of 

4 this century under the RCP8.5 scenario from a high-resolution version of the Community 

5 Atmospheric Model, CAM5 (Rhoades et al. 2016), Figure source: Lawrence Berkeley National 

6 Laboratory

7
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1 REFERENCES

2 Abatzoglou, J.T. and A.P. Williams, 2016: Impact of anthropogenic climate change on wildfire 

3 across western US forests. Proceedings of the National Academy of Sciences, 113, 11770-

4 11775. http://dx.doi.org/10.1073/pnas.1607171113 

5 http://www.pnas.org/content/113/42/11770.abstract

6 Angélil, O., D. Stone, M. Wehner, C.J. Paciorek, H. Krishnan, and W. Collins, 2016: An 

7 independent assessment of anthropogenic attribution statements for recent extreme 

8 temperature and rainfall events. Journal of Climate, in press, null.

9 http://dx.doi.org/10.1175/JCLI-D-16-0077.1 

10 Armstrong, W.H., M.J. Collins, and N.P. Snyder, 2014: Hydroclimatic flood trends in the 

11 northeastern United States and linkages with large-scale atmospheric circulation patterns. 

12 Hydrological Sciences Journal, 59, 1636-1655.

13 http://dx.doi.org/10.1080/02626667.2013.862339 

14 Barnett, T.P. and D.W. Pierce, 2009: Sustainable water deliveries from the Colorado River in a 

15 changing climate. Proceedings of the National Academy of Sciences, 106, 7334-7338.

16 http://dx.doi.org/10.1073/pnas.0812762106 

17 Barnett, T.P., D.W. Pierce, H.G. Hidalgo, C. Bonfils, B.D. Santer, T. Das, G. Bala, A.W. Wood, 

18 T. Nozawa, A.A. Mirin, D.R. Cayan, and M.D. Dettinger, 2008: Human-induced changes in 

19 the hydrology of the western United States. Science, 319, 1080-1083.

20 http://dx.doi.org/10.1126/science.1152538 

21 Bennet, H.H., F.H. Fowler, F.C. Harrington, R.C. Moore, J.C. Page, M.L. Cooke, H.A. Wallace, 

22 and R.G. Tugwell, 1936: A Report of the Great Plains Area Drought Committee. In: (FERI), 

23 N.D.N. (ed.), Hopkins Papers Franklin D. Roosevelt Library, Hyde Park, NY.

24 Berghuijs, W.R., R.A. Woods, C.J. Hutton, and M. Sivapalan, 2016: Dominant flood generating 

25 mechanisms across the United States. Geophysical Research Letters, 43, 4382-4390.

26 http://dx.doi.org/10.1002/2016GL068070 

27 Bindoff, N.L., P.A. Stott, K.M. AchutaRao, M.R. Allen, N. Gillett, D. Gutzler, K. Hansingo, G. 

28 Hegerl, Y. Hu, S. Jain, I.I. Mokhov, J. Overland, J. Perlwitz, R. Sebbari, and X. Zhang, 2013: 

29 Detection and Attribution of Climate Change: from Global to Regional. Climate Change 

30 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment 

31 Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. 

32 Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, 

33 Eds. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 

34 867–952. http://dx.doi.org/10.1017/CBO9781107415324.022 www.climatechange2013.org
CSSR TOD: DO NOT CITE, QUOTE OR DISTRIBUTE Chapter 8

302

1 Bond, N.A., M.F. Cronin, H. Freeland, and N. Mantua, 2015: Causes and impacts of the 2014 

2 warm anomaly in the NE Pacific. Geophysical Research Letters, 42, 3414-3420.

3 http://dx.doi.org/10.1002/2015GL063306 

4 Brown, P.M., E.K. Heyerdahl, S.G. Kitchen, and M.H. Weber, 2008: Climate effects on 

5 historical fires (1630–1900) in Utah. International Journal of Wildland Fire, 17, 28-39.

6 http://dx.doi.org/10.1071/WF07023 

7 Cayan, D., K. Kunkel, C. Castro, A. Gershunov, J. Barsugli, A. Ray, J. Overpeck, M. Anderson, 

8 J. Russell, B. Rajagopalan, I. Rangwala, and P. Duffy, 2013: Ch. 6: Future climate: Projected 

9 average. Assessment of Climate Change in the Southwest United States: A Report Prepared 

10 for the National Climate Assessment. Garfin, G., A. Jardine, R. Merideth, M. Black, and S. 

11 LeRoy, Eds. Island Press, Washington, D.C., 153-196.

12 http://swccar.org/sites/all/themes/files/SW-NCA-color-FINALweb.pdf

13 Cayan, D.R., T. Das, D.W. Pierce, T.P. Barnett, M. Tyree, and A. Gershunov, 2010: Future 

14 dryness in the southwest US and the hydrology of the early 21st century drought. 

15 Proceedings of the National Academy of Sciences, 107, 21271-21276.

16 http://dx.doi.org/10.1073/pnas.0912391107 

17 Cheng, L., M. Hoerling, A. AghaKouchak, B. Livneh, X.-W. Quan, and J. Eischeid, 2016: How 

18 Has Human-Induced Climate Change Affected California Drought Risk? Journal of Climate,

19 29, 111-120. http://dx.doi.org/10.1175/JCLI-D-15-0260.1 

20 Christensen, N.S. and D.P. Lettenmaier, 2007: A multimodel ensemble approach to assessment 

21 of climate change impacts on the hydrology and water resources of the Colorado River 

22 Basin. Hydrology and Earth System Sciences, 11, 1417-1434. http://dx.doi.org/10.5194/hess23 11-1417-2007 

24 Coats, S., B.I. Cook, J.E. Smerdon, and R. Seager, 2015: North American pancontinental 

25 droughts in model simulations of the last millennium. Journal of Climate, 28, 2025-2043.

26 http://dx.doi.org/10.1175/JCLI-D-14-00634.1 

27 Collins, M., R. Knutti, J. Arblaster, J.-L. Dufresne, T. Fichefet, P. Friedlingstein, X. Gao, W.J. 

28 Gutowski, T. Johns, G. Krinner, M. Shongwe, C. Tebaldi, A.J. Weaver, and M. Wehner, 

29 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. Climate 

30 Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth 

31 Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. 

32 Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and 

33 P.M. Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom and New 

34 York, NY, USA, 1029–1136. http://dx.doi.org/10.1017/CBO9781107415324.024 

35 www.climatechange2013.org
CSSR TOD: DO NOT CITE, QUOTE OR DISTRIBUTE Chapter 8

303

1 Cook, E.R., C.A. Woodhouse, C.M. Eakin, D.M. Meko, and D.W. Stahle, 2004: Long-Term 

2 Aridity Changes in the Western United States. Science, 306, 1015-1018.

3 http://dx.doi.org/10.1126/science.1102586 

4 Dai, A., 2013: Increasing drought under global warming in observations and models. Nature 

5 Climate Change, 3, 52-58. http://dx.doi.org/10.1038/nclimate1633 

6 Diffenbaugh, N.S. and M. Scherer, 2013: Likelihood of July 2012 U.S. temperatures in pre7 industrial and current forcing regimes [in "Explaining Extremes of 2013 from a Climate 

8 Perspective"]. Bulletin of the American Meteorological Society, 94, S6-S9.

9 http://dx.doi.org/10.1175/BAMS-D-13-00085.1 

10 Diffenbaugh, N.S., D.L. Swain, and D. Touma, 2015: Anthropogenic warming has increased 

11 drought risk in California. Proceedings of the National Academy of Sciences, 112, 3931-

12 3936. http://dx.doi.org/10.1073/pnas.1422385112 

13 Frei, A., K.E. Kunkel, and A. Matonse, 2015: The Seasonal Nature of Extreme Hydrological 

14 Events in the Northeastern United States. Journal of Hydrometeorology, 16, 2065-2085.

15 http://dx.doi.org/10.1175/JHM-D-14-0237.1 

16 Gochis, D., R. Schumacher, K. Friedrich, N. Doesken, M. Kelsch, J. Sun, K. Ikeda, D. Lindsey, 

17 A. Wood, B. Dolan, S. Matrosov, A. Newman, K. Mahoney, S. Rutledge, R. Johnson, P. 

18 Kucera, P. Kennedy, D. Sempere-Torres, M. Steiner, R. Roberts, J. Wilson, W. Yu, V. 

19 Chandrasekar, R. Rasmussen, A. Anderson, and B. Brown, 2015: The Great Colorado Flood 

20 of September 2013. Bulletin of the American Meteorological Society, 96, 1461-1487.

21 http://dx.doi.org/10.1175/BAMS-D-13-00241.1 

22 Hirsch, R.M. and K.R. Ryberg, 2012: Has the magnitude of floods across the USA changed with 

global CO2 23 levels? Hydrological Sciences Journal, 57, 1-9.

24 http://dx.doi.org/10.1080/02626667.2011.621895 

25 Hoerling, M., J. Eischeid, A. Kumar, R. Leung, A. Mariotti, K. Mo, S. Schubert, and R. Seager, 

26 2014: Causes and Predictability of the 2012 Great Plains Drought. Bulletin of the American 

27 Meteorological Society, 95, 269-282. http://dx.doi.org/10.1175/BAMS-D-13-00055.1 

28 Hoerling, M., D. Lettenmaier, D. Cayan, and B. Udall, 2009: Reconciling future Colorado River 

29 flows. Southwest Hydrology, 8.

30 Hoerling, M.P., J.K. Eischeid, X.-W. Quan, H.F. Diaz, R.S. Webb, R.M. Dole, and D.R. 

31 Easterling, 2012: Is a Transition to Semipermanent Drought Conditions Imminent in the U.S. 

32 Great Plains? Journal of Climate, 25, 8380-8386. http://dx.doi.org/10.1175/JCLI-D-12-

33 00449.1 
CSSR TOD: DO NOT CITE, QUOTE OR DISTRIBUTE Chapter 8

304

1 Hoerling, M., M. Chen, R. Dole, J. Eischeid, A. Kumar, J.W. Nielsen-Gammon, P. Pegion, J. 

2 Perlwitz, X.-W. Quan, and T. Zhang, 2013: Anatomy of an extreme event. Journal of 

3 Climate, 26, 2811–2832. http://dx.doi.org/10.1175/JCLI-D-12-00270.1

4 Kapnick, S.B. and T.L. Delworth, 2013: Controls of Global Snow under a Changed Climate. 

5 Journal of Climate, 26, 5537-5562. http://dx.doi.org/10.1175/JCLI-D-12-00528.1 

6 Karl, T.R., J.T. Melillo, and T.C. Peterson, eds. Global Climate Change Impacts in the United 

7 States. ed. Karl, T.R., J.T. Melillo, and T.C. Peterson. 2009, Cambridge University Press: 

8 New York, NY. 189. http://downloads.globalchange.gov/usimpacts/pdfs/climate-impacts9 report.pdf

10 Klos, P.Z., T.E. Link, and J.T. Abatzoglou, 2014: Extent of the rain-snow transition zone in the 

11 western U.S. under historic and projected climate. Geophysical Research Letters, 41, 4560-

12 4568. http://dx.doi.org/10.1002/2014GL060500 http://dx.doi.org/10.1002/2014GL060500

13 Livneh, B. and M.P. Hoerling, 2016: The Physics of Drought in the U.S. Central Great Plains. 

14 Journal of Climate, 29, 6783-6804. http://dx.doi.org/10.1175/JCLI-D-15-0697.1 

15 Mallakpour, I. and G. Villarini, 2015: The changing nature of flooding across the central United 

16 States. Nature Climate Change, 5, 250-254. http://dx.doi.org/10.1038/nclimate2516 

17 Mao, Y., B. Nijssen, and D.P. Lettenmaier, 2015: Is climate change implicated in the 2013–2014 

18 California drought? A hydrologic perspective. Geophysical Research Letters, 42, 2805-2813.

19 http://dx.doi.org/10.1002/2015GL063456 

20 Masson-Delmotte, V., M. Schulz, A. Abe-Ouchi, J. Beer, A. Ganopolski, J.F. González Rouco, 

21 E. Jansen, K. Lambeck, J. Luterbacher, T. Naish, T. Osborn, B. Otto-Bliesner, T. Quinn, R. 

22 Ramesh, M. Rojas, X. Shao, and A. Timmermann, 2013: Information from Paleoclimate 

23 Archives. Climate Change 2013: The Physical Science Basis. Contribution of Working 

24 Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. 

25 Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, 

26 V. Bex, and P.M. Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom 

27 and New York, NY, USA, 383–464. http://dx.doi.org/10.1017/CBO9781107415324.013 

28 www.climatechange2013.org

29 McCabe, G.J. and D.M. Wolock, 2014: Spatial and temporal patterns in conterminous United 

30 States streamflow characteristics. Geophysical Research Letters, 41, 6889-6897.

31 http://dx.doi.org/10.1002/2014GL061980 

32 Meehl, G.A., C. Tebaldi, G. Walton, D. Easterling, and L. McDaniel, 2009: Relative increase of 

33 record high maximum temperatures compared to record low minimum temperatures in the 

34 US. Geophysical Research Letters, 36, L23701. http://dx.doi.org/10.1029/2009GL040736 
CSSR TOD: DO NOT CITE, QUOTE OR DISTRIBUTE Chapter 8

305

1 Merz, B., S. Vorogushyn, S. Uhlemann, J. Delgado, and Y. Hundecha, 2012: HESS Opinions 

2 "More efforts and scientific rigour are needed to attribute trends in flood time series". 

3 Hydrology and Earth System Sciences, 16, 1379-1387. http://dx.doi.org/10.5194/hess-16-

4 1379-2012 

5 Milly, P.C.D. and K.A. Dunne, 2016: Potential evapotranspiration and continental drying. 

6 Nature Climate Change, 6, 946-969. http://dx.doi.org/10.1038/nclimate3046 

7 Munoz, S.E., K.E. Gruley, A. Massie, D.A. Fike, S. Schroeder, and J.W. Williams, 2015: 

8 Cahokia’s emergence and decline coincided with shifts of flood frequency on the Mississippi 

9 River. Proceedings of the National Academy of Sciences, 112, 6319-6324.

10 http://dx.doi.org/10.1073/pnas.1501904112 

11 Najafi, M.R. and H. Moradkhani, 2015: Multi-model ensemble analysis of runoff extremes for 

12 climate change impact assessments. Journal of Hydrology, 525, 352-361.

13 http://dx.doi.org/http://dx.doi.org/10.1016/j.jhydrol.2015.03.045 

14 http://www.sciencedirect.com/science/article/pii/S0022169415002164

15 NOAA, 2008: Drought: Public fact sheet. National Oceanic and Atmospheric Administration, 

16 National Weather Service, Washington, D.C. 

17 http://www.nws.noaa.gov/os/brochures/climate/DroughtPublic2.pdf

18 NOAA, 2016: State of the Climate: Drought for June 2016. National Oceanic and Atmospheric 

19 Administration, National Centers for Environmental Information, Asheville, NC. 

20 http://www.ncdc.noaa.gov/sotc/drought/201606

21 O'Gorman, P.A., 2014: Contrasting responses of mean and extreme snowfall to climate change. 

22 Nature, 512, 416-418. http://dx.doi.org/10.1038/nature13625 

23 Pall, P.C.M.P., M.F. Wehner, D.A. Stone, C.J. Paciorek, and W.D. Collins, 2016: Diagnosing 

24 anthropogenic contributions to heavy Colorado rainfall in September 2013. Weather and 

25 Climate Extremes, in review.

26 Partain Jr., J.L., S. Alden, U.S. Bhatt, P.A. Bieniek, B.R. Brettschneider, R. Lader, P.Q. Olsson, 

27 T.S. Rupp, H. Strader, R.L.T. Jr., J.E. Walsh, A.D. York, and R.H. Zieh, 2016: An 

28 assessment of the role of anthropogenic climate change in the Alaska fire season of 2015. 

29 Bulletin of the American Meteorological Society, 97, in press.

30 http://dx.doi.org/10.1175/BAMS-D-16-0149.1 

31 Pierce, D.W., T.P. Barnett, H.G. Hidalgo, T. Das, C. Bonfils, B.D. Santer, G. Bala, M.D. 

32 Dettinger, D.R. Cayan, A. Mirin, A.W. Wood, and T. Nozawa, 2008: Attribution of declining 

33 western US snowpack to human effects. Journal of Climate, 21, 6425-6444.

34 http://dx.doi.org/10.1175/2008JCLI2405.1 
CSSR TOD: DO NOT CITE, QUOTE OR DISTRIBUTE Chapter 8

306

1 Pierce, D.W. and D.R. Cayan, 2013: The Uneven Response of Different Snow Measures to 

2 Human-Induced Climate Warming. Journal of Climate, 26, 4148-4167.

3 http://dx.doi.org/10.1175/jcli-d-12-00534.1 

4 Rhoades, C.C., et, and Al., 2016: Projecting 21st Century Snowpack Trends in Western USA 

5 Mountains Using Variable-Resolution CESM. Climate Dynamics, In press.

6 Rupp, D.E., P.W. Mote, N. Massey, F.E.L. Otto, and M.R. Allen, 2013: Human influence on the 

7 probability of low precipitation in the central United States in 2012 [in "Explaining Extremes 

8 of 2013 from a Climate Perspective"]. Bulletin of the American Meteorological Society, 94,

9 S2-S6. http://dx.doi.org/10.1175/BAMS-D-13-00085.1 

10 Seager, R., M. Hoerling, S. Schubert, H. Wang, B. Lyon, A. Kumar, J. Nakamura, and N. 

11 Henderson, 2015: Causes of the 2011–14 California Drought. Journal of Climate, 28, 6997-

12 7024. http://dx.doi.org/10.1175/JCLI-D-14-00860.1 

13 Seager, R., M. Hoerling, D.S. Siegfried, h. Wang, B. Lyon, A. Kumar, J. Nakamura, and N. 

14 Henderson, 2014: Causes and predictability of the 2011-14 California drought. 40 pp. NOAA 

15 Drought Task Force Narrative Team. http://cpo.noaa.gov/MAPP/californiadroughtreport

16 Sheffield, J., E.F. Wood, and M.L. Roderick, 2012: Little change in global drought over the past 

17 60 years. Nature, 491, 435-438. http://dx.doi.org/10.1038/nature11575 

18 Swain, D., M. Tsiang, M. Haughen, D. Singh, A. Charland, B. Rajarthan, and N.S. Diffenbaugh, 

19 2014: The extraordinary California drought of 2013/14: Character, context and the role of 

20 climate change [in "Explaining Extremes of 2013 from a Climate Perspective"]. Bulletin of 

21 the American Meteorological Society, 95, S3-S6. http://dx.doi.org/10.1175/1520-0477-

22 95.9.S1.1 

23 Walsh, J., D. Wuebbles, K. Hayhoe, J. Kossin, K. Kunkel, G. Stephens, P. Thorne, R. Vose, M. 

24 Wehner, J. Willis, D. Anderson, S. Doney, R. Feely, P. Hennon, V. Kharin, T. Knutson, F. 

25 Landerer, T. Lenton, J. Kennedy, and R. Somerville, 2014: Ch. 2: Our changing climate. 

26 Climate Change Impacts in the United States: The Third National Climate Assessment. 

27 Melillo, J.M., T.C. Richmond, and G.W. Yohe, Eds. U.S. Global Change Research Program, 

28 Washington, D.C., 19-67. http://dx.doi.org/10.7930/J0KW5CXT 

29 Wang, H., S. Schubert, R. Koster, Y.-G. Ham, and M. Suarez, 2014: On the Role of SST Forcing 

30 in the 2011 and 2012 Extreme U.S. Heat and Drought: A Study in Contrasts. Journal of 

31 Hydrometeorology, 15, 1255-1273. http://dx.doi.org/10.1175/JHM-D-13-069.1 

32 Wehner, M., D.R. Easterling, J.H. Lawrimore, R.R. Heim Jr, R.S. Vose, and B.D. Santer, 2011: 

33 Projections of future drought in the continental United States and Mexico. Journal of 

34 Hydrometeorology, 12, 1359-1377. http://dx.doi.org/10.1175/2011JHM1351.1 
CSSR TOD: DO NOT CITE, QUOTE OR DISTRIBUTE Chapter 8

307

1 Williams, A.P., R. Seager, J.T. Abatzoglou, B.I. Cook, J.E. Smerdon, and E.R. Cook, 2015: 

2 Contribution of anthropogenic warming to California drought during 2012–2014. 

3 Geophysical Research Letters, 42, 6819-6828. http://dx.doi.org/10.1002/2015GL064924 

4 Williams, I.N. and M.S. Torn, 2015: Vegetation controls on surface heat flux partitioning, and 

5 land-atmosphere coupling. Geophysical Research Letters, 42, 9416-9424.

6 http://dx.doi.org/10.1002/2015GL066305 

7 Woodhouse, C.A., S.T. Gray, and D.M. Meko, 2006: Updated streamflow reconstructions for the 

8 Upper Colorado River Basin. Water Resources Research, 42.

9 http://dx.doi.org/10.1029/2005WR004455 

10 Yoon, J.-H., B. Kravitz, P.J. Rasch, S.-Y.S. Wang, R.R. Gillies, and L. Hipps, 2015: Extreme 

11 Fire Season in California: A Glimpse Into the Future? Bulletin of the American 

12 Meteorological Society, 96, S5-S9. http://dx.doi.org/10.1175/bams-d-15-00114.1 

13 http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-15-00114.1
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1 9. Extreme Storms

2 KEY FINDINGS

3 1. Human activities have contributed substantially to observed ocean-atmosphere variability in 

4 the Atlantic Ocean (medium confidence), and these changes have contributed to the observed 

5 increasing trend in North Atlantic hurricane activity since the 1970s (medium confidence).

6 2. For Atlantic and eastern North Pacific hurricanes and western North Pacific typhoons,

7 increases are projected in precipitation rates (high confidence) and intensity (medium 

8 confidence). The frequency of the most intense of these storms is projected to increase in the 

9 Atlantic and western North Pacific (low confidence) and in the eastern North Pacific (medium 

10 confidence).

11 3. Tornado activity in the United States has become more variable, particularly over the 2000s, 

12 with a decrease in the number of days per year experiencing tornadoes, and an increase in the 

13 number of tornadoes on these days (high confidence). Confidence in past trends for hail and 

14 severe thunderstorm winds, however, is low. Climate models consistently project 

15 environmental changes that would putatively support an increase in the frequency and 

16 intensity of severe thunderstorms (a category that combines tornadoes, hail, and winds), 

17 especially over regions that are currently prone to these hazards, but confidence in the details 

18 of this increase is low.

19 4. There has been a trend toward earlier snowmelt and a decrease in snowstorm frequency on 

20 the southern margins of climatologically snowy areas (medium confidence). Winter storm 

21 tracks have shifted northward since 1950 over the Northern Hemisphere (medium 

22 confidence). Projections of winter storm frequency and intensity over the United States vary 

23 from increasing to decreasing depending on region, but model agreement is poor and 

24 confidence is low. Potential linkages between the frequency and intensity of severe winter 

25 storms in the United States and accelerated warming in the Arctic have been postulated, but 

26 they are complex, and, to some extent, controversial, and confidence in the connection is 

27 currently low.

28 5. The frequency and severity of landfalling “atmospheric rivers” on the U. S. West Coast 

29 (narrow streams of moisture that account for 30%–40% of precipitation and snowpack in the 

30 region and are associated with severe flooding events) will increase as a result of increasing 

31 evaporation and resulting higher atmospheric water vapor that occurs with increasing 

32 temperature. (Medium confidence)

33 9.1 Introduction

34 Quantifying how broad-scale average climate influences the behavior of extreme storms is 

35 particularly challenging, in part because extreme storms are comparatively short-lived events and 
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1 occur within an environment of largely random variability. Additionally, because the physical 

2 mechanisms linking climate change and extreme storms can manifest in a variety of ways, even 

3 the sign of the changes in the extreme storms can vary in a warming climate. This makes 

4 detection and attribution of trends in extreme storm characteristics more difficult than detection 

5 and attribution of trends in the larger environment in which the storms evolve (e.g., Ch. 6: 

6 Temperature Change). Despite the challenges, good progress is being made for a variety of storm 

7 types, such as tropical cyclones, severe convective storms (thunderstorms), winter storms, and 

8 atmospheric river events.

9 9.2 Tropical Cyclones (Hurricanes, Typhoons)

10 Detection and attribution (Ch. 3: Detection and Attribution) of past changes in tropical cyclone 

11 (TC) behavior remain a challenge due to the nature of the historical data, which are highly 

12 heterogeneous in both time and among the various regions that collect and analyze the data

13 (Kossin et al. 2013; Klotzbach and Landsea 2015; Walsh et al. 2016). While there are ongoing 

14 efforts to reanalyze and homogenize the data (e.g., Landsea et al. 2015; Kossin et al. 2013), there 

15 is still low confidence that any reported long-term (multidecadal to centennial) increases in TC

16 activity are robust, after accounting for past changes in observing capabilities (which is 

17 unchanged from the IPCC AR5 assessment statement [Hartmann et al. 2013]). This is not meant 

18 to imply that no such increases have occurred, but rather that the data are not of a high enough 

19 quality to determine this with much certainty. 

20 Both theory and numerical modeling simulations (in general) indicate an increase in TC intensity 

21 in a warmer world, and the models generally show an increase in the number of very intense TCs 

22 (Bindoff et al. 2013; Christensen et al. 2013; Walsh et al. 2015; Knutson et al. 2015). In some 

23 cases, climate models can be used to make attribution statements about TCs without formal 

24 detection (see also Ch. 3: Detection and Attribution). For example, there is evidence that, in 

25 addition to the effects of El Niño, anthropogenic forcing made the extremely active 2014 

26 Hawaiian hurricane season substantially more likely, although no significant rising trend in TC 

27 frequency near Hawai‘i was detected (Murakami et al. 2015). 

28 Changes in frequency and intensity are not the only measures of TC behavior that may be 

29 affected by climate variability and change, and there is evidence that the locations where TCs 

30 reach their peak intensity has migrated poleward over the past 30 years in the Northern and 

31 Southern Hemispheres, apparently in concert with environmental changes associated with the 

32 independently observed expansion of the tropics (Kossin et al. 2014). The poleward migration in 

33 the western North Pacific (Kossin et al. 2016), which includes a number of United States 

34 Territories, appears particularly robust and remains significant over the past 60–70 years after 

35 accounting for the known modes of natural variability in the region (Figure 9.1). The migration, 

36 which can substantially change patterns of TC hazard exposure and mortality risk, is also evident 

37 in 21st century Coupled Model Intercomparison Project Phase 5 (CMIP5) projections following 

38 the RCP8.5 emissions trajectories, suggesting a possible link to human activities. Further 
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1 analysis comparing observed past TC behavior with climate model historical forcing runs (and 

2 with model control runs simulating multidecadal internal climate variability alone) are needed to 

3 better understand this process, but it is expected that this will be an area of heightened future 

4 research.

5 [INSERT FIGURE 9.1 HERE:

6 Figure 9.1: Poleward migration, in degrees of latitude, of the location of annual-mean TC peak 

7 lifetime intensity in the western N. Pacific ocean, after accounting for the known regional modes 

8 of interannual (El Niño–Southern Oscillation; ENSO) and interdecadal (Pacific Decadal 

9 Oscillation; PDO) variability. The time series shows residuals of the multivariate regression of 

10 annually-averaged latitude of TC peak lifetime intensity onto the mean Niño-3.4 and PDO 

11 indices. Data are taken from the Joint Typhoon Warning Center (JTWC). Shading shows 95% 

12 confidence bounds for the trend. Annotated values at lower-right show the mean migration rate 

13 and its 95% confidence interval in degrees per decade for the period 1945–2013. (Figure source: 

14 redrawn from Kossin et al. 2016; © American Meteorological Society. Used with permission.)]

15 In the Atlantic, observed multidecadal variability of the ocean and atmosphere, which TCs are 

16 shown to respond to, has been attributed (Ch. 3: Detection and Attribution) to natural internal 

17 variability via meridional overturning ocean circulation changes (Delworth and Mann 2000), 

18 natural external variability caused by volcanic eruptions (Thompson and Solomon 2009; Evan 

19 2012) and Saharan dust outbreaks (Evan et al. 2009, 2011), and anthropogenic external forcing 

20 via greenhouse gases and sulfate aerosols (Mann and Emanuel 2006; Booth et al. 2012; 

21 Dunstone et al. 2013). Determining the relative contributions of each mechanism to the observed 

22 multidecadal variability in the Atlantic, and even whether natural or anthropogenic factors have 

23 dominated, is presently a very active area of research and debate, and no consensus has yet been 

24 reached (Carslaw et al. 2013; Zhang et al. 2013; Tung and Zhao 2013; Mann et al. 2014; Stevens 

25 2015; Sobel et al. 2016). Despite the level of disagreement about the relative magnitude of 

26 human influences, there is broad agreement in the literature that human factors have had a 

27 measurable impact on the observed oceanic and atmospheric variability in the North Atlantic,

28 and there is medium confidence that this has contributed to the observed increase in hurricane 

29 activity since the 1970s. This is essentially unchanged from the Intergovernmental Panel on 

30 Climate Change Fifth Assessment Report (IPCC AR5) statement (Bindoff et al. 2013), although

31 the post-AR5 literature has only served to further support this statement (Kossin et al. 2015).

32 This is expected to remain an active research topic in the foreseeable future.

33 The IPCC AR5 consensus TC projections for the late 21st century (IPCC Figure 14.17; 

34 Christensen et al. 2013) include an increase in global mean TC intensity, precipitation rate, and 

35 frequency of very intense (Saffir-Simpson Category 4–5) TCs, and a decrease, or little change, in 

36 global tropical cyclone frequency. Since the IPCC AR5, some studies have provided additional 

37 support for this consensus, and some have challenged it. For example, a recent study (Fig. 9.2) 

38 projects increased mean TC intensity and occurrence of Saffir-Simpson Category 4–5 storms in 

39 the Atlantic Ocean basin and in most, but not all, other TC-supporting basins (Knutson et al. 
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1 2015). However, another recent (post-AR5) study proposed that increased thermal stratification 

2 of the upper ocean in CMIP5 climate warming scenarios should substantially reduce the 

3 warming-induced intensification of TCs estimated in previous studies (Huang et al. 2015). 

4 Follow-up studies, however, estimate that the effect of such increased stratification is relatively 

5 small, reducing the projected intensification of TCs by only about 10%–15% (Emanuel 2015; 

6 Tuleya et al. 2016). 

7 Another recent study challenged the IPCC AR5 consensus projections by simulating increased 

8 global TC frequency over the 21st century under the RCP8.5 scenario (Emanuel 2013). 

9 However, another modeling study has found that neither direct analysis of CMIP5-class 

10 simulations, nor indirect inferences from the simulations (such as those of Emanuel 2013), could 

11 reproduce the sign of the change in TC frequency projected in a warmer world by high12 resolution TC-permitting climate models (Wehner et al. 2015), which adds uncertainty to the 

13 results of Emanuel (2013). 

14 In summary, despite new research that challenges some aspects of the AR5 consensus for late 

15 21st century projected TC activity, it remains likely that global mean tropical cyclone maximum 

16 wind speeds and precipitation rates will increase; and it is more likely than not that the global 

17 frequency of occurrence of TCs will either decrease or remain essentially the same. Confidence 

18 in projected global increases of intensity and tropical cyclone precipitation rates is medium and 

19 high, respectively, as there is some consistency among studies and at least a fair degree of 

20 consensus. Confidence in projected increases in the frequency of very intense TCs is generally 

21 lower (medium in the eastern North Pacific and low in the western North Pacific and Atlantic) 

22 due to comparatively fewer studies available and due to the competing influences of projected 

23 reductions in overall storm frequency and increased mean intensity on the frequency of the most 

24 intense storms. Both the magnitude and sign of projected changes in individual ocean basins 

25 appears to depend on the large-scale pattern of changes to atmospheric circulation and ocean 

26 surface temperature (e.g., Knutson et al. 2015). Projections of these regional patterns of change 

27 — apparently critical for TC projections — are uncertain, leading to uncertainty in regional TC 

28 projections.

29 [INSERT FIGURE 9.2 HERE:

30 Figure 9.2: Simulated occurrence of tropical cyclones of at least Category 4 intensity (surface 

31 winds of at least 59 m/s [132 mph]) for (a) present-day or (b) late-twenty-first-century (RCP4.5; 

32 CMIP5 multimodel ensemble) conditions; unit: storms per decade. Simulated tropical cyclone 

33 tracks were obtained using the GFDL hurricane model to resimulate (at higher resolution) the 

34 tropical cyclone cases originally obtained from the HiRAM C180 global mode. Occurrence 

35 refers to the number of days, over a 20-year period, in which a storm exceeding 59 m/s (132 

36 mph) intensity was centered within the 10° × 10° grid region. (c) Difference in occurrence rate 

37 between late twenty-first century and present day [(b) minus (a)]. White regions are regions 

38 where no tropical storms occurred in the simulations [in (a) and (b)] or where the difference 
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1 between the experiments is zero [in (c)]. (Figure source: redrawn from Knutson et al. 2015; © 

2 American Meteorological Society. Used with permission.).]

3 ----- START BOX 9.1 HERE -----

4 Box 9.1: U.S. Landfalling Major Hurricane “Drought”

5 The last major hurricane (Saffir-Simpson Category 3 or higher) to make landfall in the

6 continental United States was Wilma in 2005. The current 11-year (2006–2016) absence of U.S. 

7 major hurricane landfall events (sometimes colloquially referred to as a “hurricane drought”) is 

8 unprecedented in the historical records dating back to the mid-19th century, and has occurred in 

9 tandem with average to above-average basin-wide major hurricane counts. Is the absence of U.S. 

10 landfalling major hurricanes due to random luck, or are there systematic changes in climate 

11 driving this?

12 One recent study indicates that the absence of U.S. landfalling major hurricanes cannot readily 

13 be attributed to any sustained changes in the climate patterns that affect hurricanes (Hall and 

14 Heried 2015). Based on a statistical analysis of the historical North Atlantic hurricane database, 

15 the study found no evidence for memory in major U.S. landfalls from one year to the next and 

16 concluded that the 11-year absence of U.S. landfalling major hurricanes is random. Another 

17 recent study did identify a systematic pattern of atmosphere/ocean conditions that vary in such a 

18 way that conditions conducive to hurricane intensification in the deep tropics occur in concert 

19 with conditions conducive to weakening near the U.S. coast (Kossin 2016). This result suggests a 

20 possible relationship between climate and hurricanes; increasing basin-wide hurricane counts are 

21 associated with decreasing fraction of major hurricanes making U.S. landfall, as major 

22 hurricanes approaching the U.S. coast are more likely to weaken during active North Atlantic 

23 hurricane periods (such as the present period). It is unclear to what degree this relationship has 

24 affected absolute hurricane landfall counts during the recent active hurricane period from the 

25 mid-1990s, as the basin-wide number and landfalling fraction are in opposition (that is, there are 

26 more major hurricanes but a smaller fraction make landfall as major hurricanes). It is also 

27 unclear how this relationship may change as the climate continues to warm. 

28 A third recent study (Hart et al. 2016) shows that the extent of the absence is sensitive to 

29 uncertainties in the historical data and even small variations in the definition of a major 

30 hurricane, which is somewhat arbitrary. It is also sensitive to the definition of U.S. landfall, 

31 which is a geopolitical-border-based constraint and has no physical meaning. In fact, many areas 

32 outside of the U.S. border have experienced major hurricane landfalls in the past 11 years. In this 

33 sense, the frequency of U.S. landfalling major hurricanes is not a particularly robust metric with 

34 which to study questions about hurricane activity and its relationship with climate variability. 

35 Furthermore, the 11-year absence of U.S. landfalling major hurricanes is not a particularly 

36 relevant metric in terms of coastal hazard exposure and risk. For example, Hurricanes Ike (2008), 

37 Irene (2011), and Sandy (2012), and most recently Hurricane Matthew (2016) brought severe 
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1 impacts to the U.S. coast despite not making landfall in the United States as major hurricanes. In 

2 the case of Hurricane Matthew, the center came within about 40 miles of the Florida coast while 

3 Matthew was a major hurricane, which is close enough to significantly impact the coast but not 

4 close enough to break the “drought” as it’s defined. 

5 In summary, the 11-year absence of U.S. landfalling major hurricanes is anomalous. There is 

6 some evidence that systematic atmosphere/ocean variability has reduced the fraction of 

7 hurricanes making U.S. landfall since the mid-1990s, but this is at least partly countered by 

8 increased basin-wide numbers, and the net effect on landfall rates is unclear. Moreover, there is a 

9 large random element, and the metric itself suffers from lack of physical basis due to the 

10 arbitrary intensity threshold and geopolitically based constraints. Additionally, U.S. coastal risk, 

11 particularly from storm surge and freshwater flooding, depends strongly on storm size, 

12 propagation speed and direction, and rainfall rates. There is some danger, in the form of evoking 

13 complacency, in placing too much emphasis on the recent absence of a specific subset of 

14 hurricanes.

15 ----- END BOX 9.1 HERE -----

16 9.3 Severe Convective Storms (Thunderstorms)

17 Tornado and severe thunderstorm events cause significant loss of life and property: more than 

18 one-third of the $1 billion weather disasters in the United States during the past 25 years were 

19 due to such events, and relative to other extreme weather, the damages from convective weather 

20 hazards have undergone the largest increase (Smith and Katz 2013). A particular challenge in 

21 quantifying the existence and intensity of these events arises from the data source: rather than 

22 measurements, the occurrence of tornadoes and severe thunderstorms is determined by visual 

23 sightings by eyewitnesses (such as “storm spotters” and law enforcement officials). The 

24 reporting has been susceptible to changes in population density, modifications to reporting 

25 procedures and training, the introduction of video and social media, and so on. These have led to 

26 systematic, non-meteorological biases in the long-term data record.

27 Nonetheless, judicious use of the report database has revealed important information about 

28 tornado trends. Since the 1970s, the United States has experienced a decrease in the number of 

29 days per year on which tornadoes occur, but an increase in the number of tornadoes that form on 

30 such days (Brooks et al. 2014). One important implication is that the frequency of days with 

31 large numbers of tornadoes—tornado outbreaks—appears to be increasing (Figure 9.3). The 

32 extent of the season over which such tornado activity occurs is increasing as well: although 

33 tornadoes in the United States are observed in all months of the year, an earlier calendar-day start 

34 to the season of high activity is emerging. In general, there is more interannual variability, or 

35 volatility, in tornado occurrence (Tippett 2014: see also Elsner et al. 2015).
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1 [INSERT FIGURE 9.3 HERE:

2 Figure 9.3: Annual tornado activity in the United States over the period 1955–2013. The black 

3 squares indicate the number of days per year with at least one tornado rated (E)F1 or greater, and 

4 the black circles and line show the decadal mean line of such tornado days. The red triangles 

5 indicate the number of days per year with more than 30 tornadoes rated (E)F1 or greater, and the 

6 red circles and line show the decadal mean of these tornado outbreaks. (Figure source: redrawn 

7 from Brooks et al. 2014)]

8 Evaluations of hail and (non-tornadic) thunderstorm wind reports have thus far been less 

9 revealing. Although there is evidence of an increase in the number of hail days per year, the 

10 inherent uncertainty in reported hail size reduces the confidence in such a conclusion (Allen and 

11 Tippett 2015). Thunderstorm wind reports have proven to be even less reliable, because, as 

12 compared to tornadoes and hail, there is less tangible visual evidence; thus, although the United 

13 States has lately experienced several significant thunderstorm wind events (sometimes referred 

14 to as “derechos”), the lack of studies that explore long-term trends in wind events and the 

15 uncertainties in the historical data preclude any robust assessment.

16 It is possible to bypass the use of reports by exploiting the fact that the temperature, humidity, 

17 and wind in the larger vicinity—or “environment”—of a developing thunderstorm ultimately 

18 control the intensity, morphology, and hazardous tendency of the storm. Thus, the premise is that 

19 quantifications of the vertical profiles of temperature, humidity, and wind that can be used as a 

20 proxy for actual severe thunderstorm occurrence. In particular, a thresholded product of 

21 convective available potential energy (CAPE) and vertical wind shear over a surface-to-6 km 

22 layer (S06) constitutes one widely used means of representing the frequency of severe 

23 thunderstorms (Brooks et al. 2003). This environmental-proxy approach avoids the biases and 

24 other issues with eyewitness storm reports and is readily evaluated using the relatively coarse 

25 global data sets and global climate models. It has the disadvantage of assuming that a

26 thunderstorm will necessarily form and then realize its environmental potential. 

27 Upon employing Global Climate Models (GCMs) to evaluate CAPE and S06, a consistent 

28 finding among a growing number of proxy-based studies is a projected increase in the frequency 

29 of severe thunderstorm environments in the United States over the mid- to late 21st century (Van 

30 Klooster and Roebber 2009; Diffenbaugh et al. 2013; Gensini et al. 2014; Seely and Romps 

31 2015). The most robust projected increases in frequency are over the central United States, 

32 during March-April-May (MAM) (Diffenbaugh et al. 2013). Based on the increased frequency of 

33 very high CAPE, increases in storm intensity are also projected over this same period (see also 

34 Del Genio et al. 2007).

35 Key limitations of the environmental proxy approach are being addressed through the 

36 applications of high-resolution dynamical downscaling, wherein sufficiently fine model grids are 

37 used so that individual thunderstorms are explicitly resolved, rather than implicitly represented 

38 (as through environmental proxies). The individually modeled thunderstorms can then be 
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1 quantified and assessed in terms of severity (Trapp et al. 2011; Robinson et al. 2013; Gensini and 

2 Mote 2014). A comprehensive approach using a dynamically downscaled GCM over 30-year

3 historical and future climate periods showed the following: 1) a relatively large increase in the 

4 severe thunderstorm occurrence during the early part of MAM within the southeastern United 

5 States; 2) a northward and eastward expansion of the occurrence frequency, especially during 

6 MAM; and 3) a significant increase in the frequency in June-July-August (JJA), particularly in 

7 the northern Great Plains (Hoogewind et al. 2016). 

8 The computational expense of high-resolution dynamical downscaling makes it difficult to 

9 generate model ensembles over long time periods, and thus to assess the uncertainty of the 

10 downscaled projections. Because these dynamical downscaling implementations focus on the 

11 statistics of storm occurrence rather than on faithful representations of individual events, they 

12 have generally been unconcerned with specific extreme convective events in history. So, for 

13 example, such downscaling does not address whether the intensity of an event like the Joplin, 

14 Missouri, tornado of May 22, 2011, would be amplified under projected future climates. 

15 Recently, the “pseudo-global warming” (PGW) methodology (see Schär et al. 1996), which is a 

16 variant of dynamical downscaling, has been adapted to address these and related questions. As an 

17 example, when the parent “supercell” of select historical tornado events forms under the climate 

18 conditions projected during the late 21st century, it becomes a more intense supercell rather than 

19 a benign, unorganized thunderstorm (Trapp and Hoogewind 2016). The intensity and, by 

20 extension, the severity of these supercells fall short of the expectations based on CAPE.

21 However, the updrafts simulated under PGW are relatively more intense, but not in proportion to 

22 the projected higher levels of CAPE.

23 9.4 Winter Storms

24 The frequency of large snowfall years has decreased in the southern United States and Pacific 

25 Northwest and increased in the northern United States (see Ch. 7: Precipitation Change). The 

26 winters of 2013/2014 and 2014/2015 have contributed to this trend. They were characterized by 

27 frequent storms and heavier-than-normal snowfalls in the Midwest and Northeast and drought in 

28 the western United States. These were related to blocking (a large-scale pressure pattern with 

29 little or no movement) of the wintertime circulation in the Pacific sector of the Northern 

30 Hemisphere (e.g., Marinaro et al. 2015) that put this part of the United States in the primary 

31 winter storm track, while at the same time reducing the number of winter storms in California, 

32 causing severe drought conditions (Chang et al. 2015). While some observational studies suggest 

33 a linkage between blocking affecting the U.S. climate and enhanced Arctic warming (arctic 

34 amplification), specifically for an increase in highly amplified jet stream patterns in winter over 

35 the United States (Francis and Skific 2015), other studies show mixed results (Barnes and 

36 Polvani 2015; Perlwitz et al. 2015; Screen et al. 2015). Therefore, a definitive understanding of 

37 the effects of arctic amplification on midlatitude winter weather remains elusive, and other 
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1 explanations have been offered for the weather patterns of recent winters, such as anomalously 

2 strong Pacific trade winds (Yang et al. 2015).

3 Analysis of storm tracks indicates that there has been an increase in winter storm frequency and 

4 intensity since 1950, with a slight shift in tracks toward the poles (Wang et al. 2006, 2012; Vose 

5 et al. 2014). Current global climate models (CMIP5) do in fact predict an increase in 

6 extratropical cyclone (ETC) frequency over the eastern United States, including the most intense 

7 ETCs, under the high RCP8.5 emission scenario (Colle et al. 2013). However, there are large 

8 model-to-model differences in the realism of ETC simulations and in the projected changes. 

9 Moreover, projected ETC changes have large regional variations, including a decreased total 

10 frequency in the North Atlantic, further highlighting the complexity of the response to climate 

11 change.

12 9.5 Atmospheric Rivers 

13 The term “atmospheric rivers” (ARs) refers to the relatively narrow streams of moisture transport 

14 that often occur within and across midlatitudes (Zhu and Newell 1998) (Figure 9.4), in part 

15 because they often transport as much water as in the Amazon River (Newell et al. 1992). While 

16 ARs occupy less than 10% of the circumference of the Earth at any given time, they account for 

17 90% of the poleward moisture transport across midlatitudes. In many regions of the world, they 

18 account for a substantial fraction of the precipitation (Guan and Waliser 2015), and thus water 

19 supply, often delivered in the form of an extreme weather and precipitation event (Figure 9.4). 

20 For example, ARs account for 30%–40% of the typical snow pack in the Sierra Nevada 

21 mountains and annual precipitation in the U.S. West Coast states (Guan et al. 2010; Dettinger et 

22 al. 2011)—an essential summertime source of water for agriculture, consumption, and ecosystem 

23 health. However, this vital source of water is also associated with severe flooding, with 

24 observational evidence showing a close connection between historically high streamflow events 

25 and floods with landfalling AR events, in the west and other sectors of the United States (Ralph 

26 et al. 2006; Neiman et al. 2011; Moore et al. 2012). More recently, research has also 

27 demonstrated that ARs are often found to be critical in ending droughts in the western United 

28 States (Dettinger 2013).

29 [INSERT FIGURE 9.4 HERE:

30 Figure 9.4: (upper left) Atmospheric rivers depicted in Special Sensor Microwave Imager

31 (SSM/I) measurements of total column water vapor leading to extreme precipitation events at 

32 landfall locations. (upper right) Annual mean frequency of atmospheric river occurrence (for 

33 example, 12% means about 1 every 8 days) and their integrated moisture transport (IVT) (Guan 

34 and Waliser 2015). (lower left) ARs are the dominant synoptic storms for the U.S. west coast in 

35 terms of extreme precipitation (Ralph and Dettinger 2012) and (lower right) supply a large 

36 fraction of the annual precipitation in the U.S. west coast states (Dettinger et al. 2011). [Figure 

37 source: (upper left) Ralph et al. 2011, (upper right) Guan and Waliser 2015, (lower left) Ralph 
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1 and Dettinger 2012, (lower right), Dettinger et al. 2011; left panels, © American Meteorological 

2 Society. Used with permission.]]

3 Given the important role that ARs play in the water supply of the western United States and their 

4 role in weather and water extremes in the west and occasionally other parts of the United States 

5 (e.g., Rutz et al. 2014), it is critical to examine how climate change and the expected 

6 intensification of the global water cycle and atmospheric transports (e.g., Held and Soden 2006; 

7 Lavers et al. 2015) are projected to impact ARs (e.g., Dettinger and Ingram 2013). Under climate 

8 change conditions, ARs may be altered in a number of ways, namely their frequency, intensity, 

9 duration, and locations. In association with landfalling ARs, any of these would be expected to 

10 result in impacts on hazards and water supply given the discussion above. Assessments of ARs in 

11 climate change projections for the United States have been undertaken for central California 

12 from CMIP3 (Dettinger et al. 2011) and a number of studies for the West Coast of North

13 America (Warner et al. 2015; Payne and Magnusdottir 2015; Gao et al. 2015; Radic et al. 2015; 

14 Hagos et al. 2016), and these studies have uniformly shown that ARs are likely to become more 

15 frequent and intense in the future. For example, one recent study reveals a large increase of AR 

16 days along the West Coast by the end of the 21st century in the RCP8.5 scenario, with fractional 

17 increases between 50% and 600%, depending on the seasons and landfall locations (Gao et al. 

18 2015). Results from these studies (and Lavers et al. 2013 for ARs impacting the United 

19 Kingdom) show that these AR changes were predominantly driven by increasing atmospheric 

20 specific humidity, with little discernible change in the low-level winds. The higher atmospheric 

21 water vapor content in a warmer climate is to be expected because of an increase in saturation 

22 water vapor pressure with air temperature (Ch. 2: Scientific Basis). While the thermodynamic 

23 effect appears to dominate the climate change impact on ARs, leading to projected increases in 

24 ARs, there is evidence for a dynamical effect (that is, location change) related to the projected 

25 poleward shift of the subtropical jet that diminished the thermodynamic effect in the southern 

26 portion of the West Coast of North America (Gao et al. 2015).

27 The evidence for considerable increases in the number and intensity of ARs depends (as do all 

28 climate changes studies based on dynamical models) on the model fidelity in representing ARs 

29 and their interactions with the global climate/circulation. Additional confidence comes from 

30 studies that show qualitatively similar increases while also providing evidence that the models 

31 represent AR frequency, transports, and spatial distributions relatively well compared to 

32 observations (Payne and Magnusdottir 2015; Hagos et al. 2016). A caveat associated with 

33 drawing conclusions from any given study or differences between two is that they typically use 

34 different detection methodologies that are typically tailored to a regional setting (cf. Guan and 

35 Waliser 2015). Additional research is warranted to examine these storms from a global 

36 perspective, with additional and more in-depth process-oriented diagnostics/metrics. Stepping 

37 away from the sensitivities associated with defining atmospheric rivers, one study examined the 

38 intensification of the integrated vapor transport (IVT), easily and unambiguously defined (Lavers 

39 et al. 2015). That study found that for the RCP8.5 scenario, multimodel mean IVT and the IVT 
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1 associated with extremes above 95% percentile increase by 30%–40% in the North Pacific.

2 These results, along with the uniform findings of the studies above examining projected changes 

3 in ARs for the western North America and the United Kingdom, give high confidence that the 

4 frequency of AR storms will increase in association with rising global temperatures. 

5
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1 TRACEABLE ACCOUNTS

2 Key Finding 1

3 Human activities have contributed substantially to observed ocean-atmosphere variability in the 

4 Atlantic Ocean (medium confidence), and these changes have contributed to the observed 

5 increasing trend in North Atlantic hurricane activity since the 1970s (medium confidence).

6 Description of evidence base

7 The Key Finding and supporting text summarizes extensive evidence documented in the climate 

8 science literature and are similar to statements made in previous national (NCA3; Melillo et al., 

9 2014) and international (IPCC 2013) assessments. Data limitations are documented in Kossin et 

10 al. (2013) and references therein. Contributions of natural and anthropogenic factors in observed 

11 multidecadal variability are quantified in Carslaw et al. 2013; Zhang et al. 2013; Tung and Zhao 

12 2013; Mann et al. 2014; Stevens 2015; Sobel et al. 2016; Walsh et al. 2015.

13 Major uncertainties

14 Key remaining uncertainties are due to known and substantial heterogeneities in the historical 

15 tropical cyclone data and lack of robust consensus in determining the precise relative 

16 contributions of natural and anthropogenic factors in past variability of the tropical environment.

17 Assessment of confidence based on evidence and agreement, including short description of 

18 nature of evidence and level of agreement 

19 ☐ Very High 

20 ☐ High

21 X	 Medium 

22 ☐ Low 

23 Although the range of estimates of natural versus anthropogenic contributions in the literature is 

24 fairly broad, virtually all studies identify a measurable, and generally substantial, anthropogenic 

25 influence. This does constitute a consensus for human contribution to the increases in tropical 

26 cyclone activity since 1970. 

27 Summary sentence or paragraph that integrates the above information

28 The key message and supporting text summarizes extensive evidence documented in the climate 

29 science peer-reviewed literature. The uncertainties and points of consensus that were described 

30 in the NCA3 and IPCC assessments have continued.

31

32 Key Finding 2 

33 For Atlantic and eastern North Pacific hurricanes and western North Pacific typhoons, increases 

34 are projected in precipitation rates (high confidence) and intensity (medium confidence). The 

35 frequency of the most intense of these storms is projected to increase in the Atlantic and western 

36 North Pacific (low confidence) and in the eastern North Pacific (medium confidence).
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1 Description of evidence base

2 The Key Finding and supporting text summarizes extensive evidence documented in the climate 

3 science literature and are similar to statements made in previous national (NCA3; Melillo et al. 

4 2014) and international (IPCC 2013) assessments. Since these assessments, more recent 

5 downscaling studies have further supported these assessments (e.g., Knutson et al. 2015), though 

6 pointing out that the changes (future increased intensity and tropical cyclone precipitation rates) 

7 may not occur in all basins. 

8 Major uncertainties

9 A key uncertainty remains the lack of a supporting detectable anthropogenic signal in the 

10 historical data to add further confidence to these projections. As such, confidence in the 

11 projections is based on agreement among different modeling studies and physical understanding 

12 (for example, potential intensity theory for tropical cyclone intensities and the expectation of

13 stronger moisture convergence, and thus higher precipitation rates, in tropical cyclones in a 

14 warmer environment containing greater amounts of environmental atmospheric moisture). 

15 Additional uncertainty stems from uncertainty in both the projected pattern and magnitude of 

16 future sea surface temperatures (Knutson et al. 2015).

17 Assessment of confidence based on evidence and agreement, including short description of 

18 nature of evidence and level of agreement

19 ☐ Very High 

20 x☐ High

21 x☐ Medium

22 x☐ Low 

23 Confidence is rated as high in tropical cyclone rainfall projections and medium in intensity 

24 projections since there are a number of publications supporting these overall conclusions, fairly 

25 well established theory, generally consistency among different studies, varying methods used in 

26 studies, and still a fairly strong consensus among studies. However, a limiting factor for 

27 confidence in the results is the lack of a supporting detectable anthropogenic contribution in 

28 observed tropical cyclone data. 

29 There is low to medium confidence for increased occurrence of the most intense tropical cyclones 

30 for most basins, as there are relatively few formal studies that focus on these changes, and the 

31 change in occurrence of such storms would be enhanced by increased intensities, but reduced by 

32 decreased overall frequency of tropical cyclones.

33 Summary sentence or paragraph that integrates the above information

34 Models are generally in agreement that tropical cyclones will be more intense and have higher 

35 precipitation rates, at least in most basins. Given the agreement between models and support of 

36 theory and mechanistic understanding, there is medium to high confidence in the overall 

37 projection, although there is some limitation on confidence levels due to the lack of a supporting 

38 detectable anthropogenic contribution to tropical cyclone intensities or precipitation rates.
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1 Key Finding 3

2 Tornado activity in the United States has become more variable, particularly over the 2000s, with 

3 a decrease in the number of days per year experiencing tornadoes, and an increase in the number 

4 of tornadoes on these days (high confidence). Confidence in past trends for hail and severe 

5 thunderstorm winds, however, is low. Climate models consistently project environmental 

6 changes that would putatively support an increase in the frequency and intensity of severe 

7 thunderstorms (a category that combines tornadoes, hail, and winds), especially over regions that 

8 are currently prone to these hazards, but confidence in the details of this increase is low.

9 Description of evidence base

10 Evidence for the first and second statement comes from the U.S. database of tornado reports. 

11 There are well known biases in this database, but application of an intensity threshold (≥ a rating 

12 of 1 on the [Enhanced] Fujita scale) and the quantification of tornado activity in terms of tornado 

13 days instead of raw numbers of reports are thought to reduce these biases. It is not known at this 

14 time whether the variability and trends are necessarily due to climate change.

15 The third statement is based on projections from a wide range of climate models, including 

16 GCMs and RCMs, run over the past 10 years (e.g., see the review by Brooks 2013). The 

17 evidence is derived from an “environmental-proxy” approach, which herein means that severe18 thunderstorm occurrence is related to the occurrence of two key environmental parameters, 

19 CAPE and vertical wind shear. A limitation of this approach is the assumption that the 

20 thunderstorm will necessarily form and then realize its environmental potential. This assumption 

21 is indeed violated, albeit at levels that vary by region and season. 

22 Major uncertainties

23 Regarding the first and second statements, there is still some uncertainty in the database, even 

24 when the data are filtered. The major uncertainty in the third statement equates to the 

25 aforementioned limitation (that is, the thunderstorm will necessarily form and then realize its

26 environmental potential). 

27 Assessment of confidence based on evidence and agreement, including short description of 

28 nature of evidence and level of agreement

29 High: That the variability in tornado activity has increased.

30 Medium: That the severe-thunderstorm environmental conditions will change with a changing 

31 climate, but

32 Low: on the precise (geographical and seasonal) realization of the environmental conditions as 

33 actual severe thunderstorms.

34 Summary sentence or paragraph that integrates the above information

35 Analyses and projections of tornado and severe thunderstorm trends depend on careful 

36 treatments of the historical record and on novel approaches on the use of climate model 

37 simulations.
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1 Key Finding 4

2 There has been a trend toward earlier snowmelt and a decrease in snowstorm frequency on the 

3 southern margins of climatologically snowy areas (medium confidence). Winter storm tracks 

4 have shifted northward since 1950 over the Northern Hemisphere (medium confidence). 

5 Projections of winter storm frequency and intensity over the United States vary from increasing 

6 to decreasing depending on region, but model agreement is poor and confidence is low. Potential 

7 linkages between the frequency and intensity of severe winter storms in the United States and 

8 accelerated warming in the Arctic have been postulated, but they are complex, and, to some 

9 extent, controversial, and confidence in the connection is currently low.

10 Description of evidence base

11 The Key Finding and supporting text summarizes evidence documented in the climate science 

12 literature.

13 Evidence for changes in winter storm track changes are documented in a small number of studies 

14 (Wang et al. 2006, 2012). Future changes are documented in one study (Colle et al. 2013), but 

15 there are large model-to-model differences. The effects of arctic amplification on U.S. winter 

16 storms have been studied, but the results are mixed (Francis and Skific 2015; Barnes and Polvani 

17 2015; Perlwitz et al. 2015; Screen et al. 2015), leading to considerable uncertainties. 

18 Major uncertainties

19 Key remaining uncertainties relate to the sensitivity of observed snow changes to the spatial 

20 distribution of observing stations, and to historical changes in station location and observing 

21 practices. There is conflicting evidence about the effects of arctic amplification on CONUS 

22 winter weather.

23 Assessment of confidence based on evidence and agreement, including short description of 

24 nature of evidence and level of agreement

25 Very High 

26 x☐ High

27 x Medium 

28 x☐ Low 

29 There is high confidence that warming has resulted in earlier snowmelt and decreased snowfall 

30 on the warm margins of areas with consistent snowpack based on a number of observational 

31 studies. There is medium confidence that Northern Hemisphere storm tracks have shifted north 

32 based on a small number of studies. There is low confidence in future changes in winter storm 

33 frequency and intensity based on conflicting evidence from analysis of climate model 

34 simulations.

35 Summary sentence or paragraph that integrates the above information

36 Decreases in snowfall on southern and low elevation margins of currently climatologically 

37 snowy areas are likely but winter storm frequency and intensity changes are uncertain.
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1 Key Finding 5

2 The frequency and severity of landfalling “atmospheric rivers” on the U. S. West Coast (narrow 

3 streams of moisture that account for 30%–40% of precipitation and snowpack in the region and 

4 are associated with severe flooding events) will increase as a result of increasing evaporation and 

5 resulting higher atmospheric water vapor that occurs with increasing temperature. (Medium

6 confidence)

7 Description of evidence base

8 The Key Finding and supporting text summarizes evidence documented in the climate science 

9 literature. 

10 Evidence for the expectation of an increase in the frequency and severity of landfalling 

11 atmospheric rivers on the US West Coast comes from the CMIP-based climate change projection 

12 studies of Dettinger et al. 2011; Warner et al. 2015; Payne and Magnusdottir 2015; Gao et al. 

13 2015; Radic et al. 2015; and Hagos et al. 2016. The close connection between atmospheric rivers 

14 and water availability and flooding is based on the present-day observation studies of Guan et al.

15 2010; Dettinger et al. 2011; Ralph et al. 2006; Neiman et al. 2011; Moore et al. 2012; and 

16 Dettinger 2013. 

17 Major uncertainties

18 A modest uncertainty remains in the lack of a supporting detectable anthropogenic signal in the 

19 historical data to add further confidence to these projections. However, the overall increase in 

20 atmospheric rivers projected/expected is based to very large degree on the very high confidence

21 there is that the atmospheric water vapor will increase. Thus, increasing water vapor coupled 

22 with little projected change in wind structure/intensity still indicates increases in the 

23 frequency/intensity of atmospheric rivers. A modest uncertainty arises in quantifying the 

24 expected change at a regional level (for example, northern Oregon vs southern Oregon) given 

25 that there are some changes expected in the position of the jet stream that might influence the 

26 degree of increase for different locations along the west coast. 

27 Assessment of confidence based on evidence and agreement, including short description of 

28 nature of evidence and level of agreement

29 ☐ Very High 

30 ☐High

31 x Medium 

32 ☐ Low 

33 Summary sentence or paragraph that integrates the above information

34 Increases in atmospheric river frequency and intensity are expected along the U.S. west coast, 

35 leading to the likelihood of more frequent flooding conditions, with uncertainties remaining in 

36 the details of the spatial structure of theses along the coast (for example, northern vs southern 

37 California)
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1 FIGURES

2

3 Figure 9.1: Poleward migration, in degrees of latitude, of the location of annual-mean tropical 

4 cyclone (TC) peak lifetime intensity in the western North Pacific Ocean, after accounting for the 

5 known regional modes of interannual (El Niño–Southern Oscillation; ENSO) and interdecadal 

6 (Pacific Decadal Oscillation; PDO) variability. The time series shows residuals of the 

7 multivariate regression of annually-averaged latitude of TC peak lifetime intensity onto the mean 

8 Niño-3.4 and PDO indices. Data are taken from the Joint Typhoon Warning Center (JTWC). 

9 Shading shows 95% confidence bounds for the trend. Annotated values at lower-right show the 

10 mean migration rate and its 95% confidence interval in degrees per decade for the period 1945–

11 2013. (Figure source: redrawn from Kossin et al. 2016; © American Meteorological Society. 

12 Used with permission.).
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1

2 Figure 9.2: Simulated occurrence of tropical cyclones of at least Category 4 intensity (surface 

3 winds of at least 59 m/s or 130 mph) for a) present-day or b) late 21st century (RCP4.5; CMIP5 

4 multimodel ensemble) conditions; unit: storms per decade. Simulated tropical cyclone tracks 

5 were obtained using the GFDL hurricane model to resimulate (at higher resolution) the tropical 

6 cyclone cases originally obtained from the HiRAM C180 global mode. Occurrence refers to the 

7 number of days, over a 20-year period, in which a storm exceeding 59 m/s intensity was centered 

8 within the 10° × 10° grid region. c) Difference in occurrence rate between late 21st century and 

9 present day [(b) minus (a)]. White regions are regions where no tropical storms occurred in the 

10 simulations [in (a) and (b)] or where the difference between the experiments is zero [in (c)].

11 (Figure source: redrawn from Knutson et al. 2015; © American Meteorological Society. Used 

12 with permission.).

13
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1

2 Figure 9.3: Annual tornado activity in the United States over the period 1955–2013. The black 

3 squares indicate the number of days per year with at least one tornado rated (E)F1 or greater, and 

4 the black circles and line show the decadal mean line of such tornado days. The red triangles 

5 indicate the number of days per year with more than 30 tornadoes rated (E)F1 or greater, and the 

6 red circles and line show the decadal mean of these tornado outbreaks. (Figure source: redrawn 

7 from Brooks et al. 2014)
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1

2 Figure 9.4: (upper left) Atmospheric rivers depicted in Special Sensor Microwave Imager 

3 (SSM/I) measurements of SSM/I total column water vapor leading to extreme precipitation

4 events at landfall locations. (upper right) Annual mean frequency of atmospheric river 

5 occurrence (for example, 12% means about 1 every 8 days) and their integrated moisture 

6 transport (IVT) (Guan and Waliser 2015). (lower left) ARs are the dominant synoptic storms for 

7 the U.S. west coast in terms of extreme precipitation (Ralph and Dettinger 2012) and (lower 

8 right) supply a large fraction of the annual precipitation in the U.S. west coast states (Dettinger et 

9 al. 2011). [Figure source: (upper left) Ralph et al. 2011, (upper right) Guan and Waliser 2015, 

10 (lower left) Ralph and Dettinger 2012, (lower right), Dettinger et al. 2011; left panels, © 

11 American Meteorological Society. Used with permission.]

12
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 9

328

1 REFERENCES

2 Allen, J.T. and M.K. Tippett, 2015: The Characteristics of United States Hail Reports: 1955-

3 2014. Electronic Journal of Severe Storms Meteorology.

4 Barnes, E.A. and L.M. Polvani, 2015: CMIP5 Projections of Arctic Amplification, of the North 

5 American/North Atlantic Circulation, and of Their Relationship. Journal of Climate, 28, 

6 5254-5271. http://dx.doi.org/10.1175/JCLI-D-14-00589.1 

7 Bindoff, N.L., P.A. Stott, K.M. AchutaRao, M.R. Allen, N. Gillett, D. Gutzler, K. Hansingo, G. 

8 Hegerl, Y. Hu, S. Jain, I.I. Mokhov, J. Overland, J. Perlwitz, R. Sebbari, and X. Zhang, 2013: 

9 Detection and Attribution of Climate Change: from Global to Regional. Climate Change

10 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment 

11 Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. 

12 Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, 

13 Eds. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 

14 867–952. http://dx.doi.org/10.1017/CBO9781107415324.022 www.climatechange2013.org

15 Booth, B.B.B., N.J. Dunstone, P.R. Halloran, T. Andrews, and N. Bellouin, 2012: Aerosols 

16 implicated as a prime driver of twentieth-century North Atlantic climate variability. Nature,

17 484, 228-232. http://dx.doi.org/10.1038/nature10946 

18 Brooks, H.E., 2013: Severe thunderstorms and climate change. Atmospheric Research, 123, 129-

19 138. http://dx.doi.org/10.1016/j.atmosres.2012.04.002 

20 Brooks, H.E., G.W. Carbin, and P.T. Marsh, 2014: Increased variability of tornado occurrence in 

21 the United States. Science, 346, 349-352. http://dx.doi.org/10.1126/science.1257460 

22 Brooks, H.E., J.W. Lee, and J.P. Craven, 2003: The spatial distribution of severe thunderstorm 

23 and tornado environments from global reanalysis data. Atmospheric Research, 67–68, 73-94. 

24 http://dx.doi.org/10.1016/S0169-8095(03)00045-0 

25 Carslaw, K.S., L.A. Lee, C.L. Reddington, K.J. Pringle, A. Rap, P.M. Forster, G.W. Mann, D.V. 

26 Spracklen, M.T. Woodhouse, L.A. Regayre, and J.R. Pierce, 2013: Large contribution of 

27 natural aerosols to uncertainty in indirect forcing. Nature, 503, 67-71. 

28 http://dx.doi.org/10.1038/nature12674 

29 Chang, E.K.M., C. Zheng, P. Lanigan, A.M.W. Yau, and J.D. Neelin, 2015: Significant 

30 modulation of variability and projected change in California winter precipitation by 

31 extratropical cyclone activity. Geophysical Research Letters, 42, 5983-5991. 

32 http://dx.doi.org/10.1002/2015GL064424 

33 Christensen, J.H., K. Krishna Kumar, E. Aldrian, S.-I. An, I.F.A. Cavalcanti, M. de Castro, W. 

34 Dong, P. Goswami, A. Hall, J.K. Kanyanga, A. Kitoh, J. Kossin, N.-C. Lau, J. Renwick, D.B. 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 9

329

1 Stephenson, S.-P. Xie, and T. Zhou, 2013: Climate Phenomena and their Relevance for 

2 Future Regional Climate Change. Climate Change 2013: The Physical Science Basis. 

3 Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental 

4 Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. 

5 Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. Cambridge University Press, 

6 Cambridge, United Kingdom and New York, NY, USA, 1217–1308. 

7 http://dx.doi.org/10.1017/CBO9781107415324.028 www.climatechange2013.org

8 Colle, B.A., Z. Zhang, K.A. Lombardo, E. Chang, P. Liu, and M. Zhang, 2013: Historical 

9 Evaluation and Future Prediction of Eastern North American and Western Atlantic 

10 Extratropical Cyclones in the CMIP5 Models during the Cool Season. Journal of Climate,

11 26, 6882-6903. http://dx.doi.org/10.1175/JCLI-D-12-00498.1 

12 Del Genio, A.D., M.S. Yao, and J. Jonas, 2007: Will moist convection be stronger in a warmer 

13 climate? Geophysical Research Letters, 34, 5. http://dx.doi.org/10.1029/2007GL030525 

14 Delworth, L.T. and E.M. Mann, 2000: Observed and simulated multidecadal variability in the 

15 Northern Hemisphere. Climate Dynamics, 16, 661-676. 

16 http://dx.doi.org/10.1007/s003820000075 

17 Dettinger, M.D., 2013: Atmospheric Rivers as Drought Busters on the U.S. West Coast. Journal 

18 of Hydrometeorology, 14, 1721-1732. http://dx.doi.org/10.1175/JHM-D-13-02.1 

19 Dettinger, M.D. and B.L. Ingram, 2013: The Coming Megafloods. Scientific American, 308, 64-

20 71. http://dx.doi.org/10.1038/scientificamerican0113-64 

21 Dettinger, M.D., F.M. Ralph, T. Das, P.J. Neiman, and D.R. Cayan, 2011: Atmospheric rivers, 

22 floods and the water resources of California. Water, 3, 445-478. 

23 http://dx.doi.org/10.3390/w3020445 http://www.mdpi.com/2073-4441/3/2/445/pdf

24 Diffenbaugh, N.S., M. Scherer, and R.J. Trapp, 2013: Robust increases in severe thunderstorm 

25 environments in response to greenhouse forcing. Proceedings of the National Academy of 

26 Sciences, 110, 16361-16366. http://dx.doi.org/10.1073/pnas.1307758110 

27 Dunstone, N.J., D.M. Smith, B.B.B. Booth, L. Hermanson, and R. Eade, 2013: Anthropogenic 

28 aerosol forcing of Atlantic tropical storms. Nature Geoscience, 6, 534-539. 

29 http://dx.doi.org/10.1038/ngeo1854 

30 Elsner, J.B., S.C. Elsner, and T.H. Jagger, 2015: The increasing efficiency of tornado days in the 

31 United States. Climate Dynamics, 45, 651-659. http://dx.doi.org/10.1007/s00382-014-2277-

32 3 

33 Emanuel, K., 2015: Effect of Upper-Ocean Evolution on Projected Trends in Tropical Cyclone 

34 Activity. Journal of Climate, 28, 8165-8170. http://dx.doi.org/10.1175/JCLI-D-15-0401.1 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 9

330

1 Emanuel, K.A., 2013: Downscaling CMIP5 climate models shows increased tropical cyclone 

2 activity over the 21st century. Proceedings of the National Academy of Sciences, 110, 12219-

3 12224. http://dx.doi.org/10.1073/pnas.1301293110 

4 Evan, A.T., 2012: Atlantic hurricane activity following two major volcanic eruptions. Journal of 

5 Geophysical Research, 117, D06101. http://dx.doi.org/10.1029/2011JD016716 

6 Evan, A.T., G.R. Foltz, D. Zhang, and D.J. Vimont, 2011: Influence of African dust on ocean7 atmosphere variability in the tropical Atlantic. Nature Geoscience, 4, 762-765. 

8 http://dx.doi.org/10.1038/ngeo1276 

9 Evan, A.T., D.J. Vimont, A.K. Heidinger, J.P. Kossin, and R. Bennartz, 2009: The role of 

10 aerosols in the evolution of tropical North Atlantic Ocean temperature anomalies. Science,

11 324, 778-781. http://dx.doi.org/10.1126/science.1167404 

12 Francis, J. and N. Skific, 2015: Evidence linking rapid Arctic warming to mid-latitude weather 

13 patterns. Philosophical Transactions of the Royal Society A: Mathematical, Physical and 

14 Engineering Sciences, 373. http://dx.doi.org/10.1098/rsta.2014.0170 

15 Gao, Y., J. Lu, L.R. Leung, Q. Yang, S. Hagos, and Y. Qian, 2015: Dynamical and 

16 thermodynamical modulations on future changes of landfalling atmospheric rivers over 

17 western North America. Geophysical Research Letters, 42, 7179-7186. 

18 http://dx.doi.org/10.1002/2015GL065435 

19 Gensini, V.A. and T.L. Mote, 2014: Estimations of Hazardous Convective Weather in the United 

20 States Using Dynamical Downscaling. Journal of Climate, 27, 6581-6589. 

21 http://dx.doi.org/10.1175/JCLI-D-13-00777.1 

22 Gensini, V.A., C. Ramseyer, and T.L. Mote, 2014: Future convective environments using 

23 NARCCAP. International Journal of Climatology, 34, 1699-1705. 

24 http://dx.doi.org/10.1002/joc.3769 

25 Guan, B., N.P. Molotch, D.E. Waliser, E.J. Fetzer, and P.J. Neiman, 2010: Extreme snowfall 

26 events linked to atmospheric rivers and surface air temperature via satellite measurements. 

27 Geophysical Research Letters, 37, n/a-n/a. http://dx.doi.org/10.1029/2010GL044696 

28 Guan, B. and D.E. Waliser, 2015: Detection of atmospheric rivers: Evaluation and application of 

29 an algorithm for global studies. Journal of Geophysical Research: Atmospheres, 120, 12514-

30 12535. http://dx.doi.org/10.1002/2015JD024257 

31 Hagos, S.M., L.R. Leung, J.-H. Yoon, J. Lu, and Y. Gao, 2016: A projection of changes in 

32 landfalling atmospheric river frequency and extreme precipitation over western North 

33 America from the Large Ensemble CESM simulations. Geophysical Research Letters, 43, 

34 1357-1363. http://dx.doi.org/10.1002/2015GL067392 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 9

331

1 Hall, T. and K. Hereid, 2015: The frequency and duration of U.S. hurricane droughts. 

2 Geophysical Research Letters, 42, 3482-3485. http://dx.doi.org/10.1002/2015GL063652

3 Hart, R.E., D.R. Chavas, and M.P. Guishard, 2016: The Arbitrary Definition of the Current 

4 Atlantic Major Hurricane Landfall Drought. Bulletin of the American Meteorological Society,

5 97, 713-722. http://dx.doi.org/10.1175/BAMS-D-15-00185.1 

6 Hartmann, D.L., A.M.G. Klein Tank, M. Rusticucci, L.V. Alexander, S. Brönnimann, Y. 

7 Charabi, F.J. Dentener, E.J. Dlugokencky, D.R. Easterling, A. Kaplan, B.J. Soden, P.W. 

8 Thorne, M. Wild, and P.M. Zhai, 2013: Observations: Atmosphere and Surface. Climate 

9 Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth 

10 Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. 

11 Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and 

12 P.M. Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom and New 

13 York, NY, USA, 159–254. http://dx.doi.org/10.1017/CBO9781107415324.008 

14 www.climatechange2013.org

15 Held, I.M. and B.J. Soden, 2006: Robust responses of the hydrological cycle to global warming. 

16 Journal of Climate, 19, 5686-5699. http://dx.doi.org/10.1175/jcli3990.1 

17 Hoogewind, K.A., M.E. Baldwin, and R.J. Trapp, 2016: Climate change and hazardous 

18 convective weather in the United States: Insights from high-resolution dynamical 

19 downscaling. Journal of Climate, Submitted.

20 Huang, P., I.I. Lin, C. Chou, and R.-H. Huang, 2015: Change in ocean subsurface environment to 

21 suppress tropical cyclone intensification under global warming. Nature Communications, 6, 

22 7188. http://dx.doi.org/10.1038/ncomms8188 http://dx.doi.org/10.1038/ncomms8188

23 IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group 

24 I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

25 Cambridge University Press, Cambridge, UK and New York, NY, 1535 pp. 

26 http://dx.doi.org/10.1017/CBO9781107415324 www.climatechange2013.org

27 Klotzbach, P.J. and C.W. Landsea, 2015: Extremely Intense Hurricanes: Revisiting Webster et 

28 al. (2005) after 10 Years. Journal of Climate, 28, 7621-7629. 

29 http://dx.doi.org/10.1175/JCLI-D-15-0188.1 

30 Knutson, T.R., J.J. Sirutis, M. Zhao, R.E. Tuleya, M. Bender, G.A. Vecchi, G. Villarini, and D. 

31 Chavas, 2015: Global Projections of Intense Tropical Cyclone Activity for the Late Twenty32 First Century from Dynamical Downscaling of CMIP5/RCP4.5 Scenarios. Journal of 

33 Climate, 28, 7203-7224. http://dx.doi.org/10.1175/JCLI-D-15-0129.1 

34 Kossin, J., 2016: Hurricane intensification along U. S. coast suppressed during active hurricane 

35 periods. Nature, doi:10.1038/nature20783
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 9

332

1 Kossin, J.P., K.A. Emanuel, and S.J. Camargo, 2016: Past and Projected Changes in Western 

2 North Pacific Tropical Cyclone Exposure. Journal of Climate, 29, 5725-5739. 

3 http://dx.doi.org/10.1175/JCLI-D-16-0076.1 

4 Kossin, J.P., K.A. Emanuel, and G.A. Vecchi, 2014: The poleward migration of the location of 

5 tropical cyclone maximum intensity. Nature, 509, 349-352. 

6 http://dx.doi.org/10.1038/nature13278 

7 Kossin, J.P., T.R. Karl, T.R. Knutson, K.A. Emanuel, K.E. Kunkel, and J.J. O’Brien, 2015: 

8 Reply to “Comments on ‘Monitoring and Understanding Trends in Extreme Storms: State of 

9 Knowledge’”. Bulletin of the American Meteorological Society, 96, 1177-1179. 

10 http://dx.doi.org/10.1175/BAMS-D-14-00261.1 

11 Kossin, J.P., T.L. Olander, and K.R. Knapp, 2013: Trend analysis with a new global record of 

12 tropical cyclone intensity. Journal of Climate, 26, 9960-9976. 

13 http://dx.doi.org/10.1175/JCLI-D-13-00262.1 

14 Landsea, C., J. Franklin, and J. Beven, 2015: The revised Atlantic hurricane database 

15 (HURDAT2) In: (NHC), N.H.C. (ed.). NHC, Miami, FL.

16 Lavers, D.A., R.P. Allan, G. Villarini, B. Lloyd-Hughes, D.J. Brayshaw, and A.J. Wade, 2013: 

17 Future changes in atmospheric rivers and their implications for winter flooding in Britain. 

18 Environmental Research Letters, 8, 034010. http://dx.doi.org/10.1088/1748-

19 9326/8/3/034010 

20 Lavers, D.A., F.M. Ralph, D.E. Waliser, A. Gershunov, and M.D. Dettinger, 2015: Climate 

21 change intensification of horizontal water vapor transport in CMIP5. Geophysical Research 

22 Letters, 42, 5617-5625. http://dx.doi.org/10.1002/2015GL064672 

23 Mann, M.E. and K.A. Emanuel, 2006: Atlantic hurricane trends linked to climate change. Eos, 

24 Transactions of the American Geophysical Union, 87, 233-244. 

25 http://dx.doi.org/10.1029/2006EO240001 

26 Mann, M.E., B.A. Steinman, and S.K. Miller, 2014: On forced temperature changes, internal 

27 variability, and the AMO. Geophysical Research Letters, 41, 3211-3219. 

28 http://dx.doi.org/10.1002/2014GL059233 

29 Marinaro, A., S. Hilberg, D. Changnon, and J.R. Angel, 2015: The North Pacific–Driven Severe 

30 Midwest Winter of 2013/14. Journal of Applied Meteorology and Climatology, 54, 2141-

31 2151. http://dx.doi.org/10.1175/JAMC-D-15-0084.1 

32 Melillo, J.M., T.C. Richmond, and G.W. Yohe, eds. Climate Change Impacts in the United 

33 States: The Third National Climate Assessment. 2014, U.S. Global Change Research 

34 Program: Washington, D.C. 842. http://dx.doi.org/10.7930/J0Z31WJ2. 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 9

333

1 Moore, B.J., P.J. Neiman, F.M. Ralph, and F.E. Barthold, 2012: Physical Processes Associated 

2 with Heavy Flooding Rainfall in Nashville, Tennessee, and Vicinity during 1–2 May 2010: 

3 The Role of an Atmospheric River and Mesoscale Convective Systems. Monthly Weather 

4 Review, 140, 358-378. http://dx.doi.org/10.1175/MWR-D-11-00126.1 

5 Murakami, H., G.A. Vecchi, T.L. Delworth, K. Paffendorf, L. Jia, R. Gudgel, and F. Zeng, 2015: 

6 Investigating the Influence of Anthropogenic Forcing and Natural Variability on the 2014 

7 Hawaiian Hurricane Season. Bulletin of the American Meteorological Society, 96, S115-

8 S119. http://dx.doi.org/10.1175/BAMS-D-15-00119.1 

9 Neiman, P.J., L.J. Schick, F.M. Ralph, M. Hughes, and G.A. Wick, 2011: Flooding in Western 

10 Washington: The Connection to Atmospheric Rivers. Journal of Hydrometeorology, 12, 

11 1337-1358. http://dx.doi.org/10.1175/2011JHM1358.1 

12 Newell, R.E., N.E. Newell, Y. Zhu, and C. Scott, 1992: Tropospheric rivers? – A pilot study. 

13 Geophysical Research Letters, 19, 2401-2404. http://dx.doi.org/10.1029/92GL02916 

14 Payne, A.E. and G. Magnusdottir, 2015: An evaluation of atmospheric rivers over the North 

15 Pacific in CMIP5 and their response to warming under RCP 8.5. Journal of Geophysical 

16 Research: Atmospheres, 120, 11,173-11,190. http://dx.doi.org/10.1002/2015JD023586 

17 Perlwitz, J., M. Hoerling, and R. Dole, 2015: Arctic Tropospheric Warming: Causes and 

18 Linkages to Lower Latitudes. Journal of Climate, 28, 2154-2167. 

19 http://dx.doi.org/10.1175/JCLI-D-14-00095.1

20 Radić, V., A.J. Cannon, B. Menounos, and N. Gi, 2015: Future changes in autumn atmospheric 

21 river events in British Columbia, Canada, as projected by CMIP5 global climate models. 

22 Journal of Geophysical Research: Atmospheres, 120, 9279-9302. 

23 http://dx.doi.org/10.1002/2015JD023279 

24 Ralph, F.M. and M.D. Dettinger, 2012: Historical and National Perspectives on Extreme West 

25 Coast Precipitation Associated with Atmospheric Rivers during December 2010. Bulletin of 

26 the American Meteorological Society, 93, 783-790. http://dx.doi.org/10.1175/BAMS-D-11-

27 00188.1 

28 Ralph, F.M., P.J. Neiman, G.A. Wick, S.I. Gutman, M.D. Dettinger, D.R. Cayan, and A.B. 

29 White, 2006: Flooding on California's Russian River: Role of atmospheric rivers. 

30 Geophysical Research Letters, 33, L13801. http://dx.doi.org/10.1029/2006GL026689 

31 Robinson, E.D., R.J. Trapp, and M.E. Baldwin, 2013: The Geospatial and Temporal 

32 Distributions of Severe Thunderstorms from High-Resolution Dynamical Downscaling. 

33 Journal of Applied Meteorology and Climatology, 52, 2147-2161. 

34 http://dx.doi.org/10.1175/JAMC-D-12-0131.1 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 9

334

1 Rutz, J.J., W.J. Steenburgh, and F.M. Ralph, 2014: Climatological Characteristics of 

2 Atmospheric Rivers and Their Inland Penetration over the Western United States. Monthly 

3 Weather Review, 142, 905-921. http://dx.doi.org/10.1175/MWR-D-13-00168.1 

4 Schär, C., C. Frei, D. Lüthi, and H.C. Davies, 1996: Surrogate climate-change scenarios for 

5 regional climate models. Geophysical Research Letters, 23, 669-672. 

6 http://dx.doi.org/10.1029/96GL00265 

7 Screen, J.A., C. Deser, and L. Sun, 2015: Projected changes in regional climate extremes arising 

8 from Arctic sea ice loss. Environmental Research Letters, 10, 084006. 

9 http://dx.doi.org/10.1088/1748-9326/10/8/084006 

10 Seeley, J.T. and D.M. Romps, 2015: The Effect of Global Warming on Severe Thunderstorms in 

11 the United States. Journal of Climate, 28, 2443-2458. http://dx.doi.org/10.1175/JCLI-D-14-

12 00382.1 

13 Smith, A.B. and R.W. Katz, 2013: U.S. billion-dollar weather and climate disasters: Data 

14 sources, trends, accuracy and biases. Natural Hazards, 67, 387-410. 

15 http://dx.doi.org/10.1007/s11069-013-0566-5 

16 Sobel, A.H., S.J. Camargo, T.M. Hall, C.-Y. Lee, M.K. Tippett, and A.A. Wing, 2016: Human 

17 influence on tropical cyclone intensity. Science, 353, 242-246. 

18 http://dx.doi.org/10.1126/science.aaf6574 

19 Stevens, B., 2015: Rethinking the Lower Bound on Aerosol Radiative Forcing. Journal of 

20 Climate, 28, 4794-4819. http://dx.doi.org/10.1175/JCLI-D-14-00656.1 

21 Thompson, D.W.J. and S. Solomon, 2009: Understanding Recent Stratospheric Climate Change. 

22 Journal of Climate, 22, 1934-1943. http://dx.doi.org/10.1175/2008JCLI2482.1 

23 Tippett, M.K., 2014: Changing volatility of U.S. annual tornado reports. Geophysical Research 

24 Letters, 41, 6956-6961. http://dx.doi.org/10.1002/2014GL061347 

25 Trapp, R.J. and K.A. Hoogewind, 2016: The Realization of Extreme Tornadic Storm Events 

26 under Future Anthropogenic Climate Change. Journal of Climate, 29, 5251-5265. 

27 http://dx.doi.org/10.1175/JCLI-D-15-0623.1 

28 Trapp, R.J., E.D. Robinson, M.E. Baldwin, N.S. Diffenbaugh, and B.R.J. Schwedler, 2011: 

29 Regional climate of hazardous convective weather through high-resolution dynamical 

30 downscaling. Climate Dynamics, 37, 677-688. http://dx.doi.org/10.1007/s00382-010-0826-y 

31 Tuleya, R.E., M. Bender, T.R. Knutson, J.J. Sirutis, B. Thomas, and I. Ginis, 2016: Impact of 

32 Upper-Tropospheric Temperature Anomalies and Vertical Wind Shear on Tropical Cyclone 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 9

335

1 Evolution Using an Idealized Version of the Operational GFDL Hurricane Model. Journal of 

2 the Atmospheric Sciences, 73, 3803-3820. http://dx.doi.org/10.1175/JAS-D-16-0045.1 

3 Tung, K.-K. and J. Zhou, 2013: Using data to attribute episodes of warming and cooling in 

4 instrumental records. Proceedings of the National Academy of Sciences. 

5 http://dx.doi.org/10.1073/pnas.1212471110 

6 Van Klooster, S.L. and P.J. Roebber, 2009: Surface-Based Convective Potential in the 

7 Contiguous United States in a Business-as-Usual Future Climate. Journal of Climate, 22, 

8 3317-3330. http://dx.doi.org/10.1175/2009JCLI2697.1 

9 Vose, R.S., S. Applequist, M.A. Bourassa, S.C. Pryor, R.J. Barthelmie, B. Blanton, P.D. 

10 Bromirski, H.E. Brooks, A.T. DeGaetano, R.M. Dole, D.R. Easterling, R.E. Jensen, T.R. 

11 Karl, R.W. Katz, K. Klink, M.C. Kruk, K.E. Kunkel, M.C. MacCracken, T.C. Peterson, K. 

12 Shein, B.R. Thomas, J.E. Walsh, X.L. Wang, M.F. Wehner, D.J. Wuebbles, and R.S. Young, 

13 2014: Monitoring and understanding changes in extremes: Extratropical storms, winds, and 

14 waves. Bulletin of the American Meteorological Society, 95, 377-386. 

15 http://dx.doi.org/10.1175/BAMS-D-12-00162.1 

16 Walsh, K.J.E., S.J. Camargo, G.A. Vecchi, A.S. Daloz, J. Elsner, K. Emanuel, M. Horn, Y.-K. 

17 Lim, M. Roberts, C. Patricola, E. Scoccimarro, A.H. Sobel, S. Strazzo, G. Villarini, M. 

18 Wehner, M. Zhao, J.P. Kossin, T. LaRow, K. Oouchi, S. Schubert, H. Wang, J. Bacmeister, 

19 P. Chang, F. Chauvin, C. Jablonowski, A. Kumar, H. Murakami, T. Ose, K.A. Reed, R. 

20 Saravanan, Y. Yamada, C.M. Zarzycki, P.L. Vidale, J.A. Jonas, and N. Henderson, 2015: 

21 Hurricanes and Climate: The U.S. CLIVAR Working Group on Hurricanes. Bulletin of the 

22 American Meteorological Society, 96, 997-1017. http://dx.doi.org/10.1175/BAMS-D-13-

23 00242.1 

24 Walsh, K.J.E., J.L. McBride, P.J. Klotzbach, S. Balachandran, S.J. Camargo, G. Holland, T.R. 

25 Knutson, J.P. Kossin, T.-c. Lee, A. Sobel, and M. Sugi, 2016: Tropical cyclones and climate 

26 change. Wiley Interdisciplinary Reviews: Climate Change, 7, 65-89. 

27 http://dx.doi.org/10.1002/wcc.371 

28 Wang, X.L., Y. Feng, G.P. Compo, V.R. Swail, F.W. Zwiers, R.J. Allan, and P.D. Sardeshmukh, 

29 2012: Trends and low frequency variability of extra-tropical cyclone activity in the ensemble 

30 of twentieth century reanalysis. Climate Dynamics, 40, 2775-2800. 

31 http://dx.doi.org/10.1007/s00382-012-1450-9 

32 Wang, X.L., V.R. Swail, and F.W. Zwiers, 2006: Climatology and changes of extratropical 

33 cyclone activity: Comparison of ERA-40 with NCEP-NCAR reanalysis for 1958-2001. 

34 Journal of Climate, 19, 3145-3166. http://dx.doi.org/10.1175/JCLI3781.1 
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1 Warner, M.D., C.F. Mass, and E.P.S. Jr., 2015: Changes in Winter Atmospheric Rivers along the 

2 North American West Coast in CMIP5 Climate Models. Journal of Hydrometeorology, 16, 

3 118-128. http://dx.doi.org/10.1175/JHM-D-14-0080.1 

4 Wehner, M., Prabhat, K.A. Reed, D. Stone, W.D. Collins, and J. Bacmeister, 2015: Resolution 

5 Dependence of Future Tropical Cyclone Projections of CAM5.1 in the U.S. CLIVAR 

6 Hurricane Working Group Idealized Configurations. Journal of Climate, 28, 3905-3925. 

7 http://dx.doi.org/10.1175/JCLI-D-14-00311.1 

8 Yang, X., G.A. Vecchi, T.L. Delworth, K. Paffendorf, L. Jia, R. Gudgel, F. Zeng, and S.D. 

9 Underwood, 2015: Extreme North America Winter Storm Season of 2013/14: Roles of 

10 Radiative Forcing and the Global Warming Hiatus. Bulletin of the American Meteorological 

11 Society, 96, S25-S28. http://dx.doi.org/10.1175/BAMS-D-15-00133.1 

12 Zhang, R., T.L. Delworth, R. Sutton, D.L.R. Hodson, K.W. Dixon, I.M. Held, Y. Kushnir, J. 

13 Marshall, Y. Ming, R. Msadek, J. Robson, A.J. Rosati, M. Ting, and G.A. Vecchi, 2013: 

14 Have aerosols caused the observed Atlantic multidecadal variability? Journal of the 

15 Atmospheric Sciences, 70, 1135-1144. http://dx.doi.org/10.1175/jas-d-12-0331.1 

16 Zhu, Y. and R.E. Newell, 1998: A Proposed Algorithm for Moisture Fluxes from Atmospheric 

17 Rivers. Monthly Weather Review, 126, 725-735. http://dx.doi.org/10.1175/1520-

18 0493(1998)126<0725:APAFMF>2.0.CO;2
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1 10. Changes in Land Cover and Terrestrial Biogeochemistry

2 KEY FINDINGS

3 1. Changes in land use and land cover due to human activities produce changes in surface 

4 albedo and in atmospheric aerosol and greenhouse gas concentrations. These combined 

5 effects have recently been estimated to account for 40% ± 16% of the human-caused global 

6 radiative forcing from 1850 to 2010 (high confidence). As a whole, the terrestrial biosphere

7 (soil, plants) is a net “sink” for carbon (drawing down carbon from the atmosphere) and this 

8 sink has steadily increased since 1980, in part due to CO2 fertilization (very high confidence). 

9 The future strength of the land sink is uncertain and dependent on ecosystem feedbacks; the 

10 possibility of the land becoming a net carbon source cannot be excluded (very high 

11 confidence).

12 2. The increased occurrence and severity of drought has led to large changes in plant 

13 community structure with subsequent effects on carbon distribution and cycling within 

14 ecosystems (for example, forests, grasslands). Uncertainties about future land use changes 

15 (for example, policy or mitigation measures) and about how climate change will affect land 

16 cover change make it difficult to project the magnitude and sign of future climate feedbacks 

17 from land cover changes. (High confidence)

18 3. Since 1901, the consecutive number of both frost-free days and the length of the 

19 corresponding growing season has increased for all regions of the United States. However,

20 there is important variability at smaller scales, with some locations showing decreases of as 

21 much as one to two weeks. Plant productivity has not increased linearly with the increased 

22 number of frost-free days or with the longer growing season due to temperature thresholds 

23 and requirements for growth as well as seasonal limitations in water and nutrient availability

24 (very high confidence). Future consequences of changes to the growing season for plant 

25 productivity are uncertain. 

26 4. Surface temperatures are often higher in urban areas than in surrounding rural areas, for a 

27 number of reasons including the concentrated release of heat from buildings, vehicles, and 

28 industry. In the United States, this urban heat island (UHI) effect results in daytime 

29 temperatures 0.9°–7.2°F (0.5°–4.0°C) higher and nighttime temperatures 1.8°– 4.5°F (1.0°–

30 2.5°C) higher in urban areas, with larger temperature differences in humid regions (primarily 

31 the eastern United States) and in cities with larger populations. The UHI effect will 

32 strengthen in the future as the spatial extent and population of urban areas grow. (High 

33 confidence)

34
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1 10.1 Introduction

2 Direct changes in land use by humans are contributing to radiative forcing by altering land cover 

3 and therefore albedo, contributing to climate change (Chapter 2: Physical Drivers of Climate 

4 Change). This forcing is spatially variable in both magnitude and sign; globally averaged it is

5 negative (climate cooling; Figure 2.3). Climate changes, in turn, are altering the biogeochemistry 

6 of land ecosystems through extended growing seasons, increased numbers of frost-free days, 

7 altered productivity in agricultural and forested systems, longer fire seasons, and urban-induced 

8 thunderstorms (Galloway et al. 2014). These changes in land use and land cover interact with 

9 local, regional, and global climate processes (Brown et al. 2014). The resulting changes alter 

10 Earth’s albedo, the carbon cycle, and atmospheric aerosols, constituting a mix of positive and 

11 negative feedbacks to climate change (Ward et al. 2014; Figure 10.1 and Chapter 2, Section 

12 2.6.2). Thus, changes to terrestrial ecosystems are a direct driver of climate change and they are 

13 altered by climate change in ways that affect both ecosystem productivity and, through 

14 feedbacks, the climate itself. 

15 The concept that longer growing seasons are increasing productivity in some agriculture and 

16 forested ecosystems was discussed in the Third National Climate Assessment (NCA3; Melillo et 

17 al. 2014). However, there are other consequences to a lengthened growing season that can offset 

18 these gains in productivity. Here we discuss these emerging complexities (Section 10.3.1) as well 

19 as discussing other aspects of how climate change is altering and interacting with terrestrial 

20 ecosystems.

21 [INSERT FIGURE 10.1 HERE:

22 Figure 10.1. A graphical representation of climate interactions with land use and land cover.

23 (Figure source: Ward et al. 2014)]

24

25 10.2 Terrestrial Ecosystem Interactions with the Climate System

26 This report discusses changes in temperature (Ch. 6: Temperature Change), precipitation (Ch. 7: 

27 Precipitation Change), hydrology (Ch. 8: Droughts, Floods, and Hydrology) and extreme events 

28 (Ch. 9: Extreme Storms). The intersections of these topics affect the phenology, or onset of 

29 growth through senescence; of land cover and biogeochemistry through biophysical land surface 

30 properties such as albedo, energy, and hydrologic processes; and biogeochemical cycles such as 

31 carbon, nitrogen, and water (Ward et al. 2014; Figure 10.2). Satellite observations and ecosystem 

32 models suggests that biogeochemical interactions of carbon dioxide (CO2) fertilization and 

33 nitrogen (N) deposition and land cover change are responsible for global greening (25%–50%) 

34 and 4% of the Earth browning between 1982 and 2009 (Zhu et al. 2016; Mao et al. 2016). A 

35 recent analysis shows large-scale greening in the Arctic and boreal regions of North America and 

36 browning in the boreal forests of eastern Alaska for the period 1984–2012 (Ju and Masek 2016). 

37 While several studies have documented significant green-up periods, the lengthening of the 

38 growing season (Chapter 10.3.1) also alters the timing of green-up (onset of growth) and brown-
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1 down (senescence); where ecosystems become depleted of water resources as a result of this, the 

2 actual period of productive growth can be truncated (Adams et al. 2015). This section discusses 

3 how changes in temperature, the hydrologic cycle, extreme events, and biogeochemistry interact 

4 with land cover change. 

5 [INSERT FIGURE 10.2 HERE:

6 Figure 10.2. Radiative forcings for land use/land cover change and other anthropogenic impacts 

7 estimated for the year 2010 referenced to the year 1850. Total anthropogenic radiative forcings

8 (Myhre et al. 2013) are shown for comparison (yellow). Error lines represent uncertainties in 

9 total anthropogenic RF for the IPCC bars and uncertainties in land use/land cover change

10 radiative forcings. The “SUM” bars show the total radiative forcings when all forcing agents are 

11 combined. (Figure source: Ward et al. 2014)]

12 10.2.1 Temperature Change

13 Interactions between temperature changes, land cover, and biogeochemistry are more complex 

14 than commonly assumed. Previous research suggested a fairly direct relationship between 

15 increasing temperatures, longer growing seasons (see Section 10.3.1), increasing plant 

16 productivity (e.g. Walsh et al. 2014a), and therefore also an increase in CO2 uptake. Without 

17 water or nutrient limitations, increased CO2 concentrations and warm temperatures have been 

18 shown to extend the growing season, which may contribute to longer periods of plant activity 

19 and carbon uptake, but do not affect reproduction rates (Reyes-Fox et. al. 2014). However, there 

20 are other processes that offset benefits of a longer growing season, such as changes in water 

21 availability and demand for water (e.g., Georgakakos et al. 2014; Hibbard et al. 2014). For 

22 instance, increased dry conditions can lead to wildfire (e.g., Hatfield et al. 2014; Joyce et al. 

23 2014) and urban temperatures can contribute to urban-induced thunderstorms in the southeastern 

24 United States (Ashley et al. 2012). Temperature benefits of early onset of plant development in a 

25 longer growing season can be offset by (1) freeze damage caused by late-season frosts; (2) limits

26 to growth because of shortening of the photoperiod later in the season; or (3) by shorter chilling 

27 periods required for leaf unfolding by many plants (Fu et al. 2013; Gu et al. 2008). In the case of 

28 the 2012 draught, a warm spring reduced the carbon cycle impact of the drought by inducing 

29 earlier carbon uptake (Wolf et al. 2016). New evidence points to longer temperature-driven 

30 growing seasons for grasslands that may facilitate earlier onset of growth, but also that 

31 senescence is typically earlier (Fridley et al. 2016). In addition to changing CO2 uptake, higher 

32 temperatures can also enhance soil decomposition rates, thereby adding more CO2 to the 

33 atmosphere. Similarly temperature, as well as changes in the seasonality and intensity of 

34 precipitation, can influence nutrient and water availability (leading to both shortages and 

35 excesses) thereby influencing rates and magnitudes of decomposition (Galloway et al. 2014). 

36
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1 10.2.2 Water Cycle Changes

2 The global hydrological cycle is expected to intensify under climate change as a consequence of 

3 increased temperatures in the troposphere. The consequences of the increased water-holding 

4 capacity of a warmer atmosphere are longer and more frequent droughts and less frequent but 

5 more severe precipitation events and cyclonic activity (see Ch. 9: Extreme Storms for an in6 depth discussion of extreme storms). More intense rain events and storms can lead to flooding 

7 and ecosystem disturbances, thereby altering ecosystem function and carbon cycle dynamics. For 

8 an extensive review of precipitation changes and droughts, floods, and hydrology, see Chapters 7 

9 and 8 in this report.

10 From the perspective of the land biosphere, drought has strong effects on ecosystem productivity 

11 and carbon storage by reducing photosynthesis and increasing the risk of wildfire, pest 

12 infestation, and disease susceptibility. Thus, droughts of the future will affect carbon uptake and 

13 storage, leading to feedbacks to the climate system (See Chapter 11 for Arctic/climate/wildfire 

14 feedbacks; also see Schlesinger et al. 2016). Reduced productivity as a result of extreme drought 

15 events can also extend for several years post-drought (i.e., drought legacy effects; Frank et al. 

16 2016; Reichstein et al. 2013; Anderegg et al. 2015). The area of ecosystems under active drought 

17 and drought recovery is increasing (Schwalm et al. in review Nature), and recent work suggests 

18 that as drought events become more severe and frequent, the period between drought events may 

19 become shorter than ecosystem drought recovery time, leading to widespread degradation of land 

20 carbon sinks (Schwalm et al. in review Nature). In 2011, the most severe drought on record in 

21 Texas led to statewide regional tree mortality of 6.2%, or nearly nine times greater than the 

22 average annual mortality in this region (approximately 0.7%) (Moore et al. 2016). The net effect 

23 on carbon storage was estimated to be a redistribution of 24–30 Tg C from the live to dead tree 

24 carbon pool, which is equal to 6%–7% of pre-drought live tree carbon storage in Texas state 

25 forestlands (Moore et al. 2016). Another way to think about this redistribution is that the single 

26 Texas drought event equals approximately 36% of annual global carbon losses due to 

27 deforestation and land use change (Cias et al. 2013). The projected increases in temperatures and 

28 in the magnitude and frequency of heavy precipitation events, changes to snowpack, and changes 

29 in the subsequent water availability for agriculture and forestry may lead to similar rates of 

30 mortality or changes in land cover. Increasing frequency and intensity of drought across northern 

31 ecosystems reduces total observed organic matter export, leads to oxidized wetland soils, and 

32 releases stored contaminants into streams after rain events (Szkokan-Emilson et al. 2016). The 

33 consequences of drought and changes in the growing season have also increased demand for 

34 irrigation water in all major agricultural areas of the United States from 2000 to 2008, resulting 

35 in unsustainable use of groundwater resources (Marston et al. 2015).

36 10.2.3 Biogeochemistry

37 Terrestrial biogeochemical cycles play a key role in Earth’s climate system, including by 

38 affecting land–atmosphere fluxes of many aerosol precursors and greenhouse gases, including 
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1 carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). As such, changes in the 

2 terrestrial ecosphere can drive climate change. At the same time, biogeochemical cycles are 

3 sensitive to changes in climate and atmospheric composition. 

4 Historically, increased atmospheric CO2 concentrations have led to increased plant production 

5 (known as CO2 fertilization) and longer-term storage of carbon in biomass and soils (SOCCR-2 

6 Chapter 12). Whether increased atmospheric CO2 will continue to lead to long-term storage of 

7 carbon in terrestrial ecosystems depends on whether CO2 fertilization simply intensifies the rate 

8 of short-term carbon cycling (for example, by stimulating respiration, root exudation, and high 

9 turnover root growth) or whether the additional carbon is used by plants to build more wood or 

10 tissues that, once senesced, decompose into long-lived soil organic matter (SOCCR-2 Chapter 

11 19). Under increased CO2 concentrations plants have been observed to optimize water use due to 

12 reduced stomatal conductance, thereby increasing water use efficiency (Keenan et al. 2013; 

13 SOCCR-2 Chapter 17). This change in water use efficiency can affect plants’ tolerance to stress 

14 and specifically to drought (SOCCR-2 Chapter 17). Due to the complex interactions of the 

15 processes that govern terrestrial biogeochemical cycling, terrestrial ecosystem responses to CO2

16 remains one of the largest uncertainties in predicting future climate change (Chapter 2: Physical 

17 Drivers of Climate Change).

18 Nitrogen is a principal nutrient for plant growth and can limit or stimulate plant productivity (and 

19 carbon uptake), depending on availability. As a result, increased nitrogen deposition and natural 

20 nitrogen-cycle responses to climate change will influence the global carbon cycle. For example, 

21 nitrogen limitation can limit the CO2 fertilization response of plants to elevated atmospheric CO2 

22 (e.g., Norby et al. 2010; Zaehle et al. 2010; SOCCR-2 Chapter 17). Conversely, increased 

23 decomposition of soil organic matter in response to climate warming increases nitrogen 

24 mineralization. This shift of nitrogen from soil to vegetation can increase ecosystem carbon 

25 storage (Melillo et al. 2011; Cias et al. 2013). While the effects of increased nitrogen deposition 

26 may counteract some nitrogen limitation on CO2 fertilization, the importance of nitrogen in 

27 future carbon–climate interactions is not clear. Nitrogen dynamics are being integrated into the 

28 simulation of land carbon cycle modeling, but only two of the models in CMIP5 included 

29 coupled carbon–nitrogen interactions (Knutti and Sedlacek 2013).

30 Many factors, including climate, atmospheric CO2 concentrations, and nitrogen deposition rates

31 influence the structure of the plant community and therefore the amount and biochemical quality 

32 of inputs into soils (Jandl et al. 2007; McLauchlan 2006; Smith et al. 2007; SOCCR-2 Chapter 

33 12). For example, though CO2 losses from soils may decrease with greater nitrogen deposition, 

34 increased emissions of other greenhouse gases, such as methane (CH4) and nitrous oxide (N2O), 

35 can offset the reduction in CO2 (Liu and Greaver 2009; SOCCR-2 Chapter 12). The dynamics of 

36 soil organic carbon under the influences of climate change are poorly understood and therefore 

37 not well represented in models. As a result, there is high uncertainty in soil carbon stocks in 

38 model simulations (Todd-Brown et al. 2013; Tian et al. 2015; SOCCR-2 Chapter 12).
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1 Future emissions of many aerosol precursors are expected to be affected by a number of climate2 related factors, in part because of changes in aerosol and aerosol precursors from the terrestrial 

3 biosphere. For example, volatile organic compounds (VOCs) are a significant source of 

4 secondary organic aerosols, and biogenic sources of VOCs exceed emissions from the industrial 

5 and transportation sectors (Guenther et al. 2006). Isoprene is one of the most important biogenic 

6 VOCs, and isoprene emissions are strongly dependent on temperature and light, as well as other 

7 factors like plant type and leaf age (Guenther et al. 2006). Higher temperatures are expected to 

8 lead to an increase in biogenic VOC emissions. Atmospheric CO2 concentration can also affect 

9 isoprene emissions (e.g., Rosenstiel et al. 2003). Changes in biogenic VOC emissions can impact 

10 aerosol formation and feedbacks with climate (Chapter 2: Physical Drivers of Climate Change). 

11 Increased biogenic VOC emissions can also impact ozone and the atmospheric oxidizing 

12 capacity (Pyle et al. 2007). Conversely, increases in nitrogen oxide (NOx) pollution produce 

13 tropospheric ozone (O3), which has damaging effects on vegetation. For example, a recent study 

14 estimated yield losses for maize and soybean production of up to 5% to 10% due to increases in

15 O3 (McGrath et al. 2015).

16 While the climate influences land cover and biogeochemistry, the converse is also true (Kalnay 

17 and Cai 2003). Changes in land cover and land use, including deforestation, afforestation, land 

18 cultivation, and development together impact surface albedo, CO2, CH4, O3, and aerosols. With 

all forcing agents considered together, one study estimated that 0.9 W/m2 19 , or 40% of the present20 day human-caused radiative forcing can be attributed to land use and land cover change (Ward et 

al. 2014). Continued land-use change is expected to contribute between 0.9 and 1.9 W/m2 21 to 

22 direct radiative forcing by 2100 (Ward et al. 2014). The net radiative forcing due specifically to

23 fire, after accounting for short-lived forcing agents O3 and aerosols, in addition to long-lived 

24 greenhouse gases and land albedo change, both now and in the future, is estimated to be near25 zero due to regrowth of forests following fires offsetting the release of CO2 in the fire (Ward and 

26 Mahowald 2015). 

27 10.2.4 Extreme Events and Disturbance

28 This section builds on the physical overview provided in earlier chapters to frame how the 

29 intersects of climate, extreme events, and disturbance affect regional land cover and 

30 biogeochemistry. In addition to overall trends in temperature (Ch. 7) and precipitation (Ch. 8), 

31 changes in modes of variability such as the Pacific Decadal Oscillation (PDO) and the El Niño–

32 Southern Oscillation (ENSO) (Ch. 5: Circulation and Variability) can contribute to drought 

33 cycles in the United States, which leads to unanticipated changes in disturbance regimes in the 

34 terrestrial biosphere (e.g., Kam et al., 2014). Drought may exacerbate the rate of plant invasions 

35 by non-native species in rangelands and grasslands (Moore et al. 2016). Land cover changes such 

36 as encroachment and invasion of non-native species can in turn lead to increased frequency of 

37 disturbance such as fires. Disturbance events alter soil moisture, which in addition to being 

38 affected by evapotranspiration and precipitation (Ch. 8: Droughts, Flood and Hydrology), is 

39 controlled by canopy and rooting architecture as well as soil physics. Invasive plants may be 
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1 directly responsible for changes in fire regimes through increased biomass, changes in the 

2 distribution of flammable biomass, increased flammability, and altered timing of fuel drying, 

3 while others may be “fire followers” whose abundances increase as a result of shortening the 

4 fire return interval (e.g., Lambert et al. 2010). Changes in land cover resulting from fire and 

5 alteration of disturbance regimes affects long-term carbon exchange between the atmosphere and 

6 biosphere (e.g., Moore et al. 2016). Recent extensive diebacks and changes in plant cover due to 

7 drought have interacted with regional carbon cycle dynamics including carbon release from 

8 biomass and reductions in carbon uptake from the atmosphere, though plant re-growth may 

9 offset emissions (Vose et al. 2016). The current meteorological drought in California (described 

10 in Ch. 8: Droughts, Floods and Hydrology), combined with warming, will result in long-term 

11 changes in land cover, leading to increased probability of drought and wildfire and in ecosystem 

12 shifts (Diffenbaugh et al. 2015). California’s recent drought has also resulted in measureable 

13 canopy water losses, posing long-term hazards to forest health and biophysical feedbacks to 

14 regional climate (Anderegg et al. 2015; Asner et al. 2016; Mann and Gleick 2015). Multi-year, or 

15 severe meteorological and hydrologic (see Ch. 8: Droughts, Floods, and Hydrology for 

16 definitions) droughts can also affect stream biogeochemistry and riparian ecosystems by

17 concentrating sediments and nutrients (Vose et al. 2016).

18 Changes in the variability of hurricanes and winter storm events (Ch. 9: Extreme Storms) also 

19 affect the terrestrial biosphere, as shown in studies comparing historic and future (projected) 

20 extreme events in the western United States and how these translate into changes in the regional 

21 water balance, fire, and streamflow. Composited across 10 global climate models (GCMs) 

22 summer (June–August) water-balance deficit in the future (2030–2059) increases compared to 

23 that under historical (1916–2006) conditions. Portions of the Southwest that have significant 

24 monsoon precipitation, and some mountainous areas of the Pacific Northwest are exempt from 

25 this deficit (Littell et al. 2016). Projections for 2030–2059 suggest that the Columbia Basin, 

26 upper Snake River, southeastern California, and southwestern Oregon may exceed extreme low 

27 flows less frequently than they did historically (1916–2006). Given the historical relationships 

28 between fire occurrence and drought indicators such as water-balance deficit and streamflow, 

29 climate change can be expected to have significant effects on fire occurrence and area burned 

30 (Littell et al. 2016, 2011; Elsner et al. 2010). 

31 Climate change in the northern high latitudes is directly contributing to increased fire occurrence 

32 (Ch. 11: Arctic); in the coterminous United States, climate-induced changes in fires, changes in 

33 direct human ignitions, and land management practices all significantly contribute to wildfire

34 trends. Wildfires in the western United States are often ignited by lightning, but management 

35 practices such as fire suppression contribute to fuels and amplify the intensity and spread of 

36 wildfire. Unintentional ignition by campfires or intentional human ignitions are also 

37 compounded by increasingly dry and vulnerable fuels, which build up with fire suppression or 

38 human settlements. 
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1 10.3 Climate Indicators and Agricultural and Forest Responses

2 Agricultural production has changed the surface of the Earth and influenced hydrology as well as 

3 the flux and distribution of carbon. Recent studies indicate a correlation between the expansion 

4 of agriculture and the global amplitude of the CO2 uptake and emissions (Zeng et al. 2014; Gray 

5 et al. 2014). Conversely, agricultural production is increasingly disrupted by climate and extreme 

6 weather events, and these impacts are expected to be augmented by mid-century and beyond for 

7 most crops (Lobell and Tebaldi 2014; Challinor et al. 2014) and livestock. Precipitation extremes 

8 put pressure on agricultural soil and water assets and lead to increased irrigation, shrinking 

9 aquifers, and ground subsidence. While human adaptation of crop management has mitigated 

10 many near-term consequences, climate change effects on agriculture will have long-term impacts 

11 on food security (Brown et al. 2015). Global Climate Models (GCMs) differ with regard to how 

12 increasing atmospheric CO2 concentrations affect plant productivity through CO2 fertilization or 

13 downregulation as well as the strength of carbon cycle feedbacks (Anav et al. 2013; Chapter 2: 

14 Physical Drivers of Climate Change). When CO2 effects on photosynthesis and transpiration are 

15 removed from Global Gridded Crop Models, simulated response to climate across the models is 

16 comparable, suggesting that model parameterizations representing these process remains 

17 uncertain (Rosenzweig et al. 2014). 

18 10.3.1 Changes in the Frost-Free and Growing Seasons

19 The growing season is the part of the year in which temperatures are favorable to plant growth. A 

20 basic metric by which this is measured is the frost-free season. The U. S. Department of 

21 Agriculture Natural Resources Conservation Service defines the frost-free period using a range 

22 of thresholds. They calculate the average date of the last day with temperature below 24°F, 28°F,

23 and 32°F in the spring and the average date of the first day with temperature below 24°F, 28°F, 

24 and 32°F in the fall, at various probabilities. They then define the frost-free season at three index 

25 temperatures (32°F, 28°F, and 24°F), also with a range of probabilities. Fixed temperature 

26 thresholds (for example, temperature below 32°F) are often used when discussing growing 

27 season; however, different plant cover-types (for example, forest, agricultural, shrub, tundra) 

28 have different temperature thresholds for growth, and different requirements/thresholds for 

29 chilling (Zhang et al. 2011; Hatfield et al. 2014). For the purposes of this report, we use the

30 metric with a 32°F threshold to define the change in the number of “frost-free” days, and a 

31 temperature threshold of 41°F as a first-order measure of how the growing season length has 

32 changed over the observational record (Zhang et al. 2011). 

33 The NCA3 reported an increase in the growing season by as much as several weeks as a result of 

34 higher temperatures occurring earlier and later in the year (e.g., Walsh et al. 2014b; Hatfield et 

35 al. 2014; Joyce et al. 2014). NCA3 used a threshold of 32°F—that is to say, the frost-free 

36 season—to define the growing season. An update to this finding is presented in Figures 10.3 and 

37 10.4, which show changes in the frost-free and growing season, respectively, as defined above. 

38 Overall, the length of the frost-free season has increased in the contiguous United States during 
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1 the past century (Figure 10.3). The growing season changes are more variable: Growing season

2 length increased until the late 1930s, declined slightly until the early 1970s, increased again until 

3 about 1990, and remained quasi-stable thereafter (Figure 10.4). This contrasts somewhat with 

4 changes in the length of the frost-free season presented in NCA3, which showed a continuing 

5 increase after 1980. This discrepancy is attributable to the temperature thresholds used in each 

6 indicator to define the start and end of a season. Specifically, the growing season length (41°F

7 threshold) is more conservative than the 32°F frost-free threshold because the latter captures 

8 more days in winter, which has larger temperature trends. 

9 The lengthening of the growing season has been somewhat greater in the northern and western 

10 United States, which had increases of 1–2 weeks in many locations. In contrast, some areas in 

11 the Midwest, Southern Great Plains, and the Southeast had decreases of a week or more between

12 the periods 1986–2015 and 1901–1960. These differences reflect the more general pattern of 

13 warming and cooling nationwide (Ch. 6: Temperature Changes). Observations and models have 

14 verified that the growing season has generally increased plant productivity over most of the 

15 United States (Mao et al. 2016).

16 Consistent with increases in growing season length and the coldest temperature of the year, plant 

17 hardiness zones have shifted northward in many areas (Daly et al. 2012). The widespread 

18 increase in temperature has also impacted the distribution of other climate zones in parts of the 

19 United States. For instance, there have been moderate changes in the range of the temperate and 

20 continental climate zones of the eastern United States since 1950 (Chan and Wu 2015) as well as 

21 changes in the coverage of some extreme climate zones in the western United States. In 

22 particular, the spatial extent of the “alpine tundra” zone has decreased in high-elevation areas 

23 (Diaz and Eischeid 2007) while the extent of the “hot arid” zone has increased in the Southwest 

24 (Grundstein 2008).

25 The period over which plants are actually productive, that is, their true growing season, is a 

26 function of multiple climate factors including air temperature, number of frost days, and rainfall, 

27 as well as biophysical factors including soil physics, daylight hours, and the biogeochemistry of 

28 ecosystems (EPA 2016). Further, while growing season length is generally referred to in the 

29 context of agricultural productivity, the factors that govern which plant types will grow in a 

30 given location are common to all plants whether they are in agricultural, natural, or managed 

31 landscapes. Changes in both the length and the seasonality of the growing season, in concert with 

32 local environmental conditions, can have multiple effects on agricultural productivity and land 

33 cover more generally.

34 In the context of agriculture, a longer growing season could allow for the diversification of 

35 cropping systems or allow multiple harvests within a growing season. For example, shifts in 

36 cold-hardy zones across the contiguous United States suggest widespread expansion of thermally 

37 suitable areas for the cultivation of cold-intolerant perennial agriculture (Parker and Abatzoglou 

38 2016). However, changes in available water, conversion from dry to irrigated farming, and 
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1 changes in sensible and latent heat exchange associated with these shifts need to be considered. 

2 Increasingly dry conditions under a longer growing season can alter terrestrial organic matter 

3 export and catalyze oxidation of wetland soils, releasing stored contaminants (for example, 

4 copper and nickel) into streamflow after rainfall (Szkokan-Emilson et al. 2016). Similarly, a 

5 longer growing season, particularly in years where water is limited, is not due to warming alone, 

6 but is exacerbated by higher atmospheric CO2 concentrations that extend the active period of 

7 growth by plants (Reyes-Fox et al. 2014). Longer growing seasons can also limit the types of 

8 crops that can be grown, encourage invasive species or weed growth, or increase demand for 

9 irrigation, possibly beyond the limits of water availability. They could also disrupt the function 

10 and structure of a region’s ecosystems and could, for example, alter the range and types of 

11 animal species in the area.

12 A longer and seasonally-shifted growing season also affects the role of terrestrial ecosystems in 

13 the carbon cycle. Neither seasonality of growing season (spring and summer) nor carbon, water, 

14 and energy fluxes should be interpreted separately when analyzing the impacts of climate 

15 extremes such as drought (Ch. 8: Droughts, Floods, and Hydrology; Sippel et al. 2016; Wolf et 

16 al. 2016). Observations and data-driven model studies suggest that losses in net terrestrial carbon 

17 uptake during record warm springs followed by severely hot and dry summers can be largely 

18 offset by carbon gains in record-exceeding warmth and early arrival of spring (Wolf et al. 2016). 

19 Depending on soil physics and land cover, a cool spring, however, can deplete soil water 

20 resources less rapidly, making the subsequent impacts of precipitation deficits less severe (Sippel 

21 et al. 2016). Depletion of soil moisture through early plant activity in a warm spring can 

22 potentially amplify summer heating, a typical lagged direct effect of an extremely warm spring 

23 (Frank et al. 2015). Ecosystem responses to the phenological changes of timing and extent of 

24 growing season and subsequent biophysical feedbacks are therefore strongly dependent on the 

25 timing of climate extremes (Ch. 8: Droughts, Floods, and Hydrology; Ch. 9: Extreme Storms; 

26 Sippel et al. 2016).

27 The global Coupled Model Intercomparison Project Phase 5 (CMIP5) analyses did not explicitly 

28 explore future changes to the growing season. Many projected changes in North American 

29 climate are generally consistent across CMIP5 models, but there is substantial inter-model 

30 disagreement in projections of some metrics important to biophysical systems’ productivity, 

31 including the sign of regional precipitation changes and extreme heat events across the northern 

32 United States (Maloney et al. 2014). 

33 [INSERT FIGURE 10.3 HERE:

34 Figure 10.3. Change in number of days since 1901 between the last spring occurrence of 32°F

35 and first fall occurrence of 32°F for NCA4 regions of the United States. This change is expressed 

36 as the difference between the average number of frost-free days in 1986–2015 minus that in 

37 1901–1960. (Figure source: updated from Walsh et al. 2010)]

38
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1 [INSERT FIGURE 10.4 HERE:

2 Figure 10.4. The length of the growing season in the contiguous 48 states compared with a long3 term average (1895–2015), where “growing season” is defined by a temperature threshold of 

4 41°F. For each year, the line represents the number of days shorter or longer than average. The 

5 line was smoothed using an 11-year moving average. Choosing a different long-term average for 

6 comparison would not change the shape of the data over time. (Figure source: Kunkel et al.

7 2016, EPA 2016)]

8 10.3.2 Water Availability and Drought

9 Drought is generally parameterized in most agricultural models as limited water availability, and 

10 is an integrated response of both meteorological and agricultural drought, as described in Chapter 

11 8 (Droughts, Floods and Hydrology). However, physiological as well as biophysical processes 

12 that influence land cover and biogeochemistry interact with drought through stomatal closure 

13 induced by elevated atmospheric CO2 levels. This has direct impacts on plant transpiration, 

14 atmospheric latent heat fluxes, and soil moisture, thereby influencing local and regional climate. 

15 Drought is often offset by management through groundwater withdrawals, with increasing 

16 pressure on these resources to maintain plant productivity. This results in indirect climate effects 

17 by altering land surface exchange of water and energy with the atmosphere (Marston et al. 2015). 

18 10.3.3 Forestry Considerations

19 Climate change and land cover change in forested areas interact in many ways, such as through 

20 changes in mortality rates driven by changes in the frequency and magnitude of fire, insect 

21 infestations, and disease. In addition to the direct economic benefits of forestry, unquantified 

22 societal benefits include ecosystem services, like protection of watersheds and wildlife habitat, 

23 and recreation and human health value. United States forests and related wood products also 

24 absorb and store the equivalent of 16% of all CO2 emitted by fossil fuel burning in the United 

25 States each year. Climate change is expected to reduce the carbon sink strength of forests overall. 

26 Effective management of forests offers the opportunity to reduce future climate change (for 

27 example, as given in proposals for Reduced Emissions from Deforestation and forest 

28 Degradation, or REDD+ (https://www.forestcarbonpartnership.org/what-redd), and in the Paris 

29 Agreement (see Ch. 14: Mitigation for more on the Paris Agreement) by capturing and storing 

30 carbon in forest ecosystems and long-term wood products (Lippke et al. 2011). Afforestation in 

31 the United States has the potential to capture and store 225 million tons of additional carbon per 

32 year from 2010 to 2110 (EPA 2005; King et al. 2007). However, the projected maturation of 

33 United States forests (Wear and Coulston 2015) and land-cover change, in particular the

34 expansion of urban and suburban areas along with projected increased demands for food and 

35 bioenergy, threaten the extent of forests and their carbon storage potential (McKinley et al. 

36 2011).
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1 Large-scale die-off and disturbances resulting from climate change have potential effects beyond 

2 the biogeochemical and carbon cycle effects. Biogeophysical feedbacks can strengthen or reduce 

3 climate forcing. The low albedo of boreal forests provides a positive feedback, but those albedo 

4 effects are mitigated in tropical forests through evaporative cooling; for temperate forests, the 

5 evaporative effects are less clear (Bonan 2008). Changes in surface albedo, evaporation, and 

6 surface roughness can have feedbacks to local temperatures that are larger than the feedback due 

7 to the change in carbon sequestration (Jackson et al. 2008). Forest management frameworks 

8 (e.g., afforestation, deforestation, avoided deforestation) that account for biophysical (e.g., land 

9 surface albedo, surface roughness) properties can be used as climate protection or mitigation 

10 strategies (Anderson et al. 2011).

11 Changes in growing season length, combined with drought and accompanying wildfire are 

12 reshaping California’s mountain ecosystems. The California drought led to the lowest snowpack 

13 in 500 years, the largest wildfires in post-settlement history, greater than 23% stress mortality in 

14 Sierra mid-elevation forests, and associated post-fire erosion. It is anticipated that slow recovery, 

15 possibly to different ecosystem types, with numerous shifts to species’ ranges will result in long16 term changes to land surface biophysical as well as ecosystem structure and function in this 

17 region (Asner et al. 2016; http://www.fire.ca.gov/treetaskforce/).

18 While changes in forest stocks, composition, and the ultimate use of forest products can 

19 influence net emissions and climate, the future net changes in forest stocks continue to be 

20 uncertain (US Department of State 2016). This uncertainty is due to a combination of 

21 uncertainties in future population size, population distribution and subsequent land-use change, 

22 harvest trends, wildfire management practices (for example, large-scale thinning of forests), and 

23 the impact of maturing U.S. forests. 

24 10.4 Urban Responses and Feedbacks to Climate Change: Urban Heat Island 

25 The urban heat island (UHI) effect is a well-known phenomenon in which urban environments 

26 often retain more heat than nearby rural environments, and it has a profound effect on the quality 

27 of life of the world’s growing urban population (Shepherd 2013). The UHI is characterized by 

28 increased surface and canopy temperatures as a result of heat-retaining asphalt and concrete, a 

29 lack of vegetation, and anthropogenic generation of heat and greenhouse gasses (Shepherd 

30 2013). Based on land surface temperature measurements, the UHI effect increases urban 

31 temperature by 2.9°C (5.2°F) on average, but it has been measured at 8°C (14.4°F) in cities built 

32 in areas dominated by temperate forests (Imhoff et al. 2010). In arid regions, however, urban 

33 areas can be greater than 2°C (3.6°F) cooler than surrounding shrublands (Bounoua et al. 2015). 

34 Similarly, urban settings lose up to 12% of precipitation through impervious surface runoff,

35 versus just over 3% loss to runoff in vegetated regions. Carbon losses from the biosphere to the 

36 atmosphere through urbanization account for almost 2% of the continental total, a significant 

37 proportion given that urban areas only account for around 1% of land in the United States 

38 (Bounoua et al. 2015).
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1 According to the World Bank, over 81% of the United States population currently resides in 

2 urban settings (http://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS?locations=US).

3 Mitigation efforts are often stalled by the lack of quantitative data and understanding of the 

4 various factors that contribute to UHI. A recent study set out to quantitatively determine 

5 contributions to the intensity of UHI across North America (Zhao et al. 2014). The study found 

6 that population strongly influenced nighttime UHI, but that daytime UHI varied spatially 

7 following precipitation gradients. The model applied in this study indicated that the spatial 

8 variation in the UHI signal was controlled most strongly by impacts on the atmospheric 

9 convection efficiency. Because of the impracticality of managing convection efficiency, results 

10 from Zhao et al. (2014) support albedo management as an efficient strategy to mitigate UHI on a 

11 large scale.

12
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1 TRACEABLE ACCOUNTS

2 Key Finding 1

3 Changes in land use and land cover due to human activities produce changes in surface albedo 

4 and in atmospheric aerosol and greenhouse gas concentrations. These combined effects have 

5 recently been estimated to account for 40% ± 16% of the human-caused global radiative forcing 

6 from 1850 to 2010 (high confidence). As a whole, the terrestrial biosphere (soil, plants) is a net 

7 “sink” for carbon (drawing down carbon from the atmosphere) and this sink has steadily 

8 increased since 1980, in part due to CO2 fertilization (very high confidence). The future strength 

9 of the land sink is uncertain and dependent on ecosystem feedbacks; the possibility of the land 

10 becoming a net carbon source cannot be excluded (very high confidence). 

11 Description of evidence base

12 Integrative modeling studies that combine climate models with models that simulate changes in 

13 land cover and land use have provided updated estimates climate forcing due to feedbacks 

14 among climate variables and land. Changes in land cover and land use are estimated to contribute 

40% of present climate forcing (0.9 W/m

2

), and are estimated to contribute 0.9 to 1.9 W/m2 15 in 

16 the year 2100 (Ward et al. 2015). This research is grounded in long-term observations that have 

17 been documented for over 40 years. For example, studies have documented physical land surface 

18 processes such as albedo, surface roughness, sensible and latent heat exchange, and land use and 

19 land cover change that interact with regional atmospheric processes (e.g., Marotz et al. 1975; 

20 Barnston and Schickendanz 1984; Alpert and Mandel 1986; Pielke and Zeng 1989; Pielke et 

21 2007). 

22 IPCC, 2013: Summary for Policymakers states: “From 1750 to 2011, CO2 emissions from fossil 

23 fuel combustion and cement production have released 375 [345 to 405] Gt C to the atmosphere, 

24 while deforestation and other land use change are estimated to have released 180 [100 to 260] 

25 GtC. This results in cumulative anthropogenic emissions of 555 [470 to 640] Gt C. {6.3 and 

26 WGI, Chapter 14 states for North America: “In summary, it is very likely that by mid-century the 

27 anthropogenic warming signal will be large compared to natural variability such as that 

28 stemming from the NAO, ENSO, PNA, PDO, and the NAMS in all North America regions 

29 throughout the year.”

30 Major uncertainties

31 Uncertainty exists in the future land cover and land use change. 

32 Assessment of confidence based on evidence and agreement, including short description of 

33 nature of evidence and level of agreement

34 ☐ Certain (100%)

35 X Very High

36 ☐ High

37 ☐ Medium
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1 ☐ Low

2 The existing impact on climate forcing has high confidence. The future forcing has lower 

3 confidence because it is difficult to estimate changes in land cover and land use into the future. 

4 However, if existing trends in land use and land cover change continue, the contribution of land 

5 cover to forcing will increase with high confidence. 

6 If appropriate, estimate likelihood of impact or consequence, including short description of 

7 basis of estimate

8 ☐Greater than 9 in 10 / Very Likely

9 ☐Greater than 2 in 3 / Likely

10 ☐ About 1 in 2 / As Likely as Not

11 ☐ Less than 1 in 3 / Unlikely

12 ☐ Less than 1 in 10 / Very Unlikely

13 Summary sentence or paragraph that integrates the above information

14 The key finding is based on basic physics that has been well established for decades. Specific 

15 assessments, however, have not yet been made with regards to land cover and the climate 

16 system.

17

18 Key Finding 2

19 The increased occurrence and severity of drought has led to large changes in plant community 

20 structure with subsequent effects on carbon distribution and cycling within ecosystems (for 

21 example, forests, grasslands). Uncertainties about future land use changes (for example, policy or 

22 mitigation measures) and about how climate change will affect land cover change make it 

23 difficult to project the magnitude and sign of future climate feedbacks from land cover changes. 

24 (High confidence)

25 Description of evidence base

26 From the perspective of the land biosphere, drought has strong effects on ecosystem productivity 

27 and carbon storage by reducing microbial activity and photosynthesis, and increasing the risk of 

28 wildfire, pest infestation, and disease susceptibility. Thus, droughts of the future will affect 

29 carbon uptake and storage, leading to feedbacks to the climate system (Schlesinger et al. 2016). 

30 Reduced productivity as a result of extreme drought events can also extend for several years 

31 post-drought (i.e., drought legacy effects; Frank et al. 2016; Reichstein et al. 2013; Anderegg et 

32 al. 2015).

33 The most severe drought on record in Texas led to statewide regional tree mortality of 6.2%, or 

34 nearly 9X greater than the average annual mortality in this region of ~0.7% (Moore et al. 2016). 

35 The net effect on carbon storage was estimated to be a redistribution of 24–30 Tg C from the live 

36 to dead tree carbon pool, which is equal to 6%–7% of pre-drought live tree carbon storage in 
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1 Texas state forestlands or (Moore et al. 2016). This redistribution is that one singular event 

2 equals ~36% of global carbon losses due to deforestation and land use change (Cias et al. 2013).

3 Major uncertainties

4 Major uncertainties include how future land use/land cover changes will occur as a result of 

5 policy and/or mitigation strategies in addition to climate change 

6 Assessment of confidence based on evidence and agreement, including short description of 

7 nature of evidence and level of agreement

8 ☐ Certain (100%)

9 ☐ Very High

10 X High

11 ☐ Medium

12 ☐ Low

13 If appropriate, estimate likelihood of impact or consequence, including short description of 

14 basis of estimate

15 ☐Greater than 9 in 10 / Very Likely

16 ☐Greater than 2 in 3 / Likely

17 ☐ About 1 in 2 / As Likely as Not

18 ☐ Less than 1 in 3 / Unlikely

19 ☐ Less than 1 in 10 / Very Unlikely

20 Summary sentence or paragraph that integrates the above information

21 Future interactions between land cover and the climate system are uncertain and depend on both 

22 human decision making and the evolution of the climate system.

23

24 Key Finding 3

25 Since 1901, the consecutive number of both frost-free days and the length of the corresponding 

26 growing season has increased for all regions of the United States. However, there is important 

27 variability at smaller scales, with some locations showing decreases of as much as one to two 

28 weeks. Plant productivity has not increased linearly with the increased number of frost-free days 

29 or with the longer growing season due to temperature thresholds and requirements for growth as 

30 well as seasonal limitations in water and nutrient availability (very high confidence). Future 

31 consequences of changes to the growing season for plant productivity are uncertain. 

32 Description of evidence base

33 Without nutrient limitations, increased CO2 concentrations and warm temperatures have been 

34 shown to extend the growing season, which may contribute to longer periods of plant activity 

35 and carbon uptake, but do not affect reproduction rates (Reyes-Fox et. al. 2014). However, other 
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1 confounding variables that coincide with climate change (for example, drought, increased ozone, 

2 and reduced photosynthesis due to increased or extreme heat) can offset increased growth 

3 associated with longer growing seasons (Adams et al. 2015).

4 Major uncertainties

5 Uncertainties exist in feedbacks among variables that impact the length of the growing season. 

6 Assessment of confidence based on evidence and agreement, including short description of 

7 nature of evidence and level of agreement

8 ☐ Certain (100%)

9 X Very High

10 ☐ High

11 ☐ Medium

12 ☐ Low

13 If appropriate, estimate likelihood of impact or consequence, including short description of 

14 basis of estimate

15 ☐Greater than 9 in 10 / Very Likely

16 ☐Greater than 2 in 3 / Likely

17 ☐ About 1 in 2 / As Likely as Not

18 ☐ Less than 1 in 3 / Unlikely

19 ☐ Less than 1 in 10 / Very Unlikely

20 Summary sentence or paragraph that integrates the above information

21 Changes in growing season length and interactions with climate, biogeochemistry and land cover 

22 were covered in 12 chapters of NCA3, with no real summary statement. This key finding 

23 provides a summary of the complex nature of the growing season.

24

25 Key Finding 4

26 Surface temperatures are often higher in urban areas than in surrounding rural areas, for a 

27 number of reasons including the concentrated release of heat from buildings, vehicles, and 

28 industry. In the United States, this urban heat island (UHI) effect results in daytime temperatures 

29 0.9°–7.2°F (0.5°–4.0°C) higher and nighttime temperatures 1.8°– 4.5°F (1.0°–2.5°C) higher in 

30 urban areas, with larger temperature differences in humid regions (primarily the eastern United 

31 States) and in cities with larger populations. The UHI effect will strengthen in the future as the 

32 spatial extent and population of urban areas grow. (High confidence)

33 Description of evidence base

34 The urban heat island (UHI) effect is correlated with the extent of impervious surfaces, which 

35 alter albedo or the saturation of radiation. The urban-rural difference that defines the UHI is 
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1 greatest for cities built in temperate forest ecosystems. The average temperature increase is 

2 2.9°C, except for urban areas in biomes with arid and semiarid climates (Imhoff et al. 2010)

3 Major uncertainties

4 No major uncertainties. 

5 Assessment of confidence based on evidence and agreement, including short description of

6 nature of evidence and level of agreement

7 ☐ Very High

8 X High

9 ☐Medium

10 ☐ Low

11 Land surface temperature estimates are taken from the MODIS-Aqua (MOD11A2) satellite 

12 sensor with 99% confidence.

13 If appropriate, estimate likelihood of impact or consequence, including short description of 

14 basis of estimate

15 ☐Greater than 9 in 10 / Very Likely

16 ☐ Greater than 2 in 3 / Likely

17 ☐ About 1 in 2 / As Likely as Not

18 ☐ Less than 1 in 3 / Unlikely

19 ☐ Less than 1 in 10 / Very Unlikely

20 Summary sentence or paragraph that integrates the above information

21 The key finding is based on satellite land surface measurements and analyzed by Imhoff et al. 

22 (2010). Bonoua et al. (2015) and Shepherd (2013) provide specific updates with regards to the 

23 influence of urban heat islands and their intersects with the climate system.

24
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1 FIGURES

2

3 Figure 10.1. A graphical representation of climate interactions with land use and land cover.

4 (Figure source: Ward et al., 2014)
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1

2 Figure 10.2. Radiative forcings for land use/land cover change and other anthropogenic impacts 

3 estimated for the year 2010 referenced to the year 1850 (Ward et al. 2014). Total anthropogenic 

4 radiative forcings from (Myhre et al. 2013) are shown for comparison (yellow). Error lines 

5 represent uncertainties in total anthropogenic RF for the IPCC bars and uncertainties in land 

6 use/land cover change radiative forcings (Ward et al. 2014). The “SUM” bars show the total 

7 radiative forcings when all forcing agents are combined. (Figure source: Ward et al. 2014)

8
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1

2 Figure 10.3. Change in number of days since 1901 between the last spring occurrence of 32°F 

3 and first fall occurrence of 32°F for NCA4 regions of the United States. This change is 

4 expressed as the difference between the average number of frost-free days in 1986-2015 minus 

5 that 1901-1960. (Figure source: updated from Walsh et al. 2010).

6
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1

2 Figure 10.4. The length of the growing season in the contiguous 48 states compared with a long3 term average (1895–2015), where “growing season” is defined by a temperature threshold of 

4 41°F. For each year, the line represents the number of days shorter or longer than average. The 

5 line was smoothed using an 11-year moving average. Choosing a different long-term average for 

6 comparison would not change the shape of the data over time. (Figure source: Kunkel et al.

7 2016, EPA 2016).

8
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1 REFERENCES

2 Adams, H.D., A.D. Collins, S.P. Briggs, M. Vennetier, L.T. Dickman, S.A. Sevanto, N. Garcia3 Forner, H.H. Powers, and N.G. McDowell, 2015: Experimental drought and heat can delay 

4 phenological development and reduce foliar and shoot growth in semiarid trees. Global 

5 Change Biology, 21, 4210-4220. http://dx.doi.org/10.1111/gcb.13030 

6 Alpert, P. and M. Mandel, 1986: Wind variability—An indicator for a mesoclimatic change in 

7 Israel. Journal of Climate and Applied Meteorology, 25, 1568-1576. 

8 http://dx.doi.org/10.1175/1520-0450(1986)025<1568:wvifam>2.0.co;2 

9 Anav, A., P. Friedlingstein, M. Kidston, L. Bopp, P. Ciais, P. Cox, C. Jones, M. Jung, R. 

10 Myneni, and Z. Zhu, 2013: Evaluating the land and ocean components of the global carbon 

11 cycle in the CMIP5 earth system models. Journal of Climate, 26, 6801-6843. 

12 http://dx.doi.org/10.1175/jcli-d-12-00417.1 

13 Anderegg, W.R.L., C. Schwalm, F. Biondi, J.J. Camarero, G. Koch, M. Litvak, K. Ogle, J.D. 

14 Shaw, E. Shevliakova, A.P. Williams, A. Wolf, E. Ziaco, and S. Pacala, 2015: Pervasive 

15 drought legacies in forest ecosystems and their implications for carbon cycle models. Science,

16 349, 528-532. http://dx.doi.org/10.1126/science.aab1833 

17 Anderson, R.G., J.G. Canadell, J.T. Randerson, R.B. Jackson, B.A. Hungate, D.D. Baldocchi, 

18 G.A. Ban-Weiss, G.B. Bonan, K. Caldeira, L. Cao, N.S. Diffenbaugh, K.R. Gurney, L.M. 

19 Kueppers, B.E. Law, S. Luyssaert, and T.L. O'Halloran, 2011: Biophysical considerations in 

20 forestry for climate protection. Frontiers in Ecology and the Environment, 9, 174-182. 

21 http://dx.doi.org/10.1890/090179 

22 Ashley, W.S., M.L. Bentley, and J.A. Stallins, 2012: Urban-induced thunderstorm modification 

23 in the southeast United States. Climatic Change, 113, 481-498. 

24 http://dx.doi.org/10.1007/s10584-011-0324-1 

25 Asner, G.P., P.G. Brodrick, C.B. Anderson, N. Vaughn, D.E. Knapp, and R.E. Martin, 2016: 

26 Progressive forest canopy water loss during the 2012–2015 California drought. Proceedings 

27 of the National Academy of Sciences, 113, E249-E255. 

28 http://dx.doi.org/10.1073/pnas.1523397113 

29 Barnston, A.G. and P.T. Schickedanz, 1984: The effect of irrigation on warm season 

30 precipitation in the southern Great Plains. Journal of Climate and Applied Meteorology, 23,

31 865-888. http://dx.doi.org/10.1175/1520-0450(1984)023<0865:TEOIOW>2.0.CO;2 

32 Bonan, G.B., 2008: Forests and climate change: Forcings, feedbacks, and the climate benefits of 

33 forests. Science, 320, 1444-1449. http://dx.doi.org/10.1126/science.1155121 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 10

360

1 Bounoua, L., P. Zhang, G. Mostovoy, K. Thome, J. Masek, M. Imhoff, M. Shepherd, D. 

2 Quattrochi, J. Santanello, J. Silva, R. Wolfe, and A.M. Toure, 2015: Impact of urbanization on 

3 US surface climate. Environmental Research Letters, 10, 084010. 

4 http://dx.doi.org/10.1088/1748-9326/10/8/084010 

5 Brown, D.G., C. Polsky, P. Bolstad, S.D. Brody, D. Hulse, R. Kroh, T.R. Loveland, and A. 

6 Thomson, 2014: Ch. 13: Land Use and Land Cover Change. Climate Change Impacts in the 

7 United States: The Third National Climate Assessment. Melillo, J.M., Terese (T.C.) 

8 Richmond, and G.W. Yohe, Eds. U.S. Global Change Research Program, Washington, DC, 

9 318-332. http://dx.doi.org/10.7930/J05Q4T1Q 

10 http://nca2014.globalchange.gov/report/sectors/land-use-and-land-cover-change

11 Brown, M.E., J.M. Antle, P. Backlund, E.R. Carr, W.E. Easterling, M.K. Walsh, C. Ammann, 

12 W. Attavanich, C.B. Barrett, M.F. Bellemare, V. Dancheck, C. Funk, K. Grace, J.S.I. Ingram, 

13 H. Jiang, H. Maletta, T. Mata, A. Murray, M. Ngugi, D. Ojima, B. O'Neill, and C. Tebaldi, 

14 2015: Climate Change, Global Food Security, and the U.S. Food System. 146 pp. U.S. Global 

15 Change Research Program. 

16 http://www.usda.gov/oce/climate_change/FoodSecurity2015Assessment/FullAssessment.pdf

17 Challinor, A.J., J. Watson, D.B. Lobell, S.M. Howden, D.R. Smith, and N. Chhetri, 2014: A 

18 meta-analysis of crop yield under climate change and adaptation. Nature Climate Change, 4,

19 287-291. http://dx.doi.org/10.1038/nclimate2153 

20 Chan, D. and Q. Wu, 2015: Significant anthropogenic-induced changes of climate classes since 

21 1950. Scientific Reports, 5, 13487. http://dx.doi.org/10.1038/srep13487 

22 Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, R. DeFries, J. 

23 Galloway, M. Heimann, C. Jones, C. Le Quéré, R.B. Myneni, S. Piao, and P. Thornton, 2013: 

24 Carbon and Other Biogeochemical Cycles. Climate Change 2013: The Physical Science 

25 Basis. Contribution of Working Group I to the Fifth Assessment Report of the 

26 Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. 

27 Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. 

28 Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 465–

29 570. http://dx.doi.org/10.1017/CBO9781107415324.015 www.climatechange2013.org

30 Daly, C., M.P. Widrlechner, M.D. Halbleib, J.I. Smith, and W.P. Gibson, 2012: Development of 

31 a new USDA plant hardiness zone map for the United States. Journal of Applied Meteorology 

32 and Climatology, 51, 242-264. http://dx.doi.org/10.1175/2010JAMC2536.1 

33 Diaz, H.F. and J.K. Eischeid, 2007: Disappearing “alpine tundra” Köppen climatic type in the 

34 western United States. Geophysical Research Letters, 34, n/a-n/a. 

35 http://dx.doi.org/10.1029/2007GL031253 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 10

361

1 Diffenbaugh, N.S., D.L. Swain, and D. Touma, 2015: Anthropogenic warming has increased 

2 drought risk in California. Proceedings of the National Academy of Sciences, 112, 3931-3936. 

3 http://dx.doi.org/10.1073/pnas.1422385112 

4 Elsner, M.M., L. Cuo, N. Voisin, J.S. Deems, A.F. Hamlet, J.A. Vano, K.E.B. Mickelson, S.Y. 

5 Lee, and D.P. Lettenmaier, 2010: Implications of 21st century climate change for the 

6 hydrology of Washington State. Climatic Change, 102, 225-260. 

7 http://dx.doi.org/10.1007/s10584-010-9855-0 

8 EPA, 2005: Greenhouse Gas Mitigation Potential in U.S. Forestry and Agriculture. EPA 430-R9 05-006. U.S. Environmental Protection Agency, Washington, D.C.

10 EPA, 2016: Climate Change Indicators in the United States, 2016. 4th edition. EPA 430-R-16-

11 004, 96 pp. U.S. Environmental Protection Agency, Washington, D.C. 

12 https://www.epa.gov/sites/production/files/2016-08/documents/climate_indicators_2016.pdf

13 Frank, D., M. Reichstein, M. Bahn, K. Thonicke, D. Frank, M.D. Mahecha, P. Smith, M. van der 

14 Velde, S. Vicca, F. Babst, C. Beer, N. Buchmann, J.G. Canadell, P. Ciais, W. Cramer, A. 

15 Ibrom, F. Miglietta, B. Poulter, A. Rammig, S.I. Seneviratne, A. Walz, M. Wattenbach, M.A. 

16 Zavala, and J. Zscheischler, 2015: Effects of climate extremes on the terrestrial carbon cycle: 

17 concepts, processes and potential future impacts. Global Change Biology, 21, 2861-2880. 

18 http://dx.doi.org/10.1111/gcb.12916 

19 Fridley, J.D., J.S. Lynn, J.P. Grime, and A.P. Askew, 2016: Longer growing seasons shift 

20 grassland vegetation towards more-productive species. Nature Clim. Change, 6, 865-868. 

21 http://dx.doi.org/10.1038/nclimate3032 

22 Fu, Y.H., H. Zhao, S. Piao, M. Peaucelle, S. Peng, G. Zhou, P. Ciais, M. Huang, A. Menzel, J. 

23 Penuelas, Y. Song, Y. Vitasse, Z. Zeng, and I.A. Janssens, 2015: Declining global warming 

24 effects on the phenology of spring leaf unfolding. Nature, 526, 104-107. 

25 http://dx.doi.org/10.1038/nature15402 

26 Galloway, J.N., W.H. Schlesinger, C.M. Clark, N.B. Grimm, R.B. Jackson, B.E. Law, P.E. 

27 Thornton, A.R. Townsend, and R. Martin, 2014: Ch. 15: Biogeochemical Cycles. Climate 

28 Change Impacts in the United States: The Third National Climate Assessment. Melillo, J.M., 

29 Terese (T.C.) Richmond, and G.W. Yohe, Eds. U.S. Global Change Research Program, 

30 Washington, DC, 350-368. http://dx.doi.org/10.7930/J0X63JT0 

31 http://nca2014.globalchange.gov/report/sectors/biogeochemical-cycles

32 Georgakakos, A., P. Fleming, M. Dettinger, C. Peters-Lidard, T.C. Richmond, K. Reckhow, K. 

33 White, and D. Yates, 2014: Ch. 3: Water resources. Climate Change Impacts in the United 

34 States: The Third National Climate Assessment. Melillo, J.M., T.C. Richmond, and G.W. 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 10

362

1 Yohe, Eds. U.S. Global Change Research Program, Washington, D.C., 69-112. 

2 http://dx.doi.org/10.7930/J0G44N6T 

3 Gray, J.M., S. Frolking, E.A. Kort, D.K. Ray, C.J. Kucharik, N. Ramankutty, and M.A. Friedl, 

4 2014: Direct human influence on atmospheric CO2 seasonality from increased cropland 

5 productivity. Nature, 515, 398-401. http://dx.doi.org/10.1038/nature13957 

6 Grundstein, A., 2008: Assessing climate change in the contiguous United States using a modified 

7 Thornthwaite climate classification scheme. The Professional Geographer, 60, 398-412. 

8 http://dx.doi.org/10.1080/00330120802046695 

9 Gu, L., P.J. Hanson, W. Mac Post, D.P. Kaiser, B. Yang, R. Nemani, S.G. Pallardy, and T. 

10 Meyers, 2008: The 2007 eastern US spring freezes: Increased cold damage in a warming 

11 world? BioScience, 58, 253-262. http://dx.doi.org/10.1641/b580311 

12 Guenther, A., T. Karl, P. Harley, C. Wiedinmyer, P.I. Palmer, and C. Geron, 2006: Estimates of 

13 global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and 

14 Aerosols from Nature). Atmospheric Chemistry and Physics, 6, 3181-3210. 

15 http://dx.doi.org/10.5194/acp-6-3181-2006 

16 Hatfield, J., G. Takle, R. Grotjahn, P. Holden, R.C. Izaurralde, T. Mader, E. Marshall, and D. 

17 Liverman, 2014: Ch. 6: Agriculture. Climate Change Impacts in the United States: The Third 

18 National Climate Assessment. Melillo, J.M., Terese (T.C.) Richmond, and G.W. Yohe, Eds. 

19 U.S. Global Change Research Program, Washington, DC, 150-174. 

20 http://dx.doi.org/10.7930/J02Z13FR 

21 http://nca2014.globalchange.gov/report/sectors/agriculture

22 Hibbard, K., T. Wilson, K. Averyt, R. Harriss, R. Newmark, S. Rose, E. Shevliakova, and V. 

23 Tidwell, 2014: Ch. 10: Energy, Water, and Land Use. Climate Change Impacts in the United 

24 States: The Third National Climate Assessment. Melillo, J.M., Terese (T.C.) Richmond, and 

25 G.W. Yohe, Eds. U.S. Global Change Research Program, Washington, DC, 257-281. 

26 http://dx.doi.org/10.7930/J0JW8BSF http://nca2014.globalchange.gov/report/sectors/energy27 water-and-land

28 Imhoff, M.L., P. Zhang, R.E. Wolfe, and L. Bounoua, 2010: Remote sensing of the urban heat 

29 island effect across biomes in the continental USA. Remote Sensing of Environment, 114,

30 504-513. http://dx.doi.org/10.1016/j.rse.2009.10.008 

31 IPCC, 2013: Summary for policymakers. Climate Change 2013: The Physical Science Basis. 

32 Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental 

33 Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. 

34 Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. Cambridge University Press, 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 10

363

1 Cambridge, United Kingdom and New York, NY, USA, 1–30. 

2 http://dx.doi.org/10.1017/CBO9781107415324.004 

3 Jackson, R.B., J.T. Randerson, J.G. Canadell, R.G. Anderson, R. Avissar, D.D. Baldocchi, G.B. 

4 Bonan, K. Caldeira, N.S. Diffenbaugh, C.B. Field, B.A. Hungate, E.G. Jobbágy, L.M. 

5 Kueppers, D.N. Marcelo, and D.E. Pataki, 2008: Protecting climate with forests. 

6 Environmental Research Letters, 3, 044006. http://stacks.iop.org/1748-9326/3/i=4/a=044006

7 Jandl, R., M. Lindner, L. Vesterdal, B. Bauwens, R. Baritz, F. Hagedorn, D.W. Johnson, K. 

8 Minkkinen, and K.A. Byrne, 2007: How strongly can forest management influence soil 

9 carbon sequestration? Geoderma, 137, 253-268. 

10 http://dx.doi.org/10.1016/j.geoderma.2006.09.003 

11 Joyce, L.A., S.W. Running, D.D. Breshears, V.H. Dale, R.W. Malmsheimer, R.N. Sampson, B. 

12 Sohngen, and C.W. Woodall, 2014: Ch. 7: Forests. Climate Change Impacts in the United 

13 States: The Third National Climate Assessment. Melillo, J.M., Terese (T.C.) Richmond, and 

14 G.W. Yohe, Eds. U.S. Global Change Research Program, Washington, DC, 175-194. 

15 http://dx.doi.org/10.7930/J0Z60KZC http://nca2014.globalchange.gov/report/sectors/forests

16 Ju, J. and J.G. Masek, 2016: The vegetation greenness trend in Canada and US Alaska from 

17 1984–2012 Landsat data. Remote Sensing of Environment, 176, 1-16. 

18 http://dx.doi.org/10.1016/j.rse.2016.01.001 

19 Kalnay, E. and M. Cai, 2003: Impact of urbanization and land-use change on climate. Nature,

20 423, 528-531. http://dx.doi.org/10.1038/nature01675 

21 Kam, J., J. Sheffield, and E.F. Wood, 2014: Changes in drought risk over the contiguous United 

22 States (1901–2012): The influence of the Pacific and Atlantic Oceans. Geophysical Research 

23 Letters, 41, 5897-5903. http://dx.doi.org/10.1002/2014GL060973 

24 Keenan, T.F., D.Y. Hollinger, G. Bohrer, D. Dragoni, J.W. Munger, H.P. Schmid, and A.D. 

25 Richardson, 2013: Increase in forest water-use efficiency as atmospheric carbon dioxide 

26 concentrations rise. Nature, 499, 324-327. http://dx.doi.org/10.1038/nature12291 

27 King, S.L., D.J. Twedt, and R.R. Wilson, 2006: The role of the wetland reserve program in 

28 conservation efforts in the Mississippi River alluvial valley. Wildlife Society Bulletin (1973-

29 2006), 34, 914-920. http://www.jstor.org/stable/4134299

30 Knutti, R. and J. Sedlacek, 2013: Robustness and uncertainties in the new CMIP5 climate model 

31 projections. Nature Climate Change, 3, 369-373. http://dx.doi.org/10.1038/nclimate1716 

32 Lambert, A.M., C.M. D'Antonio, and T.L. Dudley, 2010: Invasive species and fire in California 

33 ecosystems. Fremontia, 38, 29-36. 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 10

364

1 Lippke, B., E. Oneil, R. Harrison, K. Skog, L. Gustavsson, and R. Sathre, 2011: Life cycle 

2 impacts of forest management and wood utilization on carbon mitigation : knowns and 

3 unknowns. Carbon management, 2, 303-333. http://dx.doi.org/10.4155/CMT.11.24 

4 Littell, J.S., M.M. Elsner, G.S. Mauger, E.R. Lutz, A.F. Hamlet, and E.P. Salathé, 2011: 

5 Regional climate and hydrologic change in the northern U.S. Rockies and Pacific Northwest: 

6 Internally consistent projections of future climate for resource management. University of 

7 Washington, Seattle. https://cig.uw.edu/publications/regional-climate-and-hydrologic-change8 in-the-northern-u-s-rockies-and-pacific-northwest-internally-consistent-projections-of-future9 climate-for-resource-management/

10 Littell, J.S., D.L. Peterson, K.L. Riley, Y.-Q. Liu, and C.H. Luce, 2016: Fire and Drought. 

11 Effects of Drought on Forests and Rangelands in the United States: A comprehensive science 

12 synthesis. Vose, J., J.S. Clark, C. Luce, and T. Patel-Weynand, Eds. U.S. Department of 

13 Agriculture, Forest Service, Washington Office, Washington, DC, 135-150. 

14 http://www.treesearch.fs.fed.us/pubs/50261

15 Liu, L.L. and T.L. Greaver, 2009: A review of nitrogen enrichment effects on three biogenic 

GHGs: The CO2 16 sink may be largely offset by stimulated N2O and CH4 emission. Ecology 

17 Letters, 12, 1103-1117. http://dx.doi.org/10.1111/j.1461-0248.2009.01351.x 

18 Lobell, D.B. and C. Tebaldi, 2014: Getting caught with our plants down: The risks of a global 

19 crop yield slowdown from climate trends in the next two decades. Environmental Research 

20 Letters, 9, 074003. http://dx.doi.org/10.1088/1748-9326/9/7/074003 

21 Maloney, E.D., S.J. Camargo, E. Chang, B. Colle, R. Fu, K.L. Geil, Q. Hu, X. Jiang, N. Johnson, 

22 K.B. Karnauskas, J. Kinter, B. Kirtman, S. Kumar, B. Langenbrunner, K. Lombardo, L.N. 

23 Long, A. Mariotti, J.E. Meyerson, K.C. Mo, J.D. Neelin, Z. Pan, R. Seager, Y. Serra, A. Seth, 

24 J. Sheffield, J. Stroeve, J. Thibeault, S.-P. Xie, C. Wang, B. Wyman, and M. Zhao, 2014: 

25 North American climate in CMIP5 experiments: Part III: Assessment of twenty-first-century 

26 projections. Journal of Climate, 27, 2230-2270. http://dx.doi.org/10.1175/JCLI-D-13-00273.1 

27 Mann, M.E. and P.H. Gleick, 2015: Climate change and California drought in the 21st century. 

28 Proceedings of the National Academy of Sciences, 112, 3858-3859. 

29 http://dx.doi.org/10.1073/pnas.1503667112 

30 Mao, J., A. Ribes, B. Yan, X. Shi, P.E. Thornton, R. Seferian, P. Ciais, R.B. Myneni, H. 

31 Douville, S. Piao, Z. Zhu, R.E. Dickinson, Y. Dai, D.M. Ricciuto, M. Jin, F.M. Hoffman, B. 

32 Wang, M. Huang, and X. Lian, 2016: Human-induced greening of the northern extratropical 

33 land surface. Nature Climate Change, 6, 959-963. http://dx.doi.org/10.1038/nclimate3056 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 10

365

1 Marotz, G.A., J. Clark, J. Henry, and R. Standfast, 1975: Cloud fields over irrigated areas in 

2 southwestern Kansas—Data and speculations. The Professional Geographer, 27, 457-461. 

3 http://dx.doi.org/10.1111/j.0033-0124.1975.00457.x 

4 Marston, L., M. Konar, X. Cai, and T.J. Troy, 2015: Virtual groundwater transfers from 

5 overexploited aquifers in the United States. Proceedings of the National Academy of Sciences,

6 112, 8561-8566. http://dx.doi.org/10.1073/pnas.1500457112 

7 McGrath, J.M., A.M. Betzelberger, S. Wang, E. Shook, X.-G. Zhu, S.P. Long, and E.A. 

8 Ainsworth, 2015: An analysis of ozone damage to historical maize and soybean yields in the 

9 United States. Proceedings of the National Academy of Sciences, 112, 14390-14395. 

10 http://dx.doi.org/10.1073/pnas.1509777112 

11 McKinley, D.C., M.G. Ryan, R.A. Birdsey, C.P. Giardina, M.E. Harmon, L.S. Heath, R.A. 

12 Houghton, R.B. Jackson, J.F. Morrison, B.C. Murray, D.E. Pataki, and K.E. Skog, 2011: A 

13 synthesis of current knowledge on forests and carbon storage in the United States. Ecological 

14 Applications, 21, 1902-1924. http://dx.doi.org/10.1890/10-0697.1 

15 http://128.104.77.228/documnts/pdf2011/fpl_2011_mckinley001.pdf

16 McLauchlan, K., 2006: The nature and longevity of agricultural impacts on soil carbon and 

17 nutrients: A review. Ecosystems, 9, 1364-1382. http://dx.doi.org/10.1007/s10021-005-0135-1 

18 Melillo, J.M., S. Butler, J. Johnson, J. Mohan, P. Steudler, H. Lux, E. Burrows, F. Bowles, R. 

19 Smith, L. Scott, C. Vario, T. Hill, A. Burton, Y.M. Zhou, and J. Tang, 2011: Soil warming, 

20 carbon-nitrogen interactions, and forest carbon budgets. Proceedings of the National Academy 

21 of Sciences, 108, 9508-9512. http://dx.doi.org/10.1073/pnas.1018189108 

22 http://www.pnas.org/content/108/23/9508.full.pdf+html

23 Melillo, J.M., T.C. Richmond, and G.W. Yohe, eds. Climate Change Impacts in the United 

24 States: The Third National Climate Assessment. 2014, U.S. Global Change Research Program: 

25 Washington, D.C. 842. http://dx.doi.org/10.7930/J0Z31WJ2. 

26 Moore, G.W., C.B. Edgar, J.G. Vogel, R.A. Washington-Allen, Rosaleen G. March, and R. 

27 Zehnder, 2016: Tree mortality from an exceptional drought spanning mesic to semiarid 

28 ecoregions. Ecological Applications, 26, 602-611. http://dx.doi.org/10.1890/15-0330 

29 Myhre, G., D. Shindell, F.-M. Bréon, W. Collins, J. Fuglestvedt, J. Huang, D. Koch, J.-F. 

30 Lamarque, D. Lee, B. Mendoza, T. Nakajima, A. Robock, G. Stephens, T. Takemura, and H. 

31 Zhang, 2013: Anthropogenic and Natural Radiative Forcing. Climate Change 2013: The 

32 Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of 

33 the Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. 

34 Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 10

366

1 Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 659–

2 740. http://dx.doi.org/10.1017/CBO9781107415324.018 www.climatechange2013.org

3 Norby, R.J., E.H. DeLucia, B. Gielen, C. Calfapietra, C.P. Giardina, J.S. King, J. Ledford, H.R. 

4 McCarthy, D.J.P. Moore, R. Ceulemans, P. De Angelis, A.C. Finzi, D.F. Karnosky, M.E. 

5 Kubiske, M. Lukac, K.S. Pregitzer, G.E. Scarascia-Mugnozza, W.H. Schlesinger, and R. 

6 Oren, 2005: Forest response to elevated CO2 is conserved across a broad range of 

7 productivity. Proceedings of the National Academy of Sciences of the United States of 

8 America, 102, 18052-18056. http://dx.doi.org/10.1073/pnas.0509478102 

9 Parker, L.E. and J.T. Abatzoglou, 2016: Projected changes in cold hardiness zones and suitable 

10 overwinter ranges of perennial crops over the United States. Environmental Research Letters,

11 11, 034001. http://dx.doi.org/10.1088/1748-9326/11/3/034001 

12 Pielke, R.A., J. Adegoke, A. BeltráN-Przekurat, C.A. Hiemstra, J. Lin, U.S. Nair, D. Niyogi, and 

13 T.E. Nobis, 2007: An overview of regional land-use and land-cover impacts on rainfall. Tellus 

14 B, 59, 587-601. http://dx.doi.org/10.1111/j.1600-0889.2007.00251.x 

15 Pielke Sr, R. and X. Zeng, 1989: Influence on severe storm development of irrigated land. 

16 National Weather Digest 14, 16-17. 

17 Pyle, J.A., N. Warwick, X. Yang, P.J. Young, and G. Zeng, 2007: Climate/chemistry feedbacks 

18 and biogenic emissions. Philosophical Transactions of the Royal Society A: Mathematical, 

19 Physical and Engineering Sciences, 365, 1727-40. http://dx.doi.org/10.1098/rsta.2007.2041 

20 Reichstein, M., M. Bahn, P. Ciais, D. Frank, M.D. Mahecha, S.I. Seneviratne, J. Zscheischler, C. 

21 Beer, N. Buchmann, D.C. Frank, D. Papale, A. Rammig, P. Smith, K. Thonicke, M. van der 

22 Velde, S. Vicca, A. Walz, and M. Wattenbach, 2013: Climate extremes and the carbon cycle. 

23 Nature, 500, 287-295. http://dx.doi.org/10.1038/nature12350 

24 Reyes-Fox, M., H. Steltzer, M.J. Trlica, G.S. McMaster, A.A. Andales, D.R. LeCain, and J.A. 

25 Morgan, 2014: Elevated CO2 further lengthens growing season under warming conditions. 

26 Nature, 510, 259-262. http://dx.doi.org/10.1038/nature13207 

27 Rosenstiel, T.N., M.J. Potosnak, K.L. Griffin, R. Fall, and R.K. Monson, 2003: Increased CO2 

28 uncouples growth from isoprene emission in an agriforest ecosystem. Nature, 421, 256-259. 

29 http://dx.doi.org/10.1038/nature01312 

30 Rosenzweig, C., J. Elliott, D. Deryng, A.C. Ruane, C. Müller, A. Arneth, K.J. Boote, C. 

31 Folberth, M. Glotter, N. Khabarov, K. Neumann, F. Piontek, T.A.M. Pugh, E. Schmid, E. 

32 Stehfest, H. Yang, and J.W. Jones, 2014: Assessing agricultural risks of climate change in the 

33 21st century in a global gridded crop model intercomparison. Proceedings of the National 

34 Academy of Sciences, 111, 3268-3273. http://dx.doi.org/10.1073/pnas.1222463110 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 10

367

1 Schlesinger, W.H., M.C. Dietze, R.B. Jackson, R.P. Phillips, C.C. Rhoads, L.E. Rustad, and J.M. 

2 Vose, 2016: Forest biogeochemistry in response to drought. Effects of Drought on Forests and 

3 Rangelands in the United States: A comprehensive science synthesis. Vose, J., J.S. Clark, C. 

4 Luce, and T. Patel-Weynand, Eds. U.S. Department of Agriculture, Forest Service, 

5 Washington Office, Washington, DC, 97-106. http://www.treesearch.fs.fed.us/pubs/50261

6 Schwalm, C.R., W.R.L. Anderegg, F. Biondi, G. Koch, M. Litvak, K. Ogle, J.D. Shaw, A. Wolf, 

7 D.N. Huntzinger, and A.M. Michalak, In review: Global patterns of drought recovery. Nature. 

8 Shepherd, J.M., 2013: Impacts of Urbanization on Precipitation and Storms: Physical Insights 

9 and Vulnerabilities Climate Vulnerability: Understanding and Addressing Threats to 

10 Essential Resources. Academic Press, Oxford, 109-125. http://dx.doi.org/10.1016/B978-0-12-

11 384703-4.00503-7 

12 Sippel, S., J. Zscheischler, and M. Reichstein, 2016: Ecosystem impacts of climate extremes 

13 crucially depend on the timing. Proceedings of the National Academy of Sciences, 113, 5768-

14 5770. http://dx.doi.org/10.1073/pnas.1605667113 

15 Smith, P., S.J. Chapman, W.A. Scott, H.I.J. Black, M. Wattenbach, R. Milne, C.D. Campbell, A. 

16 Lilly, N. Ostle, P.E. Levy, D.G. Lumsdon, P. Millard, W. Towers, S. Zaehle, and J.U. Smith, 

17 2007: Climate change cannot be entirely responsible for soil carbon loss observed in England 

18 and Wales, 1978–2003. Global Change Biology, 13, 2605-2609. 

19 http://dx.doi.org/10.1111/j.1365-2486.2007.01458.x 

20 SOCCR-2, To be released in 2017, per website. 

21 Szkokan-Emilson, E.J., B.W. Kielstra, S.E. Arnott, S.A. Watmough, J.M. Gunn, and A.J. 

22 Tanentzap, 2016: Dry conditions disrupt terrestrial–aquatic linkages in northern catchments. 

23 Global Change Biology, Early view. http://dx.doi.org/10.1111/gcb.13361 

24 Tian, H., C. Lu, J. Yang, K. Banger, D.N. Huntzinger, C.R. Schwalm, A.M. Michalak, R. Cook, 

25 P. Ciais, D. Hayes, M. Huang, A. Ito, A.K. Jain, H. Lei, J. Mao, S. Pan, W.M. Post, S. Peng, 

26 B. Poulter, W. Ren, D. Ricciuto, K. Schaefer, X. Shi, B. Tao, W. Wang, Y. Wei, Q. Yang, B. 

27 Zhang, and N. Zeng, 2015: Global patterns and controls of soil organic carbon dynamics as 

28 simulated by multiple terrestrial biosphere models: Current status and future directions. 

29 Global Biogeochemical Cycles, 29, 775-792. http://dx.doi.org/10.1002/2014GB005021 

30 Todd-Brown, K.E.O., J.T. Randerson, W.M. Post, F.M. Hoffman, C. Tarnocai, E.A.G. Schuur, 

31 and S.D. Allison, 2013: Causes of variation in soil carbon simulations from CMIP5 Earth 

32 system models and comparison with observations. Biogeosciences, 10, 1717-1736. 

33 http://dx.doi.org/10.5194/bg-10-1717-2013 

34 U.S. Department of State, 2016: Second Biennial Report of the United States of America. 75 pp., 

35 Washington, DC. 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 10

368

1 http://unfccc.int/files/national_reports/biennial_reports_and_iar/submitted_biennial_reports/ap

2 plication/pdf/2016_second_biennial_report_of_the_united_states_.pdf

3 Vose, J., J.S. Clark, C. Luce, and T. Patel-Weynand, eds. Effects of Drought on Forests and 

4 Rangelands in the United States: A comprehensive science synthesis. 2016, U.S. Department 

5 of Agriculture, Forest Service, Washington Office: Washington, DC. 289. 

6 http://www.treesearch.fs.fed.us/pubs/50261

7 Walsh, J., D. Wuebbles, K. Hayhoe, J. Kossin, K. Kunkel, G. Stephens, P. Thorne, R. Vose, M. 

8 Wehner, J. Willis, D. Anderson, S. Doney, R. Feely, P. Hennon, V. Kharin, T. Knutson, F. 

9 Landerer, T. Lenton, J. Kennedy, and R. Somerville, 2014: Ch. 2: Our changing climate. 

10 Climate Change Impacts in the United States: The Third National Climate Assessment. 

11 Melillo, J.M., T.C. Richmond, and G.W. Yohe, Eds. U.S. Global Change Research Program, 

12 Washington, D.C., 19-67. http://dx.doi.org/10.7930/J0KW5CXT 

13 Walsh, J., D. Wuebbles, K. Hayhoe, J. Kossin, K. Kunkel, G. Stephens, P. Thorne, R. Vose, M. 

14 Wehner, J. Willis, D. Anderson, V. Kharin, T. Knutson, F. Landerer, T. Lenton, J. Kennedy, 

15 and R. Somerville, 2014: Appendix 4: Frequently Asked Questions. Climate Change Impacts 

16 in the United States: The Third National Climate Assessment. Melillo, J.M., Terese (T.C.) 

17 Richmond, and G.W. Yohe, Eds. U.S. Global Change Research Program, Washington, DC, 

18 790-820. http://dx.doi.org/10.7930/J0G15XS3 

19 Ward, D.S. and N.M. Mahowald, 2015: Local sources of global climate forcing from different 

20 categories of land use activities. Earth System Dynamics, 6, 175-194. 

21 http://dx.doi.org/10.5194/esd-6-175-2015 

22 Ward, D.S., N.M. Mahowald, and S. Kloster, 2014: Potential climate forcing of land use and 

23 land cover change. Atmospheric Chemistry and Physics, 14, 12701-12724. 

24 http://dx.doi.org/10.5194/acp-14-12701-2014 

25 Wear, D.N. and J.W. Coulston, 2015: From sink to source: Regional variation in U.S. forest 

26 carbon futures. Scientific Reports, 5, 16518. http://dx.doi.org/10.1038/srep16518 

27 Wolf, S., T.F. Keenan, J.B. Fisher, D.D. Baldocchi, A.R. Desai, A.D. Richardson, R.L. Scott, 

28 B.E. Law, M.E. Litvak, N.A. Brunsell, W. Peters, and I.T. van der Laan-Luijkx, 2016: Warm 

29 spring reduced carbon cycle impact of the 2012 US summer drought. Proceedings of the 

30 National Academy of Sciences, 113, 5880-5885. http://dx.doi.org/10.1073/pnas.1519620113 

31 Zaehle, S., P. Friedlingstein, and A.D. Friend, 2010: Terrestrial nitrogen feedbacks may 

32 accelerate future climate change. Geophysical Research Letters, 37, L01401. 

33 http://dx.doi.org/10.1029/2009GL041345 
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1 Zeng, N., F. Zhao, G.J. Collatz, E. Kalnay, R.J. Salawitch, T.O. West, and L. Guanter, 2014: 

2 Agricultural green revolution as a driver of increasing atmospheric CO2 seasonal amplitude. 

3 Nature, 515, 394-397. http://dx.doi.org/10.1038/nature13893 

4 Zhang, X., L. Alexander, G.C. Hegerl, P. Jones, A.K. Tank, T.C. Peterson, B. Trewin, and F.W. 

5 Zwiers, 2011: Indices for monitoring changes in extremes based on daily temperature and 

6 precipitation data. Wiley Interdisciplinary Reviews: Climate Change, 2, 851-870. 

7 http://dx.doi.org/10.1002/wcc.147 

8 Zhao, L., X. Lee, R.B. Smith, and K. Oleson, 2014: Strong contributions of local background 

9 climate to urban heat islands. Nature, 511, 216-219. http://dx.doi.org/10.1038/nature13462 

10 Zhu, Z., S. Piao, R.B. Myneni, M. Huang, Z. Zeng, J.G. Canadell, P. Ciais, S. Sitch, P. 

11 Friedlingstein, A. Arneth, C. Cao, L. Cheng, E. Kato, C. Koven, Y. Li, X. Lian, Y. Liu, R. 

12 Liu, J. Mao, Y. Pan, S. Peng, J. Penuelas, B. Poulter, T.A.M. Pugh, B.D. Stocker, N. Viovy, 

13 X. Wang, Y. Wang, Z. Xiao, H. Yang, S. Zaehle, and N. Zeng, 2016: Greening of the Earth 

14 and its drivers. Nature Climate Change, 6, 791-795. http://dx.doi.org/10.1038/nclimate3004
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1 11. Arctic Changes and their Effects on Alaska

2 and the Rest of the United States

3 KEY FINDINGS

4 1. For both the State of Alaska and for the Arctic as a whole, near-surface air temperature is 

5 increasing at a rate more than twice as fast as the global-average temperature. (Very high 

6 confidence)

7 2. Rising Alaskan permafrost temperatures are causing permafrost to thaw and become more 

8 discontinuous; this releases additional CO2 and CH4 resulting in additional warming (high 

9 confidence). The overall magnitude of the permafrost-carbon feedback is uncertain.

10 3. Arctic sea ice and Greenland Ice Sheet mass loss are accelerating and Alaskan mountain 

11 glaciers continue to melt (very high confidence). Alaskan coastal sea ice loss rates exceed the 

12 Arctic average (very high confidence). Observed sea and land ice loss across the Arctic is 

13 occurring faster than climate models predict (very high confidence). Melting trends are 

14 expected to continue resulting in late summers becoming nearly ice-free for the Arctic ocean

15 by mid-century (very high confidence). 

16 4. Human activities have contributed to rising surface temperature, sea ice loss since 1979, and

17 glacier mass loss observed across the Arctic. (High confidence)

18 5. Atmospheric circulation patterns connect the climates of the Arctic and the United States. 

19 The mid-latitude circulation influences Arctic climate change (medium to high confidence).

20 In turn, current evidence suggests that Arctic warming is influencing mid-latitude circulation 

21 over the continental United States and affecting weather patterns, but the mechanisms are not 

22 well understood (low to medium confidence).

23 11.1. Introduction

24 Climate changes in Alaska and across the Arctic continue to outpace changes occurring across 

25 the globe. The Arctic is a complex system integral to Earth’s climate, influencing global surface 

26 energy and moisture budgets, atmospheric and oceanic circulations, and geosphere–biosphere 

27 feedbacks. Resulting from its high sensitivity to radiative forcing and its role in amplifying 

28 warming, the Arctic cryosphere is a key indicator of the global climate state. Accelerated melting 

29 of multiyear sea ice cover, mass loss from the Greenland Ice Sheet (GrIS), reduction of terrestrial 

30 snow cover, and permafrost degradation are stark examples of the rapid Arctic system-wide 

31 response to global warming. These changes in Arctic sea ice, land ice, surface temperature, and 

32 permafrost influence global climate by affecting sea level, the carbon cycle, and potentially 

33 atmospheric and oceanic circulation patterns. Arctic climate change has altered the global 

34 climate in the past (Knies et al. 2014) and will influence climate in the future. Strongly coupled 
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1 to the Arctic, climate change in Alaska is apparent; the connection between climate changes in 

2 the Arctic and the continental United States is a topic of current research.

3 Adaptation, mitigation, and policy decisions depend on projections of future Alaskan and Arctic 

4 climate. Aside from uncertainties due to natural variability, scientific uncertainty, and 

5 greenhouse gas emissions uncertainty (see Chapter 4), additional unique uncertainties in our 

6 understanding of Arctic processes thwart projections, including shortcomings in mixed-phase 

7 cloud processes (Wyser et al. 2008); boundary layer processes (Bourassa et al. 2013); sea ice 

8 mechanics (Bourassa et al. 2013); and ocean currents, eddies, and tides that affect the advection

9 of heat into and around the Arctic Ocean (Maslowski et al. 2012, 2014). The inaccessibility of 

10 the Arctic has made it difficult to sustain the kind of high-quality observations of the atmosphere, 

11 ocean, land, and ice required to improve physically based models, stunting scientific progress. 

12 Improved data quality and increased observational coverage would help address important Arctic 

13 science questions. 

14 Despite these challenges, this chapter documents significant scientific progress and knowledge 

15 about how the Alaskan and Arctic climate has changed and will continue to change.

16 11.2. Arctic Changes 

17 11.2.1. Alaska and Arctic Temperature 

18 Surface temperature—an essential component of the Arctic climate system—both drives and 

19 signifies change, fundamentally controlling the melting of sea ice, land ice, and snow. Further, 

20 the vertical profile of temperature modulates the exchange of mass, energy, and momentum

21 between the surface and atmosphere, and influences other components such as clouds (Kay and 

22 Gettelman 2009; Pavelsky et al. 2011; Taylor et al. 2015). Arctic temperatures exhibit significant 

23 spatial and interannual variability resulting from interactions and feedbacks between sea ice, 

24 snow cover, atmospheric heat transports, vegetation, clouds, water vapor, and the surface energy 

25 budget (Overland et al. 2015b; Johannessen et al. 2016; Overland and Wang 2016).

26 Satellite observations show that the Arctic has warmed at rates more than twice as fast as the 

27 global average—by +0.60 ± 0.07°C (1.08° ± 0.13°F) per decade since 1981—and that North 

28 American land regions north of 64°N (including Alaska) have warmed +0.54 ± 0.09°C (0.97° ±

29 0.16°F) per decade (Hartmann et al. 2013; Overland et al. 2014; Comiso and Hall 2014). Strong 

30 surface temperature warming has occurred across Alaska, especially on the North Slope during 

31 autumn. For example, Barrow’s warming since 1979 exceeds 3.8°C (7°F) in September, 6.6°C 

32 (12°F) in October, and 5.5°C (10°F) in November (Wendler et al. 2014). While Alaska state33 wide annual mean temperature changes since 1949 are dominated by decadal variability like the 

34 Pacific Decadal Oscillation (Hartmann and Wendler 2005; McAfee 2014; see Ch. 5), records in 

35 2014 and 2015 broke previous marks by more than 0.5°C (1.0°F) (see Ch. 6).
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1 The enhanced warming of Alaska and the Arctic is a robust feature of the climate response to 

2 anthropogenic forcing (Collins et al. 2013; Taylor et al. 2013). There is very likely an 

3 anthropogenic contribution to Alaskan surface temperature warming over the past 50 years

4 (Bindoff et al. 2013; Gillett et al. 2008; Najafi et al. 2015). However, it is likely that other 

5 anthropogenic forcings (mostly aerosols) have partially offset the greenhouse gas warming since 

6 1913 by up to 60% at high latitudes and that natural forcing has not contributed to the long-term 

7 warming in a discernable way (Najafi et al. 2015). According to this study, Arctic warming to 

8 date would have been larger without the offsetting aerosols influence. It is virtually certain that 

9 Arctic surface temperatures continue to increase faster than the global mean through the 21st

10 century (Christensen et al. 2013). 

11 11.2.2. Arctic Sea Ice Change

12 Arctic sea ice strongly influences Alaskan, Arctic, and global climate by modulating exchanges

13 of mass, energy, and momentum between the ocean and the atmosphere. Variations in Arctic sea 

14 ice cover also influence atmospheric temperature and humidity, wind patterns, clouds, ocean 

15 temperature, thermal stratification, and ecosystem productivity (Kay and Gettelman 2009; Kay et 

16 al. 2010; Pavelsky et al. 2011; Boisvert et al. 2013; Vaughan et al. 2013; Solomon et al. 2014; 

17 Taylor et al. 2015; Boisvert et al. 2015a,b; Johannessen et al. 2016). Arctic sea ice exhibits 

18 significant interannual, spatial, and seasonal variability driven by atmospheric wind patterns and 

19 cyclones, atmospheric temperature and humidity structure, clouds, radiation, sea ice dynamics, 

20 and the ocean (Ogi and Wallace 2007; Kwok and Untersteiner 2011; Stroeve et al. 2012a,b; Ogi 

21 and Rigor 2013; Carmack et al. 2015). Overwhelming evidence indicates that the character of 

22 Arctic sea ice is rapidly changing, marking the beginning of the “New Arctic” era.

23 Observational evidence indicates Arctic-wide sea ice decline since 1979, accelerating melt since 

24 2000, and the fastest melt along the Alaskan coast (Stroeve et al. 2014a,b; Comiso and Hall 

25 2014; Wendler et al. 2014). Although sea ice loss is found in all months, satellite observations 

26 show the fastest loss in late summer and autumn (Stroeve et al. 2014a). Since 1979, the annual 

27 average Arctic sea ice extent has decreased at a rate of 3.5%–4.1% per decade, accelerating since 

28 2000 (Vaughan et al. 2013; Stroeve et al. 2014a,b; Comiso and Hall 2014). Regional sea ice melt

29 along the Alaskan coasts exceeds the Arctic average rates with declines in the Beaufort and 

30 Chukchi Seas of −4.1% and −4.7% per decade, respectively. The annual minimum and 

31 maximum sea ice extent have decreased over the last 35 years by −13.3% and −2.5% per decade, 

32 respectively (Perovich et al. 2015). The ten lowest September sea ice extents over the satellite 

33 period have all occurred in the last ten years, the lowest in 2012. The 2016 September sea ice 

34 minimum tied with 2007 for the second lowest on record, but rapid refreezing resulted in the 

35 September monthly average extent being the fifth lowest. Despite the rapid initial refreezing, 

36 October and November sea ice extent is again in record low territory due to anomalously warm 

37 temperatures in the marginal seas around Alaska.
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1 Other important characteristics of Arctic sea ice have also changed, including thickness, age, and 

2 melt season length. Sea ice thickness is monitored using an array of satellite, aircraft, and vessel 

3 measurements (Vaughan et al. 2013). The thickness of the Arctic sea ice during winter between 

4 1980 and 2008 has decreased between 1.3 and 2.3 meters (4.3 and 7.5 feet) (Vaughan et al. 

5 2013). The age distribution of sea ice has become younger since 1988 where the extent of first6 and multiyear sea ice has decreased in September by −11.5 ± 2.1% and −13.5 ± 2.5% per decade, 

7 respectively (Vaughan et al. 2013; Perovich et al. 2015). Figure 11.1 shows the September sea 

8 ice extent and age in 1984 and 2016, illustrating significant reductions in sea ice age (Tschudi et 

9 al. 2016). Younger, thinner sea ice is more susceptible to melt, therefore reductions in age and 

10 thickness imply a stronger interannual sea ice albedo feedback and larger interannual variability.

11 [INSERT FIGURE 11.1 HERE:

12 Figure 11.1: September sea ice extent (all gray-scale colors) and age shown for (a) 1984 and (b) 

13 2016, illustrating significant reductions in sea ice extent and age (thickness). Bar graph in the 

14 lower right of each panel illustrates the sea ice area covered within each age category. (Figure 

15 source: NASA Science Visualization Studio (http://svs.gsfc.nasa.gov/cgi16 bin/details.cgi?aid=4489); data: Tschudi et al. 2016)

17 Sea ice melt season has lengthened Arctic-wide by at least five days per decade since 1979, with 

18 larger regional changes (Stroeve et al. 2014b; Parkinson 2014). Some of the largest observed 

19 changes in sea ice melt season (Figure 11.2) are found along Alaska’s northern and western 

20 coasts, lengthening the melt season by 20–30 days per decade and increasing the annual number 

21 of ice-free days by more than 90 (Parkinson 2014). Summer sea ice retreat along coastal Alaska 

22 has led to a longer open water seasons making the Alaskan coastline more vulnerable to erosion 

23 (Melillo 2014; Gibbs and Richmond 2015).

24 [INSERT FIGURE 11.2 HERE:

25 Figure 11.2: 35-year trends in Arctic sea ice melt season length in days per decade from passive 

26 microwave satellite observations, illustrating that the sea ice season has shortened by more than 

27 60 and as much as 90 days in coastal Alaska over the last 30 years. (Figure source: adapted from 

28 Parkinson 2014)]

29 There is very likely an anthropogenic contribution to the observed changes in the Alaska and 

30 Arctic sea ice since 1979 (Bindoff et al. 2013). Internal climate variability alone could not have 

31 caused recently observed record low Arctic sea ice extents (Zhang and Knutson 2013).

32 Additional sea ice loss across the Arctic is virtually certain to result in late summers very likely 

becoming nearly ice-free (areal extent less than 106 km2 or approximately 3.9x105 mi2 33 ) by mid34 century (Collins et al. 2013; Snape and Forster 2014). Natural variability (Wettstein and Deser 

35 2014), future emissions, and model uncertainties (Gagne et al. 2015; Stroeve and Notz, 2015; 

36 Swart et al. 2015) all influence sea ice projections. A key message from the Third National 

37 Climate Assessment (NCA3; Melillo et al. 2014) was that Arctic sea ice is disappearing. The
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1 fundamental conclusion of this assessment the same, additional research corroborates the NCA3 

2 statement. 

3 11.2.3. Arctic Ocean and Marginal Seas

4 SEA SURFACE TEMPERATURE

5 Arctic Ocean sea surface temperatures (SSTs) have increased since 1982. Satellite-observed 

6 Arctic Ocean SSTs, poleward of 60°N, exhibit a trend of +0.09 ± 0.01°C (+0.16 ± 0.02°F) per 

7 decade (Comiso and Hall 2014). Arctic Ocean SST is controlled by a combination of factors,

8 including solar radiation and energy transport from ocean currents and atmospheric winds. 

9 Summertime Arctic Ocean SST trends and pattern strongly couple with sea ice extent; however,

10 clouds, ocean color, upper-ocean thermal structure, and atmospheric circulation also play a role 

11 (Ogi and Rigor 2013; Rhein et al. 2013). Along coastal Alaska, SSTs in the Chukchi Sea exhibit 

12 a statistically significant (95% confidence) trend of 0.5 ± 0.3°C (+0.9 ± 0.5°F) per decade 

13 (Timmermans and Proshutinksy 2015).

14 Arctic Ocean temperatures also increased at depth (Polyakov et al. 2012; Rhein et al. 2013). 

15 Since 1970, Arctic Ocean Intermediate Atlantic Water (AW)—located between 150 and 900

16 meters—has warmed by 0.48 ± 0.05°C (0.86 ± 0.09°F) per decade; the most recent decade being 

17 the warmest (Polyakov et al. 2012). The observed AW warming is unprecedented in the last 

18 1,150 years (Spielhagen et al. 2011; Jungclaus et al. 2014). The influence of AW warming on 

19 future Alaska and Arctic sea ice loss is unclear (Döscher et al. 2014; Carmack et al. 2015).

20 ALASKAN SEA LEVEL RISE

21 The Alaskan coastline is vulnerable to sea level rise; however, strong regional variability exists 

22 in current trends and future projections. Sea level rise trends from the National Water Level 

23 Observation Network around Alaska reveal regional variations in the observed rate of sea level 

24 rise, with most stations experiencing slower rises than the global average. Several stations along 

25 Alaska’s southern coast have observed rises three times slower than the global values due to 

26 isostatic rebound and the proximity to Alaskan melting glaciers (Church et al. 2013; Ch. 12: Sea 

27 Level Rise). Tide gauge data show sea levels rising faster along the northern coast of Alaska but 

28 still slower than the global average. The largest future sea level rise in the Arctic is expected 

29 along the North Alaskan coast, exceeding a foot by 2100, but the magnitude depends

30 significantly on the radiative forcing scenario and could reach 0.6 meters (approx. 2 feet)

31 (Church et al. 2013).

32 SALINITY

33 Arctic Ocean salinity influences the freezing temperature of sea ice (less salty water freezes more 

34 readily) and the density profile representing the integrated effects of freshwater transport, river 

35 runoff, evaporation, and sea ice processes. Arctic Ocean salinity exhibits multidecadal 

36 variability, hampering the assessment of long-term trends (Rawlins et al. 2010). Emerging 
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1 evidence suggests that the Arctic Ocean and marginal sea salinity has decreased in recent years 

2 despite short-lived regional salinity increases between 2000 and 2005 (Rhein et al. 2013).

3 Increased river runoff, rapid melting of sea and land ice, and changes in freshwater transport 

4 have influenced observed Arctic Ocean salinity (Rhein et al. 2013; Köhl and Serra 2014).

5 OCEAN ACIDIFICATION

6 Arctic Ocean acidification is occurring at a faster rate than the rest of the globe (Mathis et al. 

7 2015; Ch. 13: Ocean Acidification). Coastal Alaska and its ecosystems are especially vulnerable 

8 to ocean acidification because of the high sensitivity of Arctic Ocean water chemistry to changes 

9 in sea ice, respiration of organic matter, upwelling, and increasing river runoff (Mathis et al. 

10 2015). Sea ice loss and a longer melt season contribute to increased vulnerability of the Arctic 

11 Ocean to acidification by lowering total alkalinity, permitting greater upwelling, and influencing

12 the primary production characteristics in coastal Alaska (Arrigo et al. 2008; Cai et al. 2010; Hunt 

13 et al. 2011; Stabeno et al. 2012; Mathis et al. 2012; Bates et al. 2014). Global-scale modeling 

14 studies suggest that the largest and most rapid changes in pH are being observed and will 

15 continue to occur along Alaska’s coast, indicating that ocean acidification may increase enough 

16 by the 2030s to significantly influence coastal ecosystems (Mathis et al. 2015).

17 11.2.4. Boreal Wildfires

18 A global phenomenon with natural (lightning) and human-caused ignition sources, wildfire 

19 represents a critical ecosystem process that renews terrestrial habitats. Recent decades have seen 

20 increased forest fire activity in Alaska. Historically, however, wildfires have been less frequent 

21 and smaller in Alaska compared to the rest of the globe (Flannigan et al. 2009; Hu et al. 2015). 

22 Shortened land snow cover seasons and higher temperatures make the Arctic more vulnerable to 

23 wildfire (Flannigan et al. 2009; Hu et al. 2015; Young et al. 2016). Total area burned and the 

number of large fires (those with area greater than 1000 km2or 386 mi2 24 ) in Alaska exhibits 

25 significant interannual and decadal scale variability, from influences of atmospheric circulation 

26 patterns and controlled burns, but have likely increased since 1959 (Kasischke and Turetsky 

27 2006). The most recent decade has seen an unusually large number of severe wildfire years in 

28 Alaska, for which the risk of severe fires has likely increased by 33%–50% as a result of 

29 anthropogenic climate change (Partain et al. 2016) and is projected to increase by up to a factor 

30 of four by the end of the century (Young et al. 2016). Alaska’s fire season is also likely

31 lengthening—a trend expected to continue (Flannigan et al. 2009; Sanford et al. 2015). 

32 Thresholds in temperature and precipitation shape Arctic fire regimes, and projected increases in

33 future lightning activity imply increased vulnerability to future climate change (Flannigan et al. 

34 2009; Young et al. 2016). Alaskan tundra and forest wildfires will likely increase under warmer 

35 and drier conditions (Sanford et al. 2015; French et al. 2015) and potentially result in a transition 

36 into a fire regime unprecedented in the last 10,000 years (Kelly et al. 2013). Total area burned is 

37 projected to increase between 25% and 53% by the end of the century (Joly et al. 2012). Existing

38 studies do not demonstrate that the observed increased in forest fire activity over the historical 
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1 period has been highly unusual in comparison to natural variability. Rather, these studies have 

2 relied on model calculation to infer the human contribution. The degree of forestry management

3 is a confounding factor which complicates attribution of changes to anthropogenic climate 

4 change. We conclude that there is medium confidence for a human-caused climate change 

5 contribution to increased forest fire activity in Alaska in recent decades.

6 Boreal forests and tundra contain large stores of carbon, approximately 50% of the total global 

7 soil carbon (McGuire et al. 2009). Increased fire activity could deplete these stores, releasing

8 them to the atmosphere to serve as an additional source of atmospheric CO2 and alter the carbon 

9 cycle (McGuire et al. 2009; Kelly et al. 2016). Additionally, increased fires in Alaska may also 

10 enhance the degradation of Alaska’s permafrost, blackening the ground, reducing surface albedo, 

11 and removing protective vegetation.

12 11.2.5. Snow Cover and Permafrost 

13 Snow cover, like sea ice, possesses a high albedo and serves as a climate feedback. Snow cover 

14 extent has significantly decreased across the Northern Hemisphere and Alaska over the last 

15 decade (Derksen and Brown 2012; see also Ch. 7: Precipitation Change and Ch. 10: Land 

16 Cover). Northern Hemisphere June snow cover decreased by more than 50% between 1967 and 

17 2012 (Brown and Robinson 2011; Vaughan et al. 2013), at trend of −19.8% per decade (Derksen 

18 et al. 2015). May snow cover has also declined, at −7.3% per decade, due to reduced winter 

19 accumulation from warmer temperatures. Regional trends in snow cover duration vary, with 

20 some showing earlier onsets while others show later onsets (Derksen et al. 2015). In Alaska, the 

2016 May statewide snow coverage 595,000 km2 (~372,000 mi2 21 ) was the lowest on record dating 

22 back to 1967; the snow coverage of 2015 was the second lowest and 2014 was the fourth lowest. 

23 Declining snow cover is expected to continue; however, the evolution of Arctic ecosystems, 

24 including the observed tundra shrub expansion (Myers-Smith et al. 2011), can alter the snow 

25 depth, melt dynamics, and the local surface energy budget influencing melt.

26 Alaska and Arctic permafrost characteristics have responded to increased temperatures and 

27 reduced snow cover in most regions since the 1980s (AMAP 2011; Vaughan et al. 2013). The 

28 permafrost warming rate varies regionally; however, colder permafrost is warming faster than 

29 warmer permafrost (Vaughan et al. 2013; Romanovsky et al. 2015). This feature is most evident 

30 across Alaska, where permafrost on the North Slope is warming more rapidly than in the interior. 

31 Permafrost temperatures across the North Slope at various depths ranging from 12 to 20 meters

32 (39 to 65 feet) have warmed between 0.2° and 0.7°C (0.3° and 1.3°F) per decade since 2000 

33 (Figure 11.3; Romanovsky et al. 2016). Trends in the permafrost active layer show strong 

34 regional variations (AMAP 2011; Shiklomanov et al. 2012); however, active layer thickness 

35 increased across much of the Arctic (Vaughan et al. 2013). Uncertainties in future permafrost 

36 warming and active layer deepening in Alaska are due to poorly understood deep soil, ice wedge, 

37 and thermokarst processes that may accelerate the thaw (Koven et al. 2015a; Liljedahl et al. 
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1 2016). Continued degradation of permafrost and a transition from continuous to discontinuous 

2 permafrost is expected over the 21st century (Vaughan et al. 2013). 

3 [INSERT FIGURE 11.3 HERE:

4 Figure 11.3: Time series of annual mean permafrost temperatures (units: °F) at various depths 

5 from 12 to 20 meters (39 to 65 feet) from 1977 through 2015 at several sites across Alaska,

6 including the North Slope continuous permafrost region, and the discontinuous permafrost in 

7 Alaska and northwestern Canada. Solid lines represent the linear trends drawn to highlight that 

8 permafrost temperatures are warming faster in the colder, coastal permafrost regions than the 

9 warmer interior regions. (Figure Source: adapted from Romanovsky et al. 2016; © American 

10 Meteorological Society, used with permission)]

11 11.2.6. Continental Ice Sheets and Mountain Glaciers

12 Mass loss from ice sheets and glaciers influences sea level rise (see Ch. 12: Sea Level Rise), the 

13 oceanic thermohaline circulation, and the global energy budget. Changes in GrIS can also 

14 influence Alaskan climate by altering Arctic-wide and midlatitude circulation patterns (Section 

15 11.3.1). Observational and modeling studies indicate that GrIS and glaciers in Alaska are out of 

16 balance with current climate conditions and losing mass (Vaughan et al. 2013; Zemp et al. 2015). 

17 In recent years, mass loss has accelerated and is expect to continue (Zemp et al. 2015; Harig and 

18 Simons 2016).

19 Dramatic changes have occurred across GrIS, particularly at its margins. GrIS average annual 

20 mass loss from January 2003 to May 2013 was −244 ± 6 Gt per year (Harig and Simons 2016). 

21 Increased surface melt, runoff, and increased outlet glacier discharge from warmer surface air 

22 temperatures are the primary factors contributing to mass loss (Howat et al. 2008; van den 

23 Broeke et al. 2009; Rignot et al. 2010; Straneo et al. 2011; Khan et al. 2014). The effects of 

24 warmer air and ocean temperatures on the GrIS mass balance can be amplified by ice dynamical 

25 feedbacks, such as faster sliding, greater calving, and increased submarine melting (Joughin et al. 

26 2008; Holland et al. 2008a; Rignot et al. 2010; Bartholomew et al. 2011). Shallow ocean 

27 warming and regional changes in the ocean and atmospheric circulation are increasing mass loss 

28 (Dupont and Alley 2005; Lim et al. 2016; Tedesco et al. 2016). The underlying mechanisms of 

29 the recent speed-up of discharge remain unclear (Straneo et al. 2010; Johannessen et al. 2011);

30 however, warming subsurface ocean temperatures, atmospheric warming (Velicogna 2009; van 

31 den Broeke et al. 2009, Andresen et al. 2012), and meltwater penetration to the glacier bed 

32 (Johannessen et al. 2011, Mernild et al. 2012) very likely contribute. 

33 Annual average ice mass from Arctic-wide glaciers has decreased every year since 1984 (AMAP

34 2011; Pelto 2015; Zemp et al. 2015), with significant losses in Alaska, especially over the past 

35 two decades (Figure 11.4; Vaughan et al. 2013; Sharp et al. 2015; Harig and Simons 2016). 

36 Glacial mass loss around the Gulf of Alaska region has declined steadily since 2003 (Harig and 

37 Simons 2016). NASA’s Gravity Recovery and Climate Experiment (GRACE) indicates mass 
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1 loss from the northern and southern parts of the Gulf of Alaska region of −36 ± 4 Gt per year and 

2 −4 ± 3 Gt per year, respectively (Harig and Simons 2016). Studies show imbalances in Alaskan 

3 glaciers, indicating that melt will continue through the 21st century (Vaughan et al. 2013; Zemp 

4 et al. 2015; Mengel et al. 2016). Multiple data sets indicate that it is extremely likely that Alaskan 

5 glaciers have lost mass over the last 50 years and will continue to do so.

6 [INSERT FIGURE 11.4 HERE:

Figure 11.4: Time series of the cumulative climatic mass balance (units: kg/m

2 7 ) in five Arctic 

8 regions and cumulative for the Pan-Arctic from the World Glacier Monitoring Service (WGMS 

9 2016; Wolken et al. 2016; solid lines), and Alaskan glacial mass loss observed from NASA 

10 GRACE (Harig and Simons 2016; dashed blue line). (Figure source: Harig and Simons 2016 and 

11 Wolken et al. 2016; © American Meteorological Society, used with permission)]

12 11.3. Arctic Feedbacks on the Lower 48 and Globally

13 11.3.1. Linkages between Arctic Warming and Lower Latitudes

14 Midlatitude circulation is an important factor of Arctic climate and climate change (Rigor et al. 

15 2002; Graversen 2006; Perlwitz et al. 2015; Francis et al. 2016; Screen et al. 2012; Park et al. 

16 2015; Lee 2014; Lee et al. 2011; Ding et al. 2014; Screen and Francis 2016; Overland et al. 

17 2016). The extent to which enhanced Arctic surface warming and sea ice loss influence the large18 scale atmospheric circulation and midlatitude weather and climate extremes has become an 

19 active research area (Francis et al. 2016; Overland et al. 2016). Several pathways have been 

20 proposed (see studies referred in Cohen et al. 2014 and Barnes and Screen 2015): reduced 

21 meridional temperature gradient, a more sinuous jet-stream, trapped atmospheric waves, 

22 modified storm tracks, weakened stratospheric polar vortex. While modeling studies link a 

23 reduced meridional temperature gradient to fewer cold temperature extremes in the continental 

24 United States (Ayarzagüena and Screen 2016; Sun et al. 2016; Screen et al. 2015a,b), other 

25 studies hypothesize that a slower jet stream may amplify Rossby waves and increase the 

26 frequency of atmospheric blocking, causing more persistent and extreme weather in midlatitudes 

27 (Francis and Vavrus 2012).

28 Multiple observational studies suggest that the concurrent changes in the Arctic and Northern 

29 Hemisphere large-scale circulation since the 1990s did not occur by chance, but were caused by 

30 arctic amplification (Cohen et al. 2014; Vihma 2014; Barnes and Screen 2015). Reanalysis data 

31 suggest a relationship between arctic amplification and observed changes in persistent circulation 

32 phenomena like blocking and planetary wave amplitude (Francis and Skific 2015; Francis and

33 Vavrus 2012; Francis and Vavrus 2015). Robust empirical evidence is lacking because the Arctic 

34 sea ice observational record is too short (Overland et al. 2015a) or because the atmospheric 

35 response to arctic amplification depends on the prior state of the atmospheric circulation

36 reducing detectability (Overland et al. 2016). Furthermore, it is not possible to draw conclusions 

37 regarding the direction of the relationship between Arctic warming and midlatitude circulation 
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1 based on empirical correlation and covariance analyses alone. Observational analyses have been 

2 combined with modeling studies to test causality statements. 

3 Studies with simple models and Atmospheric General Circulation Models (AGCMs) provide 

4 evidence that Arctic warming can affect midlatitude jet streams and location of storm tracks

5 (Barnes and Screen 2015; Francis et al. 2016; Overland et al. 2016). In addition, analysis of 

6 CMIP5 models forced with increasing greenhouse gases suggests that the magnitude of arctic 

7 amplification affects the future midlatitude jet position, specifically during boreal winter (Barnes 

8 and Polvani 2015). However, the effect of arctic amplification on blocking is not clear (Hoskins 

9 and Woollings 2015; Ch. 5: Circulation and Variability).

10 Regarding attribution, AGCM simulations forced with observed changes in Arctic sea ice 

11 suggest that the sea ice loss effect on recent circulation changes is small compared to natural 

12 variability (Screen et al. 2012; Perlwitz et al. 2015). These simulations do not support the 

13 hypothesis that Arctic sea ice loss is the principal factor in the recently emerged “Warm Arctic, 

14 Cold Continents” pattern and related enhanced occurrence of cold winters in the continental 

15 United States (Sigmond and Fyfe 2016; Sun et al. 2016). While several studies find a significant 

16 influence of reduced sea ice in the Barents and Kara Seas (northeast of Scandinavia) on observed 

17 cooling over Eurasia (Overland et al. 2016 and references therein), others suggest that the 

18 cooling results from an internally generated circulation change (Sun et al. 2016; McCusker et al. 

19 2016). Furthermore, models cannot reproduce the observed “Warm Arctic, Cold Continent” 

20 trend pattern, nor do they reproduce the linkage between arctic amplification and lower latitude 

21 climate due to significant model errors, including incorrect sea ice–atmosphere coupling and 

22 poor representation of stratospheric processes (Cohen et al. 2013; Francis et al. 2016). The nature 

23 and magnitude of arctic amplification’s influence on U.S. weather over the coming decades 

24 remains an open question.

25 11.3.2. Freshwater Effects on Ocean Circulation

26 The addition of freshwater to the Arctic Ocean from melting sea ice and land ice can influence 

27 ocean salinity, altering ocean circulation, density stratification, and sea ice characteristics. River 

28 runoff is increasing into the Arctic Ocean, driven by land ice melt (Nummelin et al. 2016). 

29 Changes in the thermohaline circulation that result from freshening the North Atlantic Ocean can 

30 be abrupt and occur over just a few decades (see Ch. 15: Potential Surprises).

31 Melting Arctic sea and land ice combined with time-varying atmospheric forcing (Giles et al. 

32 2012; Köhl and Serra 2014) control Arctic Ocean freshwater export to the North Atlantic. Large33 scale circulation variability in the central Arctic not only controls the redistribution and storage 

34 of freshwater in the Arctic (Köhl and Serra 2014) but also the export volume (Morison et al. 

35 2012). Increased freshwater fluxes can weaken open ocean convection and deep water formation 

36 in the Labrador and Irminger seas, weakening the Atlantic meridional overturning circulation 

37 (AMOC; Rahmstorf et al. 2015; Yang et al. 2016). AMOC-associated poleward heat transport 
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1 substantially contributes to North American and continental European climate; any AMOC slow2 down could have implications for global climate change as well (Smeed et al. 2014).

3 11.3.3. Thawing permafrost and methane hydrates effects on CO2 and CH4 emissions

4 Permafrost contains large stores of carbon, as do methane hydrates. Though the total contribution 

5 of these carbon stores to global methane emission is uncertain, Alaska’s permafrost contains 

6 some of the richest organic carbon soils in the Arctic (Mishra and Riley 2012; Schuur et al. 

7 2015). Thus, warming Alaska permafrost is not only a concern for the Arctic climate but for the 

8 global carbon cycle. Current methane emissions from Alaskan Arctic tundra and boreal forests 

9 contribute a small fraction of the global CH4 budget (Chang et al. 2014). Methane emissions in 

10 the cold season (after snowfall) are greater than summer emissions in Alaska, and methane 

11 emissions in upland tundra are greater than in wetland tundra (Zona et al. 2016).

12 The permafrost–carbon feedback represents the additional release of CO2 and CH4 from thawing 

13 permafrost soils providing additional radiative forcing, a source of a potential surprise (see Ch. 

14 15: Potential Surprises). Thawing permafrost makes previously frozen organic matter available 

15 for microbial decomposition, resulting in the release of CO2 and CH4. The specific condition

16 under which microbial decomposition occurs, aerobic or anaerobic, determines whether CO2 or 

17 CH4 is released (Schadel et al. 2016). This distinction has significant implications for future 

18 climate change, as CH4 is more than 20 times stronger a greenhouse gas than CO2. Combined 

19 data and modeling studies suggest a global sensitivity of the permafrost feedback between -14 

20 and −19 GtC per K (approx. 25 to 34 GtC per °F) (Koven et al. 2015a,b) resulting in a 120 ± 85 

21 Gt release of carbon from permafrost and a global temperature increase of +0.29 ± 0.21 °C 

22 (+0.52 ± 0.38°F) by 2100 (Schaefer et al. 2014). In the coming decades, enhanced high-latitude 

23 plant growth and its associated CO2 sink (Friedlingstein et al. 2006) should partially offset the 

24 increased emissions from permafrost thaw (Schaefer et al. 2014; Schuur et al. 2015); thereafter, 

25 decomposition is expected to dominate uptake. Permafrost thaw is occurring faster than models

26 predict due to poorly understood deep soil, ice wedge, and thermokarst processes (Koven et al. 

27 2015; Liljedahl et al. 2016). There is high confidence in the positive sign of the permafrost28 carbon feedback, but low confidence in the feedback magnitude (Vaughan et al. 2013; Fisher et 

29 al. 2014).

30 Significant stores of CH4, in the form of methane hydrates (also called clathrates), lie below 

31 permafrost and under the global ocean. The estimated total global inventory of methane hydrates 

32 ranges from 500 to 3,000 GtC (Archer 2007; Ruppel 2011; Piñero et al. 2013). Methane hydrates 

33 are solid compounds formed at high pressures and cold temperatures trapping methane gas 

34 within the crystalline structure of water. In the Arctic Ocean and along the shallow coastal 

35 Alaskan seas, methane hydrates form on shallow but cold continental shelves and may be 

36 vulnerable to small increases in ocean temperature (Bollman et al. 2010; Ruppel 2011). 
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1 Rising sea levels and warming oceans have a competing influence on methane hydrate stability

2 (Bollman et al. 2010; Hunter et al. 2013). Studies indicate that the temperature effect dominates 

3 and that the overall influence is likely a destabilizing effect. Projected warming rates for the 21st

4 century Arctic Ocean are not expected to lead to sudden or catastrophic destabilization of sea 

5 floor methane hydrates (Kretschmer et al. 2015; AMIP 2015). It is likely that most of the 

6 methane hydrate deposits will remain stable for the foreseeable future (the next few thousand 

7 years). However, deposits off of coastal Alaska are among the most vulnerable and are expected

8 to begin dissociating and release small amounts of CH4 during the 21st century (Archer 2007; 

9 Ruppel 2011; Hunter et al. 2013; Kretschmer et al. 2015).

10
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1 TRACEABLE ACCOUNTS

2 Key Finding 1

3 For both the State of Alaska and for the Arctic as a whole, near-surface air temperature is

4 increasing at a rate more than twice as fast as the global-average temperature. (Very high 

5 confidence)

6 Description of evidence base

7 The Key Finding is supported by observational evidence from ground-based observing stations, 

8 satellites, and data-model temperature analyses documented in the climate science literature from 

9 multiple sources and analysis techniques (Hartmann et al. 2013; Overland et al. 2014; Comiso 

10 and Hall 2014; Wendler et al. 2014). Additionally, climate models have predicted enhanced 

11 Arctic warming for more than 40 years, indicating that we have a solid grasp on the underlying 

12 physics and positive feedbacks driving the accelerated Arctic warming (Collins et al. 2013; 

13 Taylor et al. 2013). These studies are discussed in the chapter text. Lastly, similar statements 

14 have been made previously in NCA3 (Melillo et al. 2014), IPCC AR5 (2013), and in other 

15 Arctic-specific assessments such as the Arctic Climate Impacts Assessment (ACIA 2005) and 

16 Snow, Water, Ice and Permafrost in the Arctic (AMAP 2011).

17 Major Uncertainties

18 The lack of high quality and restricted spatial resolution of surface and ground temperature data 

19 over many Arctic land regions and essentially no measurements over the Central Arctic Ocean 

20 hampers the ability to better refine the rate of Arctic warming and completely restricts our ability 

21 to quantify and detect regional trends, especially over the sea ice. Climate models generally 

22 produce an Arctic warming between 2 to 3 times the global mean warming. A key uncertainty is 

23 our quantitative knowledge of the contributions from individual feedback processes in driving 

24 the accelerated Arctic warming. Reducing this uncertainty will help constrain projections of 

25 future Arctic warming.

26 Assessment of confidence based on evidence and agreement, including short description of 

27 nature of evidence and level of agreement

28 x Very High 

29 ☐ High

30 ☐ Medium 

31 ☐ Low 

32 There is very high confidence that the Arctic surface and air temperatures have warmed across 

33 Alaska and the Arctic at a much faster rate than the global average. The surface temperature has 

34 warmed twice as much as the global average given the combination of observational evidence 

35 from multiple sources and the ability of climate models to capture and explain the root causes of 

36 the accelerated Arctic warming.
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1 If appropriate, estimate likelihood of impact or consequence, including short description of 

2 basis of estimate

3 x Greater than 9 in 10 / Very Likely 

4 ☐ Greater than 2 in 3 / Likely

5 ☐ About 1 in 2 / As Likely as Not

6 ☐ Less than 1 in 3 / Unlikely 

7 ☐ Less than 1 in 10 / Very Unlikely

8 It is very likely that the accelerated rate of Arctic warming will have a significant consequence 

9 for the United States due to accelerated land and sea ice melt driving changes in the ocean 

10 including sea level rise threatening our coastal communities and freshening of sea water that is 

11 influencing marine ecology and potentially altering the thermohaline circulation. 

12 Summary sentence or paragraph that integrates the above information

13 It is very likely that surface and air temperatures across Alaska and the Arctic have warmed and 

14 will continue to warm faster than the rest of the globe. This Key Finding is supported by a large 

15 amount of observational and modeling evidence documented in the climate science peer16 reviewed literature. Similar to statements have been made previously international and United 

17 States based assessments of Arctic climate change (ACIA 2005; AMAP 2011; IPCC AR5 2013; 

18 NCA3). The primary key uncertainty is our inability to provide high quality regional trends of 

19 warming in the Arctic due to the lack of high quality surface temperature measurements in 

20 isolated land regions and over the Central Arctic Ocean. Accelerated Arctic warming impacts 

21 and threatens our coastal community and marine ecosystems.

22

23 Key Finding 2

24 Rising Alaskan permafrost temperatures are causing permafrost to thaw and become more 

25 discontinuous; this releases additional CO2 and CH4 resulting in additional warming (high 

26 confidence). The overall magnitude of the permafrost-carbon feedback is uncertain.

27 Description of evidence base

28 The Key Finding is supported by observational evidence of warming permafrost temperatures, a 

deepening active layer, laboratory incubation experiments of CO2 and CH4 29 release, and model 

30 studies published in the peer reviewed climate science literature (Vaughan et al. 2013; Fisher et 

31 al. 2014; Schuur et al. 2015; Koven et al. 2015a,b; Liljedahl et al. 2016). This evidence is 

32 documented in the chapter text.

33 Major Uncertainties

34 A major limiting factor is the sparse observations of permafrost in Alaska and remote areas 

35 across the Arctic. Major uncertainties are related to deep soil, ice wedging, and thermokarst 

36 processes. Uncertainties also exist in relevant soil processes during and after permafrost thaw, 

37 especially those that control unfrozen soil carbon storage and plant carbon uptake and net 
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1 ecosystem exchange. Many processes with the potential to drive rapid permafrost thaw (such as 

2 thermokarst) are not included in current earth system models. 

3 Assessment of confidence based on evidence and agreement, including short description of 

4 nature of evidence and level of agreement

5 ☐ Very High 

6 x High

7 ☐ Medium 

8 ☐ Low 

9 There is high confidence that permafrost is thawing, becoming discontinuous, releasing CO2 and 

CH4 10 . Physically based argument indicated that the feedback is positive. This confidence level is 

11 justified based on observations of rapidly changing permafrost characteristics.

12 If appropriate, estimate likelihood of impact or consequence, including short description of 

13 basis of estimate

14 ☐ Greater than 9 in 10 / Very Likely 

15 x Greater than 2 in 3 / Likely

16 ☐ About 1 in 2 / As Likely as Not

17 ☐ Less than 1 in 3 / Unlikely 

18 ☐ Less than 1 in 10 / Very Unlikely

19 Thawing permafrost has significant impacts to the global carbon cycle and serves as a source of 

20 CO2 and CH4 emission. Additionally, thawing permafrost will significantly impact Arctic 

21 infrastructures as crumbling buildings, roads, and bridges are being observed.

22 Summary sentence or paragraph that integrates the above information

Permafrost is thawing, becoming more discontinuous, and releasing CO2 and CH4 23 . Observational 

24 and modeling evidence documented in the peer-reviewed climate literature indicates that the sign 

25 of the permafrost feedbacks is (high confidence). Although, the magnitude of the permafrost26 carbon feedback is uncertain. A number of major uncertainties exist including deep soil and ice 

27 wedge processes, plant carbon uptake, and the role of rapid permafrost thaw processes, such as 

28 thermokarst. Progress is hindered by the lack of accurate data in remote Arctic regions. Impacts 

29 of permafrost thaw are likely significant for both the physical climate system and ecosystem 

30 services.

31

32 Key Finding 3

33 Arctic sea ice and Greenland Ice Sheet mass loss are accelerating and Alaskan mountain glaciers 

34 continue to melt (very high confidence). Alaskan coastal sea ice loss rates exceed the Arctic 

35 average (very high confidence). Observed sea and land ice loss across the Arctic is occurring 

36 faster than climate models predict (very high confidence). Melting trends are expected to
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1 continue resulting in late summers becoming nearly ice-free for the Arctic ocean by mid-century 

2 (very high confidence). 

3 Description of evidence base 

4 The Key Finding is supported by observational evidence from multiple ground-based, satellite 

5 observational techniques (passive microwave, laser and radar altimetry, and gravimetry) 

6 documented in the climate science literature (Vaughan et al. 2013; Comiso and Hall 2014; 

7 Stroeve et al. 2014a; Zemp et al. 2015; Harig and Simons 2016). Multiple sources and 

8 independent analysis techniques discussed in the chapter text support the Key Finding. Similar 

9 statements have been made previously in NCA3 (Melillo et al. 2014) and IPCC AR5 (Vaughan 

10 et al. 2013).

11 Major uncertainties 

12 Key uncertainties remain in the quantification and modeling of key physical processes that 

13 contribute to the acceleration of land and sea ice melting. Climate models are unable to capture 

14 the rapid pace of observed sea and land ice melt over the last 15 years; a major factor is our 

15 inability to quantify and accurately model the physical processes driving the accelerated melting. 

16 The interactions between atmospheric circulation, ice dynamics and thermodynamics, clouds, 

17 and specifically the influence on the surface energy budget are key uncertainties. Mechanisms 

18 controlling marine-terminating glacier dynamics, specifically the roles of atmospheric warming, 

19 seawater intrusions under floating ice shelves, and the penetration of surface meltwater to the 

20 glacier bed, are key uncertainties in projecting Greenland Ice Sheet melt. 

21 Assessment of confidence based on evidence and agreement, including short description of 

22 nature of evidence and level of agreement 

23 x Very High 

24 ☐ High

25 ☐ Medium 

26 ☐ Low 

27 There is very high confidence that Arctic sea and land ice melt is accelerating given the multiple 

28 observational sources and analysis technique documented in the peer reviewed climate science 

29 literature.

30 If appropriate, estimate likelihood of impact or consequence, including short description of 

31 basis of estimate

32 x Greater than 9 in 10 / Very Likely 

33 ☐ Greater than 2 in 3 / Likely

34 ☐ About 1 in 2 / As Likely as Not

35 ☐ Less than 1 in 3 / Unlikely 

36 ☐ Less than 1 in 10 / Very Unlikely

37 It is very likely that accelerating Arctic land and sea melt impacts the United States. Accelerating 

38 Arctic Ocean sea ice melt is affecting coastal erosion in Alaska and important Alaskan fisheries, 
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1 by changing Arctic Ocean chemistry and the vulnerability to ocean acidification in the region. 

2 Greenland Ice Sheet and Alaska mountain glacier melt drives sea level rise threatening coastal 

3 communities in the United State and worldwide, influencing marine ecology, and potentially 

4 altering the thermohaline circulation.

5 Summary sentence or paragraph that integrates the above information

6 Ice melting across the Arctic continues steadily continues and in some cases is accelerating (very 

7 high confidence). The Key Finding is supported by observational evidence from multiple data 

8 sources and independent analysis techniques documented in the climate science literature. 

9 Similar statements have been made previously in international and United States based climate 

10 science assessments (Vaughan et al. 2013; Melillo et al. 2014). Key uncertainties remain in the 

11 contributions of the sea and land ice melting to individual physical mechanisms and the inability 

12 to accurately model the rate of melting. Accelerating Arctic sea and land ice melt impacts our 

13 coastal communities and marine ecosystems, especially in Alaska.

14

15 Key Finding 4

16 Human activities have contributed to rising surface temperature, sea ice loss since 1979, and 

17 glacier mass loss observed across the Arctic. (High confidence).

18 Description of evidence base

19 The Key Finding is supported by many attribution studies including a wide array of climate 

20 models documented in the climate science literature (Gillett et al. 2008; Bindoff et al. 2013; 

21 Christensen et al. 2013; Najafi et al. 2015). Multiple independent analysis techniques and studies 

22 discussed in the chapter text support the Key Finding. 

23 Major uncertainties

24 A major limiting factor in our ability to attribute Arctic sea ice and glacier melt to human 

25 activities to the significant natural climate variability in the Arctic. Longer data records and a 

26 better understanding of the physical mechanisms that drive natural climate variability in the 

27 Arctic are required to reduce this uncertainty. Another major uncertainty is the ability of climate 

28 models to capture the relevant physical processes and climate changes at a fine regional scale.

29 Assessment of confidence based on evidence and agreement, including short description of 

30 nature of evidence and level of agreement

31 x Very High 

32 ☐ High

33 ☐ Medium 

34 ☐ Low 

35 There is very high confidence that human activities have contributed to Arctic sea ice and 

36 melting glaciers given the multiple independent studies documented in the peer reviewed climate 

37 science literature and discussed in the chapter text.
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1 If appropriate, estimate likelihood of impact or consequence, including short description of 

2 basis of estimate

3 x Greater than 9 in 10 / Very Likely 

4 ☐ Greater than 2 in 3 / Likely

5 ☐ About 1 in 2 / As Likely as Not

6 ☐ Less than 1 in 3 / Unlikely 

7 ☐ Less than 1 in 10 / Very Unlikely

8 Arctic sea ice and glacier mass loss impacts the United States by affecting coastal erosion in 

9 Alaska and key Alaskan fisheries through an increased vulnerability to ocean acidification. 

10 Glacier mass loss is a significant driver of sea level rise threatening coastal communities in the 

11 United States and worldwide, influencing marine ecology, and potentially altering the 

12 thermohaline circulation.

13 Summary sentence or paragraph that integrates the above information

14 It is very likely that human activities have contributed to Arctic sea ice and glacier mass loss in 

15 recent years. The Key Finding is supported by an array of independent analysis techniques using 

16 different climate models documented in the climate science literature. Key uncertainties remain 

17 in attribution studies including understanding and characteristics of Arctic climate system natural 

18 variability and the modeling of the Arctic climate system. Arctic sea ice and glacier melt 

19 influence U.S. coastal communities and marine ecosystems, especially in Alaska.

20

21 Key Finding 5

22 Atmospheric circulation patterns connect the climates of the Arctic and the United States. The 

23 mid-latitude circulation influences Arctic climate change (medium to high confidence). In turn, 

24 current evidence suggests that Arctic warming is influencing mid-latitude circulation over the 

25 continental United States and affecting weather patterns, but the mechanisms are not well 

26 understood (low to medium confidence).

27

28 Description of evidence base

29 The Key Finding addresses recent observations of coherent changes in Arctic climate and 

30 midlatitude circulation. While we have a very good understanding of the impact of midlatitude 

31 circulation on the Arctic climate (Rigor et al. 2002; Graverson et al. 2006; Screen et al. 2012; 

32 Perlwitz et al. 2015), the research on the impact of Arctic climate on midlatitude circulation is 

33 rapidly evolving, including observational analysis and modeling studies with several review 

34 papers available (Cohen et al. 2014; Barnes and Screen 2015; Vihma 2014). An assessment of 

35 current literature is provided in the text.

36 Major uncertainties 

37 A major limiting factor is our understanding and modeling of natural climate variability in the 

38 Arctic. Longer data records and a better understanding of the physical mechanisms that drive 
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1 natural climate variability in the Arctic are required to reduce this uncertainty. The inability of 

2 climate models to accurately capture interactions between sea ice and the atmospheric circulation 

3 and polar stratospheric processes limits our current understanding.

4 Assessment of confidence based on evidence and agreement, including short description of 

5 nature of evidence and level of agreement 

6 ☐Very High 

7 ☐ High

8 ☐ Medium 

9 x Low 

10 Low confidence on the detection of an impact of Arctic warming on midlatitude climate is based 

11 on short observational data record, model uncertainty, and lack of physical understanding.

12 Summary sentence or paragraph that integrates the above information

13 Research on linkages between Arctic and lower latitudes is rapidly evolving with progress 

14 limited by short observational data record and model uncertainty.

15
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1 FIGURES

2

3

4 Figure 11.1: September sea ice extent (all gray-scale colors) and age shown for (a) 1984 and (b) 

5 2016, illustrating significant reductions in sea ice extent and age (thickness). Bar graph is the 

6 lower right of each panel illustrates the sea ice area covered within each age category. (Figure 

7 source: NASA Science Visualization Studio (http://svs.gsfc.nasa.gov/cgi8 bin/details.cgi?aid=4489); data: Tschudi et al. 2016)

9
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1

2 Figure 11.2: 35-year trends in Arctic sea ice melt season length, in days per decade, from 

3 passive microwave satellite observations, illustrating that the sea ice season has shortened by 

4 more than 60 days in coastal Alaska over the last 30 years. (Figure source: adapted from 

5 Parkinson 2014).

6
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1

2

3 Figure 11.3: Time series of annual mean permafrost temperatures (units: °F) at various depths 

4 from 12 to 20 meters (39 to 65 feet) from 1977 through 2015 at several sites across Alaska 

5 including the North Slope continuous permafrost region and the discontinuous permafrost in 

6 Alaska and northwestern Canada. Solid lines represent the linear trends drawn to highlight that 

7 permafrost temperatures are warming faster in the colder, coastal permafrost regions than the 

8 warmer interior regions. (Figure Source: adapted from Romanovsky et al. 2016; © American 

9 Meteorological Society, used with permission)

10
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1

Figure 11.4: Time series of the cumulative climatic mass balance (units: kg/m

2 2 ) in five Arctic 

3 regions and cumulative for the Pan-Arctic from the World Glacier Monitoring Service (WGMS 

4 2016; Wolken et al. 2016; solid lines) and Alaskan glacial mass loss observed from NASA 

5 GRACE (Harig and Simons 2016; dashed blue line). (Figure source: Harig and Simons 2016 and 

6 Wolken et al. 2016; © American Meteorological Society, used with permission)

7
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1 REFERENCES

2 ACIA, 2005: Arctic Climate Impact Assessment. 1042 pp. ACIA Secretariat and Cooperative 

3 Institute for Arctic Research. http://www.acia.uaf.edu/pages/scientific.html

4 AMAP, 2011: Snow, Water, Ice and Permafrost in the Arctic (SWIPA): Climate Change and the 

5 Cryosphere. 538 pp., Oslo, Norway. http://www.amap.no/documents/download/1448

6 Andresen, C.S., F. Straneo, M.H. Ribergaard, A.A. Bjork, T.J. Andersen, A. Kuijpers, N. 

7 Norgaard-Pedersen, K.H. Kjaer, F. Schjoth, K. Weckstrom, and A.P. Ahlstrom, 2012: Rapid 

8 response of Helheim Glacier in Greenland to climate variability over the past century. Nature 

9 Geoscience, 5, 37-41. http://dx.doi.org/10.1038/ngeo1349 

10 Archer, D., 2007: Methane hydrate stability and anthropogenic climate change. Biogeosciences,

11 4, 521-544. http://dx.doi.org/10.5194/bg-4-521-2007 

12 Arrigo, K.R., G. van Dijken, and S. Pabi, 2008: Impact of a shrinking Arctic ice cover on marine 

13 primary production. Geophysical Research Letters, 35, L19603. 

14 http://dx.doi.org/10.1029/2008GL035028 

15 Ayarzagüena, B. and J.A. Screen, 2016: Future Arctic sea ice loss reduces severity of cold air 

16 outbreaks in midlatitudes. Geophysical Research Letters, 43, 2801-2809. 

17 http://dx.doi.org/10.1002/2016GL068092 

18 Barnes, E.A. and L.M. Polvani, 2015: CMIP5 Projections of Arctic Amplification, of the North 

19 American/North Atlantic Circulation, and of Their Relationship. Journal of Climate, 28,

20 5254-5271. http://dx.doi.org/10.1175/JCLI-D-14-00589.1 

21 Barnes, E.A. and J.A. Screen, 2015: The impact of Arctic warming on the midlatitude jet-stream: 

22 Can it? Has it? Will it? Wiley Interdisciplinary Reviews: Climate Change, 6, 277-286. 

23 http://dx.doi.org/10.1002/wcc.337 

24 Bartholomew, I.D., P. Nienow, A. Sole, D. Mair, T. Cowton, M.A. King, and S. Palmer, 2011: 

25 Seasonal variations in Greenland Ice Sheet motion: Inland extent and behaviour at higher 

26 elevations. Earth and Planetary Science Letters, 307, 271-278. 

27 http://dx.doi.org/10.1016/j.epsl.2011.04.014 

28 Bates, N.R., R. Garley, K.E. Frey, K.L. Shake, and J.T. Mathis, 2014: Sea-ice melt 

29 CO<sub>2</sub>–carbonate chemistry in the western Arctic Ocean: meltwater contributions 

30 to air–sea CO<sub>2</sub> gas exchange, mixed-layer properties and rates of net 

31 community production under sea ice. Biogeosciences, 11, 6769-6789. 

32 http://dx.doi.org/10.5194/bg-11-6769-2014 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 11

394

1 Bindoff, N.L., P.A. Stott, K.M. AchutaRao, M.R. Allen, N. Gillett, D. Gutzler, K. Hansingo, G. 

2 Hegerl, Y. Hu, S. Jain, I.I. Mokhov, J. Overland, J. Perlwitz, R. Sebbari, and X. Zhang, 2013: 

3 Detection and Attribution of Climate Change: from Global to Regional. Climate Change 

4 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment 

5 Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. 

6 Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, 

7 Eds. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 

8 867–952. http://dx.doi.org/10.1017/CBO9781107415324.022 www.climatechange2013.org

9 Boisvert, L.N., T. Markus, and T. Vihma, 2013: Moisture flux changes and trends for the entire 

10 Arctic in 2003–2011 derived from EOS Aqua data. Journal of Geophysical Research: 

11 Oceans, 118, 5829-5843. http://dx.doi.org/10.1002/jgrc.20414 

12 Boisvert, L.N., D.L. Wu, and C.L. Shie, 2015: Increasing evaporation amounts seen in the Arctic 

13 between 2003 and 2013 from AIRS data. Journal of Geophysical Research: Atmospheres,

14 120, 6865-6881. http://dx.doi.org/10.1002/2015JD023258 

15 Boisvert, L.N., D.L. Wu, T. Vihma, and J. Susskind, 2015: Verification of air/surface humidity 

16 differences from AIRS and ERA-Interim in support of turbulent flux estimation in the Arctic. 

17 Journal of Geophysical Research: Atmospheres, 120, 945-963. 

18 http://dx.doi.org/10.1002/2014JD021666 

19 Bollmann, M., T. Bosch, F. Colijn, R. Ebinghaus, R. Froese, K. Güssow, S. Khalilian, S. Krastel, 

20 A. Körtzinger, M. Langenbuch, M. Latif, B. Matthiessen, F. Melzner, A. Oschlies, S. 

21 Petersen, A. Proelß, M. Quaas, J. Reichenbach, T. Requate, T. Reusch, P. Rosenstiel, J.O. 

22 Schmidt, K. Schrottke, H. Sichelschmidt, U. Siebert, R. Soltwedel, U. Sommer, K. 

23 Stattegger, H. Sterr, R. Sturm, T. Treude, A. Vafeidis, C.v. Bernem, J.v. Beusekom, R. Voss, 

24 M. Visbeck, M. Wahl, K. Wallmann, and F. Weinberger, 2010: World ocean review: Living 

25 with the oceans. maribus gGmbH.

26 Bourassa, M.A., S.T. Gille, C. Bitz, D. Carlson, I. Cerovecki, C.A. Clayson, M.F. Cronin, W.M. 

27 Drennan, C.W. Fairall, R.N. Hoffman, G. Magnusdottir, R.T. Pinker, I.A. Renfrew, M. 

28 Serreze, K. Speer, L.D. Talley, and G.A. Wick, 2013: High-Latitude Ocean and Sea Ice 

29 Surface Fluxes: Challenges for Climate Research. Bulletin of the American Meteorological 

30 Society, 94, 403-423. http://dx.doi.org/10.1175/BAMS-D-11-00244.1 

31 Brown, R.D. and D.A. Robinson, 2011: Northern Hemisphere spring snow cover variability and 

32 change over 1922–2010 including an assessment of uncertainty. The Cryosphere, 5, 219-229. 

33 http://dx.doi.org/10.5194/tc-5-219-2011 

34 Cai, W.-J., L. Chen, B. Chen, Z. Gao, S.H. Lee, J. Chen, D. Pierrot, K. Sullivan, Y. Wang, X. 

35 Hu, W.-J. Huang, Y. Zhang, S. Xu, A. Murata, J.M. Grebmeier, E.P. Jones, and H. Zhang, 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 11

395

1 2010: Decrease in the CO<sub>2</sub> Uptake Capacity in an Ice-Free Arctic Ocean Basin. 

2 Science, 329, 556-559. http://dx.doi.org/10.1126/science.1189338 

3 Carmack, E., I. Polyakov, L. Padman, I. Fer, E. Hunke, J. Hutchings, J. Jackson, D. Kelley, R. 

4 Kwok, C. Layton, H. Melling, D. Perovich, O. Persson, B. Ruddick, M.-L. Timmermans, J. 

5 Toole, T. Ross, S. Vavrus, and P. Winsor, 2015: Toward Quantifying the Increasing Role of 

6 Oceanic Heat in Sea Ice Loss in the New Arctic. Bulletin of the American Meteorological 

7 Society, 96, 2079-2105. http://dx.doi.org/10.1175/BAMS-D-13-00177.1 

8 Christensen, J.H., K. Krishna Kumar, E. Aldrian, S.-I. An, I.F.A. Cavalcanti, M. de Castro, W. 

9 Dong, P. Goswami, A. Hall, J.K. Kanyanga, A. Kitoh, J. Kossin, N.-C. Lau, J. Renwick, D.B. 

10 Stephenson, S.-P. Xie, and T. Zhou, 2013: Climate Phenomena and their Relevance for 

11 Future Regional Climate Change. Climate Change 2013: The Physical Science Basis. 

12 Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental 

13 Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. 

14 Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. Cambridge University Press, 

15 Cambridge, United Kingdom and New York, NY, USA, 1217–1308. 

16 http://dx.doi.org/10.1017/CBO9781107415324.028 www.climatechange2013.org

17 Church, J.A., P.U. Clark, A. Cazenave, J.M. Gregory, S. Jevrejeva, A. Levermann, M.A. 

18 Merrifield, G.A. Milne, R.S. Nerem, P.D. Nunn, A.J. Payne, W.T. Pfeffer, D. Stammer, and 

19 A.S. Unnikrishnan, 2013: Sea Level Change. Climate Change 2013: The Physical Science 

20 Basis. Contribution of Working Group I to the Fifth Assessment Report of the 

21 Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. 

22 Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. 

23 Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1137–

24 1216. http://dx.doi.org/10.1017/CBO9781107415324.026 www.climatechange2013.org

25 Cohen, J., J. Jones, J.C. Furtado, and E. Tzipermam, 2013: Warm Arctic, cold continents: A 

26 common pattern related to Arctic sea ice melt, snow advance, and extreme winter weather. . 

27 Oceanography, 26, 150-160. http://dx.doi.org/10.5670/oceanog.2013.70 

28 http://tos.org/oceanography/assets/docs/26-4_cohen.pdf

29 Cohen, J., J.A. Screen, J.C. Furtado, M. Barlow, D. Whittleston, D. Coumou, J. Francis, K. 

30 Dethloff, D. Entekhabi, J. Overland, and J. Jones, 2014: Recent Arctic amplification and 

31 extreme mid-latitude weather. Nature Geoscience, 7, 627-637. 

32 http://dx.doi.org/10.1038/ngeo2234 

33 Collins, M., R. Knutti, J. Arblaster, J.-L. Dufresne, T. Fichefet, P. Friedlingstein, X. Gao, W.J. 

34 Gutowski, T. Johns, G. Krinner, M. Shongwe, C. Tebaldi, A.J. Weaver, and M. Wehner, 

35 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. Climate 

36 Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth 

37 Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 11

396

1 Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and 

2 P.M. Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom and New 

3 York, NY, USA, 1029–1136. http://dx.doi.org/10.1017/CBO9781107415324.024 

4 www.climatechange2013.org

5 Comiso, J.C. and D.K. Hall, 2014: Climate trends in the Arctic as observed from space. Wiley 

6 Interdisciplinary Reviews: Climate Change, 5, 389-409. http://dx.doi.org/10.1002/wcc.277 

7 Derksen, C. and R. Brown, 2012: Snow [in Arctic Report Card 2012]. 

8 http://www.arctic.noaa.gov/report12/snow.html

9 Derksen, C., R. Brown, L. Mudryk, and K. Luojus, 2015: Terrestrial snow cover [in Arctic 

10 Report Card 2015]. http://www.arctic.noaa.gov/reportcard/snow_cover.html

11 Ding, Q., J.M. Wallace, D.S. Battisti, E.J. Steig, A.J.E. Gallant, H.-J. Kim, and L. Geng, 2014: 

12 Tropical forcing of the recent rapid Arctic warming in northeastern Canada and Greenland. 

13 Nature, 509, 209-212. http://dx.doi.org/10.1038/nature13260 

14 Döscher, R., T. Vihma, and E. Maksimovich, 2014: Recent advances in understanding the Arctic 

15 climate system state and change from a sea ice perspective: a review. Atmospheric Chemistry 

16 and Physics, 14, 13571-13600. http://dx.doi.org/10.5194/acp-14-13571-2014 

17 Dupont, T.K. and R.B. Alley, 2005: Assessment of the importance of ice-shelf buttressing to ice18 sheet flow. Geophysical Research Letters, 32, n/a-n/a. 

19 http://dx.doi.org/10.1029/2004GL022024 

20 Fisher, J.B., M. Sikka, W.C. Oechel, D.N. Huntzinger, J.R. Melton, C.D. Koven, A. Ahlström, 

21 M.A. Arain, I. Baker, J.M. Chen, P. Ciais, C. Davidson, M. Dietze, B. El-Masri, D. Hayes, C. 

22 Huntingford, A.K. Jain, P.E. Levy, M.R. Lomas, B. Poulter, D. Price, A.K. Sahoo, K. 

23 Schaefer, H. Tian, E. Tomelleri, H. Verbeeck, N. Viovy, R. Wania, N. Zeng, and C.E. Miller, 

24 2014: Carbon cycle uncertainty in the Alaskan Arctic. Biogeosciences, 11, 4271-4288. 

25 http://dx.doi.org/10.5194/bg-11-4271-2014 http://www.biogeosciences.net/11/4271/2014/

26 Flannigan, M., B. Stocks, M. Turetsky, and M. Wotton, 2009: Impacts of climate change on fire 

27 activity and fire management in the circumboreal forest. Global Change Biology, 15, 549-

28 560. http://dx.doi.org/10.1111/j.1365-2486.2008.01660.x 

29 Francis, J. and N. Skific, 2015: Evidence linking rapid Arctic warming to mid-latitude weather 

30 patterns. Philosophical Transactions of the Royal Society A: Mathematical, Physical and 

31 Engineering Sciences, 373. http://dx.doi.org/10.1098/rsta.2014.0170 

32 Francis, J.A. and S.J. Vavrus, 2012: Evidence linking Arctic amplification to extreme weather in 

33 mid-latitudes. Geophysical Research Letters, 39, L06801. 

34 http://dx.doi.org/10.1029/2012GL051000 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 11

397

1 Francis, J.A. and S.J. Vavrus, 2015: Evidence for a wavier jet stream in response to rapid Arctic 

2 warming. Environmental Research Letters, 10, 014005. http://dx.doi.org/10.1088/1748-

3 9326/10/1/014005 

4 Francis, J.A., S.J. Vavrus, and J. Cohen, 2016: Amplified Arctic Warming and Mid-Latitude 

5 Weather: Emerging ConnectionsWiREs. WIRES, Submitted. 

6 French, N.H.F., L.K. Jenkins, T.V. Loboda, M. Flannigan, R. Jandt, L.L. Bourgeau-Chavez, and 

7 M. Whitley, 2015: Fire in arctic tundra of Alaska: past fire activity, future fire potential, and 

8 significance for land management and ecology. International Journal of Wildland Fire, 24,

9 1045-1061. http://dx.doi.org/10.1071/WF14167 

10 http://www.publish.csiro.au/paper/WF14167

11 Friedlingstein, P., P. Cox, R. Betts, L. Bopp, W.v. Bloh, V. Brovkin, P. Cadule, S. Doney, M. 

12 Eby, I. Fung, G. Bala, J. John, C. Jones, F. Joos, T. Kato, M. Kawamiya, W. Knorr, K. 

13 Lindsay, H.D. Matthews, T. Raddatz, P. Rayner, C. Reick, E. Roeckner, K.-G. Schnitzler, R. 

14 Schnur, K. Strassmann, A.J. Weaver, C. Yoshikawa, and N. Zeng, 2006: Climate–Carbon 

15 Cycle Feedback Analysis: Results from the C4MIP Model Intercomparison. Journal of 

16 Climate, 19, 3337-3353. http://dx.doi.org/10.1175/JCLI3800.1 

17 Gagné, M.È., N.P. Gillett, and J.C. Fyfe, 2015: Impact of aerosol emission controls on future 

18 Arctic sea ice cover. Geophysical Research Letters, 42, 8481-8488. 

19 http://dx.doi.org/10.1002/2015GL065504 

20 Gibbs, A.E. and B.M. Richmond, 2010: National assessment of shoreline change: Historical 

21 shoreline change along the north coast of Alaska, U.S.–Canadian border to Icy Cape. U.S. 

22 Geological Survey Open-File Report 2015–1048. 96 pp. U.S. Department of the Interior, 

23 U.S. Geological Survey. http://pubs.usgs.gov/of/2015/1048/pdf/ofr2015-1048.pdf

24 Giles, K.A., S.W. Laxon, A.L. Ridout, D.J. Wingham, and S. Bacon, 2012: Western Arctic 

25 Ocean freshwater storage increased by wind-driven spin-up of the Beaufort Gyre. Nature 

26 Geoscience, 5, 194-197. http://dx.doi.org/10.1038/ngeo1379 

27 Gillett, N.P., D.A. Stone, P.A. Stott, T. Nozawa, A.Y. Karpechko, G.C. Hegerl, M.F. Wehner, 

28 and P.D. Jones, 2008: Attribution of polar warming to human influence. Nature Geoscience,

29 1, 750-754. http://dx.doi.org/10.1038/ngeo338 

30 Graversen, R.G., 2006: Do Changes in the Midlatitude Circulation Have Any Impact on the 

31 Arctic Surface Air Temperature Trend? Journal of Climate, 19, 5422-5438. 

32 http://dx.doi.org/10.1175/JCLI3906.1 

33 Harig, C. and F.J. Simons, 2016: Ice mass loss in Greenland, the Gulf of Alaska, and the 

34 Canadian Archipelago: Seasonal cycles and decadal trends. Geophysical Research Letters,

35 43, 3150-3159. http://dx.doi.org/10.1002/2016GL067759 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 11

398

1 Hartmann, B. and G. Wendler, 2005: The Significance of the 1976 Pacific Climate Shift in the 

2 Climatology of Alaska. Journal of Climate, 18, 4824-4839. 

3 http://dx.doi.org/10.1175/JCLI3532.1 

4 Hartmann, D.L., A.M.G. Klein Tank, M. Rusticucci, L.V. Alexander, S. Brönnimann, Y. 

5 Charabi, F.J. Dentener, E.J. Dlugokencky, D.R. Easterling, A. Kaplan, B.J. Soden, P.W. 

6 Thorne, M. Wild, and P.M. Zhai, 2013: Observations: Atmosphere and Surface. Climate 

7 Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth 

8 Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. 

9 Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and 

10 P.M. Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom and New 

11 York, NY, USA, 159–254. http://dx.doi.org/10.1017/CBO9781107415324.008 

12 www.climatechange2013.org

13 Holland, D.M., R.H. Thomas, B. de Young, M.H. Ribergaard, and B. Lyberth, 2008: 

14 Acceleration of Jakobshavn Isbrae triggered by warm subsurface ocean waters. Nature 

15 Geoscience, 1, 659-664. http://dx.doi.org/10.1038/ngeo316 

16 Hoskins, B. and T. Woollings, 2015: Persistent Extratropical Regimes and Climate Extremes. 

17 Current Climate Change Reports, 1, 115-124. http://dx.doi.org/10.1007/s40641-015-0020-8 

18 Howat, I.M., I. Joughin, M. Fahnestock, B.E. Smith, and T.A. Scambos, 2008: Synchronous 

19 retreat and acceleration of southeast Greenland outlet glaciers 2000–06: ice dynamics 

20 and coupling to climate. Journal of Glaciology, 54, 646-660. 

21 http://dx.doi.org/10.3189/002214308786570908 

22 Hu, F.S., P.E. Higuera, P. Duffy, M.L. Chipman, A.V. Rocha, A.M. Young, R. Kelly, and M.C. 

23 Dietze, 2015: Arctic tundra fires: natural variability and responses to climate change. 

24 Frontiers in Ecology and the Environment, 13, 369-377. http://dx.doi.org/10.1890/150063 

25 Hunt, G.L., Jr., K.O. Coyle, L.B. Eisner, E.V. Farley, R.A. Heintz, F. Mueter, J.M. Napp, J.E. 

26 Overland, P.H. Ressler, S. Salo, and P.J. Stabeno, 2011: Climate impacts on eastern Bering 

27 Sea foodwebs: A synthesis of new data and an assessment of the Oscillating Control 

28 Hypothesis. ICES Journal of Marine Science, 68, 1230-1243. 

29 http://dx.doi.org/10.1093/icesjms/fsr036 

30 Hunter, S.J., D.S. Goldobin, A.M. Haywood, A. Ridgwell, and J.G. Rees, 2013: Sensitivity of 

31 the global submarine hydrate inventory to scenarios of future climate change. Earth and 

32 Planetary Science Letters, 367, 105-115. http://dx.doi.org/10.1016/j.epsl.2013.02.017 

33 IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group 

34 I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 11

399

1 Cambridge University Press, Cambridge, UK and New York, NY, 1535 pp. 

2 http://dx.doi.org/10.1017/CBO9781107415324 www.climatechange2013.org

3 Johannessen, O.M., A. Korablev, V. Miles, M.W. Miles, and K.E. Solberg, 2011: Interaction 

4 Between the Warm Subsurface Atlantic Water in the Sermilik Fjord and Helheim Glacier in 

5 Southeast Greenland. Surveys in Geophysics, 32, 387-396. http://dx.doi.org/10.1007/s10712-

6 011-9130-6 

7 Johannessen, O.M., S.I. Kuzmina, L.P. Bobylev, and M.W. Miles, 2016: Surface air temperature 

8 variability and trends in the Arctic: New amplification assessment and regionalisation. Tellus 

9 A, 68. http://dx.doi.org/10.3402/tellusa.v68.28234 

10 Joly, K., P.A. Duffy, and T.S. Rupp, 2012: Simulating the effects of climate change on fire 

11 regimes in Arctic biomes: implications for caribou and moose habitat. Ecosphere, 3, 1-18. 

12 http://dx.doi.org/10.1890/ES12-00012.1 

13 Joughin, I., S.B. Das, M.A. King, B.E. Smith, I.M. Howat, and T. Moon, 2008: Seasonal 

14 Speedup Along the Western Flank of the Greenland Ice Sheet. Science, 320, 781-783. 

15 http://dx.doi.org/10.1126/science.1153288 

16 Jungclaus, J.H., K. Lohmann, and D. Zanchettin, 2014: Enhanced 20th-century heat transfer to 

17 the Arctic simulated in the context of climate variations over the last millennium. Climate of 

18 the Past, 10, 2201-2213. http://dx.doi.org/10.5194/cp-10-2201-2014 

19 Kasischke, E.S. and M.R. Turetsky, 2006: Recent changes in the fire regime across the North 

20 American boreal region—Spatial and temporal patterns of burning across Canada and 

21 Alaska. Geophysical Research Letters, 33, n/a-n/a. http://dx.doi.org/10.1029/2006GL025677 

22 Kay, J.E. and A. Gettelman, 2009: Cloud influence on and response to seasonal Arctic sea ice 

23 loss. Journal of Geophysical Research, 114, D18204. 

24 http://dx.doi.org/10.1029/2009JD011773 

25 Kay, J.E., K. Raeder, A. Gettelman, and J. Anderson, 2011: The Boundary Layer Response to 

26 Recent Arctic Sea Ice Loss and Implications for High-Latitude Climate Feedbacks. Journal 

27 of Climate, 24, 428-447. http://dx.doi.org/10.1175/2010JCLI3651.1 

28 Kelly, R., M.L. Chipman, P.E. Higuera, I. Stefanova, L.B. Brubaker, and F.S. Hu, 2013: Recent 

29 burning of boreal forests exceeds fire regime limits of the past 10,000 years. Proceedings of 

30 the National Academy of Sciences, 110, 13055-13060. 

31 http://dx.doi.org/10.1073/pnas.1305069110 

32 Kelly, R., H. Genet, A.D. McGuire, and F.S. Hu, 2016: Palaeodata-informed modelling of large 

33 carbon losses from recent burning of boreal forests. Nature Climate Change, 6, 79-82. 

34 http://dx.doi.org/10.1038/nclimate2832 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 11

400

1 Khan, S.A., K.H. Kjaer, M. Bevis, J.L. Bamber, J. Wahr, K.K. Kjeldsen, A.A. Bjork, N.J. 

2 Korsgaard, L.A. Stearns, M.R. van den Broeke, L. Liu, N.K. Larsen, and I.S. Muresan, 2014: 

3 Sustained mass loss of the northeast Greenland ice sheet triggered by regional warming. 

4 Nature Climate Change, 4, 292-299. http://dx.doi.org/10.1038/nclimate2161 

5 Knies, J., P. Cabedo-Sanz, S.T. Belt, S. Baranwal, S. Fietz, and A. Rosell-Melé, 2014: The 

6 emergence of modern sea ice cover in the Arctic Ocean. Nature Communications, 5, 5608. 

7 http://dx.doi.org/10.1038/ncomms6608 

8 Köhl, A. and N. Serra, 2014: Causes of Decadal Changes of the Freshwater Content in the Arctic 

9 Ocean. Journal of Climate, 27, 3461-3475. http://dx.doi.org/10.1175/JCLI-D-13-00389.1 

10 Koven, C.D., D.M. Lawrence, and W.J. Riley, 2015: Permafrost carbon−climate feedback is 

11 sensitive to deep soil carbon decomposability but not deep soil nitrogen dynamics. 

12 Proceedings of the National Academy of Sciences, 112, 3752-3757. 

13 http://dx.doi.org/10.1073/pnas.1415123112 

14 Koven, C.D., E.A.G. Schuur, C. Schädel, T.J. Bohn, E.J. Burke, G. Chen, X. Chen, P. Ciais, G. 

15 Grosse, J.W. Harden, D.J. Hayes, G. Hugelius, E.E. Jafarov, G. Krinner, P. Kuhry, D.M. 

16 Lawrence, A.H. MacDougall, S.S. Marchenko, A.D. McGuire, S.M. Natali, D.J. Nicolsky, D. 

17 Olefeldt, S. Peng, V.E. Romanovsky, K.M. Schaefer, J. Strauss, C.C. Treat, and M. Turetsky, 

18 2015: A simplified, data-constrained approach to estimate the permafrost carbon–climate 

19 feedback. Philosophical Transactions of the Royal Society A: Mathematical, Physical and 

20 Engineering Sciences, 373. http://dx.doi.org/10.1098/rsta.2014.0423 

21 Kretschmer, K., A. Biastoch, L. Rüpke, and E. Burwicz, 2015: Modeling the fate of methane 

22 hydrates under global warming. Global Biogeochemical Cycles, 29, 610-625. 

23 http://dx.doi.org/10.1002/2014GB005011 

24 Kwok, R. and N. Untersteiner, 2011: The thinning of Arctic sea ice. Physics Today, 64, 36-41. 

25 http://dx.doi.org/10.1063/1.3580491 

26 Lee, S., 2014: A theory for polar amplification from a general circulation perspective. Asia27 Pacific Journal of Atmospheric Sciences, 50, 31-43. http://dx.doi.org/10.1007/s13143-014-

28 0024-7 

29 Lee, S., T. Gong, N. Johnson, S.B. Feldstein, and D. Pollard, 2011: On the Possible Link 

30 between Tropical Convection and the Northern Hemisphere Arctic Surface Air Temperature 

31 Change between 1958 and 2001. Journal of Climate, 24, 4350-4367. 

32 http://dx.doi.org/10.1175/2011JCLI4003.1 

33 Liljedahl, A.K., J. Boike, R.P. Daanen, A.N. Fedorov, G.V. Frost, G. Grosse, L.D. Hinzman, Y.

34 Iijma, J.C. Jorgenson, N. Matveyeva, M. Necsoiu, M.K. Raynolds, V.E. Romanovsky, J. 

35 Schulla, K.D. Tape, D.A. Walker, C.J. Wilson, H. Yabuki, and D. Zona, 2016: Pan-Arctic 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 11

401

1 ice-wedge degradation in warming permafrost and its influence on tundra hydrology. Nature 

2 Geoscience, 9, 312-318. http://dx.doi.org/10.1038/ngeo2674 

3 Lim, Y.-K., D.S. Siegfried, M.J.N. Sophie, N.L. Jae, M.M. Andrea, I.C. Richard, Z. Bin, and V. 

4 Isabella, 2016: Atmospheric summer teleconnections and Greenland Ice Sheet surface mass 

5 variations: insights from MERRA-2. Environmental Research Letters, 11, 024002. 

6 http://dx.doi.org/10.1088/1748-9326/11/2/024002 

7 Maslowski, W., J. Clement Kinney, M. Higgins, and A. Roberts, 2012: The future of Arctic sea 

8 ice. Annual Review of Earth and Planetary Sciences, 40, 625-654. 

9 http://dx.doi.org/10.1146/annurev-earth-042711-105345 

10 Maslowski, W., J. Clement Kinney, S.R. Okkonen, R. Osinski, A.F. Roberts, and W.J. Williams, 

11 2014: The Large Scale Ocean Circulation and Physical Processes Controlling Pacific-Arctic 

12 Interactions. The Pacific Arctic Region: Ecosystem Status and Trends in a Rapidly Changing 

13 Environment. Grebmeier, M.J. and W. Maslowski, Eds. Springer Netherlands, Dordrecht, 

14 101-132. http://dx.doi.org/10.1007/978-94-017-8863-2_5 

15 Mathis, J.T., J.N. Cross, W. Evans, and S.C. Doney, 2015: Ocean acidification in the surface 

16 waters of the Pacific-Arctic boundary regions. Oceanography, 28, 122-135. 

17 http://dx.doi.org/10.5670/oceanog.2015.36 http://tos.org/oceanography/assets/docs/28-

18 2_mathis2.pdf

19 Mathis, J.T., R.S. Pickart, R.H. Byrne, C.L. McNeil, G.W.K. Moore, L.W. Juranek, X. Liu, J. 

20 Ma, R.A. Easley, M.M. Elliot, J.N. Cross, S.C. Reisdorph, F. Bahr, J. Morison, T. 

21 Lichendorf, and R.A. Feely, 2012: Storm-induced upwelling of high pCO2 waters onto the 

22 continental shelf of the western Arctic Ocean and implications for carbonate mineral 

23 saturation states. Geophysical Research Letters, 39, L16703. 

24 http://dx.doi.org/10.1029/2012GL051574 

25 McAfee, S.A., 2014: Consistency and the Lack Thereof in Pacific Decadal Oscillation Impacts 

26 on North American Winter Climate. Journal of Climate, 27, 7410-7431. 

27 http://dx.doi.org/10.1175/JCLI-D-14-00143.1 

28 McCusker, K.E., J.C. Fyfe, and M. Sigmond, 2016: Twenty-five winters of unexpected Eurasian 

29 cooling unlikely due to Arctic sea-ice loss. Nature Geosci, 9, 838-842. 

30 http://dx.doi.org/10.1038/ngeo2820 

31 McGuire, A.D., L.G. Anderson, T.R. Christensen, S. Dallimore, L. Guo, D.J. Hayes, M. 

32 Heimann, T.D. Lorenson, R.W. MacDonald, and N. Roulet, 2009: Sensitivity of the carbon 

33 cycle in the Arctic to climate change. Ecological Monographs, 79, 523-555. 

34 http://dx.doi.org/10.1890/08-2025.1 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 11

402

1 Melillo, J.M., T.C. Richmond, and G.W. Yohe, eds. Climate Change Impacts in the United 

2 States: The Third National Climate Assessment. 2014, U.S. Global Change Research 

3 Program: Washington, D.C. 842. http://dx.doi.org/10.7930/J0Z31WJ2. 

4 Melillo, J.M., T.C. Richmond, and G.W. Yohe, eds. Highlights of Climate Change Impacts in the 

5 United States: The Third National Climate Assessment. 2014, U.S. Global Change Research 

6 Program: Washington, DC. 148. http://dx.doi.org/10.7930/J0H41PB6. 

7 http://nca2014.globalchange.gov/highlights

8 Mengel, M., A. Levermann, K. Frieler, A. Robinson, B. Marzeion, and R. Winkelmann, 2016: 

9 Future sea level rise constrained by observations and long-term commitment. Proceedings of 

10 the National Academy of Sciences, 113, 2597-2602. 

11 http://dx.doi.org/10.1073/pnas.1500515113 

12 Mernild, S.H., J.K. Malmros, J.C. Yde, and N.T. Knudsen, 2012: Multi-decadal marine- and 

13 land-terminating glacier recession in the Ammassalik region, southeast Greenland. The 

14 Cryosphere, 6, 625-639. http://dx.doi.org/10.5194/tc-6-625-2012 

15 Mishra, U. and W.J. Riley, 2012: Alaskan soil carbon stocks: spatial variability and dependence 

16 on environmental factors. Biogeosciences, 9, 3637-3645. http://dx.doi.org/10.5194/bg-9-

17 3637-2012 http://www.biogeosciences.net/9/3637/2012/

18 Morison, J., R. Kwok, C. Peralta-Ferriz, M. Alkire, I. Rigor, R. Andersen, and M. Steele, 2012: 

19 Changing Arctic Ocean freshwater pathways. Nature, 481, 66-70. 

20 http://dx.doi.org/10.1038/nature10705 

21 Myers-Smith, I.H., B.C. Forbes, M. Wilmking, M. Hallinger, T. Lantz, D. Blok, K.D. Tape, M. 

22 Macias-Fauria, U. Sass-Klaassen, E. Lévesque, S. Boudreau, P. Ropars, L. Hermanutz, A. 

23 Trant, L.S. Collier, S. Weijers, J. Rozema, S.A. Rayback, N.M. Schmidt, G. Schaepman24 Strub, S. Wipf, C. Rixen, C.B. Ménard, S. Venn, S. Goetz, L. Andreu-Hayles, S. Elmendorf, 

25 V. Ravolainen, J. Welker, P. Grogan, H.E. Epstein, and D.S. Hik, 2011: Shrub expansion in 

26 tundra ecosystems: dynamics, impacts and research priorities. Environmental Research 

27 Letters, 6, 045509. http://dx.doi.org/10.1088/1748-9326/6/4/045509 

28 http://stacks.iop.org/1748-9326/6/i=4/a=045509

29 Najafi, M.R., F.W. Zwiers, and N.P. Gillett, 2015: Attribution of Arctic temperature change to 

30 greenhouse-gas and aerosol influences. Nature Climate Change, 5, 246-249. 

31 http://dx.doi.org/10.1038/nclimate2524 

32 Nummelin, A., M. Ilicak, C. Li, and L.H. Smedsrud, 2016: Consequences of future increased 

33 Arctic runoff on Arctic Ocean stratification, circulation, and sea ice cover. Journal of 

34 Geophysical Research: Oceans, 121, 617-637. http://dx.doi.org/10.1002/2015JC011156 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 11

403

1 Ogi, M. and I.G. Rigor, 2013: Trends in Arctic sea ice and the role of atmospheric circulation. 

2 Atmospheric Science Letters, 14, 97-101. http://dx.doi.org/10.1002/asl2.423 

3 Ogi, M. and J.M. Wallace, 2007: Summer minimum Arctic sea ice extent and the associated 

4 summer atmospheric circulation. Geophysical Research Letters, 34, L12705. 

5 http://dx.doi.org/10.1029/2007GL029897 

6 Overland, J., J.A. Francis, R. Hall, E. Hanna, S.-J. Kim, and T. Vihma, 2015: The Melting Arctic 

7 and Midlatitude Weather Patterns: Are They Connected? Journal of Climate, 28, 7917-7932. 

8 http://dx.doi.org/10.1175/JCLI-D-14-00822.1 

9 Overland, J., E. Hanna, I. Hanssen-Bauer, S.-J. Kim, J. Walsh, M. Wang, and U. Bhatt, 2014: Air 

10 temperature [in Arctic Report Card 2014]. 

11 http://www.arctic.noaa.gov/report14/air_temperature.html

12 Overland, J., E. Hanna, I. Hanssen-Bauer, S.-J. Kim, J. Wlash, M. Wang, and U.S. Bhatt, 2015: 

13 [The Arctic] Arctic air temperature [in “State of the Climate in 2014”]. Bulletin of the

14 American Meteorological Society, 96, S128-S129. 

15 http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1 

16 Overland, J.E. and M. Wang, 2016: Recent Extreme Arctic Temperatures are due to a Split Polar 

17 Vortex. Journal of Climate, 29, 5609-5616. http://dx.doi.org/10.1175/JCLI-D-16-0320.1 

18 Park, H.-S., S. Lee, S.-W. Son, S.B. Feldstein, and Y. Kosaka, 2015: The Impact of Poleward 

19 Moisture and Sensible Heat Flux on Arctic Winter Sea Ice Variability. Journal of Climate,

20 28, 5030-5040. http://dx.doi.org/10.1175/JCLI-D-15-0074.1 

21 Parkinson, C.L., 2014: Spatially mapped reductions in the length of the Arctic sea ice season. 

22 Geophysical Research Letters, 41, 4316-4322. http://dx.doi.org/10.1002/2014GL060434 

23 Partain Jr., J.L., S. Alden, U.S. Bhatt, P.A. Bieniek, B.R. Brettschneider, R. Lader, P.Q. Olsson, 

24 T.S. Rupp, H. Strader, R.L.T. Jr., J.E. Walsh, A.D. York, and R.H. Zieh, 2016: An 

25 assessment of the role of anthropogenic climate change in the Alaska fire season of 2015. 

26 Bulletin of the American Meteorological Society, 97, in press. 

27 http://dx.doi.org/10.1175/BAMS-D-16-0149.1 

28 Pavelsky, T.M., J. Boé, A. Hall, and E.J. Fetzer, 2011: Atmospheric inversion strength over polar 

29 oceans in winter regulated by sea ice. Climate Dynamics, 36, 945-955. 

30 http://dx.doi.org/10.1007/s00382-010-0756-8 

31 Pelto, M.S., 2015: [Global Climate] Alpine glaciers [in “State of the Climate in 2014”]. Bulletin 

32 of the American Meteorological Society, 96, S19-S20. 

33 http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 11

404

1 Perlwitz, J., M. Hoerling, and R. Dole, 2015: Arctic Tropospheric Warming: Causes and 

2 Linkages to Lower Latitudes. Journal of Climate, 28, 2154-2167. 

3 http://dx.doi.org/10.1175/JCLI-D-14-00095.1 

4 Perovich, D., W. Meier, M. Tschudi, S. Farrell, S. Gerland, and S. Hendricks, 2015: Sea ice [in 

5 Arctic Report Cart 2015]. http://www.arctic.noaa.gov/reportcard/sea_ice.html

6 Piñero, E., M. Marquardt, C. Hensen, M. Haeckel, and K. Wallmann, 2013: Estimation of the 

7 global inventory of methane hydrates in marine sediments using transfer functions. 

8 Biogeosciences, 10, 959-975. http://dx.doi.org/10.5194/bg-10-959-2013 

9 Polyakov, I.V., A.V. Pnyushkov, and L.A. Timokhov, 2012: Warming of the Intermediate 

10 Atlantic Water of the Arctic Ocean in the 2000s. Journal of Climate, 25, 8362-8370. 

11 http://dx.doi.org/10.1175/JCLI-D-12-00266.1 

12 Rahmstorf, S., J.E. Box, G. Feulner, M.E. Mann, A. Robinson, S. Rutherford, and E.J. 

13 Schaffernicht, 2015: Exceptional twentieth-century slowdown in Atlantic Ocean overturning 

14 circulation. Nature Climate Change, 5, 475-480. http://dx.doi.org/10.1038/nclimate2554 

15 Rawlins, M.A., M. Steele, M.M. Holland, J.C. Adam, J.E. Cherry, J.A. Francis, P.Y. Groisman, 

16 L.D. Hinzman, T.G. Huntington, D.L. Kane, J.S. Kimball, R. Kwok, R.B. Lammers, C.M. 

17 Lee, D.P. Lettenmaier, K.C. McDonald, E. Podest, J.W. Pundsack, B. Rudels, M.C. Serreze, 

18 A. Shiklomanov, Ø. Skagseth, T.J. Troy, C.J. Vörösmarty, M. Wensnahan, E.F. Wood, R. 

19 Woodgate, D. Yang, K. Zhang, and T. Zhang, 2010: Analysis of the Arctic System for 

20 Freshwater Cycle Intensification: Observations and Expectations. Journal of Climate, 23,

21 5715-5737. http://dx.doi.org/10.1175/2010JCLI3421.1 

22 Rhein, M., S.R. Rintoul, S. Aoki, E. Campos, D. Chambers, R.A. Feely, S. Gulev, G.C. Johnson, 

23 S.A. Josey, A. Kostianoy, C. Mauritzen, D. Roemmich, L.D. Talley, and F. Wang, 2013: 

24 Observations: Ocean. Climate Change 2013: The Physical Science Basis. Contribution of 

25 Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate 

26 Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. 

27 Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. Cambridge University Press, Cambridge, 

28 United Kingdom and New York, NY, USA, 255–316. 

29 http://dx.doi.org/10.1017/CBO9781107415324.010 www.climatechange2013.org

30 Rignot, E., M. Koppes, and I. Velicogna, 2010: Rapid submarine melting of the calving faces of 

31 West Greenland glaciers. Nature Geoscience, 3, 187-191. http://dx.doi.org/10.1038/ngeo765 

32 Rigor, I.G., J.M. Wallace, and R.L. Colony, 2002: Response of Sea Ice to the Arctic Oscillation. 

33 Journal of Climate, 15, 2648-2663. http://dx.doi.org/10.1175/1520-

34 0442(2002)015<2648:ROSITT>2.0.CO;2 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 11

405

1 Romanovsky, V.E., S.L. Smith, H.H. Christiansen, N.I. Shiklomanov, D.A. Streletskiy, D.S. 

2 Drozdov, G.V. Malkova, N.G. Oberman, A.L. Kholodov, and S.S. Marchenko, 2015: [The 

3 Arctic] Terrestrial permafrost [in “State of the Climate in 2014”]. Bulletin of the American 

4 Meteorological Society, 96, S139-S141. 

5 http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1 

6 Romanovsky, V.E., S.L. Smith, K. Isaksen, N.I. Shiklomanov, D.A. Streletskiy, A.L. Kholodov, 

7 H.H. Christiansen, D.S. Drozdov, G.V. Malkova, and S.S. Marchenko, 2016: [The Arctic] 

8 Terrestrial permafrost [in “State of the Climate in 2015”]. Bulletin of the American 

9 Meteorological Society, 97, S149-S152. 

10 http://dx.doi.org/10.1175/2016BAMSStateoftheClimate.1 

11 Ruppel, C.D., 2011: Methane Hydrates and Contemporary Climate Change. Nature Education 

12 Knowledge, 3. 

13 Sanford, T., R. Wang, and A. Kenwa, 2015: The age of Alaskan wildfires. Climate Central, 

14 Princeton, NJ.

15 Schadel, C., M.K.F. Bader, E.A.G. Schuur, C. Biasi, R. Bracho, P. Capek, S. De Baets, K. 

16 Diakova, J. Ernakovich, C. Estop-Aragones, D.E. Graham, I.P. Hartley, C.M. Iversen, E. 

17 Kane, C. Knoblauch, M. Lupascu, P.J. Martikainen, S.M. Natali, R.J. Norby, J.A. 

18 O/'Donnell, T.R. Chowdhury, H. Santruckova, G. Shaver, V.L. Sloan, C.C. Treat, M.R. 

19 Turetsky, M.P. Waldrop, and K.P. Wickland, 2016: Potential carbon emissions dominated by 

20 carbon dioxide from thawed permafrost soils. Nature Climate Change, 6, 950-953. 

21 http://dx.doi.org/10.1038/nclimate3054 

22 Schaefer, K., H. Lantuit, E.R. Vladimir, E.A.G. Schuur, and R. Witt, 2014: The impact of the 

23 permafrost carbon feedback on global climate. Environmental Research Letters, 9, 085003. 

24 http://dx.doi.org/10.1088/1748-9326/9/8/085003 

25 Schuur, E.A.G., A.D. McGuire, C. Schadel, G. Grosse, J.W. Harden, D.J. Hayes, G. Hugelius, 

26 C.D. Koven, P. Kuhry, D.M. Lawrence, S.M. Natali, D. Olefeldt, V.E. Romanovsky, K. 

27 Schaefer, M.R. Turetsky, C.C. Treat, and J.E. Vonk, 2015: Climate change and the 

28 permafrost carbon feedback. Nature, 520, 171-179. http://dx.doi.org/10.1038/nature14338 

29 Screen, J.A., C. Deser, and I. Simmonds, 2012: Local and remote controls on observed Arctic 

30 warming. Geophysical Research Letters, 39, L10709. 

31 http://dx.doi.org/10.1029/2012GL051598 

32 Screen, J.A., C. Deser, and L. Sun, 2015: Reduced Risk of North American Cold Extremes due 

33 to Continued Arctic Sea Ice Loss. Bulletin of the American Meteorological Society, 96, 1489-

34 1503. http://dx.doi.org/10.1175/BAMS-D-14-00185.1 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 11

406

1 Screen, J.A., C. Deser, and L. Sun, 2015: Projected changes in regional climate extremes arising 

2 from Arctic sea ice loss. Environmental Research Letters, 10, 084006. 

3 http://dx.doi.org/10.1088/1748-9326/10/8/084006 

4 Screen, J.A. and J.A. Francis, 2016: Contribution of sea-ice loss to Arctic amplification is 

5 regulated by Pacific Ocean decadal variability. Nature Climate Change, 6, 856-860. 

6 http://dx.doi.org/10.1038/nclimate3011 

7 Sharp, M., G. Wolken, D. Burgess, J.G. Cogley, L. Copland, L. Thomson, A. Arendt, B. 

8 Wouters, J. Kohler, L.M. Andreassen, S. O’Neel, and M. Pelto, 2015: [Global Climate] 

9 Glaciers and ice caps outside Greenland [in “State of the Climate in 2014”]. Bulletin of the 

10 American Meteorological Society, 96, S135-S137. 

11 http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1 

12 Shiklomanov, N.E., D.A. Streletskiy, and F.E. Nelson. Northern Hemisphere component of the 

13 global Circumpolar Active Layer Monitory (CALM) program. in Proceedings of the 10th 

14 International Conference on Permafrost. 2012. Salekhard, Russia.

15 Sigmond, M. and J.C. Fyfe, 2016: Tropical Pacific impacts on cooling North American winters. 

16 Nature Climate Change, advance online publication. 

17 http://dx.doi.org/10.1038/nclimate3069 

18 Smeed, D.A., G.D. McCarthy, S.A. Cunningham, E. Frajka-Williams, D. Rayner, W.E. Johns, 

19 C.S. Meinen, M.O. Baringer, B.I. Moat, A. Duchez, and H.L. Bryden, 2014: Observed 

20 decline of the Atlantic meridional overturning circulation 2004–2012. Ocean Science,

21 10, 29-38. http://dx.doi.org/10.5194/os-10-29-2014 

22 Snape, T.J. and P.M. Forster, 2014: Decline of Arctic sea ice: Evaluation and weighting of 

23 CMIP5 projections. Journal of Geophysical Research: Atmospheres, 119, 546-554. 

24 http://dx.doi.org/10.1002/2013JD020593 

25 Solomon, A., M.D. Shupe, O. Persson, H. Morrison, T. Yamaguchi, P.M. Caldwell, and G.d. 

26 Boer, 2014: The Sensitivity of Springtime Arctic Mixed-Phase Stratocumulus Clouds to 

27 Surface-Layer and Cloud-Top Inversion-Layer Moisture Sources. Journal of the Atmospheric 

28 Sciences, 71, 574-595. http://dx.doi.org/10.1175/JAS-D-13-0179.1 

29 Spielhagen, R.F., K. Werner, S.A. Sørensen, K. Zamelczyk, E. Kandiano, G. Budeus, K. Husum, 

30 T.M. Marchitto, and M. Hald, 2011: Enhanced Modern Heat Transfer to the Arctic by Warm 

31 Atlantic Water. Science, 331, 450-453. http://dx.doi.org/10.1126/science.1197397 

32 Stabeno, P.J., E.V. Farley, Jr., N.B. Kachel, S. Moore, C.W. Mordy, J.M. Napp, J.E. Overland, 

33 A.I. Pinchuk, and M.F. Sigler, 2012: A comparison of the physics of the northern and 

34 southern shelves of the eastern Bering Sea and some implications for the ecosystem. Deep 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 11

407

1 Sea Research Part II: Topical Studies in Oceanography, 65-70, 14-30. 

2 http://dx.doi.org/10.1016/j.dsr2.2012.02.019 

3 Straneo, F., R.G. Curry, D.A. Sutherland, G.S. Hamilton, C. Cenedese, K. Vage, and L.A. 

4 Stearns, 2011: Impact of fjord dynamics and glacial runoff on the circulation near Helheim 

5 Glacier. Nature Geoscience, 4, 322-327. http://dx.doi.org/10.1038/ngeo1109 

6 Straneo, F., G.S. Hamilton, D.A. Sutherland, L.A. Stearns, F. Davidson, M.O. Hammill, G.B. 

7 Stenson, and A. Rosing-Asvid, 2010: Rapid circulation of warm subtropical waters in a 

8 major glacial fjord in East Greenland. Nature Geoscience, 3, 182-186. 

9 http://dx.doi.org/10.1038/ngeo764 

10 Stroeve, J., A. Barrett, M. Serreze, and A. Schweiger, 2014: Using records from submarine, 

11 aircraft and satellites to evaluate climate model simulations of Arctic sea ice thickness. The 

12 Cryosphere, 8, 1839-1854. http://dx.doi.org/10.5194/tc-8-1839-2014 

13 Stroeve, J. and D. Notz, 2015: Insights on past and future sea-ice evolution from combining 

14 observations and models. Global and Planetary Change, 135, 119-132. 

15 http://dx.doi.org/10.1016/j.gloplacha.2015.10.011 

16 Stroeve, J.C., V. Kattsov, A. Barrett, M. Serreze, T. Pavlova, M. Holland, and W.N. Meier, 

17 2012: Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations. Geophysical 

18 Research Letters, 39, L16502. http://dx.doi.org/10.1029/2012GL052676 

19 Stroeve, J.C., T. Markus, L. Boisvert, J. Miller, and A. Barrett, 2014: Changes in Arctic melt 

20 season and implications for sea ice loss. Geophysical Research Letters, 41, 1216-1225. 

21 http://dx.doi.org/10.1002/2013GL058951 

22 Stroeve, J.C., M.C. Serreze, M.M. Holland, J.E. Kay, J. Malanik, and A.P. Barrett, 2012: The 

23 Arctic’s rapidly shrinking sea ice cover: A research synthesis. Climatic Change, 110, 1005-

24 1027. http://dx.doi.org/10.1007/s10584-011-0101-1 

25 Sun, L., J. Perlwitz, and M. Hoerling, 2016: What caused the recent “Warm Arctic, Cold 

26 Continents” trend pattern in winter temperatures? Geophysical Research Letters, 43, 5345-

27 5352. http://dx.doi.org/10.1002/2016GL069024 

28 Swart, N.C., J.C. Fyfe, E. Hawkins, J.E. Kay, and A. Jahn, 2015: Influence of internal variability 

29 on Arctic sea-ice trends. Nature Climate Change, 5, 86-89. 

30 http://dx.doi.org/10.1038/nclimate2483 

31 Taylor, P.C., M. Cai, A. Hu, J. Meehl, W. Washington, and G.J. Zhang, 2013: A Decomposition 

32 of Feedback Contributions to Polar Warming Amplification. Journal of Climate, 26, 7023-

33 7043. http://dx.doi.org/10.1175/JCLI-D-12-00696.1 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 11

408

1 Taylor, P.C., S. Kato, K.-M. Xu, and M. Cai, 2015: Covariance between Arctic sea ice and 

2 clouds within atmospheric state regimes at the satellite footprint level. Journal of 

3 Geophysical Research: Atmospheres, 120, 12656-12678. 

4 http://dx.doi.org/10.1002/2015JD023520 

5 Tedesco, M., T. Mote, X. Fettweis, E. Hanna, J. Jeyaratnam, J.F. Booth, R. Datta, and K. Briggs, 

6 2016: Arctic cut-off high drives the poleward shift of a new Greenland melting record. 

7 Nature Communications, 7, 11723. http://dx.doi.org/10.1038/ncomms11723 

8 Timmermans, M.-L. and A. Proshutinsky, 2015: [The Arctic] Sea surface temperature [in “State 

9 of the Climate in 2014”]. Bulletin of the American Meteorological Society, 96, S147-S148. 

10 http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1 

11 Tschudi, M., C. Fowler, J. Maslanik, J.S. Stewart, and W. Meier, 2016: EASE-Grid Sea Ice Age, 

12 Version 3. In: NASA (ed.). National Snow and Ice Data Center Distributed Active Archive 

13 Center, Boulder, CO.

14 van den Broeke, M., J. Bamber, J. Ettema, E. Rignot, E. Schrama, W.J. van de Berg, E. van 

15 Meijgaard, I. Velicogna, and B. Wouters, 2009: Partitioning Recent Greenland Mass Loss. 

16 Science, 326, 984-986. http://dx.doi.org/10.1126/science.1178176 

17 Vaughan, D.G., J.C. Comiso, I. Allison, J. Carrasco, G. Kaser, R. Kwok, P. Mote, T. Murray, F. 

18 Paul, J. Ren, E. Rignot, O. Solomina, K. Steffen, and T. Zhang, 2013: Observations: 

19 Cryosphere. Climate Change 2013: The Physical Science Basis. Contribution of Working 

20 Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. 

21 Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, 

22 V. Bex, and P.M. Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom 

23 and New York, NY, USA, 317–382. http://dx.doi.org/10.1017/CBO9781107415324.012 

24 www.climatechange2013.org

25 Velicogna, I., 2009: Increasing rates of ice mass loss from the Greenland and Antarctic ice sheets 

26 revealed by GRACE. Geophysical Research Letters, 36, L19503. 

27 http://dx.doi.org/10.1029/2009GL040222 

28 Vihma, T., 2014: Effects of Arctic Sea Ice Decline on Weather and Climate: A Review. Surveys 

29 in Geophysics, 35, 1175-1214. http://dx.doi.org/10.1007/s10712-014-9284-0 

30 Wendler, G., B. Moore, and K. Galloway, 2014: Strong Temperature Increase and Shrinking Sea 

31 Ice in Arctic Alaska. The Open Atmospheric Science Journal, 8, 7-15. 

32 http://dx.doi.org/10.2174/1874282301408010007 

33 http://benthamopen.com/contents/pdf/TOASCJ/TOASCJ-8-7.pdf
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 11

409

1 Wettstein, J.J. and C. Deser, 2014: Internal Variability in Projections of Twenty-First-Century 

2 Arctic Sea Ice Loss: Role of the Large-Scale Atmospheric Circulation. Journal of Climate,

3 27, 527-550. http://dx.doi.org/10.1175/JCLI-D-12-00839.1 

4 WGMS, 2016: Fluctuations of Glaciers Database. World Glacier Monitoring Service, Zurich, 

5 Switzerland.

6 Wolken, G., M. Sharp, L.M. Andreassen, A. Arendt, D. Burgess, J.G. Cogley, L. Copland, J. 

7 Kohler, S. O’Neel, M. Pelto, L. Thomson, and B. Wouters, 2016: [The Arctic] Glaciers and 

8 ice caps outside Greenland [in “State of the Climate in 2015”]. Bulletin of the American 

9 Meteorological Society, 97, S142-S145. 

10 http://dx.doi.org/10.1175/2016BAMSStateoftheClimate.1 

11 Wyser, K., C.G. Jones, P. Du, E. Girard, U. Willén, J. Cassano, J.H. Christensen, J.A. Curry, K. 

12 Dethloff, J.-E. Haugen, D. Jacob, M. Køltzow, R. Laprise, A. Lynch, S. Pfeifer, A. Rinke, M. 

13 Serreze, M.J. Shaw, M. Tjernström, and M. Zagar, 2008: An evaluation of Arctic cloud and 

14 radiation processes during the SHEBA year: simulation results from eight Arctic regional 

15 climate models. Climate Dynamics, 30, 203-223. http://dx.doi.org/10.1007/s00382-007-

16 0286-1 

17 Yang, Q., T.H. Dixon, P.G. Myers, J. Bonin, D. Chambers, and M.R. van den Broeke, 2016: 

18 Recent increases in Arctic freshwater flux affects Labrador Sea convection and Atlantic 

19 overturning circulation. Nature Communications, 7, 10525. 

20 http://dx.doi.org/10.1038/ncomms10525 

21 Young, A.M., P.E. Higuera, P.A. Duffy, and F.S. Hu, 2016: Climatic thresholds shape northern 

22 high-latitude fire regimes and imply vulnerability to future climate change. Ecography, n/a23 n/a. http://dx.doi.org/10.1111/ecog.02205 

24 Zemp, M., H. Frey, I. Gärtner-Roer, S.U. Nussbaumer, M. Hoelzle, F. Paul, W. Haeberli, F. 

25 Denzinger, A.P. Ahlstrøm, B. Anderson, S. Bajracharya, C. Baroni, L.N. Braun, B.E. 

26 Cáceres, G. Casassa, G. Cobos, L.R. Dávila, H. Delgado Granados, M.N. Demuth, L. 

27 Espizua, A. Fischer, K. Fujita, B. Gadek, A. Ghazanfar, J.O. Hagen, P. Holmlund, N. Karimi, 

28 Z. Li, M. Pelto, P. Pitte, V.V. Popovnin, C.A. Portocarrero, R. Prinz, C.V. Sangewar, I. 

29 Severskiy, O. Sigurðsson, A. Soruco, R. Usubaliev, and C. Vincent, 2015: Historically 

30 unprecedented global glacier decline in the early 21st century. Journal of Glaciology, 61,

31 745-762. http://dx.doi.org/10.3189/2015JoG15J017 

32 Zhang, R. and T.R. Knutson, 2013: The role of global climate change in the extreme low 

33 summer Arctic sea ice extent in 2012 [in "Explaining Extremes of 2012 from a Climate 

34 Perspective"]. Bulletin of the American Meteorological Society, 94, S23-S26. 

35 http://dx.doi.org/10.1175/BAMS-D-13-00085.1 
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1 Zona, D., B. Gioli, R. Commane, J. Lindaas, S.C. Wofsy, C.E. Miller, S.J. Dinardo, S. Dengel, 

2 C. Sweeney, A. Karion, R.Y.-W. Chang, J.M. Henderson, P.C. Murphy, J.P. Goodrich, V. 

3 Moreaux, A. Liljedahl, J.D. Watts, J.S. Kimball, D.A. Lipson, and W.C. Oechel, 2016: Cold 

4 season emissions dominate the Arctic tundra methane budget. Proceedings of the National 

5 Academy of Sciences, 113, 40-45. http://dx.doi.org/10.1073/pnas.1516017113
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1 12. Sea Level Rise

2 KEY FINDINGS

3 1. Global mean sea level (GMSL) has risen by about 8–9 inches (about 20-23 cm) since 1880, 

4 with about 3 of those inches (about 7 cm) occurring since 1990 (very high confidence). 

5 Human-caused climate change has made a substantial contribution to GMSL rise since 1900 

6 (high confidence), contributing to a rate of rise faster than during any comparable period 

7 since at least 800 BCE (medium confidence). 

8 2. Relative to the year 2000, GMSL is very likely to rise by 0.3–0.6 feet (9-18 cm) by 2030, 

9 0.5–1.2 feet (15-38 cm) by 2050, and 1 to 4 feet (30-130 cm) by 2100 (very high confidence 

10 in lower bounds; medium confidence in upper bounds for 2030 and 2050; low confidence in 

11 upper bounds for 2100). Emissions pathways have little effect on projected GMSL rise in the 

12 first half of the century, but significantly affect projections for the second half of the century 

13 (high confidence). Emerging science regarding ice sheet stability suggests that, for high 

14 emissions, a GMSL rise exceeding 8 feet (2.4 m) by 2100 cannot be ruled out. 

15 3. Relative sea level (RSL) rise in this century will vary along U.S. coastlines due, in part, to: 

16 changes in Earth’s gravitational field and rotation from melting of land ice, changes in ocean 

17 circulation, and vertical land motion (very high confidence). For almost all future GMSL rise 

18 scenarios, RSL rise is likely to be greater than the global average in the U.S. Northeast and 

19 the western Gulf of Mexico. In intermediate and low GMSL rise scenarios, it is likely to be 

20 less than the global average in much of the Pacific Northwest and Alaska. For high GMSL 

21 rise scenarios, it is likely to be higher than the global average along all U.S. coastlines outside 

22 Alaska (high confidence). 

23 4. The annual occurrences of daily tidal flooding—exceeding local thresholds for minor 

24 impacts to infrastructure—have increased 5- to 10-fold since the 1960s in several U.S. 

25 coastal cities (very high confidence). Rates of increase, which are accelerating in over 25 

26 Atlantic and Gulf Coast cities, are fastest where elevation thresholds are lower, local RSL 

27 rise is higher, or extreme variability is less (very high confidence). Tidal flooding will 

28 continue increasing in depth and frequency in similar manners this century (very high 

29 confidence). 

30 5. The projected increase in the intensity of hurricanes in the North Atlantic could increase the 

31 probability of extreme coastal flooding along the U.S. Atlantic and Gulf Coasts beyond what 

32 would be projected based solely on RSL rise. However, there is low confidence in the 

33 magnitude of the increase in intensity and the associated flood risk amplification, and it could 

34 be offset or amplified by other factors, such as changes in hurricane frequency or tracks.
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1 12.1 Introduction

2 Sea level rise is closely linked to increasing global temperatures. Thus, even as uncertainties 

3 remain about just how much sea level may rise this century, it is virtually certain that sea level 

4 rise this century and beyond will pose a growing challenge to coastal communities, 

5 infrastructure, and ecosystems, both through more frequent and extreme coastal flooding and 

6 more subtle coastal geomorphological changes associated with increases in mean sea level. 

7 Assessment of change requires consideration of physical causes, historical evidence, and 

8 projections. Vulnerability and risk-based perspectives point to the need for emphasis on how 

9 changing sea levels alter the coastal zone and interact with coastal flood risk at local scales. 

10 12.2 Physical Factors Contributing to Sea Level Rise

11 Sea level change is driven by a variety of mechanisms operating at different spatial and temporal 

12 scales. Global mean sea level (GMSL) rise is primarily driven by two factors: 1) increased 

13 volume from thermal expansion of the ocean as it warms, and 2) increased mass from melt 

14 additions of ice locked in mountain glaciers and the Antarctic and Greenland ice sheets. Satellite 

15 (altimeter and GRACE) and in situ (Argo) measurements show that, since 2005, about one third 

16 of GMSL rise has been from steric changes (thermal expansion) and about two-thirds from the 

17 addition of mass to the ocean, primarily from melting land-based ice (Llovel et al. 2014; 

18 Leuliette 2015; Merrifield et al. 2015; Chambers et al. 2016). The overall amount (mass) of 

19 ocean water, and thus sea level, is also affected to a lesser extent by changes in global land water 

20 storage associated with dams and reservoirs, groundwater extraction, and global precipitation 

21 anomalies (Reager et al. 2016; Rietbroek et al. 2016; Wada et al. 2016), such as associated with 

22 the El Niño–Southern Oscillation (ENSO). 

23 Sea level and its changes are not uniform globally, for several reasons. First, atmosphere–ocean 

24 dynamics—driven by ocean circulation, winds, and other factors—are associated with 

25 differences in the height of the sea surface, as are differences in density arising from the 

26 distribution of heat and salinity in the ocean. Changes in any of these factors will affect sea27 surface height. For example, a weakening of the Gulf Stream may have contributed to enhanced 

28 sea level rise in the ocean environment extending to the northeastern U.S. coast, a trend that 

29 many models project will continue into the future (Yin and Goddard 2013). 

30 Second, the location of land ice melting imparts distinct regional “static-equilibrium 

31 fingerprints” on sea level, based on gravitational, rotational, and crustal deformation effects 

32 (Mitrovica et al. 2011) (Figure 12.1a–d). For example, sea level falls near a melting ice sheet 

33 because of the resulting changes in the distribution of mass on the planet and thus in the planet’s 

34 gravitational field. 

35 Third, the Earth’s mantle is still moving in response to the loss of the great North American 

36 (Laurentide) and European ice sheets of the Last Glacial Maximum; the associated changes in 

37 the height of the land, the shape of the ocean basin, and the Earth’s gravitational field give rise to 
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1 glacial-isostatic adjustment (Figure 12.1e). For example, underneath the cores of the great ice 

2 sheets of the Last Glacial Maximum, such as in Hudson Bay and in Scandinavia, post-glacial 

3 rebound of the land is causing relative sea levels (RSL) to fall. Slightly farther away from the 

4 cores, such as along most of the east coast of the United States, subsidence of the bulge that 

5 flanked the ice sheet is causing RSL to rise. Finally, a variety of other factors can cause local 

6 vertical land movement; these include natural sediment compaction, compaction caused by local 

7 extraction of groundwater and fossil fuels, and tectonics (Zervas et al. 2013; Kopp et al. 2014, 

8 Wöppelmann and Marcos 2016) (Figure 12.1f).

9 Compared to many climate variables, the trend signal for sea level change tends to be large 

10 relative to natural variability. However, at interannual timescales, changes in ocean dynamics, 

11 density, and wind can cause substantial sea level variability in some regions. For example, there 

12 has been a multidecadal suppression of sea level rise off the Pacific coast (Bromirski et al. 2011) 

13 and large year-to-year variations in sea level along the Northeast U.S. coast (Goddard et al. 

14 2015). Local land height changes have also varied dramatically on decadal timescales in some 

15 locations, such as along the western Gulf Coast, where rates of subsurface extraction of fossil 

16 fuels and groundwater have varied over time (Galloway et al. 1999). 

17 [INSERT FIGURE 12.1 HERE:

18 Figure 12.1: (a–d) Static-equilibrium fingerprints of the RSL effect of land ice melt, in units of 

19 feet of RSL change per feet of GMSL change, for mass loss from (a) Greenland, (b) West 

20 Antarctica, (c) East Antarctica, and (d) the median projected combination of melting glaciers, 

21 after Kopp et al. (2014, 2015). (e) Model projections of the rate of RSL rise due to glacial22 isostatic adjustment (units of feet.century), after Kopp et al. (2015). (f) Tide gauge-based 

23 estimates of the non-climatic, long-term contribution to RSL rise, including the effects of glacial 

24 isostatic adjustment, tectonics, and sediment compaction (Kopp et al. 2014) [Figure source: (a)–

25 (d) Kopp et al. (2015), (e) adapted from Kopp et al. (2015); (f) adapted from Sweet et al. (In 

26 Review)]

27 12.3 Paleo Sea Level

28 Geological records of temperature and sea level indicate that, during past warm periods, GMSL 

29 was higher than it is today (Dutton et al. 2015). During the Last Interglacial stage, about 125,000 

30 years ago, global mean temperature was about 1°C (1.8°F) above the preindustrial level (that is, 

31 comparable to current temperatures), and polar temperatures were comparable to those projected 

32 for 1°C –2°C (1.8°F –3.6°F) of global mean warming above the preindustrial level. At this time, 

33 GMSL was about 6–9 meters (about 20–30 feet) higher than today (Dutton and Lambeck 2012; 

34 Kopp et al. 2009). This geological benchmark may indicate the probable long-term response of 

35 GMSL to the minimum magnitude of temperature change projected for the current century. 

36 Geologists can reconstruct rates of GMSL change over the last few millennia using proxies such 

37 as the heights of fossil coral reefs and the different populations of salinity-sensitive microfossils 
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1 within salt marsh sediments (Shennan et al. 2015). Since the disappearance of the last remnants 

2 of the North American (Laurentide) Ice Sheet about 7,000 years ago (Carlson et al. 2008), 

3 GMSL has been relatively stable, with total GMSL rise since 7,000 years ago estimated at about 

4 4 meters (about 13 feet), most of which occurred between 7,000 and 4,000 years ago (Lambeck 

5 et al. 2014). The Third National Climate Assessment (NCA3) noted, based on a geological data 

6 set from North Carolina (Kemp et al. 2011), that the 20th century GMSL rise was much faster 

7 than at any time over the past 2,000 years. Since NCA3, high-resolution sea level reconstructions 

8 have been developed for multiple locations, and a new global analysis of such reconstructions 

9 strengthens this finding (Kopp et al. 2016). Over the last 2,000 years, prior to the industrial era, 

10 GMSL exhibited small fluctuations of about ±8 cm (3 inches), with a significant decline of about 

11 8 cm (3 inches) between the years 1000 and 1400 CE coinciding with about 0.2°C (0.4°F) of 

12 global mean cooling (Kopp et al. 2016). The rate of rise in the last century, about 14 cm/century 

13 (5.5 inches/century), was faster than during any century since at least 800 BCE (Kopp et al. 

14 2016; Figure 12.2a).

15 [INSERT FIGURE 12.2 HERE:

16 Figure 12.2: (a) GMSL rise from −500 to 1900 CE, from Kopp et al. (2016)’s geological and tide 

17 gauge-based reconstruction [blue], from 1900 to 2010 from Hay et al. (2015)’s tide gauge-based 

18 reconstruction [black], and from 1992 to 2015 from the satellite-based reconstruction updated 

19 from Nerem et al. (2010) [magenta]; (b) GMSL rise from 1800 to 2100, based on (a) from 1800 

20 to 2015, the six Interagency (Sweet et al., In Review) GMSL scenarios (black, blue, cyan, green, 

21 orange and red curves), the very likely ranges in 2100 for different RCPs from this chapter 

22 (colored boxes), and lines augmenting the very likely ranges by the difference between the 

23 median Antarctic contribution of Kopp et al. 2014 and the various median Antarctic projections 

24 of DeConto and Pollard (2016). (c) Rates of change from 1993 to 2014 in sea surface height 

25 from satellite altimetry data; from Kopp et al. (2015) using data updated from Church and White 

26 (2011). [Figure source: (a) adapted from Nerem et al. (2010), Kopp et al. (2016) and Hay et al. 

27 (2015), (b) adapted from Sweet et al. (In Review), (c) adapted from Kopp et al. (2015)]

28 12.4 Recent Past Trends (20th and 21st Centuries)

29 12.4.1 Global Tide Gauge Network and Satellite Observations

30 A global tide gauge network provides the century-long observations of local RSL, whereas 

31 satellite altimetry provides broader coverage of sea surface heights outside the polar regions 

32 starting in 1993. GMSL can be estimated through statistical analyses of either data set. GMSL 

33 trends over the 1901–1990 period vary slightly (Hay et al. 2015: 1.2 ± 0.2 mm/year [0.5 

34 inches/decade]; Church and White 2011: 1.5 ± 0.2 mm/year [0.6 inches/decade]) with 

35 differences amounting to about 1 inch over 90 years. Both data sets indicate similar rates since 

36 1880. Thus, these results indicate about 13-16 cm (5–6 inches) of GMSL rise from 1880 to 1990.
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1 Tide gauge analyses indicate that GMSL rose at a considerably faster rate of about 3 mm/year 

2 (1.2 inches/decade) since 1993 (Hay et al. 2015; Church and White 2011), a result supported by 

3 satellite data indicating a trend of 3.4 ± 0.4 mm/year (1.3 inches/decade) over 1993–2015 

4 (update to Nerem et al. 2010) (Figure 12.2a). These results indicate an additional GMSL rise of 

5 about 7 cm (3 inches) rise since 1990. Thus total GMSL rise since 1880 is about 20-23 cm (8–9 

6 inches). 

7 Comparison of results from a variety of approaches supports the conclusion that a substantial 

8 fraction of GMSL rise since 1900 is attributable to human-caused climate change (Kopp et al. 

9 2016; Slangen et al. 2016; Jevrejeva et al. 2009; Dangendorf et al. 2015; Becker et al. 2014; 

10 Marcos and Amores, 2014; Slangen et al. 2014; Marzeion et al. 2014; Marcos et al. 2016). 

11 After accounting for background rates inherent in RSL rise rates due to long-term factors like 

12 glacial isostatic adjustment, four notable patterns of regional sea surface height variability exist 

13 (Figure 12.2c): a slower-than-global increase off of the U.S. Pacific Coast between about 1980 

14 and 2011, with a faster-than-global rise subsequently; a faster-than-global increase in the western 

15 tropical Pacific in the 1990s and 2000s, with a slower rise in this decade; a faster-than-global 

16 increase in sea level rise in the U.S. Northeast since the 1970s; and a slower-than-global increase 

17 in the U.S. Southeast since the 1970s. 

18 The slowdown in sea level rise along the U.S. Pacific Coast and acceleration in the western 

19 tropical Pacific was associated with changes in average winds linked to the Pacific Decadal 

20 Oscillation (PDO) (Bromirski et al. 2011; Zhang and Church 2012; Merrifield 2011), and 

21 appears to have reversed since about 2012 (Hamlington et al. 2016). The acceleration in the U.S. 

22 Northeast and slowdown in the U.S. Southeast appear to be tied to changes in the Gulf Stream 

23 (Yin and Goddard 2013; Ezer 2013; Kopp 2013; Kopp et al. 2015b), although whether these 

24 changes represent natural variability or a long-term trend remains uncertain (Rahmstorf et al. 

25 2015).

26 12.4.2 Ocean Heat Uptake

27 About 93% of the global warming due to anthropogenic greenhouse emissions heating since the 

28 1970s has occurred in the ocean (Rhein et al. 2013), leading to temperature increases both at the 

29 surface and at depth. This ocean warming acts as a buffer to climate change, slowing down the 

30 rate of surface warming (e.g., Nieves et al. 2015), while also leading to ocean thermal expansion 

31 and thus contributing to sea level rise. 

Between 1970 and 2012, the upper ocean (0–700 meters; 0–2300 feet) warmed by about 0.2o 32 C 

(0.4°F), corresponding to a heat storage of 0.27 ±0.04 W/m2 33 (Watts per square meter) (Abraham 

et al. 2013). For shorter and more recent periods, ocean heat uptake varies: 0.30 ±0.04 W/m2 34

during 1980–2012 and 0.25–0.46 W/m2 35 during 1993–2012. Decadal variability in ocean heat 

36 uptake is mostly attributed to volcanic eruptions, which indirectly leads to cooling (for example, 
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1 1991 Pinatubo eruption, Fasulo et al. 2016) and to ENSO phases (with El Niños warming and La 

2 Niñas cooling) (Abraham et al. 2013). 

3 Previous estimates of the ocean uptake were confined to the upper ocean and had sparse spatial 

4 and temporal coverage. Recent studies, based on data from the Argo network of floats that 

5 extend to depths of 2000 meters (6600 feet), revise earlier estimates upwards to about 0.4–0.6 

W/m2 6 over 2006–2013, with approximately half occurring in the upper 500 m (1600 feet) and 

7 half below (Roemmich et al. 2015). This uptake explains about a third of the GMSL rise (Llovel 

8 et al. 2014, Leuliette, 2015; Chambers et al. 2016) and corresponds to a steric height increase of 

9 about 1.0 ± 0.5 mm/year (0.2 inches/decade) for the period 2005–2013. The role of the deep 

10 ocean (below 2,000 meters [3300 feet]) in ocean heat uptake remains uncertain, both in the 

11 magnitude but also the sign of the uptake (Purkey and Johnson 2010; Llovel et al. 2014). On 

12 interannual scales, ENSO dominates the signal of ocean heat uptake, and the North Atlantic is 

13 the most vigorous region for uptake, whereas on decadal timescales the Southern Ocean accounts 

14 for about 67%–80% of the uptake (Roemmich et al. 2015; Abraham et al. 2013). 

15 12.4.3 Ice Sheet Gravity and Altimetry and Visual Observations

16 Since NCA3, Antarctica and Greenland have continued to lose ice mass, with stronger evidence 

17 accumulating that mass loss is accelerating.

18 Studies using repeat gravimetry (GRACE satellites), repeat altimetry, GPS monitoring, and 

19 input-output calculations generally agree on accelerating mass loss in Antarctica (Shepherd et al. 

20 2012; Scambos and Shuman 2016; Seo et al. 2015; Martín-Español et al. 2016). Together, these 

21 indicate mass loss of roughly 100 Gt/year (gigatonnes/year) over the last decade (a contribution 

22 to GMSL of about 0.3 mm/year [0.1 inches/decade]). Positive accumulation-rate anomalies in 

23 East Antarctica, especially in Dronning Maud Land, have contributed to the trend of slight 

24 growth there (e.g., Seo et al. 2015; Martín-Español et al. 2016), but this is more than offset by 

25 mass loss elsewhere, especially in West Antarctica along the coast facing the Amundsen Sea 

26 (Sutterley et al. 2014; Mouginot et al. 2014), Totten Glacier in East Antarctica (Khazendar et al. 

27 2013; Li et al. 2015), and along the Antarctic Peninsula (Seo et al. 2015; Martín-Español et al. 

28 2016). Floating ice shelves around Antarctica are losing mass at an accelerating rate (Paolo et al. 

29 2015). Mass loss from floating ice shelves does not directly affect GMSL, but does allow faster 

30 flow of ice from the ice sheet into the ocean. 

31 Estimates of mass loss in Greenland based on mass balance from input-output, repeat 

32 gravimetry, repeat altimetry, and aerial imagery as discussed in Chapter 11 reveal a recent 

33 acceleration (Khan et al. 2014). Mass loss averaged approximately 75 Gt/year (about 0.2 

34 mm/year [0.08 inches/decade] GMSL rise) from 1900 to 1983, continuing at a similar rate of 

35 approximately 74 Gt/year through 2003 before accelerating to 186 Gt/year (about 0.5 mm/year 

36 [0.2 inches/decade] GMSL rise) from 2003 to 2010 (Kjeldsen et al. 2015). Shorter-term 

37 interannual variability exists (see Chapter 11), such as during the exceptional melt year from 
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1 April 2012 to April 2013, which produced mass loss of approximately 560 Gt (1.6 mm/year [0.6 

2 inches/decade]) (Tedesco et al. 2013). Accelerating mass loss over the record is clear and has 

3 reversed a long-term trend of slow thickening linked to continuing evolution of the ice sheet 

4 from the end of the last ice age (MacGregor et al. 2016).

5 12.5 Projected Sea Level Rise 

6 12.5.1 Scenarios of Global Mean Sea Level Rise

7 No single physical model is capable of accurately representing all of the major processes 

8 contributing to GMSL and regional/local RSL rise. Accordingly, the U.S. Interagency Sea Level 

9 Rise Task force (Sweet et al. In Review and henceforth referred to as “Interagency”) has revised 

10 the GMSL rise scenarios for the United States, and now provides six scenarios that can be used 

11 for assessment and risk-framing purposes (Figure 12.2b; Table 12.1). The low scenario of 30 cm 

12 (1 foot) GMSL rise by 2100 is consistent with a continuation of the recent approximately 3 

13 mm/year (1.2 inches/decade) rate of rise through to 2100, while the five other scenarios span a 

14 range of GMSL rise between 50 and 250 cm (1.6 and 8.2 feet) in 2100. The highest scenario of 

15 250 cm is consistent with several literature estimates of the maximum physically plausible level 

16 of 21st century sea level rise (e.g., Pfeffer et al. 2008; Sriver et al. 2012; Miller et al. 2013; Kopp

17 et al. 2014). The Interagency GMSL scenario interpretations are shown in Table 12.2. 

18 The Interagency scenario approach is similar to local RSL rise scenarios used for all coastal U.S. 

19 Department of Defense installations worldwide (Hall et al. 2016). The Interagency approach 

20 starts with a probabilistic projection framework to generate time series and regional projections 

21 consistent with each GMSL rise scenario for 2100 (Kopp et al. 2014). That framework combines 

22 probabilistic estimates of contributions to GMSL and regional RSL rise from ocean processes, 

23 cryospheric processes, geological processes, and anthropogenic land-water storage. Pooling the 

24 Kopp et al. (2014) projections across RCP2.6, 4.5, and 8.5, the probabilistic projections are 

25 filtered to identify pathways consistent with each of these 2100 levels with median (and 17th and 

26 83rd percentiles) picked from each of the filtered subsets. 

27 Table 12.1. The Interagency GMSL rise scenarios, cm (feet) relative to 2000. All values are 19-

28 year averages of GMSL centered at the identified year. To convert from a 1991–2009 tidal datum 

29 to the 1983–2001 tidal datum, add 2.4 cm (0.9 inches).

Scenario 2020 2030 2050 2100

Low 6 (0.2) 9 (0.3) 16 (0.5) 30 (1.0)

Intermediate-Low 8 (0.3) 13 (0.4) 24 (0.8) 50 (1.6)
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Intermediate 10 (0.3) 16 (0.5) 34 (1.1) 100 (3.3)

Intermediate-High 10 (0.3) 19 (0.6) 44 (1.4) 150 (4.9)

High 11 (0.4) 21 (0.7) 54 (1.8) 200 (6.6)

Extreme 11 (0.4) 24 (0.8) 63 (2.1) 250 (8.2)

1

2 Table 12.2. Interpretations of the Interagency GMSL rise scenarios

Scenario Interpretation

Low Continuing current rate of GMSL rise, as calculated since 1993

Low end of very likely range under RCP2.6

Intermediate-Low Modest increase in rate

Middle of likely range under RCP2.6

Low end of likely range under RCP4.5

Low end of very likely range under RCP8.5

Intermediate High end of very likely range under RCP4.5

High end of likely range under RCP8.5

Middle of likely range under RCP4.5 when accounting for 

possible ice cliff instabilities

Intermediate-High Slightly above high end of very likely range under RCP8.5

Middle of likely range under RCP8.5 when accounting for 

possible ice cliff instabilities

High High end of very likely range under RCP8.5 when accounting for 

possible ice cliff instabilities

Extreme Consistent with estimates of physically possible “worst case”
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1 12.5.2 Probabilities of Different Sea Level Rise Scenarios

2 Several studies have estimated the probabilities of different amounts of GMSL rise under 

3 different emissions pathways (e.g., Church et al. 2013; Kopp et al. 2014; Slangen et al. 2014; 

4 Grinsted et al. 2015; Kopp et al. 2016; Mengel et al. 2016) using a variety of methods, including 

5 both statistical and physical models. Most of these studies are in general agreement that GMSL 

6 rise by 2100 is very likely to be between about 25–80 cm (0.8-2.6 feet) under RCP2.6, 35–95 cm 

7 (1.1-3.1 feet) under RCP4.5, and 50–130 cm (1.6-4.3 feet) under RCP8.5. The probability of 

8 exceeding the amount of GMSL in 2100 under the Interagency scenarios is shown in Table 12.3.

9 However, emerging science suggests that these projections may understate the probability of 

10 faster-than-expected ice sheet melt, particularly for high-end warming scenarios. While these 

11 probability estimates are consistent with the assumption that the relationship between global 

12 temperature and GMSL in the coming century will be similar to that observed over the last two 

13 millennia (Rahmstorf 2007; Kopp et al. 2016), emerging positive feedbacks (self-amplifying 

14 cycles) in the Antarctic Ice Sheet especially (Rignot et al. 2014; Joughin et al. 2014) may 

15 invalidate that assumption. Physical feedbacks that until recently were not incorporated into ice 

16 sheet models (Pollard et al. 2015) could add about 60–110 cm (2.0–3.6 feet) to central estimates 

17 of current-century sea level rise under RCP8.5, 20–50 cm (0.7–1.6 feet) to central estimates of 

18 sea level rise under RCP4.5, and 0–10 cm (0–0.3 feet) to central estimates of sea level rise under 

19 RCP2.6 (DeConto and Pollard 2016). Examples of these interrelated processes include marine 

20 ice sheet instability, ice cliff instability, and ice shelf hydrofracturing. Processes underway in 

21 Greenland may also be leading to accelerating high-end melt risk. Much of the research has 

22 focused on changes in surface albedo driven by the melt-associated unmasking and concentration 

23 of impurities in snow and ice (Tedesco et al. 2016). However, ice dynamics at the bottom of the 

24 ice sheet may be important as well, through interactions with surface runoff or a warming ocean. 

25 As an example of the latter, Jakobshavn Isbræ, Kangerdlugssuaq Glacier, and the Northeast 

26 Greenland ice stream may be vulnerable to marine ice sheet instability (Khan et al. 2014).

27 Table 12.3. Probability of exceeding the Interagency GMSL scenarios in 2100 per Kopp et al. 

28 (2014). New evidence regarding the Antarctic ice sheet, if sustained, may significantly increase 

29 the probability of the intermediate-high, high and extreme scenarios, particularly for RCP8.5, but 

30 these results have not yet been incorporated into a probabilistic analysis.

Scenario RCP2.6 RCP4.5 RCP8.5

Low 94% 98% 100%

Intermediate-Low 49% 73% 96%
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Intermediate 2% 3% 17%

Intermediate-High 0.4% 0.5% 1.3%

High 0.1% 0.1% 0.3%

Extreme 0.05% 0.05% 0.1%

1

2 12.5.3 Sea Level Rise after 2100

3 GMSL rise will not stop in 2100, and so it is useful to consider extensions of GMSL rise 

4 scenarios beyond this point (e.g., Kopp et al. 2014). By 2200, the 0.3–2.5 meters (1.0–8.2 feet) 

5 range spanned by the six Interagency GMSL scenarios increases to 0.3–9.5 meters (1.0–31 feet) 

6 as shown in Table 12.4. Excluding the possible effects of still-emerging science regarding ice 

7 cliffs and ice shelves, it is very likely that GMSL by 2200 will rise by 1.0–3.7 meters (3.3–12 

8 feet) under RCP8.5, 0.4–2.7 meters (1.3–8.9 feet) under RCP4.5, and 0.3–2.4 meters (1.0–7.9 

9 feet) under RCP2.6 (Kopp et al. 2014).

10 Under most projections, GMSL rise will also not stop in 2200. The concept of a “sea level rise 

11 commitment” refers to the long-term projected sea level rise were the planet’s temperature to be 

12 stabilized at a given level. The paleo sea level record suggests that even 2°C (3.6°F) of global 

13 average warming above the preindustrial temperature may represent a commitment to several 

14 meters of rise, with one modeling study suggesting a 2,000-year commitment of 2.3 m/°C (4.2 

15 feet/°F) (Levermann et al. 2013). This relationship suggests that emissions through to 2100 

16 would lock in a likely 2,000-year GMSL rise commitment of about 0.7–4.2 meters (2.3–13.8 

17 feet) under RCP2.6, about 1.7–5.6 meters (5.6–18.4 feet) under RCP4.5, and about 4.3–9.9 

18 meters (14.1–32.5 feet) under RCP8.5 (Strauss et al. 2015). However, as with the 21st century 

19 projections, emerging science regarding the sensitivity of the Antarctic Ice Sheet may increase 

20 the estimated sea level rise over the next millennium, especially for high-emissions pathways 

21 (DeConto and Pollard 2016). Large-scale climate geoengineering might reduce these 

22 commitments (Irvine et al. 2009; Applegate and Keller 2015), but may not be able to avoid lock23 in of significant change (Lenton 2011; Barrett et al. 2014; Markusson et al. 2014; Sillmann et al. 

24 2015).

25
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1 Table 12.4. Post-2100 extensions of the Interagency GMSL rise scenarios in cm (feet)

Scenario 2100 2120 2150 2200

Low 30 (1.0) 34 (1.1) 37 (1.2) 39 (1.3)

Intermediate-Low 50 (1.6) 60 (2.0) 73 (2.4) 95 (3.1)

Intermediate 100 (3.3) 129 (4.2) 182 (6.0) 283 (9.3)

Intermediate-High 150 (4.9) 203 (6.7) 305 (10.0) 511 (16.8)

High 200 (6.6) 282 (9.3) 434 (14.2) 748 (24.5)

Extreme 250 (8.2) 357 (11.7) 554 (18.2) 972 (31.9)

2

3 12.5.4 Regional Projections of Sea Level Change

4 Because the different factors contributing to sea level change give rise to different spatial 

5 patterns, projecting future RSL change at specific locations requires not just an estimate of 

6 GMSL change but estimates of the different processes contributing to GMSL change—each of 

7 which has a different associated spatial pattern—as well as of the processes contributing 

8 exclusively to regional or local change. Based on the process-level projections of the Interagency 

9 scenarios, several key regional patterns are apparent in future U.S. RSL rise as shown for the 1-

10 meter (3.3 feet) Interagency scenario in 2100 (Figure 12.1 a-f, 12.3a,).

11 (1) RSL rise due to Antarctic Ice Sheet melt is greater than GMSL rise along all U.S. 

12 coastlines due to static-equilibrium effects.

13 (2) RSL rise due to Greenland Ice Sheet melt is less than GMSL rise in the continental U.S. 

14 due to static-equilibrium effects. This effect is especially strong in the Northeast.

15 (3) RSL rise is additionally augmented in the Northeast by the effects of glacial isostatic 

16 adjustment. The Northeast is also exposed to rise due to reductions in the Atlantic 

17 meridional overturning circulation. 

18 (4) The western Gulf of Mexico and parts of the U.S. Atlantic Coast south of New York are 

19 currently experiencing significant RSL rise caused by the withdrawal of groundwater 
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1 (along the Atlantic Coast) and of both fossil fuels and groundwater (along the Gulf 

2 Coast). Continuation of these practices will further amplify RSL rise.

3 (5) The proximity of the Alaskan glaciers to Alaska and the Pacific Northwest reduces 

4 regional RSL rise, due to both the ongoing glacial isostatic adjustment to past glacier 

5 shrinkage and to the static-equilibrium effects of projected future losses

6 (6) Because they are far from all glaciers and ice sheets, RSL rise in Hawai‘i and other 

7 Pacific islands due to any source of melting land ice is amplified by the static-equilibrium 

8 effects.

9 [INSERT FIGURE 12.3 HERE:

10 Figure 12.3: (a) RSL rise (feet) in 2100 under the median-local Interagency Intermediate (1-

11 meter) Scenario, (b) tidal floods (days per year) exceeding NOAA thresholds for minor impacts 

12 at NOAA tide gauges through 2015, from Sweet and Marra (2016), (c) water level heights 

13 associated with a local 5-year recurrence probability, after Sweet et al. 2014, and (d) future 

14 decade the 5-year becomes a 0.2-year event under the Interagency Intermediate scenario, after 

15 Sweet and Park (2014); black dots imply that a 5-year to 0.2-year frequency change does not 

16 unfold by 2200 under the Intermediate scenario. [Figure source: (a), (c), and (d) Sweet et al. (In 

17 Review), (b) adapted from Sweet and Marra (2016)]

18 12.6 Extreme Water Levels

19 12.6.1 Observations

20 Coastal flooding during extreme high-water events has become deeper due to local RSL rise and 

21 more frequent from a fixed-elevation perspective (Menendez and Woodworth 2010; Kemp and 

22 Horton 2013; Sweet et al. 2013; Hall et al. 2016). Trends in annual frequencies surpassing local 

23 emergency preparedness thresholds for minor tidal flooding (i.e., “nuisance” levels of about 30-

24 60 cm [1–2 feet]) that flood infrastructure and trigger coastal flood “advisories” by NOAA’s 

25 National Weather Service (NWS) have increased 5- to 10-fold or more since the 1960s along the 

26 U.S. coastline (Sweet et al. 2014), as shown in Figure 12.3b. Locations experiencing such trend 

27 changes (based upon fits of flood days per year of Sweet and Park 2014) include Atlantic City 

28 and Sandy Hook, NJ; Philadelphia, PA; Baltimore and Annapolis, MD; Norfolk, VA; 

29 Wilmington, NC; Charleston, SC; Savannah, GA; Mayport and Key West, FL; Port Isabel, TX, 

30 La Jolla, CA and Honolulu, HI. In fact, over the last several decades, minor tidal flood rates have 

31 been accelerating within several (> 25) East and Gulf Coast cities with established elevation 

32 thresholds for minor (nuisance) flood impacts, fastest where elevation thresholds are lower, local 

33 RSL rise is higher, and extreme variability less (Ezer and Atkinson 2014; Sweet et al. 2014; 

34 Sweet and Park 2014).

35 Trends in extreme water levels (for example, monthly maxima) in excess of mean sea levels (for 

36 example, monthly means) exist, but are not commonplace (Menendez and Woodworth 2010; 
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1 Talke et al. 2014; Wahl and Chambers 2015; Reed et al. 2015; Marcos et al. 2016). More 

2 common are regional time dependencies in high-water probabilities, which can co-vary on an 

3 interannual basis with climatic and other patterns (Menendez and Woodworth 2010; Grinsted et 

4 al. 2013; Marcos et al. 2015; Woodworth and Menendez 2015; Wahl and Chambers 2016; 

5 Mawdsley and Haigh 2016; Sweet et al. 2016). These patterns are often associated with 

6 anomalous oceanic and atmospheric conditions (Feser et al. 2015; Colle et al. 2015). For 

7 instance, the probability of experiencing minor tidal flooding is compounded during El Niño 

8 along portions of the West and mid-Atlantic Coasts (Sweet and Park 2014) from a combination 

9 of higher sea levels and enhanced synoptic forcing and storm surge frequency (Sweet and Zervas 

10 2011; Thompson et al. 2013; Hamlington et al. 2015; Woodworth and Menendez 2015).

11 12.6.2 Influence of Projected Sea Level Rise on Coastal Flood Frequencies

12 The frequency and depth of high-water extreme events experienced locally in the future will 

13 continue to increase as local RSL rises (Tebaldi et al. 2012; Kopp et al. 2014; Horton et al. 2011; 

14 Woodruff et al. 2013; Buchanan et al. 2016; Hall et al. 2016). Under the probabilistic RSL 

15 projections of Kopp et al. 2014, at tide-gauge locations along the contiguous U.S. coastline, a 

16 median 8-fold increase (range of 1.1- to 430-fold increase) is expected by 2050 in the annual 

17 number of floods exceeding the elevation of the current 100-year flood event (measured with 

18 respect to a 1991–2009 baseline sea level) (Buchanan et al. 2016). Under the same forcing, the 

19 frequency of minor tidal flooding (with recurrence intervals generally <1 year [Sweet et al. 

20 2014]) will increase even more so in the coming decades (Sweet and Park 2014; Moftakhari et 

21 al. 2015). Probabilities of both minor and major high-water events are expected to increase more 

22 rapidly at locations with less overall extreme variability and/or greater amounts of local RSL rise 

23 (Hunter 2012; Tebaldi et al. 2012; Kopp et al. 2014; Sweet and Park 2014). For example, Figure 

24 12.3d shows the decade in which the frequency of moderate-level flooding—defined as a water 

25 level locally with a 5-year recurrence interval (Figure 12.3c) and that typically triggers an 

26 issuance of a coastal flood “warning” by NOAA’s NWS (Sweet et al. In Review)—will increase 

27 25-fold in response to local RSL rise associated with the Interagency 1-meter (3.3 feet) scenario 

28 for 2100 (Figure 12.3a). Under the 1-meter scenario, many cities along the mid- and Southeast 

29 Atlantic, western Gulf, California, and the Island States and Territories may experience a 25-fold 

30 increase in moderate flooding over the next three decades. 

31 12.6.3 Waves and Impacts 

32 The combination of a storm surge at high tide with additional dynamical effects from waves 

33 (Stockton et al. 2006; Sweet et al. 2015) creates the most damaging coastal hydraulic conditions 

34 (Moritz et al. 2015). Simply with higher-than-normal sea levels, wave action increases the 

35 likelihood for extensive coastal erosion (Barnard et al. 2011; Theuerkauf et al. 2014; Serafin and 

36 Ruggiero 2014) and low-island overwash (Hoeke et al. 2013). Wave runup is often the largest 

37 water level component during extreme events where storm surge is constrained by bathymetry 

38 (Tebaldi et al. 2012; Woodruff et al. 2013; Hall et al. 2016), as depicted in Figure 12.3c. On an 
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1 interannual basis, wave impacts are correlated across the Pacific Ocean with phases of ENSO 

2 (Stopa and Cheung 2014; Barnard et al. 2015). Over the last half century, there has been an 

3 increasing trend in wave height and power within the North Pacific Ocean (Bromirski et al. 

4 2013; Erikson et al. 2015) that is modulated by the PDO (Aucan et al. 2012; Bromirski et al. 

5 2013). Resultant increases in wave run-up have been more of a factor than RSL rise in terms of 

6 impacts along the U.S. Northwest Pacific Coast over the last several decades (Ruggiero 2013). In 

7 the Northwest Atlantic Ocean, no long-term trends in wave power have been observed over the 

8 last half century (Bromirski and Cayan 2015), though hurricane activity drives interannual 

9 variability (Bromirski and Kossin 2008). In terms of future conditions this century, increases in 

10 mean and maximum seasonal wave heights are projected within parts of the northeast Pacific, 

11 northwest Atlantic, and Gulf of Mexico (Graham et al. 2013; Wang et al. 2014; Erikson et al. 

12 2015; Shope et al. 2016).

13 12.6.4 Sea Level Rise, Changing Storm Characteristics, and Their Interdependencies

14 Future probabilities of extreme coastal floods will depend upon the amount of local RSL rise, 

15 changes in coastal storm characteristics, and their interdependencies. For instance, there have 

16 been more storms producing concurrent locally extreme storm surge and rainfall (not captured in 

17 tide gauge data) along the U.S. East and Gulf Coasts over the last 65 years, with flooding further 

18 compounded by local RSL rise (Wahl et al. 2015). Hemispheric-scale extratropical cyclones may 

19 experience a northward shift this century, with some studies projecting an overall decrease in 

20 storm number (Colle et al. 2015 and references therein). The research is mixed about strong 

21 extratropical storms; studies find potential increases in frequency and intensity in some regions, 

22 like within the Northeast (Colle et al. 2013), whereas others project decreases in strong 

23 extratropical storms in some regions (e.g,, Zappa et al. 2013). In terms of tropical cyclones, 

24 model projections for the North Atlantic mostly agree that intensities and precipitation rates will 

25 increase this century (see Chapter 9), although some model evidence suggests that track changes 

26 could dampen the effect in the U.S. Mid-Atlantic and Northeast (Hall and Yonekura 2013). 

27 Despite such uncertainties, the combination of sea level rise and tropical cyclone changes is 

28 projected to increase extreme coastal flood probabilities along U.S. coastlines (Grinsted et al. 

29 2013; Lin et al. 2012; Little et al. 2015; Knutson et al. 2013, 2015; Lin et al. 2016). In addition, 

30 RSL increases are projected to cause a nonlinear increase in storm surge heights in shallow 

31 bathymetry environments (Smith et al. 2010; Atkinson et al. 2013; Bilskie et al. 2014; Passeri et 

32 al. 2015; Bilskie et al. 2016), and extend wave propagation and impacts landward (Smith et al. 

33 2010; Atkinson et al. 2013).

34 12.6.5 Interactions between sea level rise and coastal impacts

35 While outside the scope of this chapter, it is important to note the myriad of other potential 

36 impacts associated with RSL rise, wave action, and increases in coastal flooding. These impacts 

37 include loss of life, damage to infrastructure and the built environment, salinization of 

38 groundwater, mobilization of pollutants, changing sediment budgets, coastal erosion, and 
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1 ecosystem changes such as marsh loss and threats to endangered flora and fauna (Wong et al. 

2 2013). While all these impacts are inherently important, some also have the potential to influence 

3 local rates of RSL rise and the extent of wave-driven and coastal flooding impacts. For example, 

4 there is evidence that wave action and flooding of beaches and marshes can induce changes in 

5 coastal geomorphology, such as sediment build up, that may iteratively modify the future flood 

6 risk profile of communities and ecosystems (Lentz et al. 2016). 

7
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1 TRACEABLE ACCOUNTS

2 Key Finding 1

3 Global mean sea level (GMSL) has risen by about 8–9 inches (about 20-23 cm) since 1880, with 

4 about 3 of those inches (about 7 cm) occurring since 1990 (very high confidence). Human-caused 

5 climate change has made a substantial contribution to GMSL rise since 1900 (high confidence), 

6 contributing to a rate of rise faster than during any comparable period since at least 800 BCE 

7 (medium confidence). 

8 Description of evidence base

9 Multiple researchers, using different statistical approaches, have integrated tide gauge records to 

10 estimate GMSL rise since the late nineteenth century (e.g., Church and White 2006, 2011; Hay et 

11 al. 2015; Jevrejeva et al. 2009). The most recent published rate estimates are 1.2 ± 0.2 (Hay et al. 

12 2015) or 1.5 ± 0.2 (Church and White 2011) mm/year over 1901–1990. Both data sets indicate 

13 similar rates since 1880. Thus, these results indicate about 13-16 cm (5–6 inches) of GMSL rise 

14 from 1880 to 1990. Tide gauge analyses indicate that GMSL rose at a considerably faster rate of 

15 about 3 mm/year (1.2 inches/decade) since 1993 (Hay et al. 2015; Church and White 2011), a 

16 result supported by satellite data indicating a trend of 3.4 ± 0.4 mm/year (1.3 inches/decade) over 

17 1993–2015 (update to Nerem et al. 2010) (Figure 12.2a). These results indicate an additional 

18 GMSL rise of about 7 cm (3 inches) rise since 1990. Thus total GMSL rise since 1880 is about 

19 20-23 cm (8–9 inches). 

20 The finding regarding the historical context of the 20th century change is based upon Kopp et al. 

21 (2016), who conducted a meta-analysis of geological RSL reconstructions, spanning the last 

22 3000 years, from 24 localities around the world, as well as tide gauge data from 66 sites and the 

23 tide gauge based GMSL reconstruction of Hay et al. (2015). By constructing a spatio-temporal 

24 statistical model of these data sets, they identified the common global sea level signal over the 

25 last three millennia, and its uncertainties. They found a 95% probability that the average rate of 

26 GMSL change from 1900–2000 was faster than during any previous century since at least 800 

27 BCE.

28 The finding regarding the substantial human contribution is based upon several lines of evidence. 

29 Kopp et al. (2016), based on the long-term historical relationship between temperature and rate 

30 of sea-level change, found that GMSL rise would extremely likely have been <59% of observed 

31 in the absence of 20th century global warming, and that it is very likely that GMSL has been 

32 higher since 1960 than it would have been without 20th century global warming. Using a variety 

33 of models for individual components, Slangen et al. (2016) found that 69% ± 31% out of the 

34 87% ± 20% of GMSL rise over 1970–2005 that their models simulated was attributable to 

35 anthropogenic forcing, and that 37% ± 38% out of 74% ± 22% simulated was attributable over 

36 1900–2005. Jevrejeva et al. (2009), using the relationship between forcing and GMSL over 1850 

37 and 2001 and CMIP3 models, found that ~75% of GMSL rise in the 20th century is attributable 

38 to anthropogenic forcing. Marcos and Amores (2014), using CMIP5 models, found that ~87% of 
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1 ocean heat uptake since 1970 in the top 700 m of the ocean has been due to anthropogenic 

2 forcing. Slangen et al. (2014), using CMIP5, found that anthropogenic forcing was required to 

3 explain observed thermosteric SLR over 1957–2005. Marzeion et al. (2014) found that 25% ± 

4 35% of glacial loss over 1851–2010, and 69% ± 24% over 1991–2010, was attributable to 

5 anthropogenic forcing. Dangendorf et al (2015), based on time series analysis, found that >45% 

6 of observed GMSL trend since 1900 cannot (with 99% probability) be explained by multi7 decadal natural variability. Becker et al. (2014), based on time series analysis, found a 99% 

8 probability that at least 1.0 or 1.3 mm/yr of GMSL rise over 1880–2010 is anthropogenic.

9 Major uncertainties

10 Uncertainties in reconstructed GMSL change relate to the sparsity of tide gauge records, 

11 particularly before the middle of the twentieth century, and to different statistical approaches for 

12 estimating GMSL change from these sparse records. Uncertainties in reconstructed GMSL 

13 change before the twentieth century also relate to the sparsity of geological proxies for sea level 

14 change, the interpretation of these proxies, and the dating of these proxies. Uncertainty in 

15 attribution relates to the reconstruction of past changes and the magnitude of unforced 

16 variability.

17 Assessment of confidence based on evidence and agreement, including short description of 

18 nature of evidence and level of agreement

19 x Very High

20 x High

21 x Medium

22 ☐ Low

23 Confidence is very high in the rate of GMSL rise since 1900, based on multiple different 

24 approaches to estimating GMSL rise from tide gauges and satellite altimetry. It is high in the 

25 substantial human contribution to GMSL rise since 1900, based on both statistical and physical 

26 modeling evidence. It is medium that the magnitude of the observed rise since 1900 is 

27 unprecedented in the context of the previous 2700 years, based on meta-analysis of geological 

28 proxy records.

29 Summary sentence or paragraph that integrates the above information

30 This key finding is based upon multiple analyses of tide gauge and satellite altimetry records, on 

31 a meta-analysis of multiple geological proxies for pre-instrumental sea level change, and on both 

32 statistical and physical analyses of the human contribution to GMSL rise since 1900.

33

34 Key Finding 2

35 Relative to the year 2000, GMSL is very likely to rise by 0.3–0.6 feet (9-18 cm) by 2030, 0.5–1.2 

36 feet (15-38 cm) by 2050, and 1 to 4 feet (30-130 cm) by 2100 (very high confidence in lower 

37 bounds; medium confidence in upper bounds for 2030 and 2050; low confidence in upper bounds 
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1 for 2100). Emissions pathways have little effect on projected GMSL rise in the first half of the 

2 century, but significantly affect projections for the second half of the century (high confidence). 

3 Emerging science regarding ice sheet stability suggests that, for high emissions, a GMSL rise 

4 exceeding 8 feet (2.4 m) by 2100 cannot be ruled out. 

5 Description of evidence base

6 The lower bound of the very likely range is based on a continuation of the observed 

7 approximately 3 mm/year rate of GMSL rise. The upper end of the very likely range is based 

8 upon estimates for RCP 8.5 from three studies producing fully probabilistic projections across 

9 multiple RCPs. Kopp et al. (2014) fused multiple sources of information accounting for the 

10 different individual process contributing to GMSL rise. Kopp et al. (2016) constructed a semi11 empirical sea level model calibrated to the Common Era sea level reconstruction. Mengel et al. 

12 (2016) constructed a set of semi-empirical models of the different contributing processes. All 

13 three studies show negligible RCP dependence in the first half of this century, becoming more 

14 prominent in the second half of the century.

15 To estimate the effect of incorporating the DeConto and Pollard (2016) projections of Antarctic 

16 ice sheet melt, we note that Kopp et al. (2014)’s median projection of Antarctic melt in 2100 is 4 

17 cm (1.6 inches) (RCP8.5), 5 cm (2 inches) (RCP4.5), or 6 cm (2.4 inches) (RCP2.6). By contrast, 

18 DeConto and Pollard (2016)’s ensemble mean projections are (varying the assumptions for the 

19 size of Pliocene mass loss and the bias correction in the Amundsen Sea) 64–114 cm (2.1–3.7 

20 feet) for RCP8.5, 26–58 cm (0.9–1.9 feet) for RCP4.5, and 2–14 cm (0.1–0.5 foot) for RCP2.6. 

21 Thus we conclude that DeConto and Pollard (2016)’s projection would lead to a 60–110 cm 

22 (2.0–3.6 feet) increase in median RCP8.5 projections, a 21–53 cm (0.7–1.7 feet) increase in 

23 median RCP4.5 projections, and a −-4–10 cm (−0.1–0.3 foot) increase in median RCP2.6 

24 projections.

25 Very likely ranges, 2030 relative to 2000 in cm (feet)

Kopp et al. 

(2014)

Kopp et al. 

(2016)

Mengel et al. 

(2016)

RCP8.5 11-18 (0.4-0.6) 8-15 (0.3-0.5) 7-12 (0.2-0.4)

RCP4.5 10-18 (0.3-0.6) 8-15 (0.3-0.5) 7-12 (0.2-0.4)

RCP2.6 10-18 (0.3-0.6) 8-15 (0.3-0.5) 7-12 (0.2-0.4)

26

27
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1 Very likely ranges, 2050 relative to 2000 in cm (feet)

Kopp et al. 

(2014)

Kopp et al. 

(2016)

Mengel et al. 

(2016)

RCP8.5 21-38 (0.7-1.2) 16-34 (0.5-1.1) 15-28 (0.5-0.9)

RCP4.5 18-35 (0.6-1.1) 15-31 (0.5-1.0) 14-25 (0.5-0.8)

RCP2.6 18-33 (0.6-1.1) 14-29 (0.5-1.0) 13-23 (0.4-0.8)

2

3 Very likely ranges, 2100 relative to 2000 in cm (feet)

Kopp et al. 

(2014)

Kopp et al. 

(2016)

Mengel et al. 

(2016)

RCP8.5 55-121 (1.8-

4.0)

52-131 (1.7-

4.3)

57-131 (1.9-

4.3)

RCP4.5 36-93 (1.2-3.1) 33-85 (1.1-2.8) 37-77 (1.2-2.5)

RCP2.6 29-82 (1.0-2.7) 24-61 (0.8-2.0) 28-56 (0.9-1.8)

4

5 Major uncertainties

6 Since NCA3, multiple different approaches have been used to generate probabilistic projections 

7 of GMSL rise, conditional upon the RCPs. These approaches are in general agreement. However, 

8 emerging results indicate that marine-based sectors of the Antarctic Ice Sheet are more unstable 

9 than previous modeling indicated. The rate of ice sheet mass changes remains challenging to 

10 project.

11 Assessment of confidence based on evidence and agreement, including short description of 

12 nature of evidence and level of agreement

13 x Very High

14 x High

15 x Medium

16 x Low
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1 There is very high confidence that future GMSL rise over the next several decades will be at 

2 least as fast as a continuation of the historical trend over the last quarter century would indicate. 

3 There is medium confidence in the upper end of very likely ranges for 2030 and 2050. Due to 

4 possibly large ice sheet contributions, there is low confidence in the upper end of very likely 

5 ranges for 2100. Based on multiple projection methods, there is high confidence that differences 

6 between emission scenarios are small before 2050 but significant beyond 2050.

7 Summary sentence or paragraph that integrates the above information

8 This key finding is based upon multiple methods for estimating the probability of future sea level 

9 change and on new modeling results regarding the stability of the West Antarctic Ice Sheet.

10

11 Key Finding 3

12 Relative sea level (RSL) rise in this century will vary along U.S. coastlines due, in part, to: 

13 changes in Earth’s gravitational field and rotation from melting of land ice, changes in ocean 

14 circulation, and vertical land motion (very high confidence). For almost all future GMSL rise 

15 scenarios, RSL rise is likely to be greater than the global average in the U.S. Northeast and the 

16 western Gulf of Mexico. In intermediate and low GMSL rise scenarios, it is likely to be less than 

17 the global average in much of the Pacific Northwest and Alaska. For high GMSL rise scenarios, 

18 it is likely to be higher than the global average along all U.S. coastlines outside Alaska (high 

19 confidence). 

20 Description of evidence base

21 The processes that cause geographic variability in RSL change are reviewed by Kopp et al. 

22 (2015). Long tide gauge data sets show the RSL rise caused by vertical land motion due to 

23 glacio-isostatic adjustment and fluid withdrawal along many U.S. coastlines (PSMSL 2016). 

24 These observations are corroborated by glacio-isostatic adjustment models, by GPS observations, 

25 and by geological data (e.g., Engelhart and Horton 2012). The physics of the gravitational, 

26 rotational and flexural “static-equilibrium fingerprint” response of sea level to redistribution of 

27 mass from land ice to the oceans is well-established (Farrell and Clark 1976; Mitrovica et al. 

28 2011). GCM studies indicate the potential for a Gulf Stream contribution to sea level rise in the 

29 U.S. northeast (Yin et al. 2009; Yin and Goddard 2011). Kopp et al. (2014) and Slangen et al. 

30 (2014) accounted for land motion (only glacial isostatic adjustment for Slangen et al.), 

31 fingerprint, and ocean dynamic responses. Comparing projections of local RSL change and 

32 GMSL change in these studies indicate that local rise is likely to be greater than the global 

33 average along the U.S. Atlantic and Gulf coasts and less than the global average in most of the 

34 Pacific Northwest.

35 Sea level rise projections in this report are developed by an Interagency Sea Level Rise Task 

36 Force (Sweet et al. In Review)

37
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1 Major uncertainties

2 Since NCA3, multiple authors have produced global or regional studies synthesizing the major 

3 process that causes global and local sea level change to diverge. The largest sources of 

4 uncertainty in the geographic variability of sea-level change are ocean dynamic sea level change 

5 and, for those regions where sea level fingerprints for Greenland and Antarctica differ from the 

6 global mean in different directions, the relative contributions of these two sources to projected 

7 sea level change.

8 Assessment of confidence based on evidence and agreement, including short description of 

9 nature of evidence and level of agreement

10 x Very High

11 x High

12 ☐ Medium

13 ☐ Low

14 Because of the enumerated physical processes, there is very high confidence that RSL change 

15 will vary across U.S. coastlines. There is high confidence in the likely differences of RSL change 

16 from GMSL change under different levels of GMSL change, based on projections incorporating 

17 the different relevant processes. 

18 Summary sentence or paragraph that integrates the above information

19 The part of the key finding regarding the existence of geographic variability is based upon a 

20 broader observational, modeling, and theoretical literature. The specific differences are based 

21 upon the scenarios described by the Interagency Sea Level Rise Task Force (Sweet et al. In 

22 Review)

23

24 Key Finding 4

25 The annual occurrences of daily tidal flooding—exceeding local thresholds for minor impacts to 

26 infrastructure—have increased 5- to 10-fold since the 1960s in several U.S. coastal cities (very 

27 high confidence). Rates of increase, which are accelerating in over 25 Atlantic and Gulf Coast 

28 cities, are fastest where elevation thresholds are lower, local RSL rise is higher, or extreme 

29 variability is less (very high confidence). Tidal flooding will continue increasing in depth and 

30 frequency in similar manners this century (very high confidence).

31 Description of evidence base

32 Sweet et al. (2014) examined 45 NOAA tide gauge locations with hourly data since 1980 and 

33 Sweet and Park (2014) examined a subset of these (27 locations) with hourly data prior to 1950, 

34 all with a National Weather Service elevation threshold established for minor “nuisance” flood 

35 impacts. Using linear or quadratic fits of annual number of days exceeding the minor thresholds, 

36 Sweet and Park (2014) find differences in trend-derived values since 1960 greater than 10-fold at 

37 8 locations, greater than 5-fold at 6 locations and greater than 3-fold at 7 locations. Sweet et al. 
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1 (2014), Sweet and Park (2014), and Ezer and Atkinson (2014) find that annual minor tidal flood 

2 frequencies since 1980 are accelerating along locations on the East and Gulf Coasts (> 25 

3 locations, Sweet et al. 2014) due to continued exceedance of a typical high-water distribution 

4 above elevation thresholds for minor impacts. 

5 Historical changes over last 60 years in flood probabilities have occurred most rapidly where 

6 RSL rates were highest and where tide ranges and extreme variability is less (Sweet and Park 

7 2014). In terms of future rates of changes in extreme event probabilities relative to fixed 

8 elevations, Hunter (2012), Tebaldi et al. (2012), Kopp et al. (2014) and Sweet and Park (2014) 

9 all find that locations with less extreme variability and higher RSL rise rates are most prone.

10 Major uncertainties

11 Minor flooding probabilities has been only assessed where a tide gauge is present with >30 years 

12 data and that has a NOAA National Weather Service elevation threshold for impacts established. 

13 There are likely many other locations experiencing similar flooding patterns, but an expanded 

14 assessment is not possible at this time.

15 Assessment of confidence based on evidence and agreement, including short description of 

16 nature of evidence and level of agreement

17 x Very High

18 ☐ High

19 ☐ Medium

20 ☐ Low

21 There is very high confidence that exceedance probabilities of high-tide flooding at dozens of 

22 local-specific elevation thresholds have significantly increased over the last half century, often in 

23 an accelerated fashion, and will continue to do this century.

24 Summary sentence or paragraph that integrates the above information

25 This key finding is based upon several studies finding historic and projecting future changes in 

26 high-water probabilities for local-specific elevation thresholds for flooding.

27

28 Key Finding 5

29 The projected increase in the intensity of hurricanes in the North Atlantic could increase the 

30 probability of extreme coastal flooding along the U.S. Atlantic and Gulf Coasts beyond what 

31 would be projected based solely on RSL rise. However, there is low confidence in the magnitude 

32 of the increase in intensity and the associated flood risk amplification, and it could be offset or 

33 amplified by other factors, such as changes in hurricane frequency or tracks.

34 Description of evidence base

35 Model-based projections of tropical storms and related major storm surges within the North 

36 Atlantic mostly agree that intensities and frequencies of the most intense storms will increase this 
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1 century (Grinsted et al. 2013; Lin et al. 2012; Little et al. 2015; Knutson et al. 2013; Lin et al.

2 2016). However, the projection of increased hurricane intensity is more robust across models 

3 than the projection of increased frequency of the most intense storms, since a number of models 

4 project a decrease in the overall number of tropical storms and hurricanes in the North Atlantic, 

5 although high-resolution models generally project increased mean hurricane intensity (e.g., 

6 Knutson et al. 2013). In addition, there is model evidence for a change in tropical cyclone tracks 

7 in warm years that minimizes the increase in landfalling hurricanes in the U.S. Mid-Atlantic or 

8 Northeast (Hall and Yonekura 2013). 

9 Major uncertainties

10 Uncertainties remain large with respect to the precise change in future risk of a major coastal 

11 impact at a specific location from changes in the most intense tropical cyclone characteristics and 

12 tracks beyond changes imposed from local sea level rise.

13 Assessment of confidence based on evidence and agreement, including short description of 

14 nature of evidence and level of agreement

15 ☐ Very High

16 ☐ High

17 ☐ Medium

18 x Low

19 There is low confidence that flood risk at specific location will be amplified from a major 

20 tropical storm this century.

21 Summary sentence or paragraph that integrates the above information

22 This key finding is based upon several modeling studies of future hurricane characteristics and 

23 associated increases in major storm surge risk amplification.

24
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1 FIGURES

2

3 Figure 12.1: (a–d) Static-equilibrium fingerprints of the RSL effect of land ice melt, in units of 

4 feet of RSL change per feet of GMSL change, for mass loss from (a) Greenland, (b) West 

5 Antarctica, (c) East Antarctica, and (d) the median projected combination of melting glaciers, 

6 after Kopp et al. (2014, 2015). (e) Model projections of the rate of RSL rise due to glacial7 isostatic adjustment (units of feet.century), after Kopp et al. (2015). (f) Tide gauge-based 

8 estimates of the non-climatic, long-term contribution to RSL rise, including the effects of glacial 

9 isostatic adjustment, tectonics, and sediment compaction (Kopp et al. 2014) [Figure source: (a)–

10 (d) Kopp et al. (2015), (e) adapted from Kopp et al. (2015); (f) adapted from Sweet et al. (In 

11 Review)]
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1

2 Figure 12.2: (a) GMSL rise from −500 to 1900 CE, from Kopp et al. (2016)’s geological and 

3 tide gauge-based reconstruction [blue], from 1900 to 2010 from Hay et al. (2015)’s tide gauge4 based reconstruction [black], and from 1992 to 2015 from the satellite-based reconstruction 

5 updated from Nerem et al. (2010) [magenta]; (b) GMSL rise from 1800 to 2100, based on (a) 

6 from 1800 to 2015, the six Interagency (Sweet et al., In Review) GMSL scenarios (black, blue, 

7 cyan, green, orange and red curves), the very likely ranges in 2100 for different RCPs from this 

8 chapter (colored boxes), and lines augmenting the very likely ranges by the difference between 

9 the median Antarctic contribution of Kopp et al. 2014 and the various median Antarctic 

10 projections of DeConto and Pollard (2016). (c) Rates of change from 1993 to 2014 in sea surface 

11 height from satellite altimetry data; from Kopp et al. (2015) using data updated from Church and 

12 White (2011). [Figure source: (a) adapted from Nerem et al. (2010), Kopp et al. (2016) and Hay 

13 et al. (2015), (b) adapted from Sweet et al. (In Review), (c) adapted from Kopp et al. (2015)]
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1

2 Figure 12.3: (a) RSL rise (feet) in 2100 under the median-local Interagency Intermediate (1-

3 meter) Scenario, (b) tidal floods (days per year) exceeding NOAA thresholds for minor impacts 

4 at NOAA tide gauges through 2015, from Sweet and Marra (2016), (c) water level heights 

5 associated with a local 5-year recurrence probability, after Sweet et al. 2014, and (d) future 

6 decade the 5-year becomes a 0.2-year event under the Interagency Intermediate scenario, after 

7 Sweet and Park (2014); black dots imply that a 5-year to 0.2-year frequency change does not 

8 unfold by 2200 under the Intermediate scenario. [Figure source: (a), (c), and (d) Sweet et al. (In 

9 Review), (b) adapted from Sweet and Marra (2016)]

10
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1 REFERENCES

2 Abraham, J.P., M. Baringer, N.L. Bindoff, T. Boyer, L.J. Cheng, J.A. Church, J.L. Conroy, C.M. 

3 Domingues, J.T. Fasullo, J. Gilson, G. Goni, S.A. Good, J.M. Gorman, V. Gouretski, M. 

4 Ishii, G.C. Johnson, S. Kizu, J.M. Lyman, A.M. Macdonald, W.J. Minkowycz, S.E. Moffitt, 

5 M.D. Palmer, A.R. Piola, F. Reseghetti, K. Schuckmann, K.E. Trenberth, I. Velicogna, and 

6 J.K. Willis, 2013: A review of global ocean temperature observations: Implications for ocean 

7 heat content estimates and climate change. Reviews of Geophysics, 51, 450-483. 

8 http://dx.doi.org/10.1002/rog.20022 

9 Applegate, P.J. and K. Keller, 2015: How effective is albedo modification (solar radiation 

10 management geoengineering) in preventing sea-level rise from the Greenland Ice Sheet? 

11 Environmental Research Letters, 10, 084018. http://dx.doi.org/10.1088/1748-

12 9326/10/8/084018 

13 Atkinson, J., J.M. Smith, and C. Bender, 2013: Sea-level rise effects on storm surge and 

14 nearshore waves on the Texas coast: Influence of landscape and storm characteristics. 

15 Journal of Waterway, Port, Coastal, and Ocean Engineering, 139, 98-117. 

16 http://dx.doi.org/10.1061/(ASCE)WW.1943-5460.0000187 

17 Aucan, J., R. Hoeke, and M.A. Merrifield, 2012: Wave-driven sea level anomalies at the Midway 

18 tide gauge as an index of North Pacific storminess over the past 60 years. Geophysical 

19 Research Letters, 39, L17603. http://dx.doi.org/10.1029/2012GL052993 

20 Barnard, P.L., J. Allan, J.E. Hansen, G.M. Kaminsky, P. Ruggiero, and A. Doria, 2011: The 

21 impact of the 2009–10 El Niño Modoki on U.S. West Coast beaches. Geophysical Research 

22 Letters, 38, L13604. http://dx.doi.org/10.1029/2011GL047707 

23 Barnard, P.L., A.D. Short, M.D. Harley, K.D. Splinter, S. Vitousek, I.L. Turner, J. Allan, M. 

24 Banno, K.R. Bryan, A. Doria, J.E. Hansen, S. Kato, Y. Kuriyama, E. Randall-Goodwin, P. 

25 Ruggiero, I.J. Walker, and D.K. Heathfield, 2015: Coastal vulnerability across the Pacific 

26 dominated by /Southern Oscillation. Nature Geoscience, 8, 801-807. 

27 http://dx.doi.org/10.1038/ngeo2539 

28 Barrett, S., T.M. Lenton, A. Millner, A. Tavoni, S. Carpenter, J.M. Anderies, F.S. Chapin Iii, A.-

29 S. Crepin, G. Daily, P. Ehrlich, C. Folke, V. Galaz, T. Hughes, N. Kautsky, E.F. Lambin, R. 

30 Naylor, K. Nyborg, S. Polasky, M. Scheffer, J. Wilen, A. Xepapadeas, and A. de Zeeuw, 

31 2014: Climate engineering reconsidered. Nature Climate Change, 4, 527-529. 

32 http://dx.doi.org/10.1038/nclimate2278 

33 Becker, M., M. Karpytchev, and S. Lennartz-Sassinek, 2014: Long-term sea level trends: Natural 

34 or anthropogenic? Geophysical Research Letters, 41, 5571-5580. 

35 http://dx.doi.org/10.1002/2014GL061027 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 12

438

1 Bilskie, M.V., S.C. Hagen, K. Alizad, S.C. Medeiros, D.L. Passeri, H.F. Needham, and A. Cox, 

2 2016: Dynamic simulation and numerical analysis of hurricane storm surge under sea level

3 rise with geomorphologic changes along the northern Gulf of Mexico. Earth's Future, 4, 177-

4 193. http://dx.doi.org/10.1002/2015EF000347 

5 Bilskie, M.V., S.C. Hagen, S.C. Medeiros, and D.L. Passeri, 2014: Dynamics of sea level rise 

6 and coastal flooding on a changing landscape. Geophysical Research Letters, 41, 927-934. 

7 http://dx.doi.org/10.1002/2013GL058759 

8 Bromirski, P.D. and D.R. Cayan, 2015: Wave power variability and trends across the North 

9 Atlantic influenced by decadal climate patterns. Journal of Geophysical Research: Oceans,

10 120, 3419-3443. http://dx.doi.org/10.1002/2014JC010440 

11 Bromirski, P.D., D.R. Cayan, J. Helly, and P. Wittmann, 2013: Wave power variability and 

12 trends across the North Pacific. Journal of Geophysical Research: Oceans, 118, 6329-6348. 

13 http://dx.doi.org/10.1002/2013JC009189 

14 Bromirski, P.D. and J.P. Kossin, 2008: Increasing hurricane wave power along the U.S. Atlantic 

15 and Gulf coasts. Journal of Geophysical Research, 113, C07012. 

16 http://dx.doi.org/10.1029/2007JC004706 

17 Bromirski, P.D., A.J. Miller, R.E. Flick, and G. Auad, 2011: Dynamical suppression of sea level 

18 rise along the Pacific coast of North America: Indications for imminent acceleration. Journal 

19 of Geophysical Research, 116, C07005. http://dx.doi.org/10.1029/2010JC006759 

20 Buchanan, M.K., R.E. Kopp, M. Oppenheimer, and C. Tebaldi, 2016: Allowances for evolving 

21 coastal flood risk under uncertain local sea-level rise. Climatic Change, 137, 347-362. 

22 http://dx.doi.org/10.1007/s10584-016-1664-7 

23 Carlson, A.E., A.N. LeGrande, D.W. Oppo, R.E. Came, G.A. Schmidt, F.S. Anslow, J.M. 

24 Licciardi, and E.A. Obbink, 2008: Rapid early Holocene deglaciation of the Laurentide ice 

25 sheet. Nature Geoscience, 1, 620-624. http://dx.doi.org/10.1038/ngeo285 

26 Chambers, D.P., A. Cazenave, N. Champollion, H. Dieng, W. Llovel, R. Forsberg, K. von 

27 Schuckmann, and Y. Wada, 2016: Evaluation of the Global Mean Sea Level Budget between 

28 1993 and 2014. Surveys in Geophysics, 1-19. http://dx.doi.org/10.1007/s10712-016-9381-3 

29 Church, J.A., P.U. Clark, A. Cazenave, J.M. Gregory, S. Jevrejeva, A. Levermann, M.A. 

30 Merrifield, G.A. Milne, R.S. Nerem, P.D. Nunn, A.J. Payne, W.T. Pfeffer, D. Stammer, and 

31 A.S. Unnikrishnan, 2013: Sea Level Change. Climate Change 2013: The Physical Science 

32 Basis. Contribution of Working Group I to the Fifth Assessment Report of the 

33 Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. 

34 Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 12

439

1 Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1137–

2 1216. http://dx.doi.org/10.1017/CBO9781107415324.026 www.climatechange2013.org

3 Church, J.A. and N.J. White, 2011: Sea-level rise from the late 19th to the early 21st century. 

4 Surveys in Geophysics, 32, 585-602. http://dx.doi.org/10.1007/s10712-011-9119-1 

5 Colle, B.A., J.F. Booth, and E.K.M. Chang, 2015: A Review of Historical and Future Changes of 

6 Extratropical Cyclones and Associated Impacts Along the US East Coast. Current Climate 

7 Change Reports, 1, 125-143. http://dx.doi.org/10.1007/s40641-015-0013-7 

8 Colle, B.A., Z. Zhang, K.A. Lombardo, E. Chang, P. Liu, and M. Zhang, 2013: Historical 

9 Evaluation and Future Prediction of Eastern North American and Western Atlantic 

10 Extratropical Cyclones in the CMIP5 Models during the Cool Season. Journal of Climate,

11 26, 6882-6903. http://dx.doi.org/10.1175/JCLI-D-12-00498.1 

12 Dangendorf, S., M. Marcos, A. Müller, E. Zorita, R. Riva, K. Berk, and J. Jensen, 2015: 

13 Detecting anthropogenic footprints in sea level rise. Nature Communications, 6, 7849. 

14 http://dx.doi.org/10.1038/ncomms8849 

15 DeConto, R.M. and D. Pollard, 2016: Contribution of Antarctica to past and future sea-level rise. 

16 Nature, 531, 591-597. http://dx.doi.org/10.1038/nature17145 

17 Dutton, A., A.E. Carlson, A.J. Long, G.A. Milne, P.U. Clark, R. DeConto, B.P. Horton, S. 

18 Rahmstorf, and M.E. Raymo, 2015: Sea-level rise due to polar ice-sheet mass loss during 

19 past warm periods. Science, 349. http://dx.doi.org/10.1126/science.aaa4019 

20 Dutton, A. and K. Lambeck, 2012: Ice Volume and Sea Level During the Last Interglacial. 

21 Science, 337, 216-219. http://dx.doi.org/10.1126/science.1205749 

22 Engelhart, S.E. and Horton, B.P., 2012. Holocene sea level database for the Atlantic coast of the 

23 United States. Quaternary Science Reviews, 54, pp.12-25

24 Erikson, L.H., C.A. Hegermiller, P.L. Barnard, P. Ruggiero, and M. van Ormondt, 2015: 

25 Projected wave conditions in the Eastern North Pacific under the influence of two CMIP5 

26 climate scenarios. Ocean Modelling, 96, Part 1, 171-185. 

27 http://dx.doi.org/10.1016/j.ocemod.2015.07.004 

28 Ezer, T., 2013: Sea level rise, spatially uneven and temporally unsteady: Why the U.S. East 

29 Coast, the global tide gauge record, and the global altimeter data show different trends. 

30 Geophysical Research Letters, 40, 5439-5444. http://dx.doi.org/10.1002/2013GL057952 

31 Ezer, T. and L.P. Atkinson, 2014: Accelerated flooding along the U.S. East Coast: On the impact 

32 of sea-level rise, tides, storms, the Gulf Stream, and the North Atlantic Oscillations. Earth's 

33 Future, 2, 362-382. http://dx.doi.org/10.1002/2014EF000252 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 12

440

1 Farrell, W. E., and J. A. Clark, 1976: On postglacial sea level. Geophys. J. Roy. Astrophys. Soc., 

2 46, 647–667

3 Fasullo, J.T., R.S. Nerem, and B. Hamlington, 2016: Is the detection of accelerated sea level rise 

4 imminent? Scientific Reports, 6, 31245. http://dx.doi.org/10.1038/srep31245 

5 Feser, F., M. Barcikowska, O. Krueger, F. Schenk, R. Weisse, and L. Xia, 2015: Storminess over 

6 the North Atlantic and northwestern Europe—A review. Quarterly Journal of the Royal 

7 Meteorological Society, 141, 350-382. http://dx.doi.org/10.1002/qj.2364 

8 Galloway, D., D.R. Jones, and S.E. Ingebritsen, 1999: Land Subsidence in the United States. 6 

9 pp. U.S. Geological Survey, Reston, VA.

10 Goddard, P.B., J. Yin, S.M. Griffies, and S. Zhang, 2015: An extreme event of sea-level rise 

11 along the Northeast coast of North America in 2009–2010. Nature Communications, 6, 6346. 

12 http://dx.doi.org/10.1038/ncomms7346 

13 Graham, N.E., D.R. Cayan, P.D. Bromirski, and R.E. Flick, 2013: Multi-model projections of 

14 twenty-first century North Pacific winter wave climate under the IPCC A2 scenario. Climate 

15 Dynamics, 40, 1335-1360. http://dx.doi.org/10.1007/s00382-012-1435-8 

16 Grinsted, A., S. Jevrejeva, R.E.M. Riva, and D. Dahl-Jensen, 2015: Sea level rise projections for 

17 northern Europe under RCP8.5. Climate Research, 64, 15-23. 

18 http://dx.doi.org/10.3354/cr01309 

19 Grinsted, A., J.C. Moore, and S. Jevrejeva, 2013: Projected Atlantic hurricane surge threat from 

20 rising temperatures. Proceedings of the National Academy of Sciences, 110, 5369-5373. 

21 http://dx.doi.org/10.1073/pnas.1209980110 

22 Hall, J.A., S. Gill, J. Obeysekera, W. Sweet, K. Knuuti, and J. Marburger, 2016: Regional Sea 

23 Level Scenarios for Coastal Risk Management: Managing the Uncertainty of Future Sea 

24 Level Change and Extreme Water Levels for Department of Defense Coastal Sites 

25 Worldwide. 224 pp. U.S. Department of Defense, Strategic Environmental Research and 

26 Development Program, Alexandria VA.

27 Hall, T. and E. Yonekura, 2013: North American Tropical Cyclone Landfall and SST: A 

28 Statistical Model Study. Journal of Climate, 26, 8422-8439. http://dx.doi.org/10.1175/jcli-d29 12-00756.1 

30 Hamlington, B.D., S.H. Cheon, P.R. Thompson, M.A. Merrifield, R.S. Nerem, R.R. Leben, and 

31 K.Y. Kim, 2016: An ongoing shift in Pacific Ocean sea level. Journal of Geophysical 

32 Research: Oceans, 121, 5084-5097. http://dx.doi.org/10.1002/2016JC011815 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 12

441

1 Hamlington, B.D., R.R. Leben, K.Y. Kim, R.S. Nerem, L.P. Atkinson, and P.R. Thompson, 

2 2015: The effect of the El Niño-Southern Oscillation on U.S. regional and coastal sea level. 

3 Journal of Geophysical Research: Oceans, 120, 3970-3986. 

4 http://dx.doi.org/10.1002/2014JC010602 

5 Hay, C.C., E. Morrow, R.E. Kopp, and J.X. Mitrovica, 2015: Probabilistic reanalysis of 

6 twentieth-century sea-level rise. Nature, 517, 481-484. 

7 http://dx.doi.org/10.1038/nature14093 

8 Hoeke, R.K., K.L. McInnes, J.C. Kruger, R.J. McNaught, J.R. Hunter, and S.G. Smithers, 2013: 

9 Widespread inundation of Pacific islands triggered by distant-source wind-waves. Global 

10 and Planetary Change, 108, 128-138. http://dx.doi.org/10.1016/j.gloplacha.2013.06.006 

11 Horton, R.M., V. Gornitz, D.A. Bader, A.C. Ruane, R. Goldberg, and C. Rosenzweig, 2011: 

12 Climate hazard assessment for stakeholder adaptation planning in New York City. Journal of

13 Applied Meteorology and Climatology, 50, 2247-2266. 

14 http://dx.doi.org/10.1175/2011JAMC2521.1 

15 Hunter, J., 2012: A simple technique for estimating an allowance for uncertain sea-level rise. 

16 Climatic Change, 113, 239-252. http://dx.doi.org/10.1007/s10584-011-0332-1 

17 Irvine, P.J., D.J. Lunt, E.J. Stone, and A. Ridgwell, 2009: The fate of the Greenland Ice Sheet in 

18 a geoengineered, high CO 2 world. Environmental Research Letters, 4, 045109. 

19 http://dx.doi.org/10.1088/1748-9326/4/4/045109 

20 Jevrejeva, S., A. Grinsted, and J.C. Moore, 2009: Anthropogenic forcing dominates sea level rise 

21 since 1850. Geophysical Research Letters, 36, n/a-n/a. 

22 http://dx.doi.org/10.1029/2009GL040216 

23 Joughin, I., B.E. Smith, and B. Medley, 2014: Marine Ice Sheet Collapse Potentially Under Way 

24 for the Thwaites Glacier Basin, West Antarctica. Science, 344, 735-738. 

25 http://dx.doi.org/10.1126/science.1249055 

26 Kemp, A.C. and B.P. Horton, 2013: Contribution of relative sea-level rise to historical hurricane 

27 flooding in New York City. Journal of Quaternary Science, 28, 537-541. 

28 http://dx.doi.org/10.1002/jqs.2653 

29 Kemp, A.C., B.P. Horton, J.P. Donnelly, M.E. Mann, M. Vermeer, and S. Rahmstorf, 2011: 

30 Climate related sea-level variations over the past two millennia. Proceedings of the National 

31 Academy of Sciences, 108, 11017-11022. http://dx.doi.org/10.1073/pnas.1015619108 

32 Khan, S.A., K.H. Kjaer, M. Bevis, J.L. Bamber, J. Wahr, K.K. Kjeldsen, A.A. Bjork, N.J. 

33 Korsgaard, L.A. Stearns, M.R. van den Broeke, L. Liu, N.K. Larsen, and I.S. Muresan, 2014: 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 12

442

1 Sustained mass loss of the northeast Greenland ice sheet triggered by regional warming. 

2 Nature Climate Change, 4, 292-299. http://dx.doi.org/10.1038/nclimate2161 

3 Khazendar, A., M.P. Schodlok, I. Fenty, S.R.M. Ligtenberg, E. Rignot, and M.R. van den 

4 Broeke, 2013: Observed thinning of Totten Glacier is linked to coastal polynya variability. 

5 Nature Communications, 4, 2857. http://dx.doi.org/10.1038/ncomms3857 

6 Kjeldsen, K.K., N.J. Korsgaard, A.A. Bjørk, S.A. Khan, J.E. Box, S. Funder, N.K. Larsen, J.L. 

7 Bamber, W. Colgan, M. van den Broeke, M.-L. Siggaard-Andersen, C. Nuth, A. 

8 Schomacker, C.S. Andresen, E. Willerslev, and K.H. Kjær, 2015: Spatial and temporal 

9 distribution of mass loss from the Greenland Ice Sheet since AD 1900. Nature, 528, 396-400. 

10 http://dx.doi.org/10.1038/nature16183 

11 Knutson, T.R., J.J. Sirutis, G.A. Vecchi, S. Garner, M. Zhao, H.-S. Kim, M. Bender, R.E. 

12 Tuleya, I.M. Held, and G. Villarini, 2013: Dynamical downscaling projections of twenty13 first-century Atlantic hurricane activity: CMIP3 and CMIP5 model-based scenarios. Journal 

14 of Climate, 27, 6591-6617. http://dx.doi.org/10.1175/jcli-d-12-00539.1 

15 Knutson, T.R., J.J. Sirutis, M. Zhao, R.E. Tuleya, M. Bender, G.A. Vecchi, G. Villarini, and D. 

16 Chavas, 2015: Global Projections of Intense Tropical Cyclone Activity for the Late Twenty17 First Century from Dynamical Downscaling of CMIP5/RCP4.5 Scenarios. Journal of 

18 Climate, 28, 7203-7224. http://dx.doi.org/10.1175/JCLI-D-15-0129.1 

19 Kopp, R.E., 2013: Does the mid-Atlantic United States sea level acceleration hot spot reflect 

20 ocean dynamic variability? Geophysical Research Letters, 40, 3981-3985. 

21 http://dx.doi.org/10.1002/grl.50781 

22 Kopp, R.E., C.C. Hay, C.M. Little, and J.X. Mitrovica, 2015: Geographic Variability of Sea23 Level Change. Current Climate Change Reports, 1, 192-204. 

24 http://dx.doi.org/10.7282/T37W6F4P 

25 Kopp, R.E., B.P. Horton, A.C. Kemp, and C. Tebaldi, 2015: Past and future sea-level rise along

26 the coast of North Carolina, USA. Climatic Change, 132, 693-707. 

27 http://dx.doi.org/10.1007/s10584-015-1451-x 

28 Kopp, R.E., R.M. Horton, C.M. Little, J.X. Mitrovica, M. Oppenheimer, D.J. Rasmussen, B.H. 

29 Strauss, and C. Tebaldi, 2014: Probabilistic 21st and 22nd century sea-level projections at a 

30 global network of tide-gauge sites. Earth's Future, 2, 383-406. 

31 http://dx.doi.org/10.1002/2014EF000239 

32 Kopp, R.E., A.C. Kemp, K. Bittermann, B.P. Horton, J.P. Donnelly, W.R. Gehrels, C.C. Hay, 

33 J.X. Mitrovica, E.D. Morrow, and S. Rahmstorf, 2016: Temperature-driven global sea-level 

34 variability in the Common Era. Proceedings of the National Academy of Sciences, 113,

35 E1434-E1441. http://dx.doi.org/10.1073/pnas.1517056113 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 12

443

1 Kopp, R.E., F.J. Simons, J.X. Mitrovica, A.C. Maloof, and M. Oppenheimer, 2009: Probabilistic 

2 assessment of sea level during the last interglacial stage. Nature, 462, 863-867. 

3 http://dx.doi.org/10.1038/nature08686 

4 Lambeck, K., H. Rouby, A. Purcell, Y. Sun, and M. Sambridge, 2014: Sea level and global ice 

5 volumes from the Last Glacial Maximum to the Holocene. Proceedings of the National 

6 Academy of Sciences, 111, 15296-15303. http://dx.doi.org/10.1073/pnas.1411762111 

7 Lenton, T.M., 2011: Early warning of climate tipping points. Nature Climate Change, 1, 201-

8 209. http://dx.doi.org/10.1038/nclimate1143 

9 Lentz, E.E., E.R. Thieler, N.G. Plant, S.R. Stippa, R.M. Horton, and D.B. Gesch, 2016: 

10 Evaluation of dynamic coastal response to sea-level rise modifies inundation likelihood. 

11 Nature Clim. Change, 6, 696-700. http://dx.doi.org/10.1038/nclimate2957 

12 Leuliette, E.W., 2015: The Balancing of the Sea-Level Budget. Current Climate Change 

13 Reports, 1, 185-191. http://dx.doi.org/10.1007/s40641-015-0012-8 

14 Levermann, A., P.U. Clark, B. Marzeion, G.A. Milne, D. Pollard, V. Radic, and A. Robinson, 

15 2013: The multimillennial sea-level commitment of global warming. Proceedings of the 

16 National Academy of Sciences, 110, 13745-13750. 

17 http://dx.doi.org/10.1073/pnas.1219414110 

18 Li, X., E. Rignot, M. Morlighem, J. Mouginot, and B. Scheuchl, 2015: Grounding line retreat of 

19 Totten Glacier, East Antarctica, 1996 to 2013. Geophysical Research Letters, 42, 8049-8056. 

20 http://dx.doi.org/10.1002/2015GL065701 

21 Lin, N., K. Emanuel, M. Oppenheimer, and E. Vanmarcke, 2012: Physically based assessment of 

22 hurricane surge threat under climate change. Nature Climate Change, 2, 462-467. 

23 http://dx.doi.org/10.1038/nclimate1389 

24 Lin, N., R.E. Kopp, B.P. Horton, and J.P. Donnelly, 2016: Hurricane Sandy’s flood frequency 

25 increasing from year 1800 to 2100. Proceedings of the National Academy of Sciences, 113,

26 12071-12075. http://dx.doi.org/10.1073/pnas.1604386113 

27 http://www.pnas.org/content/113/43/12071.abstract

28 Little, C.M., R.M. Horton, R.E. Kopp, M. Oppenheimer, and S. Yip, 2015: Uncertainty in 

29 Twenty-First-Century CMIP5 Sea Level Projections. Journal of Climate, 28, 838-852. 

30 http://dx.doi.org/10.1175/JCLI-D-14-00453.1 

31 Llovel, W., J.K. Willis, F.W. Landerer, and I. Fukumori, 2014: Deep-ocean contribution to sea 

32 level and energy budget not detectable over the past decade. Nature Climate Change, 4,

33 1031-1035. http://dx.doi.org/10.1038/nclimate2387 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 12

444

1 MacGregor, J.A., W.T. Colgan, M.A. Fahnestock, M. Morlighem, G.A. Catania, J.D. Paden, and 

2 S.P. Gogineni, 2016: Holocene deceleration of the Greenland Ice Sheet. Science, 351, 590-

3 593. http://dx.doi.org/10.1126/science.aab1702 

4 Marcos, M. and A. Amores, 2014: Quantifying anthropogenic and natural contributions to 

5 thermosteric sea level rise. Geophysical Research Letters, 41, 2502-2507. 

6 http://dx.doi.org/10.1002/2014GL059766 

7 Marcos, M., F.M. Calafat, Á. Berihuete, and S. Dangendorf, 2015: Long-term variations in 

8 global sea level extremes. Journal of Geophysical Research: Oceans, 120, 8115-8134. 

9 http://dx.doi.org/10.1002/2015JC011173 

10 Marcos, M., B. Marzeion, S. Dangendorf, A.B.A. Slangen, H. Palanisamy, and L. Fenoglio11 Marc, 2016: Internal Variability Versus Anthropogenic Forcing on Sea Level and Its 

12 Components. Surveys in Geophysics, 1-20. http://dx.doi.org/10.1007/s10712-016-9373-3 

13 Markusson, N., F. Ginn, N. Singh Ghaleigh, and V. Scott, 2014: ‘In case of emergency press 

14 here’: framing geoengineering as a response to dangerous climate change. Wiley 

15 Interdisciplinary Reviews: Climate Change, 5, 281-290. http://dx.doi.org/10.1002/wcc.263 

16 Martín-Español, A., A. Zammit-Mangion, P.J. Clarke, T. Flament, V. Helm, M.A. King, S.B. 

17 Luthcke, E. Petrie, F. Rémy, N. Schön, B. Wouters, and J.L. Bamber, 2016: Spatial and 

18 temporal Antarctic Ice Sheet mass trends, glacio-isostatic adjustment, and surface processes 

19 from a joint inversion of satellite altimeter, gravity, and GPS data. Journal of Geophysical 

20 Research: Earth Surface, 121, 182-200. http://dx.doi.org/10.1002/2015JF003550 

21 Marzeion, B., J.G. Cogley, K. Richter, and D. Parkes, 2014: Attribution of global glacier mass 

22 loss to anthropogenic and natural causes. Science, 345, 919-921. 

23 http://dx.doi.org/10.1126/science.1254702 

24 Mawdsley, R.J. and I.D. Haigh, 2016: Spatial and Temporal Variability and Long-Term Trends 

25 in Skew Surges Globally. Frontiers in Marine Science, 3. 

26 http://dx.doi.org/10.3389/fmars.2016.00029 

27 Menéndez, M. and P.L. Woodworth, 2010: Changes in extreme high water levels based on a 

28 quasi-global tide-gauge data set. Journal of Geophysical Research, 115, C10011. 

29 http://dx.doi.org/10.1029/2009JC005997 

30 Mengel, M., A. Levermann, K. Frieler, A. Robinson, B. Marzeion, and R. Winkelmann, 2016: 

31 Future sea level rise constrained by observations and long-term commitment. Proceedings of 

32 the National Academy of Sciences, 113, 2597-2602. 

33 http://dx.doi.org/10.1073/pnas.1500515113 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 12

445

1 Merrifield, M.A., 2011: A shift in western tropical Pacific sea level trends during the 1990s. 

2 Journal of Climate, 24, 4126-4138. http://dx.doi.org/10.1175/2011JCLI3932.1 

3 Merrifield, M.A., P. Thompson, E. Leuliette, G.T. Mitchum, D.P. Chambers, S. Jevrejeva, R.S. 

4 Nerem, M. Menéndez, W. Sweet, B. Hamlington, and J.J. Marra, 2015: [Global Oceans] Sea 

5 level variability and change [in “State of the Climate in 2014”]. Bulletin of the American 

6 Meteorological Society, 96, S82-S85. 

7 http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1 

8 Miller, K.G., R.E. Kopp, B.P. Horton, J.V. Browning, and A.C. Kemp, 2013: A geological 

9 perspective on sea-level rise and its impacts along the U.S. mid-Atlantic coast. Earth's 

10 Future, 1, 3-18. http://dx.doi.org/10.1002/2013EF000135 

11 Miller, K.G., J.D. Wright, J.V. Browning, A. Kulpecz, M. Kominz, T.R. Naish, B.S. Cramer, Y. 

12 Rosenthal, W.R. Peltier, and S. Sosdian, 2012: High tide of the warm Pliocene: Implications 

13 of global sea level for Antarctic deglaciation. Geology. http://dx.doi.org/10.1130/g32869.1

14 Mitrovica, J. X., Gomez, N., Morrow, E., Hay, C., Latychev, K., & Tamisiea, M. E. (2011). On 

15 the robustness of predictions of sea level fingerprints. Geophysical Journal International, 

16 187(2), 729-742

17 Moftakhari, H.R., A. AghaKouchak, B.F. Sanders, D.L. Feldman, W. Sweet, R.A. Matthew, and 

18 A. Luke, 2015: Increased nuisance flooding along the coasts of the United States due to sea 

19 level rise: Past and future. Geophysical Research Letters, 42, 9846-9852. 

20 http://dx.doi.org/10.1002/2015GL066072 

21 Moritz, H., K. White, B. Gouldby, W. Sweet, P. Ruggiero, M. Gravens, P. O'Brien, H. Moritz, T. 

22 Wahl, N.C. Nadal-Caraballo, and W. Veatch, 2015: USACE adaptation approach for future 

23 coastal climate conditions. Proceedings of the Institution of Civil Engineers - Maritime 

24 Engineering, 168, 111-117. http://dx.doi.org/10.1680/jmaen.15.00015 

25 Mouginot, J., E. Rignot, and B. Scheuchl, 2014: Sustained increase in ice discharge from the 

26 Amundsen Sea Embayment, West Antarctica, from 1973 to 2013. Geophysical Research 

27 Letters, 41, 1576-1584. http://dx.doi.org/10.1002/2013GL059069 

28 Nerem, R.S., D.P. Chambers, C. Choe, and G.T. Mitchum, 2010: Estimating mean sea level 

29 change from the TOPEX and Jason altimeter missions. Marine Geodesy, 33, 435-446. 

30 http://dx.doi.org/10.1080/01490419.2010.491031 

31 Nieves, V., J.K. Willis, and W.C. Patzert, 2015: Recent hiatus caused by decadal shift in Indo32 Pacific heating. Science, 349, 532-535. http://dx.doi.org/10.1126/science.aaa4521 

33 Paolo, F.S., H.A. Fricker, and L. Padman, 2015: Volume loss from Antarctic ice shelves is 

34 accelerating. Science, 348, 327-331. http://dx.doi.org/10.1126/science.aaa0940 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 12

446

1 Passeri, D.L., S.C. Hagen, S.C. Medeiros, M.V. Bilskie, K. Alizad, and D. Wang, 2015: The 

2 dynamic effects of sea level rise on low-gradient coastal landscapes: A review. Earth's 

3 Future, 3, 159-181. http://dx.doi.org/10.1002/2015EF000298 

4 Pfeffer, W.T., J.T. Harper, and S. O'Neel, 2008: Kinematic constraints on glacier contributions to 

5 21st-century sea-level rise. Science, 321, 1340-1343. 

6 http://dx.doi.org/10.1126/science.1159099 

7 Pollard, D., R.M. DeConto, and R.B. Alley, 2015: Potential Antarctic Ice Sheet retreat driven by 

8 hydrofracturing and ice cliff failure. Earth and Planetary Science Letters, 412, 112-121. 

9 http://dx.doi.org/10.1016/j.epsl.2014.12.035 

10 Purkey, S.G. and G.C. Johnson, 2010: Warming of Global Abyssal and Deep Southern Ocean 

11 Waters between the 1990s and 2000s: Contributions to Global Heat and Sea Level Rise 

12 Budgets. Journal of Climate, 23, 6336-6351. http://dx.doi.org/10.1175/2010JCLI3682.1 

13 Rahmstorf, S., 2007: A Semi-Empirical Approach to Projecting Future Sea-Level Rise. Science,

14 315, 368-370. http://dx.doi.org/10.1126/science.1135456 

15 Rahmstorf, S., J.E. Box, G. Feulner, M.E. Mann, A. Robinson, S. Rutherford, and E.J. 

16 Schaffernicht, 2015: Exceptional twentieth-century slowdown in Atlantic Ocean overturning 

17 circulation. Nature Climate Change, 5, 475-480. http://dx.doi.org/10.1038/nclimate2554 

18 Reager, J.T., A.S. Gardner, J.S. Famiglietti, D.N. Wiese, A. Eicker, and M.-H. Lo, 2016: A 

19 decade of sea level rise slowed by climate-driven hydrology. Science, 351, 699-703. 

20 http://dx.doi.org/10.1126/science.aad8386 

21 Reed, A.J., M.E. Mann, K.A. Emanuel, N. Lin, B.P. Horton, A.C. Kemp, and J.P. Donnelly, 

22 2015: Increased threat of tropical cyclones and coastal flooding to New York City during the 

23 anthropogenic era. Proceedings of the National Academy of Sciences, 112, 12610-12615. 

24 http://dx.doi.org/10.1073/pnas.1513127112 

25 Rhein, M., S.R. Rintoul, S. Aoki, E. Campos, D. Chambers, R.A. Feely, S. Gulev, G.C. Johnson, 

26 S.A. Josey, A. Kostianoy, C. Mauritzen, D. Roemmich, L.D. Talley, and F. Wang, 2013: 

27 Observations: Ocean. Climate Change 2013: The Physical Science Basis. Contribution of 

28 Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate 

29 Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. 

30 Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. Cambridge University Press, Cambridge, 

31 United Kingdom and New York, NY, USA, 255–316. 

32 http://dx.doi.org/10.1017/CBO9781107415324.010 www.climatechange2013.org

33 Rietbroek, R., S.-E. Brunnabend, J. Kusche, J. Schröter, and C. Dahle, 2016: Revisiting the 

34 contemporary sea-level budget on global and regional scales. Proceedings of the National 

35 Academy of Sciences, 113, 1504-1509. http://dx.doi.org/10.1073/pnas.1519132113 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 12

447

1 Rignot, E., J. Mouginot, M. Morlighem, H. Seroussi, and B. Scheuchl, 2014: Widespread, rapid 

2 grounding line retreat of Pine Island, Thwaites, Smith, and Kohler glaciers, West Antarctica, 

3 from 1992 to 2011. Geophysical Research Letters, 41, 3502-3509. 

4 http://dx.doi.org/10.1002/2014GL060140 

5 Roemmich, D., J. Church, J. Gilson, D. Monselesan, P. Sutton, and S. Wijffels, 2015: Unabated 

6 planetary warming and its ocean structure since 2006. Nature Climate Change, 5, 240-245. 

7 http://dx.doi.org/10.1038/nclimate2513 

8 Ruggiero, P., 2013: Is the Intensifying Wave Climate of the U.S. Pacific Northwest Increasing 

9 Flooding and Erosion Risk Faster Than Sea-Level Rise? Journal of Waterway, Port, Coastal, 

10 and Ocean Engineering, 139, 88-97. http://dx.doi.org/10.1061/(ASCE)WW.1943-

11 5460.0000172 

12 Scambos, T. and C. Shuman, 2016: Comment on ‘Mass gains of the Antarctic ice sheet exceed 

13 losses’ by H. J. Zwally and others. Journal of Glaciology, 62, 599-603. 

14 http://dx.doi.org/10.1017/jog.2016.59 

15 Seo, K.-W., C.R. Wilson, T. Scambos, B.-M. Kim, D.E. Waliser, B. Tian, B.-H. Kim, and J. 

16 Eom, 2015: Surface mass balance contributions to acceleration of Antarctic ice mass loss 

17 during 2003–2013. Journal of Geophysical Research: Solid Earth, 120, 3617-3627. 

18 http://dx.doi.org/10.1002/2014JB011755 

19 Serafin, K.A. and P. Ruggiero, 2014: Simulating extreme total water levels using a time20 dependent, extreme value approach. Journal of Geophysical Research: Oceans, 119, 6305-

21 6329. http://dx.doi.org/10.1002/2014JC010093 

22 Shennan, I., A.J. Long, and B.P. Horton, eds. Handbook of Sea-Level Research. 2015, John 

23 Wiley & Sons, Ltd. 581. http://dx.doi.org/10.1002/9781118452547. 

24 Shepherd, A., E.R. Ivins, A. Geruo, V.R. Barletta, M.J. Bentley, S. Bettadpur, K.H. Briggs, D.H. 

25 Bromwich, R. Forsberg, N. Galin, M. Horwath, S. Jacobs, I. Joughin, M.A. King, J.T.M. 

26 Lenaerts, J. Li, S.R.M. Ligtenberg, A. Luckman, S.B. Luthcke, M. McMillan, R. Meister, G. 

27 Milne, J. Mouginot, A. Muir, J.P. Nicolas, J. Paden, A.J. Payne, H. Pritchard, E. Rignot, H. 

28 Rott, L. Sandberg Sørensen, T.A. Scambos, B. Scheuchl, E.J.O. Schrama, B. Smith, A.V. 

29 Sundal, J.H. van Angelen, W.J. van de Berg, M.R. van den Broeke, D.G. Vaughan, I. 

30 Velicogna, J. Wahr, P.L. Whitehouse, D.J. Wingham, D. Yi, D. Young, and H.J. Zwally, 

31 2012: A reconciled estimate of ice-sheet mass balance. Science, 338, 1183-1189. 

32 http://dx.doi.org/10.1126/science.1228102 

33 Shope, J.B., C.D. Storlazzi, L.H. Erikson, and C.A. Hegermiller, 2016: Changes to extreme wave 

34 climates of islands within the Western Tropical Pacific throughout the 21st century under 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 12

448

1 RCP 4.5 and RCP 8.5, with implications for island vulnerability and sustainability. Global 

2 and Planetary Change, 141, 25-38. http://dx.doi.org/10.1016/j.gloplacha.2016.03.009 

3 Sillmann, J., T.M. Lenton, A. Levermann, K. Ott, M. Hulme, F. Benduhn, and J.B. Horton, 2015: 

4 Climate emergencies do not justify engineering the climate. Nature Climate Change, 5, 290-

5 292. http://dx.doi.org/10.1038/nclimate2539 

6 Slangen, A.B.A., M. Carson, C.A. Katsman, R.S.W. van de Wal, A. Köhl, L.L.A. Vermeersen, 

7 and D. Stammer, 2014: Projecting twenty-first century regional sea-level changes. Climatic 

8 Change, 124, 317-332. http://dx.doi.org/10.1007/s10584-014-1080-9 

9 Slangen, A.B.A., J.A. Church, C. Agosta, X. Fettweis, B. Marzeion, and K. Richter, 2016: 

10 Anthropogenic forcing dominates global mean sea-level rise since 1970. Nature Climate 

11 Change, advance online publication. http://dx.doi.org/10.1038/nclimate2991 

12 Slangen, A.B.A., J.A. Church, X. Zhang, and D. Monselesan, 2014: Detection and attribution of 

13 global mean thermosteric sea level change. Geophysical Research Letters, 41, 5951-5959. 

14 http://dx.doi.org/10.1002/2014GL061356 

15 Smith, J.M., M.A. Cialone, T.V. Wamsley, and T.O. McAlpin, 2010: Potential impact of sea 

16 level rise on coastal surges in southeast Louisiana. Ocean Engineering, 37, 37-47. 

17 http://dx.doi.org/10.1016/j.oceaneng.2009.07.008

18 Sriver, R. L., N. M. Urban, R. Olson, and K. Keller (2012), Toward a physically plausible upper 

19 bound of sea-level rise projections, Climatic Change, 115(3-4), 893–902, 

20 doi:10.1007/s10584-012-0610-6

21 Stockdon, H.F., R.A. Holman, P.A. Howd, and A.H. Sallenger Jr, 2006: Empirical 

22 parameterization of setup, swash, and runup. Coastal Engineering, 53, 573-588. 

23 http://dx.doi.org/10.1016/j.coastaleng.2005.12.005 

24 Stopa, J.E. and K.F. Cheung, 2014: Periodicity and patterns of ocean wind and wave climate. 

25 Journal of Geophysical Research: Oceans, 119, 5563-5584. 

26 http://dx.doi.org/10.1002/2013JC009729 

27 Strauss, B.H., S. Kulp, and A. Levermann, 2015: Carbon choices determine US cities committed 

28 to futures below sea level. Proceedings of the National Academy of Sciences, 112, 13508-

29 13513. http://dx.doi.org/10.1073/pnas.1511186112 

30 Sutterley, T.C., I. Velicogna, E. Rignot, J. Mouginot, T. Flament, M.R. van den Broeke, J.M. 

31 van Wessem, and C.H. Reijmer, 2014: Mass loss of the Amundsen Sea Embayment of West 

32 Antarctica from four independent techniques. Geophysical Research Letters, 41, 8421-8428. 

33 http://dx.doi.org/10.1002/2014GL061940 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 12

449

1 Sweet, W., M. Menendez, A. Genz, J. Obeysekera, J. Park, and J. Marra, 2016: In Tide’s Way: 

2 Southeast Florida’s September 2015 Sunny-day Flood [in "Explaining Extremes of 2015 

3 from a Climate Perspective"]. Bulletin of the American Meteorological Society, 97, In press. 

4 http://dx.doi.org/10.1175/BAMS-D-16-0117.1 

5 Sweet, W., J. Park, J. Marra, C. Zervas, and S. Gill, 2014: Sea Level Rise and Nuisance Flood 

6 Frequency Changes around the United States. NOAA Technical Report NOS CO-OPS 073, 

7 58 pp. U.S. Department of Commerce, National Oceanic and Atmospheric Administration, 

8 National Ocean Service, Silver Spring, MD. 

9 http://tidesandcurrents.noaa.gov/publications/NOAA_Technical_Report_NOS_COOPS_073.

10 pdf

11 Sweet, W.V., R.E. Kopp, C. Weaver, J. Obeysekera, R. Horton, E.R. Thieler, and C. Zervas, In 

12 review: Global and Regional Sea Level Rise Scenarios for the United States. NOAA.

13 Sweet, W.V. and J.J. Marra, 2016: State of U.S. Nuisance Tidal Flooding. Supplement to State of 

14 the Climate: National Overview for May 2016. 5 pp. NOAA National Centers for 

15 Environmental Information. http://www.ncdc.noaa.gov/monitoring16 content/sotc/national/2016/may/sweet-marra-nuisance-flooding-2015.pdf

17 Sweet, W.V. and J. Park, 2014: From the extreme to the mean: Acceleration and tipping points 

18 of coastal inundation from sea level rise. Earth's Future, 2, 579-600. 

19 http://dx.doi.org/10.1002/2014EF000272 

20 Sweet, W.V., J. Park, S. Gill, and J. Marra, 2015: New ways to measure waves and their effects 

21 at NOAA tide gauges: A Hawaiian-network perspective. Geophysical Research Letters, 42,

22 9355-9361. http://dx.doi.org/10.1002/2015GL066030 

23 Sweet, W.V. and C. Zervas, 2011: Cool-Season Sea Level Anomalies and Storm Surges along 

24 the U.S. East Coast: Climatology and Comparison with the 2009/10 El Niño. Monthly 

25 Weather Review, 139, 2290-2299. http://dx.doi.org/10.1175/MWR-D-10-05043.1 

26 Sweet, W.V., C. Zervas, S. Gill, and J. Park, 2013: Hurricane Sandy inundation probabilities of 

27 today and tomorrow [in "Explaining Extremes of 2012 from a Climate Perspective"]. Bulletin 

28 of the American Meteorological Society, 94, S17-S20. http://dx.doi.org/10.1175/BAMS-D29 13-00085.1 

30 Talke, S.A., P. Orton, and D.A. Jay, 2014: Increasing storm tides in New York Harbor, 1844–

31 2013. Geophysical Research Letters, 41, 3149-3155. 

32 http://dx.doi.org/10.1002/2014GL059574 

33 Tebaldi, C., B.H. Strauss, and C.E. Zervas, 2012: Modelling sea level rise impacts on storm 

34 surges along US coasts. Environmental Research Letters, 7, 014032. 

35 http://dx.doi.org/10.1088/1748-9326/7/1/014032 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 12

450

1 Tedesco, M., S. Doherty, X. Fettweis, P. Alexander, J. Jeyaratnam, and J. Stroeve, 2016: The 

2 darkening of the Greenland ice sheet: trends, drivers, and projections (1981–2100). The 

3 Cryosphere, 10, 477-496. http://dx.doi.org/10.5194/tc-10-477-2016 

4 Tedesco, M., X. Fettweis, T. Mote, J. Wahr, P. Alexander, J.E. Box, and B. Wouters, 2013: 

5 Evidence and analysis of 2012 Greenland records from spaceborne observations, a regional 

6 climate model and reanalysis data. The Cryosphere, 7, 615-630. http://dx.doi.org/10.5194/tc7 7-615-2013 

8 Theuerkauf, E.J., A.B. Rodriguez, S.R. Fegley, and R.A. Luettich, 2014: Sea level anomalies 

9 exacerbate beach erosion. Geophysical Research Letters, 41, 5139-5147. 

10 http://dx.doi.org/10.1002/2014GL060544 

11 Thompson, P.R., G.T. Mitchum, C. Vonesch, and J. Li, 2013: Variability of Winter Storminess 

12 in the Eastern United States during the Twentieth Century from Tide Gauges. Journal of 

13 Climate, 26, 9713-9726. http://dx.doi.org/10.1175/JCLI-D-12-00561.1 

14 Wada, Y., M.-H. Lo, P.J.F. Yeh, J.T. Reager, J.S. Famiglietti, R.-J. Wu, and Y.-H. Tseng, 2016: 

15 Fate of water pumped from underground and contributions to sea-level rise. Nature Climate 

16 Change, 6, 777-780. http://dx.doi.org/10.1038/nclimate3001 

17 Wahl, T. and D.P. Chambers, 2015: Evidence for multidecadal variability in US extreme sea 

18 level records. Journal of Geophysical Research: Oceans, 120, 1527-1544. 

19 http://dx.doi.org/10.1002/2014JC010443 

20 Wahl, T. and D.P. Chambers, 2016: Climate controls multidecadal variability in U. S. extreme 

21 sea level records. Journal of Geophysical Research: Oceans, 121, 1274-1290. 

22 http://dx.doi.org/10.1002/2015JC011057 

23 Wang, X.L., Y. Feng, and V.R. Swail, 2014: Changes in global ocean wave heights as projected 

24 using multimodel CMIP5 simulations. Geophysical Research Letters, 41, 1026-1034. 

25 http://dx.doi.org/10.1002/2013GL058650 

26 Wong, P.P. et al. in Climate Change 2013: The Physical Science Basis. (eds Stocker, T.F. et al.) 

27 361-409 (Cambridge Univ Press, 2014).

28 Woodruff, J.D., J.L. Irish, and S.J. Camargo, 2013: Coastal flooding by tropical cyclones and 

29 sea-level rise. Nature, 504, 44-52. http://dx.doi.org/10.1038/nature12855 

30 Woodworth, P.L. and M. Menéndez, 2015: Changes in the mesoscale variability and in extreme 

31 sea levels over two decades as observed by satellite altimetry. Journal of Geophysical 

32 Research: Oceans, 120, 64-77. http://dx.doi.org/10.1002/2014JC010363 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 12

451

1 Wöppelmann, G. and M. Marcos, 2016: Vertical land motion as a key to understanding sea level 

2 change and variability. Reviews of Geophysics, 54, 64-92. 

3 http://dx.doi.org/10.1002/2015RG000502 

4 Yin, J. and P.B. Goddard, 2013: Oceanic control of sea level rise patterns along the East Coast of 

5 the United States. Geophysical Research Letters, 40, 5514-5520. 

6 http://dx.doi.org/10.1002/2013GL057992

7 Yin, J., M.E. Schlesinger, and R.J. Stouffer, 2009: Model projections of rapid sea-level rise on 

8 the northeast coast of the United States. Nature Geoscience, 2, 262-266. 

9 http://dx.doi.org/10.1038/ngeo462 

10 Zappa, G., L.C. Shaffrey, K.I. Hodges, P.G. Sansom, and D.B. Stephenson, 2013: A Multimodel 

11 Assessment of Future Projections of North Atlantic and European Extratropical Cyclones in 

12 the CMIP5 Climate Models. Journal of Climate, 26, 5846-5862. 

13 http://dx.doi.org/10.1175/jcli-d-12-00573.1 

14 Zervas, C., S. Gill, and W.V. Sweet, 2013: Estimating vertical land motion from long-term tide 

15 gauge records. 22 pp. NOAA.

16 Zhang, X. and J.A. Church, 2012: Sea level trends, interannual and decadal variability in the 

17 Pacific Ocean. Geophysical Research Letters, 39, L21701. 

18 http://dx.doi.org/10.1029/2012GL053240
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 13

452

1 13. Ocean Changes: Warming, Stratification, Circulation, 

2 Acidification, and Deoxygenation

3 KEY FINDINGS

4 1. The world’s oceans have absorbed more than 90% of the excess heat caused by 

5 greenhouse warming since the mid 20th Century, making them warmer and altering 

6 global and regional circulation patterns and climate feedbacks (very high confidence). 

7 Surface oceans have warmed by about 0.45°F (0.25°C) globally since the 1970s (very 

8 high confidence). The Atlantic meridional overturning circulation (AMOC) has slowed 

9 since preindustrial times (high confidence). Regionally, eastern boundary upwelling, such 

10 as along the U.S. West Coast, that sustains fisheries and controls local climate has 

11 intensified (high confidence).

12 2. The world’s oceans are currently absorbing more than a quarter of the carbon dioxide 

13 emitted to the atmosphere annually from human activities (very high confidence), making 

14 them more acidic with potential detrimental impacts to marine ecosystems. The rate of 

15 acidification is unparalleled in at least the past 66 million years (medium confidence). 

16 Acidification is regionally increased along U.S. coastal systems as a result of intensified 

17 upwelling (for example, in the Pacific Northwest) (high confidence), changes in 

18 freshwater inputs (for example, Gulf of Maine) (medium confidence), and nutrient input

19 (for example, urbanized estuaries) (high confidence).

20 3. Increasing sea surface temperatures, rising sea levels, and changing patterns of 

21 precipitation, winds, nutrients, and ocean circulation are contributing to overall declining 

22 oxygen concentrations at intermediate depths in various ocean locations and in many 

23 coastal areas. Over the last half century, major oxygen losses have occurred in inland 

24 seas, estuaries, and in the coastal and open ocean (high confidence).

25 4. In coastal regions, local biogeochemical processes can result in increased acidification 

26 co-occurring with eutrophication and/or where riverine loads include naturally corrosive 

27 materials of geological origin. All local areas of deoxygenation also tend to be areas of 

28 acidification, due to intensified respiration (very high confidence).

29 5. Under a high future scenario (RCP8.5), the AMOC is projected to decline by 6 Sverdrups

(1 x 106 m3 30 /sec), global average ocean acidity is projected to increase by 100% to 150%) 

31 (very high confidence), and ocean oxygen levels are projected to decrease by 4% (high 

32 confidence) by 2100 relative to preindustrial values. Under a low future scenario

33 (RCP2.6), global average ocean acidity is projected to increase by 35% and oxygen

34 projected to decrease by 2% by 2100. Larger acidity increases and oxygen declines are 

35 projected in some regions and in intermediate and mode waters (medium confidence).
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1 13.1 Warming, Stratification, and Circulation Changes

2 13.1.1 General Background

3 Anthropogenic perturbations to the global earth system have included important changes to the 

4 oceans. These changes will be distinguishable from the background natural variability in nearly 

5 half of the global open ocean within a decade, with important consequences for marine 

6 ecosystems and their services (Gattuso et al. 2015).

7 As discussed in Chapter 12, between 1971–2010, the upper ocean (0–200 m depth) warmed by 

8 about 0.25°C (0.45° F) globally (Rhein et al. 2013). Between 1950 and 2009, the North Atlantic 

9 gained heat at the rate of about 0.07°C/decade (Hoegh-Guldberg et al. 2014). Such trends are 

10 attributed to the combination of global warming and natural climatic variations ,such as the 

11 Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation (Trenberth et al. 2014; 

12 Steinman et al. 2015). 

13 The uptake of heat by oceans alleviated some atmospheric warming. However, as the ocean 

14 warms, its efficiency in taking up heat and greenhouse gases is decreasing because, among other 

15 reasons, the surface ocean has become more stratified. Surface ocean stratification has increased 

16 by about 4% during the period 1971 to 2010 (Ciais et al. 2013) due to thermal heating and 

17 freshening from increased freshwater inputs (precipitation and evaporation changes, land and 

18 sea-ice melting). In addition, changes in stratification are associated with suppression of tropical 

19 cyclone intensification (Mei et al. 2015), retreat of the polar ice sheets (Straneo and Heimbach 

20 2013), and reductions of the convective mixing at higher latitudes that ventilates the deep ocean 

21 through the Atlantic meridional overturning circulation (AMOC) (Rahmstorf et al. 2015). Ocean 

22 heat uptake therefore represents a significant feedback that controls the climate transient 

23 response (see Ch. 2) while it is also responsible for one third of the sea level rise (see Ch. 12). 

24 In addition to hydrographic changes, ocean circulation patterns are altered in a warming climate. 

25 Through a complex interplay with surface heat fluxes and winds, increased sea surface 

26 temperatures (SSTs) have led to intensification and a poleward shift of the western boundary 

27 currents in all ocean basins (Yang et al. 2016). The Gulf Stream, in contrast to other western 

28 boundary currents, is expected to slow down because of the weakening of the AMOC (Yang et 

29 al. 2016). On the other hand, the expected slowdown of the AMOC will be counteracted by the 

30 warming of the deep ocean (below 700 m [3000 ft]) which will tend to strengthen the AMOC 

31 (Patara and Böning, 2014). Any slowdown of the AMOC will result in less heat and CO2

32 absorbed by the ocean from the atmosphere, which is a positive feedback to climate change (see 

33 also Ch. 2).

34 Significant changes to ocean stratification and circulation can also be observed regionally, along 

35 the eastern ocean boundaries and the equatorial waveguide. In these areas, wind-driven 

36 upwelling brings colder, nutrient- and carbon-rich water to the surface; this upwelled water is 

37 more efficient in heat and CO2 uptake. Some upwelling regions are already experiencing an 
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1 intensification and greater number of upwelling events due to climate change (Hoegh-Guldberg 

2 et al. 2014), while others, such as the California Current, are experiencing fewer (by about 23%–

3 40%) but stronger upwelling events (Hoegh-Guldberg et al. 2014; Sydeman et al. 2014; Jacox et 

4 al. 2014). 

5 The Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC 2013)

6 concluded that there is low confidence in the current understanding of how eastern upwelling 

7 systems will be altered under future climate change because of the obscuring role of multi8 decadal climate variability (Ciais et al. 2013). However, subsequent studies showed that by 2100, 

9 upwelling is predicted to start earlier, end later, and intensify in three of the four major eastern 

10 boundary upwelling systems (not in the California Current; Wang et al. 2015). Southern Ocean 

11 upwelling will intensify while the Atlantic equatorial upwelling systems will weaken (Hoegh12 Guldberg et al. 2014; Wang et al. 2015). The intensification is attributed to the strengthening of 

13 regional coastal winds as observations already show (Sydeman et al. 2014a) and model 

14 projections estimate for the 21st Century (Rykaczewski et al. 2015; Wang et al. 2015).

15 13.1.2 Coastal Changes

16 Coastal boundary systems have warmed by 0.14°–0.80°C from 1950 to 2009 (Hoegh-Guldberg 

17 et al. 2014). This is consistent with changes in adjacent deeper waters. For example, the Gulf of 

18 Mexico and Caribbean Sea have warmed by 0.31° and 0.50°C, respectively, from 1982 to 2006 

19 (Chollett et al. 2012; Hoegh-Guldberg et al. 2014; Muller-Karger et al. 2015). Warming in 

20 tropical seas is leading to increased rates of stress in biological systems like coral reefs.

21 Sea level is an important variable that affects coastal ecosystems. Global sea level rose very 

22 rapidly at the end of the last glaciation, as glaciers and the polar ice sheets melted at their fringes. 

23 On average around the globe, sea level is estimated to have risen at rates exceeding 2.5 mm/year 

24 between about 8,000 and 6,000 years before present. These rates steadily decreased to less than 

25 2.0 mm/year through about 4,000 years ago and stabilized at less than 0.4 mm/year through the 

26 late 1800s. Global sea level rise has accelerated again within the last 100 years, and now 

27 averages about 1 to 2 mm/year (Thompson et al. 2016). 

28 Stronger offshore upwelling combined with cross-shelf advection brings nutrients from the 

29 deeper ocean but also increased offshore transport (Bakun et al. 2015). The net nutrient load in 

30 the coastal regions is responsible for increased productivity and ecosystem function. There is 

31 some evidence that coastal upwelling in mid- to high-latiude eastern boundary regions has 

32 increased (Garcia-Reyes et al. 2015), but in more tropical areas of the western Atlantic, such as 

33 in the Caribbean Sea, it decreased between 1990 and 2010 (Taylor et al. 2012; Astor et al. 2013). 

34 This led to a decrease in primary productivity in the southern Caribbean Sea (Taylor et al. 2012). 

35 There is still much uncertainty on the direction in which upwelling systems may go with regards 

36 to upwelling strength in different locations. Each coastal upwelling center has unique features 

37 and should be evaluated locally in the context of regional changes.
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1 Dust transported from continental desert regions to the marine environment deposits nutrients 

2 such as iron, nitrogen and phosphorus, and trace metals that stimulate growth of phytoplankton 

3 and increase marine productivity (Jickells and Moore 2015). U.S. continental and coastal regions 

4 experience large dust deposition fluxes originating from Sahara on the Eastern boundary and 

5 from the Central Asia and China Subcontinent in the Northwest (Chiapello 2014). Greenhouse 

6 gas emissions and other anthropogenic interference, such as agricultural activity and land use 

7 changes, may play an important role on the variability and strength of these dust sources (e.g., 

8 Mulitza et al. 2010). 

9 Additionally, oxidized nitrogen, released during high-temperature combustion over land, and 

10 reduced nitrogen, released from intensive agriculture, are emitted in areas of high population in 

11 North America and are carried away and deposited through wet or dry deposition over coastal 

12 and open ocean ecosystems via the local wind circulation. Wet deposition of pollutants produced 

13 in urban areas is known to play an important role in changes of ecosystem structure in coastal 

14 and open ocean systems.

15 13.2 Ocean Acidification

16 13.2.1 General Background

17 In addition to causing changes in climate, increasing atmospheric levels of carbon dioxide (CO2)

18 from the burning of fossil fuels and other human activities (for example, changes in land use and 

19 cement production) have a direct effect on the world’s oceans’ chemistry (Orr et al., 2005; Feely 

20 et al. 2009). Chemical changes in seawater as a result of the uptake of CO2 include increasing 

21 concentrations of dissolved inorganic carbon (DIC), the production of carbonic acid 

22 (acidification, or lowering of pH), an increase in the partial pressure of CO2 (pCO2,sw), and a 

23 decrease in the concentration of carbonate ions (Figure 13.1). Ocean surface waters have become

24 30% more acidic over the last 150 years as they have absorbed large amounts of carbon dioxide 

25 from the atmosphere (Feely et al. 2004). Since the preindustrial period, the oceans have absorbed 

26 approximately 27% of all carbon dioxide emitted to the atmosphere. The oceans are currently 

27 absorbing about a quarter of human-caused carbon dioxide emitted to the atmosphere, leading to 

28 ocean acidification and the potential alteration of marine ecosystems (Le Quéré et al. 2015).

29 [INSERT FIGURE 13.1 HERE:

30 Figure 13.1: Trends in surface (< 50 m) ocean carbonate chemistry calculated from observations 

31 obtained at the Hawai‘i Ocean Time-series (HOT) Program in the North Pacific 1988–2015. 

32 Data obtained from the Hawai‘i Ocean Time-series Data Organization & Graphical System 

33 (HOT-DOGS, http://hahana.soest.hawaii.edu/hot/hot-dogs/index.html). Seawater xCO2 (μatm),

34 pHTotal, and carbonate ion concentration (μmol/kg) were derived using CO2SYS v2.1 by pairing 

35 direct measures of total alkalinity (TA) with dissolved inorganic carbon (DIC) when available (n 

36 = 824). In cases where this pairing was not possible, then pH and DIC were instead adopted (n =

37 16). As a final option, if neither of those pairings was an option then pHTotal and TA were 
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1 adopted (n = 18). The calculations were performed using K1, K2 from Millero, 2010, the KHSO4

2 dissociation constant of Dickon, total scale pH, and Uppstrom, 1974 for [B]T. Atmospheric mole 

3 fraction CO2 (red) data were obtained from NOAA’s Earth System Research Laboratory 

4 CarbonTracker (Peters et al. 2007). (Figure source: NOAA)]

5 13.2.2 Open Ocean Acidification

6 Open ocean waters experience changes in carbonate chemistry more reflective of large-scale 

7 physical oceanic processes, including the global uptake of atmospheric CO2 and the entrainment 

8 of acidified subsurface waters due to vertical mixing and upwelling. The rate of ocean 

9 acidification within these waters closely approximates the rate of atmospheric CO2 increase 

10 subsequently modified by multidecadal variability (for example, the Atlantic Multidecadal 

11 Oscillation and Pacific Decadal Oscillation) processes thereby exhibiting long-term secular 

12 changes in ocean carbonate chemistry, as measured on decadal time-scales (Bates et al. 2014; 

13 Gledhill et al. 2015).

14 13.2.3 Coastal Acidification

15 In contrast to open ocean waters, coastal shelf and nearshore waters are strongly influenced by a 

16 number of additional processes. Along the Pacific Coast, upwelling brings deep waters enriched 

17 in CO2 due to deep water respiration processes, thereby exhibiting much lower pH than that of 

18 surface waters (Feely et al. 2009; Harris et al. 2013). Along the East Coast, upwelling is less 

19 prominent than along the Pacific Coast but does occur in limited locations. However, the coastal 

20 waters of the East Coast and mid-Atlantic are far more influenced by freshwater inputs, which 

21 contribute varying amounts of dissolved inorganic carbon (DIC), total alkalinity (TA), dissolved 

22 and particulate organic carbon, and nutrients from riverine and estuarine sources, all of which 

23 significantly alter the local biogeochemistry of the receiving water mass (Gledhill et al. 2015). 

24 Coastal waters can episodically experience riverine plumes that are corrosive to calcium 

25 carbonate (Salisbury et al. 2008). While these processes have persisted historically, climate26 induced increases in high-intensity precipitation events, particularly in the northeast, can yield 

27 larger freshwater plumes extending further from the coast. Nutrient loading promotes organic 

28 matter production by autotrophy that can result in intense respiration by heterotrophs and drive 

29 up local CO2 concentrations to a degree dependent upon hydrographic conditions (including 

30 stratification and residence time) (Waldbusser & Salisbury 2014). Coastal acidification generally 

31 exhibits higher-frequency variability and short-term episodic events relative to open-ocean 

32 acidification (Borges & Gypens 2010; Waldbusser & Salisbury 2014; Hendriks et al. 2015). 

33 However, long-term changes in nutrient loading and/or precipitation may also impart long-term 

34 secular changes in coastal acidification magnitude and occurrence.

35 13.2.4 Latitudinal Variation

36 Higher-latitude systems are typically less buffered against pH change and can exhibit low 

37 carbonate ion conditions (important in carbonate mineral formation and preservation), in large 
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1 part due to their lower temperatures (Gledhill et al. 2015, Bates & Mathis 2009). This means that 

2 they will experience seasonally corrosive conditions sooner than low-latitude systems. For 

3 example, observations have shown that the northeastern Pacific Ocean, including the Arctic and 

4 sub-Arctic seas, is particularly susceptible to ocean acidification because the degree of carbonate

5 mineral supersaturation is reduced due to their lower temperatures (Bates & Mathis 2009). The 

6 waters along the northwestern Atlantic, including areas along the Scotian Shelf and Gulf of 

7 Maine, are also sensitive to acidification (for example, they exhibit a greater change in pH in 

8 response to a given input of CO2), and have lower buffering capacity due to significant riverine 

9 input (Gledhill et al. 2015; Jiang et al. 2010). However, it is important to note that the absolute 

10 rate of change in low-latitude systems with respect to carbonate mineral saturation state is 

11 considerably faster than that of the higher-latitudes (Friedrich et al. 2012).

12 13.2.5 Paleo Evidence

13 Evidence suggests that the current ocean acidification rate is the fastest in the last 66 million 

14 years and possibly even the past 300 million years (Honisch et al. 2012; Zeebe et al 2016). There 

15 is no known period in the past 66 million years that has shown rates of CO2 increase similar to 

16 those experienced in the late 20th and early 21st centuries. The Paleo-Eocene Thermal Maximum 

17 (PETM; around 56 million years ago) is often referenced as the closest analogue to the present 

18 CO2 increase and related change in ocean chemistry. However, at the PETM, only about 2500–

19 4500 PgC of CO2 were released into the atmosphere over 4000 years. The rate of release was 

20 between 0.6 and 1.1 Pg/year (Zeebe et al, 2016) compared to the current 10 Pg/year. Thus, the 

21 relatively slower rate of CO2 increase at the PETM likely led to relatively small change in 

22 carbonate ion concentration compared with contemporary acidification, due to the ability of rock 

23 weathering to buffer the change (Zeebe et al. 2016). However, others have argued that the PETM 

24 may have resulted from an abrupt pulse of CO2, perhaps even faster than current emission rates, 

25 albeit with a lesser total emission volume (Wright and Shaller 2013). 

26 13.2.6 Projected Changes

27 Projections indicate that in higher emissions pathways, such as SRES A1fi or RCP8.5, pH of the 

28 open surface ocean could be reduced from the current, average level of 8.1 to as low as an 

29 average of 7.8 by the end of the century (Fig 13.2), and the ocean volume occupied by corrosive 

30 waters could expand from 76% in the 1990s to 91% in 2100 (Gattuso et al. 2015). Regional 

31 changes in the rate of acidification may vary significantly from the global mean, however, with 

32 some regions acidifying faster than others (Turley et al. 2010). Recent observational data from 

33 the Arctic Basin show that the Beaufort Sea became undersaturated, for part of the year, with 

34 respect to aragonite in 2001, and the other continental shelf seas in the region will all acidify 

35 enough to become undersaturated, seasonally, with respect to aragonite by the end of the century 

36 (Chukchi Sea in about 2033; Bering Sea in about 2062); this undersaturation will put tremendous 

37 pressure on the diverse ecosystems that support some of the largest commercial and subsistence 
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1 fisheries in the world (Mathis et al. 2015). The Gulf of Maine will likely experience hypercapnia 

2 (pCO2 > 1000 μatm) throughout much of the fall before 2100 (McNeil & Sasse 2016). 

3 Earth System Models predict that surface waters of the Southern Polar Ocean will be 

4 undersaturated with respect to aragonite by 2100 (Caldeira and Wickett 2003; Orr et al. 2005). 

5 But, these projections are highly uncertain due to the lack of measurements in the Southern 

6 Ocean; more observational data are needed to better constrain the natural variability. Calcium 

7 carbonate saturation levels in the Western Antarctic Peninsula and the Arctic Ocean could 

8 decrease dramatically if there is increased freshwater from sea ice and glacier melt into the ocean 

9 (Chierici and Fransson 2009, Yamamoto-Kawai et al. 2009). 

10 [INSERT FIGURE 13.2 HERE:

11 Figure 13.2: Predicted change in pH in 2090–2099 relative to 1990–1999 under RCP8.5, based 

12 on the Community Earth System Models–Large Ensemble Experiments CMIP5. (Figure source: 

13 adapted from Bopp et al. 2013)]

14 13.3 Ocean Deoxygenation

15 13.3.1 General Background

16 Oxygen is essential to most life in the ocean, governing a host of biogeochemical and biological 

17 processes. Oxygen influences metabolic, physiological, reproductive, behavioral, and ecological 

18 processes, ultimately shaping the composition, diversity, abundance, and distribution of 

19 organisms from microbes to whales. Increasingly, climate-induced oxygen loss (deoxygenation) 

20 has become evident locally, regionally, and globally. Deoxygenation can be attributed to 

21 anthropogenic nutrient input as well as CO2 emissions. In addition, acidification (section 13.2) 

22 can co-occur with deoxygenation as a result of warming-enhanced biological respiration

23 (Breitburg et al. 2015). As organisms respire, O2 is consumed and CO2 is produced. 

24 Understanding the combined effect of both low O2 and low pH on marine ecosystems is an area 

25 of active research (Gobler et al. 2014). Warming also raises biological metabolic rates which, in 

26 combination with intensified coastal and estuarine stratification, exacerbates eutrophication27 induced hypoxia. We now see earlier onset and longer periods of seasonal hypoxia in many 

28 eutrophic sites, most of which occur in areas that are also warming (Altieri and Gedan 2015). 

29 13.3.2 Climate Drivers of Ocean Deoxygenation

30 Global ocean deoxygenation is a direct effect of warming. Ocean warming reduces the solubility 

31 of oxygen (that is, warmer water can hold less oxygen). The increased temperature of global 

32 oceans accounts for about 15% of current global oxygen loss (Helm et al. 2011), although 

33 changes in temperature and oxygen are not uniform throughout the ocean (Roemmich et al. 

34 2015). Warming also exerts direct influence on thermal stratification and enhances salinity 

35 stratification through ice melt and climate change-associated precipitation effects. Intensified 

36 stratification leads to reduced ventilation (mixing of oxygen into the ocean interior) and accounts 
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1 for up to 85% of global ocean oxygen loss (Helm et al. 2011). Effects of ocean temperature 

2 change and stratification on oxygen loss are strongest in intermediate or mode waters at bathyal 

3 depths (in general, 200–3000 meters), and also nearshore and in the open ocean; these changes 

4 are especially evident in tropical and subtropical waters globally, in the Eastern Pacific (Stramma 

5 et al. 2010), and in the Southern Ocean (Helm et al. 2011).

6 There are also other, less direct effects of global temperature increase. Warming on land reduces 

7 terrestrial plant water efficiency (through effects on stomata), leading to greater runoff into 

8 coastal zones and further enhancing hypoxia potential because greater run off means more 

9 nutrient transport (Reay et al. 2008). Warming can induce dissociation of frozen methane in gas 

10 hydrates buried on continental margins, leading to further drawdown of oxygen through aerobic 

11 methane oxidation in the water column (Boetius and Wenzhöfer 2013). On eastern ocean 

12 boundaries, warming enhances the land–sea temperature differential, causing increased 

13 upwelling due to higher winds with (a) greater nutrient input leading to production, sinking, 

14 decay, and biochemical drawdown of oxygen and (b) upwelling of naturally low-oxygen, high15 CO2 waters onto the upper slope and shelf environments (Sydeman et al. 2014, Feely et al. 

16 2009). Taken together, the effects of warming are manifested as expanding and shoaling oxygen 

17 minimum zones (OMZs) in open waters and increased eutrophication-induced hypoxia in coastal 

18 areas.

19 Changes in precipitation, winds, circulation, airborne nutrients, and sea level can also contribute 

20 to ocean deoxygenation. Projected increases in precipitation in some regions will intensify 

21 stratification, reducing vertical mixing and ventilation, and intensify nutrient input to coastal 

22 waters through excess runoff. Coastal wetlands that might remove these nutrients before they 

23 reach the ocean may be lost through rising sea level, further exacerbating hypoxia. Some 

24 observations of oxygen decline are linked to regional changes in circulation involving low25 oxygen water masses. Enhanced fluxes of airborne iron and nitrogen are interacting with natural 

26 climate variability and contributing to fertilization, enhanced respiration, and oxygen loss in the 

27 tropical Pacific (Ito et al. 2016). In contrast to the many sources of climate-induced oxygen loss, 

28 the projected increase in incidence and intensity of cyclones and hurricanes will induce mixing, 

29 which can ameliorate hypoxia locally (Rabalais et al. 2009).

30 13.3.3 Biogeochemical Feedbacks of Deoxygenation to Climate and Elemental Cycles

31 Climate patterns and ocean circulation have a large effect on global nitrogen and oxygen cycles, 

32 which in turn affect phosphorus and trace metal availability and generate feedbacks to the 

33 atmosphere and oceanic production. Climate-driven changes in the depth of the tropical and 

34 subtropical thermocline control the volume of suboxic waters (< 5 micromolar O2), which in turn

35 controls loss of fixed nitrogen through denitrification, influencing the supply of nitrite, which 

36 limits global ocean productivity (Codispoti et al. 2001; Deutsch et al. 2011). The extent of 

37 suboxia also regulates the production of the greenhouse gas nitrous oxide (N2O); as oxygen 

38 declines, greater N2O production may intensify global warming (Gruber 2008). Production of
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1 hydrogen sulfide (H2S, which is highly toxic) and intensified phosphorus recycling can occur at 

2 low oxygen levels (Wallmann 2003). Other feedbacks may emerge as OMZ shoaling diminishes 

3 the depths of diurnal vertical migrations by fish and invertebrates, and as their huge biomass and 

4 associated oxygen consumption deplete oxygen (Bianchi et al. 2013).

5 13.3.4 Past Trends

6 Over hundreds of millions of years, oxygen has varied dramatically in the atmosphere and ocean, 

7 and has been linked to the gain and loss of biodiversity (Knoll and Carroll 1999; McFall-Ngai et 

8 al. 2013). Variation in oxygenation in the paleo record is very sensitive to climate─with clear 

9 links to temperature and often CO2 variation (Falkowski et al. 2011). OMZs expand and contract 

10 in synchrony with warming and cooling events, respectively (Robinson et al. 2007). Episodic 

11 climate events that involve rapid temperature increases over decades, followed by a cool period 

12 lasting a few hundred years, lead to major fluctuations in the intensity of Pacific and Indian 

13 Ocean oxygen minimum zones. These events are associated with rapid variations in North 

14 Atlantic Deep Water formation (Schmittner et al. 2007). Ocean oxygen fluctuates on glacial15 interglacial timescales of thousands of years in the Eastern Pacific (Galbraith et al. 2004; Moffitt 

16 et al. 2015). 

17 13.3.5 Modern observations (last 50+ years)

18 Long-term oxygen records made over the last 50 years reflect oxygen declines in inland seas 

19 (Justic et al. 1987; Zaitzev 1992; Conley et al. 2011), in estuaries (Brush 2009; Gilbert et al. 

20 2005), and in coastal waters (Rabalais et al. 2007, 2010; Booth et al. 2012; Baden et al. 1990). 

21 The number of coastal, eutrophication-induced hypoxic sites in the United States has grown

22 dramatically over the past 40 years (Diaz and Rosenberg 2008, Hypoxia Tracker). Over larger 

scales, global syntheses show hypoxic waters have expanded by 4.5 million km2 23 at a depth of 

24 200 meters (Stramma et al. 2010), with widespread loss of oxygen in the Southern Ocean (Helm 

25 et al. 2011), Western Pacific (Takatani et al. 2012), and North Atlantic (Stendardo and Gruber 

26 2012). Overall oxygen declines have been greater in the coastal ocean than the open ocean 

27 (Gilbert et al. 2010) and often greater inshore than offshore (Bograd et al. 2015). The emergence 

28 of a deoxygenation signal in regions with naturally high oxygen variability will unfold over 

29 longer time periods (20 – 50 years from now) (Long et al. 2016).

30 13.3.6 Projected changes - Results from ocean models

31 GLOBAL MODELS

32 Global models generally agree that ocean deoxygenation is occurring; this finding is also 

33 reflected in in situ observations from past 50 years. Compilations of 10 Earth System models 

34 predict a global average loss of oxygen of −3.5% (for the RCP8.5 pathway) to −1.8% (RCP2.6) 

35 by 2100, but much stronger losses regionally, and in intermediate and mode waters (Bopp et al. 

36 2013) (Figure 13.2). The North Pacific, North Atlantic, Southern Ocean, subtropical South 
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1 Pacific, and South Indian Oceans all are expected to experience deoxygenation, with O2

2 decreases of as much as 17% in the North Pacific by 2100 for the RCP8.5 pathway. However, 

3 the tropical Atlantic and tropical Indian Oceans show increasing O2 concentrations. In the many 

4 areas where oxygen is declining, high natural variability makes it difficult to identify 

5 anthropogenically forced trends (Long et al. 2016).

6 [INSERT FIGURE 13.3 HERE:

7 Figure 13.3: Predicted change between the periods 1981–2000 and 2081–2100 in dissolved 

8 oxygen, based on the Community Earth System Models – Large Ensemble Experiments. (Figure 

9 source: redrawn from Long et al. 2016)]

10 REGIONAL MODELS

11 Regional models are critical because many oxygen drivers are local, influenced by bathymetry, 

12 winds, circulation, and fresh water and nutrient inputs. Most eastern boundary upwelling areas 

13 are predicted to experience intensified upwelling to 2100 (Wang et al. 2015). 

14 Particularly notable for the western United States, variation in trade winds in the eastern Pacific 

15 can affect nutrient inputs, leading to centennial periods of oxygen decline or oxygen increase 

16 distinct from global oxygen decline (Deutsch et al. 2014). Oxygen dynamics in the Eastern 

17 Tropical Pacific are highly sensitive to equatorial circulation changes (Montes et al. 2014). 

18 Regional modeling also shows that year-to-year variability in precipitation in the central United 

19 States affects the nitrate–N flux by the Mississippi River and the extent of hypoxia in the Gulf of 

20 Mexico (Donner and Scavia 2007). A host of climate influences linked to warming and increased 

21 precipitation are predicted to lower dissolved oxygen in Chesapeake Bay (Najjar et al. 2010).

22
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1 TRACEABLE ACCOUNTS

2 Key Finding 1

3 The world’s oceans have absorbed more than 90% of the excess heat caused by greenhouse 

4 warming since the mid 20th Century, making them warmer and altering global and regional 

5 circulation patterns and climate feedbacks (very high confidence). Surface oceans have warmed 

6 by about 0.45°F (0.25°C) globally since the 1970s (very high confidence). The Atlantic 

7 meridional overturning circulation (AMOC) has slowed since preindustrial times (high 

8 confidence). Regionally, eastern boundary upwelling, such as along the U.S. West Coast, that 

9 sustains fisheries and controls local climate has intensified (high confidence).

10 Description of evidence base

11 The Key Finding and supporting text summarizes the evidence documented in climate science 

12 literature, including IPCC 2013 and thereafter. The oceanic warming has been documented in a 

13 variety of data sources, most notably the WOCE (http://www.nodc.noaa.gov/woce/wdiu/) and 

14 the ARGO database (https://www.nodc.noaa.gov/argo/). The AMOC changes over the 20th

15 Century are inferred from a number of high quality SST observations and a suite of climate 

16 models that participated in IPCC 2013 (Rahmstorf et al. 2015). The eastern boundary upwelling 

17 trends are based on a number of observational studies which looked at intensification of 

18 upwelling favorable winds along the eastern boundaries (Garcia-Reyes et al. 2015, Sydeman et 

19 al. 2014a).

20 Major uncertainties

21 Uncertainties in the magnitude of ocean warming stem from the disparate measurements over the 

22 last century. There is less uncertainty in 0–700 m warming trends, whereas there is only a short 

23 record of measurements from 700–2000 m. Data on warming trends in the deep ocean are even 

24 more sparse (Roemmich et al. 2015). The estimated change of the strength of the AMOC of 

25 about 2 Sv over the 20th Century is made using an inferred method (Rahmstorf et al. 2015) and 

26 not direct observations of AMOC. Direct observational records are short (after 2004, RAPID 

27 array). There are regional uncertainties in the magnitude of the eastern boundary intensification 

28 associated with uncertainties in the measurement of the wind speeds (Sydenman et al. 2014a). 

29 Assessment of confidence based on evidence and agreement, including short description of 

30 nature of evidence and level of agreement

31 x	Very High 

32 ☐ High

33 ☐ Medium 

34 ☐ Low

35 Confidence on ocean warming is very high based on the agreement of different methods. AMOC 

36 and eastern boundary upwelling changes are indirectly estimated, but based on high quality 

37 measurements, hence the high confidence.
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1 Summary sentence or paragraph that integrates the above information

2 We have very high confidence in measurements that show that the oceans are warming, the 

3 overturning circulation is slowing down, and the eastern boundary upwelling is intensified.

4

5 Key Finding 2

6 The world’s oceans are currently absorbing more than a quarter of the carbon dioxide emitted to 

7 the atmosphere annually from human activities (very high confidence), making them more acidic 

8 with potential detrimental impacts to marine ecosystems. The rate of acidification is unparalleled 

9 in at least the past 66 million years (medium confidence). Acidification is regionally increased

10 along U.S. coastal systems as a result of intensified upwelling (for example, in the Pacific 

11 Northwest) (high confidence), changes in freshwater inputs (for example, Gulf of Maine)

12 (medium confidence), and nutrient input (for example, urbanized estuaries) (high confidence).

13 Description of evidence base

14 The Key Finding and supporting text summarizes the evidence documented in climate science 

15 literature, including IPCC 2013 and thereafter. Evidence with regards to the magnitude of the 

16 ocean sink is obtained from multiple biogeochemical and transport ocean models, and two 

17 observation-based estimates from the 1990s for the uptake of the anthropogenic CO2. Estimates 

18 of the carbonate system (DIC and Alkalinity) were based on multiple survey cruises in the global 

19 ocean in the 1990s (WOCE, JGOFS). The data is available from the Carbon Dioxide Information 

20 Analysis Center (http:// cdiac.esd.ornl.gov/oceans/). Rates of change associated with the 

21 Palaeocene-Eocene Thermal Maximum (PETM, 56 mya) were derived using stable carbon and 

22 oxygen isotope records preserved in the sedimentary record from the New Jersey shelf using 

23 time series analysis and carbon cycle–climate modelling. This evidence supports a carbon release 

24 during the onset of the PETM over no less than 4,000 years, yielding a maximum sustained 

carbon release rate of less than 1.1 Pg C yr−1 25 (Zeebe R. et al. 2016). Evidence for increased 

26 upwelling along Pacific Northwest has been documented (Feely et al. 2008; Harris et al. 2013)

27 Major uncertainties

28 In 2014 the ocean sink was 2.6 ± 0.5 GtC (10.6 GtCO2), equivalent to 26% of the total emissions 

29 attributed to fossil fuel use and land use changes (Le Quéré et al. 2015). Estimates of PETM 

30 ocean acidification event evidenced in the geological record remains a matter of some debate 

within the community. Evidence for the 1.1 Pg C yr- 31 cited by Zeebe et al. (2016), could be biased 

32 as a result of brief pulses of carbon input above average rates of emissions where they to 

33 transpire on timescales ≲ 40 years. 

34 Assessment of confidence based on evidence and agreement, including short description of 

35 nature of evidence and level of agreement

36 ☐Very High 

37 x High
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1 ☐ Medium

2 ☐ Low 

3 The magnitude of the ocean carbon sink is known at a high confidence level because it is 

4 estimated using a series of disparate data sources and methods of analysis, while the magnitude 

5 of the interannual variability is based only on model studies.

6 Summary sentence or paragraph that integrates the above information

7 We have very high confidence in evidence that the oceans absorb about a quarter of the carbon 

8 dioxide emitted in the atmosphere and hence become more acidic. Current climate acidification 

9 is unprecedented in the past 66 million years.

10

11 Key Finding 3

12 Increasing sea surface temperatures, rising sea levels, and changing patterns of precipitation, 

13 winds, nutrients, and ocean circulation are contributing to overall declining oxygen 

14 concentrations at intermediate depths in various ocean locations and in many coastal areas. Over 

15 the last half century, major oxygen losses have occurred in inland seas, estuaries, and in the 

16 coastal and open ocean (high confidence).

17 Description of evidence base

18 The Key Finding and supporting text summarizes the evidence documented in climate science 

19 literature including IPCC 2013 and thereafter. Evidence arises from extensive global 

20 measurements of the World Ocean Circulation Experiment (WOCE) after 1989 and individual 

21 profiles before that (Helm et al. 2011). Uncertainties in long-term decreases of the global 

22 averaged oxygen concentration amount to 25% in the upper 1000 m for the period 1970–1992 

23 and 28% for the period 1993–2003.

24 Major uncertainties

25 Remaining uncertainties relate to regional variability driven by mesoscale eddies and intrinsic 

26 climate variability such as ENSO. 

27 Assessment of confidence based on evidence and agreement, including short description of 

28 nature of evidence and level of agreement

29 ☐Very High 

30 x High

31 ☐ Medium 

32 ☐ Low 

33 Confidence levels for the uncertainties quoted above are based on the 90th percentile.

34
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1 Summary sentence or paragraph that integrates the above information

2 Major ocean deoxygenation is taking place in bodies of water inland, at estuaries, and in the 

3 coastal and the open ocean. Regionally, the phenomenon is exacerbated by local changes in 

4 weather, ocean circulation and continental inputs to the oceans.

5

6 Key Finding 4

7 In coastal regions, local biogeochemical processes can result in increased acidification co8 occurring with eutrophication and/or where riverine loads include naturally corrosive materials 

9 of geological origin. All local areas of deoxygenation also tend to be areas of acidification, due 

10 to intensified respiration (very high confidence).

11 Description of evidence base

12 Coastal carbon and acidification surveys have been executed along the U.S. coastal Large 

13 Marine Ecosystem since at least 2007, documenting significantly elevated pCO2 and low pH 

14 conditions relative to oceanic waters. Data are archived and available for public access at NOAA 

15 NCEI (http://www.nodc.noaa.gov/oceanacidification/). Based on these data the relative

16 importance of respiratory DIC has been described within numerous studies including Feely et al. 

17 2008; Hauri et al. 2009 2013; and Gruber et al. 2012.

18 Major uncertainties

19 Coastal waters exhibit considerable high frequency variability (both time and space) in carbonate 

20 and oxygen dynamics which is poorly constrained by the existing U.S. observing assets, making 

21 it difficult to determine the full range of conditions exposed to organisms in these waters. 

22 Furthermore, most observing assets provide only surface measures which may underestimate of 

23 the corrosive conditions experienced by marine organisms at depth or at the benthos. 

24 Quantifying both the magnitudes and trends in nutrient supply to the coast via both terrestrial 

25 input and upwelling remains a central challenge to contemporary coastal biogeochemistry and 

26 coastal oceanography science. 

27 Assessment of confidence based on evidence and agreement, including short description of 

28 nature of evidence and level of agreement

29 x	Very High 

30 ☐ High

31 ☐ Medium 

32 ☐ Low 

33 The fact that respiratory processes significantly alter the local oxygen and carbonate chemistry 

34 conditions has been well established and is documented in the rich history of coastal 

35 biogeochemistry literature dating since at least the second half of the twentieth century (Zhang

36 2014).
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1 Summary sentence or paragraph that integrates the above information

2 TBD

3

4 Key Finding 5

5 Under a high future scenario (RCP8.5), the AMOC is projected to decline by 6 Sverdrups (1 x 

106 m3 6 /sec), global average ocean acidity is projected to increase by 100% to 150%) (very high 

7 confidence), and ocean oxygen levels are projected to decrease by 4% (high confidence) by 2100 

8 relative to preindustrial values. Under a low future scenario (RCP2.6), global average ocean

9 acidity is projected to increase by 35% and oxygen projected to decrease by 2% by 2100. Larger 

10 acidity increases and oxygen declines are projected in some regions and in intermediate and 

11 mode waters (medium confidence).

12 Description of evidence base

13 The Key Finding is based on evidence from 10 of the latest generation Earth System Models 

14 which include 6 distinct biogeochemical models that were included in the latest IPCC (2014) 

15 Assessment Report.

16 Major uncertainties

17 Uncertainties (as estimated from the inter-model spread) in the global mean are moderate mainly 

18 because when ocean oxygen content exhibits low interannual variability when globally averaged. 

19 Assessment of confidence based on evidence and agreement, including short description of 

20 nature of evidence and level of agreement

21 ☐ Very High 

22 x High

23 ☐ Medium 

24 ☐ Low 

25 The confidence level is based on globally integrated O2 distributions in a variety of ocean 

26 models. Although the global mean exhibits low interannual variability, regional contrasts which 

27 are large.

28 Summary sentence or paragraph that integrates the above information

29 For the 21st Century, ocean overturning circulation is expected to slow down and oceanic pH 

30 and oxygen concentrations will continue decrease.

31
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1 FIGURES

2

3 Figure 13.1: Trends in surface (< 50 m) ocean carbonate chemistry calculated from observations 

4 obtained at the Hawai‘i Ocean Time-series (HOT) Program in the North Pacific 1988–2015. 

5 Data obtained from the Hawai‘i Ocean Time-series Data Organization & Graphical System 

6 (HOT-DOGS, http://hahana.soest.hawaii.edu/hot/hot-dogs/index.html). Seawater xCO2 (μatm),

7 pHTotal, and carbonate ion concentration (μmol/kg) were derived using CO2SYS v2.1 by pairing 

8 direct measures of total alkalinity (TA) with dissolved inorganic carbon (DIC) when available (n 

9 = 824). In cases where this pairing was not possible, then pH and DIC were instead adopted (n =

10 16). As a final option, if neither of those pairings was an option then pHTotal and TA were 

11 adopted (n = 18). The calculations were performed using K1, K2 from Millero, 2010, the KHSO4

12 dissociation constant of Dickon, total scale pH, and Uppstrom, 1974 for [B]T. Atmospheric mole 

13 fraction CO2 (red) were obtained from NOAA’s Earth System Research Laboratory 

14 CarbonTracker (Peters et al. 2007). (Figure source: NOAA).

15
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1 Cites for Figure 13.1:

2 Peters et al., 2007, "An atmospheric perspective on North American carbon dioxide exchange: 

3 CarbonTracker", PNAS, November 27, 2007 , vol. 104, no. 48, 18925-18930, with updates 

4 documented at http://carbontracker.noaa.gov.

5 Dickson, Journal of Chemical Thermodynamics, 22:113-127, 1990.

6 Millero, Marine and Freshwater Research, v. 61, p. 139-142, 2010

7 Dickson, A. G. and Riley, J. P., Marine Chemistry 7:89-99, 1979.

8 Uppstrom, Leif, Deep-Sea Research 21:161-162, 1974

9

10

11

12

13 Figure 13.2 Predicted change in pH in 2090–2099 relative to 1990–1999 under RCP8.5, based 

14 on the Community Earth System Models – Large Ensemble Experiments CMIP5 (Figure source: 

15 adapted from Bopp et al. 2013).
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1

2 Figure 13.3: Predicted change between the periods 1981–2000 and 2081–2100 in dissolved 

3 oxygen, based on the Community Earth System Models – Large Ensemble Experiments (Figure 

4 source: redrawn from Long et al. 2016).

5
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 13

470

1 REFERENCES

2 Altieri, A.H. and K.B. Gedan, 2015: Climate change and dead zones. Global Change Biology,

3 21, 1395-1406. http://dx.doi.org/10.1111/gcb.12754 

4 Baden, S.P., L.O. Loo, L. Pihl, and R. Rosenberg, 1990: Effects of eutrophication on benthic 

5 communities including fish — Swedish west coast. Ambio, 19, 113-122. 

6 www.jstor.org/stable/4313676

7 Bakun, A., B.A. Black, S.J. Bograd, M. García-Reyes, A.J. Miller, R.R. Rykaczewski, and W.J. 

8 Sydeman, 2015: Anticipated Effects of Climate Change on Coastal Upwelling Ecosystems. 

9 Current Climate Change Reports, 1, 85-93. http://dx.doi.org/10.1007/s40641-015-0008-4 

10 Bates, N.R., Y.M. Astor, M.J. Church, K. Currie, J.E. Dore, M. González-Dávila, L. Lorenzoni, 

11 F. Muller-Karger, J. Olafsson, and J.M. Santana-Casiano, 2014: A time-series view of 

12 changing ocean chemistry due to ocean uptake of anthropogenic CO2 and ocean 

13 acidification. Oceanography, 27, 126–141. http://dx.doi.org/10.5670/oceanog.2014.16 

14 Bates, N.R. and J.T. Mathis, 2009: The Arctic Ocean marine carbon cycle: evaluation of air-sea 

15 CO<sub>2</sub> exchanges, ocean acidification impacts and potential feedbacks. 

16 Biogeosciences, 6, 2433-2459. http://dx.doi.org/10.5194/bg-6-2433-2009 

17 Bianchi, D., E.D. Galbraith, D.A. Carozza, K.A.S. Mislan, and C.A. Stock, 2013: Intensification 

18 of open-ocean oxygen depletion by vertically migrating animals. Nature Geoscience, 6, 545-

19 548. http://dx.doi.org/10.1038/ngeo1837 

20 Boetius, A. and F. Wenzhofer, 2013: Seafloor oxygen consumption fuelled by methane from 

21 cold seeps. Nature Geoscience, 6, 725-734. http://dx.doi.org/10.1038/ngeo1926 

22 Bograd, S.J., M.P. Buil, E.D. Lorenzo, C.G. Castro, I.D. Schroeder, R. Goericke, C.R. Anderson, 

23 C. Benitez-Nelson, and F.A. Whitney, 2015: Changes in source waters to the Southern 

24 California Bight. Deep Sea Research Part II: Topical Studies in Oceanography, 112, 42-52. 

25 http://dx.doi.org/10.1016/j.dsr2.2014.04.009 

26 Booth, J.A.T., E.E. McPhee-Shaw, P. Chua, E. Kingsley, M. Denny, R. Phillips, S.J. Bograd, 

27 L.D. Zeidberg, and W.F. Gilly, 2012: Natural intrusions of hypoxic, low pH water into 

28 nearshore marine environments on the California coast. Continental Shelf Research, 45, 108-

29 115. http://dx.doi.org/10.1016/j.csr.2012.06.009 

30 www.sciencedirect.com/science/article/pii/S0278434312001653

31 Bopp, L., L. Resplandy, J.C. Orr, S.C. Doney, J.P. Dunne, M. Gehlen, P. Halloran, C. Heinze, T. 

32 Ilyina, R. Séférian, J. Tjiputra, and M. Vichi, 2013: Multiple stressors of ocean ecosystems in 

33 the 21st century: projections with CMIP5 models. Biogeosciences, 10, 6225-6245. 

34 http://dx.doi.org/10.5194/bg-10-6225-2013 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 13

471

1 Borges, A.V. and N. Gypens, 2010: Carbonate chemistry in the coastal zone responds more 

2 strongly to eutrophication than ocean acidification. Limnology and Oceanography, 55, 346-

3 353. http://dx.doi.org/10.4319/lo.2010.55.1.0346 

4 Breitburg, D.L., J. Salisbury, J.M. Bernhard, W.-J. Cai, S. Dupont, S.C. Doney, K.J. Kroeker, 

5 L.A. Levin, W.C. Long, L.M. Milke, S.H. Miller, B. Phelan, U. Passow, B.A. Seibel, A.E. 

6 Todgham, and A.M. Tarrant, 2015: And on Top of All That... Coping with Ocean 

7 Acidification in the Midst of Many Stressors. Oceanography, 28, 48-61. 

8 http://dx.doi.org/10.5670/oceanog.2015.31 

9 Brush, G.S., 2009: Historical Land Use, Nitrogen, and Coastal Eutrophication: A 

10 Paleoecological Perspective. Estuaries and Coasts, 32, 18-28. 

11 http://dx.doi.org/10.1007/s12237-008-9106-z 

12 Caldeira, K. and M.E. Wickett, 2003: Anthropogenic carbon and ocean pH. Nature, 425, 365. 

13 Chiapello, I., 2014: Dust Observations and Climatology. Mineral Dust: A Key Player in the 

14 Earth System. Knippertz, P. and J.-B.W. Stuut, Eds. Springer Netherlands, Dordrecht, 149-

15 177. http://dx.doi.org/10.1007/978-94-017-8978-3_7 

16 Chierici, M. and A. Fransson, 2009: Calcium carbonate saturation in the surface water of the 

17 Arctic Ocean: undersaturation in freshwater influenced shelves. Biogeosciences, 6, 2421-

18 2431. http://dx.doi.org/10.5194/bg-6-2421-2009 

19 Chollett, I., F.E. Müller-Karger, S.F. Heron, W. Skirving, and P.J. Mumby, 2012: Seasonal and 

20 spatial heterogeneity of recent sea surface temperature trends in the Caribbean Sea and 

21 southeast Gulf of Mexico. Marine Pollution Bulletin, 64, 956-965. 

22 http://dx.doi.org/10.1016/j.marpolbul.2012.02.016 

23 www.sciencedirect.com/science/article/pii/S0025326X1200094X

24 Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, R. DeFries, J. 

25 Galloway, M. Heimann, C. Jones, C. Le Quéré, R.B. Myneni, S. Piao, and P. Thornton, 

26 2013: Carbon and Other Biogeochemical Cycles. Climate Change 2013: The Physical 

27 Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the 

28 Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. 

29 Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. 

30 Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 465–

31 570. http://dx.doi.org/10.1017/CBO9781107415324.015 www.climatechange2013.org

32 Codispoti, L.A., J.A. Brandes, J.P. Christensen, A.H. Devol, S.W.A. Naqvi, H.W. Paerl, and T. 

33 Yoshinari, 2001: The oceanic fixed nitrogen and nitrous oxide budgets: Moving targets as we 

34 enter the anthropocene? Scientia Marina, 65, 85-105. 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 13

472

1 http://dx.doi.org/10.3989/scimar.2001.65s285 

2 scientiamarina.revistas.csic.es/index.php/scientiamarina/article/view/684/700

3 Conley, D.J., J. Carstensen, J. Aigars, P. Axe, E. Bonsdorff, T. Eremina, B.-M. Haahti, C. 

4 Humborg, P. Jonsson, J. Kotta, C. Lännegren, U. Larsson, A. Maximov, M.R. Medina, E. 

5 Lysiak-Pastuszak, N. Remeikaitė-Nikienė, J. Walve, S. Wilhelms, and L. Zillén, 2011: 

6 Hypoxia Is Increasing in the Coastal Zone of the Baltic Sea. Environmental Science & 

7 Technology, 45, 6777-6783. http://dx.doi.org/10.1021/es201212r 

8 Deutsch, C., W. Berelson, R. Thunell, T. Weber, C. Tems, J. McManus, J. Crusius, T. Ito, T. 

9 Baumgartner, V. Ferreira, J. Mey, and A. van Geen, 2014: Centennial changes in North 

10 Pacific anoxia linked to tropical trade winds. Science, 345, 665-668. 

11 http://dx.doi.org/10.1126/science.1252332 

12 Deutsch, C., H. Brix, T. Ito, H. Frenzel, and L. Thompson, 2011: Climate-Forced Variability of 

13 Ocean Hypoxia. Science, 333, 336-339. http://dx.doi.org/10.1126/science.1202422 

14 Diaz, R.J. and R. Rosenberg, 2008: Spreading dead zones and consequences for marine 

15 ecosystems. Science, 321, 926-929. http://dx.doi.org/10.1126/science.1156401 

16 Donner, S.D. and D. Scavia, 2007: How climate controls the flux of nitrogen by the Mississippi 

17 River and the development of hypoxia in the Gulf of Mexico. Limnology and Oceanography,

18 52, 856-861. http://dx.doi.org/10.4319/lo.2007.52.2.0856 

19 Falkowski, P.G., T. Algeo, L. Codispoti, C. Deutsch, S. Emerson, B. Hales, R.B. Huey, W.J. 

20 Jenkins, L.R. Kump, L.A. Levin, T.W. Lyons, N.B. Nelson, O.S. Schofield, R. Summons, 

21 L.D. Talley, E. Thomas, F. Whitney, and C.B. Pilcher, 2011: Ocean deoxygenation: Past, 

22 present, and future. Eos, Transactions American Geophysical Union, 92, 409-410. 

23 http://dx.doi.org/10.1029/2011EO460001 

24 Feely, R.A., S.C. Doney, and S.R. Cooley, 2009: Ocean acidification: Present conditions and 

25 future changes in a high-CO2 world. Oceanography, 22, 36-47. 

26 http://dx.doi.org/10.5670/oceanog.2009.95 www.tos.org/oceanography/archive/22-

27 4_feely.pdf

28 Feely, R.A., C.L. Sabine, J.M. Hernandez-Ayon, D. Ianson, and B. Hales, 2008: Evidence for 

29 Upwelling of Corrosive "Acidified" Water onto the Continental Shelf. Science, 320, 1490-

30 1492. http://dx.doi.org/10.1126/science.1155676 

31 Feely, R.A., C.L. Sabine, K. Lee, W. Berelson, J. Kleypas, V.J. Fabry, and F.J. Millero, 2004: 

32 Impact of Anthropogenic CO<sub>2</sub> on the CaCO<sub>3</sub> System in the 

33 Oceans. Science, 305, 362-366. http://dx.doi.org/10.1126/science.1097329 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 13

473

1 Friedrich, T., A. Timmermann, A. Abe-Ouchi, N.R. Bates, M.O. Chikamoto, M.J. Church, J.E. 

2 Dore, D.K. Gledhill, M. Gonzalez-Davila, M. Heinemann, T. Ilyina, J.H. Jungclaus, E. 

3 McLeod, A. Mouchet, and J.M. Santana-Casiano, 2012: Detecting regional anthropogenic 

4 trends in ocean acidification against natural variability. Nature Climate Change, 2, 167-171. 

5 http://dx.doi.org/10.1038/nclimate1372 

6 Galbraith, E.D., M. Kienast, T.F. Pedersen, and S.E. Calvert, 2004: Glacial-interglacial 

7 modulation of the marine nitrogen cycle by high-latitude O2 supply to the global 

8 thermocline. Paleoceanography, 19, n/a-n/a. http://dx.doi.org/10.1029/2003PA001000 

9 García-Reyes, M., W.J. Sydeman, D.S. Schoeman, R.R. Rykaczewski, B.A. Black, A.J. Smit, 

10 and S.J. Bograd, 2015: Under Pressure: Climate Change, Upwelling, and Eastern Boundary 

11 Upwelling Ecosystems. Frontiers in Marine Science, 2. 

12 http://dx.doi.org/10.3389/fmars.2015.00109 

13 Gattuso, J.-P., A. Magnan, R. Billé, W.W.L. Cheung, E.L. Howes, F. Joos, D. Allemand, L. 

14 Bopp, S.R. Cooley, C.M. Eakin, O. Hoegh-Guldberg, R.P. Kelly, H.-O. Pörtner, A.D. 

15 Rogers, J.M. Baxter, D. Laffoley, D. Osborn, A. Rankovic, J. Rochette, U.R. Sumaila, S. 

16 Treyer, and C. Turley, 2015: Contrasting futures for ocean and society from different 

17 anthropogenic CO<sub>2</sub> emissions scenarios. Science, 349. 

18 http://dx.doi.org/10.1126/science.aac4722 

19 Gilbert, D., N.N. Rabalais, R.J. Díaz, and J. Zhang, 2010: Evidence for greater oxygen decline 

20 rates in the coastal ocean than in the open ocean. Biogeosciences, 7, 2283-2296. 

21 http://dx.doi.org/10.5194/bg-7-2283-2010 

22 Gilbert, D., B. Sundby, C. Gobeil, A. Mucci, and G.-H. Tremblay, 2005: A seventy-two-year 

23 record of diminishing deep-water oxygen in the St. Lawrence estuary: The northwest Atlantic 

24 connection. Limnology and Oceanography, 50, 1654-1666. 

25 http://dx.doi.org/10.4319/lo.2005.50.5.1654 

26 Gledhill, D.K., M.M. White, J. Salisbury, H. Thomas, I. Mlsna, M. Liebman, B. Mook, J. Grear, 

27 A.C. Candelmo, R.C. Chambers, C.J. Gobler, C.W. Hunt, A.L. King, N.N. Price, S.R. 

28 Signorini, E. Stancioff, C. Stymiest, R.A. Wahle, J.D. Waller, N.D. Rebuck, Z.A. Wang, T.L. 

29 Capson, J.R. Morrison, S.R. Cooley, and S.C. Doney, 2015: Ocean and coastal acidification 

30 off New England and Nova Scotia. Oceanography, 28, 182-197. 

31 http://dx.doi.org/10.5670/oceanog.2015.41 tos.org/oceanography/assets/docs/28-

32 2_gledhill.pdf

33 Gobler, C.J., E.L. DePasquale, A.W. Griffith, and H. Baumann, 2014: Hypoxia and Acidification 

34 Have Additive and Synergistic Negative Effects on the Growth, Survival, and 

35 Metamorphosis of Early Life Stage Bivalves. PLoS ONE, 9, e83648. 

36 http://dx.doi.org/10.1371/journal.pone.0083648 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 13

474

1 Gruber, N., 2008: Chapter 1 - The Marine Nitrogen Cycle: Overview and Challenges. Nitrogen 

2 in the Marine Environment (2nd Edition). Academic Press, San Diego, 1-50. 

3 http://dx.doi.org/10.1016/B978-0-12-372522-6.00001-3 

4 Gruber, N., C. Hauri, Z. Lachkar, D. Loher, T.L. Frölicher, and G.K. Plattner, 2012: Rapid 

5 progression of ocean acidification in the California Current System. Science, 337, 220-223. 

6 http://dx.doi.org/10.1126/science.1216773 www.sciencemag.org/content/337/6091/220.short

7 Harris, K.E., M.D. DeGrandpre, and B. Hales, 2013: Aragonite saturation state dynamics in a 

8 coastal upwelling zone. Geophysical Research Letters, 40, 2720-2725. 

9 http://dx.doi.org/10.1002/grl.50460 

10 Hauri, C., N. Gruber, A.M.P. McDonnell, and M. Vogt, 2013: The intensity, duration, and 

11 severity of low aragonite saturation state events on the California continental shelf. 

12 Geophysical Research Letters, 40, 3424-3428. http://dx.doi.org/10.1002/grl.50618 

13 Hauri, C., N. Gruber, G.-K. Plattner, S. Alin, R.A. Feely, B. Hales, and P.A. Wheeler, 2009: 

14 Ocean acidification in the California Current System. Oceanography, 22, 60-71. 

15 http://dx.doi.org/10.5670/oceanog.2009.97 

16 Helm, K.P., N.L. Bindoff, and J.A. Church, 2011: Observed decreases in oxygen content of the 

17 global ocean. Geophysical Research Letters, 38, n/a-n/a. 

18 http://dx.doi.org/10.1029/2011GL049513 

19 Hendriks, I.E., C.M. Duarte, Y.S. Olsen, A. Steckbauer, L. Ramajo, T.S. Moore, J.A. Trotter, 

20 and M. McCulloch, 2015: Biological mechanisms supporting adaptation to ocean 

21 acidification in coastal ecosystems. Estuarine, Coastal and Shelf Science, 152, A1-A8. 

22 http://dx.doi.org/10.1016/j.ecss.2014.07.019 

23 Hoegh-Guldberg, O., R. Cai, E.S. Poloczanska, P.G. Brewer, S. Sundby, K. Hilmi, V.J. Fabry, 

24 and S. Jung, 2014: The Ocean--Supplementary material. Climate Change 2014: Impacts, 

25 Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II 

26 to the Fifth Assessment Report of the Intergovernmental Panel of Climate Change. Barros, 

27 V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. 

28 Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. 

29 Mastrandrea, and L.L. White, Eds. Cambridge University Press, Cambridge, United 

30 Kingdom and New York, NY, USA, 1655-1731. ipcc.ch/pdf/assessment31 report/ar5/wg2/supplementary/WGIIAR5-Chap30_OLSM.pdf

32 Hönisch, B., A. Ridgwell, D.N. Schmidt, E. Thomas, S.J. Gibbs, A. Sluijs, R. Zeebe, L. Kump, 

33 R.C. Martindale, S.E. Greene, W. Kiessling, J. Ries, J.C. Zachos, D.L. Royer, S. Barker, 

34 T.M.M. Jr., R. Moyer, C. Pelejero, P. Ziveri, G.L. Foster, and B. Williams, 2012: The 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 13

475

1 geological record of ocean acidification. Science, 335, 1058-1063. 

2 http://dx.doi.org/10.1126/science.1208277 

3 IPCC, 2014: Climate Change 2013: The Physical Science Basis. Contribution of Working Group 

4 I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, 

5 T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, 

6 and P.M. Midgley (Eds.), 1535 pp. Cambridge University Press, Cambridge, UK and New 

7 York, NY. www.climatechange2013.org

8 Ito, T., A. Nenes, M.S. Johnson, N. Meskhidze, and C. Deutsch, 2016: Acceleration of oxygen 

9 decline in the tropical Pacific over the past decades by aerosol pollutants. Nature Geosci, 9,

10 443-447. http://dx.doi.org/10.1038/ngeo2717 

11 Jacox, M.G., A.M. Moore, C.A. Edwards, and J. Fiechter, 2014: Spatially resolved upwelling in 

12 the California Current System and its connections to climate variability. Geophysical 

13 Research Letters, 41, 3189-3196. http://dx.doi.org/10.1002/2014GL059589 

14 Jiang, L.-Q., W.-J. Cai, R.A. Feely, Y. Wang, X. Guo, D.K. Gledhill, X. Hu, F. Arzayus, F. 

15 Chen, J. Hartmann, and L. Zhang, 2010: Carbonate mineral saturation states along the U.S. 

16 East Coast. Limnology and Oceanography, 55, 2424-2432. 

17 http://dx.doi.org/10.4319/lo.2010.55.6.2424 

18 Jickells, T. and C.M. Moore, 2015: The Importance of Atmospheric Deposition for Ocean 

19 Productivity. Annual Review of Ecology, Evolution, and Systematics, 46, 481-501. 

20 http://dx.doi.org/10.1146/annurev-ecolsys-112414-054118 

21 Justić, D., T. Legović, and L. Rottini-Sandrini, 1987: Trends in oxygen content 1911–1984 and 

22 occurrence of benthic mortality in the northern Adriatic Sea. Estuarine, Coastal and Shelf 

23 Science, 25, 435-445. http://dx.doi.org/10.1016/0272-7714(87)90035-7 

24 Knoll, A.H. and S.B. Carroll, 1999: Early Animal Evolution: Emerging Views from Comparative 

25 Biology and Geology. Science, 284, 2129-2137. 

26 http://dx.doi.org/10.1126/science.284.5423.2129 

27 Le Quéré, C., R. Moriarty, R.M. Andrew, J.G. Canadell, S. Sitch, J.I. Korsbakken, P. 

28 Friedlingstein, G.P. Peters, R.J. Andres, T.A. Boden, R.A. Houghton, J.I. House, R.F. 

29 Keeling, P. Tans, A. Arneth, D.C.E. Bakker, L. Barbero, L. Bopp, J. Chang, F. Chevallier, 

30 L.P. Chini, P. Ciais, M. Fader, R.A. Feely, T. Gkritzalis, I. Harris, J. Hauck, T. Ilyina, A.K. 

31 Jain, E. Kato, V. Kitidis, K. Klein Goldewijk, C. Koven, P. Landschützer, S.K. Lauvset, N. 

32 Lefèvre, A. Lenton, I.D. Lima, N. Metzl, F. Millero, D.R. Munro, A. Murata, J.E.M.S. Nabel, 

33 S. Nakaoka, Y. Nojiri, K. O'Brien, A. Olsen, T. Ono, F.F. Pérez, B. Pfeil, D. Pierrot, B. 

34 Poulter, G. Rehder, C. Rödenbeck, S. Saito, U. Schuster, J. Schwinger, R. Séférian, T. 

35 Steinhoff, B.D. Stocker, A.J. Sutton, T. Takahashi, B. Tilbrook, I.T. van der Laan-Luijkx, 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 13

476

1 G.R. van der Werf, S. van Heuven, D. Vandemark, N. Viovy, A. Wiltshire, S. Zaehle, and N. 

2 Zeng, 2015: Global Carbon Budget 2015. Earth System Science Data, 7, 349-396. 

3 http://dx.doi.org/10.5194/essd-7-349-2015 

4 Long, M.C., C. Deutsch, and T. Ito, 2016: Finding forced trends in oceanic oxygen. Global 

5 Biogeochemical Cycles, 30, 381-397. http://dx.doi.org/10.1002/2015GB005310 

6 Mantoura, R.F.C., J.M. Martin, and R. Wollast, eds. Ocean margin processes in global change. 

7 1991, John Wiley and Sons Ltd. 469. 

8 Mathis, J.T., S.R. Cooley, N. Lucey, S. Colt, J. Ekstrom, T. Hurst, C. Hauri, W. Evans, J.N. 

9 Cross, and R.A. Feely, 2015: Ocean acidification risk assessment for Alaska’s fishery sector. 

10 Progress in Oceanography, 136, 71-91. http://dx.doi.org/10.1016/j.pocean.2014.07.001 

11 www.sciencedirect.com/science/article/pii/S0079661114001141

12 McFall-Ngai, M., M.G. Hadfield, T.C.G. Bosch, H.V. Carey, T. Domazet-Lošo, A.E. Douglas, 

13 N. Dubilier, G. Eberl, T. Fukami, S.F. Gilbert, U. Hentschel, N. King, S. Kjelleberg, A.H. 

14 Knoll, N. Kremer, S.K. Mazmanian, J.L. Metcalf, K. Nealson, N.E. Pierce, J.F. Rawls, A. 

15 Reid, E.G. Ruby, M. Rumpho, J.G. Sanders, D. Tautz, and J.J. Wernegreen, 2013: Animals 

16 in a bacterial world, a new imperative for the life sciences. Proceedings of the National 

17 Academy of Sciences, 110, 3229-3236. http://dx.doi.org/10.1073/pnas.1218525110 

18 McNeil, B.I. and T.P. Sasse, 2016: Future ocean hypercapnia driven by anthropogenic 

19 amplification of the natural CO2 cycle. Nature, 529, 383-386. 

20 http://dx.doi.org/10.1038/nature16156 

21 Mei, W., S.-P. Xie, F. Primeau, J.C. McWilliams, and C. Pasquero, 2015: Northwestern Pacific 

22 typhoon intensity controlled by changes in ocean temperatures. Science Advances, 1. 

23 http://dx.doi.org/10.1126/sciadv.1500014 

24 Moffitt, S.E., R.A. Moffitt, W. Sauthoff, C.V. Davis, K. Hewett, and T.M. Hill, 2015: 

25 Paleoceanographic Insights on Recent Oxygen Minimum Zone Expansion: Lessons for 

26 Modern Oceanography. PLoS ONE, 10, e0115246. 

27 http://dx.doi.org/10.1371/journal.pone.0115246 

28 Montes, I., B. Dewitte, E. Gutknecht, A. Paulmier, I. Dadou, A. Oschlies, and V. Garçon, 2014: 

29 High-resolution modeling of the Eastern Tropical Pacific oxygen minimum zone: Sensitivity 

30 to the tropical oceanic circulation. Journal of Geophysical Research: Oceans, 119, 5515-

31 5532. http://dx.doi.org/10.1002/2014JC009858 

32 Mulitza, S., D. Heslop, D. Pittauerova, H.W. Fischer, I. Meyer, J.-B. Stuut, M. Zabel, G. 

33 Mollenhauer, J.A. Collins, H. Kuhnert, and M. Schulz, 2010: Increase in African dust flux at 

34 the onset of commercial agriculture in the Sahel region. Nature, 466, 226-228. 

35 http://dx.doi.org/10.1038/nature09213 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 13

477

1 Muller-Karger, F.E., J.P. Smith, S. Werner, R. Chen, M. Roffer, Y. Liu, B. Muhling, D. Lindo2 Atichati, J. Lamkin, S. Cerdeira-Estrada, and D.B. Enfield, 2015: Natural variability of 

3 surface oceanographic conditions in the offshore Gulf of Mexico. Progress in 

4 Oceanography, 134, 54-76. http://dx.doi.org/10.1016/j.pocean.2014.12.007 

5 www.sciencedirect.com/science/article/pii/S0079661114002171

6 Najjar, R.G., C.R. Pyke, M.B. Adams, D. Breitburg, C. Hershner, M. Kemp, R. Howarth, M.R. 

7 Mulholland, M. Paolisso, D. Secor, K. Sellner, D. Wardrop, and R. Wood, 2010: Potential 

8 climate-change impacts on the Chesapeake Bay. Estuarine, Coastal and Shelf Science, 86, 1-

9 20. http://dx.doi.org/10.1016/j.ecss.2009.09.026 

10 Orr, J.C., V.J. Fabry, O. Aumont, L. Bopp, S.C. Doney, R.A. Feely, A. Gnanadesikan, N. 

11 Gruber, A. Ishida, F. Joos, R.M. Key, K. Lindsay, E. Maier-Reimer, R. Matear, P. Monfray, 

12 A. Mouchet, R.G. Najjar, G.-K. Plattner, K.B. Rodgers, C.L. Sabine, J.L. Sarmiento, R. 

13 Schlitzer, R.D. Slater, I.J. Totterdell, M.-F. Weirig, Y. Yamanaka, and A. Yool, 2005: 

14 Anthropogenic ocean acidification over the twenty-first century and its impact on calcifying 

15 organisms. Nature, 437, 681-686. http://dx.doi.org/10.1038/nature04095 

16 Patara, L. and C.W. Böning, 2014: Abyssal ocean warming around Antarctica strengthens the 

17 Atlantic overturning circulation. Geophysical Research Letters, 41, 3972-3978. 

18 http://dx.doi.org/10.1002/2014GL059923 

19 Peters, W., A.R. Jacobson, C. Sweeney, A.E. Andrews, T.J. Conway, K. Masarie, J.B. Miller, 

20 L.M.P. Bruhwiler, G. Pétron, A.I. Hirsch, D.E.J. Worthy, G.R. van der Werf, J.T. Randerson, 

21 P.O. Wennberg, M.C. Krol, and P.P. Tans, 2007: An atmospheric perspective on North 

22 American carbon dioxide exchange: CarbonTracker. Proceedings of the National Academy of 

23 Sciences, 104, 18925-18930. http://dx.doi.org/10.1073/pnas.0708986104 

24 Rabalais, N.N., R.J. Díaz, L.A. Levin, R.E. Turner, D. Gilbert, and J. Zhang, 2010: Dynamics 

25 and distribution of natural and human-caused hypoxia. Biogeosciences, 7, 585-619. 

26 http://dx.doi.org/10.5194/bg-7-585-2010 

27 Rabalais, N.N., R.E. Turner, R.J. Díaz, and D. Justić, 2009: Global change and eutrophication of 

28 coastal waters. ICES Journal of Marine Science: Journal du Conseil, 66, 1528-1537. 

29 http://dx.doi.org/10.1093/icesjms/fsp047 

30 icesjms.oxfordjournals.org/content/66/7/1528.abstract

31 Rabalais, N.N., R.E. Turner, B.K. Sen Gupta, D.F. Boesch, P. Chapman, and M.C. Murrell, 

32 2007: Hypoxia in the northern Gulf of Mexico: Does the science support the Plan to Reduce, 

33 Mitigate, and Control Hypoxia? Estuaries and Coasts, 30, 753-772. 

34 http://dx.doi.org/10.1007/bf02841332 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 13

478

1 Reay, D.S., F. Dentener, P. Smith, J. Grace, and R.A. Feely, 2008: Global nitrogen deposition 

2 and carbon sinks. Nature Geoscience, 1, 430-437. http://dx.doi.org/10.1038/ngeo230 

3 Rhein, M., S.R. Rintoul, S. Aoki, E. Campos, D. Chambers, R.A. Feely, S. Gulev, G.C. Johnson, 

4 S.A. Josey, A. Kostianoy, C. Mauritzen, D. Roemmich, L.D. Talley, and F. Wang, 2013: 

5 Observations: Ocean. Climate Change 2013: The Physical Science Basis. Contribution of 

6 Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate 

7 Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. 

8 Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. Cambridge University Press, Cambridge, 

9 United Kingdom and New York, NY, USA, 255–316. 

10 http://dx.doi.org/10.1017/CBO9781107415324.010 www.climatechange2013.org

11 Robinson, R.S., A. Mix, and P. Martinez, 2007: Southern Ocean control on the extent of 

12 denitrification in the southeast Pacific over the last 70 ka. Quaternary Science Reviews, 26,

13 201-212. http://dx.doi.org/10.1016/j.quascirev.2006.08.005 

14 www.sciencedirect.com/science/article/pii/S0277379106002411

15 Roemmich, D., J. Church, J. Gilson, D. Monselesan, P. Sutton, and S. Wijffels, 2015: Unabated 

16 planetary warming and its ocean structure since 2006. Nature Climate Change, 5, 240-245. 

17 http://dx.doi.org/10.1038/nclimate2513 

18 Salisbury, J., M. Green, C. Hunt, and J. Campbell, 2008: Coastal Acidification by Rivers:A 

19 Threat to Shellfish? Eos, Transactions American Geophysical Union, 89, 513-513. 

20 http://dx.doi.org/10.1029/2008EO500001 

21 Schmittner, A., E.D. Galbraith, S.W. Hostetler, T.F. Pedersen, and R. Zhang, 2007: Large 

22 fluctuations of dissolved oxygen in the Indian and Pacific oceans during Dansgaard23 Oeschger oscillations caused by variations of North Atlantic Deep Water subduction. 

24 Paleoceanography, 22, n/a-n/a. http://dx.doi.org/10.1029/2006PA001384 

25 Smith, S.V. and J.T. Hollibaugh, 1993: Coastal metabolism and the oceanic organic carbon 

26 balance. Reviews of Geophysics, 31, 75-89. http://dx.doi.org/10.1029/92RG02584 

27 Steinman, B.A., M.E. Mann, and S.K. Miller, 2015: Atlantic and Pacific multidecadal 

28 oscillations and Northern Hemisphere temperatures. Science, 347, 988-991. 

29 http://dx.doi.org/10.1126/science.1257856 

30 Stendardo, I. and N. Gruber, 2012: Oxygen trends over five decades in the North Atlantic. 

31 Journal of Geophysical Research, 117, C11004. http://dx.doi.org/10.1029/2012JC007909 

32 Stramma, L., S. Schmidtko, L.A. Levin, and G.C. Johnson, 2010: Ocean oxygen minima 

33 expansions and their biological impacts. Deep Sea Research Part I: Oceanographic Research 

34 Papers, 57, 587-595. http://dx.doi.org/10.1016/j.dsr.2010.01.005 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 13

479

1 Straneo, F. and P. Heimbach, 2013: North Atlantic warming and the retreat of Greenland's outlet 

2 glaciers. Nature, 504, 36-43. http://dx.doi.org/10.1038/nature12854 

3 Sydeman, W.J., M. García-Reyes, D.S. Schoeman, R.R. Rykaczewski, S.A. Thompson, B.A. 

4 Black, and S.J. Bograd, 2014: Climate change and wind intensification in coastal upwelling 

5 ecosystems. Science, 345, 77-80. http://dx.doi.org/10.1126/science.1251635 

6 Takatani, Y., D. Sasano, T. Nakano, T. Midorikawa, and M. Ishii, 2012: Decrease of dissolved 

7 oxygen after the mid-1980s in the western North Pacific subtropical gyre along the 137°E 

8 repeat section. Global Biogeochemical Cycles, 26, n/a-n/a. 

9 http://dx.doi.org/10.1029/2011GB004227 

10 Taylor, G.T., F.E. Muller-Karger, R.C. Thunell, M.I. Scranton, Y. Astor, R. Varela, L.T. 

11 Ghinaglia, L. Lorenzoni, K.A. Fanning, S. Hameed, and O. Doherty, 2012: Ecosystem 

12 responses in the southern Caribbean Sea to global climate change. Proceedings of the 

13 National Academy of Sciences, 109, 19315-19320. 

14 http://dx.doi.org/10.1073/pnas.1207514109 www.pnas.org/content/109/47/19315.abstract

15 Thompson, P.R., B.D. Hamlington, F.W. Landerer, and S. Adhikari, 2016: Are long tide gauge 

16 records in the wrong place to measure global mean sea level rise? Geophysical Research 

17 Letters, 43, 10,403-10,411. http://dx.doi.org/10.1002/2016GL070552 

18 Trenberth, K.E., J.T. Fasullo, and M.A. Balmaseda, 2014: Earth’s Energy Imbalance. Journal of 

19 Climate, 27, 3129-3144. http://dx.doi.org/10.1175/jcli-d-13-00294.1 

20 Turley, C., M. Eby, A.J. Ridgwell, D.N. Schmidt, H.S. Findlay, C. Brownlee, U. Riebesell, V.J. 

21 Fabry, R.A. Feely, and J.P. Gattuso, 2010: The societal challenge of ocean acidification. 

22 Marine Pollution Bulletin, 60, 787-792. http://dx.doi.org/10.1016/j.marpolbul.2010.05.006 

23 www.sciencedirect.com/science/article/pii/S0025326X1000192X

24 Waldbusser, G.G. and J.E. Salisbury, 2014: Ocean Acidification in the Coastal Zone from an 

25 Organism's Perspective: Multiple System Parameters, Frequency Domains, and Habitats. 

26 Annual Review of Marine Science, 6, 221-247. http://dx.doi.org/10.1146/annurev-marine27 121211-172238 

28 Wallmann, K., 2003: Feedbacks between oceanic redox states and marine productivity: A model 

29 perspective focused on benthic phosphorus cycling. Global Biogeochemical Cycles, 17, n/a30 n/a. http://dx.doi.org/10.1029/2002GB001968 

31 Walsh, J.J., ed. On the Nature of Continental Shelves. 1988, Academic Press. 520. 

32 Wang, D., T.C. Gouhier, B.A. Menge, and A.R. Ganguly, 2015: Intensification and spatial 

33 homogenization of coastal upwelling under climate change. Nature, 518, 390-394. 

34 http://dx.doi.org/10.1038/nature14235 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 13

480

1 Wollast, R., 1998: Evaluation and Comparison of the Global Carbon Cycle in the Coastal Zone 

2 and in the Open Ocean. The Sea: Processes and Methods. Brink, K.H. and A.R. Robinson, 

3 Eds. Harvard University Press, Cambridge, Mass., 213-252. 

4 Wright, J.D. and M.F. Schaller, 2013: Evidence for a rapid release of carbon at the Paleocene5 Eocene thermal maximum. Proceedings of the National Academy of Sciences, 110, 15908-

6 15913. http://dx.doi.org/10.1073/pnas.1309188110 

7 www.pnas.org/content/110/40/15908.abstract

8 Yamamoto-Kawai, M., F.A. McLaughlin, E.C. Carmack, S. Nishino, and K. Shimada, 2009: 

9 Aragonite undersaturation in the Arctic ocean: Effects of ocean acidification and sea ice melt. 

10 Science, 326, 1098-1100. http://dx.doi.org/10.1126/science.1174190 

11 Yang, H., G. Lohmann, W. Wei, M. Dima, M. Ionita, and J. Liu, 2016: Intensification and 

12 poleward shift of subtropical western boundary currents in a warming climate. Journal of 

13 Geophysical Research: Oceans, 121, 4928-4945. http://dx.doi.org/10.1002/2015JC011513 

14 Zaitsev, Y.P., 1992: Recent changes in the trophic structure of the Black Sea. Fisheries 

15 Oceanography, 1, 180-189. http://dx.doi.org/10.1111/j.1365-2419.1992.tb00036.x 

16 Zeebe, R.E., A. Ridgwell, and J.C. Zachos, 2016: Anthropogenic carbon release rate 

17 unprecedented during the past 66 million years. Nature Geoscience, 9, 325-329. 

18 http://dx.doi.org/10.1038/ngeo2681 

19 Zhang, J., 2014: Coastal Biogeochemical Cycles. In: Harff, J., M. Meschede, S. Petersen, and J. 

20 Thiede (eds.), Encyclopedia of Marine Geosciences. Springer.
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 14

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1 14. Perspectives on Climate Change Mitigation

2 KEY FINDINGS

3 1. There will be a delay of decades or longer between significant actions that reduce CO2

4 emissions and reductions in atmospheric CO2 concentrations that contribute to surface 

5 warming. This delay—the result of the long lifetime of CO2 in the atmosphere and the 

6 time lag in the response of atmospheric CO2 concentrations following a reduction in 

7 emissions—means that near-term changes in climate will be largely determined by past 

8 and present greenhouse gas emissions, modified by natural variability. (Very high 

9 confidence)

10 2. Limiting the global-mean temperature increase to 3.6°F (2°C) above pre-industrial levels

11 requires significant reductions in global CO2 emissions relative to present-day emission 

12 rates. Given the near-linear relationship between cumulative CO2 emissions and global 

13 temperature response, cumulative emissions would likely have to stay below 1,000 GtC

14 for a 2°C objective, leaving about 400 GtC still to be emitted globally. Assuming future 

15 global emissions follow the RCP4.5 scenario, the total, cumulative emissions 

16 commensurate with the 2°C objective would likely be reached between 2051 and 2065, 

17 while under the RCP8.5 scenario, the timing would likely fall between 2043 and 2050.

18 (High confidence)

19 3. Successful implementation of the first round of National Determined Commitments under 

20 the Paris agreement is a large step towards the objective of limiting global warming to

21 3.6°F (2°C). Even greater greenhouse gas emission reductions are required beyond 2030 

22 in order to increase the likelihood of achieving the 2°C goal; indeed, substantial 

23 (although smaller) reductions after 2030 would be required to achieve even the lesser 

24 goal of significantly reducing the likelihood of a global mean temperature increase 

25 greater than 7.2°F (4°C). (High confidence)

26 4. If projected atmospheric CO2 concentrations are not sufficiently low to prevent warming 

27 of 2°C or more, climate-intervention strategies such as technological CO2 removal or 

28 solar radiation management may gain attention as additional means to limit or reduce 

29 temperature increases. Assessing the technical feasibility, costs, risks, co-benefits and 

30 governance challenges of these additional measures, which are as-yet unproven at scale, 

31 would be of value to decision makers. (Medium confidence)

32 Introduction

33 This chapter addresses three topics. First, it addresses the science underlying the timing of 

34 when and how greenhouse gas (GHG) mitigation activities that occur in the present affect the 

35 climate of the future. In other words, when do we see the benefits of a greenhouse gas 
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1 emission reduction activity? And how do these benefits manifest themselves? Second, it 

2 addresses a number of questions in light of the Paris agreement adopted in December 2015 

3 (UNFCCC 2015) that builds upon the UN Framework Convention on Climate Change 

4 (UNFCCC; see Section 14.2): if fully implemented, what is the first round of mitigation 

5 commitments under the Paris agreement estimated to achieve in terms of avoiding increases 

6 in globally-averaged temperature and associated climate change in the future? And how 

7 significant of a role does the Paris agreement play in terms of the oft-cited 2°C (3.6°F) target 

8 or even a 1.5°C (2.7°F) target? Third, looking beyond the Paris agreement (which does not 

9 set emission reduction targets past the year 2030), what global-scale emissions pathways are 

10 estimated to be necessary by mid-century and beyond in order to have a high likelihood of 

11 achieving the 2°C or 1.5°C targets? As part of this last question, this chapter briefly 

12 addresses the status of climate intervention and geoengineering research and proposals and 

13 how these types of mitigation actions could possibly play a role in avoiding future climate 

14 change.

15 14.1 The Timing of Benefits from Mitigation Actions 

16 14.1.1 Inherent Delays in the Climate System

17 Carbon dioxide (CO2) concentrations in the atmosphere are directly affected by human 

18 activities. The long lifetime of CO2 in the atmosphere (Joos et al. 2013), coupled with the 

19 time lag in the response of the climate system to atmospheric forcing (Tebaldi and 

20 Friedlingstein 2013), has timing implications for the benefits of mitigation actions. Benefits 

21 of action to improve some environmental issues, such as air or water quality, can be almost 

22 immediately detectable and attributable to the specific actions that took place. However, the 

23 climate benefits (such as the avoided warming) of individual GHG mitigation actions 

24 generally will not be discernible in the short term because of an insufficient scale of the 

25 mitigation and because changes in climate parameters are generally globally diffuse and 

26 occur many decades into the future after the mitigation activity (e.g., Tebaldi and 

27 Friedlingstein 2013).

28 The world is committed to some degree of warming and associated climate change resulting 

29 from emissions to date. There is ample literature (Paltsev et al. 2015; IPCC 2013) showing 

30 that long-term risks (around and beyond 2050) of climate change can be strongly influenced 

31 by the GHG emissions pathways, which includes the emissions path over the near term. In 

32 the nearer term, the global climate will not be strongly influenced via changes to the 

33 emissions path; natural variability and the earth system response to past and current GHG 

34 emissions will be stronger factors. Some studies have nevertheless shown the potential for 

35 some near-term benefits. For example, one study found that, even at the regional scale, heat 

36 waves would already be significantly more severe by the 2030s in a non-mitigation scenario 

37 compared to a moderate mitigation scenario (Tebaldi and Wehner 2016).
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1 The mitigation of short-lived climate pollutants (SLCPs)—such as black carbon, ozone, 

2 methane and some hydrofluorocarbons (HFCs)—has been highlighted as a way to achieve 

3 more rapid climate benefits (e.g., Zaelke and Borgford-Parnell 2015). SLCPs are substances 

4 with an atmospheric lifetime shorter (for example, weeks to a decades) than CO2. Hence, 

5 mitigation of SLCP emissions produce more rapid radiative responses and, in some cases, 

6 strong regional (for example, for black carbon) and health co-benefits (for example, for black 

7 carbon and methane). Long-term reductions and/or avoidances of methane and HFC

8 emissions could be a significant contribution to staying at or below a 2°C global-mean 

9 temperature increase (Hayhoe et al. 1998; Shah 2015). A recent amendment to the Montreal 

10 Protocol will regulate global HFC production and consumption in order to avoid substantial 

11 CO2-eq emissions in coming decades. However, given that economic and technological 

12 factors tend to couple CO2 and SLCP emissions, it is thought that significant SLCP emissions 

13 reduction would be a co-benefit of CO2 mitigation, and, conversely, stringent near-term 

14 SLCP mitigation could potentially increase allowable CO2 budgets for avoiding warming 

15 beyond 2°C, by up to 25% according to Rogelj et al. (2016). 

16 14.1.2 Stock and Stabilization

17 Cumulative CO2 emissions will largely determine long-term global mean temperature change

18 and there is a nearly linear relationship between cumulative CO2 emissions and global mean 

19 temperature increases (IPCC 2013, Figure SPM 10; see Chapter 4.). Thus, for a 2°C (3.6°F) 

20 or any desired global mean temperature objective, an estimated range of allowable 

21 cumulative CO2 emissions from the current period onward can be calculated. This issue is 

22 illustrated here and discussed in the context of the Paris agreement.

23 To date, human activities, primarily burning fossil fuels and deforestation, have emitted more 

24 than 600 Pg or GtC into the atmosphere since pre-industrial times. To meet the 2°C (3.6°F) 

25 objective called for under the Paris Agreement, approximately 400 GtC more CO2 could be 

26 emitted globally. In order to meet the 1.5°C (2.7°F) objective, only about 150 GtC more of 

27 CO2 could be emitted. Assuming a stabilized global emission rate at just under the current 

28 value of 10 GtC per year, this would permit around 40 more years of CO2 emissions for the 

29 2°C objective, and just 15 years for 1.5°C objective. Assuming future global emissions 

30 follow the RCP4.5 scenario, the total, cumulative emissions commensurate with the 2°C

31 objective would likely be reached between 2051 and 2065, while under the RCP8.5 scenario, 

32 the timing would likely fall between 2043 and 2050. For the 1.5°C objective, the cumulative 

33 carbon limit of 750 GtC would likely be reached under RCP4.5 between 2028 and 2041 and 

34 under RCP8.5 between 2026 and 2036. 

35 The analysis of remaining CO2 emissions above is more generally expressed in units of net 

36 CO2-eq emissions, which include a range of climate forcing agents in addition to CO2

37 (Chapter 2). Since non-CO2 forcing agents have projections of net positive forcing, 

38 incorporating these agents into the analysis reduces the allowable range of CO2-only 
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1 emissions and increases the uncertainty regarding the amount and timing of net CO2-eq 

2 emissions consistent with achieving specific temperature objectives.

3 The cumulative carbon emissions estimated to be compatible with a given global temperature 

4 objective can be compared to known fossil-fuel reserves, and their associated CO2 emissions 

5 if burnt, to calculate how much of these reserves may be used, in the absence of widespread 

6 carbon capture and storage. If the 2°C objective is to be met, no further CO2 emissions would 

7 be allowed for one third of oil reserves, half of gas reserves, and over 80% of coal reserves 

8 (McGlade and Ekins 2015).

9 14.2 Estimated Climate Implications of the Paris Agreement

10 In December of 2015 in Paris, building upon the UNFCCC, 195 countries adopted the first11 ever agreement under which all nations committed to put forward GHG mitigation targets, 

12 and to back these targets by domestic measures. Leading up to Paris, nations submitted

13 Intended Nationally Determined Contributions (INDCs), where each country developed its 

14 own annual emissions targets, timeframes, and mechanisms that were suited to their own 

15 national circumstances. As a country formally joins the Paris agreement, the country's INDC 

16 becomes, by default, its first NDC (Nationally Determined Commitment). The first

17 commitments under the Paris agreement extend to 2025 or 2030, and take a wide range of 

18 forms. Some, like the U.S. goal of 26% to 28% reductions in annual emissions by 2025 

19 relative to 2005 levels, are absolute emissions reduction goals. Others, like China’s goal for 

20 its emissions to peak on or before 2030 or India’s goal to “to reduce the emissions intensity 

21 of its GDP by 33 to 35 percent by 2030 from 2005 level,” are relative, not absolute, 

22 emissions reduction goals. As a consequence of this country-by-country, bottom-up 

23 approach, uncertainty exists as to the net annual emissions reductions implied by this first 

24 round of NDCs.

25 The agreement also contains the objectives of “holding the increase in the global average 

26 temperature to well below 2°C above pre-industrial levels and pursuing efforts to limit the 

27 temperature increase to 1.5°C above pre-industrial levels.” Furthermore, stated goals are to 

28 “aim to reach global peaking of greenhouse gas emissions as soon as possible, recognizing 

29 that peaking will take longer for developing country Parties, and to undertake rapid 

30 reductions thereafter in accordance with best available science, so as to achieve a balance 

31 between anthropogenic emissions by sources and removals by sinks of greenhouse gases in 

32 the second half of this century.” The balance implies net-zero emissions, whereby future CO2

33 emissions, for example, would be fully offset through CO2 removal by terrestrial carbon 

34 sinks, other enhanced removal processes, or carbon capture and storage mechanisms.

35
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1 14.2.1 Temperature Implications from Current Commitments

2 under the Paris Agreement

3 Analyses have been undertaken to estimate the global emissions and global temperature 

4 implications of the successful implementation of the first round of NDCs (Rogelj et al. 2016; 

5 Sanderson et al. 2016; Climate Action Tracker 2015; Fawcett et al. 2015; UNFCCC 2015).

6 These estimates generally find that 1) successful implementation of the first round of NDCs 

7 would reduce GHG emissions growth by 2030 relative to a situation where these goals did 

8 not exist, though global emissions are still not expected to be lower in 2030 than in 2015, and 

9 2) the NDCs are important in order to keep the 2°C objective within reach, but that the NDCs 

10 are, by themselves, insufficient to achieve this ambitious objective. According to one study, 

11 the NDCs imply a median warming of 2.6°C –3.1°C by 2100, though year 2100 temperature 

12 estimates depend on assumed emissions between 2030 and 2100 (Rogelj et al. 2016). For 

13 example, Climate Action Tracker, using alternative post-2030 assumptions, put the range at 

14 3.3°C to 3.9°C. 

15 Emissions pathways consistent with the then INDCs were evaluated in the context of 

16 likelihood of global mean surface temperature change. It was found that the likelihood of 

17 meeting the objective of 2°C or less was enhanced by the INDCs, but depended strongly on 

18 subsequent policies and measures. The chief finding was that even without additional 

19 emission reductions after 2030, if implemented successfully, the INDCs provided some

20 likelihood (less than 10%) of preventing global mean surface temperature change of 2°C 

21 relative to preindustrial levels (that is, an outcome between 1.5°C and 2°C) (by Fawcett et al. 

22 2015). Greater emission reductions beyond 2030 increased the likelihood of achieving the 

23 2°C goal or lower to more than 30%, and significantly reduced the likelihood of a global 

24 mean temperature increase greater than 4°C to roughly 1% (Figure 14.1).

25 [INSERT FIGURE 14.1 HERE:

26 Figure 14.1: Global CO2 emissions and probabilistic temperature outcomes of Paris. (A) 

27 Global CO2 emissions from energy and industry (includes CO2 emissions from all fossil fuel 

28 production and use and industrial processes such as cement manufacture that also produce 

29 CO2 as a byproduct) for emissions scenarios following no policy, current policy, meeting the 

30 INDCs with no increased future ambition and meeting the INDCs with continually increasing 

31 ambition. (B) Likelihoods of different levels of increase in global mean surface temperature 

32 during the 21st century relative to preindustrial levels for the four scenarios. Although (A) 

33 shows only CO2 emissions from energy and industry, temperature outcomes are based on the 

34 full suite of GHG, aerosol, and short-lived species emissions across the full set of human 

35 activities and physical Earth systems. (Figure source: adapted from Fawcett et al. 2015)]

36 The way in which near-term goals are achieved also matters to meeting long-term climate 

37 goals. Few nations have provided detailed information as to the methods that they will use to 

38 accomplish their goals. To the extent that nations have explicitly announced policies that they 
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1 intend to use to achieve their goals, these policies and measures span a wide range forms, 

2 extending from fiscal measures (for example, carbon tax) to regulatory measures (for 

3 example, performance standards). This variety in goals and measures raises the prospect of 

4 policy interactions that can either enhance others’ efforts, (for example, technology 

5 development policies), or degrade others’ efforts. Also, near-term policies and measures 

6 could either help facilitate the deep reductions that need to follow after 2030 to limit global 

7 temperature to 2°C or less, or could frustrate that ambition.

8 14.2.2 Pathways Consistent with Paris Temperature Targets (1.5°C or 2°C) 

9 A robust feature of model climate change simulations, irrespective of the details of the 

10 emissions pathway, is the near-linear relationship between cumulative emissions and peak 

11 warming in the simulation (see Figure 14.2). To date, humanity has emitted just over 600 

12 billion tonnes of carbon, which means that even if emissions were to halt today, temperatures 

13 would be expected to stabilize at 1.5°C above the preindustrial average. An exceedance of a 

14 trillion tonnes of cumulatively emitted carbon would make it unlikely that temperature

15 increases could remain below 2°C (Allen et al. 2009).

16 [INSERT FIGURE 14.2 HERE:

17 Figure 14.2: Global mean temperature change for a number of scenarios as a function of 

18 cumulative CO2 emissions from preindustrial conditions, with time progressing along each 

19 individual line for each scenario. (Figure source: IPCC 2013; © IPCC. Used with 

20 permission.)]

21 As such, there are only a limited number of pathways which would enable the world to 

22 remain below 2°C of warming (see Figure 14.3), and almost all but the most rapid near-term 

23 mitigation would be heavily reliant on the implementation of CO2 removal from the 

24 atmosphere later in the century or other climate intervention. If global emissions are in line 

25 with the first round of NDCs by 2030, then the world would likely need to reduce effective 

26 GHG emissions to zero by 2080, and be significantly net negative by the end of the century 

27 (relying on as-yet unproven technologies to remove GHGs from the atmosphere) in order to 

28 avoid and not exceed 2°C of warming. Avoiding 1.5°C of warming would require more 

29 aggressive action still, with net zero emissions achieved by 2050 and net negative emissions 

30 thereafter. In either case, faster near-term action would significantly decrease the 

31 requirements for negative emissions in the future.

32 [INSERT FIGURE 14.3 HERE:

33 Figure 14.3: Global emissions pathways of effective global CO2 emissions which would be 

34 consistent with different temperature targets (relative to preindustrial temperatures). (Figure 

35 source: Sanderson et al. 2016)]

36
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1 14.3 The Role of Climate Intervention

2 in Meeting Ambitious Climate Targets

3 Achieving a 2°C target through emissions reductions or adapting to the impacts of a 

4 greater-than-2°C world have been acknowledged as severely challenging tasks by the 

5 international science and policy communities. As a consequence, there is increased 

6 interest by some scientists and policy makers in exploring measures designed to reduce 

7 net radiative forcing through other, as-yet untested actions, which are often referred to as

8 “geoengineering” or “climate intervention” (CI). CI approaches are generally divided into 

9 two categories: carbon dioxide (CO2) removal (CDR) and solar radiation management 

10 (SRM). CDR and SRM methods may have future roles in helping meet global 

11 temperature targets. Both methods reduce global-average temperature by reducing net 

12 global radiative forcing: CDR through reducing atmospheric CO2 concentrations and 

13 SRM through increasing Earth’s albedo. The U.S. National Academy of Sciences 

14 completed reports in 2015 examining CDR and SRM (NAS 2015a,b).

15 By removing CO2 from the atmosphere, CDR directly addresses the principal cause of 

16 climate change. Potential CDR approaches include point-source CO2 capture, direct air 

17 capture, biological methods on land (e.g., afforestation) and in the ocean (e.g., ocean 

18 fertilization), and accelerated weathering (e.g., forming calcium carbonate on land or in 

19 the oceans). While CDR is technically possible, the primary challenge is achieving the 

20 required scale of removal in a cost-effective manner. In principle, at large scale, CDR 

21 could reduce CO2 concentrations (i.e., cause negative emissions). Point-source capture 

22 and removal of CO2 is considered a particularly effective CDR method. The climate

23 value of avoided CO2 emissions is essentially equivalent to that of the atmospheric 

24 removal of the same amount. To realize the climate benefits of CDR, the removal of CO2

25 from the atmosphere must be essentially permanent—at least several centuries to 

26 millennia. In addition to high costs, CDR has the additional limitation of long 

27 implementation times.

28 By contrast, SRM approaches offer the only CI method of cooling Earth within a few 

29 years after inception. An important limitation of SRM is that it would not address damage 

30 to ocean ecosystems from increasing ocean acidification due to continued CO2 uptake. 

31 SRM could theoretically have a significant global impact even if implemented by a small 

32 number of nations, and by nations who are not also the major emitters of GHGs. Leading 

33 SRM concepts would increase Earth’s albedo through injection of sulfur gases or aerosols 

34 into the stratosphere (thereby simulating the effects of explosive volcanic eruptions) or 

35 marine cloud brightening through aerosol injection near the ocean surface. Injection of 

36 solid particles is an alternative to sulfur and yet other SRM methods could be deployed in 

37 space. Studies have evaluated the expected effort and effectiveness of various SRM 

38 methods have been proposed (NAS 2015b; Keith et al. 2014). For example, model runs
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1 were performed in the GeoMIP project using the full CMIP5 model suite to illustrate the 

2 effect of reducing top-of-the-atmosphere insolation to offset climate warming from CO2

3 (Kravitz et al. 2013). The idealized runs, which assumed an abrupt, globally-uniform 

4 insolation reduction in a 4×CO2 atmosphere, show that temperature increases are largely 

5 offset, most sea-ice loss is avoided, average precipitation changes are small, and net 

6 primary productivity increases. However, important regional changes in climate variables 

7 are likely in SRM scenarios as discussed below.

8 As global ambitions increase to avoid or remove CO2 emissions, probabilities of large 

9 increases in global temperatures in 2100 are proportionately reduced (Fawcett et al.

10 2015). Scenarios in which large-scale CDR is used to meet a 2°C limit while allowing

11 business-as-usual consumption of fossil fuels are likely not feasible with present 

12 technologies. Model SRM scenarios have been developed that show reductions in

radiative forcing up to 1 W/m2 13 with annual stratospheric injections of 1 Mt of sulfur from 

14 aircraft or other platforms (Pierce et al. 2010; Tilmes et al. 2016). Preliminary studies 

15 suggest that this could be accomplished at a cost as low as a few billion dollars per year 

16 using current technology, enabling an individual country or subnational entity to conduct 

17 activities having significant global climate impacts.

18 SRM scenarios could be designed to follow a particular radiative forcing trajectory, with 

19 adjustments made in response to monitoring of the climate effects (Keith and MacMartin 

20 2015). SRM could be used as an interim measure to avoid peaks in global average 

21 temperature and other climate parameters. The assumption is often made that SRM 

22 measures, once implemented, must continue indefinitely in order to avoid the rapid 

23 climate change that would occur if the measures were abruptly stopped. SRM could be 

24 used, however, as an interim measure to buy time for the implementation of emissions 

25 reductions and/or CDR, and SRM could be phased out as emission reductions and CDR 

26 are phased in, to avoid abrupt changes in radiative forcing (Keith and MacMartin 2015).

27 SRM via marine cloud brightening derives from changes in cloud albedo from injection 

28 of aerosol into low-level clouds, primarily over the oceans. Clouds with smaller and more 

29 numerous droplets reflect more sunlight that clouds with fewer and larger droplets. 

30 Current models provide more confidence in the effects of stratospheric injection than in 

31 marine cloud brightening (NAS 2015b). 

32 Even if the reduction in global-average radiative forcing from SRM was exactly equal to 

33 the radiative forcing from greenhouse gases, the regional and temporal patterns of these 

34 forcings would vary. Hence, SRM actions are unlikely to fully offset the effects of 

35 greenhouse gases, causing potentially important changes in atmospheric circulation and 

36 weather patterns. Also, the reduction in sunlight may have effects on agriculture and 

37 ecosystems. In general, restoring regional preindustrial temperature and precipitation

38 conditions in response to SRM actions is not expected to be possible based on ensemble 
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1 modeling studies (Ricke et al. 2010). As a consequence, the optimal climate and 

2 geopolitical value of potential SRM actions will likely involve tradeoffs between regional 

3 temperature and precipitation changes (MacMartin et al. 2013). 

4 CDR and SRM have substantial uncertainties regarding their effectiveness and 

5 unintended consequences. CDR on a large scale may disturb natural systems and have

6 important implications for land-use changes. While SRM could rapidly lower global 

7 mean temperatures, the effects on precipitation patterns, light availability, crop yields, 

8 acid rain, pollution levels, temperature gradients, and atmospheric circulation in response 

9 to such actions are less well understood than the effects of GHGs alone. Furthermore, the 

10 potential for rapid changes upon initiation (or ceasing) of a CI action would require 

11 adaptation on timescales significantly more rapid than what would otherwise be 

12 necessary. The NAS (2015a,b) and the Royal Society (Shepherd et al. 2009) recognized

13 that research on the feasibility and consequences of CI actions is incomplete and call for 

14 continued research to improve knowledge of the feasibility, risks, and benefits of CI 

15 techniques.

16 The evaluation of the suitability and advisability of potential CI actions requires a 

17 decision framework that includes important dimensions beyond scientific and technical 

18 considerations. Among these dimensions are the development of global and national 

19 governance and oversight procedures, geopolitical relations, legality, environmental, 

20 economic and societal impacts, ethical considerations, and the relationships to global 

21 climate policy and current GHG mitigation and adaptation actions. This report only 

22 acknowledges these mostly non-scientific dimensions and must forego a detailed 

23 discussion. Furthermore, it is clear that these non-scientific dimensions are likely to be 

24 the major part of a decision framework and ultimately control the adoption and 

25 effectiveness of CI actions.

26
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1 TRACEABLE ACCOUNTS

2 Key Finding 1

3 There will be a delay of decades or longer between significant actions that reduce CO2

4 emissions and reductions in atmospheric CO2 concentrations that contribute to surface 

5 warming. This delay—the result of the long lifetime of CO2 in the atmosphere and the time 

6 lag in the response of atmospheric CO2 concentrations following a reduction in emissions—

7 means that near-term changes in climate will be largely determined by past and present 

8 greenhouse gas emissions, modified by natural variability.

9 Description of evidence base 

10 Joos et al. (2013) describe the climate response of CO2 pulse emissions. IPCC (2013) stated 

11 that the best estimate of the human-induced contribution to observed warming was similar to 

12 the observed warming for the period 1951-2010. IPCC (2013) also stated that natural internal 

13 variability will continue to be a major influence on climate, particularly in the near term. 

14 Major uncertainties 

15 The Key Finding makes a general statement about the timing of the climate effect following 

16 a CO2 emission reduction activity, without specifying the exact magnitude or timing of the 

17 climate effect that could be associated with different timings or levels of CO2 reductions. 

18 Uncertainties affecting the timing of the climate response following a pulse emission (or 

19 avoidance of that emission) involve the quantity of emissions, the background concentration 

20 of CO2, and the choice of model. 

21 Assessment of confidence based on evidence and agreement, including short description 

22 of nature of evidence and level of agreement 

23 x Very High 

24 ☐ High

25 ☐ Medium 

26 ☐ Low 

27 There is a well-established understanding, based on a robust literature base, of the timing of 

28 atmospheric CO2 concentration effects following an emission of CO2. There are also well29 vetted statements in the scientific literature that the near-term climate can be expected to be 

30 most influenced by past and present GHG emissions, and internal natural variability. It 

31 follows that the qualitative key finding—that the effects of significant mitigation actions will 

32 be delayed—is well supported.

33 Summary sentence or paragraph that integrates the above information

34 The qualitative statement that there will be a delay of decades or longer between significant 

35 actions that reduce CO2 emissions and reductions in atmospheric CO2 concentrations that 

36 contribute to surface warming is well supported by the literature.
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1 Key Finding 2

2 Stabilizing the global-mean temperature increase to 2°C (3.6oF) above pre-industrial levels 

3 requires significant reductions in global CO2 emissions relative to present-day emission rates. 

4 Given the near-linear relationship between cumulative CO2 emissions and global temperature 

5 response, cumulative emissions would likely have to stay below 1,000 GtC for a 2°C 

6 objective, leaving about 400 GtC to be emitted. Assuming future global emissions follow the 

7 RCP4.5 scenario, the total, cumulative emissions commensurate with the 2°C objective 

8 would likely be reached between 2051 and 2065, while under the RCP8.5 scenario, the 

9 timing would likely fall between 2043 and 2050.

10 Description of evidence base 

11 The Key Finding is an abbreviated version of the 2°C objective described in section 12.5 of 

12 the IPCC WG1 AR5 and section E.8 of its SPM. The 1.5°C objective is a simple linear 

13 scaling as described in IPCC WG1 AR5 section 12.5.4.3 on page 1113.

14 Major uncertainties 

15 The largest uncertainty is the equilibrium response to atmospheric CO2 increases or 

16 “Equilibrium Climate Sensitivity”. Other uncertainties include the ability of the terrestrial 

17 and marine biosystems to remove CO2 from the atmosphere, as well as the effect of reducing 

18 the aerosol pollutants associated with the burning of fossil fuels.

19 Assessment of confidence based on evidence and agreement, including short description 

20 of nature of evidence and level of agreement 

21 ☐ Very High

22 X High

23 ☐ Medium 

24 ☐ Low

25 High confidence in the likelihood statement is based on high confidence in the estimate range 

26 of the equilibrium climate sensitivity. IPCC WG1 AR5, box 12.2, page 1111.

27 If appropriate, estimate likelihood of impact or consequence, including short 

28 description of basis of estimate 

29 ☐ Greater than 9 in 10 / Very Likely 

30 X Greater than 2 in 3 / Likely

31 ☐ About 1 in 2 / As Likely as Not

32 ☐ Less than 1 in 3 / Unlikely 

33 ☐ Less than 1 in 10 / Very Unlikely

34 Likely means that there is a 66% or greater chance that the objective will be met with the 

35 stated total cumulative carbon amount (source: IPCC WG1 AR5 SPM section E.8, page 27.)

36
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1 Summary sentence or paragraph that integrates the above information

2 The key finding is an abbreviated statement from the IPCC WG1 AR5 that made a thorough 

3 assessment of the relevant literature (section 12.5).

4

5 Key Finding 3

6 Successful implementation of the first round of NDCs under the Paris agreement is a large 

7 step towards the objective of limiting global warming to 2°C (3.6oF). Even greater GHG 

8 emission reductions are required beyond 2030 in order to increase the likelihood of achieving 

9 the 2°C goal; indeed, substantial (although smaller) reductions after 2030 would be required 

10 to achieve even the lesser goal of significantly reducing the likelihood of a global mean 

11 temperature increase greater than 7.2oF (4°C).

12 Description of evidence base 

13 The primary source supporting this key finding is Fawcett et al. (2015) and is also supported 

14 by Rogelj et al. (2016), Sanderson et al. (2016), and Climate Action Tracker. Each of these 

15 analyses evaluated the global climate implications of the aggregation of the individual 

16 country commitments thus far put forward under the Paris agreement. 

17 Major uncertainties 

18 The largest uncertainty lies in the assumption of “successful implementation” of the first 

19 round of NDCs; these are assumed to be fully successful but could either over- or 

20 underachieve. This in turn creates uncertainty about the extent of emission reductions that 

21 would be needed after the first round of NDCs in order to achieve the 2°C or any other 

22 objective. The response of the climate system, the climate sensitivity, is also a source of 

23 uncertainty; the Fawcett et al. analysis used the IPCC AR5 range, 1.5 to 4.5°C.

24 Assessment of confidence based on evidence and agreement, including short description 

25 of nature of evidence and level of agreement 

26 ☐ Very High

27 X High

28 ☐ Medium 

29 ☐ Low

30 There is high confidence in this key finding because a number of analyses have examined the 

31 implications of the first round of national commitments under the Paris agreement and have 

32 come to similar conclusions, as captured in this key finding.

33 Summary sentence or paragraph that integrates the above information

34 A number of analyses have estimated the implications for global mean temperature of the 

35 first round of commitments under the Paris agreement. Assuming successful implementation 

36 of this first round of commitments, along with a range of climate sensitivities, these 
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1 commitments are a large step towards the objective of limiting global warming to 2°C but 

2 greater action will be required.

3

4 Key Finding 4

5 If projected atmospheric CO2 concentrations are not sufficiently low to prevent warming of 

6 2°C or more, climate-intervention strategies such as technological CO2 removal or solar 

7 radiation management may increasingly gain attention and support as additional means to 

8 limit or reduce temperature increases. Assessing the technical feasibility, costs, risks, co9 benefits and governance challenges of these additional measures, which are as-yet unproven 

10 at scale, would be of value to decision makers.

11 Description of evidence base 

12 The key finding is a qualitative statement reasonably based on the growing literature 

13 addressing this topic. 

14 Major uncertainties 

15 The major uncertainty is how public perception and interest among policymakers in climate 

16 intervention may change over time, even independently from the level of progress made 

17 towards conventional emission reductions commensurate with the 2°C objective.

18 Assessment of confidence based on evidence and agreement, including short description 

19 of nature of evidence and level of agreement 

20 ☐Very High 

21 x High

22 ☐ Medium 

23 ☐ Low 

24 There is high confidence that climate intervention strategies may gain attention, especially if 

25 efforts to slow the buildup of atmospheric CO2 are considered inadequate by many in the 

26 scientific and policy communities.

27 Summary sentence or paragraph that integrates the above information

28 The key finding is a qualitative statement based on the growing literature on this topic. The 

29 uncertainty moving forward is the comfort level and desire among numerous stakeholders to 

30 research and potentially carry out these climate intervention strategies, particularly in light of 

31 progress the global community may make in slowing the atmospheric buildup of CO2.

32
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1 FIGURES

2

3 Figure 14.1: Global CO2 emissions and probabilistic temperature outcomes of Paris. (A) 

4 Global CO2 emissions from energy and industry (includes CO2 emissions from all fossil fuel 

5 production and use and industrial processes such as cement manufacture that also produce 

6 CO2 as a byproduct) for emissions scenarios following no policy, current policy, meeting the 

7 INDCs with no increased future ambition and meeting the INDCs with continually increasing 

8 ambition. (B) Likelihoods of different levels of increase in global mean surface temperature 

9 during the 21st century relative to preindustrial levels for the four scenarios. Although (A) 

10 shows only CO2 emissions from energy and industry, temperature outcomes are based on the 

11 full suite of GHG, aerosol, and short-lived species emissions across the full set of human 

12 activities and physical Earth systems. (Figure source: Fawcett et al. 2015)

Paris-Increased ambition

Paris-Continued ambition

Reference-Low policy

Likelihood of projected warming until 2100 (%)

0 10 20 30 40 50 60 70 80 90 100

Projected 

warming until 

2100 relative to 

preindustrial 

levels

1–1.5°C

1.5–2°C

2–3°C

3–4°C

>4°C

Reference-No policy

B Temperature probabilities

-10

0

10

20

30

40

50

60

70

80

1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090

Global CO2 emissions from energy and industry (GtCO2

/year) A Emissions pathways

2100

INDCs
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1

2 Figure 14.2: Global mean temperature change for a number of scenarios as a function of 

3 cumulative CO2 emissions from preindustrial conditions, with time progressing along each 

4 individual line for each scenario. (Figure source: IPCC 2013; © IPCC. Used with 

5 permission.)
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1

2 Figure 14.3: Global emissions pathways of effective global CO2 emissions which would be 

3 consistent with different temperature targets (relative to preindustrial temperatures). (Figure 

4 source: Sanderson et al. 2016)

5
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1 REFERENCES

2 Allen, M.R., D.J. Frame, C. Huntingford, C.D. Jones, J.A. Lowe, M. Meinshausen, and N. 

3 Meinshausen, 2009: Warming caused by cumulative carbon emissions towards the 

4 trillionth tonne. Nature, 458, 1163-1166. http://dx.doi.org/10.1038/nature08019 

5 Climate Action Tracker, 2016. http://climateactiontracker.org/global.html

6 Fawcett, A.A., G.C. Iyer, L.E. Clarke, J.A. Edmonds, N.E. Hultman, H.C. McJeon, J. Rogelj, 

7 R. Schuler, J. Alsalam, G.R. Asrar, J. Creason, M. Jeong, J. McFarland, A. Mundra, and 

8 W. Shi, 2015: Can Paris pledges avert severe climate change? Science, 350, 1168-1169. 

9 http://dx.doi.org/10.1126/science.aad5761 

10 Hayhoe, K.A.S., H.S. Kheshgi, A.K. Jain, and D.J. Wuebbles, 1998: Tradeoffs in fossil fuel 

11 use: the effects of CO2, CH4, and SO2 aerosol emissions on climate. World Resources 

12 Review, 10. 

13 IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working 

14 Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate 

15 Change. Cambridge University Press, Cambridge, UK and New York, NY, 1535 pp. 

16 http://dx.doi.org/10.1017/CBO9781107415324 www.climatechange2013.org

17 IPCC, 2013: Summary for policymakers. Climate Change 2013: The Physical Science Basis. 

18 Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental 

19 Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. 

20 Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. Cambridge University 

21 Press, Cambridge, United Kingdom and New York, NY, USA, 1–30. 

22 http://dx.doi.org/10.1017/CBO9781107415324.004 

23 Joos, F., R. Roth, J.S. Fuglestvedt, G.P. Peters, I.G. Enting, W. von Bloh, V. Brovkin, E.J. 

24 Burke, M. Eby, N.R. Edwards, T. Friedrich, T.L. Frölicher, P.R. Halloran, P.B. Holden, 

25 C. Jones, T. Kleinen, F.T. Mackenzie, K. Matsumoto, M. Meinshausen, G.K. Plattner, A. 

26 Reisinger, J. Segschneider, G. Shaffer, M. Steinacher, K. Strassmann, K. Tanaka, A. 

27 Timmermann, and A.J. Weaver, 2013: Carbon dioxide and climate impulse response 

28 functions for the computation of greenhouse gas metrics: a multi-model analysis. 

29 Atmospheric Chemistry and Physics, 13, 2793-2825. http://dx.doi.org/10.5194/acp-13-

30 2793-2013 

31 Keith, D.W., R. Duren, and D.G. MacMartin, 2014: Field experiments on solar 

32 geoengineering: report of a workshop exploring a representative research portfolio. 

33 Philosophical Transactions of the Royal Society A: Mathematical, Physical and 

34 Engineering Sciences, 372. http://dx.doi.org/10.1098/rsta.2014.0175 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 14

498

1 Keith, D.W. and D.G. MacMartin, 2015: A temporary, moderate and responsive scenario for 

2 solar geoengineering. Nature Climate Change, 5, 201-206. 

3 http://dx.doi.org/10.1038/nclimate2493 

4 Kravitz, B., K. Caldeira, O. Boucher, A. Robock, P.J. Rasch, K. Alterskjær, D.B. Karam, 

5 J.N.S. Cole, C.L. Curry, J.M. Haywood, P.J. Irvine, D. Ji, A. Jones, J.E. Kristjánsson, 

6 D.J. Lunt, J.C. Moore, U. Niemeier, H. Schmidt, M. Schulz, B. Singh, S. Tilmes, S. 

7 Watanabe, S. Yang, and J.-H. Yoon, 2013: Climate model response from the 

8 Geoengineering Model Intercomparison Project (GeoMIP). Journal of Geophysical 

9 Research: Atmospheres, 118, 8320-8332. http://dx.doi.org/10.1002/jgrd.50646 

10 MacMartin, D.G., D.W. Keith, B. Kravitz, and K. Caldeira, 2013: Management of trade-offs 

11 in geoengineering through optimal choice of non-uniform radiative forcing. Nature Clim. 

12 Change, 3, 365-368. http://dx.doi.org/10.1038/nclimate1722 

13 McGlade, C. and P. Ekins, 2015: The geographical distribution of fossil fuels unused when 

14 limiting global warming to 2 [deg]C. Nature, 517, 187-190. 

15 http://dx.doi.org/10.1038/nature14016 

16 NAS, 2015: Climate intervention: Carbon dioxide removal and reliable sequestration. The 

17 National Academies Press, Washington, DC, 154 pp. http://dx.doi.org/10.17226/18805 

18 NAS, 2015: Climate intervention: Reflecting sunlight to cool earth. The National Academies 

19 Press, Washington, DC, 260 pp. http://dx.doi.org/10.17226/18988 

20 Paltsev, S., E. Monier, J. Scott, A. Sokolov, and J. Reilly, 2015: Integrated economic and 

21 climate projections for impact assessment. Climatic Change, 131, 21-33. 

22 http://dx.doi.org/10.1007/s10584-013-0892-3 

23 Pierce, J.R., D.K. Weisenstein, P. Heckendorn, T. Peter, and D.W. Keith, 2010: Efficient 

24 formation of stratospheric aerosol for climate engineering by emission of condensible 

25 vapor from aircraft. Geophysical Research Letters, 37, L18805. 

26 http://dx.doi.org/10.1029/2010GL043975 

27 Ricke, K.L., M.G. Morgan, and M.R. Allen, 2010: Regional climate response to solar28 radiation management. Nature Geoscience, 3, 537-541. 

29 http://dx.doi.org/10.1038/ngeo915 

30 Rogelj, J., M. den Elzen, N. Höhne, T. Fransen, H. Fekete, H. Winkler, R. Schaeffer, F. Sha, 

31 K. Riahi, and M. Meinshausen, 2016: Paris Agreement climate proposals need a boost to 

32 keep warming well below 2 °C. Nature, 534, 631-639. 

33 http://dx.doi.org/10.1038/nature18307 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 14

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1 Sanderson, B.M., B.C. O'Neill, and C. Tebaldi, 2016: What would it take to achieve the Paris 

2 temperature targets? Geophysical Research Letters, 43, 7133-7142. 

3 http://dx.doi.org/10.1002/2016GL069563 

4 Shah, N., M. Wei, E.L. Virginie, and A.A. Phadke, 2015: Benefits of Leapfrogging to 

5 Superefficiency and Low Global Warming Potential Refrigerants in Room Air 

6 Conditioning.

7 Shepherd, J.G., K. Caldeira, P. Cox, J. Haigh, D. Keith, B. Launder, G. Mace, G. 

8 MacKerron, J. Pyle, S. Rayner, C. Redgwell, and A. Watson, 2009: Geoengineering the 

9 climate: Science, governance and uncertainty. Royal Society, 82 pp. 

10 http://eprints.soton.ac.uk/156647/1/Geoengineering_the_climate.pdf

11 Tebaldi, C. and P. Friedlingstein, 2013: Delayed detection of climate mitigation benefits due 

12 to climate inertia and variability. Proceedings of the National Academy of Sciences, 110,

13 17229-17234. http://dx.doi.org/10.1073/pnas.1300005110 

14 Tebaldi, C. and M.F. Wehner, 2016: Benefits of mitigation for future heat extremes under 

15 RCP4.5 compared to RCP8.5. Climatic Change, 1-13. http://dx.doi.org/10.1007/s10584-

16 016-1605-5 

17 Tilmes, S., B.M. Sanderson, and B.C. O'Neill, 2016: Climate impacts of geoengineering in a 

18 delayed mitigation scenario. Geophysical Research Letters, 43, 8222-8229. 

19 http://dx.doi.org/10.1002/2016GL070122 

20 UNFCCC, 2015: Paris Agreement. 25 pp. United Nations Framework Convention on Climate 

21 Change, [Bonn, Germany]. 

22 http://unfccc.int/files/essential_background/convention/application/pdf/english_paris_agr

23 eement.pdf

24 Zaelke, D. and N. Borgford-Parnell, 2015: The importance of phasing down 

25 hydrofluorocarbons and other short-lived climate pollutants. Journal of Environmental 

26 Studies and Sciences, 5, 169-175. http://dx.doi.org/10.1007/s13412-014-0215-7
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1 15. Potential Surprises: Compound Extremes and Tipping Elements 

2 KEY FINDINGS

3 1. Positive feedbacks (self-reinforcing cycles) within the climate system have the potential to 

4 accelerate human-induced climate change and even shift the Earth’s climate system, in part 

5 or in whole, into new states that are very different from those experienced in the recent past 

6 (for example, ones with greatly diminished ice sheets or different large-scale patterns of 

7 atmosphere or ocean circulation). Some feedbacks and potential state shifts can be modeled 

8 and quantified; others can be modeled or identified but not quantified; and some are 

9 probably still unknown. (Very high confidence)

10 2. The physical and socioeconomic impacts of compound extreme events (such as 

11 simultaneous heat and drought, wildfires associated with hot and dry conditions, or flooding 

12 associated with high precipitation on top of snow or waterlogged ground) can be greater 

13 than the sum of the parts (very high confidence). Few analyses consider the spatial or 

14 temporal correlation between extreme events.

15 3. While climate models incorporate important climate processes that can well quantified, they

16 do not include all of the processes that can contribute to positive feedbacks, correlation of 

17 extremes, and abrupt and/or irreversible changes. For this reason, future changes outside the 

18 range projected by climate models cannot be ruled out (very high confidence), and climate 

19 models are more likely to underestimate than to overestimate the amount of future change 

20 (medium confidence).

21 15.1 Introduction

22 Humanity is conducting an unprecedented experiment with the Earth’s climate system, through 

23 the large-scale combustion of fossil fuels, widespread deforestation, and the resulting release of 

24 carbon dioxide (CO2) into the atmosphere, as well as through emissions of other greenhouse 

25 gases and radiatively active substances from human activities (Ch. 2: Scientific Basis). Previous 

26 chapters have covered a variety of observed and projected changes in the climate system, 

27 including averages and extremes of temperature, precipitation, sea level, and storm events 

28 (Chapters 1, 4-13). While the distribution of climate model projections provides insight into the 

29 range of possible future change, this range is limited by the fact that models do not include or 

30 fully represent some of the known processes and components of the climate system. Therefore, 

31 there is a significant potential for our planetary experiment to result in unanticipated surprises, 

32 and a broad consensus that the further and the faster the Earth’s climate system is pushed 

33 towards warming, the greater the risk of such surprises. 

34 The climate system is made up of many subcomponents that interact in complex ways, including 

35 some that may be difficult to anticipate. Examples such as Arctic sea ice, Greenland Ice Sheet 

36 melt, and Antarctic Ice Sheet instability illustrate how positive feedbacks, or self-reinforcing 
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1 cycles, within and between systems have the potential to magnify changes in any given system. 

2 Factoring in natural variability and the existence of thresholds beyond which relatively rapid 

3 change can occur raises the possibility of a cascading series of impacts throughout the 

4 interconnected Earth system and across a broad range of temporal and spatial scales.

5 This chapter focuses on two potential types of surprises. The first arises from changing 

6 correlations in extreme events which, on their own, may not be surprising but which together can 

7 increase the likelihood of “perfect storms” of physical and/or socioeconomic impacts. Increasing 

8 correlation of extremes, either between types of events (such as paired extremes of droughts and 

9 intense rainfall) or over greater spatial or temporal scales (such as a drought occurring in 

10 multiple breadbaskets around the world or lasting for multiple decades) are often ignored by 

11 analyses that focus solely on one class of extreme. The second type of surprise arises from 

12 positive feedbacks in the climate system, which can give rise to “tipping elements” that can exist 

13 in multiple, stable but quite different states. Examples of possible tipping elements include 

14 modes of atmosphere–ocean circulation like El Niño–Southern Oscillation, ice sheets, and large15 scale ecosystems like the Amazon rainforest (Lenton et al. 2008; Kopp et al. 2016). 

16 15.2 Risk Quantification and Its Limits

17 Quantifying the risk of low-probability, high-impact events, based on models or observations, 

18 usually involves examining the tails of a probability distribution function (PDF). Robust 

19 detection, attribution, and projection of such events into the future is challenged by multiple 

20 factors, including an instrumental record (typically no more than 130 years in length across the 

21 United States) that often does not represent the full range of physical possibilities in the climate 

22 system, as well as the limitations of the statistical tools, scientific understanding, and models 

23 used to describe these processes (Zwiers et al. 2013).

24 The 2013 Boulder, Colorado, floods and the Dust Bowl of the 1930s in the central United States 

25 are two examples of extreme events whose magnitude and/or extent are unprecedented in the 

26 observational record. Statistical approaches such as Extreme Value Theory can be used to model 

27 and estimate the magnitude of rare events that may not have occurred in the observational record, 

28 such as the “1,000-year flood event” (i.e., a flood event with a 0.1% chance of occurrence in any 

29 given year) (e.g., Smith 1987). While useful for many applications, these are not physical 

30 models: they are statistical models that are typically based on the assumption that observed 

31 patterns of natural variability (that is, the sample from which the models derive their statistics) 

32 are valid beyond the observational period. Extremely rare events can also be assessed based upon 

33 paleoclimate records and physical modeling. In the paleoclimatic record, numerous abrupt 

34 changes have occurred since the last deglaciation, many larger than those recorded in the 

35 instrumental record. For example, tree ring records of drought in the western United States show 

36 abrupt, long-lasting megadroughts that were similar to but more intense and longer-lasting than 

37 the 1930s Dust Bowl (Woodhouse and Overpeck 1998). 
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1 Since climate models are based on physics rather than observational data, they are not inherently 

2 constrained to any given time period or set of physical conditions. They have been used to study 

3 the Earth in the distant past, and even the climate of other planets (e.g., Lunt et al. 2012; Navarro 

4 et al. 2014). Looking to the future, thousands of years’ worth of simulations can be generated 

5 and explored to characterize small-probability, high-risk extreme events, as well as correlated

6 extremes (see Section 15.4). However, the likelihood that such model events represent real risks 

7 is limited by well-known uncertainties in climate modeling related to parameterizations, model 

8 resolution, and limits to scientific understanding (Ch. 4: Projections). For example, conventional 

9 convective parameterizations in global climate models systematically underestimate extreme 

10 precipitation (Kang et al. 2015). In addition, models often do not accurately capture or even 

11 include the processes, such as permafrost feedbacks, by which abrupt, non-reversible change 

12 may occur (see Section 15.4). An analysis focusing on physical climate predictions over the last 

13 20 years found a tendency for scientific assessments such as those of the Intergovernmental 

14 Panel on Climate Change to under-predict rather than over-predict changes that were 

15 subsequently observed (Brysse et al. 2013).

16 15.3 Compound Extremes

17 An important aspect of surprise is the potential for compound extreme events. These can be 

18 events that occur at the same time, or in sequence (such as consecutive droughts in the same 

19 region); and in the same geographic location, or at multiple locations within a given country or 

20 around the world (such as the 2009 Australian floods and wildfires). They may consist of 

21 multiple extreme events, or of events that by themselves may not be extreme, but together 

22 produce a multi-event “occurrence” (such as a heat wave accompanied by drought; Quarantelli 

23 1986). It is possible for the net impact of these events to be less than the sum of the individual 

24 events, if their effects cancel each other out. For example, increasing carbon dioxide 

25 concentrations and acceleration of the hydrological cycle may mitigate the future impact of 

26 extremes in gross primary productivity that currently impact the carbon cycle (Zscheischler et al. 

27 2014). However, our primary concern relates to those whose effects are additive, or even 

28 multiplicative, due to compounding influences.

29 Some areas are susceptible to multiple types of extreme events that can occur simultaneously. 

30 For example, some regions are susceptible to both flooding from coastal storms and riverine 

31 flooding from snow melt, and a compound event would be the occurrence of both 

32 simultaneously. Compound events can also result from shared forcing factors, including natural 

33 cycles like El Niño–Southern Oscillation (ENSO); large-scale circulation patterns, such as the 

34 ridge observed during the current California Drought (e.g., Swain et al. 2016; see also Ch. 8: 

35 Droughts, Floods, and Hydrology); or relatively greater regional sensitivity to global change, as 

36 may occur in “hot spots” such as the western United States (Diffenbaugh and Giorgi 2012). 

37 Finally, compound events can result from positive feedbacks between individual events leading 

38 to mutual reinforcement, such as the relationship between droughts and heat waves in water39 limited areas (IPCC 2012). In a changing climate, the probability of compound events can be 
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1 altered if there is an underlying trend in non-extreme conditions, such as mean temperature, 

2 precipitation, or sea level, that alters the baseline conditions or vulnerability of a region. It can 

3 also be altered if there is a change in the frequency or intensity of individual extreme events 

4 relative to the changing mean (for example, stronger storm surges, more frequent heat waves, or 

5 heavier precipitation events).

6 The characterization of compound events in both the historical record and future projections 

7 remains challenging and largely incomplete. For example, the relationship between drought and 

8 heat, linked through soil moisture and evaporation, is one of the most widespread compound 

9 events. Globally, the occurrence of warm/dry and warm/wet conditions has increased since the 

10 1950s (Hao et al. 2013). In the future, hot summers will become more frequent, and although it is 

11 not always clear for every region whether drought frequency will change, droughts in already dry 

12 regions, such as the southwestern United States, are likely to be more intense in a warmer world 

13 due to faster evaporation and associated surface drying (Collins et al. 2013; Trenberth et al. 

14 2014; Cook et al. 2015). For other regions, however, the picture is not as clear. Recent examples 

15 of heat/drought events (in the southern Great Plains in 2011; in California, 2012–2015) have 

16 highlighted the inadequacy of traditional univariate risk assessment methods (AghaKouchak et 

17 al. 2014); yet an analysis for the contiguous United States of precipitation deficits and positive 

18 temperature anomalies in the last 30 years finds no significant trend (Serinaldi 2016).

19 Another compound event frequently discussed in the literature is wildfire driven by the 

20 combined effects of high precipitation variability (wet seasons followed by dry), elevated 

21 temperature, and low humidity. These factors increase the risk of wildfires, which, if followed by 

22 heavy rain, can in turn promote landslides and erosion. They can also radically increase 

23 emissions of greenhouse gases, as demonstrated by the amount of carbon dioxide produced by 

24 the Fort McMurray fires of May 2016—more than 10% of Canada’s annual emissions (Ch. 11: 

25 Arctic Changes).

26 A third compound event involves flooding arising from wet conditions due to precipitation or to 

27 snowmelt, which could be exacerbated by warm temperatures. These wet conditions lead to high 

28 groundwater levels, saturated soils, and/or elevated river flows, which can increase the risk of 

29 flooding associated with a given storm days or even months later (IPCC 2012).

30 Compound events may surprise in two ways. The first is if known types of compound events 

31 recur, but are stronger, longer-lasting, and/or more widespread than those experienced in the very 

32 limited observational record or projected by model simulations for the future. An example would 

33 be simultaneous drought events in different agricultural regions across the country, or even 

34 around the world, that challenge the ability of human systems to provide adequate affordable 

35 food. The second way in which they could surprise would be the emergence of new types of 

36 compound events not observed in the historical record or predicted by model simulations, either 

37 due to the limited resolution of models or due to an increase in the frequency of such events as a 

38 result of human-induced climate change, or both. An example is Hurricane Sandy, where sea 
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1 level rise, anomalously high ocean temperatures, and high tides combined to strengthen both the 

2 storm and the magnitude of storm surge associated with it (Reed et al. 2015). At the same time, a 

3 blocking ridge over Greenland—a feature whose strength and frequency may be related to both 

4 Greenland surface melt and reduced summer sea ice in the Arctic (Liu et al. 2016; see also Ch. 

5 11: Arctic Changes)—redirected the storm inland to what was, coincidentally, an exceptionally 

6 high-exposure location. 

7 [INSERT FIGURE 15.1 HERE:

8 Figure 15.1: (left) Potential climatic tipping elements affecting the Americas. (right) Wildfire 

9 and drought events from the NOAA Billion Dollar Weather Events list (1980–2016), and 

10 associated temperature and precipitation anomalies. Dot size scales with the magnitude of 

11 impact, as reflected by the cost of the event. These high-impact events occur preferentially under 

12 hotter, drier conditions. (Figure source: (left) adapted from Lenton et al. 2008, (right) original 

13 analysis.]

14 15.4 Climatic Tipping Elements 

15 Some potential climatic surprises relate to critical thresholds, sometimes called “tipping points”, 

16 in different parts of the Earth systems (e.g., Lenton et al. 2008; Collins et al. 2013; NRC 2013; 

17 Kopp et al. 2016). These different parts of the Earth system, known as tipping elements, have the 

18 potential to enter into self-amplifying cycles that commit them to shifting into a new state: for 

19 example, from one in which the Arctic Ocean is covered by ice in the summer, to one in which it 

20 is ice-free in the summer. In some potential tipping elements, these state shifts occur abruptly; in 

21 others, the commitment to a state shift may occur rapidly, but the state shift itself may take 

22 decades, centuries, or even millennia to play out. Often, the forcing that commits a tipping 

23 element to a shift in state is unknown; sometimes, it is even unclear whether a proposed tipping 

24 element actually exhibits tipping behavior. Through a combination of physical modeling, 

25 paleoclimate observations, and expert elicitations, scientists have identified possible tipping 

26 elements in atmosphere–ocean circulation, the cryosphere, the carbon cycle, and ecosystems 

27 (Table 15.1). 

28 [INSERT TABLE 15.1 HERE:

29 Table 15.1: Potential tipping elements (adapted from Kopp et al. 2016).]

30 The Atlantic meridional overturning circulation (AMOC) is a major component of global ocean 

31 circulation. Driven by the sinking of cold, dense water in the North Atlantic near Greenland, its 

32 strength is projected to decrease with warming due to freshwater input from increased 

33 precipitation, glacial melt, and melt of the Greenland Ice Sheet (Rahmstorf et al. 2015; see also 

34 discussion in Ch. 11: Arctic Changes). Given sufficient freshwater input, AMOC might collapse 

35 entirely, although the large majority of models do not produce an abrupt collapse in the 21st 

36 century (NRC 2013). A decrease in AMOC strength is, however, probable and may already be 

37 culpable for the “warming hole” observed in the North Atlantic (Rahmstorf et al. 2015), although 
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1 it is still unclear whether this decrease represents a forced change or internal variability (Cheng 

2 et al. 2016). 

3 A decrease in the strength of AMOC would accelerate sea level rise off the northeastern United 

4 States (Yin and Goddard 2013), while a full collapse could result in as much as approximately 

5 0.5 meters (1.6 feet) of regional sea level rise (Gregory and Lowe 2000; Levermann et al. 2005) 

6 and a cooling of approximately 0°F–4°F (0°C–2°C) over the United States (Jackson et al. 2015). 

7 A slowdown of the AMOC would also lead to a reduction of ocean carbon dioxide uptake, and 

8 thus an acceleration of warming (Pérez et al. 2013). 

9 The atmospheric–oceanic circulation of the equatorial Pacific drives the state shifts of the El 

10 Niño–Southern Oscillation through a set of feedbacks. This is an example of a tipping element 

11 that shifts on a sub-decadal, interannual timescale, primarily in response to internal noise. 

12 Climate model experiments suggest climate change will reduce the threshold needed to trigger 

13 extreme El Niño and extreme La Niña events. As can be seen by examining the impacts of recent 

14 El Niño and La Niña events, such a shift would have potential implications for the United States 

15 (Cai et al. 2014, 2015; for more on ENSO impacts, see Chapter 5).

16 Arctic sea ice may exhibit abrupt state shifts into summer- and perennially ice-free states. 

17 Critical positive feedbacks not captured sufficiently by global climate models could include: 

18 greater high-latitude storminess and ocean wave penetration as sea ice declines; more northerly 

19 incursions of warm air and water; melting associated with increasing water vapor; loss of 

20 multiyear ice; and albedo decreases on the sea ice surface. At the same time, however, the point 

21 at which the threshold for an abrupt shift would be crossed also depends on the role of natural 

22 variability in a changing system; the relative importance of potential stabilizing negative 

23 feedbacks—such as more efficient heat transfer from the ocean to the atmosphere in fall and 

24 winter as sea declines; and how sea ice in other seasons, as well as the climate system more 

25 generally, will respond once the first “ice free” summer occurs. It is also possible that summer 

26 sea ice may not abruptly collapse but instead respond in a manner proportional to increase in 

27 temperature (Armour et al. 2011; Ridley et al. 2012; Li et al. 2013; Wagner and Eisenman 2015). 

28 Moreover, an abrupt decrease in winter sea ice may result simply from the Arctic Ocean 

29 warming above a critical temperature for ice formation, rather than from positive feedbacks 

30 (Bathiany et al. 2016).

31 Two possible tipping elements in the carbon cycle also lie in the Arctic. The first is buried in the 

32 permafrost, where an estimated 1,300–1,600 Gt C (Schuur et al. 2015) could be released as CO2

33 or methane as the Arctic warms. The release of permafrost carbon is limited by the freeze–thaw 

34 cycle, the rate with which heat diffuses into the permafrost, the potential for organisms to cycle 

35 permafrost carbon into new biomass, and limited oxygen availability. Though the release of 

36 permafrost carbon would probably not be fast enough to trigger a runaway self-amplifying cycle 

37 leading to a permafrost-free Arctic, it still has the potential to significantly amplify both local 

38 and global warming, reduce the budget of human-caused CO2 emissions available to meet global 
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1 temperature targets such as the Paris Agreement, and drive continued warming even if human 

2 emissions stopped altogether (MacDougall et al. 2012, 2015). 

3 The second Arctic carbon cycle tipping point is an estimated 1,100 Gt of carbon in methane 

4 hydrates (equivalent to about 11,000 Gt C as CO2 using a 100-year global warming potential) 

5 frozen into the sediments of continental shelves of the Arctic Ocean (Kretschmer et al. 2015). 

6 While this reservoir has been known and discussed for several decades (e.g., Kvenvolden 1988), 

7 only recently has it been hypothesized that warming bottom water temperatures may destabilize 

8 the hydrates over timescales shorter than millennia, leading to their release into the water column 

9 and eventually the atmosphere (e.g., Archer 2007; Kretschmer et al. 2015). Recent measurements 

10 of the release of methane from these sediments in summer find that, while methane is being 

11 emitted, these emissions do not appear to reach the ocean surface in sufficient quantity to affect 

12 the atmospheric methane budget (Myhre et al. 2016). Future estimates of natural releases to the 

13 atmosphere over the next century are only a fraction of present-day anthropogenic methane 

14 emissions (Kretschmer et al. 2015; Stranne et al. 2016). These estimates, however, neglect the 

15 possibility that humans may insert themselves into the physical feedback systems. With an 

16 estimated 53% of global fossil fuel reserves in the Arctic becoming increasingly accessible in a 

17 warmer world (Lee and Holder 2001), the risks associated with this carbon being extracted and 

18 burned, further exacerbating the influence of humans on global climate, are evident (Jakob and 

19 Hilaire 2015; McGlade and Elkins 2015). Of less concern but still relevant, Arctic ocean waters 

20 themselves are a source of methane, which could increase as sea ice decreases (Kort et al. 2012).

21 The Antarctic and Greenland Ice Sheets are clear tipping elements, although they respond 

22 relatively slowly. The Greenland Ice Sheet exhibits multiple stable states as a result of feedbacks 

23 involving the elevation of the ice sheet, atmospheric dynamics, and albedo (Ridley et al. 2010; 

24 Robinson et al. 2012; Levermann et al. 2013), with some results suggesting that warming of 

25 1.6°C (2.9oF) above a preindustrial baseline could commit Greenland to an 85% reduction in ice 

26 volume and a 20 foot (6 meter) contribution to global mean sea level over the course of centuries 

27 or millennia (Robinson et al. 2012). In Antarctica, enough ice to raise global mean sea level by 

28 23 meters (75.5 feet) sits on bedrock that is below sea level (Fretwell et al. 2013). This ice is 

29 vulnerable to collapse due to a variety of feedbacks involving ocean–ice sheet interactions 

30 (Schoof 2007; Gomez et al. 2010; Ritz et al. 2015; Mengel and Levermann et al. 2014; Pollard et 

31 al. 2015). Observational evidence suggests that ice dynamics already in progress have committed 

32 parts of the West Antarctic Ice Sheet to contributing as much as 1.2 meters (3.9 feet) to global 

33 mean sea level, probably over the course of many centuries (Joughin et al. 2014; Rignot et al. 

34 2014). Plausible physical modeling indicates that, under RCP8.5 (Ch. 4: Projections), Antarctic 

35 ice could contribute 1 meter (3.28 feet) or more to global mean sea level over the remainder of 

36 this century (DeConto and Pollard 2016), with some authors arguing that rates of change could 

37 be even faster (Hansen et al. 2016). 

38 Tipping elements also exist in large-scale ecosystems. For example, boreal forests such as those 

39 in southern Alaska may expand northward in response to Arctic warming. Because forests are 
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1 darker than the tundra they replace, their expansion amplifies regional warming, which in turn 

2 accelerates their expansion (Jones et al. 2009). Similarly, coral reef ecosystems, such as those in 

3 Florida, are maintained by stabilizing ecological feedbacks among corals, coralline red algae, 

4 and grazing fish and invertebrates. However, these stabilizing feedbacks can be undermined by 

5 warming, increased risk of bleaching events, spread of disease, and ocean acidification, leading 

6 to abrupt reef collapse (Hoegh-Guldberg et al. 2007).

7 15.5 Paleoclimatic Hints of Additional Potential Surprises 

8 The paleoclimatic record provides evidence for additional state shifts whose driving mechanisms 

9 are as yet poorly understood. For example, compared to reconstructions of temperature and CO2

10 from the geological record, global climate models have a tendency to underestimate the 

11 magnitude of both global mean warming in response to higher CO2 levels and the amplification

12 of this warming at high latitudes. The late Pliocene (about 3.6–2.6 million years ago), the middle 

13 Miocene (about 17–14.5 million years ago), and the early Eocene (about 56–48 million years 

14 ago)—all periods well predating the first appearance of Homo sapiens around two hundred 

15 thousand years ago (Tattersall 2009)—provide three case studies.

16 Global climate model (GCM) simulations of the late Pliocene systematically underestimate 

17 warming north of 30°N (Salzmann et al. 2013). Similarly, GCM simulations of paleoclimate 

18 during the middle Miocene (about 17–14.5 million years ago) cannot simultaneously replicate 

19 the proxy-estimated global mean temperature (approximately 8°C ± 2°C [14°F ± 4°F] warmer 

20 than preindustrial) and the approximately 40% reduction in the pole-to-equator temperature 

21 gradient relative to today (Goldner et al. 2014). Although about one-third of the global mean 

22 temperature increase can be attributed to changes in geography and vegetation, geological 

23 proxies indicate CO2 concentrations of around 400 ppm (Goldner et al. 2014; Foster et al. 2012), 

24 suggesting that as yet unidentified feedbacks must be invoked to explain climate conditions 

25 during the middle Miocene.

26 The early Eocene is characterized by the absence of permanent land ice, CO2 concentrations 

27 peaking around 1400 ± 470 ppm (Anagnostu et al. 2016), and global temperatures about 14°C ± 

28 3°C (25°F ± 5°F) warmer than the preindustrial (Caballero and Huber 2013). Like the late 

29 Pliocene and the middle Miocene, it also exhibits about half the meridional temperature gradient 

30 of today (Huber and Caballero 2011; Lunt et al. 2012). About one-third of the temperature 

31 difference is attributable to changes in geography, vegetation, and ice sheet coverage (Caballero 

32 and Huber 2013). To reproduce both the elevated global mean temperature and the reduced pole33 to-equator temperature gradient, climate models require CO2 concentrations approximately 2–5 

34 times that indicated by the proxy record (Lunt et al. 2012).

35 One possible explanation for this discrepancy is a planetary state shift that, above a particular 

36 CO2 threshold, leads to a significant increase in the sensitivity of the climate to CO2. One 

37 modeling study (Caballero and Huber 2013) suggests that an abrupt change in atmospheric 
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1 circulation (the onset of equatorial atmospheric superrotation) between 1,120 and 2,240 ppm 

2 CO2 could lead to a reduction in cloudiness and an approximate doubling of climate sensitivity. 

3 However, the critical threshold for such a transition is poorly constrained. If it happened at a 

4 lower CO2 level, it might explain the Eocene discrepancy and potentially also the Miocene 

5 discrepancy. It could also then pose a threat within the 21st century under the higher RCP8.5 

6 emissions pathway (see Ch. 4: Projections for a description of future scenarios). Regardless of 

7 the particular mechanism, the systematic paleoclimatic model–data mismatch for past warm 

8 climates suggests that GCMs are omitting at least one, and probably more, processes crucial to 

9 future warming, especially in polar regions. For this reason, future changes outside the range 

10 projected by climate models cannot be ruled out, and climate models are more likely to 

11 underestimate than to overestimate the amount of future change (medium confidence).

12
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1 TRACEABLE ACCOUNTS

2 Key Finding 1

3 Positive feedbacks (self-reinforcing cycles) within the climate system have the potential to 

4 accelerate human-induced climate change and even shift the Earth’s climate system, in part or in 

5 whole, into new states that are very different from those experienced in the recent past. These 

6 states might include, for example, ones with greatly diminished ice sheets or different large-scale 

7 patterns of atmosphere or ocean circulation. Some feedbacks and potential state shifts can be 

8 modeled and quantified; others can be modeled or identified but not quantified; and some are 

9 probably still unknown (very high confidence).

10 Description of evidence base

11 This key finding is based on a large body of scientific literature recently summarized by Lenton 

12 et al. (2008), NRC (2013), and Kopp et al. (2016). As NRC (2013, page vii) states, “A study of 

13 Earth’s climate history suggests the inevitability of ‘tipping points’—thresholds beyond which 

14 major and rapid changes occur when crossed—that lead to abrupt changes in the climate system” 

15 and (page xi), “Can all tipping points be foreseen? Probably not. Some will have no precursors, 

16 or may be triggered by naturally occurring variability in the climate system. Some will be 

17 difficult to detect, clearly visible only after they have been crossed and an abrupt change 

18 becomes inevitable.” As IPCC AR5 WG1 Chapter 12, section 12.5.5 (Collins et al. 2013) further 

19 states, “A number of components or phenomena within the Earth system have been proposed as 

20 potentially possessing critical thresholds (sometimes referred to as tipping points) beyond which 

21 abrupt or nonlinear transitions to a different state ensues.” Collins et al. (2013) further 

22 summarizes critical thresholds that can be modeled and others that can only be identified.

23 Major uncertainties

24 The largest uncertainties are: 1) whether proposed tipping elements actually undergo critical 

25 transitions, 2) the magnitude and timing of forcing that will be required to initiate critical 

26 transitions in tipping elements, 3) the speed of the transition once it has been triggered, 4) the 

27 characteristics of the new state that results from such transition, and 5) the potential for new 

28 tipping elements to exist that are yet unknown.

29 Assessment of confidence based on evidence and agreement, including short description of 

30 nature of evidence and level of agreement

31 X Very High

32 ☐ High 

33 ☐ Medium

34 ☐ Low
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1 Very high confidence in the likelihood of the existence of positive feedbacks and tipping 

2 elements statement is based on a large body of literature published over the last 25 years that 

3 draws from basic physics, observations, paleoclimate data, and modeling.

4 Very high confidence that some feedbacks can be quantified, others are known but cannot be 

5 quantified, and others may yet exist that are currently unknown.

6 Summary sentence or paragraph that integrates the above information

7 The key finding is based on NRC (2013) and IPCC AR4 WG1 Chapter 12 section 12.5.5 (IPCC 

8 2007), which made a thorough assessment of the relevant literature.

9 Key Finding 2

10 The physical and socioeconomic impacts of compound extreme events (such as simultaneous 

11 heat and drought, wildfires associated with hot and dry conditions, or flooding associated with 

12 high precipitation on top of snow or waterlogged ground) can be greater than the sum of the parts 

13 (very high confidence). Few analyses consider the spatial or temporal correlation between 

14 extreme events.

15 Description of evidence base

16 This key finding is based on a large body of scientific literature summarized in the 2012 IPCC 

17 Special Report on Extremes (IPCC 2012). The report’s Summary for Policymakers (page 6) 

18 states, “exposure and vulnerability are key determinants of disaster risk and of impacts when risk 

19 is realized...extreme impacts on human, ecological, or physical systems can result from 

20 individual extreme weather or climate events. Extreme impacts can also result from non-extreme 

21 events where exposure and vulnerability are high or from a compounding of events or their 

22 impacts. For example, drought, coupled with extreme heat and low humidity, can increase the 

23 risk of wildfire.”

24 Major uncertainties

25 The largest uncertainties are in the temporal congruence of the events and the compounding 

26 nature of their impacts.

27 Assessment of confidence based on evidence and agreement, including short description of 

28 nature of evidence and level of agreement

29 x Very High

30 ☐ High 

31 ☐ Medium

32 ☐ Low

33 Very high confidence that the impacts of multiple events could exceed the sum of the impacts of 

34 events occurring individually.
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1 Summary sentence or paragraph that integrates the above information

2 The key finding is based on the 2012 IPCC SREX report, particularly section 3.1.3 on compound 

3 or multiple events, which presents a thorough assessment of the relevant literature.

4 Key Finding 3

5

6 While climate models incorporate important climate processes that can well quantified, they do 

7 not include all of the processes that contribute to positive feedbacks, correlation of extremes, and 

8 abrupt and/or irreversible changes. For this reason, future changes outside the range projected by 

9 climate models cannot be ruled out (very high confidence), and climate models are more likely to 

10 underestimate than to overestimate the amount of future change (medium confidence).

11 Description of evidence base

12 This key finding is based on the conclusions of IPCC AR5 WG1 (IPCC 2013); what is and is not 

13 included in the latest generation of CMIP5 models is summarized in Chapter 9 of this report. The 

14 second half of this key finding is further supported by the tendency of global climate models to 

15 underestimate, relative to geological reconstructions, the magnitude of both global mean 

16 warming and the amplification of warming at high latitudes in past warm climates (e.g., 

17 Salzmann et al. 2013; Goldner et al. 2014; Caballeo and Huber 2013; Lunt et al. 2012).

18 Major uncertainties

19 The largest uncertainties are structural: are the models including all the important components 

20 and relationships necessary to model the feedbacks and if so, are these correctly represented in

21 the models?

22 Assessment of confidence based on evidence and agreement, including short description of 

23 nature of evidence and level of agreement

24 X Very High

25 ☐ High 

26 X Medium

27 ☐ Low

28 Very high confidence that the models are incomplete representations of the real world; medium 

29 confidence that their tendency is to under- rather than over-estimate the amount of future 

30 change.

31 Summary sentence or paragraph that integrates the above information

32 The key finding is based on the IPCC AR5 WG1 Chapter 9 (IPCC 2013), as well as systematic 

33 paleoclimatic model/data comparisons.

34
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1 TABLE

2 Table 15.1: Potential tipping elements (adapted from Kopp et al., 2016).

Candidate Climatic Tipping 

Element

State Shift Main impact pathways

Atmosphere–ocean circulation

Atlantic meridional overturning 

circulation

Collapse regional temperature and precipitation; 

global mean temperature; regional sea 

level

El Niño-Southern Oscillation Increase in amplitude regional temperature and precipitation

Equatorial atmospheric 

superrotation

Initiation cloud cover; climate sensitivity

Regional North Atlantic 

convection

Collapse regional temperature and precipitation

Cryosphere

Antarctic Ice Sheet Collapse in ice volume sea level; albedo

Arctic sea ice Collapse in summertime and/or 

perennial area

regional temperature and precipitation; 

albedo

Greenland Ice Sheet Collapse in ice volume sea level; albedo

Carbon cycle

Methane hydrates Massive release greenhouse gas emissions

Permafrost carbon Massive release greenhouse gas emissions

Ecosystem
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Amazon rainforest Dieback, Transition to grasslands ecosystem services; greenhouse gas 

emissions

Boreal forest Dieback, Transition to grasslands ecosystem services; greenhouse gas 

emissions; albedo

Coral reefs Die-off ecosystem services

1
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1 FIGURE

2

3

4 Figure 15.1: (left) Potential climatic tipping elements affecting the Americas (adapted from 

5 Lenton et al. 2008). (right) Wildfire and drought events from the NOAA Billion Dollar Weather 

6 Events list (1980–2016), and associated temperature and precipitation anomalies. Dot size scales 

7 with the magnitude of impact, as reflected by the cost of the event. These high-impact events 

8 occur preferentially under hot, dry conditions. (Figure source: adapted from Lenton et al. 2008)

9
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 15

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1 REFERENCES

2 AghaKouchak, A., L. Cheng, O. Mazdiyasni, and A. Farahmand, 2014: Global warming and 

3 changes in risk of concurrent climate extremes: Insights from the 2014 California drought. 

4 Geophysical Research Letters, 41, 8847-8852. http://dx.doi.org/10.1002/2014GL062308 

5 Anagnostou, E., E.H. John, K.M. Edgar, G.L. Foster, A. Ridgwell, G.N. Inglis, R.D. Pancost, 

6 D.J. Lunt, and P.N. Pearson, 2016: Changing atmospheric CO2 concentration was the 

7 primary driver of early Cenozoic climate. Nature, 533, 380-384. 

8 http://dx.doi.org/10.1038/nature17423 

9 Archer, D., 2007: Methane hydrate stability and anthropogenic climate change. Biogeosciences,

10 4, 521-544. http://dx.doi.org/10.5194/bg-4-521-2007 

11 Armour, K.C., I. Eisenman, E. Blanchard-Wrigglesworth, K.E. McCusker, and C.M. Bitz, 2011: 

12 The reversibility of sea ice loss in a state-of-the-art climate model. Geophysical Research 

13 Letters, 38, L16705. http://dx.doi.org/10.1029/2011GL048739 

14 Bathiany, S., D. Notz, T. Mauritsen, G. Raedel, and V. Brovkin, 2016: On the Potential for 

15 Abrupt Arctic Winter Sea Ice Loss. Journal of Climate, 29, 2703-2719. 

16 http://dx.doi.org/10.1175/JCLI-D-15-0466.1 

17 Brysse, K., N. Oreskes, J. O’Reilly, and M. Oppenheimer, 2013: Climate change prediction: 

18 Erring on the side of least drama? Global Environmental Change, 23, 327-337. 

19 http://dx.doi.org/10.1016/j.gloenvcha.2012.10.008 

20 Caballero, R. and M. Huber, 2013: State-dependent climate sensitivity in past warm climates and 

21 its implications for future climate projections. Proceedings of the National Academy of 

22 Sciences, 110, 14162-14167. http://dx.doi.org/10.1073/pnas.1303365110 

23 Cai, W., S. Borlace, M. Lengaigne, P. van Rensch, M. Collins, G. Vecchi, A. Timmermann, A. 

24 Santoso, M.J. McPhaden, L. Wu, M.H. England, G. Wang, E. Guilyardi, and F.-F. Jin, 2014: 

25 Increasing frequency of extreme El Nino events due to greenhouse warming. Nature Climate 

26 Change, 4, 111-116. http://dx.doi.org/10.1038/nclimate2100 

27 Cai, W., G. Wang, A. Santoso, M.J. McPhaden, L. Wu, F.-F. Jin, A. Timmermann, M. Collins, 

28 G. Vecchi, M. Lengaigne, M.H. England, D. Dommenget, K. Takahashi, and E. Guilyardi, 

29 2015: Increased frequency of extreme La Nina events under greenhouse warming. Nature 

30 Climate Change, 5, 132-137. http://dx.doi.org/10.1038/nclimate2492 

31 Cheng, J., Z. Liu, S. Zhang, W. Liu, L. Dong, P. Liu, and H. Li, 2016: Reduced interdecadal 

32 variability of Atlantic Meridional Overturning Circulation under global warming. 

33 Proceedings of the National Academy of Sciences, 113, 3175-3178. 

34 http://dx.doi.org/10.1073/pnas.1519827113 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 15

516

1 Collins, M., R. Knutti, J. Arblaster, J.-L. Dufresne, T. Fichefet, P. Friedlingstein, X. Gao, W.J. 

2 Gutowski, T. Johns, G. Krinner, M. Shongwe, C. Tebaldi, A.J. Weaver, and M. Wehner, 

3 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. Climate 

4 Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth 

5 Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. 

6 Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and 

7 P.M. Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom and New 

8 York, NY, USA, 1029–1136. http://dx.doi.org/10.1017/CBO9781107415324.024 

9 www.climatechange2013.org

10 Cook, B.I., T.R. Ault, and J.E. Smerdon, 2015: Unprecedented 21st century drought risk in the 

11 American Southwest and Central Plains. Science Advances, 1. 

12 http://dx.doi.org/10.1126/sciadv.1400082 

13 DeConto, R.M. and D. Pollard, 2016: Contribution of Antarctica to past and future sea-level rise. 

14 Nature, 531, 591-597. http://dx.doi.org/10.1038/nature17145 

15 Diffenbaugh, N.S. and F. Giorgi, 2012: Climate change hotspots in the CMIP5 global climate 

16 model ensemble. Climatic Change, 114, 813-822. http://dx.doi.org/10.1007/s10584-012-

17 0570-x 

18 Foster, G.L., C.H. Lear, and J.W.B. Rae, 2012: The evolution of pCO2, ice volume and climate 

19 during the middle Miocene. Earth and Planetary Science Letters, 341–344, 243-254. 

20 http://dx.doi.org/10.1016/j.epsl.2012.06.007 

21 Fretwell, P., H.D. Pritchard, D.G. Vaughan, J.L. Bamber, N.E. Barrand, R. Bell, C. Bianchi, 

22 R.G. Bingham, D.D. Blankenship, G. Casassa, G. Catania, D. Callens, H. Conway, A.J. 

23 Cook, H.F.J. Corr, D. Damaske, V. Damm, F. Ferraccioli, R. Forsberg, S. Fujita, Y. Gim, P. 

24 Gogineni, J.A. Griggs, R.C.A. Hindmarsh, P. Holmlund, J.W. Holt, R.W. Jacobel, A. 

25 Jenkins, W. Jokat, T. Jordan, E.C. King, J. Kohler, W. Krabill, M. Riger-Kusk, K.A. 

26 Langley, G. Leitchenkov, C. Leuschen, B.P. Luyendyk, K. Matsuoka, J. Mouginot, F.O. 

27 Nitsche, Y. Nogi, O.A. Nost, S.V. Popov, E. Rignot, D.M. Rippin, A. Rivera, J. Roberts, N. 

28 Ross, M.J. Siegert, A.M. Smith, D. Steinhage, M. Studinger, B. Sun, B.K. Tinto, B.C. Welch, 

29 D. Wilson, D.A. Young, C. Xiangbin, and A. Zirizzotti, 2013: Bedmap2: improved ice bed, 

30 surface and thickness datasets for Antarctica. The Cryosphere, 7, 375-393. 

31 http://dx.doi.org/10.5194/tc-7-375-2013 

32 Goldner, A., N. Herold, and M. Huber, 2014: The challenge of simulating the warmth of the 

33 mid-Miocene climatic optimum in CESM1. Climate of the Past, 10, 523-536. 

34 http://dx.doi.org/10.5194/cp-10-523-2014 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 15

517

1 Gomez, N., J.X. Mitrovica, P. Huybers, and P.U. Clark, 2010: Sea level as a stabilizing factor for 

2 marine-ice-sheet grounding lines. Nature Geoscience, 3, 850-853. 

3 http://dx.doi.org/10.1038/ngeo1012 

4 Gregory, J.M. and J.A. Lowe, 2000: Predictions of global and regional sea-level rise using 

5 AOGCMs with and without flux adjustment. Geophysical Research Letters, 27, 3069-3072. 

6 http://dx.doi.org/10.1029/1999GL011228 

7 Hansen, J., M. Sato, P. Hearty, R. Ruedy, M. Kelley, V. Masson-Delmotte, G. Russell, G. 

8 Tselioudis, J. Cao, E. Rignot, I. Velicogna, B. Tormey, B. Donovan, E. Kandiano, K. 

9 von Schuckmann, P. Kharecha, A.N. Legrande, M. Bauer, and K.W. Lo, 2016: Ice melt, sea 

10 level rise and superstorms: evidence from paleoclimate data, climate modeling, and modern 

11 observations that 2 °C global warming could be dangerous. Atmospheric Chemistry and 

12 Physics, 16, 3761-3812. http://dx.doi.org/10.5194/acp-16-3761-2016 

13 Hao, Z., A. AghaKouchak, and T.J. Phillips, 2013: Changes in concurrent monthly precipitation 

14 and temperature extremes. Environmental Research Letters, 8, 034014. 

15 http://dx.doi.org/10.1088/1748-9326/8/3/034014 

16 Hoegh-Guldberg, O., P.J. Mumby, A.J. Hooten, R.S. Steneck, P. Greenfield, E. Gomez, C.D. 

17 Harvell, P.F. Sale, A.J. Edwards, K. Caldeira, N. Knowlton, C.M. Eakin, R. Iglesias-Prieto, 

18 N. Muthiga, R.H. Bradbury, A. Dubi, and M.E. Hatziolos, 2007: Coral reefs under rapid 

19 climate change and ocean acidification. Science, 318, 1737-1742. 

20 http://dx.doi.org/10.1126/science.1152509 

21 Huber, M. and R. Caballero, 2011: The early Eocene equable climate problem revisited. Climate 

22 of the Past, 7, 603-633. http://dx.doi.org/10.5194/cp-7-603-2011 

23 IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group 

24 I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. 

25 Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. 

26 Miller, Eds. Cambridge University Press, Cambridge. U.K, New York, NY, USA, 996 pp. 

27 www.ipcc.ch/publications_and_data/publications_ipcc_fourth_assessment_report_wg1_repor

28 t_the_physical_science_basis.htm

29 IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change 

30 Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on 

31 Climate Change. Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. 

32 Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (Eds.), 

33 582 pp. Cambridge University Press, Cambridge, UK and New York, NY. http://ipcc34 wg2.gov/SREX/images/uploads/SREX-All_FINAL.pdf
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 15

518

1 IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group 

2 I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

3 Cambridge University Press, Cambridge, UK and New York, NY, 1535 pp. 

4 http://dx.doi.org/10.1017/CBO9781107415324 www.climatechange2013.org

5 Jackson, L.C., R. Kahana, T. Graham, M.A. Ringer, T. Woollings, J.V. Mecking, and R.A. 

6 Wood, 2015: Global and European climate impacts of a slowdown of the AMOC in a high 

7 resolution GCM. Climate Dynamics, 45, 3299-3316. http://dx.doi.org/10.1007/s00382-015-

8 2540-2 

9 Jakob, M. and J. Hilaire, 2015: Climate science: Unburnable fossil-fuel reserves. Nature, 517,

10 150-152. http://dx.doi.org/10.1038/517150a 

11 Jones, C., J. Lowe, S. Liddicoat, and R. Betts, 2009: Committed terrestrial ecosystem changes 

12 due to climate change. Nature Geoscience, 2, 484-487. http://dx.doi.org/10.1038/ngeo555 

13 Joughin, I., B.E. Smith, and B. Medley, 2014: Marine Ice Sheet Collapse Potentially Under Way 

14 for the Thwaites Glacier Basin, West Antarctica. Science, 344, 735-738. 

15 http://dx.doi.org/10.1126/science.1249055 

16 Kang, I.-S., Y.-M. Yang, and W.-K. Tao, 2015: GCMs with implicit and explicit representation 

17 of cloud microphysics for simulation of extreme precipitation frequency. Climate Dynamics,

18 45, 325-335. http://dx.doi.org/10.1007/s00382-014-2376-1 

19 Kopp, R.E., R.L. Shwom, G. Wagner, and J. Yuan, 2016: Tipping elements and climate–

20 economic shocks: Pathways toward integrated assessment. Earth's Future, 4, 346-372. 

21 http://dx.doi.org/10.1002/2016EF000362 

22 Kort, E.A., S.C. Wofsy, B.C. Daube, M. Diao, J.W. Elkins, R.S. Gao, E.J. Hintsa, D.F. Hurst, R. 

23 Jimenez, F.L. Moore, J.R. Spackman, and M.A. Zondlo, 2012: Atmospheric observations of 

24 Arctic Ocean methane emissions up to 82[deg] north. Nature Geoscience, 5, 318-321. 

25 http://dx.doi.org/10.1038/ngeo1452 

26 Kretschmer, K., A. Biastoch, L. Rüpke, and E. Burwicz, 2015: Modeling the fate of methane 

27 hydrates under global warming. Global Biogeochemical Cycles, 29, 610-625. 

28 http://dx.doi.org/10.1002/2014GB005011 

29 Kvenvolden, K.A., 1988: Methane hydrate — A major reservoir of carbon in the shallow 

30 geosphere? Chemical Geology, 71, 41-51. http://dx.doi.org/10.1016/0009-2541(88)90104-0 

31 Lee, S.-Y. and G.D. Holder, 2001: Methane hydrates potential as a future energy source. Fuel 

32 Processing Technology, 71, 181-186. http://dx.doi.org/10.1016/S0378-3820(01)00145-X 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 15

519

1 Lenton, T.M., H. Held, E. Kriegler, J.W. Hall, W. Lucht, S. Rahmstorf, and H.J. Schellnhuber, 

2 2008: Tipping elements in the Earth's climate system. Proceedings of the National Academy 

3 of Sciences, 105, 1786-1793. http://dx.doi.org/10.1073/pnas.0705414105 

4 Levermann, A., P.U. Clark, B. Marzeion, G.A. Milne, D. Pollard, V. Radic, and A. Robinson, 

5 2013: The multimillennial sea-level commitment of global warming. Proceedings of the 

6 National Academy of Sciences, 110, 13745-13750. 

7 http://dx.doi.org/10.1073/pnas.1219414110 

8 Levermann, A., A. Griesel, M. Hofmann, M. Montoya, and S. Rahmstorf, 2005: Dynamic sea 

9 level changes following changes in the thermohaline circulation. Climate Dynamics, 24, 347-

10 354. http://dx.doi.org/10.1007/s00382-004-0505-y 

11 Li, C., D. Notz, S. Tietsche, and J. Marotzke, 2013: The Transient versus the Equilibrium 

12 Response of Sea Ice to Global Warming. Journal of Climate, 26, 5624-5636. 

13 http://dx.doi.org/10.1175/JCLI-D-12-00492.1 

14 Liu, J., Z. Chen, J. Francis, M. Song, T. Mote, and Y. Hu, 2016: Has Arctic Sea Ice Loss 

15 Contributed to Increased Surface Melting of the Greenland Ice Sheet? Journal of Climate,

16 29, 3373-3386. http://dx.doi.org/10.1175/JCLI-D-15-0391.1 

17 Lunt, D.J., T. Dunkley Jones, M. Heinemann, M. Huber, A. LeGrande, A. Winguth, C. Loptson, 

18 J. Marotzke, C.D. Roberts, J. Tindall, P. Valdes, and C. Winguth, 2012: A model–data 

19 comparison for a multi-model ensemble of early Eocene atmosphere–ocean simulations: 

20 EoMIP. Climate of the Past, 8, 1717-1736. http://dx.doi.org/10.5194/cp-8-1717-2012 

21 MacDougall, A.H., C.A. Avis, and A.J. Weaver, 2012: Significant contribution to climate 

22 warming from the permafrost carbon feedback. Nature Geoscience, 5, 719-721. 

23 http://dx.doi.org/10.1038/ngeo1573 

24 MacDougall, A.H., K. Zickfeld, R. Knutti, and H.D. Matthews, 2015: Sensitivity of carbon 

25 budgets to permafrost carbon feedbacks and non-CO 2 forcings. Environmental Research 

26 Letters, 10, 125003. http://dx.doi.org/10.1088/1748-9326/10/12/125003 

27 McGlade, C. and P. Ekins, 2015: The geographical distribution of fossil fuels unused when 

28 limiting global warming to 2 [deg]C. Nature, 517, 187-190. 

29 http://dx.doi.org/10.1038/nature14016 

30 Mengel, M. and A. Levermann, 2014: Ice plug prevents irreversible discharge from East 

31 Antarctica. Nature Climate Change, 4, 451-455. http://dx.doi.org/10.1038/nclimate2226 

32 Myhre, C.L., B. Ferré, S.M. Platt, A. Silyakova, O. Hermansen, G. Allen, I. Pisso, N. 

33 Schmidbauer, A. Stohl, J. Pitt, P. Jansson, J. Greinert, C. Percival, A.M. Fjaeraa, S.J. O'Shea, 

34 M. Gallagher, M. Le Breton, K.N. Bower, S.J.B. Bauguitte, S. Dalsøren, S. 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 15

520

1 Vadakkepuliyambatta, R.E. Fisher, E.G. Nisbet, D. Lowry, G. Myhre, J.A. Pyle, M. Cain, 

2 and J. Mienert, 2016: Extensive release of methane from Arctic seabed west of Svalbard 

3 during summer 2014 does not influence the atmosphere. Geophysical Research Letters, 43,

4 4624-4631. http://dx.doi.org/10.1002/2016GL068999 

5 Navarro, T., J.B. Madeleine, F. Forget, A. Spiga, E. Millour, F. Montmessin, and A. Määttänen, 

6 2014: Global climate modeling of the Martian water cycle with improved microphysics and 

7 radiatively active water ice clouds. Journal of Geophysical Research: Planets, 119, 1479-

8 1495. http://dx.doi.org/10.1002/2013JE004550 

9 NRC, 2013: Abrupt Impacts of Climate Change: Anticipating Surprises. The National 

10 Academies Press, Washington, DC, 222 pp. http://dx.doi.org/10.17226/18373 

11 Pérez, F.F., H. Mercier, M. Vazquez-Rodriguez, P. Lherminier, A. Velo, P.C. Pardo, G. Roson, 

12 and A.F. Rios, 2013: Atlantic Ocean CO2 uptake reduced by weakening of the meridional 

13 overturning circulation. Nature Geoscience, 6, 146-152. http://dx.doi.org/10.1038/ngeo1680 

14 Pollard, D., R.M. DeConto, and R.B. Alley, 2015: Potential Antarctic Ice Sheet retreat driven by 

15 hydrofracturing and ice cliff failure. Earth and Planetary Science Letters, 412, 112-121. 

16 http://dx.doi.org/10.1016/j.epsl.2014.12.035 

17 Quarantelli, E.L., 1986: Disaster Crisis Management. 10 pp. University of Delaware, Newark, 

18 DE. http://udspace.udel.edu/handle/19716/487

19 Rahmstorf, S., J.E. Box, G. Feulner, M.E. Mann, A. Robinson, S. Rutherford, and E.J. 

20 Schaffernicht, 2015: Exceptional twentieth-century slowdown in Atlantic Ocean overturning 

21 circulation. Nature Climate Change, 5, 475-480. http://dx.doi.org/10.1038/nclimate2554 

22 Reed, A.J., M.E. Mann, K.A. Emanuel, N. Lin, B.P. Horton, A.C. Kemp, and J.P. Donnelly, 

23 2015: Increased threat of tropical cyclones and coastal flooding to New York City during the 

24 anthropogenic era. Proceedings of the National Academy of Sciences, 112, 12610-12615. 

25 http://dx.doi.org/10.1073/pnas.1513127112 

26 Ridley, J., J.M. Gregory, P. Huybrechts, and J. Lowe, 2010: Thresholds for irreversible decline 

27 of the Greenland ice sheet. Climate Dynamics, 35, 1049-1057. 

28 http://dx.doi.org/10.1007/s00382-009-0646-0 

29 Ridley, J.K., J.A. Lowe, and H.T. Hewitt, 2012: How reversible is sea ice loss? The Cryosphere,

30 6, 193-198. http://dx.doi.org/10.5194/tc-6-193-2012 

31 Rignot, E., J. Mouginot, M. Morlighem, H. Seroussi, and B. Scheuchl, 2014: Widespread, rapid 

32 grounding line retreat of Pine Island, Thwaites, Smith, and Kohler glaciers, West Antarctica, 

33 from 1992 to 2011. Geophysical Research Letters, 41, 3502-3509. 

34 http://dx.doi.org/10.1002/2014GL060140 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 15

521

1 Ritz, C., T.L. Edwards, G. Durand, A.J. Payne, V. Peyaud, and R.C.A. Hindmarsh, 2015: 

2 Potential sea-level rise from Antarctic ice-sheet instability constrained by observations. 

3 Nature, 528, 115-118. http://dx.doi.org/10.1038/nature16147 

4 Robinson, A., R. Calov, and A. Ganopolski, 2012: Multistability and critical thresholds of the 

5 Greenland ice sheet. Nature Climate Change, 2, 429-432. 

6 http://dx.doi.org/10.1038/nclimate1449 

7 Salzmann, U., A.M. Dolan, A.M. Haywood, W.-L. Chan, J. Voss, D.J. Hill, A. Abe-Ouchi, B. 

8 Otto-Bliesner, F.J. Bragg, M.A. Chandler, C. Contoux, H.J. Dowsett, A. Jost, Y. Kamae, G. 

9 Lohmann, D.J. Lunt, S.J. Pickering, M.J. Pound, G. Ramstein, N.A. Rosenbloom, L. Sohl, C. 

10 Stepanek, H. Ueda, and Z. Zhang, 2013: Challenges in quantifying Pliocene terrestrial 

11 warming revealed by data-model discord. Nature Climate Change, 3, 969-974. 

12 http://dx.doi.org/10.1038/nclimate2008 

13 Schoof, C., 2007: Ice sheet grounding line dynamics: Steady states, stability, and hysteresis. 

14 Journal of Geophysical Research, 112, F03S28. http://dx.doi.org/10.1029/2006JF000664 

15 Schuur, E.A.G., A.D. McGuire, C. Schadel, G. Grosse, J.W. Harden, D.J. Hayes, G. Hugelius, 

16 C.D. Koven, P. Kuhry, D.M. Lawrence, S.M. Natali, D. Olefeldt, V.E. Romanovsky, K. 

17 Schaefer, M.R. Turetsky, C.C. Treat, and J.E. Vonk, 2015: Climate change and the 

18 permafrost carbon feedback. Nature, 520, 171-179. http://dx.doi.org/10.1038/nature14338 

19 Serinaldi, F., 2016: Can we tell more than we can know? The limits of bivariate drought analyses 

20 in the United States. Stochastic Environmental Research and Risk Assessment, 30, 1691-

21 1704. http://dx.doi.org/10.1007/s00477-015-1124-3 

22 Smith, J.A., 1987: Estimating the upper tail of flood frequency distributions. Water Resources 

23 Research, 23, 1657-1666. http://dx.doi.org/10.1029/WR023i008p01657 

24 Stranne, C., M. O'Regan, G.R. Dickens, P. Crill, C. Miller, P. Preto, and M. Jakobsson, 2016: 

25 Dynamic simulations of potential methane release from East Siberian continental slope 

26 sediments. Geochemistry, Geophysics, Geosystems, 17, 872-886. 

27 http://dx.doi.org/10.1002/2015GC006119 

28 Swain, D.L., D.E. Horton, D. Singh, and N.S. Diffenbaugh, 2016: Trends in atmospheric patterns 

29 conducive to seasonal precipitation and temperature extremes in California. Science 

30 Advances, 2. http://dx.doi.org/10.1126/sciadv.1501344 

31 Tattersall, I., 2009: Human origins: Out of Africa. Proceedings of the National Academy of 

32 Sciences, 106, 16018-16021. http://dx.doi.org/10.1073/pnas.0903207106 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Chapter 15

522

1 Trenberth, K.E., A. Dai, G. van der Schrier, P.D. Jones, J. Barichivich, K.R. Briffa, and J. 

2 Sheffield, 2014: Global warming and changes in drought. Nature Climate Change, 4, 17-22. 

3 http://dx.doi.org/10.1038/nclimate2067 

4 Wagner, T.J.W. and I. Eisenman, 2015: How Climate Model Complexity Influences Sea Ice 

5 Stability. Journal of Climate, 28, 3998-4014. http://dx.doi.org/10.1175/JCLI-D-14-00654.1 

6 Woodhouse, C.A. and J.T. Overpeck, 1998: 2000 Years of Drought Variability in the Central 

7 United States. Bulletin of the American Meteorological Society, 79, 2693-2714. 

8 http://dx.doi.org/10.1175/1520-0477(1998)079<2693:YODVIT>2.0.CO;2 

9 Yin, J. and P.B. Goddard, 2013: Oceanic control of sea level rise patterns along the East Coast of 

10 the United States. Geophysical Research Letters, 40, 5514-5520. 

11 http://dx.doi.org/10.1002/2013GL057992 

12 Zscheischler, J., M. Reichstein, J. von Buttlar, M. Mu, J.T. Randerson, and M.D. Mahecha, 2014: 

13 Carbon cycle extremes during the 21st century in CMIP5 models: Future evolution and 

14 attribution to climatic drivers. Geophysical Research Letters, 41, 8853-8861. 

15 http://dx.doi.org/10.1002/2014GL062409 

16 Zwiers, F.W., L.V. Alexander, G.C. Hegerl, T.R. Knutson, J.P. Kossin, P. Naveau, N. Nicholls, 

17 C. Schär, S.I. Seneviratne, and X. Zhang, 2013: Climate Extremes: Challenges in Estimating 

18 and Understanding Recent Changes in the Frequency and Intensity of Extreme Climate and 

19 Weather Events. Climate Science for Serving Society: Research, Modeling and Prediction 

20 Priorities. Asrar, G.R. and J.W. Hurrell, Eds. Springer Netherlands, Dordrecht, 339-389. 

21 http://dx.doi.org/10.1007/978-94-007-6692-1_13
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1 Appendix A. Observational Datasets Used in Climate Studies 

2 Climate Datasets

3 Observations, including those from satellites, mobile platforms, field campaigns and ground4 based networks, provide the basis of knowledge on many temporal and spatial scales for 

5 understanding the changes occurring in the Earth’s climate system. These observations also 

6 inform the development, calibration, and evaluation of numerical models of the physics, 

7 chemistry, and biology being used in analyzing the past changes in climate and for making future 

8 projections. As all observational data collected by support from Federal agencies are required to 

9 be made available free of charge with machine readable metadata, everyone can access these 

10 products for their personal analysis and research, and for informing decisions. Many of these 

11 datasets are accessible through web services.

12 Many long-running observations worldwide have provided us with long-term records necessary 

13 for investigating climate change and its impacts. These include made many important climate 

14 variables such as surface temperature, sea ice extent, sea level rise, and streamflow. Perhaps one 

15 of the most iconic climatic datasets, that of atmospheric carbon dioxide measured at Mauna Loa, 

16 HI, has been recorded since the 1950s. The U.S. and Global Historical Climatology Networks 

17 have been used as authoritative sources of recorded surface temperature increases, with some 

18 stations having continuous records going back many decades. Satellite radar altimetry data (for 

19 example, NASA’s TOPEX/JASON1,2 satellite data) have informed the development of the 

20 University of Colorado’s authoritative 20+ year record of global sea level changes. In the United 

21 States, the USGS (U.S. Geological Survey) National Water Information System contains in some 

22 instances decades of daily streamflow records which inform not only climate but land use studies 

23 as well. The U.S. Bureau of Reclamation and U.S. Army Corp of Engineers have maintained

24 data about reservoir levels for decades where applicable. Of course, datasets based on shorter25 term observations are used in conjunction with longer term records for climate study, and the 

26 U.S. programs are aimed at providing continuous data records. Methods have been developed 

27 and applied to process these data so as to account for biases, collection method, earth surface 

28 geometry, the Urban Heat Island effect, station relocations, and uncertainty (e.g., see Vose et al. 

29 2012; Rennie et al. 2014; Karl et al. 2015).

30 Even observations not designed for climate have informed climate research. These include ship 

31 logs containing descriptions of ice extent, readings of temperature and precipitation provided in 

32 newspapers, and harvest records. Today, observations recorded both manually and in automated 

33 fashions inform research agendas and are used in climate studies.

34 The U.S Global Change Research Program (USGCRP) has established the Global Change 

35 Information System (GCIS) to better coordinate and integrate the use of federal information 

36 products on changes in the global environment and the implications of those changes for society. 

37 The GCIS is an open-source, web-based resource for traceable, sound global change data, 
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1 information, and products. Designed for use by scientists, decision makers, and the public, the 

2 GCIS provides coordinated links to a select group of information products produced, maintained, 

3 and disseminated by government agencies and organizations. Currently the GCIS is aimed at the 

4 datasets used in Third National Climate Assessment (NCA3) and the USGCRP Climate and 

5 Health Assessment. It is to be updated for the datasets used in this report (The Climate Science 

6 Special Report, CSSR).

7 Satellite Temperature Datasets

8 A special look is given to the satellite temperature datasets because of controversies associated 

9 with these datasets. Satellite-borne microwave sounders such as the Microwave Sounding Unit 

10 (MSU) and Advanced Microwave Sounding Unit (AMSU) instruments operating on NOAA 

11 polar-orbiting platforms make measurements of the temperature of thick layers of the atmosphere 

12 with near global coverage. Because the long-term data record requires the piecing together of 

13 measurements made by 16 different satellites, accurate instrument intercalibration is of critical 

14 importance. Over the mission lifetime of most satellites, the instruments drift in both calibration 

15 and local measurement time. Adjustments to counter the effects of these drifts need to be 

16 developed and applied before a long-term record can be assembled. For tropospheric 

17 measurements, the most challenging of these adjustments is the adjustment for drifting 

18 measurement time, which requires knowledge of the diurnal cycle in both atmospheric and 

19 surface temperature. Current versions of the sounder-based datasets account for the diurnal cycle 

20 by either using diurnal cycles deduced from model output (Mears and Wentz 2009; Zou et al. 

21 2009) or by attempting to derive diurnal cycle from the satellite measurements themselves (an 

22 approach plagued by sampling issues and possible calibration drifts) (Christy et al. 2003; Po23 Chedley et al. 2015). Recently Mears and Wentz developed a hybrid approach, RSS Version 4.0 

24 (Mears and Wentz 2016), that resulting in an increased warming signal relative to the other 

25 approaches, particularly since 2000. Each of these methods has strengths and weaknesses, but 

26 neither has sufficient accuracy to construct an unassailable long-term record of atmospheric 

27 temperature change. The resulting datasets show a greater spread in decadal-scale trends than do 

28 the surface temperature datasets for the same period, suggesting that they may be less reliable. In 

29 Figure A.1 shows annual time series for the global mean tropospheric temperature for some 

30 recent versions of the satellite datasets. These data have been adjusted to remove the influence of 

31 stratospheric cooling (Fu and Johanson 2005). Linear trend values are shown in Table A.1.

32 [Figure A.1. Annual global (80°S-80°N) mean time series of tropospheric temperature for five

33 recent datasets. Each time series is adjusted so the mean value for the first three years is zero.

34 This accentuates the differences in the long-term changes between the datasets. Figure source: 

35 Remote Sensing Systems]

36 [Table A.1. Global Trends in Temperature Total Troposphere (TTT) since 1979 and 2000. (in 

37 degrees F per decade)]
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1 TABLE

2 Table A.1. Global Trends in Temperature Total Troposphere (TTT) since 1979 and 2000 (in 

3 degrees F per decade).

Dataset Trend (1979–2015) 

(°F/Decade)

Trend (2000–2015) 

(°F/Decade)

RSS V4.0 0.301 0.198

UAH V6Beta5 0.196 0.141

STAR V4.0 0.316 0.157

RSS V3.3 0.208 0.105

UAH V5.6 0.176 0.211

STAR V3.0 0.286 0.061

4
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1 FIGURE

2

3 Figure A.1. Annual global (80°S–80°N) mean time series of tropospheric temperature for five

4 recent datasets (see below). Each time series is adjusted so the mean value for the first three

5 years is zero. This accentuates the differences in the long-term changes between the datasets.

6 (Figure source: Remote Sensing Systems)

7 DATA SOURCES:

8 All Satellite Data are “Temperature Total Troposphere” time series calculated from TMT and TLS

9 (1.1*TMT) - (0.1*TLS). This combination reduces the effect of the lower stratosphere on the tropospheric 

10 temperature. (Fu, Qiang et al. "Contribution of stratospheric cooling to satellite-inferred tropospheric 

11 temperature trends." Nature 429.6987 (2004): 55-58.)

12 UAH. UAH Version 6.0Beta5. Yearly (yyyy) text files of TMT and TLS are available from

13 http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tmt/tmtmonamg.yyyy_6.0beta5

14 http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tls/tlsmonamg.yyyy_6.0beta5

15 Downloaded 5/15/2016.

16 UAH. UAH Version 5.6. Yearly (yyyy) text files of TMT and TLS are available from

17 http://vortex.nsstc.uah.edu/data/msu/t2/

18 http://vortex.nsstc.uah.edu/data/msu/t4/

19 Downloaded 5/15/2016.

20 RSS. RSS Version 4.0. ftp://ftp.remss.com/msu/data/netcdf/RSS_Tb_Anom_Maps_ch_TTT_V4_0.nc

21 Downloaded 5/15/2016

22 RSS. RSS Version 3.3. ftp://ftp.remss.com/msu/data/netcdf/RSS_Tb_Anom_Maps_ch_TTT_V3.3.nc

23 Downloaded 5/15/2016

24 NOAA STAR. Star Version 3.0.

25 ftp://ftp.star.nesdis.noaa.gov/pub/smcd/emb/mscat/data/MSU_AMSU_v3.0/Monthly_Atmospheric_Layer_Mea

26 n_Temperature/Merged_Deep-Layer_Temperature/NESDIS-STAR_TCDR_MSU27 AMSUA_V03R00_TMT_S197811_E201604_C20160513.nc

28 ftp://ftp.star.nesdis.noaa.gov/pub/smcd/emb/mscat/data/MSU_AMSU_v3.0/Monthly_Atmospheric_Layer_Mea

29 n_Temperature/Merged_Deep-Layer_Temperature/NESDIS-STAR_TCDR_MSU30 AMSUA_V03R00_TLS_S197811_E201604_C20160513.nc

31 Downloaded 5/18/2016.

32

33
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1 REFERENCES

2 Christy, J.R., R.W. Spencer, W.B. Norris, W.D. Braswell, and D.E. Parker, 2003: Error estimates 

3 of version 5.0 of MSU–AMSU bulk atmospheric temperatures. Journal of Atmospheric and 

4 Oceanic Technology, 20, 613-629. http://dx.doi.org/10.1175/1520-

5 0426(2003)20<613:EEOVOM>2.0.CO;2 

6 Fu, Q. and C.M. Johanson, 2005: Satellite-derived vertical dependence of tropical tropospheric 

7 temperature trends. Geophysical Research Letters, 32. 

8 http://dx.doi.org/10.1029/2004GL022266 

9 Karl, T.R., A. Arguez, B. Huang, J.H. Lawrimore, J.R. McMahon, M.J. Menne, T.C. Peterson, 

10 R.S. Vose, and H.-M. Zhang, 2015: Possible artifacts of data biases in the recent global 

11 surface warming hiatus. Science, 348, 1469-1472. http://dx.doi.org/10.1126/science.aaa5632 

12 Mears, C.A. and F.J. Wentz, 2009: Construction of the Remote Sensing Systems V3.2 

13 atmospheric temperature records from the MSU and AMSU microwave sounders. Journal of 

14 Atmospheric and Oceanic Technology, 26, 1040-1056. 

15 http://dx.doi.org/10.1175/2008JTECHA1176.1 

16 Mears, C.A. and F.J. Wentz, 2016: Sensitivity of satellite-derived tropospheric temperature 

17 trends to the diurnal cycle adjustment. Journal of Climate, 29, 3629-3646. 

18 http://dx.doi.org/10.1175/JCLI-D-15-0744.1 

19 Po-Chedley, S., T.J. Thorsen, and Q. Fu, 2015: Removing diurnal cycle contamination in 

20 satellite-derived tropospheric temperatures: Understanding tropical tropospheric trend 

21 discrepancies. Journal of Climate, 28, 2274-2290. http://dx.doi.org/10.1175/JCLI-D-13-

22 00767.1 

23 Rennie, J.J., J.H. Lawrimore, B.E. Gleason, P.W. Thorne, C.P. Morice, M.J. Menne, C.N. 

24 Williams, W.G. de Almeida, J.R. Christy, M. Flannery, M. Ishihara, K. Kamiguchi, A.M.G. 

25 Klein-Tank, A. Mhanda, D.H. Lister, V. Razuvaev, M. Renom, M. Rusticucci, J. Tandy, S.J. 

26 Worley, V. Venema, W. Angel, M. Brunet, B. Dattore, H. Diamond, M.A. Lazzara, F. Le 

27 Blancq, J. Luterbacher, H. Mächel, J. Revadekar, R.S. Vose, and X. Yin, 2014: The 

28 international surface temperature initiative global land surface databank: monthly 

29 temperature data release description and methods. Geoscience Data Journal, 1, 75-102. 

30 http://dx.doi.org/10.1002/gdj3.8 http://dx.doi.org/10.1002/gdj3.8

31 Vose, R.S., D. Arndt, V.F. Banzon, D.R. Easterling, B. Gleason, B. Huang, E. Kearns, J.H. 

32 Lawrimore, M.J. Menne, T.C. Peterson, R.W. Reynolds, T.M. Smith, C.N. Williams, and 

33 D.L. Wuertz, 2012: NOAA’s Merged Land-Ocean Surface Temperature Analysis. Bulletin of 

34 the American Meteorological Society, 93, 1677-1685. http://dx.doi.org/10.1175/BAMS-D35 11-00241.1 
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Appendix A

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1 Zou, C.-Z., M. Gao, and M.D. Goldberg, 2009: Error structure and atmospheric temperature 

2 trends in observations from the microwave sounding unit. Journal of Climate, 22, 1661-1681. 

3 http://dx.doi.org/10.1175/2008JCLI2233.1
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1 Appendix B: Weighting Strategy

2 for the Fourth National Climate Assessment

3 Introduction

4 This document briefly describes weighting strategy for use with the Climate Model 

5 Intercomparison Project, Phase 5 (CMIP5) multimodel archive in the 4th National 

6 Climate Assessment which considers both skill in the climatological performance of 

7 models over North America as well as the inter-dependency of models arising from 

8 common parameterizations or tuning practises. The method exploits information relating 

9 to the climatological mean state of a number of projection-relevant variables as well as 

10 long-term metrics representing long-term statistics of weather extremes. The weights, 

11 once computed, can be used to simply compute weighted mean and significance 

12 information from an ensemble containing multiple initial condition members from co13 dependent models of varying skill.

14 Our methodology is based on the concepts outlined in Sanderson et al. (2015), and the 

15 specific application to the Fourth National Climate Assessment (NCA4) is also described 

16 in that paper. The approach produces a single set of model weights which can be used to 

17 combine projections into a weighted mean result, with significance estimates which also 

18 treat the weighting appropriately.

19 The method, ideally, would seek to have two fundamental characteristics:

20 • If a duplicate of one ensemble member is added to the archive, the resulting mean 

21 and significance estimate for future change computed from the ensemble should 

22 not change.

23 • If a demonstrably unphysical model is added to the archive, the resulting mean 

24 and significance estimates should also not change.

25 Method

26 The analysis requires an assessment of both model skill and an estimate of inter-model 

27 relationships – for which inter-model root mean square difference is taken as a proxy. 

28 The model and observational data used here is for the contiguous United States 

29 (CONUS), and most of Canada, using high resolution data where available. Inter-model 

30 distances are computed as simple root mean square differences. Data is derived from a 

31 number of mean state fields, and a number of fields, which represent extreme behavior—

32 these are listed in Table B.1. All fields are masked to only include information from 

33 CONUS/Canada. 
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530

1 The RMSE between observations and each model can be used to produce an overall 

2 ranking for model simulations of the North American climate, Figure B.1 shows how this 

3 metric is influenced by different component variables.

4 [INSERT FIGURE B.1 HERE:

5 Figure B.1: A graphical representation of the inter-model distance matrix for CMIP5 and 

6 a set of observed values. Each row and column represents a single climate model (or 

7 observation). All scores are aggregated over seasons (individual seasons are not shown). 

8 Each box represents a pair-wise distance, where warm colors indicate a greater distance. 

9 Distances are measured as a fraction of the mean inter-model distance in the CMIP5 

10 ensemble. (Figure source: Sanderson et al. 2016)]

11 [INSERT FIGURE B.2 HERE:

12 Figure B.2: Model skill and independence weights for the CMIP-5 archive evaluated 

13 over the North American domain. Contours show the overall weighting, which is the 

14 product of the two individual weights. (Figure source: Sanderson et al. 2016)]

15 Models are downweighted for poor skill if their multivariate combined error is 

16 significantly greater than a “skill radius” term, which is a free parameter of the approach, 

17 and the calibration of this parameter is determined through a perfect model study 

18 (Sanderson et al. 2016). A pairwise distance matrix is computed to assess inter-model 

19 RMSE values for each model pair in the archive, and a model is downweighted for 

20 dependency if there exists another model with a pairwise distance to the original model 

21 significantly smaller than a “similarity radius.” This is the second parameter of the 

22 approach, which is calibrated by considering known relationships within the archive. The 

23 resulting skill and independence weights are multiplied to give an overall “combined”

24 weight—illustrated in Figure B.2 for the CMIP5 ensemble, and listed in Table B.2.

25 The weights are used in the Climate Science Special Report (CSSR) to produce weighted 

26 mean and significant maps of future change, where the following protocol is used:

27 Stippling—large changes where the weighted multimodel average change is greater than 

28 double the standard deviation of the 20-year mean from control simulations runs and 90%

29 of the weight corresponds to changes of the same sign.

30 Hatching—No significant change where the weighted multimodel average change is less 

31 than the standard deviation of the 20-year means from control simulations runs.

32 Whited out—Inconclusive where the weighted multimodel average change is greater than 

33 double the standard deviation of the 20-year mean from control runs and less than 90% of 

34 the weight corresponds to changes of the same sign.
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531

1 We illustrate the application of this method to future projections of precipitation change 

2 under RCP8.5 in Figure B.3. The weights used in the report are chosen to be 

3 conservative, minimizing the risk of overconfidence and maximizing out-of-sample 

4 predictive skill for future projections. This results (as in Figure B.3) in only modest 

5 differences in the weighted and unweighted maps. It is shown in Sanderson et al. (2016)

6 that a more aggressive weighting strategy, or one focused on a particular variable, tends 

7 to exhibit a stronger constraint on future change relative to the unweighted case. It is also 

8 notable trade-offs exist between skill and replication in the archive (evident in Figure 

9 B.2), such that the weighting for both skill and uniqueness has a compensating effect. As 

10 such, mean projections using the CMIP5 ensemble are not strongly influenced by the 

11 weighting. However, the establishment of the weighting strategy used in the CSSR 

12 provides some insurance against a potential case in future assessments where there is a 

13 highly replicated, but poorly performing model.

14 [INSERT FIGURE B.3 HERE:

15 Figure B.3: Projections of precipitation change over North America in 2080–2100, 

16 relative to 1980–2000 under RCP8.5. (a) shows the simple unweighted CMIP5 multi17 model average, using the significance methodology from (IPCC 2013), (b) shows the 

18 weighted results as outlined in section 3 for models weighted by uniqueness only, and (c) 

19 shows weighted results for models weighted by both uniqueness and skill. (Figure source: 

20 Sanderson et al. 2016)]

21
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532

1 TABLES

2 Table B.1: Observational Datasets used as observations.

3

Field Description Source Reference Years

TS Surface	Temperature	(seasonal) Livneh,Hutchinson (Hopkinson	et	al.	2012;	

Hutchinson	et	al.	2009;	

Livneh	et	al.	2013)

1950-2011

PR Mean	Precipitation	(seasonal) Livneh,Hutchinson (Hopkinson	et	al.	2012;	

Hutchinson	et	al.	2009;	

Livneh	et	al.	2013)

1950-2011

RSUT TOA	Shortwave	Flux	(seasonal) CERES-EBAF (NASA	2011) 2000-2005

RLUT TOA	Longwave	Flux	(seasonal) CERES-EBAF (NASA	2011) 2000-2005

T Vertical	Temperature	Profile	(seasonal) AIRS* (Aumann	et	al.	2003) 2002-2010

RH Vertical	Humidity	Profile	(seasonal) AIRS (Aumann	et	al.	2003) 2002-2010

PSL Surface	Pressure	(seasonal) ERA-40 (Uppala	et	al.	2005) 1970-2000

Tnn Coldest	Night Livneh,Hutchinson (Hopkinson	et	al.	2012;	

Hutchinson	et	al.	2009;	

Livneh	et	al.	2013)

1950-2011

Txn Coldest	Day Livneh,Hutchinson (Hopkinson	et	al.	2012;	

Hutchinson	et	al.	2009;	

Livneh	et	al.	2013)

1950-2011

Tnx Warmest	Night Livneh,Hutchinson (Hopkinson	et	al.	2012;	

Hutchinson	et	al.	2009;	

Livneh	et	al.	2013)

1950-2011

Txx Warmest	day Livneh,Hutchinson (Hopkinson	et	al.	2012;	

Hutchinson	et	al.	2009;	

Livneh	et	al.	2013)

1950-2011

rx5day seasonal	max.	5-day	total	precip. Livneh,Hutchinson (Hopkinson	et	al.	2012;	

Hutchinson	et	al.	2009;	

Livneh	et	al.	2013)

1950-2011

4
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533

1 Table B.2: Uniqueness, Skill and Combined weights for CMIP5

Uniqueness	weight Skill	Weight Combined

ACCESS1-0 0.60 1.69 1.02

ACCESS1-3 0.78 1.40 1.09

BNU-ESM 0.88 0.77 0.68

CCSM4 0.43 1.57 0.68

CESM1-BGC 0.44 1.46 0.64

CESM1-CAM5 0.72 1.80 1.30

CESM1-FASTCHEM 0.76 0.50 0.38

CMCC-CESM 0.98 0.36 0.35

CMCC-CM 0.89 1.21 1.07

CMCC-CMS 0.59 1.23 0.73

CNRM-CM5 0.94 1.08 1.01

CSIRO-Mk3-6-0 0.95 0.77 0.74

CanESM2 0.97 0.65 0.63

FGOALS-g2 0.97 0.39 0.38

GFDL-CM3 0.81 1.18 0.95

GFDL-ESM2G 0.74 0.59 0.44

GFDL-ESM2M 0.72 0.60 0.43

GISS-E2-H-p1 0.38 0.74 0.28

GISS-E2-H-p2 0.38 0.69 0.26

GISS-E2-R-p1 0.38 0.97 0.37

GISS-E2-R-p2 0.37 0.89 0.33

HadCM3 0.98 0.89 0.87

HadGEM2-AO 0.52 1.19 0.62

HadGEM2-CC 0.50 1.21 0.60

HadGEM2-ES 0.43 1.40 0.61

IPSL-CM5A-LR 0.79 0.92 0.72

IPSL-CM5A-MR 0.83 0.99 0.82

IPSL-CM5B-LR 0.92 0.63 0.58

MIROC-ESM 0.54 0.28 0.15

MIROC-ESM-CHEM 0.54 0.32 0.17

MIROC4h 0.97 0.73 0.71

MIROC5 0.89 1.24 1.11

MPI-ESM-LR 0.35 1.38 0.49

MPI-ESM-MR 0.38 1.37 0.52

MPI-ESM-P 0.36 1.54 0.56

MRI-CGCM3 0.51 1.35 0.68

MRI-ESM1 0.51 1.31 0.67

NorESM1-M 0.83 1.06 0.88

bcc-csm1-1 0.88 0.62 0.55

bcc-csm1-1-m 0.90 0.89 0.80

inmcm4 0.95 1.13 1.08
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Appendix B

534

1 FIGURES

2

3 Figure B.1: A graphical representation of the inter-model distance matrix for CMIP5 and 

4 a set of observed values. Each row and column represents a single climate model (or 

5 observation). All scores are aggregated over seasons (individual seasons are not shown). 

6 Each box represents a pair-wise distance, where warm colors indicate a greater distance. 

7 Distances are measured as a fraction of the mean inter-model distance in the CMIP5 

8 ensemble. (Figure source: Sanderson et al. 2016)

North America Skill (all seasons)

CESM1_CAM5

ACCESS1_0

CCSM4

MPI_ESM_P

CESM1_BGC

HadGEM2_ES

ACCESS1_3

MPI_ESM_LR

MPI_ESM_MR

MRI_CGCM3

MRI_ESM1

MIROC5

CMCC_CMS

HadGEM2_CC

CMCC_CM

HadGEM2_AO

GFDL_CM3

inmcm4

CNRM_CM5NorESM1_M

IPSL_CM5A_MR

GISS_E2_R_p1

IPSL_CM5A_LR

bcc_csm1_1_m

GISS_E2_R_p2

HadCM3

CSIRO_Mk3_6_0

BNU_ESM

GISS_E2_H_p1

MIROC4h

GISS_E2_H_p2

CanESM2

IPSL_CM5B_LR

bcc_csm1_1

GFDL_ESM2M

GFDL_ESM2G

CESM1_FASTCHEM

FGOALS_g2

CMCC_CESM

MIROC_ESM_CHEM

MIROC_ESM

pr

psl

rlut

rsut

tas

ta

hur

rx5day

tnn

tnx

txn

txx

Combined

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

Distance from obs (relative to CMIP mean)
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Appendix B

535

1

2 Figure B.2: Model skill and independence weights for the CMIP-5 archive evaluated 

3 over the North American domain. Contours show the overall weighting, which is the 

4 product of the two individual weights. (Figure source: Sanderson et al. 2016)

0.1

0.3

0.3

0.3

0.5

0.5

0.5

0.5

0.7

0.7

0.7

0.7

0.9

0.9

0.9

1.1

1.1

1.1

1.3

1.3

0.4 0.5 0.6 0.7 0.8 0.9 1

Model Independence Weight

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

North American Skill Weight

HadGEM2_AO

HadGEM2_CC

HadGEM2_ES

IPSL_CM5B_LR

MRI_CGCM3

MRI_ESM1

inmcm4

CMCC_CESM

CMCC_CM

CMCC_CMS

GFDL_CM3

GFDL_ESM2G

GFDL_ESM2M

IPSL_CM5A_LR

IPSL_CM5A_MR

MPI_ESM_LR

MPI_ESM_MR

MPI_ESM_P

CESM1_CAM5

NorESM1_M

bcc_csm1_1_m

MIROC_ESM

MIROC_ESM_CHEM

MIROC4h

CNRM_CM5

CSIRO_Mk3_6_0

MIROC5

ACCESS1_0

ACCESS1_3

CCSM4

CESM1_BGC

CESM1_FASTCHEM

CanESM2

BNU_ESM

bcc_csm1_1

GISS_E2_H_p1

GISS_E2_H_p2

GISS_E2_R_p1

GISS_E2_R_p2

FGOALS_g2

HadCM3

Combined Weight
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536

1

2 Figure B.3: Projections of precipitation change over North America in 2080–2100, 

3 relative to 1980–2000 under RCP8.5. (a) shows the simple unweighted CMIP5 multi4 model average, using the significance methodology from (IPCC 2013), (b) shows the 

5 weighted results as outlined in section 3 for models weighted by uniqueness only, and (c) 

6 shows weighted results for models weighted by both uniqueness and skill. (Figure source: 

7 Sanderson et al. 2016)
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Appendix B

537

1 REFERENCES

2 Aumann, H.H., M.T. Chahine, C. Gautier, M.D. Goldberg, E. Kalnay, L.M. McMillin, H. 

3 Revercomb, P.W. Rosenkranz, W.L. Smith, D.H. Staelin, L.L. Strow, and J. 

4 Susskind, 2003: AIRS/AMSU/HSB on the Aqua mission: Design, science objectives, 

5 data products, and processing systems. IEEE Transactions on Geoscience and Remote 

6 Sensing, 41, 253-264. http://dx.doi.org/10.1109/TGRS.2002.808356 

7 Hopkinson, R.F., M.F. Hutchinson, D.W. McKenney, E.J. Milewska, and P. Papadopol, 

8 2012: Optimizing Input Data for Gridding Climate Normals for Canada. Journal of 

9 Applied Meteorology and Climatology, 51, 1508-1518. 

10 http://dx.doi.org/10.1175/JAMC-D-12-018.1 

11 Hutchinson, M.F., D.W. McKenney, K. Lawrence, J.H. Pedlar, R.F. Hopkinson, E. 

12 Milewska, and P. Papadopol, 2009: Development and Testing of Canada-Wide 

13 Interpolated Spatial Models of Daily Minimum–Maximum Temperature and 

14 Precipitation for 1961–2003. Journal of Applied Meteorology and Climatology, 48,

15 725-741. http://dx.doi.org/10.1175/2008JAMC1979.1 

16 IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working 

17 Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate 

18 Change. Cambridge University Press, Cambridge, UK and New York, NY, 1535 pp. 

19 http://dx.doi.org/10.1017/CBO9781107415324 www.climatechange2013.org

20 Livneh, B., E.A. Rosenberg, C. Lin, B. Nijssen, V. Mishra, K.M. Andreadis, E.P. Maurer, 

21 and D.P. Lettenmaier, 2013: A Long-Term Hydrologically Based Dataset of Land 

22 Surface Fluxes and States for the Conterminous United States: Update and 

23 Extensions. Journal of Climate, 26, 9384-9392. http://dx.doi.org/10.1175/JCLI-D24 12-00508.1 

25 NASA, 2011: CERES EBAF Data Sets. 

26 https://ceres.larc.nasa.gov/products.php?product=EBAF-TOA

27 Sanderson, B.M., R. Knutti, and P. Caldwell, 2015: A Representative Democracy to 

28 Reduce Interdependency in a Multimodel Ensemble. Journal of Climate, 28, 5171-

29 5194. http://dx.doi.org/10.1175/JCLI-D-14-00362.1 

30 Sanderson, B.M., M. Wehner, and R. Knutti, 2016: Skill and Independence weighting for 

31 multi-model Assessment. Geoscientific Model Development Submitted. 

32 Uppala, S.M., P.W. KÅllberg, A.J. Simmons, U. Andrae, V.D.C. Bechtold, M. Fiorino, 

33 J.K. Gibson, J. Haseler, A. Hernandez, G.A. Kelly, X. Li, K. Onogi, S. Saarinen, N. 

34 Sokka, R.P. Allan, E. Andersson, K. Arpe, M.A. Balmaseda, A.C.M. Beljaars, L.V.D. 

35 Berg, J. Bidlot, N. Bormann, S. Caires, F. Chevallier, A. Dethof, M. Dragosavac, M. 
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538

1 Fisher, M. Fuentes, S. Hagemann, E. Hólm, B.J. Hoskins, L. Isaksen, P.A.E.M. 

2 Janssen, R. Jenne, A.P. McNally, J.F. Mahfouf, J.J. Morcrette, N.A. Rayner, R.W. 

3 Saunders, P. Simon, A. Sterl, K.E. Trenberth, A. Untch, D. Vasiljevic, P. Viterbo, and 

4 J. Woollen, 2005: The ERA-40 re-analysis. Quarterly Journal of the Royal 

5 Meteorological Society, 131, 2961-3012. http://dx.doi.org/10.1256/qj.04.176
CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE Appendix C

539

Appendix C. Acronyms and Units

AGCMs Atmospheric General Circulation Models

AIS Antarctic Ice Sheet

AMO Atlantic Multidecadal Oscillation

AMOC Atlantic meridional overturning circulation

AMSU Advanced Microwave Sounding Unit

AO Arctic Oscillation

AOD aerosol optical depth

ARs atmospheric rivers 

AW Atlantic Water 

BAMS Bulletin of the American Meteorological Society

BC black carbon

BCE Before Common Era

CAM5 Community Atmospheric Model, Version 5

CAPE convective available potential energy

CCN cloud condensation nuclei

CCSM3 Community Climate System Model, Version 3 (UCAR)

CDR carbon dioxide removal

CE Common Era

CENRS Committee on Environment, Natural Resources, and Sustainability

(National Science and Technology Council, White House)

CESM-LE Community Earth System Model Large Ensemble Project (UCAR) 

CFCs chlorofluorocarbons

CI climate intervention

CMIP5 Coupled Model Intercomparison Project, Fifth Phase (also CMIP3 and 

CMIP6)

CONUS contiguous United States

CP Central Pacific

CSSR Climate Science Special Report

DIC dissolved inorganic carbon

DJF December-January-February

DOD SERP U.S. Department of Defense, Strategic Environmental Research 

Program

DOE U.S. Department of Energy

EAIS East Antarctic Ice Sheet

ECS equilibrium climate sensitivity

ENSO El Niño-Southern Oscillation

EOF analysis empirical orthogonal function analysis

EP Eastern Pacific
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540

ERF effective radiative forcing

ESD empirical statistical downscaling

ESDMs empirical statistical downscaling models

ESMs Earth System Models

ESS Earth system sensitivity

ETC extra-tropical (or extratropical?) cyclone

ETCCDI Expert Team on Climate Change Detection Indices 

GBI Greenland Blocking Index

GCIS Global Change Information System 

GCMs global climate models

GeoMIP Geoengineering Model Intercomparison Project 

GFDL HiRAM Geophysical Fluid Dynamics Laboratory, global HIgh Resolution 

Atmospheric Model (NOAA)

GHCN Global Historical Climatology Network (National Centers for 

Environmental Information, NOAA)

GHG greenhouse gas

GMSL global mean sea level

GMT global mean temperature

GPS global positioning system

GRACE Gravity Recovery and Climate Experiment

GrIS Greenland Ice Sheet

GWP global warming potential

HadCM3 Hadley Centre Coupled Model, Version 3 

HadCRUT4 Hadley Centre Climatic Research Unit Gridded Surface Temperature 

Data Set 4

HCFCs hydrochlorofluorocarbons

HFCs hydrofluorocarbons

HOT Hawai’i Ocean Time-series

HOT-DOGS Hawai’i Ocean Time-series Data Organization & Graphical System

HURDAT2 revised Atlantic Hurricane Database (National Hurricane Center, 

NOAA) 

IAMs integrated assessment models

IAV impacts, adaptation, and vulnerability

INDCs Intended Nationally Determined Contributions

INMCM Institute for Numerical Mathematics Climate Model

IPCC Intergovernmental Panel on Climate Change

IPCC AR5 Fifth Assessment Report of the IPCC; also SPM – Summary for 

Policymakers, and WG1, WG2, WG3 – Working Groups 1-3

IPO Interdecadal Pacific Oscillation

IQA Information Quality Act
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541

IVT integrated vapor transport

JGOFS U.S. Joint Global Ocean Flux Study

JJA June-July-August

JTWC Joint Typhoon Warning Center

LCC land cover changes

LULCC land use and land cover change

MAM March-April-May

MSU Microwave Sounding Unit

NAM Northern Annular Mode

NAO North Atlantic Oscillation

NARCCAP North American Regional Climate Change Assessment Program 

(World Meteorological Organization)

NAS National Academy of Sciences

NASA National Aeronautics and Space Administration

NCA National Climate Assessment

NCA3 Third National Climate Assessment

NCA4 Fourth National Climate Assessment

NCEI National Centers for Enviromental Information (NOAA)

NDCs nationally determined contributions

NOAA National Oceanic and Atmopheric Administration

NPI North Pacific Index

NPO North Pacific oscillation

NPP net primary production

OMZs oxygen minimum zones

OSTP Office of Science and Technology Policy (White House)

PCA principle component analysis

PDO Pacific decadal oscillation

PDSI Palmer Drought Severity Index

PETM Paleo-Eocene Thermal Maximum

PFCs perfluorocarbons

PGW pseudo-global warming

PNA Pacific North American Pattern

RCMs regional climate models

RCPs Representative Concentration Pathways

RF radiative forcing

RFaci aerosol-cloud interaction (effect on RF)

RFari aerosol-radiation interaction (effect on RF)

RMSE root-mean-square-error 

RSL relative sea level

RSS remote sensing systems
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542

S06 surface-to-6 km layer

SCE snow cover extent

SGCR Subcommittee on Global Change Research (National Science and 

Technology Council, White House)

SLCFs short-lived climate forcers

SLCPs short-lived climate pollutants 

SLR sea level rise

SOC soil organic carbon

SRES Special Report on Emissions Scenarios (IPCC, 2000)

SREX Special Report on Managing the Risks of Extreme Events and 

Disasters to Advance Climate Change Adaptation (IPCC, 2012)

SRM solar radiation management

SSC Science Steering Committee

SSI solar spectral irradiance

SSPs Shared Socioeconomic Pathways

SST sea surface temperature

STAR Center for Satellite Applications and Research (NOAA)

SWCRE short-wave cloud radiative effect (on radiative fluxes)

LWCRE long-wave cloud radiative effect (on radiative fluxes)

TA total alkalinity

TC tropical cyclone

TCR transient climate response

TCRE transient climate response to cumulative carbon emissions

tOM total observed organic matter 

TOPEX/JASON1,2 Topography Experiment/Joint Altimetry Satellite Oceanography 

Network satellites (NASA)

TSI total solar irradiance

TTT temperature total troposphere

UAH University of Alabama, Huntsville

UHI urban heat island (effect)

UNFCCC United Nations Framework Convention on Climate Change

USGCRP U.S. Global Change Research Program

USGS U.S. Geological Survey

UV ultraviolet

VOCs volatile organic compounds

WAIS West Antarctic Ice Sheet

WCRP World Climate Research Programme

WMGHGs well-mixed greenhouse gases

WOCE World Ocean Circulation Experiment (JGOFS)
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543

Abbreviations and Units

C carbon

CO carbon monoxide

CH4 methane

cm centimeters

CO2 carbon dioxide

°C degrees celsius

°F degrees farenheit

GtC gigatonnes of carbon

hPA hectopascal

H2S hydrogen sulfide

H2SO4 sulfuric acid

km kilometers

m meters

mm milimeters

Mt megaton

μatm microatmosphere

N nitrogen

N2O nitrous oxide

NOx nitrogen oxides

O2 molecular oxygen

O3 ozone

OH hydroxyl radical

PgC petagrams of carbon

ppb parts per billion 

ppm parts per million

SF6 sulfur hexafluoride

SO2 sulfur dioxide

TgC terragrams of carbon (eliminate space in text)

W/m2 Watts per meters squared