Endangerment and Cause or 

Contribute Findings for 

Greenhouse Gases Under 

Section 202(a) of the Clean Air 

Act: 

EPA’s Response to Public 

Comments 

Volume 4: Validity of Future 

Projections 
Validity of Future Projections 

U. S. Environmental Protection Agency 

Office of Atmospheric Programs 

Climate Change Division 

Washington, D.C. 
FOREWORD 

This document provides responses to public comments on the U.S. Environmental Protection Agency’s 

(EPA’s) Proposed Endangerment and Cause or Contribute Findings for Greenhouse Gases Under Section 

202(a) of the Clean Air Act, published at 74 FR 18886 (April 24, 2009). EPA received comments on 

these Proposed Findings via mail, e-mail, and facsimile, and at two public hearings held in Arlington, 

Virginia, and Seattle, Washington, in May 2009. Copies of all comment letters submitted and transcripts 

of the public hearings are available at the EPA Docket Center Public Reading Room, or electronically 

through http://www.regulations.gov by searching Docket ID EPA-HQ-OAR-2009-0171. 

This document accompanies the Administrator’s final Endangerment and Cause or Contribute Findings 

for Greenhouse Gases Under Section 202(a) of the Clean Air Act (Findings) and the Technical Support 

Document (TSD), which contains the underlying science and greenhouse gas emissions data. 

EPA prepared this document in multiple volumes, with each volume focusing on a different broad 

category of comments on the Proposed Findings. This volume of the document provides responses to 

public comments regarding the validity of future projections. 

In light of the very large number of comments received and the significant overlap between many 

comments, this document does not respond to each comment individually. Rather, EPA summarized and 

provided a single response to each significant argument, assertion, and question contained within the 

totality of comments. Within each comment summary, EPA provides in parentheses one or more lists of 

Docket ID numbers for commenters who raised particular issues; however, these lists are not meant to be 

exhaustive and EPA does not individually identify each and every commenter who made a certain point in 

all instances, particularly in cases where multiple commenters expressed essentially identical arguments. 

Several commenters provided additional scientific literature to support their arguments. EPA’s general 

approach for taking such literature into consideration is described in Volume 1, Section 1.1, of this 

Response to Comments document. As with the comments, there was overlap in the literature received. 

EPA identified the relevant literature related to the significant comments, and responded to the significant 

issues raised in the literature. EPA does not individually identify each and every piece of literature 

(submitted or incorporated by reference) that made a certain point in all instances. 

Throughout this document, we provide a list of references at the end of each volume for additional 

literature cited by EPA in our responses; however, we do not repeat the full citations of literature cited in 

the TSD. 

EPA’s responses to comments are generally provided immediately following each comment summary. In 

some cases, EPA has discussed responses to specific comments or groups of similar comments in the 

Findings. In such cases, EPA references the Findings rather than repeating those responses in this 

document. 

Comments were assigned to specific volumes of this Response to Comments document based on an 

assessment of the principal subject of the comment; however, some comments inevitably overlap multiple 

subject areas. For this reason, EPA encourages the public to read the other volumes of this document 

relevant to their interests. 

iii 
TABLE OF CONTENTS 

4.0 Validity of Future Projections

4.1 Climate Models.............................................................................................................1 

4.2 Future Projections of Greenhouse Gas Emissions and Concentrations..............30 

4.3 Future Projections of Temperature ..........................................................................38 

4.4 Future Projections of Precipitation ..........................................................................47 

4.5 Future Projections of Extreme Weather Events......................................................53 

4.6 Future Projections of Sea Level Rise.......................................................................60 

4.7 Abrupt Climate Change .............................................................................................64

Acronyms and Abbreviations 

ACIA Arctic Climate Impact Assessment 

AGAGE Advanced Global Atmospheric Gases Experiment 

AMO Atlantic Multidecadal Oscillation 

AOGCM Atmosphere-Ocean General Circulation Models 

AR4 IPCC Fourth Assessment Report 

CCSP U.S. Climate Change Science Program 

CERES Clouds and the Earth’s Radiant Energy System (CERES) 

C degrees Celsius 

CH4 methane 

CO2 carbon dioxide 

ECS equilibrium climate sensitivity 

ENSO El Niño-Southern Oscillation 

EPA U.S. Environmental Protection Agency 

ERBS Earth Radiation Budget Satellite 

GHG greenhouse gases 

GCM general circulation model 

GDP gross domestic product 

Gt gigatonnes 

GtC gigatonnes of carbon 

GtCO2 gigatonnes of carbon dioxide 

hPa hectopascal 

IPCC Intergovernmental Panel on Climate Change 

IQA Information Quality Act 

ISCCP International Satellite Cloud Climatology Project 

K degrees Kelvin 

km kilometers 

LGM Last Glacial Maximum 

MER market exchange rate 

milliW/m2

 milliwatts per square meter 

MIT Massachusetts Institute of Technology 

MJO Madden-Julian Oscillation 

mm millimeters

iv 
v 

Acronyms and Abbreviations (Continued) 

MSU microwave sounding unit 

NAM Northern Annular Model 

NASA National Aeronautics and Space Administration 

NCDC National Climatic Data Center 

NOAA National Oceanic and Atmospheric Administration 

NRC National Research Council 

OH water-oxygen 

PDF probability density function 

PDO Pacific Decadal Oscillation 

ppm parts per million 

PPP purchasing power parity 

RF radiative forcing 

RSS Remote Sensing System 

SAGE Stratospheric Aerosol and Gas Experiment 

SRES IPCC Special Report on Emissions Scenarios 

SW short wave 

TAR IPCC Third Assessment Report 

TOA top of the atmosphere 

TSD Technical Support Document 

UAH University of Alabama in Huntsville 

UN United Nations 

USGCRP U.S. Global Change Research Program 

W watts 

WAIS West Antarctic Ice Sheet 

W/m2 watts per square meter 

WD water vapor dimers 

WV water vapor 

XBT expendable bathythermograph 
4.0 Validity of Future Projections 

Comment:

A number of commenters (e.g., 2898.1, 3394.1, 3411.1, 3596.2, 3722) state that specific aspects of the 

evidence about future projections summarized in the TSD do not support the Administrator’s 

endangerment finding. 

Response:

The specific issues that underlie these comments are addressed in the responses throughout this volume, 

and other volumes of the Response to Comments document. With regard to the commenters' conclusion 

that the current science does not support an endangerment finding with respect to future projections, we 

disagree based on the scientific evidence before the Administrator. See the Findings, Section IV.B, “The 

Air Pollution is Reasonably Anticipated to Endanger Both Public Health and Welfare,” for details on how 

the Administrator weighed the scientific evidence underlying her endangerment determination in general, 

and with regard to future projections in particular. 

4.1 Climate Models 

Comment (4-1):

A number of commenters (e.g., 0339, 2759, 2818, 3214.1, 3217, 3360, 3446.1) argue that the hypothesis 

of danger from human-induced climate change is founded on the use of computer models and that flaws 

in these models weaken or invalidate the conclusions of the Intergovernmental Panel on Climate Change 

(IPCC) and the Technical Support Document (TSD), many citing the poor representation of clouds in the 

models. One commenter states “Their unproven theory that CO2 is the main cause of global warming is 

only supported by computer models.” A commenter (0664) claims that the sheer complexity of the system 

makes modeling impossible at this time. Many of the following comments in this section of the Response 

to Comments document make similar arguments. 

Response (4-1):

We respond here to a theme that runs through many of the comments in this section. Many commenters 

arguments appear to assume that the findings of the assessment literature rest solely or predominantly on 

the outputs of climate models and that these outputs are flawed and unreliable. Based on our review of the 

assessment literature, EPA finds that both of these assumptions are incorrect. 

First, models are not the foundation of climate science, rather they are the tools used to better understand 

information and data from multiple sources and disciplines. Paleoclimate data, basic theory, observations 

of climate changes, and other branches of climate science together have provided (and continue to 

provide) the basis for key findings in the assessment literature. Indeed, research long before the advent of 

the computer found that the climate should respond to increased CO2 concentrations. Recently, scientists 

have used paleoclimate data about historical analogues such as the last interglacial and glacial maximum 

to estimate climate sensitivity, sea level response to temperature change, and other important climatic 

variables (Jansen et al., 2007, Hegerl et al., 2007). Computer modeling is, of course, important because it 

improves refinement of predictions, attributions, and analysis of non-linear interactions of a complex 

system, and thus climate models will continue to play a major role in understanding and projecting the 

future of the climate system. However, the characterization of a number of commenters that the projection 

and attribution findings of the IPCC, the U.S. Global Change Research Program (USGCRP), and others 

are supported only by the output of models is not accurate. 

With respect to the issues commenters raise concerning flaws in the models used for projections and 

attribution studies, it is well recognized that models are representations of complex systems and may not 

be able to perfectly represent all interactions in the system being modeled. For example, clouds are 

1 
difficult to model computationally because the physics involved in cloud formation occurs at scales 

smaller than the resolution of most climate models. Although model-based results are subject to some 

degree of inherent uncertainty, as reflected in the assessment literature and in the TSD, these uncertainties 

are acknowledged; uncertainties do not mean that the models are fatally flawed or unreliable 

representations of the climate system. Climate models have been demonstrated to successfully simulate a 

number of climatic properties, as documented in the IPCC, U.S. Climate Change Science Program 

(CCSP), National Research Council (NRC), and USGCRP reports on which the TSD primarily relies. 

Absolute certainty is not required, and in fact, the TSD summarizes both the important role and the 

limitations of models in Section 6(b), quoting Meehl et al. (2007): 

[C]onfidence in models comes from their physical basis, and their skill in representing 

observed climate and past climate changes. Models have proven to be extremely 

important tools for simulating and understanding climate, and there is considerable 

confidence that they are able to provide credible quantitative estimates of future climate 

change, particularly at larger scales. Models continue to have significant limitations, such 

as in their representation of clouds, which lead to uncertainties in the magnitude and 

timing, as well as regional details, of predicted climate change. Nevertheless, over several 

decades of model development, they have consistently provided a robust and 

unambiguous picture of significant climate warming in response to increasing greenhouse 

gases. 

Karl et al. (2009) reaches a similar conclusion, stating: 

All of the models used in this work [Karl et al., 2009] have imperfections in their 

representation of the complexities of the “real world” climate system. These are due to 

both limits in our understanding of the climate system, and in our ability to represent its 

complex behavior with available computer resources. Despite this, models are extremely 

useful, for a number of reasons. 

First, despite remaining imperfections, the current generation of climate models 

accurately portrays many important aspects of today’s weather patterns and climate. 

Models are constantly being improved, and are routinely tested against many 

observations of Earth’s climate system. Second, the fingerprint work shows that models 

capture not only our present-day climate, but also key features of the observed climate 

changes over the past century. Third, many of the large-scale observed climate changes 

(such as the warming of the surface and troposphere, and the increase in the amount of 

moisture in the atmosphere) are driven by very basic physics, which is well-represented 

in models. Fourth, climate models can be used to predict changes in climate that can be 

verified in the real world. Examples include the short-term global cooling subsequent to 

the eruption of Mount Pinatubo and the stratospheric cooling with increasing carbon 

dioxide. Finally, models are the only tools that exist for trying to understand the climate 

changes likely to be experienced over the course of this century. No period in Earth’s 

geological history provides an exact analogue for the climate conditions that will unfold 

in the coming decades. 

A CCSP report (2008c) assessed model strengths and limitations in detail, and the introduction states: 

Scientists extensively use mathematical models of Earth’s climate, executed on the most 

powerful computers available, to examine hypotheses about past and present-day 

climates. Development of climate models is fully consistent with approaches being taken 

2 
in many other fields of science dealing with very complex systems. These climate 

simulations provide a framework within which enhanced understanding of climaterelevant processes, along with improved observations, are merged into coherent 

projections of future climate change... 

The science of climate modeling has matured through finer spatial resolution, the 

inclusion of a greater number of physical processes, and comparison to a rapidly 

expanding array of observations. These models have important strengths and limitations. 

They successfully simulate a growing set of processes and phenomena; this set intersects 

with, but does not fully cover, the set of processes and phenomena of central importance 

for attribution of past climate changes and the projection of future changes. 

The consensus of the assessment literature is that models serve a useful purpose within the field of 

climate science, successfully modeling a number of processes. This is true despite the complexity of the 

system that was mentioned by one commenter. Although there are a number of limitations and 

uncertainties, they are accounted for, on a global scale, by providing a range of global mean temperature 

estimations for any given emission scenario (e.g., Figure 10.26, Meehl et al., 2007). This range, though 

important, still enables robust conclusions about the impact of greenhouse gases (GHGs) on temperature. 

A number of specific comments on model flaws are addressed by responses within this volume. It is 

EPA’s conclusion that the models have demonstrated the ability to accurately simulate many key aspects 

of climate, and in light of the key conclusions of the IPCC, CCSP, and USGCRP in regard to model skill 

and limitations, we have determined that it is fully appropriate to report model-based projections and 

attribution studies in the TSD. 

Comment (4-2):

Some commenters (0664,1309.1) argue that inability to model more than a week of weather means we 

cannot model 100 years of climate. Two commenters (2818, 3446.1) claim that 77 percent of the data 

essential for model initialization do not exist. One commenter (0639.1) cited “a NASA scientist” who 

claims that United Nations (UN) calculations did not include the appropriate boundary conditions. 

Response (4-2):

The claim that our inability to forecast the weather means that we cannot possibly understand the climate 

is a common one, and it is unfounded. It is based on the belief that “weather” and “climate” are 

essentially the same thing, with the same modeling limitations. This issue is discussed in the assessment 

literature, and the IPCC Fourth Assessment Report (Le Treut et al., 2007) provides a clear explanation of 

the difference between climate and weather: 

Climate is generally defined as average weather, and as such, climate change and weather 

are intertwined. Observations can show that there have been changes in weather, and it is 

the statistics of changes in weather over time that identify climate change. While weather 

and climate are closely related, there are important differences. A common confusion 

between weather and climate arises when scientists are asked how they can predict 

climate 50 years from now when they cannot predict the weather a few weeks from now. 

The chaotic nature of weather makes it unpredictable beyond a few days. Projecting 

changes in climate (i.e., long-term average weather) due to changes in atmospheric 

composition or other factors is a very different and much more manageable issue. 

Thus, we disagree with the assertion by commenters that a limitation in the ability to predict weather 

implies a limitation in the ability to predict climate on the 100-year timescale. 

3 
The claims that insufficient model initialization data exists, and that there are no appropriate boundary 

conditions, are related to the issue of the differences between weather and climate. A climate model 

usually divides the globe up into a large number of grid cells. Each cell has at any given point in time a 

temperature, wind direction, precipitation, gas composition, and other characteristics. The model then 

uses physics routines to determine how the characteristics of these grid cells would change for the next 

time step in the model (often several minutes of “model time”). The behavior of these grid cells defines 

the weather in the model—heat waves, storms, etc. Therefore, grid-by-grid data would be very important 

for near-term weather predictions. Indeed, the limitations of the initialization data (initial boundary 

conditions) and the resolution of the model are key factors in determining how far into the future a 

weather model can make accurate forecasts. On the other hand, 100-year climate projections are relatively 

insensitive to small changes in initial conditions. For purposes of predicting that June will be warmer than 

December, one does not necessarily need to know the exact atmospheric conditions of every square inch 

on the planet. For purposes of predicting the climate response (the average weather) to CO2

concentrations, it is also not necessary to perfectly initialize the model. Indeed, most climate models are 

“spun-up” from time periods long before the present day, rather than being initialized to match current 

conditions; this “spin-up” is done to make sure that the model is in a state that is stable and selfconsistent, which is more important than whether the model weather on the 5th of June matches the realworld weather on the 5th of June. Therefore, we find that these objections do not weaken the conclusions 

of the assessment literature as summarized in the TSD. 

Comment (4-3):

Many commenters argue that models do not capture a number of important processes. For example, 

commenters list processes such as clouds, dust, chemistry, and biological systems (0195,0546,3722), 

“Asian brown cloud” (3330.1), black carbon (3316.1,3596.1), historical albedo changes (0639.1), soil 

moisture (3596.1), various non-GHG anthropogenic forcings such as land use change and heat islands 

(3222.1), solar activity (2895), cosmic radiation (0650), and increased evaporation (1606.1) (which the 

commenter notes has not been seen because of global dimming). One commenter cites Wild (2005b) and 

Stanhill and Cohen (2009) when stating that “[c]urrent models do not consider the observed solar 

‘dimming’ and post-1985 ‘brightening.’” Commenter (3722) states that EPA failed to consider soils and 

vegetation in the carbon cycle, citing Dyson (1999) as stating that it makes no sense to consider the 

atmosphere and ocean alone. 

One commenter (3316.1) quotes the Scientific Alliance as stating that “it is sobering to note that the last 

IPCC Assessment Report, published just two years ago, makes no mention of the significant effect of 

soot.” 

Response (4-3):

We have reviewed the assessment literature in light of these comments and the referenced materials, and 

we conclude that commenters are incorrect. In fact, the models do include the most essential climaterelated processes. Many or most models include the effects of black carbon, global dimming, and other 

aerosol issues (including the atmospheric brown cloud), atmospheric chemistry, biological ecosystem 

uptake, natural variability on various timescales, land use change and historical albedo changes, and soil 

moisture and evaporation. These are all addressed in detail by the IPCC (Randall et al., 2007) and CCSP 

(2008c), which detail many of the newer advances in modeling terrestrial systems, aerosol indirect 

effects, and other properties. Figure 1.2 of Le Treut et al. (2007) and Figure 1.1 of CCSP (2008c) show 

graphical depictions of the evolution of climate models, showing that modern models now include 

chemistry, sulfates, precipitation, volcanic activity, land surface albedo, non-sulfate aerosols, rivers, 

interactive vegetation, sea ice, the carbon cycle, and overturning ocean circulation. Randall et al. note that 

carbon cycle dynamics now include soil carbon cycle, and devote a section to soil moisture feedbacks. 

4 
Global dimming refers to the reduction of sunlight reaching the surface, in large part due to increased 

aerosol emissions during the mid to late 20th century. This dimming has consequences for vertical 

temperature distributions, evaporation, ecosystem growth, and other climatic variables. Dimming in 

particular was addressed in terms of observational studies by the IPCC in Trenberth et al. (2007). Both 

submitted references (Wild, 2005b; Stanhill and Cohen, 2009) are also observational studies and are 

consistent with the IPCC treatment of these observations in that both studies show that dimming has 

reduced after 1990 (the Wild study was included in Trenberth et al.), though there is disagreement 

between the two studies in the urban bias of the dimming, with Stanhill and Cohen showing that in Israel 

the evidence is consistent with a more broad-based dimming. Any model that includes aerosols (which is 

nearly all of them) also includes the basic physics that lead to “dimming” and “brightening.” There are, 

however, continued uncertainties about historical and projected aerosol emissions, and those uncertainties 

are summarized in Section 6 of the TSD. 

Clouds are difficult to model computationally because the physics involved in cloud formation occurs at 

scales smaller than the resolution of most climate models. Therefore, clouds are represented by 

parameterizations in global climate models rather than being calculated explicitly. Because cloud 

responses to climatic change are important for both the trapping and reflection of energy, we recognize 

that clouds contribute to uncertainties in model-based results. For this reason, the TSD summarizes the 

uncertainties involved in estimating the indirect albedo effect (see Section 4[a]), some regional changes in 

precipitation (see Section 6[b]), and issues involving cloud representation (see Section 6[b]). 

With regards to the IPCC Assessment Report not mentioning soot, we note that soot and black carbon are 

both addressed at length in Forster et al. (2007). See also the response on black carbon in Volume 3 on 

commenters attributing recent temperature change to black carbon instead of GHGs. 

Historical patterns of solar activity are also included in the models. For EPA’s responses to issues 

involving the influence of cosmic radiation on climate, please refer to Volume 3 of this Response to 

Comments document. 

Comment (4-4):

One commenter (3769.1) objects to the lack of inclusion in climate models of water vapor dimers, citing 

Paynter et al. (2007). The commenter states that “their atmospheric absorption of incoming solar radiation 

in the near infrared ... may lead to a negative climate feedback as WV [water vapor] concentration 

increases in the lower troposphere.” 

Response (4-4):

In response to this comment, we reviewed the literature on water vapor dimers, and we conclude that the 

explicit inclusion in models of water vapor dimers is not likely to materially change any conclusions from 

the assessment literature regarding climate feedbacks. Water vapor dimers (WD) are phenomena that 

occur at high concentrations of water vapor when pairs of water molecules interact in ways that can 

change their light absorption properties. Therefore, the absorption properties of water in the atmosphere 

may be dependent on this effect. Some studies suggest that inclusion of this behavior can lead to an 

increase in atmospheric absorption of sunlight by a couple of percent (Chýlek and Geldart, 1997) 

compared to a modeled situation without water dimers. 

We do not find in the literature (including the referenced paper by Paynter et al., which was a laboratory 

measurement of the dimer absorption spectra and included no statements about implications for climate 

feedbacks) any support for the commenter’s assertion that WD are likely to lead to negative climate 

feedbacks, much less sufficient negative feedback to materially change projections of climate change. 

5 
Indeed, while the posited increased absorption by water vapor dimers at higher temperatures might reduce 

surface insolation at the surface by absorbing more sunlight before it reaches the ground, the increased 

absorption should also increase the warming of the troposphere. Thus, it appears that it is more likely that 

there would be a net positive feedback rather than a net negative feedback (though it would be a small 

contribution either way). This interpretation is supported by Pfeilsticker et al. (2003), which finds that 

atmospheric short wave absorption by water dimers should exert a positive climate feedback and 

additionally reduce atmospheric convection by heating the atmosphere and reducing surface evaporation. 

Pfeilsticker also states that “[a]ccordingly,WDs should be regarded as a greenhouse gas, of which the 

radiative effects are, however, already considered in most atmospheric radiative transfer models.” 

We conclude that this is an active area of research, and there are a number of questions that have yet to be 

resolved. However, we find no evidence to support the implication by the commenter that the lack of 

explicit inclusion of water vapor dimer physics in models weakens the conclusions drawn from models. 

As Pfeilsticker noted, while water dimers are not explicitly resolved, their radiative effects are already 

reflected in most radiative transfer models, and if anything their inclusion is more likely to lead to 

increased warming rather than cooling. 

Comment (4-5):

Some commenters argue that climate models do not properly include some modes of natural variability. 

For example, some commenters (e.g., 3291.1) mention the Madden-Julian Oscillation (MJO), including a 

citation of the IPCC statement that deficiencies remain in the simulations of the MJO. One commenter 

(3316.1) cites a paper by Spencer that uses a simple model to show that if the Pacific Decadal Oscillation 

(PDO) modulates cloudiness, it can explain up to two-thirds of the recent warming. One commenter 

(0454) states that a 60-year cycle of warming and cooling can explain temperature changes rather than 

CO2. 

Response (4-5):

As reviewed in response number 4-1, models accurately portray many important aspects of the climate. 

The IPCC does discuss limitations of the models with respect to the portrayal of some modes of natural 

variability, but states that these limitations are already reflected in the range of global temperature change 

projected from a given change in greenhouse gas forcing (Randall et al., 2007). Randall et al. conclude 

that: 

Despite such uncertainties, however, models are unanimous in their prediction of 

substantial climate warming under greenhouse gas increases, and this warming is of a 

magnitude consistent with independent estimates derived from other sources, such as 

from observed climate changes and past climate reconstructions. 

The Madden-Julian Oscillation is an observed variation in tropical winds and rainfall with a variation of 

30 to 90 days (Randall et al., 2007). The IPCC notes that “[s]imulation of the Madden-Julian Oscillation 

(MJO) remains unsatisfactory.” However, as stated above, these limitations are already included within 

the range of uncertainty represented by the IPCC (and summarized in the TSD). The commenter 

submitted no documentation supporting any claim that limitations in simulating the MJO undermines 

major conclusions in the assessment literature. 

With respect to the PDO study by Spencer (and the 60-year cycle of warming and cooling, which 

is likely to be a reference to the PDO), as far as we could determine this study has not passed peer 

review and currently exists as a blog post. We reviewed the blog post, and determined that it uses 

a simple energy balance model with variable ocean depths and cloud sensitivity, and an 

assumption that cloud cover variations were directly proportional to the PDO signal. The study 

6 
showed that with one combination of parameters, the PDO-cloud link could explain three quarters 

of the 20th century warming trend (another version of the blog post only attributes two thirds of 

the 20th century warming to the PDO). When Spencer included GHGs and other forcings 

compiled by James Hansen, then the fit was closer. The evidence provided by Spencer for a PDOcloud link was an analysis that showed that there was a correlation between the PDO index and 

average annual cloudiness over the global oceans measured by the Clouds and the Earth’s Radiant 

Energy System (CERES) instrument over a seven-year period. 

There are a number of issues with this study. The first is that the parameters for the simple model 

are chosen to yield a match with the global temperature and do not appear to be constrained by 

physical limits. The use of global temperature as the only test of the model is also a problem; 

most formal attribution studies based on data from the past century often include more data such 

as hemispheric, oceanic, or atmospheric temperatures in addition to global surface temperatures 

(Hegerl et al., 2007). As noted in Hegerl et al., the natural modes of variability examined do not 

produce spatial patterns of change that match historical temperature patterns. Additionally, the 

specific test used to come up with “three quarters” of warming used only the PDO and 

initialization conditions to match global temperatures, rather than including all known forcings, 

which will artificially inflate any correlation between the PDO and the temperature record. 

In particular, the method used in the paper to determine a PDO-cloud feedback is limited by only 

having seven years of data; Loeb et al. (2007) found that 15 years would be required to detect 

trends at the 90% confidence level using the CERES instrument. It is also unclear why the 

observational analysis uses only ocean data when the simple model uses global cloud shifts—the 

short-wave and long-wave ocean-only data in the Spencer blog post appear to have different 

trends than the global data reported in Peterson et al. (2009). 

Therefore, while this is an area of continuing research, we find that the Spencer study is not a 

compelling alternative to the conclusions of the assessment literature. 

Comment (4-6):

Commenters (e.g., 2883.1) argue that the lack of an ocean heat content upward trend since 2003 shows 

that climate models are incomplete. 

Response (4-6):

See our responses to comments on ocean heat content measurements in Volume 2 of this Response to 

Comments document. This lack of upward trend is both disputed and of a time period that is short for the 

purpose of trend detection. With respect to modeling, CCSP (2008c) states that ocean heat uptake 

observations are beginning to be sufficient to test models, and that models provide reasonable simulations 

of this uptake but have underestimated observed sea level rise. Because sea level rise is in part a function 

of ocean heat uptake, this is additional data suggesting that the models are not significantly 

overestimating ocean heat content trends. 

Comment (4-7):

Some commenters argue that the lack of inclusion by climate models of heat from volcanoes (especially 

subsea) and other geothermal sources implies that the models are wrong. 

Response (4-7):

7 
We have reviewed the literature in light of this comment, and we disagree with commenters that treatment 

of geothermal heat sources in models weakens the conclusions of the assessment literature. 

Our review of the literature indicates that the total heat geothermal heat flux is small in comparison to the 

change in forcing from anthropogenic factors. Sclater et al. (1981) estimated total geothermal heat 

flowing out of the Earth to be about 4.2 x 1013 watts (W)—or roughly 80 milliwatts per square meter 

(milliW/m2

) averaged over the entire planet. According to Sclater et al., most of this heat loss results from 

creation of oceanic plate, and the paper found that variations in this flux were small on short (less than 60-

million-year) time scales. Given this fairly steady flow, and the fact that 80 milliW/m2

 is roughly 20 times 

smaller than the current net anthropogenic forcing of about 1.6 W/m2

 at the top of the atmosphere, the 

contribution of changes in geothermal flux to current warming must be much less than 20 times the 

contribution of anthropogenic increases in greenhouse gas concentrations. 

We also note that geothermal fluxes have in fact been included in some climate models, as demonstrated 

in the papers by Adcroft et al. (2001) and Scott et al. (2001). They found that steady state geothermal heat 

from the ocean bottom (which they estimated at 50 milliW/m2

) did have some influence on some aspects 

of ocean circulation, but did not show that there was any significant contribution to large-scale 

atmospheric or oceanic changes. 

Therefore, we have determined that the conclusions about global-scale phenomena in the assessment 

literature are not affected by inclusion or lack of inclusion of the geothermal heat issue in the climate 

models. 

Comment (4-8):

A commenter (1924) claims that future predictions use climate models that assume that increased GHGs 

are the only influence on global temperature change, citing a 1990 paper by Wigley and Barnett. 

Response (4-8):

After reviewing the literature, we find no support for the claim that model-based climate projections from 

the assessment literature, as summarized in the TSD, assume that increased atmospheric GHG 

concentrations are the only influence on global temperature change. Wigley and Barnett (1990) included a 

simple energy balance model that assessed only the impact of GHGs, but the existence of one simple 20-

year-old model does not contradict the existence of hundreds of more complex models in use today. As 

discussed in response to other comments in this and other volumes of the Response to Comments 

document, models include many other factors that influence global temperature change, such as natural 

variability, historical volcanic eruptions (and sometimes stochastic approximations of future volcanic 

eruptions), aerosol effects, and land use change influences. Therefore, GHGs are not the “only influence” 

on global temperature increase projections in models. 

Comment (4-9):

A commenter (0553) claims that tipping points exist only in general circulation models (GCMs) and have 

never been observed in nature as a result of CO2 concentration changes. 

Response (4-9):

EPA has reviewed the assessment literature and concludes that the commenter’s claim that tipping points 

only exist in GCMs is false. For example, glacial-interglacial transitions—the times when the great ice 

sheets advance or retreat at the beginning or end of an ice age—are fairly dramatic examples of global 

tipping points initiated by modest changes in the distribution of solar forcing. Please refer to Volume 3, 

8 
Section 3.2.4: CO2 and Past Global Warming Episodes for our responses to other comments on the 

relationship between CO2 and temperature over geologic time. 

Comment (4-10):

Many commenters (0736, 0798, 1606.1, 2883.1, 2890.1, 2898.1, 3372, 3432.1, 3446.1), mention the lack 

of an observed tropospheric hotspot as a climate model flaw. 

Response (4-10):

Please refer to Volume 3 for our responses to comments on the tropospheric warming in the tropics and 

its treatment in models. 

 

Comment (4-11):

Commenters object to climate models, arguing that they have not predicted Antarctic cooling in the past 

50 years (1606.1) or a net gain of Antarctic and Greenland ice recently (1606.1), and that they 

overpredicted Antarctic sea ice loss (2883.1). 

Response (4-11):

See our responses on temperature trends in Antarctica and whether Antarctica is cooling in Volume 2, and 

attribution issues including the use of models to determine the causes of Antarctic temperature changes in 

Volume 3. 

See responses in Volume 2 of the Response to Comments document and Section 4(f) of the TSD, which 

summarize the findings of the assessment literature on recent loss of ice from the Antarctic and Greenland 

ice sheets, contradicting the assertion of the commenter that there has been a net gain of Antarctic and 

Greenland ice. 

 

Slower Antarctic sea ice retreat compared to Arctic sea ice retreat is consistent with the models, as stated 

in Meehl et al. (2007): “In 20th- and 21st-century simulations, antarctic sea ice cover is projected to 

decrease more slowly than in the Arctic (Figures 10.13c, d and 10.14), particularly in the vicinity of the 

Ross Sea where most models predict a local minimum in surface warming.” Further, Lemke et al. (2007) 

found that “the antarctic data provide evidence of a decline in sea ice extent in some regions, but there are 

insufficient data to draw firm conclusions about hemispheric changes prior to the satellite era,” therefore, 

while satellite-era Antarctic sea ice has exhibited a statistically insignificant increasing trend, it is not 

clear that the longer term trend has been positive. There are uncertainties involved in sea ice projections: 

Randall et al. (2007) note that “[d]espite notable progress in improving sea ice formulations, AOGCMs 

[Atmosphere-Ocean Global Climate Models] have typically achieved only modest progress in simulations 

of observed sea ice since the TAR [IPCC Third Assessment Report]. The relatively slow progress can 

partially be explained by the fact that improving sea ice simulation requires improvements in both the 

atmosphere and ocean components in addition to the sea ice component itself.” However, despite these 

uncertainties, the projections of reduction of sea ice extent in both hemispheres by the end of the century 

under all IPCC scenarios are robust, and the uncertainties involved do not substantially change the 

conclusions of the assessment literature. 

Comment (4-12):

Some commenters object that models greatly exaggerate warming because global warming since the 

1970s has been associated with a decrease in upper tropospheric water vapor and increase in outgoing 

longwave radiation (4003), or negative absolute humidity trends (2883.1). 

9 
Response (4-12):

We disagree that the evidence shows that upper tropospheric water vapor has declined. Please refer to 

Volume 3 for EPA’s response to comments on trends in water vapor and attribution. The trends in water 

vapor do not support the commenter’s argument that the models greatly exaggerate warming. 

Comment (4-13):

A commenter (2898.1) objects to statements on attribution from the TSD because, according to the 

commenter, models don't properly simulate important modes of natural variability (e.g., the El NiñoSouthern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), and the Atlantic Multidecadal 

Oscillation (AMO). Another commenter (3291.1) argues that model flaws, surface record flaws, and other 

uncertainties make attribution impossible, that goodness of fit is an expert judgment and therefore biased, 

and that using model averages smoothes out year-to-year variations. A third commenter (3330.1) claims 

that models are “curve fitted” to match, and that explaining global temperatures may not explain 

hemispheric differences. Another (3722) objects that claiming that the fact that models match historical 

data only if human emissions are included is an exercise in curve-fitting. Several commenters (3323.1, 

4003, 4041.1, 4932.1, and 5158) state that models and the IPCC ignore the possibility of indirect solar 

variability (Section 2.5), which if important would again be likely to have the effect of overstating the 

importance of GHGs/CO2. 

Response (4-13):

Please refer to Volume 3 for EPA’s responses to comments broadly focused on the extent to which 

observed climate change can be attributed to the observed increase in atmospheric GHG concentrations. 

Here, we concentrate on the issues raised by the commenters on the use of computer models in the 

attribution process. 

Chapter 9.4.1.4 of the IPCC Fourth Assessment Report (AR4) (Hegerl et al., 2007) discusses the 

influence of GHGs on surface temperature and describes how statistical approaches are used with various 

models in order to attribute changes to GHGs, other anthropogenic influences, and natural factors. The fit 

within this approach does not use any human judgment, nor is it an exercise in curve-fitting: the allocation 

of different weights to different sources results solely from statistically determining the best match to 

observations using different weights for various forcings. The four models shown in Figure 9.9 in Hegerl 

et al. (2007) all indicate that the net contribution of natural factors to total warming over the 20th century 

was actually a cooling influence. No scaling factor for solar or volcanic forcing would be able to explain 

the trends and patterns as well as the combinations of anthropogenic and natural forcings used in the 

IPCC and the formal attribution literature. Further, we note that the models do explain hemispheric and 

continental trends as well as global trends. 

If the underlying data on temperature and forcing were in error, the precise allocations would change. 

However, the judgment of the science community, as expressed in the CCSP, USGCRP, NRC and IPCC 

reports, is that it is unlikely that the errors in these underlying data could be large enough to substantively 

change the attribution. This judgment is expressed, for example, in the IPCC (2007b) statement that 

“[m]ost of the observed increase in global average temperatures since the mid-20th century is very likely 

due to the observed increase in anthropogenic greenhouse gas concentrations.” The commenters have not 

provided scientific literature that supports a claim that these errors are large enough to change the 

conclusions on attribution. 

 

With respect to model simulation of modes of natural variability, Randall et al. (2007) report the 

following: 

10 
Since the TAR [Third Assessment Report], developments in AOGCM formulation have 

improved the representation of large-scale variability over a wide range of time scales. 

The models capture the dominant extratropical patterns of variability including the 

Northern and Southern Annular Modes, the Pacific Decadal Oscillation, the Pacific-North 

American and Cold Ocean-Warm Land Patterns. AOGCMs simulate Atlantic multidecadal variability, although the relative roles of high- and low-latitude processes appear 

to differ between models. In the tropics, there has been an overall improvement in the 

AOGCM simulation of the spatial pattern and frequency of ENSO, but problems remain 

in simulating its seasonal phase locking and the asymmetry between El Niño and La Niña 

episodes. Variability with some characteristics of the MJO is simulated by most 

AOGCMs, but the events are typically too infrequent and too weak. 

This shows that the models used for attribution studies do capture many important aspects of natural 

variability. Additionally, Hegerl et al. (2007) find that natural modes of variability would not produce the 

appropriate spatial patterns of change, whereas anthropogenic forcings do explain those spatial patterns: 

When human factors are included, the models also simulate a geographic pattern of 

temperature change around the globe similar to that which has occurred in recent 

decades. This spatial pattern, which has features such as a greater warming at high 

northern latitudes, differs from the most important patterns of natural climate variability 

that are associated with internal climate processes, such as El Niño. 

Therefore, the accuracy with which models capture natural variability is sufficient for confidence in 

attribution studies that use models, especially when buttressed by the other, similar lines of evidence 

assessed by Hegerl et al. (2007). 

Comment (4-14):

Several commenters (3323.1, 4003, 4041.1, 4932.1, and 5158) submitted a document authored by Alan 

Carlin that states that models and the IPCC ignore the possibility that there may be other significant 

natural effects on global temperatures that we do not yet understand. According to the commenters, this 

possibility invalidates IPCC statements implying that one must assume anthropogenic sources in order to 

duplicate the temperature record. The commenters argue that the 1998 spike in global temperatures is 

very difficult to explain in any other way. The argument includes the statement that “[p]erhaps the closest 

simple ‘explanation’ for the observed changes in global temperatures is provided by the PDO and/or 

AMO together with ENSO,” and also that the satellite temperature trend should be divided into periods, a 

1978 to 1997 period with no trend, a 1998 spike, a high period from 1999 to 2006, which was possibly 

“an after-effect of the sudden surge in 1998,” and finally, a strong downward trend from 2007 to 2009. 

Carlin cites a Web site—Arrak (2008) 

(http://icecap.us/images/uploads/ThereWasNoGlobalWarmingBefore1997.pdf)—in support of this claim 

and presents a figure from Arrak (2008). 

Response (4-14):

EPA has reviewed Alan Carlin’s document, as well as the body of scientific literature represented in the 

assessment reports. We find that the assessment literature clearly demonstrates that the historical pattern 

of global temperature change is due to a combination of natural and anthropogenic factors. The peak 

temperatures in 1998 are understood to be a result of an extremely potent El Niño event added on top of a 

long-term anthropogenically driven warming trend (Easterling, 2009). Historical elements of natural 

variability such as volcanoes, solar insolation changes, and ENSO events do not provide compelling 

explanations for the long-term trend of warming over the past half century as seen in both the satellite and 

temperature records, whereas anthropogenic changes in greenhouse gases and aerosols in combination 

11 
with natural variability do provide such an explanation, both in terms of spatial and temporal patterns of 

change (Hegerl et al., 2007). 

The method of dividing a 30-year temperature period into smaller periods and deciding that the 

temperature pattern is indicative of periods of no-trend with a “step” caused by the 1998 ENSO event is 

not a rigorous scientific approach. This division was apparently made by estimating trends visually, rather 

than using any rigorous statistical methodology—indeed, Arrak (2008) (a non-peer-reviewed source) 

justifies the analysis by stating “we humans are good at pattern recognition.” Human pattern recognition 

is no substitute for statistics. This document even claims that the eruptions of Pinatubo and Chichon had 

no cooling effect because the coincident cooling was part of a visual oscillatory pattern; we find this 

claim to be at odds with the robust scientific literature on the impact of volcanic sulfate aerosols. 

Additionally, the allocation of a “strong downward trend” from 2007 to 2009 is merely looking at very 

short-term variability. As shown in Figure 2.8 of Peterson et al. (2009), due to internal model variability, 

negative short-term trends of as long as a decade are consistent with long-term warming—therefore, we 

find that this “strong downward trend” is consistent with an excursion from a longer term warming trend. 

In the half year since the publication of this report, all monthly temperatures have been above the decadal 

average, demonstrating how reliance on short-term variability is not a robust methodology for trend 

determination. Nor is the use of only one temperature dataset, the microwave sounding unit (MSU) 

satellite data, appropriate when other datasets including surface temperature, ocean heat content, and a 

number of physical indicators such as glacial melt and rising sea level all show warming during portions 

of the short-term periods when there are plateaus in the MSU dataset. 

Therefore, we find that the evidence that the Carlin document relies on for this analysis of recent 

temperature trends is very weak, and the arguments from this document do not provide reason to conclude 

that the major CCSP and IPCC assessments of the literature, relying as they do on multiple studies and 

lines of evidence, are in error. 

Comment (4-15):

Some commenters (e.g., 2898.1, 3136.1) object to regional-scale model projections. For example, one 

commenter (3136.1) states that natural variability has large impacts on local and regional trends, 

attributing Alaskan warming in specific and northwest North American trends in general from 1950 to 

1976 to shifts in the PDO (citing Hartmann and Wendler, 2005) and states that these trends occasionally 

randomly coincide with model projections. Other commenters (3316.1, 3222.1,) argue that models cannot 

project regional climate changes. Other problematic historical regional projections that have been raised 

by commenters are Antarctic and Arctic changes (3722), especially in reference to a comparison by Dr. 

Akasofu (2007), citing work by Bill Chapman, between models and observations from the Arctic Climate 

Impact Assessment (ACIA). Another commenter (3701.1) criticizes the capability of climate models to 

predict drought, precipitation, regional temperature, and temperature variability based on a number of 

studies. Commenters also criticize reliance on anomalies rather than absolute temperatures, claiming that 

models cannot predict absolute temperatures as well as they predict anomalies. Similarly, for historical 

data, a commenter (3701.1) objects that models have had a poor record of predicting climate fluctuations, 

regional-scale trends, and temperatures at specific weather stations. 

Response (4-15):

We have reviewed the TSD in light of these comments. We have determined that the TSD accurately 

represents the current state of knowledge on regional-scale model projections as expressed in the 

assessment literature and that the assessment literature has accurately represented the state of the science. 

As the TSD summarizes in Section 6(b), confidence decreases in changes projected by global climate 

models at smaller spatial scales. Many important small-scale processes cannot be represented explicitly in 

models and so must be included in approximate form as they interact with larger scale features (Randall et 

12 
al., 2007). Some of the most challenging aspects of understanding and projecting regional climate 

changes relate to possible changes in the circulation of the atmosphere and oceans, and their patterns of 

variability (Christensen et al., 2007). Nonetheless, the IPCC (2007d) concluded that recent advances in 

regional-scale modeling lead to higher confidence in projected patterns of warming and other regionalscale features, including changes in wind patterns, precipitation, and some aspects of extremes and of ice. 

With regards to Hartmann and Wendler (2005), the paper shows that there is a correlation between the 

PDO index and the temperature data used in the paper for most regions of Alaska except the near-Arctic 

regions. The paper does not, however, present a rigorous attribution analysis demonstrating a lack of 

influence of GHGs. The analysis by the authors finds that a (not statistically significant) cooling trend in 

many regions of Alaska between 1977 and 2001 “is in contrast to some theories regarding the 

atmospheric warming in an increasing greenhouse gas environment,” but local trends are a 

superimposition of natural variability and longer term trends. Given that the PDO statistic that the authors 

use to explain the increase in temperature in 1977 has a statistically significant negative trend between 

1977 and 2001, a rigorous attribution analysis should at least include that natural trend. Natural variability 

could potentially make spurious agreements between models and a single individual region such as 

Alaska; however, the conclusions of the IPCC are based on a comparison of many regions to model 

trends, giving statistical significance to those conclusions. Similarly, as stated previously, the IPCC notes 

that confidence decreases for projections at smaller spatial scales and shorter time periods, although the 

existence of uncertainty does not eliminate the value of these projections. 

With respect to the ACIA comparison, we were able to locate a peer-reviewed paper by Bill Chapman 

(Chapman, 2007), which was a comparison between models and climate for the Arctic region. While the 

analysis by Dr. Akasofu (2007) had statements such as “we found that there was no resemblance at all, 

even qualitatively,” the paper itself stated that “[p]atterns of variability of observed and simulated surface 

air temperatures agree very well across all seasons,” though it did identify certain regions of biases such 

as a strong cold bias in the models in the Barents Sea corresponding “to a region of oversimulated sea 

ice.” As summarized in the TSD, Karl et al. (2009) found that the observed decline in Arctic sea ice has 

been more rapid than projected by climate models, so this finding of oversimulated ice is consistent with 

the TSD. 

Importantly, changes in temperature on the global scale are not as sensitive to large scale forcings as are 

localized temperature patterns that depend on atmospheric wind patterns and shifting ocean currents. 

With regards to projections of temperature changes at specific weather stations, weather stations are 

addressed in Volume 2. Modeling a single weather station is the extreme case of modeling small spatial 

scales, and therefore models are not expected to replicate weather behavior at single stations. 

Finally, Section 5 of the TSD now addresses the impact of the PDO and other modes of internal 

variability on the likelihood of extreme temperatures, drought, precipitation, and small-scale projections: 

As with temperature, attributing changes in precipitation to anthropogenic forcing at 

continental or smaller scales is more challenging. One reason is that as spatial scales 

considered become smaller, the uncertainty becomes larger because internal climate 

variability is typically larger than the expected responses to forcing on these scales 

(Gutowski et al., 2008). For example, there is considerable evidence that modes of 

internal variability (such as ENSO, the Pacific Decadal Oscillation,1

 and NAM) 

 

1

 The Pacific Decadal Oscillation (PDO) is a pattern of Pacific climate variability that shifts phases on at least interdecadal time scale, usually about 20 to 30 years. The PDO is detected as warm or cool surface waters in the Pacific 

13 
substantially affect the likelihood of extreme temperature, droughts, and short-term 

precipitation extremes over North America (Gutowski et al., 2008). 

Please refer to Volume 3 for EPA’s responses to comments on temperature attribution for Alaska in 

relation to the PDO as well as responses regarding attribution for Antarctic temperature trends, and 

Volume 2 for EPA’s responses to comments on the existing temperature trends in Alaska, the Arctic, and 

the Antarctic. 

Comment (4-16):

A number of commenters (e.g., 0650, 2057, 2890.1, 3291.1, 3679.1, 3722) note that cloud modeling is 

important for accurate representation of the climate system and precipitation calculations, and yet that it is 

an area with a lot of uncertainty. Several commenters quote the IPCC—for example, one commenter 

(3291.1) provides the statement by the IPCC that “[s]ignificant uncertainties, in particular, are associated 

with the representation of clouds, and in the resulting cloud response to climate change. Consequently, 

models continue to display a substantial range of global temperature change in response to specified 

greenhouse gas forcing.” 

Response (4-16):

We agree that cloud modeling is important for accurately representing climate system and is subject to 

significant uncertainties. The TSD summarizes the findings of the assessment literature on this issue and 

acknowledges the uncertainties, as discussed in the assessment literature, involved in modeling clouds 

and precipitation. For example, in Section 6(b), the TSD states, following Meehl et al., 2007): 

Models have proven to be extremely important tools for simulating and understanding 

climate, and there is considerable confidence that they are able to provide credible 

quantitative estimates of future climate change, particularly at larger scales. Models 

continue to have significant limitations, such as in their representation of clouds, which 

lead to uncertainties in the magnitude and timing, as well as regional details, of predicted 

climate change. Nevertheless, over several decades of model development, they have 

consistently provided a robust and unambiguous picture of significant climate warming in 

response to increasing greenhouse gases. 

The TSD also addresses the confidence and limitations involved in predicting precipitation referencing 

both IPCC (2007d) and CCSP (2008c). We note that the conclusions in the scientific literature are that the 

findings are robust despite these uncertainties. A detailed analysis of uncertainties associated with cloud 

modeling and their relationship to overall model results is found in Randall et al. (2007), among other 

underlying references for the TSD. Indeed, the statement in Randall et al. (2007) directly after the quote 

provided by commenter (3291.1) is: “Despite such uncertainties, however, models are unanimous in their 

prediction of substantial climate warming under greenhouse gas increases, and this warming is of a 

magnitude consistent with independent estimates derived from other sources, such as from observed 

climate changes and past climate reconstructions.” 

Comment (4-17):

A number of commenters (e.g., 0400, 0499, 3215.1, 3340, 3596.1,) object to positive feedbacks in climate 

models. One commenter (0400) states that there has been no runaway climate change in the past despite 

large changes in CO2 and claims therefore that there must be strong negative feedbacks in the system. A 

 Ocean, north of 20°N. During a “warm,” or “positive,” phase, the west Pacific becomes cool and part of the eastern 

ocean warms; during a “cool,” or “negative,” phase, the opposite pattern occurs. 

14 
number of commenters (e.g., 0400, 3215.1, 3291.1, 3340, 3596.1) argue that some work shows that there 

is a bias towards over predicting feedbacks based on Spencer and Braswell (2008). One commenter 

(3291.1) argues that the estimate of climate sensitivity uncertainty has hardly changed since 1979 because 

modelers cannot characterize feedbacks, and another commenter (3330.1) claims that climate sensitivity 

ranges are determined by changing model parameters. 

Response (4-17):

Please refer to Section 4.3 of this volume for EPA’s responses to comments specific to climate sensitivity 

and temperature projections. In this response, we focus on the role of positive feedbacks in the modelbased studies that provide one source of climate sensitivity estimates. 

In response to comments, we have reviewed the work by Spencer and Braswell (2008), which argues that 

the observed changes in ocean temperature are due to changes in cloud cover rather than the other way 

around. Specifically, they show that a specific type of climate feedback estimation study based on using 

short-wave flux data from satellites (Forster and Gregory, 2006) can be biased if it does not account for 

the possibility that the temperature relationship between cloud cover and ocean temperatures works both 

ways. While this preliminary work is interesting, the conclusions of bias are based on a very simple 

model with a heat capacity equivalent to a 50 meter thick ocean, and it is not clear that the same results 

would be obtained from using this technique with more complex, realistic models. Certainly cloud 

feedbacks are one of the main areas of uncertainty in the climate field, but other recent papers such as 

Clement et al. (2009) continue to find that clouds contribute to positive feedbacks in the climate system. 

Additionally, the conclusion of low sensitivity from the Spencer et al. study is not consistent with the 

large body of work, as synthesized in the assessment literature, which uses multiple methods and datasets, 

including paleoclimate data, 20th century data, and responses to volcanic forcings, in order to estimate 

climate sensitivity (Hegerl et al., 2007). 

While the numerical range presented in the latest IPCC assessment for climate sensitivity bears a 

resemblance to the range presented in the Charney report in 1979, the earlier estimate was a rough 

estimate with no associated probabilities. The range presented in Hegerl et al. now includes a “likely” 

range, a “very likely lower limit,” and a “most likely” climate sensitivity, and this range is based on a 

number of different climate sensitivity studies. The remaining uncertainty range is due to the difficulty in 

characterizing feedbacks, but these ranges are estimated based on a number of observational studies as 

well as computational approaches and are therefore not merely based on changing model parameters. 

In regard to the comment that the absence of runaway climate change in the past (where runaway climate 

change refers to a small change in forcing leading to ever increasing temperatures, similar to what 

happened to the atmosphere of Venus) indicates that there must be strong negative feedbacks in the 

climate system that will prevent runaway climate change in the future, it appears that the commenter is 

confusing “runaway climate change” with “positive feedbacks.” In the assessment literature and the 

underlying science, positive feedbacks in the context of climate models do not necessarily imply runaway 

climate change. As temperature increases, the outgoing radiation increases as the fourth power of the 

temperature change (following the Stefan-Boltzman Law). Thus, a positive feedback can just mean that 

the total temperature change from a given forcing will be larger than the change would have been without 

those feedbacks; only past a certain threshold can positive feedbacks lead to runaway climate change. The 

likely range of climate sensitivities reported in the assessment literature are all below that critical 

threshold. Therefore, we find that it does not follow from the commenter’s assertion regarding the lack of 

historical runaway warming that there are no positive feedbacks. In fact, several well-understood 

examples of positive feedbacks are discussed in the assessment literature, including a water vapor 

feedback where increased temperatures lead to increased water vapor, which lead to increased 

temperature (but summing to a finite total temperature change), or sea ice-albedo feedbacks where sea ice 

15 
retreat in response to higher temperatures will lead to increased solar absorption and therefore enhanced 

temperature response of high latitudes (Hegerl et al., 2007). 

Comment (4-18):

A commenter (3722) argues that the IPCC ignores the fact that models can produce sensitivities outside of 

their “favored range” and states that Stainforth et al. (2005) discarded as “obviously wrong” modeling 

experiments that produce cooling. 

Response (4-18):

We have reviewed Stainforth et al. (2005) and other literature on climate sensitivities and determined that 

the objection to the IPCC approach of determining climate sensitivities does not give sufficient 

consideration to the process by which the IPCC range is determined. First, theoretical model analysis is 

not the only source of sensitivity estimates; as described in Section 4.3 of this volume, responses to 

volcanoes, paleoclimate data, and past-century data are all used in determining the most likely climate 

sensitivities. In addition, many different modeling studies inform the IPCC conclusions on this topic, each 

of which has undergone peer-review. Stainforth et al. (2005) itself is not cited in Hegerl et al. (2007), and 

therefore does not form part of the IPCC conclusions on climate sensitivity. 

Stainforth et al. developed a system to allow users to run climate models at home and provided each user 

with a model with randomly selected parameter sets. Certain parameter sets were excluded based on the 

model never reaching equilibrium during spin-up experiments (model drift). In six out of 414 remaining 

cases, Stainforth et al. discarded model simulations where an interaction between a simplified slab ocean 

and clouds in the Pacific lead to unphysical behavior. In these cases, one symptom of the unphysical 

behavior happened to be cooling; Figure 1b of the Stainforth paper also shows that those six “obviously 

wrong” modeling experiments were definite outliers. They were not discarding estimates just because the 

estimates produced cooling, but rather due to this unphysical behavior; it also so happens that there were 

simply no comparable outliers with equivalent unphysical behavior on the warm end of the study. 

While the Stainforth et al. paper is an interesting exploration of possible sensitivities that are consistent 

with model structure, with sensitivities ranging from less than 2°Kelvin (K) to more than 11°K, the paper 

does not constrain sensitivities nearly as much as the other approaches listed in Hegerl et al. (2007). It 

was not designed to test which parameter combinations were consistent with observations, but rather the 

paper was an exploration of what climate sensitivities could be produced in a model setting without 

leading to unphysical behaviors. 

We also note that most studies do not use a slab ocean like the Stainforth experiment, because slab oceans 

do not represent ocean behavior as accurately as more complex ocean models. The Stainforth effort used 

one because it was part of the climateprediction.net experiment using thousands of home computers to run 

many more GCM simulations than would have been possible for a single institution. The tradeoff of this 

approach was the use of a simplified ocean to reduce computation time, with the result that unphysical 

behaviors occasionally resulted. 

For these reasons, we conclude that the commenter has not presented compelling evidence in support of 

the claim that the IPCC ignores models that produce sensitivities outside of any favored range. 

Comment (4-19):

One commenter (3291.1) cited a Massachusetts Institute of Technology (MIT) study, Forest et al. (2006), 

which shows that climate models often overestimate heat uptake by the ocean. The commenter suggests 

16 
that because, as the commenter claims, models are calibrated to match the climate of the past century, 

there must be compensating errors in these models. 

Response (4-19):

We have reviewed Forest et al. (2006) and determined that it does not necessarily indicate that there are 

compensating model errors in addition to the potential errors in estimating ocean heat uptake. The study 

was referenced in the IPCC (Hegerl et al., 2007): 

Although the heat uptake in the ocean cannot be explained without invoking 

anthropogenic forcing, there is some evidence that the models have overestimated how 

rapidly heat has penetrated below the ocean’s mixed layer (Forest et al., 2006; see also 

Figure 9.15). In simulations that include natural forcings in addition to anthropogenic 

forcings, eight coupled climate models simulate heat uptake of 0.26 ± 0.06 W m–2 (±1 

standard deviation) for 1961 to 2003, whereas observations of ocean temperature changes 

indicate a heat uptake of 0.21 ± 0.04 W m–2 (Section 5.2.2.1). These could be consistent 

within their uncertainties but might indicate a tendency of climate models to overestimate 

ocean heat uptake. 

Climate models, ocean heat content data, and other data will continue to improve and be updated. 

Inconsistencies between models and data can indicate imperfections in either the models or the data. In 

the 2006 study by Forest et al., nine out of 11 TAR models were out of the 90% bounds of paired ocean 

heat uptake and climate sensitivity as determined by the study and six of 11 were outside the 99% bounds, 

but using updated methodology and comparing to more recent models, a 2008 paper by Forest et al. found 

that only five out of 11 new Fourth Assessment Report models fell outside those 90% bounds and none 

outside the 99% bounds of paired ocean heat uptake and climate sensitivity. The heat uptake results from 

the Forest et al. studies are dependent on the ocean heat content datasets, and newer results that were not 

included within either Forest et al. study have been released. These newer results (from Domingues et al. 

2008) corrected for expendable bathythermograph (XBT) dataset errors and show a larger ocean heat 

uptake than the older Levitus dataset upon which the Forest et al. studies relied. The models would 

therefore be more consistent with the new, updated observations than they were with the old observations. 

Even if models overestimate ocean heat uptake, it is not clear that there must necessarily be 

“compensating errors.” This is because most models are not tuned to match the historical record as the 

commenter claims; rather, the matching of the historical record is a result of attempting to accurately 

model various climatic processes in the present (Randall et al., 2007). Therefore, improvements in the 

accuracy of modeling these processes can actually lead to better matching of the historical record and 

improvements in projections. In the case of correcting an overestimation of ocean heat uptake, the most 

likely result is that as less heat goes into the model oceans, more heat will stay in the model atmosphere 

and projections of temperature change will increase. 

 

Please refer to Section 4.3 of this volume for EPA’s responses to comments specific to climate sensitivity, 

and Volume 3 for our response on ocean heat content and attribution. 

Comment (4-20):

One commenter (3427.1) claims that observations indicate the GHGs are not as well mixed in the 

atmosphere as assumed in models. 

Response (4-20):

The commenter did not provide any literature, and it is not clear to what observations they are referring. 

Clearly, near sources and sinks, GHG concentrations will not be at the “global average concentration,” 

17 
but it is well accepted that within a couple of years of being emitted, a given pulse of gas will be wellmixed throughout the troposphere. Further, we note that few climate models include an a priori

assumption that the gases are well-mixed; most models include atmospheric dynamics that calculate the 

movement of gases from one place to another. The result in these models is indeed a well-mixed 

atmosphere, and this matches the results of observations of background atmospheric concentrations at a 

number of remote monitoring sites in various networks (such as the Advanced Global Atmospheric Gases 

Experiment (AGAGE) network). 

Comment (4-21):

Some commenters assert that models include arbitrary coefficients that are necessary for the final result. 

One (0650) argues that a trace gas would not be sufficient to cause warming without such a coefficient, 

another (0798) that the assumption of constant relative humidity is flawed. A commenter (2888.1) stated 

that models are hardwired to assume a two-thirds of a degree Celsius (C) increase due to 1 W/m2

 of 

radiative forcing. Commenter (3722) provides an analysis by Dr. Akasofu (2007) that claims that global 

climate models cannot test attribution because they are adjusted or “tuned” to reproduce the 0.6 to 0.7 

degree Celsius rise. 

Response (4-21):

We reviewed the assessment literature in light of these comments and conclude that the commenters’ 

assertions indicate a lack of familiarity with state-of-the-art climate modeling. The commenters provided 

no literature to support their assertions. Climate models are designed to match the physical processes of 

the system to the best of the modeler’s ability. In some cases, this requires “parameterization” of certain 

processes, particularly those that take place on scales too small to capture with the resolution of the model 

such as cloud behavior or the physics of plume behavior. Parameterization of large scale processes is 

usually only the case with EMIC (Earth Models of Intermediate Complexity) models, some of which 

include tunable parameters such as climate sensitivity and ocean heat uptake. Most of the larger AOGCM 

models do not include tunable parameters for these large-scale behaviors because these behaviors are an 

emergent behavior of the system, rather than something that is controlled by the user. One of the tests for 

EMIC models is to show that they can replicate the behavior of these larger AOGCM models. 

Randall et al. (2007) does address the tuning of some parameters in AOGCMs, noting that this tuning 

must always meet at least two conditions: that the parameters remain within observationally based 

constraints and that the number of degrees of freedom from tunable parameters is less than the number of 

observational constraints used in model evaluations. Therefore, the selection of these parameters is not 

arbitrary. Randall et al. also state that where models have been tuned to give a good representation of an 

observed quantity, then agreement with that observation cannot be used to “build confidence” in that 

model. If models had been tuned to match global average surface temperature trends, which is not the 

case for most AOGCMs, then agreement with those trends would not produce confidence in the output of 

the model. 

 

With respect to concerns about assuming a constant relative humidity, we find that water vapor content is 

calculated by most models in response to temperature changes. Early work, such as Arrhenius’ 

calculations in 1896, assumed a constant relative humidity as a good first guess, but now it is explicitly 

calculated. Further, a study by Soden and Held (2006) examined the significance of this assumption, 

analyzing the difference between model calculations of humidity and the assumption of constant relative 

humidity. They found that the calculated relative humidity of most models was within 5% of being 

constant, and also stated: “Interestingly, the true feedback is consistently weaker than the constant relative 

humidity value, implying a small but robust reduction in relative humidity in all models on average...” 

18 
For these reasons, we disagree that the models include an assumption of constant relative humidity, or 

that coefficients are chosen “arbitrarily” in order to set the climate sensitivity. We note that the 

commenters did not provide peer-reviewed literature to support their claims. 

Comment (4-22):

One commenter (3160) objects that models predict meteorological surface air temperature 2 meters above 

the surface, while CO2 should only make small changes in actual ground surface temperatures, which 

would not be detectable at 2 meters. 

Response (4-22):

The commenter appears to misunderstand both how the models calculate temperatures and the nature of 

the warming caused by CO2. First, models calculate temperatures throughout the vertical structure of the 

atmosphere. The temperature 2 meters above the surface is often reported as a standardized comparison 

number, and this is considered accepted practice in the assessment literature and scientific community. 

Second, CO2 is expected to change temperatures throughout the atmosphere, not just at the surface. We 

are aware of no evidence to support the commenter’s claim that CO2 will only cause small change in 

ground surface temperatures, and the commenter provided no literature to support this view. 

Comment (4-23):

A commenter (0700.1) contends that the UN exaggerates the Planck parameter by at least one third, 

because it incorrectly takes temperature and radiant-energy values from planetary emitting surfaces 6 

miles apart, effectively repealing the fundamental equation of radiative transfer. Also, the commenter 

states that the UN fails to make any allowance for diurnal and latitudinal variations, which, according to a 

private communication to the commenter from Dr. David Evans, require a further 10% reduction in the 

value of the Planck parameter. 

Response (4-23): 

This comment reflects a lack of familiarity with how the climate models treat radiative transfer, and a 

misunderstanding of how the Earth is represented in these models. To be clear, the models used by the 

scientific community explicitly include radiative transfer equations. It appears the source of the confusion 

is that the forcings are often reported as top of the atmosphere (TOA) forcing, and the temperatures are 

often reported as surface temperature. Surface temperatures are reported because that is the altitude at 

which most humans and ecosystems exist; however, TOA forcing is reported because it is the simplest 

way to represent different climate forcings in a consistent manner. The fact that the models report the 

output in this way does not mean that the models are repealing the fundamental equations of radiative 

transfer. To the contrary, they explicitly calculate temperatures and radiative balances for every layer of 

the atmosphere in between the two altitudes, using standard thermodynamic and radiative constraint 

equations. 

 

Similarly, the models explicitly calculate both diurnal and latitudinal variations. High resolution global 

climate models have grid cells that are less than 2 degrees in latitude and longitude and have daily cycles 

and latitudinal temperature gradients that are consistent with observations. No evidence was submitted to 

support the concern of Dr. David Evans relayed by the commenter 

For these reasons, we conclude that the models treat radiative forcing appropriately and do not suffer 

from the weaknesses claimed by the commenter. 

19 
Comment (4-24):

Several commenters discussed modeling paleoclimatic conditions. One commenter (0736) objects to 

models because they do not show the medieval warming period and the following mini ice age. Another 

(1309.1) claimed that the models have never been backtested against ice core data. A third (3291.1) 

claims that historical variability in the paleoclimate record is larger than models can produce. 

Response (4-24):

We have reviewed the relevant literature on this topic and find that the comments do not accurately reflect 

models abilities with regard to paleoclimatic conditions. The properties of the little ice age and the 

medieval warm period are addressed in responses within Volume 2; here, we address the question of how 

models represent these periods. 

Models have been extensively tested against historical data on many time scales. This process isn’t simple 

because there are more unknowns for paleoclimate data than modern data. Referencing simulations for 

the past millennia, Jansen et al. (2007) note that the forcing influence of orbital variations can be 

calculated quite accurately but are not important for the last 2000 years. However, for the past 2000 years, 

the ice core record provides accurate records of greenhouse gases at almost decadal resolution. There are 

uncertainties, however, in the forcing influence of land use changes, aerosols, ozone, solar irradiance, and 

volcanic effects during the preindustrial period. Despite these uncertainties, Bertrand et al. (2002), which 

was cited in the IPCC, found that a “combination of solar and volcanic forcings can explain the Little Ice 

Age and the Medieval Warm Period.” The simulations by Bertrand et al. showed a medieval warm period 

in the interval 1000 to 1300 AD and a cold period from 1450 until an 18th century recovery using a 

particular solar irradiance history and a volcanic reconstruction based on Greenland ice sheet data. The 

simulation did not show that the medieval warm period was warmer than present, and the simulation was 

unable to reproduce 20th century temperatures without including anthropogenic forcings. 

For the last glacial maximum, Jansen et al. found that models “adequately represent many of the major 

processes that determine this past climate state.” The success of modeling the last glacial maximum 

demonstrates both that models have been tested against ice core data and that they can capture at least 

some modes of variability. However, Jansen et al. did find that models had not been used to try and 

replicate the “abrupt decadal to centennial-scale changes in the regional frequency of tropical cyclones, 

floods, decadal droughts and the intensity of the African-Asian summer monsoon,” which are very likely 

to have occurred in the last 10,000 years but whose mechanisms are not well understood. The inability to 

simulate certain poorly understood processes in the distant past does not, however, imply that models are 

not well suited to modeling near term climate change, especially given that much more data exists for the 

current climate state than for states 10,000 years ago. 

Therefore, given that models have been used to explain the little ice age, the medieval warm period, 

glacial maxima, and many other paleoclimate phenomena, we find that the assertions of the commenters 

are unsubstantiated. 

 

Comment (4-25):

Some commenters (e.g., 1924, 3291.1, 3340, 7031) claim that the models have never been sufficiently 

validated, and several (3567.1, 3679.1, 3701.1) include a reference to Green and Armstrong (2007). One 

states that mathematical models cannot be extrapolated for more than twice the validation period (1924). 

Another differentiates “calibration” from “validation” (3291.1), stating that the calibration models 

undergo to match historical observations cannot be assumed to improve future projections without using a 

validation data set. A commenter (3316.1) states that the lack of validation means model results do not 

meet Information Quality Act (IQA) requirements. One commenter (3729.1) requests that models be 

tested on weather predictions for validation, another (4003) that they make predictions a season or year 

20 
ahead. And one commenter (3702.1) argues that legal case history requires that EPA explain assumptions 

and methodologies of all models used in rulemaking and that therefore the EPA must identify and 

comprehensively discuss all models used in the underlying synthesis reports. Several commenters 

(3222.1, 3316.1) address issues with the surface temperature record: one notes that this record is used to 

validate models and therefore the usefulness of this validation is in doubt. 

Some commenters (0639.1, 3224, 3701.1) also request that model codes be made public if they are to be 

used for policy decisions. Others (3603.1, 3702.1) request that all the assumptions, uncertainties, and 

methodologies involved in models used to support the TSD be made public. 

Response (4-25):

Please refer to previous comments in this section for EPA’s responses to the difference between weather 

and climate in regards to requests that models be tested on weather, seasonal, or annual predictions. See 

Volume 2 for EPA’s responses to the validity of the surface temperature record. In this response, we 

focus on model testing and the validity of model-based data. 

We have reviewed the Green & Armstrong critiques referenced by a number of commenters, and we have 

concluded that they are flawed. Green & Armstrong evaluated Randall et al. (2007), “Climate Models and 

Their Evaluation,” by using their own set of “forecasting principles” and determined that the chapter did 

not meet their criteria for a good forecast. However, Randall et al. was an evaluation of climate models in 

the literature; it was not a forecast and it certainly was not a policy analysis or recommendation. Because 

the purpose of Randall et al. was to assess climate models and modeling, it should not be a surprise that 

many of the principles that Green & Armstrong evaluated are not addressed within the IPCC chapter. For 

example, the first two “violations” (a term used by the commenter to describe how Randall et al. did not 

meet their specifications) listed, “Describe decisions that might be affected by the forecast” and “Prior to 

forecasting, agree on actions to take assuming different possible forecasts” are antithetical to a document 

that is designed to review existing science literature. Other similar “violations” included not performing 

cost-benefit analysis, testing client understanding of methods, and tailoring the analysis to the decision. 

Nothing in the work of Randall et al. was intended to recommend or assess decisions or actions that might 

be taken in response to particular forecasts. Many of the recommendations made by the Green & 

Armstrong paper seem not to be applicable to scientific research models of complex physical systems. 

With regard to the apparent assumption by Green & Armstrong and various commenters that models are 

“calibrated” against historical global average temperature records, this is not true. See EPA’s response to 

comments about “tuning” in response 4-21 in this section. As stated in Randall et al. (2007), “[i]f the 

model has been tuned to give a good representation of a particular observed quantity, then agreement with 

that observation cannot be used to build confidence in that model.” However, because models have not 

been tuned to match global average surface temperature records, or a number of other historical climate 

variables, these variables are all valid comparisons to make for testing the models. 

We note that the code for several major climate models is indeed public, such as GISS ModelE and the 

Community Climate System Model, and therefore it is possible for outside agencies to perform their own, 

independent verification of these models. Cases cited by commenters do not indicate that the agency’s 

reliance on synthesis reports is inappropriate in any way, or that EPA is required to identify and 

comprehensively discuss all models used in the underlying synthesis reports. In fact, the cases confirm 

that agencies are granted an “extreme degree of deference” when they are “evaluating scientific data” 

Appalachian Power Co. v. EPA, 249 F.3d 1032, 1052 (D.C. Cir. 2001). In that case, the court specifically 

noted the value of models in regulations under the Clean Air Act, holding that the agency’s use of models 

would only be arbitrary and capricious “when the model bears no rational relationship to the 

characteristics of the data to which it is applied.” Id. (citations omitted). The court also noted that they can 

overturn the model only when it is “so oversimplified that the agency's conclusions from it are 

21 
unreasonable.” Id. (citations omitted). Appalachian Power does not mandate a line-by-line annotated 

defense of agency choice of science—it merely calls for an “actual reason articulated by the agency at 

some point in the rulemaking process.” Id. at 1053-1054. EPA has fully explained and justified its use of 

the assessment literature and therefore the models used in that literature; its use of these reports is very 

reasonable. See Volume 1 for explanation of the use of the assessment literature in the TSD and the 

Finding. 

Please see response number 4-36 regarding the use of models and compliance with Data Quality Act 

guidelines. 

 

Comment (4-26):

Some commenters (e.g., 0169, 0400, 0454, 0545, 0582, 0736, 0798, 2818, 2882, 2898.1, 2933, 3446.1, 

3477) argue that computer models have failed to predict recent temperature changes, especially 

temperature trends over the past decade, and are therefore flawed. Several commenters (e.g., 2883.1, 

2898.1, 3215.1, 3596.1, 3596.2) state that 30-year temperature trends were at or below the low end of 

IPCC projections, and a few commenters (2898.1, 3215.1, 3596.2) include extensive further analysis of 

these trends compared to model trends. Two comments on recent warming trends (3136.1, 3316.1) cite 

Keenlyside et al. (2008) and note that none of the models for B1, A1B, or A2 have a 15 to 20 year period 

without warming, which, according to the commenters, means that models are overpredicting warming 

and also that the delay in warming leads to a delay in any feedbacks involving ocean temperatures, and 

that therefore climate models are flawed. Another commenter (3316.1) cites Keenlyside as well as Tsonis 

et al. (2007) to point out that some modelers are trying to take the warming pause into account, whereas, 

according to the commenters, the TSD does not include any such efforts. Some commenters (4528, 4666, 

4670, and 4766) submitted a CATO-sponsored advertisement signed by a number of scientists which 

stated that: “The computer models forecasting rapid temperature change abjectly fail to explain recent 

climate behavior.” 

Response (4-26):

Please refer to Section 4.3 of this volume for EPA’s detailed comments on future projections of 

temperature. Here we confine our response to the components of the comments relevant to the 

relationship between recent temperature trends and the validity of model-based projections. 

Two recent studies have addressed whether recent temperature trends are consistent with model runs, and 

both Easterling and Wehner (2009) and Knight et al. (2009) find that the recent trends are well within 

model variability. Recent natural events not captured in the models such as the decrease in solar 

insolation and the La Niña event of the past year would lead to an expectation of less warming than the 

model average would suggest. 

Knight et al. (2009) report the following: 

ENSO-adjusted warming in the three surface temperature datasets over the last 2–25 yr 

continually lies within the 90% range of all similar-length ENSO-adjusted temperature 

changes in these simulations (Fig. 2.8b). Near-zero and even negative trends are common 

for intervals of a decade or less in the simulations, due to the model’s internal climate 

variability. The simulations rule out (at the 95% level) zero trends for intervals of 15 yr 

or more, suggesting that an observed absence of warming of this duration is needed to 

create a discrepancy with the expected present-day warming rate. 

Easterling and Wehner (2009) reach a similar conclusion: 

22 
What does this say about the variability of the climate system? Climate models are often 

criticized for producing a more or less monotonic-type response to anthropogenic forcing 

in 21st century simulations. Part of this may be due to the lack of volcanic and solar 

forcing in the SRES [IPCC Special Report on Emissions Scenarios] scenarios of 

anthropogenic forcing increase for the 21st century and part could be due to the fact that 

largescale oscillatory climate features, such as the El Niño-Southern Oscillation are not 

well simulated. However, even considering these criticisms, it is clear that the models can 

and do produce sustained multi-year periods of ‘‘cooling’’ embedded within the longerterm warming produced in the 21st century simulations. Therefore, it is reasonable to 

expect that the natural variability of the real climate system can and likely will produce 

multi-year periods of sustained ‘cooling’ or at least periods with no real trend even in the 

presence of long-term anthropogenic forced warming. Claims that global warming is not 

occurring that are derived from a cooling observed over such short time periods ignore 

this natural variability and are misleading. 

Additionally, it is important to note that even the coldest year of this decade would have been one of the 

warmest of the previous century according to the surface temperature record datasets. 

We have also reviewed the studies by Keenlyside et al. (2008) and Tsonis et al. (2007), which several 

commenters cited as evidence that some scientists believe that there may be multidecadal pauses in 

warming. The Keenlyside et al. paper uses a new methodology that involves calibrating the model oceans 

against observations, unlike most models that initialize the ocean in the preindustrial period and let the 

model respond to historical forcing. They find that this calibration improved the ability to predict certain 

types of northern Atlantic and eastern Pacific behavior for up to 10 years after the ocean calibration step, 

compared to models not using this methodology (e.g., 20th century radiative forcing simulations). The 

results from the paper “suggest that global surface temperature may not increase over the next decade, as 

natural climate variations in the North Atlantic and tropical Pacific temporarily offset the projected 

anthropogenic warming.” However, the paper also states that “[h]indcast skill for global mean 

temperature (Fig. 4) is also high, but slightly less than the twentieth century RF [radiative forcing] 

simulations.” Therefore, we do not find that the future global mean temperature trend projections from 

this paper are superior to the models from the assessment literature. Also, Keenlyside et al. make no 

claims that models are overpredicting warming in the long run; the models they use are based on the 

standard models, and the methodology applied only changes short term (decadal) projections. 

Tsonis et al. (2007) examined historical temperatures and several climate indices (the Pacific Decadal 

Oscillation, the North Atlantic Oscillation, the El Niño/Southern Oscillation, and the North Pacific 

Oscillation). The paper found that when at several junctures where these climate indices had synchronized 

behavior, followed by an increase in coupling strength during the destruction of this synchronized 

behavior, there were concurrent shifts in global mean temperatures (1910, 1940, and the late 1970s are 

identified). Tsonis et al. find that this same behavior occurs in climate model runs that they examined. 

This paper makes no claim regarding the recent temperature trends and notes that these climate 

synchronization events can be “superimposed on an anthropogenic warming trend.” Given that Tsonis et 

al. find that models have similar behavior to the natural system in this regard, these results do not show 

any flaws in the assessment models. 

 

Therefore we find neither Tsonis et al. (2007) nor Keenlyside et al. (2008) contradict the hypothesis of a 

significant anthropogenic influence on warming. Additionally, because Easterling and Wehner (2009) and 

Knight et al. (2009) show that observations are still consistent with the results of the models used in the 

IPCC and CCSP reports, there is no need to rely on the mechanisms proposed by Tsonis et al. or 

Keenlyside et al. in order to explain recent temperature trends. 

23 
Comment (4-27):

One commenter (2890.1) quotes the TSD as stating “First, despite remaining imperfections, the current 

generation of climate models accurately portrays many important aspects of today’s weather patterns and 

climate” and then goes on to state that “But being almost right is being wrong. Ether [sic] the model is 

accurate or it’s wrong.” Another commenter (3679.1) states that CCSP 3.1 is less confident about model 

results than EPA. 

Response (4-27):

We have reviewed the relevant literature in light of these comments and disagree that models cannot 

contribute useful information to climate science and assessments without being 100% accurate or that the 

TSD is inconsistent with CCSP 3.1 (2008c). We find that the TSD provides a reasonable and accurate 

summary of the assessment literature, particularly CCSP 3.1, in terms of both key findings and major 

remaining uncertainties. 

CCSP 3.1 states: 

The set of most recent climate simulations, referred to as CMIP3 models and utilized 

heavily in Working Group 1 and 2 reports of the Fourth IPCC Assessment, have received 

unprecedented scrutiny by hundreds of investigators in various areas of expertise. 

Although a number of systematic biases are present across the set of models, more 

generally the simulation strengths and weaknesses, when compared against the current 

climate, vary substantially from model to model. From many perspectives, an average 

over the set of models clearly provides climate simulation superior to any individual 

model, thus justifying the multimodel approach in many recent attribution and climate 

projection studies. 

and 

The use of computers to simulate complex systems has grown in the past few decades to 

play a central role in many areas of science. Climate modeling is one of the best 

examples of this trend and one of the great success stories of scientific simulation. 

Building a laboratory analog of the Earth’s climate system with all its complexity is 

impossible. Instead, the successes of climate modeling allow us to address many 

questions about climate by experimenting with simulations—that is, with mathematical 

models of the climate system. Despite the success of the climate modeling enterprise, the 

complexity of our Earth imposes important limitations on existing climate models. 

We reflect this in the Section 6.2(b) of the TSD, where we quote CCSP3.1, Meehl et al. (2007), Randall et 

al. (2007), and Christensen et al. (2007) on limitations of modeling, such as this description of the results 

of the CCSP (2008c) report: 

The CCSP (2008c) report Climate Models: An Assessment of Strengths and Limitations

finds that models “have been steadily improving over the past several decades,” “show 

many consistent features in their simulations and projections for the future,” and “are able 

to simulate the recorded 20th century global mean temperature in a plausible way.” 

However, it cautions that projections of precipitation in some cases remain “problematic” 

(especially at the regional scale) and that “uncertainties in the climatic effects of 

manmade aerosols (liquid and solid particles suspended in the atmosphere) constitute a 

major stumbling block” in certain modeling experiments. It adds that “uncertainties 

related to clouds increase the difficulty in simulating the climatic effects of aerosols, 

24 
since these aerosols are known to interact with clouds and potentially can change cloud 

radiative properties and cloud cover.” 

In addition, the argument of the commenter that if you are not 100% right you are wrong imposes a 

standard that is unreasonable. We confront uncertainty in many disciplines—medicine, energy, 

economics, construction, aerospace engineering—and in these areas we apply the information and models 

we have to reach reasoned conclusions. As is discussed in Section II of the Findings, neither the law nor 

common sense requires 100-percent certainty before we can reach a conclusion and act. Thus, the issue at 

hand is not “Are the models perfect?” but “Are they reasonable and useful representations of our 

understanding of the climate system?” This issue has been thoroughly reviewed by the CCSP, which 

subjected the climate models to unprecedented scrutiny, described their uncertainties and limitations, and 

concluded that climate modeling is “one of the great success stories of scientific simulation.” 

Thus, we conclude that it is reasonable and appropriate to rely on the assessment literature, and that 

climate models that do not (and cannot) accurately model every aspect of the global climate are 

nonetheless useful for attribution, projections, and understanding of climate phenomena. This is 

particularly the case when multiple models are applied, as in done in the climate assessment literature, 

and the results are examined across them all. Further, we find that no changes need be made to the 

discussion in Section 6 of the TSD. 

Comment (4-28):

One commenter (3283.1) points to a statement in the TSD that model projections don't begin to diverge 

until 2030 and asks how models can be validated against observations in that case. Commenter also 

objects to 100% probability that the vast majority of the last hundred years’ temperature change is due to 

anthropogenic GHGs without attributing changes to aerosols, land use change, measurement bias, heat 

island, and ocean cycles. 

Response (4-28):

With respect to the first part of this comment, the statement in the draft TSD, released in April 2009 along 

with the Proposed Findings, was: “Through about 2030, the warming rate is mostly insensitive to choices 

between the A2, A1B, or B1 scenarios.” This insensitivity to choice of emissions scenario is between a 

limited set of SRES greenhouse gas emission scenarios, not necessarily to climate model parameters, 

choice of climate model, or even sensitivity to policy scenarios that might involve changes in aerosol or 

other short-lived emissions species. The reason for this insensitivity is the long lifetime of most of the 

GHGs, the inertia of the climate system, and the magnitude of natural, short-term variability. Therefore, 

while it would be difficult to use temperature to distinguish between SRES scenarios on the 20-year time 

scale, it may be possible to use the temperature data to better constrain a number of other characteristics 

of models. 

This issue is distinct from the issue of model validation, and there are a number of ways in which models 

are validated that do not require more information about near-term temperature change. For example, 

models are validated against events such as the eruption of Mount Pinatubo, or based on their ability to 

simulate small-scale phenomena such as ENSO variability, or against longer timescale historical records. 

Individual components of the models can also be tested, such as the ability of carbon cycle models to 

reproduce carbon-14 oceanic uptake trends. A number of such validation tests are discussed both in 

Randall et al. (2007) and CCSP (2008c), where they discuss a number of successes as well as some 

remaining difficulties. 

 

We find the commenter’s objection to the 100% probability that the vast majority of the last 100 years of 

warming was due to anthropogenic GHGs confusing, because the TSD and the underlying assessment 

25 
literature that it summarizes do not attribute anything with 100% probability and do not attribute past 

temperature changes to only one source. IPCC (see, for example, Hegerl et al., 2007) and other 

underlying references for the TSD include an extensive analysis of how the last century’s temperature 

change is attributed to a combination of greenhouse gases, aerosols, land use changes, and other sources 

of natural variability. The conclusion of the IPCC is that most of the increase since the mid-20th century is 

very likely due to the observed increase in greenhouse gas concentration. In contrast, the IPCC does find 

that the warming of the system is unequivocal. Please refer to Volume 3 for EPA’s responses to 

comments on the attribution of observed climate change to the observed increase in GHG concentrations. 

Comment (4-29):

One commenter (3281) states that “[t]he worst case valid models forecast change in temperature due to 

anthropogenic forcings is a non-endangerment 0.27° C in 2100.” 

Response (4-29):

The commenter provides no documentation on what constitutes a valid model and no reference to the 

modeling simulation that projects a temperature change of only 0.27°C in 2100. In the large body of 

literature assessed by the IPCC, CCSP, NRC, and USGCRP reports, there are a large number of valid 

projections which show worst case temperature changes exceeding 5°C or more. See, for example, Meehl 

et al. (2007) with model mean warming over the 21st century ranging from 1.1° to 6.4° C. In light of the 

stark inconsistency between the comment and the assessment literature, and the lack of any supporting 

evidence, we disagree. 

 

Comment (4-30):

In regard to the tropospheric warming over the tropics and the level of agreement between modeled and 

observed data, one commenter (3136.1) requested that the TSD cite Douglass (2007) and Santer (2008) in 

order to show that the discrepancy is “far from settled.” 

Response (4-30):

Please refer to Volume 3 for EPA’s responses to comments specific to the anthropogenic fingerprint in 

the tropical troposphere. 

Comment (4-31):

One commenter (3136.1) objects to a statement in the TSD, asking “Why will soils be drier given 

increasing precipitation and cloud cover (the latter of which mitigates against higher evaporation)? 

Because a model says so? When, in reality, over the last 100 years, precipitation increases have been far 

greater than evapotranspiration across the U.S.?” 

Response (4-31):

The commenter objects to the statement in Section 8 of the TSD that “The IPCC (Field et al., 2007) 

reported with very high confidence that in North America disturbances like wildfire are increasing and are 

likely to intensify in a warmer future with drier soils and longer growing seasons.” 

Changes in soil moisture, and therefore dryness, are a function of the difference between water gain and 

water loss. Increased temperatures increase evaporation, and therefore, all other things being equal, 

should lead to increased dryness. However, increased precipitation (which increases water input, minus 

runoff losses) and increased cloudiness (which should decrease evaporative losses) can both reduce 

26 
dryness. Therefore, future dryness will be a function of future temperature, precipitation, and cloudiness, 

among other variables. 

The commenters provided a reference, McCabe and Wolock (2002), to support the claim that 

“precipitation increases have been far greater than evapotranspiration.” We reviewed this paper, which 

found that “[t]rends in annual surplus and annual deficit suggest that the eastern US has become slightly 

wetter and the western US has become slightly drier during the period 1895–1999.” In contrast to the 

assertion by the commenter that precipitation increases were “far greater” than evapotranspiration, the 

paper uses the term “slight” to describe the increase in annual surplus across the country, as well as 

finding drying in the western United States. As shown by the slight drying of the western United States, it 

does not follow that “increasing precipitation” implies an even distribution of precipitation geographically 

or temporally, or that increasing total precipitation will counteract the increased dryness projected for 

certain regions and seasons. As stated in Karl et al. (2009) “While it sounds counterintuitive, a warmer 

world produces both wetter and drier conditions. Even though total global precipitation increases, the 

regional and seasonal distribution of precipitation changes, and more precipitation comes in heavier rains 

(which can cause flooding) rather than light events.” 

Other observational studies support the contention that a warmer future can have drier soils in some 

regions or some seasons despite higher levels of national precipitation. The IPCC states that historically, 

“[d]espite the overall national trend towards wetter conditions, a severe drought has affected the 

southwest United States from 1999 through 2008 (see Section 4(l)), which is indicative of significant 

variability in regional precipitation patterns over time and space.” Karl et al. (2009) found that increased 

extremes of summer dryness and winter wetness consistent with future projections have already been 

observed, not just modeled. Moreover, Jansen et al. (2007) found that some evidence suggests that during 

the past 2000 years, warmer than average summer temperatures were associated with particularly 

extensive, severe, and frequent droughts. 

Model projections find that decreases in precipitation are actually likely in subtropical regions and the 

southwestern United States, even though precipitation is expected to increase globally. Karl et al. (2009) 

report model projections of future precipitation in the United States generally indicate northern areas will 

become wetter, and southern areas, particularly in the West, will become drier. Karl et al. also find that 

“[p]rojected increases in precipitation are unlikely to be sufficient to offset decreasing soil moisture and 

water availability in the Great Plains due to rising temperatures and aquifer depletion.” 

Therefore, we do not find that either observational data or model projections contradict the IPCC 

conclusions on the contributions of drier soils in a warmer future. 

Comment (4-32): 

One commenter (0634) discusses a large number of uncertainties in model simulations such as emissions 

projections, temperature and sea level rise projections, and even larger uncertainties in quantifying other 

impacts such as hurricanes, flooding, drought, etc. This commenter notes that in several cases impacts 

have already been greater than projected, such as accelerated ice melting and CO2 emissions. This 

commenter therefore sees uncertainty as a cause for worry, not inaction. 

Response (4-32):

Please refer to Volume 1 for EPA’s responses regarding uncertainty. Please refer to Volume 9 for EPA’s 

responses regarding the legal justification for acting under some continued uncertainty and the 

precautionary principle. 

27 
Comment (4-33):

One commenter (2895) states that more recent information on the rate of climate change indicates that the 

models are actually too conservative in their projections. The commenter states that the actual effects are 

occurring more rapidly and are likely to be much worse than the mid report scenarios that the scientists 

relied upon in their IPCC reports. In summary, the commenter indicates the proposed findings are correct 

and urges EPA to finalize them as proposed. 

Response (4-33): 

Although the uncertainty involved in projecting future climate change and impacts, as discussed in the 

TSD, may mean that climate change outcomes will be worse than the median projections from models, 

we have concluded that the TSD reflects the best science as currently incorporated in the assessment 

literature. 

Comment (4-34):

Two commenters (3462.1, 3603.1) assert that the models referred to by EPA in the Proposal and TSD do 

not accurately capture effects due to climate change in the U.S. as a consequence of their global coverage. 

Commenter 3462.1 stated: “We believe EPA has an obligation to define how those models can be applied 

accurately to determine U.S.-specific health impacts and not simply extrapolate conclusions from broader 

information.” Commenter 3603.1 stated: “Thus, to the extent that the TSD heavily relies on assessment 

or synthesis report that are based on modeling that is global in nature, with little focus on the U.S., it 

raises doubts about the efficacy of the TSD and accordingly the Proposed Findings to the extent that they 

may be dependent on the Revised TSD.” 

Response (4-34):

With respect to climate modeling, we agree that the TSD section on projected changes in U.S. 

temperature, precipitation patterns, and sea level relies in part on models that are necessarily global in 

scope, given that climate changes in one region can, in turn, affect climate in other regions. However, we 

disagree that our treatment of model results was inappropriately focused on global impacts or failed to 

adequately distinguish between U.S. and Canadian effects. And we also disagree that EPA’s use of 

IPCC’s regional analysis of North America is inappropriate or scientifically flawed. 

First, as made clear in the TSD, many important regional differences are discernible from the model 

projections assessed by IPCC and CCSP. For temperature, the TSD notes that warming will be regionally 

variable. It summarizes information from the assessment literature that the largest warming through 2100 

is projected to occur in winter over northern parts of Alaska, while warming near the coasts is not as 

large. For sea level rise, the TSD notes that the projected rate of sea level rise off the low-lying U.S. 

South Atlantic and Gulf coasts and for western Alaska is higher than the global average. For water 

resources, the TSD describes how precipitation intensity will increase particularly at mid and high 

latitudes where mean precipitation also increases. Other sections of the TSD also include considerable 

regional detail. To further strengthen the TSD’s focus on region-specific impacts, we have added a 

chapter to the TSD that focuses specifically on regional impacts. 

Second, for straightforward and entirely defensible scientific reasons many projections for North America 

are just as applicable to the U.S. as they are to the continent as a whole. It stands to reason, for instance, 

that if temperatures are projected to rise by a certain amount when averaged across the whole continent, 

they should be expected to rise by at least that much in areas of the U.S., such as Alaska, that both climate 

models and basic physical principles indicate are most susceptible to GHG forcing. Similarly, when the 

IPCC reports that 94% of the 355 significant observed changes to physical systems and 92% of the 455 

28 
significant observed changes to biological systems in North America were consistent with warming 

(Rosenzweig et al., 2007), it stands to reason that a majority of observed changes in the United States 

were also consistent with warming. There are important unresolved issues at finer geographic scales 

(e.g., although precipitation is expected to decrease in the southwest U.S. but increase over the rest of the 

continent, it is not clear precisely where the dividing line will fall) but the assessment literature 

appropriately denotes which outputs of continental-scale projections are robust and which are uncertain, 

and these conclusions and uncertainties are accurately and appropriately summarized in the TSD. 

Comment (4-35):

At least one commenter (3316.1) argues that EPA relied on General Circulation Model (GCM) outputs it 

could not introduce in a federal court under the Daubert standard and thus is an “impermissible basis” for 

an endangerment finding. 

Response (4-35): 

Daubert does not foreclose EPA’s use of GCM outputs. Daubert applies to evidence introduced in a 

“federal trial,” not an agency rulemaking under the applicable statute (e.g., the Clean Air Act) (Daubert v. 

Merrell Dow Pharmaceuticals, 1993, page 582). Daubert assigns trial judges “the task of ensuring that an 

expert’s testimony both rests on a reliable foundation and is relevant to the task at hand” (Id. at 597-598). 

The Daubert principles apply only at a trial where scientific evidence is presented to a “trier of fact,” not 

at the rulemaking stage. To the extent commenter suggests EPA has failed to adequately explain its use of 

GCM outputs, EPA disagrees. Please see other comments within this section for further discussion of the 

validity of EPA’s use of GCM outputs. 

Comment (4-36): 

A number of commenters (3136.1, 3372.1, 3415.1, 3548.1, 3596.1, 3679.1, 3729.1, 3747.1, and 10345) 

argue that EPA did not use models that comply with the IQA. Specifically, several of these commenters 

(3136.1, 3372.1, 3394.1, and 3747.1) argue that the models used by IPCC Fourth Assessment 

Report/Working Group 1 are incapable of reproducing behavior of the last decade. Other commenters 

(e.g., 3316.1) state that lack of validation means model results do not meet IQA requirements. 

Response (4-36): 

Please see Volume 1 for EPA’s general response to the information quality concerns submitted during the 

public comment process. The Data Quality Act does not impose its own standards or establish any 

requirements regarding the quality of agency information; instead, it requires only that an agency “issue 

guidelines” ensuring data quality. 

With respect to the IQA issues raised regarding the use of climate models, the Guidelines for Ensuring 

and Maximizing the Quality, Objectivity, Utility and Integrity of Information Disseminated by the 

Environmental Protection Agency state that the EPA should use the “best available science and 

supporting studies conducted in accordance with sound and objective scientific practices, including, when 

available, peer reviewed science and supporting studies.” The climate models and other models used in 

the IPCC and CCSP report meet the standards of the best available science available: they have been 

validated and compared against theory, observation, and other models. The models are routinely used in 

scientific practice throughout the country and the world. The TSD appropriately represents both the 

strengths and limitations of the models, as determined by the major assessment reports. Therefore, the 

model results presented in the TSD are consistent with EPA’s Guidelines. 

29 
With respect to the comment on EPA models and the IQA, the only EPA models referenced in the TSD 

and endangerment finding are those used in development of the Interim Report (U.S. EPA, 2009a) that is 

summarized in Volume 5, Section 2: Air Quality. In addition, please see previous responses to comments 

in this section on specific modeling issues. 

See Volume 1 for our response to general comments on the procedures and approaches taken by the IPCC 

and USGCRP/CCSP in their assessment reports. The TSD’s summary of model-based findings from these 

assessments is consistent with EPA’s Guidelines for Ensuring and Maximizing the Quality, Objectivity, 

Utility and Integrity of Information Disseminated by the Environmental Protection Agency (U.S. EPA, 

2002). 

4.2 Future Projections of Greenhouse Gas Emissions and Concentrations 

Comment (4-37):

A number of commenters argue that the projections of increasing CO2 emissions and concentrations over 

the next century included in the TSD are flawed, though the commenters are divided in whether they 

think that the projections are overestimates or underestimates. Many commenters claim that projections of 

both concentrations and emissions are too high. One commenter (3281.1) who claims that projections are 

too large shows results from a non-peer reviewed model that had much lower emissions projections than 

the IPCC, and also claims that the IPCC forecast CO2 concentrations for 2004 were 398 parts per million 

(ppm), showing that the IPCC overprojects CO2 concentrations. Another commenter (2895) claims that 

carbon emissions might increase at most to 7 billion or 8 billion tons per year in the next century, 

providing no supporting evidence for this assertion. Another (3440.1) states that CO2 is accumulating in 

the atmosphere at half the rate projected by the IPCC, and that projections of CO2 concentrations 

therefore need to be halved. One commenter (3394.1) requests a “better” analysis of the year-to-year 

variation in CO2 concentration growth rate asking if this variation would have any implications for 

attribution or projections. Another commenter (3756.1) claims that models are overpredicting climate 

change because they do not take into account peak oil or the response of biomass to increased CO2; this 

commenter further claims that the models already overpredict atmospheric CO2 concentrations. This 

commenter presents a biokinetic model based on fitting an equation to CO2 concentrations since 1965, 

which—coupled with lower emissions estimates—predicts that CO2 concentrations will peak at 412 ppm 

in 2034 and then decline. 

Finally, several commenters (3323.1, 3702.1, 4003, 4041.1, 4932.1, and 5158) argue that the recent 

recession is not accounted for in the old SRES projections and requests that EPA revise emissions 

scenarios based on newer data. To the extent that ambient GHG levels are relevant for future global 

temperatures, these commenters state that these emissions reductions should greatly influence the adverse 

effects of these emissions on public health and welfare. According to one commenter, the April 2009 TSD 

did not reflect the emission changes that have already occurred nor those that are likely to occur in the 

future as a result of the recession. The commenter objects to the fact that the topic is not even discussed. 

Commenters who claim that the projections are too low also discuss emissions projections and uptake. 

Commenter (3414.3) notes that the carbon cycle can be a dangerous positive feedback for global 

warming, citing an article that quots Chris Field, co-chair of IPCC Working Group II, as stating: “There is 

about 1,000 billion tons of carbon in these [permafrost] soils,” with staggering implications for global 

climate. Chris Field was further quoted as stating: “The IPCC fourth assessment didn’t consider either the 

tundra-thawing or tropical forest feedbacks in detail because they weren’t yet well understood.” One 

commenter (3475) submitted Solomon et al. (2009) on irreversible climate change and Raupach et al. 

(2007) as evidence that emissions growth of CO2 exceeds worst case IPCC emissions scenarios from 

2000 to 2004. 

30 
Response (4-37):

We have reviewed the TSD and the relevant assessment reports in light of these comments, and we 

disagree with the general assertion that the emissions and concentrations projections summarized in the 

TSD are unrealistic. The TSD summarizes the findings of the IPCC (2007) and CCSP (2007b) 

assessments. As stated in the Section 6 of the TSD regarding the IPCC scenarios: 

The main drivers of emissions are population, economic growth, technological change, 

and land use activities, including deforestation. The detailed underlying assumptions 

(including final and primary energy by major fuel types) across all scenarios, and across 

all modeling teams that produced the scenarios, can be found in IPCC (2000). The range 

of GHG emissions in the scenarios widen over time to reflect uncertainties in the 

underlying drivers. 

The IPCC scenarios thus span a large range of possible futures. As noted in the TSD, total cumulative

(1990 to 2100) CO2 emissions across the SRES scenarios range from 2,826 gigatonnes of CO2 (GtCO2) 

(or 770 Gt of carbon [GtC]) to approximately 9,322 GtCO2 (or 2,540 GtC) (IPCC, 2007c) and according 

to the IPCC (2007c), baseline annual emissions scenarios published since SRES are comparable in range 

to those presented in the SRES scenarios (25 to 135 GtCO2-equivalent per year in 2100). 

The CCSP scenarios were created using models from three research groups: 

As instructed in the Prospectus for the study, the modeling teams used model input 

assumptions they considered meaningful and plausible. The resulting reference scenarios 

provide insights into how the world might evolve without additional efforts to constrain 

GHG emissions, given various assumptions about principal drivers of these emissions 

such as population increase, economic growth, land and labor productivity growth, 

technological options, and resource endowments. 

The reference scenarios show growing emissions of GHGs over the century. Emissions of CO2 from fossil 

fuel use and industrial processes increases from less than 7 GtC per year in 2000 to between 22.5 and 24.0 

GtC per year by 2100, leading to CO2 concentrations increasing by 2.5 to more than three times preindustrial levels. Increases in non-CO2 GHGs vary more widely across reference scenarios. 

Two of the models used in the CCSP exercise also project that the global net sink of CO2 from terrestrial 

systems, including net deforestation, will grow over time mainly because of reduced deforestation and 

CO2 fertilization of plants. All three models project growing uptake of CO2 by the global oceans over the 

century. 

We recognize that the range of emissions associated with the available scenarios is large. This is because 

there is significant uncertainty in the major drivers of emissions (e.g., such as population growth, 

economic growth, and technological change). We do not believe it is possible or necessary to identify the 

most likely future emission level, although there are many reasons to posit that emissions will be higher 

than the lowest of the IPCC scenarios. Certainly, that is the conclusion of CCSP (2008b) in the absence of 

emissions mitigation policies. 

With respect to the some of the specific concerns raised by commenters, contrary to the assertion that 

emissions projections have been too high, a key result in Raupach et al. (2007) was that emissions growth 

of CO2 from 2000 to 2004 was above even the A1FI SRES scenario. Le Quéré et al. (2009) estimated that 

2008 emissions were 8.7 GtC per year; this means that emissions have already exceeded the projected 

31 
2010 fossil CO2 emissions from 33 out of 40 SRES scenarios provided at 

http://www.grida.no/publications/other/ipcc_sr/?src=/climate/ipcc/emission/ (IPCC, 2000), and exceeded 

the projected 2010 fossil CO2 emissions from all three CCSP 2.1a scenarios. In the absence of emissions 

mitigation policies, the best estimates in the literature all project continued growth in emissions; we find 

that a plateau of 7 to 8 GtC a year is unrealistic (and obsolete, given the 2008 emissions). 

As noted, the CCSP and IPCC projections all include projections of CO2 sinks. Increasing CO2 will 

continue to lead to ocean and terrestrial system uptake over the century (Denman et al., 2007). However, 

Meehl et al. (2007) found 

...unanimous agreement among the coupled climate carbon cycle models driven by 

emission scenarios run so far that future climate change would reduce the efficiency of 

the Earth system (land and ocean) to absorb anthropogenic CO2. As a result, an 

increasingly large fraction of anthropogenic CO2 would stay airborne in the atmosphere 

under a warmer climate. 

We find that the biokinetic model developed by one commenter is not a realistic alternative to the carbon 

cycle models included in the assessment literature. This biokinetic model assumes that the rate of biomass 

growth is proportional to the difference between current CO2 and pre-industrial CO2, times the total 

biomass. This means that if concentrations are stabilized—rather than the ecosystem sink eventually 

dropping to zero (as it must, as the ecosystems equilibrate to the new concentration)—the biokinetic 

model sink actually continues to grow over time. 

One commenter seems to have assumed that the airborne fraction (about 45%) means that CO2 is 

accumulating at half the rate predicted by the IPCC. However, this airborne fraction is represented in the 

IPCC predictions already. For this commenter, and the commenter who claimed that the IPCC forecast for 

2004 was 398 ppm, see the response in Volume 2 regarding the observations of CO2 concentrations 

falling within the range of IPCC CO2 concentration projections. 

The TSD acknowledges the year-to-year variability in growth rate (see our response in Volume 2). A 

more in-depth discussion of this phenomenon can be found in the IPCC (Denman et al., 2007); Figure 7.4 

demonstrates the historical variability graphically. Year-to-year variability averages out over several years 

and therefore does not matter for purposes of projections. Uncertainties in the carbon cycle on longer time 

periods are still important and are depicted in Figure 6.6 in the TSD, which shows a range of CO2

concentrations for each emission scenario depending on different carbon cycle feedback assumptions. 

Global recession can lead to a short-term decrease in carbon dioxide emissions, as highlighted by several 

commenters, though recent results (Le Quéré et al., 2009) suggest that this reduction was not drastic. Le 

Quéré et al. found that there was still an increase in global CO2 emissions of 2% from 2007 to 2008, and 

they project that the 2009 decrease in emissions will only be a return to 2007 emission levels. Therefore, 

it seems that despite the recession, actual emissions will still be at the high end of projections in 2010. 

Emissions projections are also not designed to capture short-term fluctuations such as recessions. 

We have reviewed the paper by Solomon et al. This paper shows that “the climate change that takes place 

due to increases in carbon dioxide concentration is largely irreversible for 1,000 years after emissions 

stop,” with possible consequences of this irreversible rise being “irreversible dry-season rainfall 

reductions in several regions comparable to those of the ‘dust bowl’ era and inexorable sea level rise.” 

The 1,000-year lifetime of certain CO2 emissions is reflected in the TSD, which states that “the last 20% 

[of CO2 concentrations] may remain in the atmosphere for thousands of years (Denman et al., 2007).” 

Additionally, we note the paper was referenced in Karl et al. (2009), which is one of the assessment 

reports summarized in the TSD. 

32 
With respect to the comment on dangerous carbon cycle feedbacks, we summarize information from the 

assessment literature on carbon cycle feedbacks explicitly in Section 6 (and Figure 6.6, which shows how 

uncertainty in the carbon cycle impacts future projections) of the TSD, and the TSD also references Field 

et al. (2007) that by the end of the 21st century, ecosystems in the northeast and southeast United States 

are projected to become carbon sources, while the western United States remains a carbon sink. The 

possibility of significant positive feedbacks resulting from the carbon cycle is clearly an important, 

though still uncertain area of science; the TSD also quotes Clark et al. (2008) on possible methane 

releases from wetlands and high latitude ecosystems: 

While the risk of catastrophic release of methane to the atmosphere in the next century 

appears very unlikely, it is very likely that climate change will accelerate the pace of 

persistent emissions from both hydrate sources and wetlands. Current models suggest that 

wetland emissions could double in the next century. However, since these models do not 

realistically represent all the processes thought to be relevant to future northern highlatitude CH4 emissions, much larger (or smaller) increases cannot be discounted. 

Acceleration of persistent release from hydrate reservoirs is likely, but its magnitude is 

difficult to estimate. 

Therefore, the emissions scenarios used within the assessment reports summarized in the TSD are 

credible projections for use in long-term climate modeling, even though there may be some deviations 

from observations (both high and low) of emissions or carbon sinks in the short term due to economic and 

natural variability. We note the reasons for concern regarding possible carbon feedbacks, but we conclude 

that the TSD reflects the current best estimates for CO2 emissions and concentration growth in the 

absence of emissions mitigation policies, and that any attempt by the EPA to unilaterally revise the SRES 

or CCSP scenarios is neither necessary nor warranted. 

Comment (4-38):

One commenter (1616.1) states that finite reserves of fossil fuels means that emissions will decrease soon. 

Another (3756.1) claims that models are over-predicting climate change because they do not take peak oil 

into account, citing studies by the Association for the Study of Peak Oil and the Energy Watch Group of 

Berlin for estimates of future production curves. Commenter 3756.1 also cites Monckton on a divergence 

between the growth rate of CO2 concentration and IPCC projections as evidence that the rate of growth of 

CO2 concentration is already slowing down. 

Response (4-38):

While the availability of fossil fuel reserves limits the total possible emissions, many emissions 

projections account for these limited reserves (CCSP, 2007b). According to CCSP (2007b), the three 

models included in the study include “empirically based estimates of in-ground resources of oil, coal, and 

natural gas that might ultimately be available, along with a model of the costs of extraction.” These 

models also include a number of “unconventional” sources in their resource bases, such as tar sands, shale 

oil, coal-seam gas, and in the case of one model, coal-based synthetic oils. The models also include an 

assumption that some currently unused resources become economically exploitable over time due to 

advances in technology and higher prices. Fossil fuels remain the dominant energy source in the three 

reference scenarios considered in the CCSP report, but “all three reference scenarios include a transition 

from conventional oil production to some other sources of liquid fuels based primarily on other fossil 

sources.” Because these unconventional sources result in more CO2 emissions per unit energy, this leads 

to a halt in the historic pattern of decline in carbon-to-energy ratios for the overall economy in these 

modeling scenarios. 

33 
The references by the commenter do not include any peer-reviewed sources or clear sources of data for 

the assertions of the timing of peak fossil fuel use. We recognize that there are still active debates about 

peak oil and the accuracy of the estimates of available resource by organizations such as the U.S. 

Geological Survey. The scenarios used by these modeling groups reflect the current best estimates of 

available reserves and have been reviewed in major assessment reports, and are therefore appropriate for 

inclusion in the TSD. 

With regards to the commenter’s argument regarding Monckton’s estimates of IPCC projections, please 

see Volume 2. 

Comment (4-39):

One commenter (3136.1) objects to the statement in the TSD that the SRES scenarios do not include the 

Kyoto Protocol; the commenter asserts that the statement implies that the protocol would have had a 

measurable effect. The commenter suggests the statement should be changed as follows: “...Kyoto 

Protocol, because that instrument would have done nothing measurable about global warming on the 

century time-scale.” Another commenter (3722) submits Wigley et al. (1998) to show that implementing 

Kyoto would delay sea level rise by only four years, stating that this is a “negligible impact.” 

One commenter (3394.1) notes that several analyzed emissions projections do not include new mitigation 

policies and that only the CCSP (but not SRES) scenarios include the Kyoto Protocol, and that the TSD 

does not review speculative responses to climate change. The commenter objects that it is “indefensible to 

exclude existing and virtually certain mitigation measures from that assessment,” and that exclusion of 

foreign, federal, state, and local reductions that are planned or expected is “arbitrary,” a lack of balance, 

and just designed to evade difficult questions, given that the TSD is “willing to speculate as to large GHG 

emission increases.” 

Response (4-39):

We reviewed the discussion of Kyoto Protocol in Section 6 of the TSD and disagree with the commenter 

that our statements need to be revised. The statement in the TSD regarding the IPCC scenarios is 

accurate. The IPCC Special Report on Emissions Scenarios (SRES) was published in 2000 (IPCC, 2000), 

before the Kyoto Protocol entered into force, and, as stated in the TSD, “the IPCC SRES scenarios do not 

explicitly account for implementation of the Kyoto Protocol.” The CCSP scenarios, in contrast, do 

include the Kyoto Protocol’s 2008 to 2012 targets, as well as the 2012 U.S. intensity target, but not 

targets beyond that. This is also clearly summarized in the TSD (see Box 6.2: CCSP (2007b) Reference 

Case Emission Scenarios from Synthesis and Assessment Product 2.1.) Therefore, the TSD accurately 

summarizes the treatment of these policies in both the IPCC and CCSP scenarios, and there is no need to 

include the suggested revision by the commenter. 

Wigley et al. (1998) was submitted with regard to a specific mitigation measure, the Kyoto Protocol. 

Wigley et al. show a 7% reduction in the amount of warming that is projected to occur by 2100 resulting 

from constraining Annex B nations to meet their Kyoto targets for the entire century, but allowing 

emissions to grow unconstrained in all non-Annex B nations. This scenario also results in a 4 to 7% 

reduction in sea level rise. Another similar study, Reilly et al. (1999) shows a 17% reduction in warming 

and sea level rise by 2100. The difference between the simulations relates to differences in emissions 

projections (including relative growth in emissions between Annex B and non-Annex B nations) and 

differences in underlying climate parameters such as climate sensitivity and ocean heat uptake. In either 

case, we find that a 4% to 17% reductions in these impacts are not negligible. 

See the Finding, Section III.C, Adaptation and Mitigation, for our response to comments on the treatment 

of adaptation and mitigation in the finding. 

34 
Comment (4-40):

A commenter (3394.1) notes that the results of the storyline/scenario approach are not projections, and 

that they have no probabilities associated with them. The commenter asserts that this is insufficient for 

purposes of an endangerment analysis and states “EPA would have to describe the relative probabilities of 

various future emissions projections before it could determine that the projected effects of those emissions 

endanger public health or welfare” because the endangerment finding itself states that the Administrator 

bases her finding on the “current and projected” (emphasis added by commenter) levels of the six GHGs. 

Another commenter (3679.1) objects that the word “projected” was used 285 times despite the discussion 

of storyline/scenario vs. projection. However, another commenter (3722) objects that the IPCC relies on 

“projections” rather than “predictions.” 

Response (4-40):

While neither IPCC nor CCSP put probabilities on their various baseline scenarios, all of the scenarios are 

designed to be “meaningful and plausible” (CCSP, 2007b). The fact that every major assessment exercise 

(IPCC SRES, CCSP 2.1a, and the more recent Energy Modeling Forum-21 exercise, all referenced in the 

TSD) developed scenarios that fall within the same range of emissions is indicative that these do represent 

consistent estimates of future emissions. See also response number 4-37. 

 

The language of Mass v. EPA on uncertainty was that if “the scientific uncertainty is so profound that it 

precludes EPA from making a reasoned judgment, it must say so. The statutory question is whether 

sufficient information exists for it to make an endangerment finding.” There is no requirement for being 

able to place exact probabilities on certain outcomes. As the Finding states, because scientific knowledge 

is constantly evolving, the Administrator may be called upon to make decisions while recognizing the 

uncertainties and limitations of the data or information available, as risks to public health or welfare may 

involve the frontiers of scientific or medical knowledge. 

While not included in any assessment exercise, and therefore not included in the TSD, we also note that 

some studies (e.g., Webster et al., 2002) have conducted more formal uncertainty analyses to produce a 

range of emissions projections, and the range of emissions was if anything at the high end of the IPCC 

range. Therefore, there are no reasons to indicate that any exercise by EPA to associate probabilities with 

future scenarios would provide any substantially different information to the Administrator than already 

provided by the existing scenarios represented in the assessment literature. 

We also note that the April 2009 TSD incorrectly stated that “storylines are not projections” and we have 

replaced this with “storylines are not predictions” in the final TSD. 

Comment (4-41): 

A commenter (3747.1) argues that “[f]undamental to EPA’s Proposal is the Agency’s use of and reliance 

on IPCC modeling ― “storylines” to construct and predict future scenarios. However, the authors of the 

IPCC reports explicitly note that these storylines are not predictors or forecasters of future economic, 

population, or related trajectories in the U.S. or elsewhere. Rather, as the phrase suggests, IPCC’s 

storylines are illustrative tools, not predictions. For example, none of the IPCC storylines consider 

periodic economic cycles that are directly relevant to findings of impacts.” The commenter argues that 

EPA did not adhere to the information quality guidelines under the IQA because the TSD uses IPCC 

emission scenario storyline as predictive models. 

35 
Response (4-41): 

The use of the IPCC, SRES, and Energy Modeling Forum projections for examining possible future 

scenarios is well-accepted and routine in the scientific community. Please see other responses in this 

Volume on the quality of these scenarios. We considered a wide range of scenarios, consistent with the 

approaches taken by the IPCC and CCSP. There is no indication in the EPA Guidelines for Ensuring and 

Maximizing the Quality, Objectivity, Utility and Integrity of Information Disseminated by the 

Environmental Protection Agency that there is a requirement that the Administrator rely only on 

“predictions.” According to the IPCC terminology, a “prediction” is the “most likely” projection. While 

no one scenario or projection provided has been branded “most likely,” the range of scenarios and 

projections does allow assessment of likely futures in the absence of mitigation policies. As noted in a 

previous response, papers have been published that generate scenarios probabilistically, and the median 

estimate of one example, Webster et al. (2002) falls within the range of the SRES, CCSP, and Energy 

Modeling Forum scenarios. For responses on the specific issue of considering periodic economic cycles, 

see response 4-37: just as weather prediction is not necessary for climate prediction, short term economic 

cycle prediction is not necessary for long term emissions projections. We conclude that the approach 

followed in the assessment literature, and summarized in the TSD, is accurate and sound, given the range 

of socio-economic factors (i.e., drivers of GHG emissions) that could play out over time. Further, we find 

the TSD’s discussion of the emissions scenario storylines and how they were used to be appropriate and 

consistent with how the assessment literature intended them to be used, and therefore consistent with 

EPA’s guidelines. 

Comment (4-42):

Commenter (3136.1) inquires as to why the MiniCAM projection “is clearly just a straight line,” and that 

this is important information to include in the TSD because it means complicated emission modeling is 

unnecessary. 

Response (4-42):

The commenter is apparently referring to Figure 6.2 in Section 6 of the TSD, and we disagree that the 

MiniCAM projection is “clearly just a straight line.” Although global CO2 emissions in the MiniCAM 

reference scenario appear linear, this does not imply that complicated emission modeling is unnecessary. 

This result just happens to be the outcome of the interactions between a particular set of emission drivers 

selected for the reference case. More information on the modeling approach and underlying assumptions 

for this (and other) scenarios can be found in CCSP (2007b). We summarized the range of emission 

projections available in the assessment literature in the TSD because these scenarios provide important 

insights into the drivers that lead to various future emission pathways. 

Comment (4-43):

Several commenters (e.g., 3596.1, 3596.2) request that EPA discuss the recent stabilization of methane 

(CH4) concentrations. A commenter (3596.3) argues that this stabilization is in opposition to IPCC 

projections and means future projections of climate change are “way off base,” and discusses the 

possibility of decreasing CH4 concentrations as “a logical progression” in the rate of CH4 changes over 

time. 

 

Response (4-43):

36 
Please refer to Volume 2 for EPA’s detailed response on the stabilization of CH4 concentrations over the 

last several years. Here we confine our response to the issue of methane projections. 

 

We disagree with the commenter that the stabilization in methane concentrations in the first half of this 

decade implies that CH4 concentrations will not grow in the future. As noted in the response in Volume 2, 

more recent data indicate that CH4 concentrations are growing again; Rigby et al. (2008) found a small 

increase in concentrations in 2007, which rose again in 2008. Further, examination of the underlying 

drivers of anthropogenic CH4 emissions indicates that future growth is certainly possible. Methane is 

emitted by multiple activities in the energy, waste, and agricultural sectors, and emissions from all of 

these sources could increase because of population and economic growth. Moreover, some of the major 

natural sources of CH4 emissions are highly dependent on temperature, and there is already evidence that 

methane emissions from Arctic permafrost are increasing. The TSD refers to Clark et al. (2008), which 

finds that “it is very likely that climate change will accelerate the pace of persistent emissions from both 

hydrate sources and wetlands.” 

Thus, we find that the emissions scenarios summarized in the TSD reflect the best estimates in the 

literature and are a reasonable range of possible emission futures for methane, reflecting the uncertainty 

for future emissions. Two of the CCSP models project continued growth in CH4 emissions throughout the 

21st century “as a consequence of the growth of CH4-producing activities such as ruminant livestock 

herds, natural gas use, and landfills.” However, the third emission scenario, despite expansion of these 

activities, assumes that emission control technologies will be deployed in response to local environmental 

regulations and the increasing value to capturing CH4 as natural gas prices increase. For this reason, CH4

emissions peak and decline in this third scenario. The IPCC SRES scenarios also cover a significant range 

of possible CH4 future emission pathways. Therefore we find that the assessment literature provides an 

appropriate range of scenarios given the uncertainty involved. 

For all these reasons, we disagree that the recent stabilization of CH4 concentrations means that any future 

projections showing growth in CH4 concentrations are “way off base.” 

Comment (4-44):

A commenter (4632) objects to the emission scenarios produced by the IPCC because of critiques by 

Castles and Henderson (2003a, 2003b, 2005) that highlight the use of market exchange rates (not 

purchasing power parity) and the implausible assumption that poor nations will equalize per capita 

emissions with rich nations. 

Response (4-44):

Both IPCC (2007) and CCSP (2007b) address the issue of using market exchange rate (MER) versus 

purchasing power parity (PPP) approaches in determining future gross domestic product (GDP) growth 

rates, in response to the critiques by Castles and Henderson (2003a, 2003b, 2005). The IPCC (Fisher et 

al., 2007) states the following: 

In the debate on the use of exchange rates, market exchange rates (MER) or purchasing 

power parities (PPP), evidence from the limited number of new PPP-based studies 

indicates that the choice of metric for gross domestic product (GDP), MER or PPP, does 

not appreciably affect the projected emissions, when metrics are used consistently. The 

differences, if any, are small compared to the uncertainties caused by assumptions on 

other parameters, e.g. technological change (high agreement, much evidence). 

The IPCC adds the caveat that, unlike emissions, the numerical expression of GDP does depend on 

conversion methods. CCSP (2007b) notes that while MER is used to set the base year of the models in 

37 
that assessment, “growth prospects and other parameters for the world’s economies were assessed relative 

to their own historical performance” in order to avoid potential issues arising from interactions between 

the MER/PPP issue and assumptions regarding convergence. 

While we find that both the IPCC and CCSP approaches yield credible estimates of future emissions that 

have been well supported by the literature, the robustness of conclusions based on emission projections 

developed through different means adds even more confidence that the TSD is appropriately summarizing 

the best existing science. 

Comment (4-45):

One commenter (2057) argues that the assumption that GHG levels will constantly increase is currently 

speculation. The commenter then asks if the EPA would propose a minimum CO2 level if future advances 

in carbon capture or energy usage lead to decreases in CO2. 

Response (4-45):

We have reviewed the TSD and assessment literature in light of this comment and find that the projected 

increase in GHG emissions, as expressed in the assessment literature and summarized in the TSD, is not 

speculative, and the commenter presents no evidence to suggest otherwise. Rather, we find that the range 

of projections have been well vetted, peer reviewed, and fully assessed within the scientific literature. The 

endangerment finding focuses on the risks if mitigation does not happen: it does not address the change 

in risk due to mitigation. Thus, the question of a minimum CO2 level is not pertinent to this finding. 

4.3 Future Projections of Temperature 

Comment (4-46):

A number of commenters (e.g., 0700.1, 1499.1, 2898.1, 3136.1, 3596.2) argue that the equilibrium 

climate sensitivity (ECS) is, or is likely to be, lower than the most likely value of about 3 ̊C reported in 

the IPCC AR4. 2

One commenter (3136.1) argues that “[t]he secular climate history calls for a re-assessment of the likely 

equilibrium sensitivity to a carbon dioxide doubling.” According to the commenter, observed 

temperatures patterns point to “a likely value below 3.0 ̊C.” The commenter states that evidence suggests 

that much of the early 20th-century warming was largely driven by natural variations of the sun’s output. 

If this is true, the commenter writes, then only the warming from 1976–1998 is ascribable largely to the 

CO2, suggesting that ECS is around 2.1 ̊C. 

One commenter (3596.2) makes a similar argument regarding natural variability and additionally cites 

several studies that suggest climate sensitivity is lower than the most likely value reported by IPCC. The 

commenter notes that Chylek and Lohmann (2008) derived a 95% confidence range of 1.3–2.3 ̊C for the 

climate sensitivity for a doubling of CO2 based on ice core records from the Last Glacial Maximum. The 

commenter states that Wyant et al. (2006a) finds that climate sensitivity declines to values at or below the 

low end of the IPCC range when better cloud processes are incorporated into climate models and that 

“[s]imilar conclusions indicating that observed (rather than modeled) cloud cover changes (and their 

impacts on atmospheric moisture content) suggest a lower climate sensitivity than IPCC estimates have 

been made by Spencer et al. (2007), Spencer and Braswell (2008), and Paltridge et al. (2009).” Finally, 

 

2

 In IPCC reports, equilibrium climate sensitivity refers to the equilibrium change in the annual mean global surface 

temperature following a doubling of the atmospheric equivalent CO2 concentration. 

38 
the commenter argues that observational evidence supporting a low climate sensitivity value is included 

in work by Swanson and Tsonis (2009) and Michaels et al. (2009). 

Another commenter (2898.1) argues that research conducted by Richard Lindzen and colleagues 

(Lindzen, 2009) using data from the Earth Radiation Budget Experiments and National Centers for 

Environmental Prediction shows a strong negative feedback and indicates that the sensitivity of the actual 

climate system “is narrowly constrained to about 0.5 ̊C.” This commenter references several studies that 

the commenter says call into question the IPCC analysis of ECS and help explain the findings of Lindzen 

and colleagues. Specifically, the commenter states that work by Dr. William Gray (2009) indicates that 

observations of upper tropospheric water vapor over the last three to four decades “show that upper 

tropospheric water vapor appears to undergo a small decrease while outgoing longwave radiation 

undergoes a small decrease” and that observations indicate that the specific and relative humidity of the 

middle and upper troposphere have been decreasing over the last four to five decades. The commenter 

additionally states that Spencer et al. (2007) found a strong negative cirrus cloud feedback mechanism in 

the tropical troposphere and that a study by Spencer and Braswell (2008) suggests that increases in seasurface temperature could be an effect of natural cloud variations. The commenter quotes Dr. Spencer as 

stating the following on his Web site: “[W]hen the effect of clouds-causing-temperature-change is 

accounted for, cloud feedbacks in the real climate system are strongly negative. In fact, the resulting net 

negative feedback was so strong that, if it exists on the long time scales associated with global warming, it 

would result in only 0.6 deg. C of warming by late in this century.” In addition, the commenter states that 

“an appeal to the authority of the IPCC would not suffice as a rebuttal to Lindzen, because the issue in 

dispute is precisely whether IPCC sensitivity assessments are consistent with the actual data.” 

Response (4-46):

In light of these comments, we have reexamined the relevant assessment literature, the studies submitted 

by commenters, and other recent peer-reviewed literature on ECS. On the basis of this review, we have 

determined that the TSD accurately summarizes the findings of the scientific community on the subject of 

ECS, as it is expressed in the assessment literature. We have reviewed the studies and presentations 

referenced by commenters to support claims that the true ECS value is likely to be substantially below 

3 ̊C. On the basis of this review, we have determined that important aspects of a number of these studies 

are inconsistent with much of the published literature and that the studies do not provide a sufficient basis 

on which to conclude that the IPCC conclusions are flawed. 

As noted in the TSD, IPCC (Meehl et al., 2007) has concluded that ECS is very likely greater than 1.5 ̊C 

and likely to lie in the range of 2 ̊C to 4.5 ̊C, with a most likely value of about 3 ̊C. Although ECS values 

somewhat lower than the most likely value of about 3 ̊C reported in the IPCC AR4 are plausible, we 

disagree that ECS is “likely”3

 to be lower than 3 ̊C, as several commenters stated or implied. The ECS 

value of 2.1 ̊C proposed by several commenters (3136.1, 3596.2) falls within the range of likely ECS 

values assessed by IPCC; however, as discussed further below, the commenters do not compellingly 

demonstrate that an ECS value of less than 3 ̊C is any more likely than an ECS value of 3 ̊C or greater. 

As noted by IPCC, studies to constrain climate sensitivity have used several approaches, including the use 

of AOGCMs, examination of the transient evolution of temperature (surface, upper air and ocean) over 

the last 150 years, examination of the rapid response of the global climate system to changes in the 

forcing caused by volcanic eruptions, and estimates based on palaeoclimate studies (Solomon et al., 

2007). These independent lines of evidence indicate similar most likely values and ranges for ECS and 

collectively form the basis for the key IPCC conclusions summarized in TSD. An extensive discussion of 

 

3

 According to IPCC terminology, “likely” conveys a 66 to 90% probability of occurrence. See Box 1.2 of the final 

TSD for a full description of IPCC’s uncertainty terms. 

39 
each of these lines of evidence is found in Chapter 10 of the IPCC AR4 (Meehl et al., 2007), and they are 

briefly described here. 

One main category of methods examines climate sensitivity in GCMs. Meehl et al. (2007) describe a 

number of GCM-based studies that indicate similar most likely values and ranges for ECS. Three 

probability density functions (PDFs) were obtained by comparing different variables of the simulated 

present-day climatology and variability against observations in a perturbed physics ensemble. ECS was 

found to be most likely around 3.2 ̊C and very unlikely to be below about 2 ̊C, with the upper bound 

sensitive to how model parameters are sampled and to the method used to compare with observations. 

A second category of methods uses observed changes in climate to constrain ECS. Some studies (e.g., 

Forest et al., 2006) have used instrumental records of surface, ocean, and atmospheric temperature 

changes to constrain climate sensitivity, while others (e.g., Wigley et al., 2005) analyze the forcing and 

response to major volcanic eruptions. A summary of PDFs of climate sensitivity from these methods, 

proxy data over the last millennium, estimates of radiative forcing, satellite data or subsets thereof is 

shown in Box 10.2 in Meehl et al. (2007). According to IPCC (Hegerl et al., 2007), “Results from studies 

of observed climate change and the consistency of estimates from different time periods indicate that ECS 

is very likely larger than 1.5 ̊C with a most likely value between 2 ̊C and 3 ̊C.” Hegerl et al. (2007) report 

that this supports the overall assessment based on modeling and observational studies that the most likely 

value for ECS is approximately 3 ̊C. Hegerl et al. (2007) additionally note that while considering all 

available evidence on ECS together provides a stronger constraint than individual lines of evidence, 

remaining uncertainties that are not accounted for in individual estimates and possible dependencies 

between individual lines of evidence make the upper 95% limit of ECS uncertain at present. 

One reason a high sensitivity is difficult to rule out is that a high aerosol forcing could nearly cancel GHG 

forcing over the 20th century (Hegerl et al., 2007), and a large ocean uptake can delay the emergence of a 

warming signal. As noted in the TSD, an ECS higher than 4.5 ̊C cannot be ruled out, though agreement 

for higher values is generally worse compared with values in the 2–4.5 ̊C range. 

We have reviewed the studies and presentations referenced by several commenters (2898.1, 3136.1, 

3596.2) to support their claims that the true ECS value is likely to be substantially below 3 ̊C. In the 

following paragraphs, we address the main issues these studies and presentations raise with specific 

regard to ECS. 

Regarding commenters’ claims about observations of middle- and upper-level tropospheric water vapor 

over the last three to four decades based on Gray (2009): As noted in Volume 3, Section 3.2.1: Other 

Substances with Radiative Forcing Effects, the analysis in the IPCC AR4 (Trenberth et al., 2007) stated: 

“Due to instrumental limitations, long-term changes in water vapour in the upper troposphere are difficult 

to assess,” but nonetheless concluded, “To summarise, the available data do not indicate a detectable 

trend in upper-tropospheric relative humidity. However, there is now evidence for global increases in 

upper-tropospheric specific humidity over the past two decades, which is consistent with the observed 

increases in tropospheric temperatures and the absence of any change in relative humidity.” 

Regarding commenters’ arguments based on the work of Lindzen and Gray that the water vapor feedback 

is strongly negative: As discussed in Volume 3, the hypothesis that increased CO2 forcing will lead to a 

counterbalancing decrease in water vapor is highly speculative and is not supported by the vast body of 

scientific literature. Randall et al. (2007) report that observations provide ample evidence of regionalscale increases and decreases in tropical upper-tropospheric relative humidity in response to changes in 

convention; however, they also note that these changes provide little insight into large-scale 

thermodynamic relationships important for the water vapor feedback unless considered over entire 

circulation systems. Randall et al. additionally note that broadly similar changes are found in a range of 

40 
models of different complexity and scope, and that indirect evidence for model water vapor feedback 

strength also comes from experiments that show that suppressing humidity variation from the radiation 

code in an AOGCM produces unrealistically low interannual variability. According to Randall et al. 

(2007): “Together, upper-tropospheric observational and modeling evidence provide strong support for a 

combined water vapour/lapse feedback of around the strength found in GCMs [approximately 1 W m-2 

per degree global temperature increase].” 

Regarding Dr. Spencer’s findings about a negative cirrus cloud feedback mechanism and the cloudscausing-temperature-change hypothesis: The IPCC acknowledges that, “there is some consistency 

between ISCCP [International Satellite Cloud Climatology Project], ERBS [Earth Radiation Budget 

Satellite], SAGE II [Stratospheric Aerosol and Gas Experiment] and surface observations of a reduction 

in high cloud cover during the 1990s relative to the 1980s.” Trenberth et al. (2007) note, however, that the 

variability in total surface-observed total cloud cover is not consistent with ISCCP; that there are 

substantial uncertainties in decadal trends in all data sets; and that “at present there is no clear consensus 

on changes in total cloudiness over decadal time scales.” Randall et al. (2007) note that cloud feedbacks 

remain the largest source of uncertainty in assessing ECS but that progress has been made in the 

identification of the cloud types, the dynamical regimes and regions of the globe responsible for the large 

spread of cloud feedback estimates among current models. 

On the basis of the key assessment literature conclusions described above and the numerous peerreviewed studies that form the basis for these conclusions, we find that Lindzen’s (2009) assertion that 

climate sensitivity “is narrowly constrained to about 0.5C” is inconsistent with the vast majority of 

scientific evidence on ECS. Our review of the cited presentation revealed that the methods he used to 

conclude climate sensitivity is constrained to about 0.5 ̊C are not well described and that the analysis 

appears to be based on an examination of only 16 years of data from two particular datasets (the monthly 

record of sea surface temperatures from the National Centers for Environmental Prediction and 

fluctuations in radiative flux from the Earth Radiation Budget Experiment). Commenter 2898.1 is in this 

instance attempting to use a part of a single presentation that did not undergo peer review and is based on 

research with significant temporal and spatial limitations to call into question assessment conclusions that 

are based on a large variety of peer-reviewed studies, many of which examine data over longer time 

periods. 

We have noted commenter 2898.1’s view that “an appeal to the authority of the IPCC would not suffice 

as a rebuttal to Lindzen, because the issue in dispute is precisely whether IPCC sensitivity assessments 

are consistent with the actual data.” We strongly disagree, however, that the IPCC conclusions about 

climate sensitivity are somehow inappropriate for purposes of a robust response to the comment. The 

IPCC assessment of climate sensitivity rests on several independent lines of evidence and is based on a 

rigorous assessment by numerous scientists of scores of peer-reviewed papers on the topic. Further, 

several very recent peer-reviewed papers on ECS reach conclusions that are highly consistent with the 

IPCC assessment, as described later in this response. Therefore, it is reasonable to continue to rely on the 

IPCC conclusions despite the existence of one presentation that when compared to the scope and breadth 

of the assessment literature is narrow and incomplete in its consideration of the issues. 

As previously noted, one commenter (3596.2) referenced a number of peer-reviewed studies that the 

commenter states support a low ECS value, including: Paltridge et al. (2009), Spencer and Braswell 

(2008); Wyant et al. (2006a), Chylek and Lohmann (2008) and Swanson and Tsonis (2009). We have 

reviewed these studies and find that they do not provide a sufficient basis on which to conclude that that 

an ECS value of less than 3 ̊C is any more likely than an ECS value of 3 ̊C or greater. 

Wyant et al. (2006a) calculated climate sensitivity from simulations with a SST that was increased by 2 

degrees K uniformly. They also used the same method to calculate climate sensitivity from two different 

41 
versions of the parent model. Although the model with the new cloud resolving superparameterizations4

 

had a 20% smaller climate sensitivity than one version of the parent model, it showed no decrease at all in 

climate sensitivity compared to the other version of the parent model. Therefore, it is not clear whether 

the reduction of climate sensitivity resulting from including cloud resolving superparameterizations is a 

robust finding, even for one model type. Additionally, the absolute value of climate sensitivity should not 

be compared to other estimates, as it is clear from Wyant et al. (2009) and Wyant et al. (2006b) that 

climate sensitivity calculated from a doubled-CO2 experiment usually has less negative cloud feedback 

than climate sensitivity calculated from a +2K SST experiment, as in Wyant et al. (2006a). Additionally, 

there is some evidence that the more realistic the ocean, the larger the climate sensitivity (Danabasoglu 

and Gent, 2008); though the effect from moving from a slab ocean to a full-depth ocean was small, it is 

unclear what this effect would be for a more dramatic move from a constant SST (as in Wyant et al., 

2006a) to a slab ocean. 

It is important to note that the IPCC conclusions about ECS are not based solely or even primarily on 

calculations from climate model physics; rather, historical data from a number of sources and time 

periods is often used to constrain the ECS parameter (Hegerl et al., 2007). Therefore, while this is 

interesting work that does suggest that climate sensitivity, at least in one specific model, may decrease 

when more realistic cloud resolving routines are included, there is reasonable evidence to suggest that this 

effect may be small and in any case does not apply to many other methods of estimating climate 

sensitivities. 

Paltridge et al. (2009) examine radiosonde-derived humidity data on tropospheric humidity from the 

National Centers for Environmental Prediction and find that the face-value 35-year trend in zonal-average 

annual-average specific humidity is significantly negative at all altitudes above 850 hectopascals (hPa) in 

the tropics and southern mid-latitudes and at altitudes above 600 hPA in the northern mid-latitudes. The 

authors do not quantitatively assess climate sensitivity (which is not the main focus of their study), but do 

offer several important qualifications to their conclusions on tropospheric humidity. For example, the 

authors note that radiosonde-derived data must be treated with “great caution” and that their findings 

about upper-level negative trends in specific humidity are inconsistent with climate-model calculations 

and largely inconsistent with satellite data. By ignoring these nuances and the range of studies cited in 

IPCC (Trenberth et al., 2007) that indicate radiosonde trends are suspect due to the poor quality of, and 

changes over time in, the humidity sensors, the commenter exaggerates the significance of the study visà-vis the large body of literature that underlies the IPCC conclusions, much of which focuses more 

directly on ECS. 

Chylek and Lohmann use the temperature, CO2, CH4, and dust concentration record from the Vostok ice 

core to deduce the aerosol radiative forcing during the Last Glacial Maximum (LGM) to Holocene 

transition and the climate sensitivity, and state that their results suggest a 95% likelihood of warming 

between 1.3 and 2.3 ̊K because of doubling of atmospheric concentration of CO2. In our review of the 

Chylek and Lohmann study, we determined that two peer-reviewed comments on the paper found serious 

issues with its methodology. Ganapolski and Deimling (2008) found that Chylek and Lohmann: a) did not 

properly account for the uncertainties of estimating climate sensitivity, and b) that the role of glacial 

aerosols on contributing to the LGM-Holocene cooling must have been much smaller than what Chylek 

and Lohmann inferred. Among other things, the paper notes that Chylek and Lohmann consider an 

Antarctic Last Glacial Maximum (LGM) cooling of 10.2 ̊C without attaching an uncertainty estimate to 

the value and that modeling results are not consistent with Chylek and Lohmann’s assumption that LGM 

cooling had not exceeded 5 ̊C. 

 

4

 Superparameterization (also known as Multi-Scale Modeling Framework) uses a cloud-resolving model implemented at each 

GCM grid column. According to Wyant et al. (2006a), the use of cloud-resolving models permits explicit simulation of smallerscale vertical convective motions associated with clouds and allows radiation to be calculated on a finer scale. 

42 
Hargreaves and Annan (2008) found similar flaws, and also state that Chylek and Lohmann’s analysis is 

based on the selection of local extrema in time series, which shows high temporal variability, and that the 

data points they used are not temporally coincident. Hargreaves and Annan (2008) conclude as follows: 

“When the noise of short-term natural variability is reduced by temporal averaging, the results come into 

line with previous analyses of these and similar data (e.g. Hansen et al., 1993).” 

Though commenter 3596.2 states that Swanson and Tsonis (2009) present observational evidence 

supporting a low climate sensitivity, our examination of this paper revealed that the authors’ own 

assessment of their results does not support the commenter’s statement. Swanson and Tsonis (2009) state 

the following: “Finally, it is vital to note that there is no comfort to be gained by having a climate with a 

significant degree of internal variability, even if it results in a near-term cessation of global warming. It is 

straightforward to argue that a climate with significant internal variability is a climate that is very 

sensitive to applied anthropogenic radiative anomalies [cf. Roe, 2009]. If the role of internal variability in 

the climate system is as large as this analysis would seem to suggest, warming over the 21st century may 

well be larger than that predicted by the current generation of models, given the propensity of these 

models to underestimate climate internal variability [Kravtsov and Spannagle, 2008].” Thus, we conclude 

that this paper is more supportive of a high ECS value than it is a low one. 

 

Several recent peer-reviewed studies have reached conclusions consistent with the IPCC findings 

regarding ECS. For example, Forest et al. (2008) compare observed changes in surface, upper-air, and 

deep-ocean temperature changes against simulations of 20th-century climate in which climate model 

parameters were systemically varied and estimate the effective climate sensitivity for 11 of the IPCC AR4 

AOGCMs. They derive an estimated 90% range of 2–5 ̊K, with no corresponding upper bound on ECS, 

which is largely consistent with the PDFs for ECS presented in Hegerl et al. (2007). They also carried out 

probabilistic projections of 21st century warming using the SRES scenarios from AR4. Their results are 

reported in a separate paper and “imply a significantly stronger warming in the 21st century than the AR4 

model ensemble.” 

Royer et al. (2007) estimated the long-term ECS by modeling CO2 concentrations over the past 420 

million years and comparing their results with a proxy record. They found that a long-term radiative 

forcing by CO2 of less than 1.5 ̊C is highly unlikely because lower temperatures would require 

unreasonably high levels of atmospheric CO2 to maintain the necessary feedback effect.5

 The authors find 

that the best fit between the standard version of the model and proxies for atmospheric CO2 over the 

Phanerozoic occurs for a temperature delta of 2.8 ̊C and that a temperature delta of at least 1.5 ̊C “has 

been a robust feature of the Earth’s climate system over the past 420 Myr [million years], regardless of 

temporal scaling.” 

Hansen et al. (2008) note that the ECS studies described by the IPCC generally do not include slowclimate feedbacks, such as loss of ice and spread of flora over the vast high-latitude over the high-latitude 

land area in the Northern Hemisphere, or carbon cycle and CH4 emission responses to temperature 

changes. They use palaeoclimate data to show that the long-term (i.e., millennial-scale) sensitivity could 

be in the range of 6 ̊C; however, it is important to note that climate sensitivity estimates based on 

palaeoclimate must be viewed with a degree of caution because of data uncertainties and the fact that 

climate sensitivity changes with the mean climate state and the nature of the forcing (Hegerl et al., 2007). 

 

5

 Royer et al. (2007) note that a critical factor in their approach is the effect of atmospheric CO2 level on the rate of CO2 uptake 

by weathering of calcium and magnesium silicate minerals; they report that a rise in temperature, accompanying a rise in CO2, 

increases the rate of silicate weathering, which in turn accelerates atmospheric CO2 consumption, forming a negative feedback 

loop. 

43 
Please see Volume 3 for EPA’s responses regarding natural variability and attribution of the observed 

warming and Section 4.1 of this volume for EPA’s responses to comments on the validity of model-based 

climatic projections. Comments of specific relevance to the role of positive feedbacks in the model-based 

studies that provide one source of climate sensitivity estimates are covered in response number 4-17 of 

this volume. The Spencer and Braswell (2008) study cited by a number of commenters with regard to 

feedbacks and climate sensitivity is also addressed in this response. 

In summary, we have reviewed the relevant assessment literature, the studies submitted by commenters, 

and other recent peer-reviewed literature on ECS in light of the comments received on this topic. Our 

review indicates that the studies referenced by commenters to support claims that the true ECS value is 

likely to be substantially below 3 ̊C are inconsistent with the large majority of peer-reviewed studies on 

ECS underlying the IPCC conclusions, and that the discussion of ECS in the TSD accurately reflects the 

current state of scientific knowledge on ECS as reflected in the assessment literature. Thus, the treatment 

of ECS in the TSD is accurate and sound. 

Comment (4-47):

Numerous commenters (e.g., 0700.1, 2853.1, 2898.1, 3215.1, 3217.1, 3224_CP, 3394.1, 3411.2, 3446.1, 

3729.1, 3747.1, 4003, 4509, 9733, 10499) argue that the temperature projections described in the TSD are 

not in accordance with the observed temperature record. Some of these commenters argue that global 

climate models are overestimating global temperature increases, particularly since 2000, compared with 

satellite observational data (i.e., Remote Sensing Systems [RSS], University of Alabama in Huntsville 

[UAH]). Commenters state that recent satellite data indicate a leveling or negative trend in global 

temperatures above the surface in this decade, despite an increase in CO2 emissions and concentrations 

during that same period. Commenters claim that declines occur both in surface temperatures and in 

atmospheric temperatures measured from satellites. One commenter (3217.1) states: “Before EPA 

approvingly cites the projections from global climate models, the projections should agree with actual 

temperature data. Currently the global climate models over-project global temperatures. The lack of 

temperature increase calls into question the skill of global climate models and whether they bear a relation 

to the real world.” Additionally, several commenters refer to Keenlyside et al. (2008) and Swanson and 

Tsonis (2009) as evidence that some scientists believe that there may be multidecadal pauses in warming. 

Response (4-47): 

Please refer to Volume 2 for EPA’s complete responses to comment on observed temperature. Here we 

confine our discussion of the observed temperature record to its relationship with future temperature 

projections. 

We have reviewed relevant literature in light of these comments, and we have determined that the 

temperature projections described in the TSD are in accord with the observed temperature record. It is 

true, as several commenters (e.g., 3136.1) point out, that the rate of observed temperature change in the 

last decade is, for the HadCRUT dataset, less than the projected warming of about 0.2 ̊C per decade 

reported in the TSD for a range of SRES emission scenarios; according to Knight et al. (2009), the least 

squares trend for January 1999 to December 2008 calculated from the HadCRUT3 dataset is 

+0.07±0.07 ̊C. However, as noted in the TSD (see Box 4.1: Updated Global Surface Temperature Trends 

Through 2008), the National Oceanic and Atmospheric Administration’s (NOAA’s) and National 

Aeronautics and Space Administration’s (NASA’s) trends do not show the same marked slowdown for 

the 1999–2008 period. The NOAA trend was ~0.12 C per decade while the NASA trend was ~0.19 C per 

decade. 

44 
As noted in the TSD, all three data sets show similar trends over the long term. However, as in this 

case, short-term temperature trends can differ more substantially between data sets than long-term 

ones, owing to the lower number of data points and because differences arise from the diversity of 

spatial averaging techniques and from the treatment of gaps in the data (Trenberth et al., 2007). 

Scientific best practice dictates that whenever possible more than one independent data set should be 

used to analyze both long and short term trends in a given variable. For this reason, the TSD includes 

data and calculations from not only the HadCRUT but also NOAA and NASA datasets. As discussed 

in Volume 2, short-term data sets do not appropriately inform long-term climate change trend 

questions and the relatively flat trend in some surface and satellite datasets over the last seven to 10 

years does not fundamentally alter the longer term warming signal. 

Commenters’ arguments that the period of relatively flat warming of the past seven to 10 years 

apparent in certain datasets implies that climate projections are fundamentally flawed do not take into 

account the role of internal variability. As discussed in the assessment literature and elsewhere in this 

Response to Comments document (see Volume 3), inter-annual and inter-decadal temperatures are 

substantially influenced by natural modes of internal climate variability (e.g., ENSO) that can either 

amplify or mask the long-term warning signal. 

Several studies have assessed temperature trends over the last decade in relation to these modes of 

internal variability, and these studies have determined that recent temperatures are not inconsistent 

with the IPCC temperature projections. 

As discussed in response 4-26 in this volume, Knight et al. (2009) assess model ensembles with 

different modifications to the physical parameters of the model within known uncertainties and find 

that recent temperature trends are well within model variability. Knight et al. conclude as follows: 

“Given the likelihood that internal variability contributed to the slowing of global temperature rise in 

the last decade, we expect that warming will resume in the next few years, consistent with predictions 

from near-term climate forecasts.” 

 

Easterling and Wehner (2009) find similar results. The study fits least-squares trends to running 10-

year periods in the global surface air temperature time series for the observed record, an ensemble of 

long control simulations, an ensemble of 20th-century simulations, and an ensemble of simulations 

forced with an A2 forcing from the SRES scenarios. Predictably, the control runs are symmetrical 

around a zero trend. For the 20th-century simulations, there is a shift to more positive values but still a 

significant chance of a negative decadal trend and, notably, a similar distribution to the observed 

record. For the 21st century simulations, there is still about a 5% chance of a negative decadal trend 

even without volcanic eruptions. As Easterling and Wehner (2009) note, the results demonstrate that “it 

is reasonable to expect that the natural variability of the real climate system can and likely will produce 

multi-year periods of sustained ‘cooling’ or at least periods with no real trend even in the presence of 

long-term anthropogenic forced warming.” 

On the basis of the above evidence and the analysis of the relationship between internal variability and 

temperature contained in the assessment literature [see, e.g., Trenberth et al. (2007) and Karl et al. 

(2006)], we conclude that the observed temperature record is neither inconsistent with the temperature 

projections discussed in the TSD nor calls into question the validity of climate models. To place recent 

temperature trends in context and clarify their relationship to long-term climate change, EPA has added 

the following text to Section 6(b) of the TSD: 

According to the NOAA report The State of the Climate in 2008 (Peterson and Baringer, 

2009), the recent slowdown in observed climate warming (see Box 4.1) in some datasets 

has led some to question climate predictions of substantial 21st century warming. The 

45 
study finds that climate models possess internal mechanisms of variability capable of 

reproducing the current slowdown in global temperature rise. It concludes that “[g]iven 

the likelihood that internal variability contributed to the slowing of global temperature 

rise in the last decade, we expect that warming will resume in the next few years, 

consistent with predictions from near-term climate forecasts.” 

We have reviewed the Keenlyside et al. (2008) and Swanson and Tsonis (2009) papers referenced by 

commenters. The Swanson and Tsonis (2009) paper is addressed in several locations within this Response 

to Comments document including response number 4-46 within this volume. The significance of the 

Keenlyside et al. (2008) paper vis-à-vis climate models is covered in Section 4.1 of this volume. 

Please see Volume 2 for EPA’s response to comments specific to the relationship between satellite 

records of lower atmosphere temperature and surface temperature records. 

Comment (4-48): 

A number of commenters (e.g., 2853.1, 3215.1, 3217.1, 3729.1) argue that there is a discrepancy between 

model simulations and trend observations in the tropical troposphere that calls into question model 

projections of future climate. For example, a reference document provided by commenter 5058 (Douglass 

et al., 2007) states the following: “On the whole, the evidence indicates that models trends in the 

troposphere are very likely inconsistent with observations that indicate that, since 1979, there is no 

significant long-term amplification relative to the surface. If these results continue to be supported, then 

future projections of temperature change, as depicted in the present suite of climate models, are likely too 

high.” 

Response (4-48): 

Please refer to Volume 3 for EPA’s detailed response to comments specific to the issue of simulated and 

observed temperatures in the tropical troposphere. On the basis of that response and the assessment 

literature conclusions underlying it, we conclude that the TSD is relying on the best available science in 

regard to this issue and its relationship to projected warming. 

Comment (4-49): 

A number of commenters (e.g., 3394.1, 4509) argue that flaws in the observed temperature record could 

substantially affect climate projections and result in inaccurate temperature projections. For example, one 

commenter (3394.1) argues that a number of recent studies have uncovered significant flaws, 

uncertainties, and biases in the multidecadal surface air temperature record and that these findings 

“require reanalysis of modeling results previously relied on by the IPCC and other synthesis reports cited 

by EPA.” 

Response (4-49): 

Please refer to Volume 2 for EPA’s responses to comments relating to the accuracy of the observed 

temperature record. Please refer to response number 4-47of this volume for our response to comments on 

whether the temperature projections described in the TSD are in accordance with the observed 

temperature record. These responses show that the observed temperature record does not contain flaws of 

a magnitude that would significantly affect climate projections. 

46 
Comment (4-50):

A number of commenters (e.g., 2895, 2898.1, 3217.1, 3701.1, 4509, 4632R18) argue that the temperature 

projections described in the TSD are not accurate because the underlying global climate models are 

unreliable or have not been properly validated. These commenters argue that models contain a number of 

structural and theoretical flaws, including an improper accounting of climate feedbacks. For example, one 

commenter (3330.1) claims that observational results suggest that the water vapor feedback is negative 

and reduces the warming effects of CO2, and another commenter (4509) argues that “the inability of the 

computer models to accurately model cloud effects invalidates their use in climate projections.” 

Response (4-50): 

Please see Section 4.1 of this volume for EPA’s responses to comments on the validity of model-based 

climatic projections. 

Comment (4-51): 

Numerous commenters (e.g. 3455.1, 3570.1, 3575, 4184) state their support for the Findings, noting 

future increases in global temperature as an effect of anthropogenic GHG emissions that affects health 

and welfare. 

Response (4-51): 

EPA agrees that the projected future increases in global temperature summarized in the TSD are linked to 

atmospheric GHG concentrations and have implications for public health and welfare. Please refer to 

Section IV.B. of the Findings for a description of the Administrator’s findings about the risks associated 

with rising temperature and why they support a finding of endangerment. 

4.4 Future Projections of Precipitation 

Comment (4-52):

Two commenters (3136.1 and 3596.2) note that the IPCC chapter Meehl et al. (2007) is referenced in the 

TSD to discuss a projected greater risk of drought in mid-continental areas of North America and to 

provide probabilities of extreme drought under the A2 SRES scenario. These commenters use this citation 

to in turn take issue with the implications of Figure 10.12 in Meehl et al. (2007). Part of Meehl et al. 

(2007) Figure 10.12 depicts a projected precipitation decrease for the U.S. Southwest, based on SRES 

scenario A1B, of roughly 0.2 millimeters (mm) per day. Both commenters imply that such a projection 

does not make sense by stating that “in general, there has been a secular increase in precipitation over this 

region as the planet warmed,” and provide a figure using National Climatic Data Center (NCDC) data to 

show that average annual precipitation in New Mexico (in one comment) and in the U.S. Southwest (in 

the other comment) increased slightly from 1895 to 2008. Additionally, the commenters note that 0.2 mm 

per day is less than three inches per year over the course of 100 years. One commenter (3136.1) states 

additionally that, based on the New Mexico figure provided, a 3-inch-per-year decline in precipitation is 

less than 1.5 inches below where precipitation was at the beginning of the 20th century, and that given 

other pressures on the water supply such as immigration and population growth, this does not represent a 

“significant endangerment.” Finally, a commenter (3136.1) argues that EPA should modify the 

aforementioned TSD text to refer specifically to the United States and to place the IPCC figures in 

historical context. 

Response (4-52):

We have reviewed the relevant assessment literature in light of these comments. The annual precipitation 

in the U.S. Southwest has increased over the last century, as reflected in the version of the TSD released 

47 
in April 2009, which stated that from 1901 to 2006, annual precipitation for the U.S. Southwest increased 

at an average rate of 1.3% per century. We have now revised Section 4(e) of the TSD to include updated 

information that indicates an average rate of precipitation increase for the U.S. Southwest of 3.7% from 

1901 to 2008. However, along with this average annual increase, it is important to note the significant 

temporal variability of the precipitation in the U.S. Southwest during that time, as evidenced in the graph 

below (data from NOAA’s National Climatic Data Center and used in the TSD Section 4[e]), available 

for download from www.epa.gov/climatechange/endangerment/data.html): 

Precipitation in U.S. Southwest 1901-2008

-60

-40

-20

0

20

40

60

80

1900 1920 1940 1960 1980 2000

% Annual anomaly with respect to 

1971-2000 mean

An example of this variability is where the TSD notes that “a severe drought has affected the southwest 

U.S. from 1999 through 2008.” 

We disagree with the commenters’ implication that a projected decrease in annual precipitation for the 

U.S. Southwest from future climate change is implausible or invalid given a trend over the last century of 

increasing annual precipitation, especially in light of the precipitation variability over the last century in 

the region and evidenced in the graph above. A clear conclusion from the assessment literature is that 

changes in precipitation associated with elevated atmospheric GHG concentrations will be highly regional 

along both temporal and geographic lines. To further clarify this key point, the following text has been 

added to Section 6(c) of the TSD in relation to this topic: 

Karl et al. (2009) report model projections of future precipitation in the United States 

generally indicate northern areas will become wetter, and southern areas, particularly in 

the West, will become drier. In some northern areas, warmer conditions will result in 

more precipitation falling as rain and less as snow. In southern areas, significant 

reductions in precipitation are projected in winter and spring as the subtropical dry belt 

expands, particularly in the Southwest (Karl et al., 2009). 

We also disagree with the commenter’s assertion that a roughly 3-inch-per-year decline in precipitation 

(roughly what Meehl et al. [2007] projects) for the U.S. Southwest is insignificant, relative to the average 

annual rainfall in the region for the last century. We reproduced the graph provided by a commenter 

(3596.2) using the NOAA Web site (NOAA, 2009: 

48 
http://www.ncdc.noaa.gov/oa/climate/research/cag3/sw.html), which we present at the end of this 

response, and found that the average annual precipitation for 1895–2008 for the U.S. Southwest was 

13.45 inches. A 3-inch decline by 2100 represents a decrease of greater than 20% in annual precipitation 

relative to the 1895–2008 average, which would represent a significant climate change impact to the U.S. 

Southwest, particularly given simultaneous increasing temperatures and, as the commenter points out, the 

additional nonclimatic pressures on water in the region. 

Finally, we reviewed the TSD in light of the commenter’s request for modifications to refer specifically to 

the United States and to place the IPCC figures in historical context, and we do not find such 

modifications necessary. The TSD already provides U.S.-specific information on precipitation in Section 

4e (U.S. Changes in Precipitation) and Section 6c (Projected Changes in U.S. Temperature, Precipitation 

Patterns, Sea Level Rise). These discussions are thorough and have been revised as necessary in response 

to specific comments on their content. 

Comment (4-53):

One commenter (3136.1) criticizes the TSD for stating the following on page 62: “For the contiguous 

U.S., a study in Christensen et al. (2007) finds widespread increases in extreme precipitation events under 

SRES A2 (high emissions growth).” The commenter states that EPA has “exaggerate[d] for effect” by 

citing only SRES scenario A2 while the “IPCC tend[s] to give A2, A1B, and B2 in its text.” 

Response (4-53):

We have reviewed the relevant text and disagree with the commenter’s assertion that we have 

exaggerated anything. The sentence the commenter references is found in Section 6(e) of the TSD 

statement, and it came directly from the IPCC (Christensen et al., 2007). The paper in question, that is 

49 
referenced in Christensen et al. (2007), is Diffenbaugh et al. (2005). Diffenbaugh et al. (2005) only 

compared two scenarios: a reference scenario (1961–1985) and a future scenario (SRES A2 2071-2095). 

The results were cited in IPCC 2007 and subsequently used in the TSD. 

Additionally, the latest assessment report on U.S. climate change impacts (Karl et al., 2009) states that: 

“Climate models project continued increases in the heaviest downpours during this century.” This 

statement is consistent with the quoted study results. 

Because the statement that the commenter criticized in the TSD is very clear that the study used a highemission scenario, as well as the fact that the statement above from Karl et al. (2009) is consistent with 

the study results, we disagree that we have “exaggerated for effect.” However, to increase clarity in the 

TSD regarding the issue of projected U.S. extreme precipitation events, we have added to the TSD the 

above statement from Karl et al. (2009). 

Comment (4-54): 

A number of commenters (e.g., 3722, 2750) argue that the projected changes in drought severity and 

frequency discussed in the TSD are not credible. One commenter (2750) asserts that drought prediction is 

impossible because there is “no pattern to drought occurrence.” Another commenter (3722) provides a 

graph from NOAA that, according to the commenter, “certainly indicates that modest future warming 

does not present a significant risk of increased drought.” 

Response (4-54): 

The TSD (April 2009 and final) reports the IPCC (Meehl et al., 2007) projection of a tendency for drying 

in mid-continental areas during summer, indicating a greater risk of droughts in those regions. The final 

TSD includes additional detail on this topic, noting the USGCRP conclusion (Karl et al., 2009) that 

droughts are likely to become more frequent and severe in some regions, particularly the Southwest. 

Commenters did not provide any literature or other compelling evidence in contradiction to these robust 

conclusions from the assessment literature. Therefore, we disagree that the projections described in the 

TSD lack credibility. 

We note that the graph provided by one commenter (3722) covers the entire contiguous United States. As 

such, it is not relevant to the drought projections discussed in the TSD, which are regionally specific. As 

the TSD notes, changes in precipitation will be regionally variable. In Section 6(e), the TSD states that 

droughts are likely to become more frequent and severe in some regions, particularly the Southwest; it 

does not state the droughts are likely to become more frequent or severe throughout the entire contiguous 

United States. Therefore, we disagree with the comment that the graph supports the conclusion that future 

warming does not present a significant risk of increased drought, though we note that the risk of increased 

frequency and severity of droughts is more relevant to some regions than others. 

Please refer to Volume 2 for EPA’s responses to comments on observed extreme weather events, 

including droughts. As noted in this section, EPA does not make the claim that drought has, in the 

aggregate, increased in the United States. 

Comment (4-55):

Several commenters (e.g., 3394.1) object that projections of precipitation and regional temperature are too 

uncertain to make projections of drought, especially given a number of related uncertainties in attribution 

of historic droughts, net trends, and ground water management. Additionally, commenters state that EPA 

has failed to describe the uncertainties associated with the precipitation projections in the TSD. 

50 
Response (4-55):

Although precipitation is indeed difficult to model, the assessment literature, as summarized in the TSD, 

indicates that certain robust conclusions can be drawn. For example, models consistently project 

increased drought in the American Southwest (Seager et al., 2007). This trend will, of course, be overlaid 

on continuing natural variability. Karl et al. (2009) states the following on this topic: 

Projections of changes in precipitation largely follow recently observed patterns of 

change, with overall increases in the global average but substantial shifts in where and 

how precipitation falls. Generally, higher latitudes are projected to receive more 

precipitation, while the dry belt that lies just outside the tropics expands further poleward, 

and also receives less rain. Increases in tropical precipitation are projected during rainy 

seasons (such as monsoons), and especially over the tropical Pacific. Certain regions, 

including the U.S. West (especially the Southwest) and the Mediterranean, are expected 

to become drier. The trend towards more heavy downpours is expected to continue, with 

precipitation becoming less frequent but more intense. More precipitation is expected to 

fall as rain rather than snow. 

With regard to the criticism of EPA’s description of precipitation projection uncertainties, we note that 

precipitation projections are described in two different locations in the TSD: Section 6b (Projected 

Changes in Global Temperature, Precipitation Patterns, Sea Level Rise, and Ocean Acidification) 

addresses global changes, and Section 6(c) addresses projected changes in the United States. An 

examination of both of these sections confirms that they include information on the uncertainties in the 

projections. In Section 6(b), for example, the TSD states: “Models simulate that global mean precipitation 

increases with global warming (Meehl et al., 2007). However, there are substantial spatial and seasonal 

variations. Increases in the amount of precipitation are very likely in high latitudes, while decreases are 

likely in most subtropical land regions, continuing observed patterns in recent trends in observations.” 

With regard to U.S. precipitation projections, we have added the following text to Section 6(c) of the TSD 

to better describe the relevant uncertainties and trends: “Overall, annual mean precipitation in the 

northeastern U.S. is very likely to increase and likely to decrease in the southwestern U.S.”6

 Based on our 

review, we conclude that the characterization of uncertainty regarding global and U.S. precipitation 

projections in the TSD is reasonable and appropriate. 

 

Comment (4-56):

Several commenters (3475, 6096) submit or refer to Solomon et al. (2009) as one of a number of 

examples of studies showing that the IPCC AR4 was too conservative in its estimation of emissions and 

impacts. One commenter (3475) states that Solomon et al. (2009) “predicted that peak GHG concentration 

levels of 450-600 ppmv [parts per million by volume] in this century will result in irreversible dry-season 

rainfall reductions in several regions comparable to those of the ‘dust bowl’ era...” 

Response (4-56):

We have reviewed the Solomon et al. (2009) paper and we find that its conclusions are generally 

consistent with the trends and projections described in the TSD. Solomon (2009) was incorporated by 

Karl et al. (2009), which is a USGCRP assessment report and therefore included in the TSD. 

 

6

 According to IPCC terminology, “likely” conveys a 66 to 90% probability of occurrence. . See Box 1.2 of the final 

TSD for a full description of IPCC’s uncertainty terms. 

51 
Comment (4-57):

One commenter (3136.1) states that the 2007 IPCC report “does not adequately review the topic of 

precipitation variability and its causes across the U.S.” The commenter argues that the TSD therefore 

cannot rely on the IPCC for this topic and a more in-depth look at the relevant science is required as the 

basis for an endangerment finding. 

Response (4-57):

Please refer to Volume 3 for EPA’s responses to comments generally focused on attribution, including of 

precipitation. Here, we focus on the component of the comment that is specific to precipitation variability 

and the IPCC review of this topic. 

Although significant challenges exist in projecting patterns of future precipitation, in particular, in 

projecting precipitation variability at less than a continental scale (as discussed in Section 6(b) [Projected 

Changes in Global Temperature, Precipitation Patterns, Sea Level Rise, and Ocean Acidification] of the 

TSD), we disagree with the commenter that the AR4 (Trenberth et al., 2007) does not provide an adequate 

review of precipitation variability and its causes in the United States. Trenberth et al. covers in detail the 

observed precipitation from 1900 to 2005. Time series are presented for 19 world regions (including four 

areas in the United States), along with trends at the 5° latitude/longitude grid level, and it is noted where 

the trend is significant at the 5% level. Additionally, the IPCC chapter Christensen et al. (2007) details the 

projected changes in precipitation for North America along with the degree of agreement among the 

climate models. Figure 11.12 in Christensen et al. (2007), in particular, displays the projected fractional 

changes in annual, winter, and summer precipitation specific to North America. Additionally, Figure 

11.12 shows, by color shading, the number of models out of 21 that project increases in precipitation. 

Further, as noted in the TSD, we do not rely solely on the IPCC; the TSD summarizes a number of other 

peer-reviewed assessment reports, including those by the USGCRP, CCSP, and NRC. These reports also 

review precipitation variability in the United States. For example, Figure 4.1 (reproduced below) in 

Lettenmaier et al. (2008), found in CCSP (2008e), displays the mean and coefficient of variation of 

annual precipitation in the continental United States and Alaska. 

Lettenmaier et al. (2008) concludes that: 

Precipitation variability generally is 

lowest in the humid areas, and 

highest in the arid and semi-arid 

West, with a tendency toward lower 

variability in the Pacific Northwest, 

which is more similar to that of the 

East than the rest of the West. 

CCSP (2008c) directly addresses the current 

skills of climate models in projecting 

precipitation: 

Climate model simulation of 

precipitation has improved over 

time but is still problematic. 

Correlation between models and 

observations is 50 to 60% for 

seasonal means on scales of a few 

hundred kilometers. Comparing 

52 
simulated and observed latitude-longitude precipitation maps reveals similarity of magnitud

patterns in most regions o

es and 

f the globe, with the most striking disagreements occurring in the 

tropics. 

cusses to 

and modeling ability, the observed and projected 

recipitation variability in the United States. 

Based on our review of the TSD and the underlying assessment literature in light of this comment, we 

conclude that the TSD provides an accurate and sound summary of the state of the science. It dis

the extent permitted by current understanding 

p

Comment (4-58):

One commenter (7037) references Shepherd (2005), and writes that the article conveys the view that 

“Increased precipitation, not drought, is possible over urban areas because of the presence of the urban

heat island coupled with a moister and more unstable atmosphere, resulting in more wet deposition.” 

Response (4-58):

We agree that the microclimate in urban areas differs from that of neighboring rural areas, and that 

observational studies have linked urban effects to precipitation increases over background values. This is 

discussed in the AR4 (Trenberth et al., 2007), which we cite in the TSD. Trenberth et al. (2007) states that

“Urban effects can lead to increased precipitation (5 to 25% over background values) during the summer 

months within and 50 to 75 kilometers (km) downwind of the city.” Thus, because the IPCC is aware of 

and discusses this effect, the large-scale precipitation projections which we cite in the TSD are no

into question by the com

t called 

menter’s point, and the TSD’s discussion of precipitation projections is 

reasonable and sound. 

4.5 Future Projections of Extreme Weather Events 

Comment (4-59): 

A number of commenters (e.g., 3136.1, 3347.1, 3394.1) argue that hurricane frequency is likely to 

decrease with rising temperature. Several of these commenters (e.g., 3136.1, 3394.1) refer to a study by 

Knutson et al. (2008), which, according to the commenters, indicates that future anthropogenic warming 

is anticipated to reduce the frequency of tropical storms and hurricanes throughout the Atlantic basin 

(3136.1), or that hurricanes “are likely to be substantially rarer events under projected climate chang

(3394.1). One commenter (3136.1) states that Knutson’s results show that the simulated number of 

e” 

urricanes making landfall decreases by a greater percentage than the number of hurricanes itself. 

ng 

tor’s factual claims are 

ndamentally inconsistent with the science on which she explicitly relies.”

h

Similarly, a commenter (3347.1) argues that a key reference (Karl et al., 2008) EPA used to describe how

climate change could affect hurricanes “shows that the numbers of and frequency of hurricanes maki

landfall in North America has not changed at all” and that, “[t]he Administra

fu

Response (4-59):

We have reviewed the submitted references and the relevant assessment reports in light of these 

comments, and we find that the TSD accurately summarizes the key conclusions from the assessment 

literature on this topic. The TSD already notes that “[f]requency changes in hurricanes are currently too 

uncertain for confident projections.” Thus, while it is true that tropical cyclone (i.e., tropical storms and

hurricanes) frequency may not increase and could potentially decrease because of climate change, we 

disagree with the more definitive statements commenters make that tropical cyclone frequency will 

53 
“likely” decrease. As noted in the TSD, the assessment literature indicates that projections in frequency 

changes in tropical cyclones are currently too uncertain for confident projections. 

7

Section 6(e) includes a clear summary of the findings of the literature on this topic: 

Karl et al. (2008) indicate projections in frequency changes in tropical cyclones are 

currently too uncertain for confident projections. Some modeling studies have projected a 

decrease in the number of tropical cyclones globally due to increased stability of the 

tropical atmosphere in a warmer climate, characterized by fewer weak storms and greater 

numbers of intense storms (Meehl et al., 2007). A number of modeling studies have also 

projected a general tendency for more intense but fewer storms outside the tropics, with a 

tendency towards more extreme wind events and higher ocean waves in several regions 

associated with these deepened cyclones (Meehl et al., 2007). 

We have reviewed the Knutson et al. (2008) study referenced by several commenters. The study assesses 

the changes in large-scale climate that are projected to occur by the end of the 21st century by an 

ensemble of global climate models and finds that Atlantic hurricane and tropical storm frequencies are 

reduced. The authors state that their results “do not support the notion of large increasing trends in either 

tropical storm or hurricane frequency driven by increases in atmospheric greenhouse-gas concentrations,” 

a finding that is not inconsistent with the findings of Karl et al. (2008) in the CCSP report Weather and 

Climate Extremes in a Changing Climate (CCSP, 2008i) or Meehl et al. (2008), which are cited in the 

above paragraph. Further, we note that although the simulated results of Knutson et al. (2008) suggest that 

the frequency of both tropical storms and major hurricanes in the tropical Atlantic ocean will decrease in 

the 21st century, several other studies (Ouuchi et al., 2006; Sugi et al., 2002) assessed by Gutowski et al. 

(2008) in CCSP (2008i) suggest that a future increase of tropical cyclone frequency for the North Atlantic 

is also plausible. In addition, two other studies assessed in by Gutowski et al. (Chauvin et al., 2006; 

Emanuel et al., 2008) find that, in multimodel experiments, the sign of changes in tropical cyclone 

frequency in the North Atlantic basin depends on the climate model used. Based on a thorough review of 

the literature, Gutowski et al. (2008) conclude that future projections of tropical cyclones depend on not 

only global mean climate considerations but also regional factors that can potentially affect tropical 

cyclone behavior, such as the local potential intensity, influences of vertical wind shear and other 

circulation features. 

With respect to frequency projections for tropical cyclones globally, we agree that a number of studies 

suggest that the total number of cyclones could decrease because of the effects of elevated atmospheric 

GHG concentrations. The summary from the TSD quoted above cites Meehl et al. (2007) in making this 

point. 

In light of the uncertainties involved with tropical cyclone frequency projections and the fact that Karl et 

al. (2009), the most recent major assessment report to address the issue, reaches conclusions consistent 

with previous assessment reports, we have concluded that no changes to the TSD are necessary on this 

topic. We continue to find that the following conclusion from Gutowski et al. (2008), which is based on 

their review of more than eight separate modeling studies and other relevant information, accurately 

captures the current state of knowledge: “It is unknown how late 21st century tropical cyclone frequency 

in the Atlantic and North Pacific basins will change compared to the historical period (approximately 

1950-2006).” 

 

7

 According to IPCC terminology, “likely” conveys a 66 to 90% probability of occurrence. . See Box 1.2 of the final 

TSD for a full description of IPCC’s uncertainty terms. 

54 
For EPA’s responses to comments on the validity of observed and measured data on extreme events, 

please refer to Volume 2, Section 2.5: Extreme Weather Events. 

Comment (4-60):

A number of commenters (e.g., 0339, 1927, 3136.1, 3217.1, 3291.1, 3347.3, 3394.1,3397, 4003, 11453.1) 

argue that the projections of increasing hurricane intensity reported in the April 2009 TSD are uncertain, 

outdated, or not credible. One commenter (3136.1) argues that recent research by Vecchi and Soden 

(2007a, 2007b) suggests that future changes in the pattern of sea surface temperature may act to reduce 

tropical cyclone intensity. The commenter additionally claims that the IPCC AR4 is “an outdated 

reference” on the topic of both hurricane frequency and intensity, and that, “[EPA] simply can not use the 

IPCC AR4 as a reference for expectations of future Atlantic tropical cyclones and their impacts.” 

Another commenter (3347.3) argues as follows: “EPA’s assumption that climate change will increase 

hurricane frequency and intensity is not supported by the evidence EPA cites.” In particular, the 

commenter claims that EPA did not address the “assumptions and validation” of the following statement 

from IPCC (2007d) quoted in the TSD: “Based on a range of models, it is likely that tropical cyclones 

(tropical storms and hurricanes) will become more intense, with stronger peak winds and more heavy 

precipitation associated with ongoing increases of tropical sea surface temperature.” 

Response (4-60):

We have reviewed the submitted studies and disagree that the projections of increasing tropical cyclone 

intensity8

 reported in the April 2009 TSD were not credible or have been supplanted by conflicting 

information. The discussion of tropical cyclone intensity in the April 2009 TSD was based principally on 

the IPCC AR4 (2007d) and the CCSP (2008i) report Weather and Climate Extremes in a Changing 

Climate. We have re-reviewed these assessment reports, the TSD, and the most recent assessment report 

to analyze tropical cyclone intensity, Karl et al. (2009), and we have concluded that the information from 

the April 2009 TSD continues to accurately summarize the most current and compelling science available 

on this topic as expressed in the assessment literature. Therefore, we have retained the key conclusions 

about tropical cyclone intensity from the April 2009 TSD in the Final TSD. The TSD states the following 

in Section 6(e): 

Based on a range of models, it is likely that tropical cyclones (tropical storms and 

hurricanes) will become more intense, with stronger peak winds and more heavy 

precipitation associated with ongoing increases of tropical sea surface temperatures 

(IPCC, 2007d). Karl et al. (2008) analyze model simulations and find that for each 1.8oF 

(1oC) increase in tropical sea surface temperatures, core rainfall rates will increase by 6 to 

18%, and the surface wind speeds of the strongest hurricanes will increase by about 1 to 

8%. Storm surge levels are likely to increase because of increasing hurricane intensity 

coupled with sea level rise (Karl et al., 2009). 

We disagree with the comment (3136.1) that the IPCC AR4 is an outdated reference and note that the key 

IPCC conclusions on tropical cyclone intensity are consistent with the conclusions of both the CCSP 

report Weather and Climate Extremes in a Changing Climate (2008i) and Karl et al. (2009). Consistent 

with the relevant IPCC report (2007d), Karl et al. (2009) note the following: “As ocean temperatures 

continue to increase in the future, it is likely that hurricane rainfall and wind speeds will increase in 

response to human-caused warming.” Karl et al. (2009) also retains the conclusion from CCSP (2008i) 

that model simulations suggest that for each 1 ̊C increase in tropical sea surface temperatures, the surface 

wind speeds of the strongest hurricanes will increase by about 1-8%. 

 

8 Consistent with CCSP (2008i), EPA’s statements regarding hurricane intensity refer to maximum surface wind speeds. 

55 
We have reviewed the studies that commenters reference in support of their arguments regarding 

hurricane intensity, and we have determined that these studies neither invalidate nor contradict the overall 

conclusions from the assessment literature summarized in the TSD. Gutowski et al. (2008) in CCSP 

(2008i) and Karl et al. (2009) assess the Vecchi and Soden (2007a, 2007b) studies. Gutowski et al. (2008) 

note that the results of Vecchi and Soden (2007b) show increases in potential intensity9

 for about twothirds of the area of the Atlantic basin and slight decreases in the other one-third, along with a clear 

tendency for increased vertical wind shear and reduced mid-tropospheric relative humidity in some 

regions. According to Gutowski et al., “The net effect of these composite changes remains to be modeled 

in detail, although existing global modeling studies (Oouchi et al., 2006; Bengtsson et al., 2007) suggest 

increases in the intensities and frequencies of the strongest storms.” 

As mentioned in response 4-59, several commenters reference the hurricane frequency results for the 

North Atlantic from Knutson et al. (2008). This study also obtained model results for hurricane intensity. 

In regard to the intensity of the strongest hurricanes, Knutson et al. (2008) report the following: “In 

agreement with previous studies, the frequency and intensity of the strongest hurricanes simulated by the 

model are increased for both surface wind speed (Fig. 1 b,c) and central pressure.” 

As discussed in response 4-59, there are significant uncertainties involved with projecting tropical 

cyclone frequency. This is in regard to tropical cyclone intensity as well. Gutowski et al. (2008) discuss 

the key uncertainties (e.g., the limited capacity of climate models to adequately simulate intense tropical 

cyclones and the precise influence of factors such as vertical wind shear for different regions) in some 

detail. These uncertainties are reflected in the conclusions of the assessment literature and acknowledged 

in the TSD. Consistent with the assessment literature, the TSD does not make any definitive statements 

about tropical cyclone intensity but rather states that it is “likely” that tropical cyclones will become 

intense (see Box 1.2 of the TSD for a description of the communication of uncertainty). 

In response to the comment that we did not explicitly address the “assumptions and validations” involved 

with model-based projections of hurricane intensity within the April 2009 TSD, we have added the 

following paragraph to Section 6(e) of the final TSD: 

Sources of uncertainty involved with projecting changes in tropical cyclone activity 

include the limited capacity of climate models to adequately simulate intense tropical 

cyclones and potential changes in atmospheric stability and circulation (Karl et al., 2008). 

Taking these uncertainties into consideration, Karl et al. (2009) reached the following 

conclusion on the basis of both model- and theory-based evidence: “As ocean 

temperatures continue to increase in the future, it is likely that hurricane rainfall and wind 

speeds will increase in response to human-caused warming.” 

Please refer to Section 4.1 of this volume for EPA’s responses to comments on the validity of using 

models to project future climate change. 

Comment (4-61): 

A number of commenters (e.g., 1927, 3136.1, 3347.1, 3394.1) look to historical data as evidence that 

hurricane intensity will not increase due to elevated atmospheric GHG concentrations. 

 

9

 Potential intensity refers to the maximum intensity that can be achieved by a cyclone for a given atmospheric/oceanic 

thermodynamic state. According to CCSP (2008i), tropical cyclones usually do not reach this state due to various factors that 

may include vertical shear of the horizontal wind and oceanic cooling by cyclone-induced cooling cool water from below the 

mixed layer to the surface. 

56 
Response (4-61):

Please refer to Volume 2, Section 2.6: Extreme Weather Events for EPA’s responses to comments on the 

observed record of hurricane intensity. On the basis of the science discussed in that section and within 

response number 4-60 in this section, we conclude that trends in observed tropical cyclone activity—

though uncertain in some regions and especially for earlier time periods (prior to the satellite era)—are 

not inconsistent with the future projections for hurricane intensity summarized in the TSD. Our review 

indicates that the TSD provides a sound and reasonable summary of the scientific evidence on hurricane 

intensity as assessed by the IPCC and CCSP/USGCRP. 

Comment (4-62):

A number of commenters (e.g., 2750, 3136.1) argue that the projections of potential increases in flooding 

discussed in the TSD are not well supported by the evidence or lack credibility. According to at least one 

commenter (3136.1), there is evidence that our climate is not changing, despite increases in GHGs, in 

such a way as to increase flood events. The commenter states that several studies (Small et al., 2006; Lins 

and Slack, 1999), “do not find increases in the highest streamflow (the ones associated with flooding).” 

Another commenter (3596.3) cites several studies (Starkel, 2002; Noren, 2002; and Schimmelmann, 

2003) that the commenter states “attribute flooding to solar phenomena and not increases in greenhouse 

gases.” 

Response (4-62):

We have reviewed the TSD in light of the comments and literature submitted. Section 6(e) of the TSD 

summarizes the conclusion from Meehl et al. (2007) that the intensity of precipitation events is projected 

to increase globally, particularly in tropical and high-latitude areas that experience increases in mean 

precipitation, as well as the finding from Meehl et al. (2007) that increases in heavy precipitation events 

have been linked to increases in flooding. In addition, the TSD now summarizes information from the 

most recent assessment report to assess precipitation projections and their implications for flooding, Karl 

et al. (2009). Summarizing the findings of Karl et al., the TSD states: 

Climate models consistently project that parts of the eastern United States will experience 

increased runoff, which accumulates as streamflow and can cause flooding when heavy 

precipitation persists for weeks to months in large river basins (Karl et al., 

2009). 

As noted in the TSD, Karl et al. (2009) report that heavy downpours that are now one-in-20-year 

occurrences are projected to occur about every four to 15 years by the end of this century, depending on 

location, and that the intensity of downpours is projected to increase by 10 to 25% by the end of the 

century relative to today. According to Karl et al., floods are likely to become more common and more 

intense as regional and seasonal precipitation patterns change and rainfall becomes more concentrated 

into heavy events. Karl et al. report that floods are likely to be amplified by climate change in most 

regions. 

We reviewed the Small et al. (2006) and Lins and Slack (1999) studies referenced by commenters, as well 

as other recent literature. While the studies submitted by the commenters do not find increases in the 

highest streamflows associated with flooding, but rather that increases in low to moderate streamflows 

can occur without a concomitant increase in flooding, they on their own do not constitute a compelling 

counterargument to the conclusion from the assessment literature, as summarized in the TSD, that climate 

is changing in a way that is reasonably expected to lead to increased flooding in some locations. 

Small et al. (2006) analyzed trends in annual seven-day low, average, and high flows along with seasonal 

precipitation that is averaged over individual basins in the eastern United States. The study finds that fall 

57 
precipitation and low flows have simultaneously increased across the eastern United States but that spring 

precipitation and high flows “do not show a widespread trend.” Lins and Slack (1999) reach a more 

general conclusion that “median or average streamflow is increasing, but annual maximum flows 

(including floods) are neither increasing nor decreasing.” Lins and Slack (1999), however, do not reach 

the same conclusion as the commenter regarding the implications of their results, stating that the question 

of whether increasing atmospheric CO2 will lead to more floods and droughts “is a more problematic 

issue, and one on which current climate modeling studies may soon be able to shed some light.” 

A recent study (Collins, 2009) finds evidence in contradiction to the findings of Small et al. (2006) for the 

New England region. It investigates 28 long-term annual flood series for New England watersheds with 

dominantly natural streamflow and finds that 25 of the series show upward trends, of which 40% are 

statistically significant. The authors note that the apparent disparity with the results of Small et al. (2006) 

may be due to evaluating greater number of gage records and additional years of data. They additionally 

note that their findings “are consistent with those of Hodgkins and Dudley (2005) and McCabe and 

Wolock (2002), and also make sense in light of the recent research documenting increased frequency and 

increased intensity of events in the upper 10th percentile of the daily precipitation distribution in the 

Northeast United States over the last century...” 

We note that the CCSP (2008i) report Weather and Climate Extremes in a Changing Climate states that 

other methodologies have resulted in opposite judgments about trends in high streamflow trends as 

compared with Lins and Slack (1999). CCSP reports the following: 

A series of studies by two research groups (Lins and Slack, 1999, 2005; Groisman et al., 

2001, 2004) utilized the same set of stream gages not affected by dams. This set of gages 

represents stream flow for approximately 20% of the contiguous U.S. area. The initial 

studies both examined the period 1939-1999. Differences in definitions and methodology 

resulted in opposite judgments about trends in high streamflow. Lins and Slack (1999, 

2005) reported no significant changes in high flow above the 90th percentile. On the other 

hand, Groisman et al. (2001) showed that for the same gauges, period, and territory, there 

were statistically significant regional average increases in the uppermost fractions of total 

streamflow. However, these trends became statistically insignificant after Groisman et al. 

(2004) updated the analysis to include the years 2000 through 2003, all of which 

happened to be dry years over most of the eastern United States. They concluded that “... 

during the past four dry years the contribution of the upper two 5-percentile classes to 

annual precipitation remains high or (at least) above the average while the similar 

contribution to annual streamflow sharply declined. This could be anticipated due to the 

accumulative character of high flow in large and medium rivers; it builds upon the base 

flow that remains low during dry years...” All trend estimates are sensitive to the values 

at the edges of the time series, but for high streamflow, these estimates are also sensitive 

to the mean values of the flow. 

CCSP (2008i) includes additional detail on changes in runoff that is consistent with the information 

summarized in the TSD. It notes that changes in runoff have been observed in many parts of the world, 

with increases or decreases corresponding to changes in precipitation. It additionally reports that climate 

models suggest that runoff will increase in regions where precipitation increases faster than evaporation, 

such as high Northern latitudes. 

Our review of the literature submitted by commenter 3596.3 revealed that it has essentially no relevance 

to the question of whether human-induced climate change will exacerbate the risk of flooding. The 

submitted studies do not focus on this topic but rather other factors related to past flooding episodes in the 

relatively recent and distant past. We agree with the commenter that flooding can occur due to a variety 

58 
of factors other than CO2-induced global warming but note that this fact in no way implies that humaninduced climate change will not or could not exacerbate the risk of flooding. 

In summary, we have reviewed the TSD in light of comments on streamflow and flooding trends and 

conclude that it accurately represents the current state of the science as expressed in the assessment 

literature. In light of the conclusions of the assessment literature, as well as recent studies that are 

directionally consistent, we agree that peak streamflows will likely not increase in all regions or seasons, 

and the TSD does not state that they are expected to. However, the broad and sweeping arguments several 

commenters make regarding streamflow and flooding in general, on the basis of several qualified and 

narrowly focused studies, does not provide a compelling or sufficient basis on which to conclude that the 

conclusions of the assessment summarized in the TSD are flawed. Therefore, we disagree that the 

projections of potential increases in flooding discussed in the TSD are not well supported by evidence or 

lack credibility. 

Comment (4-63): 

Several commenters (0339, 3136.1, 3722, 13091) argue that (nontropical) storms will not become more 

intense as a consequence of elevated atmospheric GHG concentrations. For example, one commenter 

(1309.1) states: “Since global warming tends to warm the polar regions more than the equatorial zones, 

one would expect a decrease in the severity of storms.” Another commenter (3722) makes a similar 

argument, quoting Lindzen (2005) as stating the following: “According to any textbook on dynamic 

meteorology, one may reasonably conclude that in a warmer world, extra-tropical storminess and weather 

variability will actually decrease.” 

Response (4-63): 

Projected future extra-tropical cyclone activity varies by region and the TSD does not suggest a global 

increase in extra-tropical cyclone frequency. In fact, Section 6(e) of the TSD states: 

The IPCC (Meehl et al., 2007) concludes model projections show fewer mid-latitude 

storms (or extratropical, primarily cold season) averaged over each hemisphere, 

associated with the poleward shift of the storm tracks that is particularly notable in the 

Southern Hemisphere, with lower central pressures for these poleward shifted storms. 

Over North America, Gutowski et al. (2008) indicate strong mid-latitude storms will be 

more frequent though the overall number of storms may decrease. 

CCSP’s (Gutowski et al., 2008) projections are drawn citing six studies published between 2005 and 

2007, all of which produce consistent results. CCSP finds these projections are also consistent with 

observed trends over the last half of the 20th century (see Volume 2, Section 2.5 regarding these observed 

trends). 

We have reviewed Dr. Lindzen’s statements regarding extra-tropical storminess and weather variability 

and other literature referenced by the commenters, and we agree that a decrease in the meridional (northto-south) temperature gradient could act to decrease some extra-tropical storm activity, especially 

equator-ward of the high latitudes. Meehl et al. (2007) refer to a modeling study that projects reduced 

extra-tropical cyclone activity over the mid-latitudes due to reduced baroclinicity (temperature contrast) 

in the lower troposphere. However, it is also clear that many other factors influence extra-tropical cyclone 

activity in addition to the north–south temperature contrasts. For example, CCSP (Gutowski et al., 2008) 

indicate that the increased storm strength in the northeast Atlantic found by some studies may be linked to 

the poleward retreat of arctic ice and a tendency toward less frequent blocking and more frequent positive 

phase of the Northern Annular Model (NAM). On the basis of our re-examination of the TSD and 

59 
assessment literature in light of the comments, we conclude that summary of the assessment literature 

projections on extra-tropical storms in the TSD is reasonable and sound. 

Comment (4-64):

A number of commenters (e.g., 2174, 3383.1) state their support for the Findings, noting future increases 

in storm intensity as one of the environmental effects of climate change. 

Response (4-64): 

We agree that the intensity of certain types of storms is likely to increase. Please refer to Section IV.B. of 

the Findings for a description of the Administrator’s findings about projected changes in storm intensity 

due to climate change and why the risks associated with these changes provide support for the 

endangerment finding. 

Comment (4-65): 

Many commenters (e.g., 3383.1, 3455.1, 3570.1, 3574.1, 3995, 4171, 4184, 9786, 10809) state their 

support for the Findings, noting future increases in precipitation and flooding as one of the environmental 

effects of climate change. 

Response (4-65): 

We agree that projected climate changes will alter precipitation patterns and is likely to amplify flooding 

in some regions. Please refer to Section IV.B of the Findings for a description of the Administrator’s 

findings about projected changes in precipitation and flooding due to climate change and why the risks 

associated with these changes provide support for the endangerment finding. 

4.6 Future Projections of Sea Level Rise 

Comment (4-66): 

Several commenters (e.g., 3136.1, 3747.1) argue that since sea level rise rates are closely tied to global 

temperature rise, the lower-than-expected rise in global temperatures (observed temperatures have, 

according to the commenters, tracked along the low end of the IPCC range of projections) implies a sea 

level rise also near the low end of the IPCC projected range (i.e., closer to 7 inches than to 23 inches). A 

number of other commenters (e.g., 3224, 11459) argue that contrary to the IPCC projections, sea level 

will not actually rise, or that the IPCC projections are not supported by facts. Commenter 3397 argues 

that in discussing sea level rise projections, the TSD relies on inadequate scientific information and does 

not adequately address uncertainty. 

Response (4-66): 

We have reviewed the referenced studies, the TSD and the underlying assessment literature in light of 

these comments. On the basis of our review, we conclude that the TSD properly summarizes the 

assessment literature regarding the current state of scientific knowledge, uncertainties regarding the 

potential effects of dynamic ice sheet processes on global sea levels and other factors, and statements of 

likelihood and probability. 

First, we disagree that recent temperatures imply lower-than-projected future temperature change for 

reasons discussed in Volume 2 and response number 4-47 in this volume. 

60 
With regard to sea level rise projections, several very recent CCSP assessment studies suggest that, if 

anything, future sea level rise is likely to be near or above the high end of the IPCC projections. In 

Section 6(b), the TSD notes that “Dynamic processes related to ice flow not included in current models 

but suggested by recent observations could increase the vulnerability of the ice sheets to warming, 

increasing future sea level rise.” Further, the TSD summarizes CCSP (2008a), which finds that the 

Greenland and West Antarctic ice sheets show acceleration of flow and thinning, and that inclusion of 

these processes in models will likely lead to sea level projections for the end of the 21st century that 

substantially exceed the projections presented in the IPCC (2007). According to the CCSP 4.1 (2009b) 

sea level rise report and Karl et al. (2009) and as noted in the TSD, Rahmstorf (2007) and others have 

suggested that a global sea level rise of 1 m (and up to 1.4 m) is plausible within this century if increased 

melting of ice sheets in Greenland and Antarctica is added to the factors included in the IPCC estimates. 

In fact, CCSP (2009b) concludes: “Therefore, thoughtful precaution suggests that a global sea level rise 

of 39 inches (100 cm) to the year 2100 should be considered for future planning and policy discussions.” 

In light of these projections from the assessment literature, we disagree that the recent temperature trend 

implies future sea level near the low end of the IPCC projected range or that sea level will not actually 

rise, as the commenters contend. We find that the discussion of sea level rise in the TSD is reasonable and 

sound and appropriately reflects key conclusions from the assessment literature. 

Comment (4-67):

A number of commenters (e.g. 3394.1, 3596.2, 3722) argue that the TSD presents incorrect or biased 

information about the likelihood that sea level rise will accelerate over time. Several of these commenters 

cite van de Wal et al. (2008) and/or Joughin et al. (2008) as evidence for their arguments. Commenter 

3596.2, for example, cites van de Wal et al. (2008) and Joughin et al. (2008) as evidence that it is unclear 

that high rates of sea level rise should be expected in the future. 

Response (4-67): 

We have reviewed the referenced studies, the TSD and the assessment literature in light of these 

comments. On the basis of this review, we conclude that the cited studies are not inconsistent with the 

assessment conclusions on future sea level rise summarized in the TSD. 

The TSD reports the IPCC (Meehl et al., 2007) conclusion that for all SRES emission scenarios the 

average rate of sea level rise during the 21st century very likely10 exceeds the 1961-2003 average rate (1.8 

± 0.5 mm yr–1). In Section 6b (Projected Changes in Global Temperature, Precipitation Patterns, Sea 

Level Rise, and Ocean Acidification), the TSD states: 

By the end of the century (2090–2099), sea level is projected by IPCC (2007d) to rise 

between 7 and 23 inches (18 and 59 cm) relative to the base period (1980–1999). These 

numbers represent the lowest and highest projections of the 5 to 95% ranges for all SRES 

scenarios considered collectively and include neither uncertainty in carbon cycle 

feedbacks nor rapid dynamical changes in ice sheet flow. In all scenarios, the average rate 

of sea level rise during the 21st century very likely exceeds the 1961 to 2003 average rate 

(0.071 to 0.02 inches [0.18 ± 0.05 cm] yr-1). Even if GHG concentrations were to be 

stabilized, sea level rise would continue for centuries due to the time scales associated 

with climate processes and feedbacks (IPCC, 2007d). Thermal expansion of ocean water 

contributes 70 to 75% of the central estimate for the rise in sea level for all scenarios 

(Meehl et al., 2007). Glaciers, ice caps, and the Greenland Ice Sheet are also projected to 

 

10 According to IPCC terminology, “very likely” conveys a 90 to 99% probability of occurrence. . See Box 1.2 of 

the final TSD for a full description of IPCC’s uncertainty terms. 

61 
add to sea level. The IPCC projects a range of sea level rise contributions from all 

glaciers, ice caps, and ice sheets between 1.6 to 9 inches (4 to 23 cm), not including the 

possibility of rapid dynamical changes. The Antarctic ice sheet is estimated to be a 

negative contributor to sea level rise over the next century under these assumptions 

(Meehl et al., 2007). 

The van de Wal et al. (2008) and Joughin et al. (2008) studies referenced by commenters do not call into 

question any of the projections quoted above. Based on our review of these studies, we find that 

commenters misrepresent their narrowly focused conclusions. 

The Joughin et al. (2008) study examined the flow rates of the Jakobshavn Isbrae glacier, several smaller 

marine terminating outlet glaciers, and a several-hundred kilometer-long stretch of surrounding ice sheet 

along the Western flank of the Greenland Ice Sheet in order to asses the effect of surface melt-water 

lubrication on ice flow. The study, which concentrates on one specific melt mechanism in one part of 

Greenland, finds that “seasonal melt’s influence on ice flow is likely confined to those regions dominated 

by ice-sheet flow.” The authors make no statements on the much broader topic of sea level rise 

projections, which were not the focus of their study. However, the study does mention recent melting 

speedups and their potential causes. It states: 

It is unlikely that recent outlet-glacier speedups (14, 19–21) were directly caused by increased 

surface meltwater lubrication during recent warmer summers. Instead, these large speedups were 

likely driven by processes that caused ice-front retreat and reduced back-stress (17, 19, 20, 22), 

such as declining sea-ice extent near calving fronts (18). The recent period of warmer summers 

(23) might also enhance ice front retreat through increased hydro-fracturing in water-filled 

crevasses near the calving front (18), which represents a process whereby surface meltwater 

influences glacial flow through means other than directly enhancing basal lubrication. 

In regard to the specific melting mechanism the study focuses on, the authors note that “Our results thus 

far suggest that surface meltwater–enhanced lubrication likely will have a substantive but not catastrophic 

effect on the Greenland Ice Sheet’s future evolution.” They also note that ice loss on the ice sheet’s 

western flank, which is comparatively free of outlet glaciers, is due largely to melt, and that “surface 

meltwater–induced speedup may influence large regions of the ice sheet in a warming climate.” 

The van de Wal et al. (2008) study presents ice velocity measurements from the major ablation area along 

the western margin of the Greenland ice sheet since 1991 and finds that a positive-feedback mechanism 

between melt rate and ice velocity “appears to be a seasonal process that may have only a limited effect 

on the response of the ice sheet to climate warming over the next decades.” Like Joughin et al. (2009), 

the study focuses on a specific process (the interaction between meltwater production and ice velocity) 

and therefore provides very little support for the broad and sweeping views expressed by commenters 

regarding future sea level rise. 

 

It is important to note that the SRES-based projections underlying the IPCC conclusion that the average 

rate of sea level rise during the 21st century will very likely exceed the 1961-2003 average rate explicitly 

exclude the possibility of rapid dynamic changes in the Greenland and WAIS ice sheets. Yet these 

possible dynamic changes are the precise topic the van de Wal et al. (2008) and Joughin et al. (2008) 

studies focus on, and the studies concentrate narrowly on a few specific mechanisms that have been 

proposed as potential contributors to dynamic changes. As discussed in the TSD [Section 6(b)], CCSP 

(2008a) found that inclusion of dynamic ice flow processes in models would likely lead to projections 

that substantially exceed the IPCC projections. The van de Wal et al. (2008) and Joughin et al. (2008) 

studies found that some of these processes may not have important contributions to glacial flow, but the 

62 
studies acknowledge that other mechanisms may increase the rate of ice loss (and therefore sea level rise) 

in response to warming. 

In summary, our review of the Joughin et al. (2008) and van de Wal et al. (2008) papers revealed that 

commenters’ interpretation of these studies is not in accord with the nuanced and narrowly focused 

conclusions of the papers. On the basis of our review of the assessment literature, the referenced studies, 

and the TSD, we find that the TSD provides an accurate and sound summary of the current state of 

scientific knowledge about future sea level rise. 

Finally, we refer the commenters to Section IV.B of the Findings, which describes the information on 

which the Administrator relied. The discussion of sea level rise in the Findings is fully consistent with the 

assessment literature conclusions summarized in the TSD. 

Comment (4-68):

One commenter argues that the IPCC’s most likely estimate of sea level rise is only 14 inches. 

Response (4-68):

IPCC (2007) provided projections of future sea level rise in the form of ranges for each emission scenario. 

For example, for the projected range for SRES A1B was approximately 8 to 19 inches (0.21–0.48 meters). 

IPCC did not provide best estimates for individual scenarios or across all scenarios. To ensure 

consistency with the findings of the assessment literature, EPA used ranges for sea level rise projections 

in the TSD. Although the best estimate suggested by the commenter is within the range provided by 

IPCC, the commenter did not provide evidence as to how they reached the conclusion that the “most 

likely” estimate of sea level is 14 inches. We are not aware of any study that has reached this conclusion. 

Additionally, as discussed in response 4-67, the IPCC range does not include dynamic processes related 

to ice flow that could lead to future sea level rise in excess of the upper end of the IPCC estimate. 

Comment (4-69): 

One commenter (3394.1) cites Bamber et al. (2009) as a reference for the following statement: “[T]he 

TSD and the Proposed Endangerment Finding rely on outdated projections of future sea level rise. The 

most recent study assessing sea level rise modeling results concludes that previous estimates, including 

those calculated by the IPCC, are roughly double currently supportable sea level rise expectations.” 

Response (4-69): 

We have reviewed the Bamber et al. (2009) study referenced by the commenter, which reassesses the 

potential contribution to eustatic (the global change due to water mass added to the oceans) and regional 

sea level from a rapid collapse of the West Antarctic Ice Sheet (WAIS). Our review revealed that the 

commenter’s interpretation of the study is only slightly reflective of its actual findings. 

Bamber et al. (2009) find that previous assessments have substantially overestimated the likely primary 

contribution of a disintegration of the WAIS, obtaining a value for the global, eustatic sea level rise 

contribution of about 3.3 meters, with important regional variations. Thus, the study suggests that the 

IPCC projections for sea level rise due to a collapse of the WAIS (which IPCC notes would occur over 

many centuries) may be somewhat too high, though we note that this remains an active area of research 

and that the study has not yet been incorporated into the assessment literature. However, the commenter’s 

broad and sweeping statement that “previous estimates [of sea level rise], including those calculated by 

IPCC, are roughly double current supportable sea level rise expectations” has no basis in the results of the 

study. This statement could be read as implying that the study suggests that the IPCC sea level rise 

63 
projections for this century or owing to other sources may be too high, when in fact it does nothing of the 

sort. Rather, Bamber et al. (2009) focus on the much narrower question of the potential contribution to sea 

level rise from an eventual collapse of the WAIS that would occur over a period of centuries. The study 

does not address near-term sea level rise projections and explicitly excludes mass losses from other 

sources such as Greenland, glaciers, and ice caps. In addition, the study finds that the maximum increase 

due to a collapse of the WAIS is concentrated along the Pacific and Atlantic seaboard of the United 

States, “where the value is about 25% greater than the global mean, even for a case of a partial collapse.” 

Therefore, for the United States, the expected change from a collapse of the WAIS would, according to 

the study, be about 4.1 meters, or only about 18% less than the lower bound of the 5–6 meter estimate 

reported by IPCC (Meehl et al., 2007). 

Comment (4-70):

Many commenters (e.g., 0362, 1672, 3383.1, 3424.1, 3455.1, 3574.1, 4171, 4184, 4249, 10809, 10838, 

11342) state their support for the Findings, noting future rise in sea levels as one of the environmental 

effects of climate change. 

Response (4-70): 

We agree that climate change is causing sea level to rise. Please refer to Section IV.B of the Findings for 

a description of the Administrator’s findings about projected sea level rise and why they provide support 

for the endangerment finding. 

4.7 Abrupt Climate Change 

Comment (4-71): 

A number of commenters (e.g., 3291, 3722, 5846, 5858, 7037, 10394,11459) argue that there is little or 

no possibility that sea levels will rise by a large amount due to climate change, even over long-time 

scales. Several commenters focus their claims specifically on 20 feet of sea level rise, arguing that such 

an increase is entirely implausible. A book submitted or referenced by a number of commenters (Singer 

and Avery, 2008) makes this claim; it argues that there is not enough ice to trigger a 20 foot rise in sea 

level. 

One commenter (3722) states that “many scientists have discredited predictions of widespread 

deglaciation, large sea level rises and resultant coastal flooding.” Citing Ollier (2007), the commenter 

goes on to argue that “[c]atastrophic scenarios of sea inundation caused by collapsing ice sheets are 

predicated on models of hypothetical glaciers and ice sheets, and not those that actually exist in 

Greenland and Antarctica. As such, these conjectures are not premised in the physics of glaciers.” 

Response (4-71): 

In light of these comments, we have reviewed the referenced materials, the assessment literature, and the 

TSD (extreme sea level rise is discussed in Section 6(f), Abrupt Climate Change and High-Impact 

Events). We find that the TSD properly summarizes the assessment literature regarding the state of the 

science on this issue and that the commenters do not provide any substantive evidence that the assessment 

literature has reached flawed conclusions. The TSD states: 

The rapid disintegration of the Greenland Ice Sheet (GIS), which would raise sea levels 

23 feet (7 meters), is another commonly discussed abrupt change. Clark et al. (2008) 

report that observations demonstrate that it is extremely likely that the Greenland Ice 

Sheet is losing mass and that this loss has very likely been accelerating since the mid64 
1990s. In the CCSP (2009c) report Past Climate Variability and Change in the Arctic and 

at High Latitudes, Alley et al. (2009) find a threshold for ice-sheet removal from 

sustained summertime warming of 9 F (5°C), with a range of uncertainties from 3.6 to 

12.6 F (2° to 7°C). Meehl et al. (2007), in the IPCC report, suggest the complete melting 

of the GIS would only require sustained warming in the range of 3.4 to 8.3 F (1.9°C to 

4.6°C) (relative to the pre-industrial temperatures) but suggest it would take many 

hundreds of years to complete. 

A collapse of the West Antarctic Ice Sheet (WAIS), which would raise seas 16 to 20 feet 

(5 to 6 meters), has been discussed as a low probability, high-impact response to global 

warming (NRC, 2002; Meehl et al., 2007). The weakening or collapse of ice shelves, 

caused by melting on the surface or by melting at the bottom by a warmer ocean, might 

contribute to a potential destabilization of the WAIS. Recent satellite and in situ 

observations of ice streams behind disintegrating ice shelves highlight some rapid 

reactions of ice sheet systems (Lemke et al., 2007). Clark et al. (2008) indicate that while 

ice is thickening over some higher elevation regions of Antarctica, substantial ice losses 

from West Antarctica and the Antarctic Peninsula are very likely occurring and that 

Antarctica is losing ice on the whole. Ice sheet models are only beginning to capture the 

small-scale dynamic processes that involve complicated interactions with the glacier bed 

and the ocean at the perimeter of the ice sheet (Meehl et al., 2007). These processes are 

not represented in the models used by the IPCC to project sea level rise. These models 

suggest Antarctica will gain mass due to increasing snowfall (although recent studies find 

no significant continent-wide trends in snow accumulation over the past several decades; 

Lemke et al., 2007), reducing sea level rise. But it is possible that acceleration of ice 

discharge could become dominant, causing a net positive contribution. Given these 

competing factors, there is presently no consensus on the long-term future of the WAIS 

or its contribution to sea level rise (Meehl et al., 2007). 

We have reviewed the materials commenters referenced in support of their contention that the science 

summarized in the TSD is inadequate and find that these references are inconsistent with the vast majority 

of peer-reviewed literature on this topic. Singer and Avery (2008) has not been peer-reviewed or 

undergone a thorough scientific evaluation of its claims. Our review of this book revealed no basis in the 

peer-reviewed literature for Singer and Avery’s claim that a sea level rise of 20 feet “cannot happen 

because there’s not enough ice to trigger it.” 

Based on our review of the relevant literature, we find that the Ollier (2007) paper, which also did not 

undergo peer-review, is narrow and incomplete in its assessment of the issues when compared to the 

scope and breadth of the assessment literature. Although the Ollier (2007) paper is correct that 

temperature and present climate are not the only determinants of ice sheet flow and mass (other factors 

such as grounding-line deposition also have important effects), a clear conclusion from the body of peerreviewed literature is that large-scale changes in climate, such as continued increases in global average 

temperature, would significantly affect ice sheet mass over time. 

As the assessment conclusions summarized in the TSD make clear, the probability of a large-scale 

collapse of the Greenland ice sheet occurring within the next several centuries is low. We note, however, 

that the body of literature contains considerable evidence sea level could rise 20 feet within the next 

several centuries were such an event to occur. 

65 
Comment (4-72): 

One commenter (3136.1) argues that “[i]n research published subsequent to the IPCC AR4, the threat of a 

very rapid sea level rise from a rapid deglaciation has been greatly downplayed.” The commenter argues 

that IPCC did not include loss from dynamic flow processes in Greenland or Antarctica because “a basis 

in the published literature is lacking” and states that since the publication of the IPCC report, several 

studies (Joughin et al., 2008; van de Wal et al., 2008) have appeared in the scientific literature suggesting 

that glacial flow rate changes in Greenland may not be great cause for concern because warming should 

not lead to catastrophic ice discharge from increasing rates of glacial flow. The commenter concludes: 

“Results such as these provide added support of the modest IPCC estimates of 21st century sea level rise 

and greatly weaken the argument for disastrous sea level rise.” 

Several other commenters (e.g., 2898.1, 3323.1, 4003, 4041.1, 4932.1, 5158) similarly argue that the 

concern for warming temperatures causing Greenland and/or the WAIS to rapidly shed their ice has been 

diminished by new results indicating little evidence for the operation of such processes. One commenter 

(2898.1) cites Anandakrishnan et al. (2007); Alley et al. (2007); and Anderson (2007) as evidence that 

“[t]he WAIS is more stable than scientists had previously assumed.” 

Response (4-72): 

We have reexamined the TSD and assessment literature in light of these comments and the referenced 

literature. On the basis of this review, we find that the findings of the studies referenced by commenters 

are largely consistent with the conclusions from the assessment literature that are summarized in the TSD. 

As discussed in response 4-67, the Joughin et al. (2008) study referenced by one commenter (3136.1) 

finds that one set of glaciers along the Western flank of the Greenland Ice Sheet is relatively insensitive to 

one specific feedback mechanism (surface-meltwater enhanced lubrication). The study concludes: 

Surface meltwater–enhanced basal lubrication has been invoked previously as a feedback 

that would hasten the Greenland Ice Sheet's demise in a warming climate (6–8). Our 

resultsshow that several fast-flowing outlet glaciers, including Jakobshavn Isbrae, are 

relatively insensitive to this process. Previously reported acceleration and near-doubling 

of speed of many outlet glaciers from other effects such as back-stress reduction are more 

than an order of magnitude greater (14, 19–21) than the observed melt-induced 

acceleration. South of Jakobshavn, however, the ice sheet's western flank is 

comparatively free of outlet glaciers, and ice loss is largely due to melt. Numerical

models appropriate to this type of sheet flow and that include a parameterization of 

surface meltwater–induced speedup predict 10 to 25% more ice loss in the 21st century 

than models without this feedback (8). Thus, surface meltwater–induced speedup may 

influence large regions of the ice sheet in a warming climate. Although our data provide 

the most comprehensive observationsto date, more data are needed to quantify the 

relative importance of melt- and calving-front–induced changes in ice flow in controlling 

near-future ice-sheet mass balance. Our resultsthus far suggest that surface meltwater–

enhanced lubrication likely will have a substantive but not catastrophic effect on the 

Greenland Ice Sheet’s future evolution. 

In addition to noting that surface melt-water enhanced lubrication likely will have a substantive effect, the 

authors note that other mechanisms of acceleration are still present and that surface meltwater-induced 

feedbacks are still applicable to other portions of the ice sheet. 

As described in response 4-67, Van de Wal et al. (2008) presents ice velocity measurements from the 

major ablation area along the western margin of the Greenland ice sheet since 1991 and finds that a 

positive-feedback mechanism between melt rate and ice velocity “appears to be a seasonal process that 

66 
may have only a limited effect on the response of the ice sheet to climate warming over the next decades.” 

However, the authors also conclude that “Longer observational records with high temporal resolution in

other ablation areas of the ice sheet are necessary to test the importance of the positive-feedback 

mechanism between meltrates and ice velocities. At present, we cannot conclude that this feedback is 

important. We do see a significant increase of the ablation rate, which is likely related to climate warming, 

but it remains to be seen if this is likely to be amplified by increasing annual ice velocities.” 

Based on our review of these studies, we conclude that neither the paper by Joughin et al. nor that by van 

de Wal et al. contradict the data that shows that the net mass balance of the Greenland ice sheet is 

currently negative, nor that there is acceleration of ablation and thinning of the ice sheets, nor that there is 

a temperature above which the Greenland ice sheet will be committed to disintegration. 

Further, we find that other recent studies such as Mernild et al. (2009), Shepherd and Wingham (2007), 

and Rignot et al. (2008) provide additional analysis of the response of Greenland’s ice sheet to warming. 

Rignot et al. conclude: 

We reconstructed the Greenland ice sheet total annual mass budget from 1958–2007. The 

ice sheet was losing mass during the warm period before the 1970s, was close to balance 

during the relatively cold 1970s and 1980s, and lost mass rapidly as climate got warmer 

in the 1990s and 2000s with no indication of a slow down. Hence, the temporal 

variability in mass balance is significant and closely follows climate fluctuations. Most 

likely, the ice sheet mass deficit in the 1925–1935 warm period was larger than in 1958. 

In the last 11 years, the total mass deficit tripled. 

The study reveals that changes in glacier flow have an important influence on ice sheet mass and lends 

further support to the conclusions of the assessment reports as summarized in the TSD, including the 

statement from Clark et al. (2008) that “observations demonstrate that it is extremely likely that 

Greenland Ice Sheet is losing mass and that this loss has very likely been accelerating since the mid1990s.” 

We have reviewed the papers submitted by commenter 2898.1 and find that these papers do not call into 

question the assessment literature conclusions on the WAIS as summarized in the TSD. The Alley (2007) 

paper finds that recent Antarctic changes cannot be attributed to sea level rise, thus “strengthening earlier 

interpretations that warming has driven ice-sheet mass loss.” The paper additionally notes that the study 

shows that the similar behavior of various ice sheets with active sedimentary systems over time indicates 

common climatic forcing, “which demonstrably can have very large and rapid effects on ice sheets.” The 

Anandakrishnan (2007) paper similarly focuses on the effects of sea level rise on glacial stability, and it 

does not directly address the effects of warming on ice-sheet mass loss. The papers suggest that the threat 

of sea level rise to ice sheet stability may not be as serious as once thought but do not assess other sources 

of instability such as surface and ocean warming. 

Anderson (2007) discusses both the Alley (2007) and Anandakrishnan (2007) papers. He notes that both 

papers demonstrate that grounding-line (the juncture between the ice shelf and the part of the ice sheet 

that is thick enough to ground on the sea floor) deposition serves to stabilize ice streams, suggesting a 

decreased role for sea level in explaining past and current changes, but also that “[r]ecent changes in the 

ice sheet have raised concern that it may be retreating again.” He adds that future research “should focus 

on other ice streams, especially those that currently display signs of instability, to get at the causes of this 

instability.” 

Shepherd and Wingham (2007) assess both the Antarctic and Greenland ice sheets with regard to glacial 

acceleration (increases in the flow rate into the sea). The authors note that satellite observations reveal 

67 
“that glacier accelerations of 20 to 100% have occurred over the last decade.” The study notes that there 

are a number of uncertainties involved in assessing whether the observed accelerations of particular ice 

streams and glaciers may be sustained, or even increase, in the future but clearly suggests that a general 

acceleration is possible. The abstract states the following: 

After a century of polar exploration, the past decade of satellite measurements has painted 

an altogether new picture of how Earth’sice sheets are changing. As global temperatures 

have risen,so have rates of snowfall, ice melting, and glacier flow. Although the balance 

between these opposing processes has varied considerably on a regional scale, data show 

that Antarctica and Greenland are each losing mass overall. Our best estimate of their 

[combined balance is about 125 gigatons per year of ice, enough to raise sea level by 0.35 

millimeters per year. This is only a modest contribution to the present rate of sea level 

rise of 3.0 millimeters per year. However, much of the loss from Antarctica 

and Greenland is the result of the flow of ice to the ocean from ice streams and glaciers, 

which has accelerated over the past decade. In both continents, there are suspected 

triggers for the accelerated ice discharge—surface and ocean warming, respectively—

and, over the course of the 21st century, these processes could rapidly counteract the 

snowfall gains predicted by present coupled climatemodels. 

In summary, we find that the referenced literature does not contradict any of the principal conclusions on 

abrupt climate change from the assessment literature, as summarized in the TSD. On the basis of our 

review of the TSD, the assessment literature, the referenced studies, and other recent literature, we find 

that the TSD provides a reasonable and sound summary of the most current and compelling science on 

potential future changes in the Greenland and West Antarctic Ice Sheets. 

Comment (4-73):

Two commenters (3394.1, 3427.1) claim that EPA conducted a “crystal ball inquiry” in terms of its 

discussion of abrupt climate change. A commenter (3394.1) further objects that if the Administrator is 

going to refer to such “high impact events” in the finding, then the TSD needs to evaluate the probability 

of such events. The commenter further argues that EPA's analysis is “little more than speculation,” and 

that most of the historical abrupt climate change examples cited were due to natural variability and that 

therefore abrupt climate change that may occur in the future could be caused naturally as well. 

Response (4-73):

With regard to potential abrupt change, the TSD summarizes the findings of the assessment literature 

regarding possible mechanisms for abrupt climate change. The TSD is clear that the potential for abrupt 

climate change is not well understood. In fact, it states: “Potential abrupt climate change implications in 

the United States are not addressed in Section 7 through 14 (the U.S. sectoral impacts) because they 

cannot be predicted with confidence, particularly for specific regions.” 

It does not follow, however, that there is no place for a summary of the science on abrupt climate change 

in the TSD, which appropriately reflects the level of certainty in the science. To the contrary, we find that 

this discussion is both reasonable and necessary. As the NRC (2002) states: 

 ...greenhouse warming and other human alterations of the Earth system may increase the 

possibility of large, abrupt, and unwelcome regional or global climatic events. The abrupt 

changes of the past are not fully explained yet, and climate models typically 

underestimate the size, speed, and extent of those changes. Hence, future abrupt changes 

cannot be predicted with confidence, and climate surprises are to be expected. 

68 
The TSD references natural causes of historic abrupt change events because until recently there were no 

anthropogenic forcings that were of the same magnitude as, for example, the orbital changes which have 

forced abrupt climate changes of the past. While it may be difficult to determine the probability of any 

given mode of abrupt change, given the historical record of other abrupt changes it would be surprising if 

a forcing of several Watts per meter square (W/m2

) did not lead to some such changes in the future. We 

note that the Administrator is not relying on the possibility of abrupt change in making her endangerment 

finding. 

Comment (4-74):

Some commenters argue that EPA understated the risks of abrupt climate change. One commenter (2895) 

objects that because the TSD in Sections 7 through 14 do not account for abrupt change (in Section 6), the 

TSD is understating likely risks to health and welfare and has made a “very conservative” finding. One 

commenter (3500.1) provided a reference (Lenton et al., 2008) listing a number of tipping points in the 

earth system. Commenter (8015) worries that we do not yet know when dangerous feedback loops 

involving retreating ice or permafrost CH4 releases will “trigger precipitous climate change which may 

lead to catastrophic conditions for billions of humans,” and urges immediate corrective actions, 

bemoaning the delay of at least a decade since the science has been well understood. 

Response (4-74):

Although we understand the commenters’ concern that the TSD may be underestimating the probability 

and severity of a number of risks, the approach we have taken has been to rely on the scientific consensus 

as expressed throughout the major assessments. The TSD includes references to a number of the potential 

tipping points described in Lenton et al., such as changes in weather patterns and megadroughts, slowing 

of the meridional overturning circulation, disintegration of major ice sheets, interactions with the polar 

ozone holes, disappearance of summer sea ice, and release of CH4 from clathrates and permafrost soils. 

Although these tipping points are of great concern, the TSD primarily summarizes the findings of the 

assessment literature for reasons discussed in the Findings and Volume 1 of this Response to Comments 

document, which for the most part do not yet make definitive statements about the probability of some of 

these events. 

Comment (4-75):

Several commenters voice their support for the findings and express concern regarding the potential to 

reach a “tipping point” (e.g., 0276, 0731, 2895, 3500.1, 4817, 8015) in GHG concentrations. Specifically, 

they mention the potential to set off large-scale events such as the collapse of the Greenland ice sheet, 

significant CH4 releases from thawing permafrost (e.g., 0742, 1520), and glacier melting leading to rising 

sea levels (4991, 9347). One commenter (9405) stated that extrapolating current trends into the future 

reveals the potential for “catastrophic harm.” 

Response (4-75); 

We agree that climate change has the potential to cause large-scale changes such as a collapse of the 

Greenland ice sheet, though, as noted in the TSD, such a change would take many hundreds of years to 

complete. Please refer to Section 6(f) of the TSD for EPA’s summary of the current scientific 

understanding of abrupt climate change and high-impact events as reflected in the assessment literature. 

69 
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