# U.S. Climate Change Science Program 

# Synthesis and Assessment Product 2.2

March 2007 

# The First State of the Carbon Cycle Report The North American Carbon Budget and Implications for the Global Carbon Cycle

U.S. Climate Change Science Program 

# Synthesis and Assessment Product 1.3 

# Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

## FEDERAL EXECUTIVE TEAM

Director, Climate Change Science Program:

........................................... William J. Brennan
Director, Climate Change Science Program Office:

................................ Peter A. Schultz
Lead Agency Principal Representative to CCSP; Deputy Under Secretary of Commerce for Oceans and Atmosphere, National Oceanic and Atmospheric Administration:

............................... Mary M. Glackin
Product Lead, Chief Scientist, Physical Sciences Division, Earth Systems Research Laboratory, National Oceanic and Atmospheric Administration

.......................................................................................... Randall M. Dole
Synthesis and Assessment Product Coordinator, Climate Change Science Program Office:

............................................... Fabien J.G. Laurier



## EDITORIAL AND PRODUCTION TEAM

Co-Chairs............................................................................................................ Randall M. Dole, NOAA Martin P. Hoerling, NOAA Siegfried Schubert, NASA Federal Advisory Committee Designated Federal Official....................... Neil Christerson, NOAA Scientific Editor .............................................................................................. Jessica Blunden, STG, Inc. Scientific Editor................................................................................................. Anne M. Waple, STG, Inc. Technical Advisor.............................................................................................. David J. Dokken, USGCRP Graphic Design Lead

............................................................................... Sara W. Veasey, NOAA Graphic Design Co-Lead

......................................................................... Deborah B. Riddle, NOAA Designer

................................................................................................... Glenn M. Hyatt, NOAA Designer

................................................................................................... Deborah Misch, STG, Inc. Designer

................................................................................................... Christian Zamarra, STG, Inc. Copy Editor

.............................................................................................. Anne Markel, STG, Inc. Copy Editor

.............................................................................................. Lesley Morgan, STG, Inc. Copy Editor

.............................................................................................. Susan Osborne, STG, Inc. Copy Editor

.............................................................................................. Susanne Skok, STG, Inc. Copy Editor

.............................................................................................. Mara Sprain, STG, Inc. Technical Support.............................................................................................. Jesse Enloe, STG, Inc.

This Synthesis and Assessment Product described in the U.S. Climate Change Science Program (CCSP) Strategic Plan, was prepared in accordance with Section 515 of the Treasury and General Government Appropriations Act for Fiscal Year 2001 (Public Law 106-554) and the information quality act guidelines issued by the Department of Commerce and NOAA pursuant to Section 515 <http://www.noaanews.noaa.gov/stories/iq.htm>. The CCSP Interagency Committee relies on Department of Commerce and NOAA certifications regarding compliance with Section 515 and Department guidelines as the basis for determining that this product conforms with Section 515. For purposes of compliance with Section 515, this CCSP Synthesis and Assessment Product is an “interpreted product” as that term is used in NOAA guidelines and is classified as “highly influential”. This document does not express any regulatory policies of the United States or any of its agencies, or provide recommendations for regulatory action.

# The First State of the Carbon Cycle Report The North American Carbon Budget and Implications for the Global Carbon Cycle

Synthesis and Assessment Product 1.3 Report by the U.S. Climate Change Science Program

and the Subcommittee on Global Change Research

EDITED BY: Randall M. Dole 

# Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change4 

### December, 2008

Members of Congress:
On behalf of the National Science and Technology Council, the U.S. Climate Change Science Program (CCSP) is pleased to transmit to the President and the Congress this Synthesis and Assessment Product (SAP) Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change . This is part of a series of 21 SAPs produced by the CCSP aimed at providing current assessments of climate change science to inform public debate, policy, and operational decisions. These reports are also intended to help the CCSP develop future program research priorities.
The CCSP’s guiding vision is to provide the Nation and the global community with the science-based knowledge needed to manage the risks and capture the opportunities associated with climate and related environmental changes. The SAPs are important steps toward achieving that vision and help to translate the CCSP’s extensive observational and research database into informational tools that directly address key questions being asked of the research community.
This SAP addresses current capabilities to integrate observations of the climate system into a consistent description of past and current conditions through the method of reanalysis. In addition, this report assesses present capabilities to attribute causes for climate variations and trends over North America during the reanalysis period, which extends from the mid-twentieth century to the present. It was developed with broad scientific input and in accordance with the Guidelines for Producing CCSP SAPs, the Information Quality Act (Section 515 of the Treasury and General Government Appropriations Act for Fiscal Year 2001 [Public Law 106-554]), and the guidelines issued by the Department of Commerce and the National Oceanic and Atmospheric Administration pursuant to Section 515.
We commend the report’s authors for both the thorough nature of their work and their adherence to an inclusive review process.

Sincerely,

John H. Marburger III Director, Office of Science and Technology Policy Executive Director, Committee on Climate Change Science and Technology Integration Samuel W. Bodman Secretary of Energy Vice Chair, Committee on Climate

Change Science and Technology Integration Carlos M. Gutierrez Secretary of Commerce Chair, Committee on Climate Change

Science and Technology Integration John H. Marburger III Director, Office of Science and Technology Policy Executive Director, Committee on Climate Change Science and Technology Integration Samuel W. Bodman Secretary of Energy Vice Chair, Committee on Climate

Change Science and Technology Integration Carlos M. Gutierrez Secretary of Commerce Chair, Committee on Climate Change

Science and Technology IntegrationITABLE OF CONTENTS Abstract

....................................................................................................................................................... VII

Preface

............................................................................................................................................................ XI

Executive Summary

................................................................................................................................. 1


## CHAPTER



# 1

............................................................................................................................................................................ 5 Introduction

1.1 Reanalysis

...................................................................................................................................................... 5 1.2 Attribution

................................................................................................................................................... 7 1.3 Connections between Reanalysis and Attribution

..................................................................... 8 1.3.1 Steps In Climate Science

..................................................................................................... 8 1.3.2 Further Comments

............................................................................................................... 8 1.4 Reanalysis Applications and Uses

....................................................................................................... 9



# 2

.......................................................................................................................................................................... 11 Reanalysis of Historical Climate Data for Key Atmospheric Features

Key Findings

....................................................................................................................................................... 11 2.1 Climate Reanalysis and its Role Within a Comprehensive Climate Observing System

...................................................................................................................... 12 2.1.1 Introductio n

......................................................................................................................................... 12 2.1.2 What is a Climate Analysis?

.......................................................................................................... 12 2.1.3 What is a Climate Reanalysis?

..................................................................................................... 16 2.1.4 What Role Does Reanalysis Play within a Climate Observing System?

............................ 17 2.2 Role of Reanalysis in Understanding Climate Processes and Evaluating Climate Models

....................................................................................................................................... 19 2.2.1 Introduction

................................................................................................................................. ....... 19 2.2.2 Assessing Systematic Errors

.......................................................................................................... 20 2.2.3 Inferences about Climate Forcing

................................................................................................ 21 2.2.4 Outlook

................................................................................................................................................ 22 2.3 Using Current Reanalyses to Identify and Understand Major Seasonal-to-Decadal

Climate Variations

................................................................................................................................. 23 2.3.1 Climate Variability

............................................................................................................................ 23 2.3.2 Reanalysis and Climate Variability

.............................................................................................. 26 2.4 Climate Trends in Surface Temperature and Precipitation Derived from Reanalyses Versus from Independent Data

......................................................................................................... 32 2.4.1 Trend Comparisons: Reanalyses Versus Independent Measurements

.............................. 33 2.4.2 Factors Complicating the Calculation of Trend

....................................................................... 36 2.4.3 Outlook

................................................................................................................................................ 38 2.5 Steps Needed to Improve Climate Reanalyis

............................................................................ 39

2.5.1 Instrument and Sampling Issues

.................................................................................................. 39 2.5.2 Modeling and Data Assimilation Issues

..................................................................................... 43II 

# 3

.......................................................................................................................................................................... 47 Attribution of the Causes of Climate Variations and Trends over North America during the Modern Reanalysis Period

Key Findings

...................................................................................................................................................... 47 Introduction

..................................................................................................................................................... 49 3.1 Climate Attribution and Scientific Methods used for Establishing Attribution

.......... 51

3.1.1 What is Attribution?

......................................................................................................................... 51 3.1.2 How is Attribution Performed?

...................................................................................................... 53 3.2 Present Understanding of North American Annual Temperature and Precipitation Climate Trends from 1951 to 2006

................................................................................................ 61 3.2.1 Summary of IPCC Fourth Assessment Repor t

......................................................................... 61 3.2.2 North American Annual Mean Temperature

........................................................................... 61 3.3 Present Understanding of United States Seasonal and Regional Differences in Temperature and Precipitation Trends from 1951 to 2006

................................................ 69 3.3.1 Introduction

........................................................................................................................................ 69 3.3.2 Temperature Trends

........................................................................................................................ 70 3.3.3 Precipitation Trends

......................................................................................................................... 76 3.4 Nature and Cause of Apparent Rapid Climate Shifts from 1951 to 2006

.................... 78

3.4.1 Introduction

........................................................................................................................................ 78 3.4.2 Defining Rapid Climate Shifts

...................................................................................................... 80 3.4.3 Mechanisms for Rapid Climate Shifts

....................................................................................... 80 3.4.4 Rapid Climate Shifts since 1950

.................................................................................................. 80 3.5 Understanding of the Causes for North American High-Impact Drought Events for 1951 to 2006

..................................................................................................................................... 83 3.5.1 Introduction

........................................................................................................................................ 83 3.5.2 Definition of Drought

...................................................................................................................... 84 3.5.3 Drought Causes

................................................................................................................................ 84 

# 4

.......................................................................................................................................................................... 93 Recommendations

.................................................................................................................... 93

4.1 Need for a Systematic Approach to Climate Analysis and Reanalysis

............................ 94 4.2 Recommendations for Improving Future Climate Analyses and Reanalyses

............... 94 4.3 Need for Improved Climate Attribution

................................................................................... 100 4.4 Recommendations for Improving Climate Attribution Capabilities

.............................. 101TABLE OF CONTENTSIII Appendix A

.................................................................................................................................. 105 Data Assimilation

................................................................................................................................... 105
Appendix B

.................................................................................................................................. 109 Data and Methods used for Attribution

................................................................................ 109 B.1 Observational Data

........................................................................................................................... 109 B.2 Climate Model Simulation Data

.................................................................................................... 109 B.2 Data Analysis and Assessment

...................................................................................................... 110

Glossary and Acronyms

..................................................................................................................... 111

References

................................................................................................................................................... 117TABLE OF CONTENTSIV 

## AUTHOR TEAM FOR THIS REPORT

Preface Convening Lead Author: Randall Dole, NOAA

Executive Summary Convening Lead Author: Randall Dole, NOAA Lead Authors: Martin Hoerling, NOAA; Siegfried Schubert, NASA
Chapter 1 Convening Lead Author: Randall Dole, NOAA Lead Author: Martin Hoerling, NOAA
Chapter 2 Convening Lead Author: Siegfried Schubert, NASA Lead Authors: Phil Arkin, University of Maryland; James Carton, University of Maryland; Eugenia Kalnay, University of Maryland; Randal Koster, NASA Contributing Authors: Randall Dole, NOAA ; Roger Pulwarty, NOAA
Chapter 3 Convening Lead Author: Martin Hoerling, NOAA Lead Authors: Gabriele Hegerl, University of Edinburgh; David Karoly, University of Melbourne; Arun Kumar, NOAA; David Rind, NASA Contributing Author: Randall Dole, NOAA
Chapter 4 Convening Lead Author: Randall Dole, NOAA Lead Authors: Martin Hoerling, NOAA; Siegfried Schubert, NASA
Appendix A Convening Lead Authors: James Carton, University of Maryland; Eugenia Kalnay, University of Maryland
Appendix B Convening Lead Author: Martin Hoerling, NOAA Lead Authors: Gabriele Hegerl, University of Edinburgh; David Karoly, University of Melbourne; Arun Kumar, NOAA; David Rind, NASA

IVV 

## ACKNOWLEDGEMENT
CCSP Synthesis and Assessment Product 1.3 (SAP 1.3) was developed with the benefit of a scientifically rigorous, first draft peer review conducted by a committee appointed by the National Research Council (NRC). Prior to their delivery to the SAP 1.3 Author Team, the NRC review comments, in turn, were reviewed in draft form by a second group of highly qualified experts to ensure that the review met NRC standards. The resultant NRC Review Report was instrumental in shaping the final version of SAP 1.3, and in improving its completeness, sharpening its focus, communicating its conclusions and recommendations, and improving its general readability.

We wish to thank the members of the NRC Review Committee: David H. Bromwich (Chair), The Ohio State University; Aiguo Dai, National Center for Atmospheric Research; Ioana M. Dima, AIR Worldwide Corporation; John W. Nielsen-Gammon, Texas A&M University; Benjamin Kirtman, University of Miami; Robert N. Miller, Oregon State University; and Andrew W. Robertson, International Research Institute for Climate and Society. The authors also thank the NRC Staff members who coordinated the process: Chris Elfring, Director, Board on Atmospheric Sciences and Climate; Maria Uhle, Study Director; Rachael Shiflett, Senior Program Assistant; and Shuba Banskota, Financial Associate.

We also thank the individuals who reviewed the NRC Report in its draft form: Mary Anne Carroll, University of Michigan; Peter R. Leavitt, Weather Information Company; Elizabeth L. Malone, Joint Global Change Research Institute; Joellen L. Russell, University of Arizona; and Andrew R. Solow, Woods Hole Oceanographic Institution.

The review process for SAP 1.3 also included a public review of the Second Draft, and we thank the individuals who participated in this important step. The Author Team carefully considered all comments, and the subsequent revisions resulted in further improvements in the quality and clarity of the report. Note that the respective review bodies were not asked to endorse the final version of SAP 1.3, as this is the responsibility of the National Science and Technology Council.

The authors would like to give special thanks to Jon Eischeid of the NOAA Earth System Research Laboratory and Sara Veasey of NOAA’s National Climatic Data Center for their great help in preparing numerous figures for publication, and to Jessica Blunden of STG, Inc., for significantly clarifying and improving the report in her role as Scientific Editor. We wish to thank Glenn Hyatt and Deborah Riddle of NOAA’s National Climatic Data Center, and Deb Misch, Susan Osborne, Susanne Skok, Mara Sprain, and Christian Zamarra for their dedication to the production of SAP 1.3. The authors also thank Neil Christerson and Krisa Arzayus of the NOAA Climate Program Office for their able support in shepherding this Product through the many steps required for its development.

VVIVIIABSTRACT



# T his Climate Change Science Program Synthesis and Assessment Product addresses current capabilities to integrate observations of the climate system into a consistent description of past and current conditions through the method of reanalysis. In addition, the Product assesses present capabilities to attribute causes for climate variations and trends over North America during the reanalysis period, which extends from the mid-twentieth century to the present. 

# T his Product reviews the strengths and limitations of current atmospheric reanalysis products. It finds that reanalysis data play a crucial role in helping to identify, describe, and understand atmospheric features associated with weather and climate variability, including high-impact events such as major droughts and floods. Reanalysis data play an important role in assessing the ability of climate models to simulate the average climate and its variations. The data also help in identifying deficiencies in representations of physical processes that produce climate model errors. 

# T he Product emphasizes that significant improvements are possible that would substantially increase the value of reanalyses for climate research, applications, and decision support. Advances are likely through developing new methods to address changes in observing systems over time, improving the historical observational database, and developing integrated Earth system models and analysis systems that include key climate elements for decision support that were not contained in initial atmospheric reanalyses, such as a carbon cycle, aerosols and other important atmospheric constituents. 

# T he Product also assesses current understanding of the causes of observed North American climate variability and trends from 1951 to 2006. This assessment is based on results from research studies, climate model simulations, and reanalysis and observational data. For annual, area-average surface temperatures over North America, more than half of the observed surface warming since 1951 is likely due to anthropogenic forcing associated with greenhouse gas forcing. However, warming due to anthropogenic greenhouse gas emissions alone is unlikely to be the main cause for regional and seasonal differences of surface temperature changes, such as the absence of a summertime warming trend over the Great Plains of the United States and the absence of a warming trend in both winter and summer over portions of the southern United States. 

# T he regional and seasonal variations in temperature trends are related to the principal atmospheric wind patterns that affect North American climate, which are well represented in climate reanalyses. It is likely that variations in regional sea surface temperatures have played an important role in forcing these atmospheric wind patterns, although there is evidence that some wind changes are also due to anthropogenic forcing. 

# I n contrast to temperature, there is no discernible trend during this period in annual average North American precipitation, although there is substantial interannual-to-decadal variability. Part of the observed variability in precipitation appears to be related to regional variations of sea surface temperatures during this period.

VIIThe U.S. Climate Change Science Program Preface

VIII 

### RECOMMENDED CITATIONS

Entire Report: CCSP, 2008: Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research [Randall Dole, Martin Hoerling, and Siegfried Schubert (eds.)]. National Oceanic and Atmospheric Administra

tion, National Climatic Data Center, Asheville, NC, 156 pp.

Preface: Dole, R., 2008: Preface. In: Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attri

bution of Causes of Observed Change. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research [Randall Dole, Martin Hoerling, and Siegfried Schubert (eds.)]. National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC, pp. XI–XIV
Executive Summary: Dole, R., M. Hoerling, and S. Schubert, 2008: Executive summary. In: Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research [Randall Dole, Martin Hoerling, and Siegfried Schu

bert (eds.)]. National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC, pp. 1–4
Chapter 1: Dole, R. and M. Hoerling, 2008: Introduction. In: Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research [Randall Dole, Martin Hoerling, and Siegfried Schubert (eds.)]. National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC, pp. 5–10
Chapter 2: Schubert, S., P. Arkin, J. Carton, E. Kalnay, and R. Koster, 2008: Reanalysis of historical climate data for key atmospheric features. In: Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research [Randall Dole, Martin Hoerling, and Siegfried Schubert (eds.)]. National Oceanic and Atmospheric Administra

tion, National Climatic Data Center, Asheville, NC, pp. 11–46
Chapter 3: Hoerling, M., G. Hegerl, D. Karoly, A. Kumar, and D. Rind, 2008: Attribution of the causes of climate variations and trends over North America during the modern reanalysis period. In: Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change. A Report by the U.S. Climate Change Science Pro

gram and the Subcommittee on Global Change Research [Randall Dole, Martin Hoerling, and Siegfried Schubert (eds.)]. National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC, pp. 47–92
Chapter 4: Dole, R., M. Hoerling, and S. Schubert, 2008: Recommendations. In: Reanalysis of Historical Climate Data for Key At

mospheric Features: Implications for Attribution of Causes of Observed Change. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research [Randall Dole, Martin Hoerling, and Siegfried Schubert (eds.)]. National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC, pp. 93–104IX Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
Appendix A: Carton, J. and E. Kalnay, 2008: Appendix A: Data assimilation. In: Reanalysis of Historical Climate Data for Key Atmo

spheric Features: Implications for Attribution of Causes of Observed Change. A Report by the U.S. Climate Change Sci

ence Program and the Subcommittee on Global Change Research [Randall Dole, Martin Hoerling, and Siegfried Schubert (eds.)]. National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC, pp. 105–106
Appendix B: Hoerling, M., G. Hegerl, D. Karoly, A. Kumar, and D. Rind, 2008: Appendix B: Data and methods used for attribution. In: Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research [Ran

dall Dole, Martin Hoerling, and Siegfried Schubert (eds.)]. National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC, pp. 107–114The U.S. Climate Change Science Program Preface

XXI Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change



# Report Motivation and Guidance for Using this Synthesis/Assessment Product Convening Lead Author: Randall Dole, NOAA/ESRLPREFACE

A primary objective of the U.S. Climate Change Science Program (CCSP) is to provide the best possible scientific information to support public discussion, and government and private sector decision making on key climate-related issues. To help meet this objective, the CCSP has iden

tified 21 Synthesis and Assessment Products (SAPs) that address its highest priority research, observational, and decision-support needs. This Product, CCSP SAP 1.3, is one of three products developed to address the first goal of the CCSP Strategic Plan: Improve knowledge of the Earth’s past and present climate and en

vironment, including its natural variability, and improve understanding of the causes of observed variability and change. This Product assesses present capabilities to describe key features of climate from the mid-twentieth century to the present through the scientific method of re

analysis. It also assesses current understanding of the causes of observed climate variability and changes over the North American region during this same period.



### P.1 OVERVIEW OF PRODUCT

New climate observations are most informative when they can be put in the context of what has occurred in the past. Are current conditions unusual or have they been observed frequently before? Are the current conditions part of a long-term trend or a manifestation of climate variability that may be expected to reverse over months, seasons, or years? Are similar or related changes occurring in other parts of the globe? What are the processes and mechanisms that can explain current conditions, and how are they similar to, or different from, what has occurred in the past?
The scientific methods of climate reanalysis and attribution are central to addressing such questions. In brief, reanalysis is a method for constructing a high-quality record of past cli

mate conditions. Attribution is the process of establishing the most likely cause (or causes) for an observed climate variation or change.
An important goal of the reanalysis efforts assessed in this Product is to provide com

prehensive, consistent, and reliable long-term datasets of temperatures, precipitation, winds, and numerous other variables that characterize the state of the climate system. Because these datasets provide continuous time records, typi

cally at six-hour intervals over several decades, they play an important role in documenting how weather and climate conditions are changing over time. The comprehensive nature of climate reanalyses also makes such datasets of great value in helping scientists to better understand the often complex relationships among variables, for example, how changes in temperatures may be connected to changes in winds, and how these in turn may be related to changes in cloudiness and precipitation.
Reanalysis datasets provide a foundation for a broad range of weather and climate research. As one measure of their extraordinary research impact, an overview paper describing one of the initial reanalyses produced in the United States is now the most widely cited paper in the geophysical sciences. Beyond their research ap

plications, products derived from reanalysis data are used in an increasing range of commercial and business applications in sectors such as energy, agriculture, water resources, and insur

ance. Some commonly used products include maps showing monthly and seasonal averages, variability and trends in temperatures, winds, precipitation and storminess.The U.S. Climate Change Science Program Preface

XII Increasingly, climate scientists are also being asked to go beyond descriptions of what are the current climate condi

tions and how they compare with the past to also explain why climate is evolving as observed; that is, to provide attribution for the causes of observed climate variations and change. The capability to attribute causes for past and current cli

mate conditions is an important factor in developing public confidence in scientific understanding of mechanisms that produce climate variability and change. Attribution also provides a scientific underpinning for predicting future climate as well as information useful for evaluating needs and options for adaptation and/or mitigation.
This Product addresses the strengths and limitations of cur

rent reanalysis products in documenting, integrating, and advancing knowledge of the climate system. It also assesses present scientific capabilities to attribute causes for weather and climate variations and trends over North America dur

ing the reanalysis period (from the mid-twentieth century to the present), including the uses, limitations, and oppor

tunities for improvement of reanalysis data applied for this purpose.
The Product is intended to be of value to the following us

ers: policymakers in assessing current scientific capabilities • to attribute causes of climate variations and change over the North American region; scientists and other users of reanalysis data through • the assessment of strengths and limitations of current reanalyses; and science program managers in developing priorities for • future observing, modeling, and analysis systems re

quired to advance national and international capabilities in climate reanalysis and attribution.
Following guidance provided by the Climate Change Science Program, this Product is written primarily for the informed lay reader. For subject matter experts, more detailed discus

sions are available through the original references cited herein. Because some terms will be new to non-specialists, a glossary and a list of acronyms and abbreviations are included at the end of this Product.



### P.2 PRIMARY FOCUS OF THE PRODUCT
Chapter 1 provides a brief, non-technical discussion of the fundamental concepts of reanalysis and attribution. Two is

sues of broad interest follow, within which specific questions are addressed: (1) the reanalysis of historical climate data for key atmospheric features, in particular, for past climate variations and trends over the reanalysis period from the mid-twentieth century to the present, and (2) attribution of the causes of climate variations and trends over North America during the same period. These topics are described in more detail below.
P.2.1 Reanalysis of Historical Climate Data for Key Atmospheric Features The availability and usefulness of reanalysis data have led to many important scientific advances as well as a broad range of new applications. However, limitations of past and current observations, models, and reanalysis methods have each contributed to uncertainties in describing climate system behavior. Chapter 2 focuses on the strengths and limitations of current reanalysis data for identifying and describing past climate variations and trends.
The first global atmospheric reanalyses were developed a little over a decade ago by NASA, NOAA (together with the National Center for Atmospheric Research [NCAR]), and the European Centre for Medium Range Weather Forecasts. These initial reanalyses were constructed by combining observations from diverse data sources within sophisticated models used for weather predictions through a process called data assimilation. Because of the origins in the use of weather models, the initial reanalyses and the majority of those conducted since that time have focused on reconstructing past atmospheric conditions. The lon

gest reanalysis, conducted by NOAA and NCAR, extends back to 1948. Because of their maturity and extensive use, atmospheric reanalyses constitute the primary focus of this Product. However, efforts are now underway to create re

analyses for other components of the Earth’s climate system, such as the ocean and land surface; emerging capabilities in these areas will also be briefly discussed.
The key questions addressed in Chapter 2 are: What is a climate reanalysis? What role does reanaly

• sis play within a comprehensive climate observing system? What can reanalysis tell us about climate processes and • their representation in models used for climate predic

tions and climate change projections? What is the capacity of current reanalyses to help iden

• tify and understand major seasonal-to-decadal climate variations, including changes in the frequency and intensity of climate extremes such as droughts? To what extent is there agreement or disagreement • between climate trends in surface temperature and precipitation derived from reanalyses and those derived from independent data; that is, from data that are not included in constructing the reanalysis? What steps would be most useful in reducing false jumps • and trends in climate time series (those that may be due to changes in observing systems or other non-physical causes) and other uncertainties in past climate condi

tions? Specifically, what contributions could be madeXIII Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
through advances in data recovery or quality control, modeling, and/or data assimilation techniques?
The assessment of capabilities and limitations of current reanalysis datasets for various purposes will be of value for determining best uses of current reanalysis products for scientific and practical purposes. This Chapter will also be useful for science program managers in developing priori

ties for improving the scientific and practical value of future climate reanalyses.

P.2.2 Attribution of the Causes of Climate Variations and Trends over North America Chapter 3 discusses current understandings of the causes of climate variations and trends over North America from the mid-twentieth century to the present, the time period encompassed by current atmospheric reanalysis products. It also addresses strengths and limitations of reanalysis products in supporting research to attribute the causes of climate variations and trends over North America during this time period. The key questions are: What is climate attribution? What are the scientific • methods used for establishing attribution? What is the present understanding • of the causes for North American climate trends in annual tempera

ture and precipitation during the reanalysis record? What is the present understanding • of causes for seasonal and regional variations in U.S. temperature and precipitation trends over the re

analysis record? What are the nature and causes • of apparent rapid climate shifts relevant to North America over the reanalysis record? What is the present understand

• ing of the causes for high-impact drought events over North America during the reanalysis record?
This Chapter will provide policy mak

ers with an assessment of current sci

entific understanding and remaining uncertainties regarding the causes of major climate variations and trends over North America since the mid-twentieth century. Resource managers and other decision makers, as well as the general public, will also benefit from this as

sessment. Finally, Chapter 4 discusses steps needed to improve na

tional capabilities in reanalysis and attribution to better address key questions in climate science and to increase the value of future reanalysis and attribution products for applications and decision making. This Chapter will be of value to scientists and research program managers who are engaged in efforts to advance national and international capabilities in climate reanalysis and attribution.



### P.3 TREATMENT OF UNCERTAINTY

Terms used in this Product to indicate the assessed likeli

hood of an outcome are consistent with those used in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report ( Climate Change 2007: The Physical Science Basis ) and summarized in Table P.1.
Terms denoting levels of confidence in findings are also consistent with the IPCC Fourth Assessment Report usage, as specified in Table P.2.

Likelihood Terminology Likelihood of occurrence/outcome
Virtually Certain more than 99 percent probability Extremely Likely more than 95 percent probability Very Likely more than 90 percent probability Likely more than 66 percent probability More Likely than Not more than 50 percent probability About as Likely as Not 33 to 66 percent probability Unlikely less than 33 percent probability Very Unlikely less than 10 percent probability Extremely Unlikely less than 5 percent probabillity Exceptionally Unlikely less than 1 percent probability Table P.1 Terminology regarding likelihood of outcome according to IPCC AR4.

Terminology Degree of confidence in being correct Very High Confidence At least nine out of ten chance of being correct High Confidence About eight out of ten chance
Medium Confidence About five out of ten chance
Low Confidence About two out of ten chance Very Low Confidence Less than one out of ten chance Table P.2 Terminology regarding degree of confidence according to IPCC AR4.The U.S. Climate Change Science Program Preface

XIV 

### P.4 SCOPE AND LIMITATIONS OF THIS PRODUCT
The time period considered in this Product is limited to that of present-day reanalysis datasets, which extend from 1948 to the present. As discussed in Chapter 4, an effort is now underway to extend reanalysis data back to at least the latter part of the nineteenth century. While initial results appear promising, this extended reanalysis project is not yet com

plete; therefore, it is not possible to assess the preliminary results in this Product.
The findings presented in this Product provide a snapshot of the current state of knowledge as of mid-2007. The fields of climate analysis, reanalysis, and attribution are cutting edge areas of climate research, with new results being obtained every month. Within the next few years new results are likely to appear that will supersede some of the key find

ings discussed in this Product; for example, with respect to the quality, types, and lengths of reanalysis records now available.
The scope of this Product was considered in light of other ongoing assessments, in particular the IPCC Fourth Assess

ment Report and other synthesis and assessment reports be

ing developed within the Climate Change Science Program. The IPCC Report emphasizes climate change at global to continental scales. This Product focuses on the United States/North American sector and considers regional climate variations and trends of specific interest to U.S. resource managers, decision makers, and the general public.EXECUTIVE SUMMARY

1 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
Convening Lead Author: Randall Dole, NOAA/ESRL

Lead Authors: Martin Hoerling, NOAA/ESRL; Siegfried Schubert, NASA/GMAO



### ES.1 P rimaryR esults and F indings
ES.1.1 Strengths and Limitations of Current Reanalysis Datasets for Repre
senting Key Atmospheric Features

KEY FINDINGS (from Chapter 2) Reanalysis plays a crucial integrating role • within a global climate observing system by producing comprehensive, long-term, objective, and consistent records of climate system components, including the atmo

sphere, oceans and land surface. 

## A mong the most common questions that climate scien

tists are asked to address are: What are current climate conditions? How do these conditions compare with the past? What are the causes for current conditions, and are the causes similar to or different from those of the past? This Climate Change Science Program (CCSP) Synthesis and Assessment Product considers such questions, fo

cusing on advances in scientific understanding obtained through the methods of reanalysis and attribution.
In climate science, a reanalysis is a method for constructing a high-quality climate record that combines a diverse array of past observations together within a model to derive a best estimate of how the climate system has evolved over time. An important goal of the reanalysis efforts assessed in this Product is to provide comprehensive, consistent, and reliable long-term datasets of temperatures, precipitation, winds, and numerous other variables that characterize the state of the climate system. The atmospheric reanalyses assessed in this Product provide a continuous, detailed record of how the atmosphere has evolved every 6 to 12 hours over periods spanning multiple decades. The Product addresses the strengths and limitations of current reanalyses in advancing scientific knowledge of the climate system. It then assesses current scientific capabilities to attribute causes for climate variations and trends over North America during the reanalysis period, which extends from the mid-twentieth century to the present. The Product concludes with recommendations to improve national capabilities in reanalysis and attribution in order to increase the value of future products for research, applications and decision making.
This Product represents a significant extension beyond the recently completed Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report ( Climate Change 2007: The Physical Science Basis ). While the IPCC Report mainly emphasized climate change at global to continental scales, this Product focuses on North America, including regional climate variations and trends that are of substantial interest to the U.S. general public, decision makers, and policy makers.

Reanalysis data play a fundamental and • unique role in studies that address the na

ture, causes, and impacts of global-scale and regional-scale climate phenomena.
Reanalysis datasets are of great value • in studies of the physical processes that produce high-impact weather and climate events such as droughts and floods, as well as other key atmospheric features that af

fect the United States, including climate variations associated with major modes ofclimate variability, such as the El NiñoSouthern Oscillation.The U.S. Climate Change Science Program Executive Summary

2 Global and regional surface temperature • trends in reanalysis datasets are broadly consistent with those obtained from tem

perature datasets constructed from surface observations not included in the reanalyses, particularly since the late 1970s. How

ever, in some regions ( e.g. , Australia) the reanalysis trends show major differences with observations.
Reanalysis precipitation trends are less • consistent with those calculated from ob

servational datasets. The differences are likely due principally to limitations in the initial reanalysis models and the methods used for integrating diverse datasets within models.
Current reanalysis data are extremely valu

• able for a host of scientific and practical applications; however, the overall quality of reanalysis products varies with latitude, altitude, time period, location and time scale, and variable of interest, such as tem

perature, winds or precipitation.
Current global reanalysis data are most • reliable in Northern Hemisphere midlati

tudes, in the middle to upper troposphere (about three to twelve miles above Earth’s surface), and for regional and larger areas. They are also most reliable for time periods ranging from one day up to several years, making reanalysis data well suited for stud

ies of midlatitude storms and short-term climate variability.
Present reanalyses are more limited in • their value for detecting long-term climate trends, although there are cases where re

analyses have been usefully applied for this purpose. Important factors constraining the value of reanalyses for trend detection include changes in observing systems over time; deficiencies in observational data quality and spatial coverage; model limi

tations in representing interactions across the land-atmosphere and ocean-atmosphere interfaces, which affect the quality of sur

face and near-surface weather and climate variables; and inadequate representation of the water cycle. At the present time, datasets constructed • for an individual variable, for example, surface temperature or precipitation, are generally superior for climate change detection. However, the integrated and comprehensive nature of reanalysis data provides a quantitative foundation for improving understanding of the processes that produce changes. These qualities make reanalysis data more useful than individual variable data sets for attributing the causes of climate variations and change.

ES.1.2 Attribution of the Causes of Climate Variations and Trends over North America during the Modern Reanalysis Period

KEY FINDINGS (from Chapter 3) Significant advances have occurred over • the past decade in capabilities to attribute causes for observed climate variations and change.
Methods now exist for establishing attri

• bution for the causes of North American climate variations and trends due to inter

nal climate variations and/or changes in external climate forcing.
Annual, area-average change for the period 1951 to 2006 across North America shows the following: Seven of the warmest ten years for annual • surface temperatures from 1951 to 2006 have occurred between 1997 and 2006.
The 56-year linear trend (1951 to 2006) • of annual surface temperature is +0.90°C ±0.1°C (1.6°F ± 0.2°F).
Virtually all of the warming since 1951 has • occurred after 1970.
More than half of this warming is • likely the result of human-caused greenhouse gas forcing of climate change.
Changes in ocean temperatures • likely ex
plain a substantial fraction of the humancaused warming of North America.3 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change There is no discernible trend in average • precipitation since 1951, in contrast to trends observed in extreme precipitation events.
Spatial variations in annual average change for the period from 1951 to 2006 across North America show the following: Observed surface temperature change has • been largest over northern and western North America, with up to +2°C (3.6°F) warming in 56 years over Alaska, the Yukon Territories, Alberta, and Saskatch

ewan.
Observed surface temperature change has • been smallest over the southern United States and eastern Canada, where no sig

nificant trends have occurred.

There is • very high confidence that changes in atmospheric wind patterns have oc

curred, based upon reanalysis data, and that these wind pattern changes are likely the physical basis for much of the spatial variations in surface temperature change over North America, especially during winter.
The spatial variations in surface tem

• perature change over North America are unlikely to be the result of anthropogenic forcing alone.
The spatial variations in surface tem

• perature change over North America are very likely influenced by changes in re

gional patterns of sea surface temperatures through the effects of sea surface tem

peratures on atmospheric wind patterns, especially during winter.
Spatial variations of seasonal average change for the period 1951 to 2006 across the United States show that: Six of the warmest ten summers and • winters for the contiguous United States average surface temperatures from 1951 to 2006 occurred recently (1997 to 2006).
During summer, surface temperatures • warmed most over western states, with insignificant change between the Rocky and Appalachian Mountains. During win

ter, surface temperatures warmed most over northern and western states, with insignificant changes over Maine and the central Gulf of Mexico.
The spatial variations in summertime sur

• face temperature change are unlikely to be the result of anthropogenic forcing alone.
The spatial variations and seasonal differ

• ences in precipitation change are unlikely to be the result of anthropogenic greenhouse gas forcing alone.
Some of the spatial variations and seasonal • differences in precipitation change and variations are likely the result of regional variations in sea surface temperatures.
An assessment to identify and attribute the causes of abrupt climate change over North America for the period 1951 to 2006 finds that: There are limitations for detecting rapid • climate shifts and distinguishing these shifts from quasi-cyclical variations be

cause current reanalysis data only extends back until to the mid-twentieth century. Reanalysis over a longer time period is needed to distinguish between these pos

sibilities with scientific confidence.

An assessment to determine trends and attribute causes for droughts for the period 1951 to 2006 shows that: It is • unlikely that a systematic change has occurred in either the frequency or area coverage of severe drought over thecontiguous United States from the midtwentieth century to the present.

It is • very likely that short-term (monthlyto-seasonal) severe droughts that have impacted North America during the past half-century are mostly due to atmospheric variability, in some cases amplified by lo

cal soil moisture conditions.

It is • likely that sea surface temperature variations have been important in forcing long-term (multi-year) severe droughts that have impacted North America during theThe U.S. Climate Change Science Program Executive Summary

4 past half-century. It is • likely that anthropogenic warm

ing has increased drought impacts over North America in recent decades through increased water stresses associated with warmer conditions, but the magnitude of the effect is uncertain.



### ES.2 RECOMMENDATIONS
The following six recommendations are aimed at improving the scientific and practical value of future reanalyses of the climate system. To better detect changes in the climate sys

• tem, improve the quality and consistency of the observational data and reduce effects of observing system changes.
Develop analysis methods that are opti

• mized for climate research and applica

tions. These methods should include uncertainty estimates for all reanalysis products.
To improve the description and understand

• ing of major climate variations that oc

curred prior to the mid-twentieth century, develop the longest possible consistent record of past climate conditions

. To improve decision support, develop • future climate reanalysis products at finer space scales ( e.g. , resolutions of 10 miles rather than 100 miles) and emphasize products that are most relevant for applica

tions, such as surface temperatures, winds, cloudiness, and precipitation.
Develop new national capabilities in analy

• sis and reanalysis that focus on variables that are of high relevance to policy and decision support. Such variables include those required to monitor changes in the carbon cycle and to understand interac

tions among Earth system components (atmosphere, ocean, land, cryosphere, and biosphere) that may lead to accelerated or diminished rates of climate change.
Develop a more coordinated, effective, and • sustained national capability in analysis and reanalysis to support climate research and applications. The following priorities are recommended for reducing uncertainties in climate attribution and realizing the benefits of this information for decision support: Develop a national capability in climate • attribution to provide regular and reliable explanations of evolving climate conditions relevant to decision making.
Focus research to better explain causes of • climate conditions at regional and local levels, including the roles of changes in land cover, land use, atmospheric aerosols, greenhouse gases, sea surface tempera

tures, and other factors that contribute to climate change.
Explore a range of methods to better • quantify and communicate findings from attribution research.

# IntroductionCHAPTER 

# 1

5 Convening Lead Author: Randall Dole, NOAA/ESRL

Lead Autho r: Martin Hoerling, NOAA/ESRL

Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change



### FUNDAMENTAL CONCEPTS

Among the most frequent questions that the public and decision makers ask climate scientists are: What do we know about past climate? What are the uncertainties in observations of climate? What do we know about the causes of cli

mate variations and change? What are the uncertainties in explaining the causes for observed climate conditions? The scientific methods of climate reanalysis and attribution play important roles in helping to address such questions. This Chapter is intended to provide readers with an initial foundation for understanding the nature and scientific roles of reanalysis and attribution, as well as their potential relevance for applications and decision making. These subjects are then discussed in detail in the remainder of the Product.



### 1.1 REANALYSIS
In atmospheric science, an analysis is a detailed representa

tion of the state of the atmosphere that is based on observa

tions (Geer, 1996). More generally, an analysis may also be performed for other parts of the climate system, such as the oceans or land surface. The analysis is often displayed as a map depicting the values of a single variable such as air temperature, wind speed, or precipitation amount, or of mul

tiple variables for a specific time period, level, and region. The daily weather maps that are presented in newspapers, on television, and in numerous other sources are familiar examples of this form of analysis (Figure 1.1a). Analyses are also performed at levels above the Earth’s surface (Figure 1.1b) in order to provide a complete depiction of atmospheric conditions throughout the depth of the atmosphere. This type of analysis enables atmospheric scientists to locate key atmo

spheric features, such as the jet stream, and plays a crucial role in weather forecasting by providing initial conditions required for models used for weather prediction. A retrospective analysis, or reanalysis , is an objective, quantitative method for producing a high quality sequence of analyses that extends over a sufficiently long time period to have value for climate applications (as well as for other purposes). An important goal of most reanalysis efforts todate has been to provide an accurate and consistent longterm data record of the global atmosphere. As discussed in Chapter 2, reanalyses have also been conducted or are in progress for the oceans and land surface. In certain cases, a reanalysis may be performed for a single variable, such as precipitation or surface temperature (Fuchs, 2007). How

ever, in many modern atmospheric reanalyses the goal is to develop an accurate and physically consistent representation of an extensive set of variables ( e.g. , winds, temperatures, pressures, etc .) required to provide a comprehensive, detailed depiction of how the atmosphere has evolved over an extend

ed period of time (typically, decades). Such comprehensive reanalyses are a major focus of this assessment.The U.S. Climate Change Science Program Chapter 1

6 The reanalysis efforts assessed in this Product estimate past conditions using a method that integrates observations from numerous datasources (Figure 1.2) together within a stateof-the art atmospheric model (or a model of another climate system component, such as the ocean or land surface). This data-model integration provides a comprehensive, high quality, temporally continuous, and physically consistent dataset of atmospheric variables for use in climate research and applications. The models provide physical consistency by constraining the analysis to be consistent with the fundamental laws that govern relationships among the different variables. Details on these methods are described in Chapter 2.
The atmospheric reanalyses assessed in the Product provide values for all atmospheric variables over the entire globe, extending in height from the Earth’s surface up to eleva

tions of approximately 30 miles. These values provide a continuous, detailed record of how the atmosphere has evolved every 6 to 12 hours over periods spanning multiple decades. Henceforth, in this Product the term reanalysis refers to this specific method for reconstruct

ing past weather and climate conditions, unless stated otherwise.
Chapter 2 describes reanalysis methods and as

sesses the strengths and limitations of current reanalysis products, including representations of seasonal-to-decadal climate variations and regional trends in surface temperatures and precipitation. Specific questions addressed in that Chapter are: What is a climate reanalysis? What role • does reanalysis play within a comprehen

sive climate observing system? What can reanalysis tell us about climate • processes and their representation in mod

els used for climate predictions and climate change projections? What is the capacity of current reanalyses • to help us identify and understand major seasonal-to-decadal climate variations, including changes in the frequency and intensity of climate extremes such as droughts? To what extent is there agreement or • disagreement between climate trends in surface temperature and precipitation Figure 1.1 Examples of map analyses for a given day (February 22, 2005) for the continental United States and adjacent regions. (a) Surface weather analysis, or “weather map”. Contours are lines of constant pressure (isobars), while green shaded areas denote precipitation. Positions of low pressure and high pressure centers, fronts and a subset of surface station locations providing observations that underpin the analysis are also shown. (b) A map of the heights (solid lines, in decameters) and temperatures (dotted lines, in °C) of a constant pressure surface, in this case the 500 millibar surface, which represents conditions at an elevation of approximately 18,000 feet. The symbols with bars and/or pennants show wind speeds and directions obtained from observations. Wind directions “blow” from the end with bars toward the open end, the open end depicting the observa

tion station location ( e.g. , winds over Denver, Colorado on this day are from the west, while those over Oakland, California are from the east). Note the strong relationship between the wind direction and the height contours, with the station winds blowing nearly parallel to the height contour lines shown in the analysis (and counter-clockwise around lows, as for example the low center just off the California coast). This is an example of a balanced relationship that is used to help construct the analyses, as discussed in Chapter 2.7 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

Figure 1.2 An illustration of some diverse types of observational systems that provide data used to construct a weather or climate analysis. Data sources include geostationary and polar-orbiting satellites, aircraft, radar, weather balloons, ships at sea and offshore buoys, and surface observing stations. Numerous other observational systems not shown also provide data that is combined to produce a comprehensive climate system analysis. derived from reanalyses and those derived from independent data; that is, from data that are not included in constructing the reanalysis? What steps would be most use

• ful in reducing false jumps and trends in climate time series (those that may be due to changes in observing systems or other non-physical causes) and other uncertainties in past climate conditions through improved reanalysis methods? Specifi

cally, what contributions could be made through advances in data recovery or quality control, modeling, and/or data assimila

tion techniques?
The assessment of capabilities and limitations of current reanalysis datasets for various purposes will be of value for determining best uses of current reanalysis products for sci

entific and practical purposes. This Chapter will also be useful for science program managers in developing priorities for improv

ing the scientific and practical value of future climate reanalyses.



### 1.2 ATTRIBUTION

The term attribute has as a common use defini

tion “to assign to a cause or source” (Webster’s II Dictionary, 1988). The Intergovernmental Panel on Climate Change (IPCC) has specifi

cally stated that “attribution of causes of climate change is the process of establishing the most likely causes for the detected change with some level of confidence” (IPCC, 2007a). The term attribution in this Product is used in the same context as the IPCC definition. However, here the scope is broadened to include observed climate variations as well as detected climate change. There are three primary reasons for expanding the scope to include climate varia

tions: (1) climate variations often have large economic impacts on regions and communities in the United States, sometimes in the billions of dollars (NCDC, 2007); (2) there is strong public interest in explanations of the causes of major short-term climate variations, for example, related to the El Niño-Southern Oscillation (ENSO), severe droughts, and other extreme events; and (3) many impacts of climate change are likely to be experienced through changes in extreme weather and climate events; that is, through changes in variability as well as changes in average conditions (IPCC, 2007b).
Methods for attributing the causes of observed climate variations and trends are discussed in Chapter 3, including the use of reanalysis data for this purpose. This Chapter focuses on observed climate variations and changes over the North American region, extending from ap

proximately 1950 to the present, the maximum time extent of current reanalysis records. The key questions are: What is climate attribution? What are the • scientific methods used for establishing attribution? What is the present understanding of the • causes for North American climate trends in annual temperature and precipitation during the reanalysis record? What is the present understanding of causes • for seasonal and regional variations in U.S. temperature and precipitation trends over the reanalysis record? The assessment of capabilities and limitations of current reanalysis datasets for various purposes will be of value for determining best

uses of current reanalysis products

for scientific and practical purposes.The U.S. Climate Change Science Program Chapter 1

8 What are the nature and causes of appar

• ent rapid climate shifts that are relevant to North America over the reanalysis record? What is the present understanding of the • causes for high-impact drought events over North America during the reanalysis record?
This Chapter will aid policy makers in as

sessing present scientific understanding and remaining uncertainties regarding the causes of major climate variations and trends over North America since the mid-twentieth century. Re

source managers and other decision makers, as well as the general public, will also benefit from this assessment, especially for those events that have high societal, economic, or environmental impacts, such as major droughts.



### 1.3 CONNECTIONS BETWEEN REANALYSIS AND ATTRIBUTION
This Product focuses on two major topics: climate reanalysis and attribution. Are there scientific connections between reanalysis and attribution and, specifically, why might reanaly

sis be useful for determining attribution? Figure 1.3 illustrates schematically some key steps commonly used in climate science including reanalysis and attribution.
1.3.1 Steps in climate science Observations provide the foundation for all of climate science. The observations are obtained from numerous disparate observing systems (see Figure 1.2) and are also distributed ir

regularly both in time and space over the Earth. These issues and others pose significant chal

lenges to scientists in evaluating present climate conditions and in comparing present conditions with those of the past.
As discussed previously, an analysis is a method for combining diverse observations to obtain a quantitative (numerical) depiction of the state of the atmosphere or, more generally, the state of the climate system at a given time (Figure 1.3). Reanalysis corresponds to the step of ap

plying the same analysis method to carefully reconstruct the past climate history. Extend

ing the record back in time enables scientists to detect climate variations and changes, and to compare present and past conditions. This reanalysis must apply consistent methods and quality-controlled data in order to accurately identify changes over time and determine how changes in different variables such as winds, temperatures, and precipitation are related. In climate science, attribution corresponds to what in medical science is called diagnosis; that is, it is the process of identifying the cause or causes of the feature of interest. As in medical science, additional “diagnostic tests” are often required to establish attribution. In climate science, these additional tests most commonly consist of controlled experiments conducted with climate models; results are compared between model outcomes when a climate forcing of interest ( e.g. , from changes in greenhouse gases or vol

canic aerosols) is either included or excluded in order to assess its potential effects.
Establishing attribution provides a scientific underpinning for predicting future climate and information useful for evaluating needs and options for adaptation and/or mitigation due to climate variability or change. Detailed discussions of climate prediction, adaptation, and mitigation are beyond the scope of this

Figure 1.3 Schematic illustrating some key steps in climate science. The shaded box indicates the general scope of this Product. The arrows show the general flow of information leading to decision support for adaptation and/or mitigation. As indicated by the open arrow, the description and understanding of past and current climate conditions included within the shaded box provide key information for developing reliable predictions of future climate and for evaluating options for adaptation and/or mitigation. Reanalysis can be considered as playing a central role in determining what has happened in the climate system, while attribution is necessary to address the question of why the changes have occurred.9 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
Product; however, recognition of such relation

ships helps illuminate potential applications of, and connections between, climate reanalysis and attribution.

1.3.2 Further comments Reanalysis can be considered as playing a cen

tral role in determining what has happened in the climate system (what has changed, and by how much?), while attribution is necessary to address the question of why the changes have occurred. As illustrated by Figure 1.3, observa

tions serve as the fundamental starting point for climate reanalysis; observations themselves are generally not sufficient to establish attribution; models incorporating fundamental understand

ing of key physical processes and their relation

ships are also required. The event of interest, e.g. , a long-term trend or other feature, such as a severe drought, must first be identified with confidence in the data in order for attribution to be meaningful. Reanalysis often plays an important role in this regard by providing a comprehensive, high quality, and continuous climate dataset spanning several decades. Physical consistency, obtained through the use of a model that incorporates the fundamental governing laws of the climate system, is also a primary feature of reanalysis datasets. Physi

cal consistency enables identification of the roles of various processes in producing climate variations and change along with correspond

ing linked patterns of variability. Thus, the method of reanalysis can contribute to more confident attribution of the processes that pro

duce responses within the climate system to a given climate forcing, as well as the expected geographical patterns and magnitudes of the responses.
One potential application of reanalysis data is in the detection of climate change. Within the IPCC, detection of climate change is defined as the process of demonstrating that climate has changed in some defined statistical sense, without providing a reason for that change. As stated earlier, attribution of the causes of climate change is the process of establishing the most likely cause for the detected change with some level of confidence. Reanalysis can play an important role in both detecting and attributing causes of climate variations and change; however, it is vital to recognize that reanalysis alone is seldom sufficient and that the best methods for both detection and attri

bution often depend on results obtained from a broad range of datasets, models, and analysis techniques.
In order to establish more definitive attribution, climate scientists perform controlled climate model experiments to determine whether esti

mated responses to particular climate forcings are consistent with the observed climate fea

tures of interest ( e.g. , a sustained temperature trend or a drought). Reanalysis data can also be of considerable value in evaluating how well climate models represent observed climate features and responses to different forcings over several decades, thereby providing important guidance of the utility of the models for estab

lishing attribution.

There are inevitable uncertainties associated with observational data, analysis techniques, and climate models. Therefore, climate change detection and attribution findings must be stated in probabilistic terms based on cur

rent knowledge, and expert judgment is often required to assess the evidence regarding particular processes (see Chapter 3). The lan

guage on uncertainty adopted in this Product is consistent with the IPCC Fourth Assessment Report (IPCC, 2007a). Finally, it is important to recognize that in complex systems, whether physical, biological, or human, it is often not one factor but the interaction among multiple factors that determines the ultimate outcome.



### 1.4 REANALYSIS APPLICATIONS AND USES
Over the past several years, reanalysis datasets have become a cornerstone for research in advancing our understanding of how and why climate has varied since the mid-twentieth century. For example, Kalnay et al. (1996), the initial overview paper on one of the first reanalysis datasets produced in the United States, has been cited more than 5500 times in the peer-reviewed literature as of mid-2008 and is currently the most widely cited paper in the geophysical sciences (ISI Web of Knowledge, <http://www.isiwebofknowledge.com/>; see also Figure 1.4). Over the past several years, reanalysis datasets have become a cornerstone for research in advancing our understanding

of how and why climate has variedsince the midtwentieth century.The U.S. Climate Change Science Program Chapter 1

10 Increasingly, reanalysis data are being used in a wide range of practical applications. One important application is to address the question: “How is the present climate similar to, or dif

ferent from, past conditions?” The short time intervals of reanalysis data (typically, every 6 to 12 hours) enable detailed studies of the time evolution of specific weather and climate events as well as comparisons with similar events in the past, providing important clues on key physical processes. Intercomparisons of different reanalyses and observational datasets help to provide a measure of the uncertainty in representations of past climate, including iden

tifying phenomena, regions, and time periods for which confidence in features is relatively high or low (Santer et al. , 2005).
Reanalysis datasets are also increasingly used for practical applications in sectors such as energy, agriculture, water resources, and insurance ( e.g. , Schwartz and George, 1998; Pryor et al ., 2006; Challinor et al ., 2005; Pul

warty, 2003; Pinto et al ., 2007). For example, a recently completed high-resolution regional reanalysis, the North American Regional Re

analysis (Mesinger et al. , 2006), focuses on improving the representation of the water cycle over North America in order to better serve water resource management needs. Chapter 2 of this Product will inform users of strengths and limitations of current reanalysis datasets, and aid in determining whether certain datasets are suited for specific purposes. Chapter 3 will be of value to policy-makers and the public in providing an assessment of current scientific understanding on the causes for observed cli

mate variations and change over North America from the mid-twentieth century to the present. Finally, Chapter 4 recommends steps needed to improve national capabilities in reanalysis and attribution in order to increase their value for scientific and practical applications. Figure 1.4 The number of published items and citations from an “ISI Web of Science” search with the key words REANALYSIS and CLIMATE.

Reanalysis datasets are increasingly used for practical applications in sectors such as energy, agriculture, water resources, and insurance.

# R eanalysis of Historical Climate Data for Key Atmospheric FeaturesCHAPTER 

# 2

11 Convening Lead Author: Siegfried Schubert, NASA

Lead Authors: Phil Arkin, Univ. of Maryland; James Carton, Univ. of

Maryland; Eugenia Kalnay, Univ. of Maryland; Randal Koster, NASA Contributing Authors: Randall Dole, NOAA; Roger Pulwarty,

NOAA



### KEY FINDINGS Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

Reanalysis plays a crucial integrating role within a global climate observing system by producing com

• prehensive, long-term, objective, and consistent records of climate system components, including the atmosphere, oceans, and land surface (Section 2.1). Reanalysis data play a fundamental and unique role in studies that address the nature, causes, and • impacts of global-scale and regional-scale climate phenomena (Section 2.3). Reanalysis datasets are of particular value in studies of the physical processes that produce high-impact • weather and climate events such as droughts and floods, as well as other key atmospheric features that affect the United States, including climate variations associated with major modes of climate variability, such as the El Niño-Southern Oscillation (Section 2.3). Global and regional surface temperature trends in reanalysis datasets are broadly consistent with • those obtained from temperature datasets constructed from surface observations not included in the reanalysis, particularly since the late 1970s. However, in some regions ( e.g. , Australia) the reanalysis trends show major differences with observations (Section 2.4). Reanalysis precipitation trends are less consistent with those calculated from observational datasets. • The differences are likely due principally to current limitations in the reanalysis models and the methods used for integrating diverse datasets within models (Section 2.4). Current reanalysis data are extremely valuable for a host of scientific and practical applications; how

• ever, the overall quality of reanalysis products varies with latitude, altitude, time period, location and time scale, and quantity or variable of interest (Sections 2.1, 2.3). Current global reanalysis data are most reliable in Northern Hemisphere mid-latitudes, in the middle • to upper troposphere (about 3 to 12 miles above Earth’s surface), and for regional and larger areas. They are also most reliable for time periods ranging from one day up to several years, making reanaly

sis data well suited for studies of mid-latitude storms and short-term climate variability (Sections 2.1, 2.2, 2.3, 2.4). Present reanalyses are more limited in their value for detecting long-term climate trends, although • there are cases where reanalyses have been usefully applied for this purpose. Important factors constraining the value of reanalyses for trend detection include: changes in observing systems over time; deficiencies in observational data quality and spatial coverage; model limitations in representing interactions across the land-atmosphere and ocean-atmosphere interfaces, which affect the quality of surface and near-surface weather and climate variables; and inadequate representation of the water cycle (Sections 2.2, 2.3, 2.4). At the present time, data sets constructed for an individual variable, for example, surface temperature • or precipitation, are generally superior for climate change detection. However, the integrated andThe U.S. Climate Change Science Program Chapter 2

12 comprehensive nature of reanalysis data provides a quantitative foundation for improving understanding of the processes that produce changes. These qualities make reanalysis data more useful than individual variable datasets for attributing the causes of climate variations and change (Section 2.4). Reanalysis data play an important role in assessing the ability of climate models to simulate • basic weather and climate variables such as the horizontal winds, temperature, and pres

sure. In addition, the adjustments or analysis increments produced during the course of a reanalysis provide a method to identify fundamental errors in the physical processes and/ or missing physics that create climate model biases (Sections 2.2, 2.3). Reanalyses have had substantial benefits for climate research and prediction, as well as for • a wide range of societal applications. Significant future improvements are possible by devel

oping new methods to address observing system inconsistencies, by developing estimates of the reanalysis uncertainties, by improving the observational database, and by developing integrated Earth system models and analysis systems that incorporate key climate elements not included in atmospheric reanalyses to date (Section 2.5).



### 2.1. climate reanalysis and ITS role within a comprehensive climate observing system

2.1.1 Introduction Weather and climate vary continuously around the world on all time scales. The observation and prediction of these variations is important to many aspects of human society. Extreme weather events can cause significant loss of life and damage to property. Seasonal to interannual changes associated with the El Niño-Southern Oscillation (ENSO) phenomenon and other modes of climate variability have substantial ef

fects on the economy. Climate change, whether natural or anthropogenic, can profoundly influ

ence social and natural environments through

out the world, with impacts that can be large and far-reaching.

Determining the nature and predictability of climate variability and change is crucial to society’s future welfare. To address the threats and opportunities associated with weather phe

nomena, an extensive weather observing system has been put in place over the past century (see Figure 2.1). Considerable resources have been invested in obtaining observations of the ocean, land, and atmosphere from satellite and surface-based systems, with plans to improve and expand these observations as a part of the Global Earth Observing System of Systems (GEOSS, 2005). Within this developing climate observing system, climate analysis plays an essential synthesizing role by combining data obtained from this diverse array of Earth system observations to enable improved descriptions and understanding of climate variations and change.
2.1.2 What is a Climate Analysis? As discussed in Chapter 1, at its most funda

mental level, an analysis is a detailed represen

tation of the state of the atmosphere and, more generally, of other Earth climate system com

ponents, such as oceans or land surface, that is based on observations. A number of techniques can be used to create an analysis from a given set of observations.
One common technique for creating an analysis is based on the expertise of human analysts, who apply their knowledge of phenomena and physical relationships to estimate values of variables between observation locations, a technique referred to as interpolation. Such subjective analysis methods were used almost exclusively before the onset of modern numeri

cal weather prediction in the 1950s and are still used for many purposes today. While these techniques have certain advantages, including the relative simplicity by which they may be produced, there are key inadequacies that limit their value for numerical weather prediction and climate research. An important practical limitation, recognized in the earliest attempts at numerical weather prediction (Richardson, 1922; Charney, 1951), was that the process of creating a detailed analysis, for example, of the global winds and temperatures through the depth of the atmosphere on a given day, is Determining the nature and predictability of climate variability and change is crucial to society’s future welfare.13 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

time consuming, often taking much longer to produce than the evolution of the weather itself. A second limitation is that physical imbalances between fields that are inevitably produced during a subjective analysis lead to forecast changes that are much larger than actually ob

served (Richardson, 1922). A third limitation is that this type of subjective analysis is not reproducible. In other words, the same analyst, given the same observational data, will gener

ally not produce an identical analysis when given multiple opportunities.
Thus, by the early 1950s the need for an auto

matic, objective analysis of atmospheric con

ditions had become apparent. The important technological advance provided by the early computers of that time, while primitive by to

day’s standards, could still perform calculations far faster than previously possible, making this a feasible goal. The first objective analyses used simple statisti

cal techniques to interpolate data values from the locations where observations were made onto uniform spatial grids that were used for the model predictions. Such techniques are still widely employed today to produce many types of analyses, such as global maps of surface tem

peratures, sea surface temperature (SST), and precipitation (Jones et al ., 1999; Hansen et al ., 2001; Doherty et al ., 1999; Huffman et al ., 1997; Xie and Arkin, 1997; Adler et al ., 2003; Fan and Van den Dool, 2008). The purely statistical approaches are less well suited for the analysis of upper air conditions in that they do not fully exploit known physical relationships among different variables of the climate system, for example, among fields of temperature, winds, and atmospheric pressure. These relationships place fundamental constraints on how weather and climate evolve in time. Therefore, statisti

cal analysis techniques are no longer used for applications that depend on relationships among Figure 2.1 The atmospheric data coverage provided by the modern observing systems on 5 September 2003 for use in reanalysis. From Simmons (2006).

The first objective analyses used simple statistical techniques

to interpolate data values from the locations where observations were made onto uniform spatial grids that were used for the model predictions.The U.S. Climate Change Science Program Chapter 2

14 variables, as in numerical weather prediction or in research to assess detailed mechanisms for climate variability and change.
An alternative objective analysis method, which is the principal focus for this Product, is to estimate the state of the climate system (or of one of its components) by combining observa

tions together within a numerical prediction model that mathematically represents the physi

cal and dynamical processes operating within the system.This observationsmodel integration is achieved through a technique called data assimilation

. One important aspect of a comprehensive cli

mate observing sys

tem achieved through data assimilation is the ability to integrate diverse surface, upper air, satellite, and other observations together into a coherent, consistent description of the state of the global climate system. Figure 2.1 shows, for example, a snapshot of the coverage provided by the different atmospheric observing systems on 5 September 2003 that can be incorporated into such an analysis scheme.

How are observations combined that have such different spatial coverage, sampling density, and error characteristics? Data assimilation mathematically combines a background field or an initial estimate produced by a numeri

cal prediction of the atmosphere (or oceans) with available observations using a method designed to minimize the overall errors in the analysis. Figure 2.2 schematically shows how data assimilation combines quality-controlled observations with a short-term model forecast (typically, in six-hour increments) to produce an analysis that attempts to minimize errors in estimates of the atmospheric state that would be present due to either the observations or model evaluated separately (for more details see Appendix A).
In practice, the quality of a global analysis is im

pacted by a multitude of practical decisions and compromises, involving the analysis methodol

ogy, quality control, the choice of observations and how they are used, and the model (see Ap

pendix A and the discussion below). Figure 2.3 compares three different reanalyses produced from the observations available for 5 Septem

ber 2003 (Figure 2.1) of the 500 millibars (mb) geopotential height distribution (the height of a mid-tropospheric pressure surface above mean sea level) and total water vapor fields. These are results from the National Centers for Envi

ronmental Prediction (NCEP)/National Center Figure 2.2 A schematic of data assimilation.

Figure 2.3 The global distribution of the mid-tropospheric pres

sure field (contours are of the 500 millibars [mb] geopotential height field) and total water vapor (shaded color; units are in mil

limeters) for 5 September 2003 from three different analyses.15 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

for Atmospheric Research (NCAR) Reanalysis 1, the NCEP/Department of Energy (DOE) Reanalysis 2, and the Japanese Meteorological Agency (JMA)/Central Research Institute of Electrical Power Industry (CRIEPI) 25-year Japanese Reanalysis (JRA-25).
The two NCEP reanalyses were carried out with basically the same system (Table 2.1, the NCEP/ DOE reanalysis system corrected some of the known errors in the NCEP/NCAR system).
The three analyses show substantial agreement in midlatitudes, especially for the pressure dis

tribution; however, there is substantial disagree

ment in the tropical moisture fields between the NCEP and JRA data. The differences indicate that there are insufficient observations and/or inadequate representation of relevant physical processes incorporated into the models that are needed to tightly constrain the analyses. Consequently, the uncertainties in the tropical moisture field are relatively large.
The numerical prediction model used for data assimilation plays a fundamental role in the analysis. It ensures an internal consistency of physical relationships among variables such as temperatures, pressure, and wind fields, and provides a detailed, three-dimensional representation of the system state at any given time, including winds, temperatures, pressures, humidity, and numerous other variables that are necessary for describing weather and climate (Appendix A). Further, the physical relation

ships among atmospheric (or oceanic) variables that are represented in the mathematical model enable the model to transfer information from times or regions with more observations to other times or areas with sparse observations. At the same time, potential errors are introduced by the use of a model (Section 2.2). Organization Time Period Model Analysis Scheme Output References

NASA Data Asssimilation Office (DAO) 1980 to 1994 2X2.5° Lat/lon- ∆x~250 km, L20 ( σ , top at 10mb), specified soil moisture Optimal Interpo

lation (OI) with incremental analysis update No longer available Schubert et al. (1993)

NOAA NCEP and NCAR (R1) 1948 to pres

ent T62 - ∆x~200km L28 ( σ , top at about 3mb) Spectral Statistical Interpolation (SSI) <http://www.cpc. ncep.noaa.gov/prod

ucts/wesley/reanaly

sis.html> Kalnay et al. (1996)

NOAA NCEP and DOE (R2) 1979 to present T62 - ∆x~200km L28 ( σ , top at about 3mb) Spectral Statistical Interpolation (SSI) <http://www.cpc. ncep.noaa.gov/prod

ucts/wesley/reanaly

sis2/> Kanamitsu et al. (2002) (Fixes errors found in R1 including fixes to PAOBS, snow, humidity, etc.) European Centre for Medium-Range Weather Fore

casts (ECM

WF) Reanalysis (ERA-15) 1979 to 1993 T106 - ∆x~125km L31( σ -p, top at 10mb) Optimal Interpola

tion (OI), 1DVAR, nonlinear normal mode initialization <http://data.ecmwf. int/data/d/era15/> Gibson et al. (1997)

ECMWF (ERA-40) 1957 to 2001 T159 - ∆x~100km L60 ( σ -p, top at 0.1mb) 3D-Var, radiance assimilation <http://data.ecmwf. int/data/d/era40_ daily/> Uppala et al. (2005)

JMA and CRIE

PI (JRA-25) 1979 to 2004 T106 - ∆x~125km L40 ( σ -p, top at 0.4mb) 3D-Var, radiance assimilation <http://jra.kishou. go.jp/index_en.html> Onogi et al. (2005)

NOAA North American Re

gional Reanaly

sis (NARR) 1979 to present ∆x= 32km L45 3D-Var, precipita

tion assimilation <http://nomads.ncdc. noaa.gov/#narr_data sets> Mesinger et al. (2006) Table 2.1 Characteristics of existing atmospheric reanlyses.

The numerical prediction model used

for data assimilation plays a fundamental role in the analysis.The U.S. Climate Change Science Program Chapter 2

16 Beginning in the 1970s, the sequence of initial atmospheric conditions or analyses needed for the emerging comprehensive global numerical weather prediction models were also used to study climate (Blackmon et al ., 1977; Lau et al ., 1978; Arkin, 1982). This unforeseen use of the analyses marked what could be considered a revolutionary step forward in climate science, enabling for the first time detailed quantitative analyses that were instrumental in advancing the identification, description, and understand

ing of many large scale climate variations, in particular, some of the major modes of climate variability described in Section 2.3. However, the frequent changes in analysis systems ( e.g.,model upgrades) needed to improve shortrange numerical weather forecasts also in

troduced false shifts in the perceived climate that rendered these initial analyses unsuitable for problems such as detecting subtle climate trends. Recognition of this fundamental issue led to recommendations for the development of a comprehensive, consistent analysis of the climate system, effectively introducing the concept of a model-based climate reanalysis (Bengtsson and Shukla, 1988; Trenberth and Olson, 1988).
2.1.3 What is a Climate Reanalysis? A climate reanalysis is an analysis performed with a fixed ( i.e. , not changing in time) nu

merical prediction model and data assimila

tion method that assimilates quality-controlled observational data over an extended time period, typically several decades, to create a long-period climate record. This use of a fixed model and data assimilation scheme differs from analyses performed for daily weather prediction. Such analyses are conducted with models using numerical and/or physical formulations as well as data assimilation schemes that are updated frequently, sometimes several times a year, giving rise to false changes in climate that limit their value for climate applications. Climate analysis also fundamen

tally differs from weather analysis in that observations throughout the system evolution are available for use, rather than simply those observations made immediately prior to the time when the forecast is initiated. While weather analysis has the goal of enabling the best short-term weather forecasts, climate analysis can be optimized to achieve other objectives such as providing a consistent description of the atmosphere over an extended time period. Current methods of climate reanalyses evolved from methods developed for short-range weather prediction, and have yet to realize their full potential for climate applications (see Chapter 4).
In the late 1980s, several reanalysis projects were initiated to develop long-term records of analyses better suited for climate purposes (Table 2.1). The products of these first reanaly

ses ( e.g. , maps of daily, monthly, and seasonal averages of temperatures, winds, and humidity) have proven to be among the most valuable and widely used in the history of climate science, as indicated both by the number of scholarly publications that rely upon them and by their widespread use in current climate services (see Section 1.4). The reanalysis projects have produced detailed atmospheric climate records that have enabled successful climate monitoring and research to be conducted. They have also provided a testbed for improving prediction models on all time scales (see Section 2.2), espe

cially for seasonal-to-interannual forecasts, as well as greatly improved basic observations and datasets prepared for their production. When extended to the present as an ongoing climate analysis, reanalysis provides decision makers with information about current climate events in relation to past events, and contributes directly to climate change assessments. Current methods of climate reanalyses evolved from methodsdeveloped for shortrange weather prediction, and have yet to realize their full potential for climate applications.17 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
2.1.4 What Role Does Reanalysis Play within a Climate Observing System? One of the key limitations of current and fore

seeable observing systems is that they do not provide complete spatial coverage of all relevant components of the climate system. Because the observing system has evolved over the last half century mainly in response to numerical weather prediction needs, it is focused primarily on the atmosphere. The system today consists of a mixture of in situ and remotely sensed ob

servations with differing spatial and temporal sampling and error characteristics (Figure 2.1). An example of the observations available for reanalysis during the modern satellite era is provided in Table 2.2. A major strength of modern data assimilation methods is the use of a model to help fill in the gaps of the observing system. The assimilation methods act as sophisticated interpolators that use the complex equations governing the atmo

sphere’s evolution together with all available observations to estimate the state of the atmo

sphere in regions with little or no observational coverage. Statistical schemes are used that ensure that, in the absence of bias with respect to the true state of the atmosphere, the observa

tions and model first guess are combined in an optimal way to jointly minimize errors that are subject to certain simplifying assumptions such that the statistics follow a normal distribution. This can be as simple as the model transporting
Table 2.2 An example of the conventional and satellite radiance data available for reanalysis during the satellite era (late 1970s to present). These are the observations used in the new NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA) reanalysis (Section 2.5.2). Data Source/Type Period Data Supplier Conventional Data Radiosondes 1970 to present NOAA/NCEP PIBAL winds 1970 to present NOAA/NCEP Wind profiles 1992/5/14 to present UCAR CDAS Convetional, ASDAR, and MDCRS aircraft reports 1970 to present NOAA/NCEP Dropsondes 1970 to present NOAA/NCEP PAOB 1978 to present NCEP CDAS GMS, METEOSAT, cloud drift IR and visible winds 1977 to present NOAA/NCEP

GOES cloud drift winds 1997 to present NOAA/NCEP EOS/Terra/MODIS winds 2002/7/01 to present NOAA/NCEP EOS/Aqua/MODIS winds 2003/9/01 to present NOAA/NCEP Surface land observations 1970 to present NOAA/NCEP Surface ship and buoy observations 1977 to present NOAA/NCEP SSM/I rain rate 1987/7 to present NASA/GSFC SSM/I V6 wind speed 1987/7 to present RSS TMI rain rate 1997/12 to present NASA/GSFC QuikSCAT surface winds 1999/7 to present JPL ERS-1 surface winds 1991/8/5 to 1996/5/21 CERSAT ERS-2 surface winds 1996/3/19 to 2001/1/17 CERSAT

Satellite Data TOVS (TIROS N, N-6, N-7, N-8) 1978/10/30 to 1985/01/01 NCAR (A)TOVS (N-9, N-10, N-11, N-12) 1985/01/01 to 1997/07/14 NOAA/NESDIS & NCAR (A)TOVS (N-14, N-15, N-16, N-17, N-18) 1995/01/19 to present NOAA/NESDIS EOS/Aqua 2002/10 to present NOAA/NESDIS SSM/I V6 (F08, F10, F11, F13, F14, F15) 1987/7 to present RSS GOES sounder T B 2001/01 to present NOAA/NCEP SBUV2 ozone (Version 8 retrievals) 1978/10 to present NASA/GSFC/Code 613.3 A major strength of modern data assimilation methods is

the use of a model to help fill in the gaps of the observing system.The U.S. Climate Change Science Program Chapter 2

18 warm air from a region that has good observa

tional coverage ( e.g. , over the United States) to a region that has little or no coverage ( e.g. , over the adjacent ocean), or a more complicated example, where the model generates a realistic low-level jet in a region where such phenomena exist but observations are limited. The latter is an example of a phenomenon that is largely generated by the model, and only indirectly constrained by observations. This example highlights both the advantages and difficulties in using reanalysis for climate studies. Through the use of a model, it allows climate scientists to estimate features that are indirectly or incom

pletely measured; however, the scientists have confidence in those estimates only if they are able to account for all model errors.

The use of a model also enables estimates of quantities and physical processes that are difficult to observe directly, such as vertical motions, surface heat fluxes, latent heating, and many of the other physical processes that determine how the atmosphere evolves over time. In general, the estimated quantities are model dependent and careful interpretation is required. Any incorrect representation of physi

cal processes (called parameterizations) will be reflected in the reanalysis to some extent. Only recently have the models improved enough to be used with some confidence in individual physical processes. Previously, most studies using assimilated data have indirectly estimated physical processes by computing them as a re

sidual of a budget that involves only variables that are well observed (Section 3.2.3). Thus, it is important to understand which quantities are strongly constrained by the observations, and which are indirectly constrained and depend on model parameterizations. In recognition of this problem, efforts have been made to docu

ment the quality of the individual products and categorize them according to how strongly they are observationally constrained ( e.g. , Kalnay et al ., 1996; Kistler et al ., 2001).
Beyond their fundamental integrating role with

in a comprehensive climate observing system, climate analysis and reanalysis can also be used to identify redundancies and gaps in the climate observing system, thus enabling the entire system to be configured more cost effectively. By directly linking products to observations, a reanalysis can be applied in conjunction with other science methods to optimize the design and efficiency of future climate observing systems and to improve the products that the system produces.
Current reanalysis data are extremely valuable for a host of climate applications. However, there are also limitations. These are due, for example, to changes in the observing systems, such as the substantial increase in satellite The use of a model enables estimates of quantities and physical processes that are difficult to observe directly, such as vertical motions, surface heat fluxes, latent heating, and many of the other physical processes that determine how the atmosphere evolves over time. Figure 2.4 Changes in the distribution and number of observations available for NASA’s Modern Era Retrospective-Analysis for Research and Applications (MERRA) reanalysis.19 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

data in 1979 and other newer remote sensing instruments (Figure 2.4). Such changes to the observing system influence the variability that is inferred from reanalyses. Therefore, inferred trends and low frequency (e.g. , decadal) vari

ability may be less reliable than shorter-term weather and climate variations ( e.g. , Figure 2.5 and discussion in Sections 2.3.2.2 and 2.4.2).
The need to periodically update the climate record in order to provide improved reanaly

ses for climate research and applications has been strongly emphasized ( e.g. , Trenberth et al ., 2002b; Bengtsson et al ., 2004a). There are several reasons for these updates: (1) to include important or extensive additional observa

tions missed in earlier analyses; (2) to correct observational data errors identified through subsequent quality-control efforts; and (3) to take advantage of scientific advances in models and data assimilation techniques, including bias correction techniques (Dee, 2005), and to incorporate new types of observations, such as satellite data not assimilated in earlier analyses. In the following Sections, the strengths and limitations of current reanalyses for address

ing specific questions defined in the Preface are discussed.



### 2.2 r OLE OF REANALYSIS IN UNDERSTANDING CLIMATE PROCESSES AND evaluating climate MODEL s

2.2.1 Introduction Global atmospheric data assimilation combines various observations of the atmosphere (see Figure 2.1) with a short-term model forecast to produce an improved estimate of the state of the atmosphere. The model used in the assimilation incorporates current scientific understanding of how the atmosphere (and more generally the cli

mate system) behaves and can ideally forecast or simulate all aspects of the atmosphere at all locations around the world.
Atmospheric data assimilation and reanalysis, in particular, can be thought of as a model simulation of past atmospheric behavior that is continually updated or adjusted by available observations. Such adjustments are necessary because the model would otherwise evolve dif

ferently from nature since it is imperfect ( i.e. , Figure 2.5 Trends and shifts in the reanalyses. The figures show the zonal mean precipitation from the GPCP observations (top panel), the ERA-40 reanalysis (bottom left panel), and the JRA-25 reanalysis (bottom right panel). Courtesy of Junye Chen and Michael Bosilovich, NASA Global Modeling and Assimilation Office (GMAO).

Atmospheric data assimilation and reanalysis, in particular,

can be thought of as

a model simulation of past atmospheric behavior that is continually updated or

adjusted by available observations.The U.S. Climate Change Science Program Chapter 2

20 our understanding about how the atmosphere behaves and our ability to represent that be

havior in computer models is limited). The adjustments must be made continually (or at least intermittently) because the information (observations) used to correct the model’s time evolution at any instant are incomplete and also contain errors. In other words, all aspects of the climate system cannot be perfectly measured. Even with a perfect model and nearly perfect observations, adjustments are necessary be

cause the model would still deviate from nature since the atmosphere is chaotic and even very small observational errors grow rapidly to im

pact the model forecast.

The above model-centric view of data assimila

tion is useful when trying to understand how reanalysis data can be applied to evaluate how well climate models represent atmospheric processes. It highlights the fact that reanalysis products are a mixture of observations and model forecasts, and their quality will therefore be impacted by the quality of the model. In large geographic regions with little observational coverage, a reanalysis will tend to move away from nature and reflect more of the model’s own behavior. Also, poorly observed quantities, such as surface evaporation, depend on the quality of the model’s representation or parameteriza

tions of the relevant physical processes ( e.g. , the model’s land surface and cloud schemes). Given that models are an integral component of reanalysis systems, how then can reanalyses be used to help understand errors in the climate models—in some cases the same models used to produce the reanalysis?
2.2.2 Assessing Systematic Errors The most straightforward approach to assess

ing systematic errors is to compare the basic reanalysis conditions ( e.g. , winds, temperature, moisture) with those that the model produces in free-running mode (a simulation that is not corrected by observations) 1 . The results of such comparisons, for example of monthly or sea

sonal average values, can indicate whether the model has systematic errors such as producing too cold or too wet in certain regions.
In general, such comparisons are only use

ful for regions and for quantities where the uncertainties in the reanalysis data are small compared to the model errors. For example, if the difference in the tropical moisture between two reanalysis products ( e.g. , NCEP/NCAR R1 and ERA-40) is as large as (or larger than) the

1 These are typically multi-year Atmospheric General Circulation Model runs started from arbitrary initial conditions and forced by the observed record of sea surface temperatures (SST). Even with a perfect model and nearly perfect observations, adjustments are necessary because the model would still deviate from nature since the atmosphere is chaotic and even very small observational errors grow rapidly to impact the model forecast. Figure 2.6 The distribution of zonally-averaged sea level pressure simulated by the various AMIP models for December, January, and February from 1979 to 1988 compared against the ECMWF (ERA-15) reanalysis (the black dots; Gibson et al. , 1997). From Gates et al ., 1999.21 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
differences between any one reanalysis product and the model results, then no conclusion can be reached about the model quality based on that comparison. This points to the need for obtain

ing reliable uncertainty and bias estimates of all reanalysis quantities ( e.g. , Dee and Todling, 2000), something that has not yet been achieved in the current generation of reanalysis efforts. In the absence of such estimates, comparing the available reanalysis datasets can provide guidance regarding uncertainties and model dependence. Such comparisons with reanaly

sis data are now routine and critical aspects of any model development and evaluation effort. ( e.g. , Atmospheric Model IntercomparisonProject [AMIP] [Gates, 1992], the troposphericstratospheric GCM-Reality Intercomparison Project for SPARC [GRIPS] [Pawson et al ., 2000], and coupled model evaluation conducted for the IPCC Fourth Assessment Report [IPCC, 2007]).
Figure 2.6 illustrates a comparison between various atmospheric models and the first Eu

ropean Centre for Me

dium-Range Weather Forecasts (ECMWF) reanalysis (ERA-15, Table 2.1).
The comparison shows considerable differenc

es among the models in the zonal mean surface pressure, especially at high latitudes. Figure 2.7 shows an example of a more in-depth evaluation of the ability of Atmospheric Gen

eral Circulation Model (AGCM) simulations forced by observed sea surface temperatures to reproduce that part of the variability associ

ated with ENSO.
In this case the compar

ison is made with the NCEP/NCAR R1 re

analysis for December, January, and February from 1950 to 1999. The comparison suggests that the models produce a very good response to the ENSO-related sea surface temperature variations.
2.2.3 Inferences about Climate Forcing While the above comparisons address errors in the description of the climate system, a more challenging problem is to address errors in the forcing or physical mechanisms (in particular the parameterizations) by which the model pro

duces and maintains climate anomalies. This in

volves quantities that are generally only weakly or indirectly constrained by observations ( e.g. , Kalnay et al ., 1996; Kistler et al ., 2001). RuizBarradas and Nigam (2005), for example, show that land/atmosphere interactions may be too efficient (make too large a contribution) in maintaining precipitation anomalies in the U.S. Great Plains in current climate models, despite rather substantial differences in the reanalyses. Nigam and Ruiz-Barradas (2006) highlight some of the difficulties encountered when trying to validate models in the presence Comparing available reanalysis datasets can provide guidance regarding

uncertainties and model dependence.

Figure 2.7 The left panels show the total variance of the winter average (December, January, February) 500mb height fields. The middle panels show that part of the total variance that is due to ENSO. The right panels show the ratio of the two variances (ENSO/Total). The top panels are from a reanalysis and the bottom panels are from atmospheric general circulation model (AGCM) simulations forced with observed sea surface temperatures. The results are computed for the period from 1950 to 1999, and plotted for the Northern Hemisphere polar cap to 20°N. The contour interval is 1000 (m 2 ) in the left and middle panels, and 0.1 in the right panels (taken from Hoerling and Kumar 2002).The U.S. Climate Change Science Program Chapter 2

22 of large differences between the reanalyses in the various components of the atmospheric water cycle ( e.g. , precipitation and evapora

tion). This problem can be alleviated to some extent by indirectly estimating the physical processes from other related quantities that are better constrained by the observations ( e.g. , Sardeshmukh, 1993). Nigam et al . (2000) show, for example, that the heating obtained from a residual approach appears to be of sufficient quality to diagnose errors in the ENSO-heating distribution in a climate model simulation.
Another approach to addressing errors in the forcing is to focus directly on the adjustments made to the model forecast during the assimi

lation ( e.g. , Schubert and Chang, 1996; Jeuken et al ., 1996; Rodwell and Palmer, 2007). These corrections can potentially provide substantial information about model limitations. Typically, the biases seen in fields, such as the monthly average temperature, are the result of complex interactions among small errors in different components of the model that grow over time. The challenge to modelers is to determine the individual potential sources of error, and ulti

mately to correct the inadequacies at the process level to improve long-term model behavior.
An important aspect of the corrections made during data assimilation is that they are ap

plied frequently (typically every six hours), such that the impact of the adjustments can be seen before they can interact with the full suite of model processes. In other words, the corrections made during the course of data assimilation give a potentially direct method for identifying errors in the physical processes that create model biases ( e.g. , Klinker and Sardeshmukh, 1992; Schubert and Chang, 1996; Kaas et al ., 1999; Danforth et al ., 2007; Rodwell and Palmer, 2007). They can also give insights into missing model physics such as dust-caused heating in the lower atmosphere (Alpert et al ., 1998), radiative heating in the stratosphere from volcanic eruptions (Andersen et al ., 2001), and impacts of land use changes (Kalnay and Cai, 2003)—processes not represented in the models used in the first reanalyses.
The development of a data assimilation system that provides unbiased estimates of the vari

ous physical processes inherent in the climate system (e.g. , precipitation, evaporation, cloud formation) is an important step in efforts to explain, or attribute (Chapter 3), the causes of climate anomalies. Therefore, reanalyses allow scientists to go beyond merely documenting what happened. Scientists can, for example, examine the processes that maintain a large pre

cipitation deficit in some region. Is the deficit maintained by local evaporative processes or by changes in the storm tracks that bring moisture to that region, or some combination of such fac

tors? As described in Chapter 3, reanalysis data provide the first steps in a process of attribution (how well the causes of climate variability are understood) that involves detection and descrip

tion of the anomalies, and an assessment of the important physical processes that contribute to their development. Ultimately, scientists seek answers to questions about the causes that can

not be addressed by reanalysis data alone. Go

ing back to the previous example, how can the role of local evaporative changes and changes in the storm tracks be separated? Model experi

mentation is required, as described in Chapter 3: here too, reanalyses play an important role in validating the model behavior.

2.2.4 Outlook There are a number of steps that can be taken to increase the value of reanalyses for identify

ing model deficiencies, including: improving our estimates of uncertainties in all reanalysis products, balancing budgets of key quantities ( e.g. , heat, water vapor, energy) (Kanamitsu and Saha, 1996; see also the next Section), and reducing the false model response to the adjust

ments made to the background forecast by the insertion of observations (the so-called model spin-up or spin-down problem), especially when the adjustments involve water vapor and the various components of the hydrological cycle (Kanamitsu and Saha, 1996; Schubert and Chang, 1996; Jeuken et al ., 1996). For example, Annan et al . (2005) proposed an ensemble fore

cast approach to estimating model parameters. These, and other approaches, hold substantial promise for obtaining optimal estimates of uncertain model parameters from reanalyses, even for the current comprehensive climate models. The development of a data assimilation system that provides unbiased estimates of the various physical processes inherent in the climate system is an important step in efforts to explain, or attribute, the causes of climate anomalies.23 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change



### 2.3. USING current reanalyses to identify and understand ma j or seasonal
- to
- decadal climate variations
In this Section the strengths and weaknesses of current reanalyses for identifying and un

derstanding climate variability are examined. This is an important step for addressing the more general issue of attribution, which was introduced in Chapter 1 and is addressed more fully in Chapter 3. Understanding the connec

tions between reanalysis, models, and attribu

tion is crucial for understanding the broader path towards attribution, as outlined in Chapter 1 (see Box 2.1). 2.3.1 Climate Variability The climate system varies greatly over space and time. The variability of the atmosphere in particular encompasses common, individual weather events, and longer-term changes af

fecting global weather patterns that can result in regional droughts or wet periods (pluvials) lasting many years. A primary research goal is to understand the causes of these long-term climate variations and to develop models that enable scientists to predict them.

On subseasonal to decadal time scales there are a number of key recurring global-scale patterns of climate variability that have pro

nounced impacts on the North American cli

mate (Table 2.3), including the Pacific/North American pattern (PNA), the Madden-Julian Section 2.3 demonstrates the value of reanalysis for identifying and understanding climate variability. By providing best estimates of the circulation patterns and other weather elements, such as moisture transport, evaporation, precipitation, and cloudiness, which are present during observed extremes—estimates that are comprehensive and consistent over space and time—reanalysis offers a unique and profound contribution to the more general problem of attribution discussed in Chapter 3. Reanalyses are especially useful for providing a global picture of the prevailing anomalous circulation patterns such as those associated with a given drought. By studying reanalysis data, investigators can hypothesize linkages between the drought and climate anomalies in other parts of the world ( e.g. , anomalies in sea surface temperatures [SSTs]).
Reanalysis is one tool for addressing the problem. A drawback of reanalysis in this context is its inability to isolate causality—to demonstrate unequivocally that one climate feature ( e.g. , anomalous SSTs) causes another ( e.g. , drought). This drawback can extend to any set of direct observations of the atmosphere. Climate model simulations that are unconstrained by the assimilation of observational data are needed in order to isolate causality, Climate models can be forced in different ways to determine whether a certain forcing will cause the model to reproduce a climate anomaly of interest. For example, if an investigator suspects, perhaps based on an analysis of reanalysis data, that anomalous SSTs caused the severe drought in the southern Great Plains during the1950s, he or she could perform two simulations with a free-running climate model, one in which the 1950s SST anomalies are imposed, and one in which they are not. If only the first simulation reproduces the drought, the investigator has evidence to support the hypothesized role of the SSTs. An additional step would be to determine the cause of the SST anomalies, which would require further experiments with a comprehensive atmosphere/ocean/land model.
These free-running modeling studies have their own deficiencies, most importantly the potential lack of realism in the climate processes simulated by an unconstrained (non-reanalysis) modeling system. This suggests an important additional role of reanalysis in the attribution problem. Not only can the reanalysis data help in the formulation of hypotheses to be tested with a free-running climate model, but it can (and should) be used to verify that the free-running model is behaving realistically, i.e. , that the variations in circulation and other climate processes in thefree-running model are consistent with what we have learned from reanalysis (see Section 2.2). Reanalysis and freerunning model simulations are complementary tools for addressing the attribution problem, each with their own strengths and weaknesses. Only the unconstrained parts of a model can be used to address attribution (causality), implying the need for free-running models, but those unconstrained parts must be evaluated for realism, implyingthe need for reanalysis. Arguably, the best approach to the attribution problem is to use the reanalysis and freerunning model approaches in tandem. BOX 2.1: The Complementary Roles of Reanalysis and Free-Running Model Simulations in the Attribution Problem

The variability of the atmosphere encompasses common, individual weather events and longer-term changes, affecting global weather patterns that can result in regional droughts

or wet periods lasting many years.The U.S. Climate Change Science Program Chapter 2

24 Oscillation (MJO), the North Atlantic Oscilla

tion (NAO) and the related Northern Annular Mode (NAM), the Quasi-Biennial Oscillation (QBO), El Niño-Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), and the Atlantic Multi-decadal Oscillation (AMO). These patterns, sometimes referred to as modes of climate variability or teleconnection pat

terns, can shift weather patterns and disrupt local climate features ( e.g. , Gutzler et al ., 1988; Hurrell, 1996).
As discussed in the following Sections, the qual

ity of the representation of these phenomena in reanalyses vary and depend on the time scales, locations, and physical processes relevant to each of these modes of variability. The last column in Table 2.3 gives the authors’ expert as

sessment of the consistency of the atmospheric manifestations of these modes (and their im

pacts on regional climate) in current reanalyses based on such general considerations. Figures 2.8 and 2.9 show examples of the con

nection between the PNA and NAO patterns and North American surface temperature and pre

cipitation variations. The spatial correspondence between the reanalysis tropospheric circulation and the independently-derived surface patterns show the potential value of the reanalysis data for interpreting the relationships between chang

es in the climate modes and regional changes in surface temperature and precipitation.
During the positive phase of the PNA pat

tern, surface temperatures over western North America tend to be above average; this can be related to an unusually strong high pressure ridge over the region as well as transport of warm Pacific air poleward along the West Coast extending to Alaska. An upper-level trough centered over the Southeast United States and the associated intensified north to south flow over the center of the continent facilitates the southward transport of Arctic air that produces a tendency toward below normal temperatures over the Gulf Coast states. This same flow Phenomenon Key reference Time scale Strength of link between atmosphere

and ocean Some impacts on North America Consistency between atmospheric

reanalyses Pacific-North American (PNA) pattern Wallace and Gutzler (1981) Subseasonal-toSeasonal Weak to moderate West coast storms Good

Madden Julian Oscillation (MJO) Madden and Julian (1994) Approximately 30-60 days Weak to moderate Atlantic hurricanes Fair to poor

North Atlantic Oscillation (NAO) Hurrell et al. (2001) Subseasonal-todecadal Moderate on long time scales East coast winters Good

Northern Annular Mode (NAM) Thompson and Wallace (2000); Wallace (2000) Subseasonal-todecadal Moderate on long time scales East coast winters Good to fair in stratosphere

El Niño-Southern Oscillation (ENSO) Philander (1990) Seasonal-to-inter

annual Strong Winter in west coast and south

ern tier of United States, Mexico,

warm season regional droughts Good to fair on longer time scales

Pacific Decadal Oscillation (PDO) Zhang et al. (1997) Decadal Strong Drought or pluvi

als over North America Fair to poor

Atlantic Multidecadal Oscilla

tion (AMO) Folland et al. (1986) Decadal Strong Drought or pluvi

als over North America, Atlantic hurricanes Fair to poor Table 2.3 Characteristics of some of the leading modes of climate variability that are known to have a substantial impact on North American climate. The last column provides a subjective assessment of the quality of the atmospheric manifestations of these modes (and their impacts on regional climate) in current atmospheric reanalyses.25 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
pattern is associated with transport of rela

tively dry polar air and a tendency to produce descending motions in the middle troposphere over the Missouri and Mississippi regions, both of which favor below normal precipitation, as observed. In contrast, the positive phase of the NAO pattern is accompanied by above average temperatures over the eastern United States and above average precipitation in the Ohio Valley. The reanalysis data of tropospheric circulation help to interpret this relationship as resulting from a northward-shifted westerly flow re

gime over the eastern United States and North Atlantic that inhibits cold air excursions while simultaneously facilitating increased moisture convergence into the region.
The above patterns arise mainly, but not exclusively, as manifestations of internal at

mospheric variability; that is, they owe their existence largely to processes that are confined to the atmosphere such as various atmospheric instabilities and nonlinear processes ( e.g. , Massacand and Davies, 2001; Cash and Lee, 2001; Feldstein, 2002, 2003; Straus and Shukla, 2002, and as discussed in Chapter 3). They are, however, also linked in varying degrees to processes external to the atmosphere such as interactions with the land surface and oceanvariations. Understanding subseasonal-todecadal climate variability requires that we understand the physical processes that produce these large-scale patterns, including how they interact with each other, and their interactions with the different climate system components (Chapter 3).
A key factor that limits scientists’ ability to fully understand such long-term variability is the lack of long-term comprehensive and consistent observations of the climate system, including observations of the land and ocean, which are critical to understanding and predict

ing atmospheric variability over seasonal and longer time periods. Observations of each of these climate system components, while im

proving with increased satellite usage, are not yet sufficient for addressing climate problems. In order to adequately address seasonal and longer period of variability, the observations need to continuously cover many decades, span the globe, include all key climate parameters, and be consistent with our best physical under

standing.
Among all components of the climate system, the atmospheric component possesses the most advanced observational capabilities. This sys

tem was developed primarily to support weather prediction, with major advances occurring first with the onset of a network of radiosondes in the 1950s and then with a near global observ

ing system provided by satellite measurements beginning in the late 1970s. The present observ

ing system is, however, still not fully adequate for many applications, and efforts continue to develop a true climate observing system that spans all climate system components and that provides continuity across space and time (GEOSS, 2005). Figure 2.8 The contours indicate the correlation between the winter

time PNA index (Wallace and Gutzler, 1981) and 500 millibar height field. The color shading indicates the correlations between the PNA index and the surface temperature (top panel) and the precipitation (bottom panel). The 500millibar height is from the NCEP/NCAR R1 reanalysis. The surface temperature and precipitation are from independent ob

servational datasets. The correlations are based on seasonally-averaged data from 1951 to 2006. The contours of correlation give an indication of the direction of the mid-tropospheric winds, and the positions of the troughs and ridges.The U.S. Climate Change Science Program Chapter 2

26 2.3.2 Reanalysis and Climate Variability One of the most important insights of the last few decades regarding the existing observa

tional record was that the investment in opera

tional weather prediction could be leveraged by harnessing the prediction infrastructure (the global models and data assimilation meth

ods for combining various observations) to develop a more consistent historical record of the atmosphere (Bengtsson and Shukla, 1988; Trenberth and Olson, 1988). This insight led to the development of several atmospheric climate reanalysis datasets (Schubert et al ., 1993; Kalnay et al ., 1996; Gibson et al ., 1997). These datasets provided the first comprehensive depictions of the global atmosphere that, in the case of the NCEP/NCAR reanalysis (Kalnay et al ., 1996), now span over 60 years. This Section summarizes how these and several follow-on reanalyses (Kanamitsu et al ., 2002; Uppala et al ., 2005; Onogi et al ., 2005; Mesinger et al ., 2006) 2 have contributed to an improved under

standing of seasonal to decadal variability of climate (Table 2.1).
The reanalysis data provide the most com

prehensive picture to date of the state of the atmosphere and its evolution. The reanalyses also provide estimates of the various physical processes, such as precipitation, cloud forma

tion, and radiative fluxes, that are required to understand the processes by which climate evolves. As the utility of current reanalyses for identifying and understanding atmospheric variability is examined, the critical roles of the model in determining the quality of the reanalysis must be recognized, and the impact of the observing system inconsistencies in both space and time must also be appreciated. When assessing the utility of the reanalyses, the nature of the problem that is being addressed must also be considered. What is the time frame? How big is the area coverage? Does the problem involve the tropics or Southern Hemisphere, which tend to be less well observed, especially before the onset of satellite observations? To what extent are water vapor and clouds or links to the land surface or the ocean important? These are important considerations because data as

similation systems used for the first reanalyses evolved from numerical weather prediction needs; however, these systems did not place a high priority on modeling links to the land and ocean, which were considered to be of second

ary importance to producing weather forecasts from a day to a week in advance.
The capacity of current reanalyses to describe and understand major seasonal-to-decadal cli

mate variations is addressed in Sections 2.3.2.1, 2.3.2.2, and 2.3.2.3 by examining three key aspects of reanalyses: their spatial character

istics, their temporal characteristics, and their internal consistency and scope. Key examples are given of where reanalyses have contributed to the understanding of seasonal-to-decadal

2 While not global, the North American Regional Reanalysis (NARR) has played an important role for studying regional climate variability. Two of its key strengths are the enhanced resolution, and the fact that precipitation observations were assimilated. Figure 2.9 The contours indicate the correlation between the winter

time NAO index (Wallace and Gutzler, 1981) and 500 millibar height field. The color shading indicates the correlations between the NAO index and the surface temperature (top panel) and the precipitation (bottom panel). The 500 millibar height is from the NCEP/NCAR R1 reanalysis. The surface temperature and precipitation are from independent ob

servational datasets. The correlations are based on seasonally-averaged data from 1951 to 2006. The contours of correlation give an indication of the direction of the mid-tropospheric winds, and the positions of the troughs and ridges.27 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
variability and where improvement is needed. This Product builds on the results of two major international workshops on reanalysis (WCRP, 1997; WCRP, 1999) by emphasizing studies that have appeared in the published literature since the last workshop.
Spatial characteristics The globally complete spatial coverage pro

vided by reanalyses, along with estimates of the physical processes that drive the atmosphere, has greatly facilitated diagnostic studies that at

tempt to identify the causes of large-scale atmo

spheric variability that have substantial impacts on North American weather and climate ( e.g. , the NAO and PNA). Substantial improvements have been made in understanding the nature of both the NAO and PNA through studies using reanalysis products. Thompson and Wallace (2000), for example, provide a global perspec

tive on the NAO, using reanalysis data to link it to the so-called Northern Hemisphere Annular Mode (NAM), noting the similarities of that mode to another annular mode in the Southern Hemisphere. Reanalysis data have also been used to link the variability of the NAO to that in the stratosphere in the sense that anomalies developing in the stratosphere propagate into the troposphere, suggesting a source of potential predictability over subseasonal time periods ( e.g. , Baldwin and Dunkerton, 1999, 2001). Detailed studies made possible by reanalysis data have contributed to the understanding that both PNA and NAO modes of variability are fundamentally internal to the atmosphere,;that is, they would exist naturally in the atmosphere without any anthropogenic or other “external” forcing ( e.g. , Massacand and Davies, 2001; Cash and Lee, 2001; Feldstein, 2002, 2003; Straus and Shukla, 2002; see also Chapter 3 on attribution). Straus and Shukla (2002) emphasized the dif

ferences between the PNA and a similar pattern of variability in the Pacific/North American re

gion that is forced primarily as an atmospheric response to the tropical sea-surface temperature changes associated with ENSO.
Reanalysis data also allow in-depth evaluations of the physical processes and global connec

tions of extreme regional climate events such as droughts or floods. For example, Mo et al

. (1997), building on several earlier studies ( e.g. , Trenberth and Branstator, 1992; Trenberth and Guillemot, 1996), capitalized on the long record of the NCEP/NCAR global reanalyses to provide a detailed analysis of the atmospheric processes linked to floods and droughts over the central United States, including precursor events connected with large-scale wave propa

gation and changes in the Great Plains low level jet (LLJ). Liu et al . (1998) used reanalysis data in conjunction with a linear model to deduce the role of various physical and dynamical processes in the maintenance of the circulation anomalies associated with the 1988 drought and 1993 flood over the United States.

Figure 2.10 Latitudinal structure of the annual cycle in temperature (K; °C is equal to K - 273.15) at pressure of 100 hPa for ERA (top left), NCEP-E (top right), NASA/DAO (bottom left), and NCEP-O (bottom right). The contour interval is 0.5 K. Temperatures lower than 195 K are shaded. From Pawson and Fiorino (1999).The U.S. Climate Change Science Program Chapter 2

28 Process studies focused on North America have benefited from the high resolution and improved precipitation fields of the North American Regional Reanalysis (NARR). The studies examine, for example, the nature and role of the LLJ ( e.g. , Weaver and Nigam, 2008), land-atmosphere interactions ( e.g. , Luo et al ., 2007), and efforts to validate precipitation processes in global climate models ( e.g. , Lee et al ., 2007). These studies highlight the leading role of reanalysis data in the diagnostic evalua

tion of large-scale climate variability and of the physical mechanisms that produce high impact regional climate anomalies.
While reanalysis data have played a funda

mental role in diagnostic studies of the leading middle- and high-latitude variability and of regional climate anomalies, there are inadequa

cies in the stratosphere—a region of the atmo

sphere particularly poorly resolved in initial reanalysis systems ( e.g. , Pawson and Fiorino, 1998a,b, 1999; Santer et al ., 2003), but better represented in more recent reanalyses, such as the ERA-40 (Santer et al ., 2004). Figure 2.10 shows an example of the substantial differences between the reanalyses that occur in the tropical stratosphere even in such a basic feature as the annual cycle of temperature.
Another area of concern is in polar regions where the reanalysis models have limitations in both the numerical representation and the modeling of physical processes ( e.g. , Walsh and Chapman, 1998; Cullather et al ., 2000; Bromwich and Wang, 2005; Bromwich et al ., 2007). In particular, reanalyses have been in

adequate in the modeled polar cloud properties and associated radiative fluxes ( e.g. , Serreze et al ., 1998).
Variations in tropical sea surface temperatures (SST), especially those associated with ENSO, are a major contributor to climate variability over North America on interannual time scales ( e.g. , Trenberth et al ., 1998). Recent studies that use reanalysis data have contributed to important new insights on the links between tropical Pacific SST variability and extratropi

cal circulation ( e.g. , Sardeshmukh et al ., 2000; Hoerling and Kumar, 2002; DeWeaver and Nigam, 2002), the global extent of the ENSO response ( e.g. , Mo, 2000; Trenberth and Caron, 2000), and its impact on weather ( e.g. , Compo et al ., 2001; Gulev et al ., 2001; Hodges et al ., 2003; Raible, 2007; Schubert et al ., 2008). Many of these studies include companion model simula

tion experiments, and the reanalyses are used to both characterize the atmospheric behavior and to validate the model results. This is an im

portant advance in climate diagnosis resulting from increased confidence in climate models, and it represents an important synergy between reanalysis and the attribution studies discussed in Chapter 3.
While the reanalyses are useful in many re

spects for addressing the problem of tropical/ extratropical connections, there are limitations in representing tropical precipitation, clouds, and other aspects of the hydrological cycle ( e.g. , Newman et al ., 2000). The Madden-Julian Oscillation is an example of a phenomenon in which the interaction between the circulation and tropical heating is fundamental to its struc

ture and evolution ( e.g. , Lin et al ., 2004)—an interaction that has not yet been well repre

sented in climate models. Current reanalysis products are inadequate for validating models because those aspects of the MJO that appear to be important for proper simulation ( e.g. , the vertical distribution of heating) are poorly constrained by observations and are therefore highly dependent on the models used in the assimilation systems ( e.g. , Tian et al ., 2006). Indirect (residual) approaches to estimate the tropical forcing from reanalyses, however, can be useful, reflecting the greater confidence placed in the estimates of certain aspects of the large-scale tropical circulation (Newman et al ., 2000; Nigam et al ., 2000). Many recent studies that use reanalysis data include companion model simulation experiments, and the reanalyses are used to both characterize the atmospheric behavior and to validate the model results.29 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
While the NAO, PNA and ENSO phenomena influence subseasonal-to-interannual climate variability, there is evidence that these modes also may vary over periods of decades or longer. Understanding that behavior, as well as other decadal-scale modes of variability such as the Pacific Decadal Oscillation and the Atlantic Multi-decadal Oscillation, require datasets that are consistent over many decades.
Temporal characteristics The observing system over the last centuryvaries greatly over time. Prior to the midtwentieth century, the observing system was primarily surface-based and limited to land areas and ship reports, although some higher observations ( e.g. , wind measurements from pilot balloons) have been made routinely since the early twentieth century ( e.g. , Brönnimann et al ., 2005). An upper-air radiosonde network of observations was initiated in the late 1940s but was primarily confined to land areas, and Northern Hemisphere midlatitudes in particu

lar. A truly global observing system arose with the onset of satellite observations in the 1970s, with numerous changes made to the observing system as new satellites were launched with updated and more capable sensors, and older systems were discontinued (Figure 2.2). The changes in the observing system, together with improved sensors and the aging and degrading of existing sensors, makes combining all avail

able observations into a consistent long-term global climate record a major challenge. Figure 2.11 provides an overview of the number of observations made at all latitudes from 1946 to 1998 that were available to the NCEP/NCAR reanalysis (Kistler et al ., 2001). These changes, especially the onset of satellite observations, have impacted the reanalysis fields, often mak

ing it difficult to separate true climate varia

tions from artificial changes associated with the evolving observing system.
The changes in the observing system have impacted the ability to study variability on interannual and longer time periods—the time scales at which changes to the observing system also tend to occur ( e.g. , Basist and Chelliah, 1997; Chelliah and Ropelewski, 2000; Kistler et al ., 2001; Trenberth et al ., 2001; Kinter et al ., 2004). The impact can be complicated, involving interactions and feedbacks with the assimilation schemes. For example, Trenberth et al . (2001) show how discontinuities in tropi

cal temperature and moisture can be traced to the bias correction of satellite radiances in the ECMWF (ERA-15) reanalyses. Changes in conventional radiosonde observations can also have impacts. For example, the Quasi-Biennial Oscillation, while clearly evident throughout the record of the NCEP/NCAR reanalysis, shows substantial secular changes in amplitude that are apparently the result of changes in the availability of tropical wind observations (Kistler et al ., 2001). The major change in the observing system associated with the onset of satellite data in the 1970s represents a particu

larly difficult and important problem because it coincides with the time of a major climate shift associated with the Pacific Decadal Oscillation ( e.g. , Pawson and Fiorino, 1999; Trenberth and Caron, 2000; Chelliah and Bell, 2004).
Despite these problems, reanalysis data can be valuable in understanding long-term atmo

spheric variability, particularly if used in con

junction with other independent observations. For example, Barlow et al . (2001) used NCEP/ NCAR reanalyses of winds and stream function for the period 1958 to 1993, in conjunction withindependent sea surface temperature, streamflow, precipitation, and other data to identify Figure 2.11 Zonal average number of all types of observations available to the NCEP/ NCAR reanalysis per 2.5° latitude-longitude box per month from 1946 to 1998. A 12-month running average has been applied. From Kistler et al. (2001).

The changes in the

observing system, together with improved sensors and the aging and degrading of existing sensors, makes combining all available observations into a consistent long-term global climate record a major challenge.The U.S. Climate Change Science Program Chapter 2

30 three leading modes of SST variability affecting long-term drought over the United States.
In general, the quality of reanalysis tends to be best at weather time scales of a day to about a week, and degrades over both shorter and longer periods of time. The changes in quality reflect both the changes in the observing sys

tem and the ability of the model to simulate the variability at the different lengths of time. For time periods of less than a day, there are several factors that degrade the quality of the analysis. These include an observing system that does not fully resolve variations shorter than one day, and deficiencies in model’s representation of the diurnal cycle ( e.g. , Higgins et al ., 1996; Betts et al ., 1998a). This issue also contributes to errors in our estimates of seasonal and longer time averages of reanalysis quantities. It is not surprising that the quality is best for the weather time scales ( e.g. , Beljaars et al ., 2006), since the analysis systems and models used thus far for atmospheric reanalyses were developed for global numerical weather prediction.
There are also important connections between the atmosphere and the land and ocean systems on seasonal and longer periods of time that can limit reanalysis quality if they are not fully understood. The assimilation systems for the land and ocean components are considerably less developed than for the atmosphere (dis

cussed further in Section 2.5). The connection between the atmosphere and the ocean in the current generation of atmospheric reanalyses is made by specifying sea surface temperatures from reconstructions of historical observations; the land is represented in a simplified form, which can also contribute to limitations in rep

resenting the diurnal cycle because the cycle is interconnected with the land surface ( e.g. , Betts et al ., 1998b).
Model errors can have particularly large impacts on quantities linked to the hydrological cycle, such as atmospheric water vapor ( e.g. , Trenberth et al ., 2005) and major tropical circulations ( e.g. , the Hadley Cell) that are relevant to understand

ing climate variations and change (Mitas and Clement, 2006). Any bias in the model can exacerbate false climate signals associated with a changing observing system, for example, a model that consistently produces conditions that are too dry in the lower atmosphere. Such a model may give a realistic tropical precipitation condition when there are few moisture observa

tions available to constrain the model, but that same model might produce unrealistic rainfall for the satellite era when it is confronted with large amounts of water vapor information that is inconsistent with the model’s average water vapor distribution (Figure 2.5).
The impacts of the changing observing systems on current reanalysis products indicate these changes have not yet been accounted for. To date, all available observations have been used in order to maximize the accuracy of the re

analysis products at any given time, but efforts to develop approaches that would reduce the inconsistencies over long time periods in the reanalysis products have been limited. This issue has been recognized, and efforts are cur

rently underway to carry out reanalyses with a subset of the full observing systems to try to minimize the changes over time ( e.g. , Compo et al ., 2006), as well as to conduct other observ

ing system sensitivity experiments that could help to understand, if not reduce, the impacts ( e.g. , Bengtsson et al ., 2004b,c; Dee, 2005; Kanamitsu and Hwang, 2006). Model bias cor

rection techniques ( e.g. , Dee and da Silva, 1998; Chepurin et al ., 2005; Danforth et al ., 2007), improvements to our models (Grassl, 2000; Randall, 2000), and improvements to historical observations including data mining, improved quality control and further cross calibration and bias correction of observations (Schubert et al ., 2006) may also help to reduce the impacts from the changing observing system. There are important connections between the atmosphere and the land and ocean systems on seasonal and longer periods of time that can limit reanalysis quality if they are not fully understood.31 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
Internal consistency and scope An advantage of the reanalysis products men

tioned earlier involves the role of the model in providing internal consistency, meaning that the model enforces certain dynamical balances that are known to exist in the atmosphere, such as the tendency for the atmosphere to be in geostrophic balance (an approximate balance of the Coriolis and pressure gradient forces) in the midlatitudes. One important implication is that the different state variables (the quantities that define the state of the atmosphere— e.g. , the winds, temperature, and pressure) depend strongly on one other. That such constraints are satisfied in the reanalysis products is important for many studies that attempt to understand the physical processes or forcing mechanisms by which the atmosphere evolves ( e.g. , the various patterns of variability mentioned above).
A fundamental advantage of model-based re

analysis products over single variable analyses of, for instance, temperature or water vapor observations, is that reanalysis products provide a comprehensive, globally complete picture of the atmosphere at any given time, together with the various forcings that determine how the atmosphere evolves over time. In principle it is possible to diagnose all aspects of how the climate system has evolved over the time period covered by the reanalyses; however, the results depend on the quality of the model as well as model characteristics and observational errors used in the reanalysis. As mentioned earlier, the models used in the current generation of re

analyses were largely developed for midlatitude numerical weather prediction and have known limitations, especially in various components of the hydrological cycle (clouds, precipitation, evaporation) that are necessary for understand

ing such important phenomena as monsoons, droughts, and various tropical phenomena.
Given that models are imperfect, can modelbased reanalysis products be used to validate model simulations (see also Section 2.2)? For example, by forcing models with the historical record of observed sea surface temperatures, can some of the major precipitation anomalies that occurred over the last century be accu

rately reproduced ( e.g. , Hoerling and Kumar, 2003; Schubert et al ., 2004; Seager et al ., 2005; Chapter 3)? As these simulations are examined for clues about how the climate system operates, there is an increasing need to validate the physi

cal processes that produce the regional climate anomalies ( e.g. , drought in the Great Plains of the United States). There is a question as to whether the reanalyses used in the validations are themselves compromised by model errors. However, evidence is growing that, at least in regions with relatively good data coverage, the reanalyses can be used to identify fundamen

tal errors in the model forcing of hydrological climate anomalies ( e.g. , Ruiz-Barradas and Nigam, 2005).
On global scales, the limitations in the assimila

tion models are shown as biases in, for example, monthly averaged heat and moisture budgets, introducing uncertainties in the physical pro

cesses that contribute to them ( e.g. , Trenberth and Guillemot, 1998; Trenberth et al ., 2001; Kistler et al ., 2001). There has been success in looking at variability of the energy budgets associated with some of the major climate variations such as ENSO ( e.g. Trenberth et al ., 2002a); however, inconsistencies in certain budgets (especially the atmospheric energy transports) limit their usefulness for estimating overall surface fluxes (Trenberth and Caron, 2001)—quantities that are important for linking the atmosphere and the ocean, as well as the atmosphere and land surface. Limitations in model-estimated clouds (and especially short wave radiation) appear to be a primary source of the problems in model fluxes both at the surface and at the top of the atmosphere ( e.g. , Shinoda et al ., 1999). Figure 2.12 shows an ex

A fundamental advantage of model-based reanalysis products is that reanalysis products provide a comprehensive, globally complete picture of the atmosphere at any given time, together with the various forcings that determine how the atmosphere evolves over time.The U.S. Climate Change Science Program Chapter 2

32 

### 2.4 climate trends in surface temperature and precipitation derived from reanalyses VERSUS from independent data
The climate of a region is defined by statistical properties of the climate system ( e.g. , averages, variances, and other statistical measures) evalu

ated over an extended period of time, typically over decades or longer. If these underlying statistical values do not change with time, the climate would be referred to as “stationary”. For example, in a stationary climate the average monthly rainfall in a specific region during the twentieth century, for instance, would be the same as that in the nineteenth, eighteenth, or any other century (within statistical sampling errors). Climate, however, is non-stationary; the underlying averages (and other statistical measures) do change over time. The climate system varies through ice ages and warmer periods with a timescale of about 100,000 years (Hays et al ., 1976). The “Little Ice Age” in the ample of implied ocean heat transport estimates provided by two different reanalyses and how they compare with the values obtained from a number of different coupled atmosphere-ocean model simulations.
Current atmospheric reanalysis models do not satisfactorily represent interactions with other important components of the climate system (ocean, land surface, cryosphere). As a result, various surface fluxes (e.g. , precipita

tion, evaporation, radiation) at the interfaces between the land and atmosphere, cryosphere and atmosphere, and the ocean and atmosphere, are generally inconsistent with one other and therefore limit the ability to fully understand the forcings and interactions of the climate system ( e.g. , Trenberth et al ., 2001). While there are important stand-alone land ( e.g. , Reichle and Koster, 2005) and ocean ( e.g. , Carton et al ., 2000) reanalysis efforts currently either in de

velopment or underway (Section 2.5), the longterm goal is a fully coupled climate reanalysis system (Tribbia et al ., 2003). The climate of a region is defined by statistical properties of the climate system evaluated over an extended period of time, typically over decades or longer. Figure 2.12 Annual mean, zonally-averaged oceanic heat transport implied by net heat flux imbalances at the sea surface, under an assumption of negligible changes in oceanic heat content. The observational based estimate, taken from Trenberth and Caron (2001) for the period February 1985 to April 1989, originates from reanalysis products from NCEP/NCAR (Kalnay et al. , 1996) and European Centre for Medium Range Weather Forecasts 40-year reanalysis (ERA40; Uppala et al. , 2005). The model aver

ages are derived from the years 1980 to 1999 in the twentieth century simulations in the Multi-Model Dataset at the Program for Climate Model Diagnosis and Intercomparison (PCMDI). The legend identifying individual models appears in Figure 8.4 of the IPCC Fourth Assessment Report (IPCC, 2007).33 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

fifteenth to nineteenth centuries (Bradley et al ., 2003) is an example of a natural climate variation (non-stationarity) with a much shorter timescale of a few centuries. Humans may be affecting climate even more quickly through their impact on atmospheric greenhouse gases (Hansen et al ., 1981).

The search for trends in climatic data is an attempt to quantify the non-stationarity of cli

mate, as reflected in changes in long-term aver

age climate values. There are various methods for accomplishing this task (see CCSP, 2006: Appendix A for a more detailed discussion). Perhaps the most common approach to calcu

lating a trend from a multiple decade dataset is to plot the data value of interest ( e.g. , rainfall) against the year of measurement. A line is fit through the points using standard regression techniques, and the resulting slope of the line is a measure of the climatic trend. A positive slope, for example, suggests that the “underly

ing climatic average” of rainfall is increasing with time over the period of interest. Such a trend calculation is limited by the overall noisi

ness of the data and by the length of the record considered. 2.4.1 Trend Comparisons: Reanalyses Versus Independent Measurements Reanalysis datasets now span several decades, as do various ground-based and space-based measurement datasets. Trends can be computed from both. A natural question is: How well do the trends computed from the reanalysis data agree with those computed from independent datasets? This question has been addressed in many independent studies. Calculating trends is one method for assessing the adequacy of reanalysis data for evaluating climate trends. The focus here is on trends in two particular variables: surface temperature at a height of two meters, referred to here as T 2m , and pre

cipitation.

Simmons et al . (2004) provide the most compre

hensive evaluation to date of reanalysis-based trends in surface temperature, T 2m . Figure 2.13, reproduced from that work which uses linear re

gression techniques, shows comparison of T 2m from observations (the CRUTEM2v dataset of Jones and Moberg, 2003), with two reanalyses (ERA-40 and NCEP/NCAR).
The period from 1958 to 2001 (left) and from 1979 to 2001 (right) were considered. All three Calculating trends is one method for assessing the adequacy of reanalysis data for evaluating climate trends. Figure 2.13 Calculated trends in near-surface (2 meter) temperature from an observational dataset (top), the ERA-40 reanalysis (middle), and the NCEP/NCAR reanalysis (bottom). Reproduced from Simmons et al. (2004).The U.S. Climate Change Science Program Chapter 2

34 datasets show generally positive trends. The reanalyses-based trends, however, are generally smaller, particularly for the longer time period. The average trend for 1958 to 2001 in the North

ern Hemisphere, is 0.19°C per decade for the observations, 0.13°C for ERA-40, and 0.14°C for NCEP/NCAR. For the shorter and more recent period, the Northern Hemisphere aver

ages are 0.30°C for the observations, 0.27°C for ERA-40, and 0.19°C for NCEP/NCAR. Sim

mons et al . (2004) consider the latter result for ERA-40 to be particularly encouraging because “the agreement is to within about 10 percent in the rate of warming of the land areas of the Northern Hemisphere since the late 1970s”. Stendel et al . (2000) note that for the ERA-15 reanalysis, which covers 1979 to 1993 using an earlier version of the modeling system, the trend in T 2m over North America and Eurasia is too small by 0.14°C per decade, relative to observations. Thus, the later ERA-40 reanalysis appears to improve significantly over the earlier ERA-15 reanalysis for T 2m temperature trends. Figure 2.13 shows that the performance of Figure 2.14 Annual tropical precipitation over land (left) and ocean (right) from four reanalyses (NCEP-R1, NCEP-R2, JRA-25, and ERA-40) and from two observational datasets (CMAP5D and GCPC5D). Reprinted from Takahashi et al. (2006).

Figure 2.15 Precipitation averaged over 10 o S-equator, 55 o -45 o W with respect to time, from (a) the NCAR/NCEP reanalysis, and (b) from an observational precipitation dataset. Reprinted from Kinter et al. (2004).35 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

ERA-40 and NCEP/NCAR varies with region, with some clear areas of large discrepancies that most likely represent reanalysis errors. Both reanalyses underestimate trends in India and Australia. The NCEP/NCAR reanalysis in par

ticular does not adequately reproduce trends in southern South America, a problem also noted by Rusticucci and Kousky (2002).
A similarly comprehensive evaluation of pre

cipitation trends from reanalyses has not been published. Takahashi et al . (2006), however, do summarize the trends in total tropical (30°S to 30°N) precipitation over the period of 1979 to 2001 (Figure 2.14) based on two sets of obser

vational data and four reanalyses.
The biggest discrepancy between the observa

tions and reanalyses is the large positive trend over the ocean for the ERA-40 reanalyses and the smaller but still positive trends for the other reanalyses, trends that are not found in the observations. Similarly, Chen and Bosilovich (2007) show that the reanalyses indicate a posi

tive precipitation trend in the 1990s when global precipitation totals are considered, whereas observational datasets do not. By starting in 1979, the tropical analysis of Takahashi et al

. (2006) misses a problem discovered by Kinter et al . (2004), who demonstrate a false precipitation trend produced by the NCEP/NCAR reanalysis in equatorial Brazil. As shown in Figure 2.15, the NCEP/NCAR reanalysis produces a strong, apparently unrealistic, increase in rainfall starting in about 1973, and thus, an unrealistic wetting trend.
Pohlmann and Greatbatch (2006) found that the NCEP/NCAR reanalysis greatly overestimates precipitation in northern Africa before the late 1960s, resulting in an unrealistic drying trend. Pavelsky and Smith (2006), in an analysis of river discharge to the Arctic Ocean, compared precipitation trends in the ERA-40 and NCEP/NCAR reanalyses with those from groundbased observations and found the reanalyses trends to be much too large, particularly for ERA-40. Figure 2.16 qualitatively summarizes these results.
River basins with an increasing precipitation trend and those with a decreasing precipita

Compared with temperature trends, reanalysis-based precipitation trends appear to be less

consistent with those calculated from observational datasets. Figure 2.16 Identification of northern Asia river basins for which the computed precipitation trend is posi

tive (a wetting trend) or negative (a drying trend), for four datasets: (top left) a dataset based on ground-based measurements of rainfall; (top right) a modified (improved) version of the first dataset; (bottom left) ERA-40 reanalysis; and (bottom right) NCEP/NCAR reanalysis. From Pavelsky and Smith (2006).The U.S. Climate Change Science Program Chapter 2

36 tion trend are identified for each dataset. For ERA-40, the vast majority of basins show an unrealistic (relative to ground observations) wetting trend.
2.4.2 Factors Complicating the Calculation of Trend The previous studies indicate that observed temperature trends are captured to a large ex

tent by the reanalyses, particularly in the latter part of the record, although some area trends ( e.g. , Australia) have been more difficult to reproduce. Compared with temperature trends, reanalysis-based precipitation trends appear to be less consistent with those calculated from observational datasets. As described below, many studies have identified sources for errors with the reanalyses that at least partly explain these inadequacies; however, trends produced from the observational datasets are also subject to errors for several reasons (see CCSP, 2006, and discussed below), such that the true inad

equacies of the reanalyses-based trends cannot be fully measured.
First, and perhaps most importantly, a false trend in the reanalysis data may result from a change in the observations being assimilated. In particular, with the onset of satellite data in the late 1970s, global-scale observations of highly variable quality increased dramatically. Consider a model that tends to “run cold” (has a negative temperature bias) when not con

strained by data. If this model is used to perform a reanalysis of the last 50 years but by necessity only ingests satellite data from the late 1970s onward, then the first half of the reanalysis will be biased cold relative to the second half, lead

ing to an artificial positive temperature trend (Figure 2.17).
Bengtsson et al . (2004a) use this reasoning to explain an apparently false trend in lower tro

posphere temperature (not surface temperature) produced by the ERA-40 reanalysis. Kalnay et al . (2006), when computing trends in surface air temperature from the NCEP/NCAR reanalysis, separate the 40-year reanalysis period into a pre-satellite and post-satellite period to avoid such issues. However, reanalyses can also be affected by non-satellite measurement system changes. Betts et al . (2005) note in reference to the surface temperature bias over Brazil that “the Brazilian surface synoptic data are not included [in the ERA-40 reanalysis] before 1967, and with its introduction, there is a marked shift in ERA-40 from a warm to a cool bias in two meter temperature”.
Reanalyses that rely solely on atmo

spheric data may miss real trends in surface temperature that are associated with land usage, such as urbanization, cropland conver

sion, changing irrigation practices, and other land use changes (Pielke et al ., 1999; Kalnay et al ., 2006). The ERA-40 reanalysis, which as

similates some station-based air temperature measurements made at the surface, is less affected by this issue than the NCEP/NCAR reanalysis, which does not. This dif

ference in station data assimilation may partially explain why ERA-40 reanalysis performs better compared with NCEP/NCAR reanalysis, as shown in Figure 2.13 (Simmons et al ., 2004). Figure 2.17 Idealized example showing how the correction of biased model data with ob

servational data during only one part of a reanalysis period, from 1979 onward, can lead to a spurious temporal trend in the reanalysis product. Reanalyses that rely solely on atmospheric data may miss real trends in surface temperature that are associated with land usage, such as urbanization, cropland conversion, changing irrigation practices, and other land use changes.37 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
As mentioned above, calculating trends from observational datasets also involves errors, and introduces additional uncertainties when compared with reanalysis products, in which values are provided on regular grids. An im

portant and challenging issue is estimating the appropriate grid-cell averaged temperature and precipitation values from point observations so that they can be directly compared with reanaly

sis products. Errors in representation may play an important role. For example, rainfall at one observation point may not be representative of rainfall over the corresponding model grid cell, which represents an area-average value. Rainfall measurements are often sparse and distributed non-randomly, for example , in the mountainous western United States, much of the precipitation falls as snow at high elevations, while most direct measurements are taken in cities and airports located at much lower eleva

tions, and are therefore not representative of total precipitation in that region. Simmons et al

. (2004) note that the gridded observational val

ues along coastlines reflect mostly land-based measurements, whereas reanalysis values for coastal grid cells reflect a mixture of ocean and land conditions. Also, producing a gridded data value from multiple stations within the cell can lead to significant problems for trend estimation because the contributing stations may have dif

ferent record lengths and other inhomogeneities over space and time (Hamlet and Lettenmaier, 2005). Jones et al . (1999) note that urban devel

opment over time at a particular sensor location can produce a positive temperature trend at the sensor that is real, but is likely unrepresentative of the large grid cell that contains it.
Observational datasets that span multiple de

cades are also subject to changes in measure

ment systems. Takahashi et al . (2006) suggest that the use of a new satellite data product (introduced in 1987) in an observational precipi

tation dataset led to a change in the character of the data. Kalnay et al . (2006) found an artificial trend in observational temperature data in

duced by changes in measurement time-of-day, measurement location, and thermometer type. Jones et al . (1999) discuss the need to adjust or omit station data as necessary to ensure a minimal impact of such changes before com

puting trends. Figure 2.18 shows the uncertainty inherent in trend computations from various observational datasets, and compared with NCSEP/NCAR reanalysis.
The top six maps show the annual temperature trends across regions over the continental United States, as computed from six differ

ent observational datasets from 1951 to 2006, and the bottom map shows the trend com

puted from the NCEP/NCAR reanalysis. Of the seven maps, the reanalysis-derived map is clearly different from the other maps; the six observations-based maps all show a warming trend in all regions except the South, whereas

Figure 2.18 Annual temperature trends across the continental United States, as determined with six observational datasets and the NCEP/ NCAR reanalysis (M. Hoerling, personal communication). Observational datasets that span multiple

decades are subject to changes

in measurement systems.The U.S. Climate Change Science Program Chapter 2

38 the reanalysis shows a general warming in the South and cooling toward the West. However, the six observations-based maps do not fully agree with one another. For example, the area of cooling in the South is smaller in the GISS and CRU datasets than in the National Cli

matic Data Center (NCDC)/Global Historical Climatology Network (GHCN) dataset. The NCDC climate division data show relatively high temperature trends in the West. These maps illustrate the fact that there is no perfect“truth” against which to evaluate the reanalysisbased trends.
There are also other sources of uncertainty forboth observations-based trends and reanalysisbased trends. The mathematical algorithm used to compute trends is important. Jones (1994a) uses the linear regression approach and the “robust trend method” of Hoaglin et al . (1983), thereby computing two similar, but not identi

cal, sets of trend values from the same dataset. Also, part of the trend estimation problem is determining whether a computed trend is real, that is, the degree to which the trend is unlikely to be the result of statistical sampling variations. Groisman et al . (2004) describe a procedure they used to determine the statisti

cal significance of computed trends, which can help alleviate this problem. Even if all surface temperature data were perfect and the trend estimation technique was not an issue, the time period chosen for computing a trend can result in sampling variations, depending, for example, on the relationship to transient events such as ENSO or volcanoes (Jones, 1994b).

2.4.3 Outlook While limitations hamper the accurate estima

tion of trends from either reanalyses or obser

vational datasets, it is the authors’ assessment that it is likely that most of the trend differ

ences shown in Figures 2.13 to 2.16 are related to limitations of the model-based reanalyses. Datasets that originate directly from surface and/or satellite observations, such as surface air temperature, precipitation, and atmospheric water vapor, will continue, at least for the near-term, to be the main tool for quantify

ing decadal and long-term climate changes. The observations-based trends are likely to be more reliable, in part because the relevant limitations in the observational data are better known and can, to a degree, be accounted for prior to trend estimation. This is less the case for existing reanalyses, which were not optimized for trend detection. Bengtsson et al . (2004a), examining various reanalysis products (though not surface temperature or precipitation), find that “there is a great deal of uncertainty in the calculation of trends from present reanalyses”. Reanalysis-based precipitation (for ERA-40 and NCAR/NCEP) and surface air tempera

ture (for NCAR/NCEP) are derived solely from the models ( i.e. , precipitation and surface temperature observations are not assimilated). Therefore, these fields are subject to inadequa

cies in model parameterization. The North American Regional Reanalysis is an important example of a reanalysis project that did employ the assimilation of observed precipitation data (Mesinger et al ., 2006), producing, as a result, more realistic precipitation products.
Reanalyses have some advantages in analyzing trends. The complexity of describing and under

standing trends is multi-faceted, and involves more than simply changes in average quantities over time. Precipitation trends, for example, can be examined in the context of the details of pre

cipitation probability distributions rather than total precipitation amount (Zolina et al ., 2004). Observed precipitation trends in the United States reflect more than just an increase in the average itself, being largely related to increases in extreme and heavy rainfall events (Karl and Knight, 1998). Heavier rainfall events seem to be decreasing over tropical land during the last 20 years, a trend that appears to be captured by reanalyses (Takahashi et al ., 2006). Warming trends often reflect nighttime warming rather than warming throughout the full 24-hour day (Karl et al ., 1991). Precipitation and temperature statistics are fundamentally tied together (Tren

berth and Shea, 2005); therefore, their trends should not be studied in isolation.
Given these and other examples of trend com

plexity, one advantage of a reanalysis dataset becomes clear: a proper analysis of the mecha

nisms of climate trends requires substantial data, and only a reanalysis provides self-con

sistent datasets that are complete in space and time over several decades. Given Figures 2.13 to 2.16, future reanalyses need to be improved to support robust trend estimation, particularly for A proper analysis of the mechanisms of climate trends requires substantial data, and only a reanalysis provides self-consistent datasets that are complete in space and time over several decades.39 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
precipitation. However, for many purposes, the comprehensive fields generated by reanalyses, together with their continuity ( i.e. , no gaps in time, which are a common feature in observa

tional data) and area coverage, provide value for understanding the causes of trends beyond what can be gained from observational datasets alone. For example, by providing trend esti

mates for midlatitude circulation patterns and other weather elements (features that tend to have a robust signal in reanalyses; see Section 2.4), reanalyses can provide insights into the nature of observed surface temperature and/or precipitation trends.



### 2.5 steps NEEDED TO IMPROVE CLIMATE REANALYSIS
As discussed previously, there are several rea

sons why the current approaches to assimilating observations for climate reanalysis can lead to false trends and patterns of climate variability. The instruments used to observe the climate may contain systematic errors, and changes in the types of instruments over time may intro

duce false trends into the observations. Even if the instruments are accurate, the sampling of the instruments across space and time changes over time and thus may improperly introduce shorter time scale or smaller space scale fea

tures, or introduce false jumps into the climate record. In addition, the numerical models used to provide a background estimate of the system state contain systematic errors that can project onto the climate analysis. In the case of the ocean, changes in the quality of the surface me

teorological forcing will be an additional source of false trends. The following Section address issues of systematic instrument and data sam

pling errors as well as model and data assimi

lation errors as a backdrop for recommending improvements in the way future reanalyses are performed. Specific recommendations are given in Chapter 4.
2.5.1 Instrument and Sampling Issues Prior to the middle of the twentieth century the atmosphere and ocean observing systems consisted mainly of surface observations of variables such as sea level pressure, winds, and surface temperature, although some upper air observations were already being routinely made early in the twentieth century (Brönnimann et al ., 2005). Much of the marine surface data are contained in the International Comprehen

sive Ocean-Atmosphere Dataset (ICOADS) (Worley et al ., 2005) but more still needs to be included. Considerable surface land data also exist, although these are currently scattered throughout several data archives, including those at the National Climatic Data Center and National Center for Atmospheric Research, and many additional surface datasets still need to be digitized. The state of this surface land data should improve as various land data recovery efforts begin (Compo et al ., 2006). Attempts to reconstruct climate for the first half of the twentieth century must rely on these surface observations almost exclusively and thus these data recovery efforts are very important (Whi

taker et al ., 2004; Compo et al ., 2006).
In 1936, the U.S. Weather Bureau began opera

tional use of the balloon-deployed radiosonde instrument, providing routine information for atmospheric pressure, temperature, humidity, and wind direction and speed used in daily weather forecasts. By the time of the Inter

national Geophysical Year of 1958, the radio

sonde network expanded globally to include Antarctica and became recognized as a central component of the historical observation net

work that climate scientists could use to study climate. As a climate observation network, radiosondes suffer from two major types of problems. First, the instruments contain inter

nal systematic errors (Haimberger, 2007). For example, the widely used Vaisala radiosondes exhibit a tendency toward dryness that needs to be removed (Zipser and Johnson, 1998; Wang et al ., 2002). Second, some radiosonde stations Attempts to reconstruct climate for the first half of the twentieth century must rely on land surface

observations almost exclusively and thus data recovery efforts are very important.The U.S. Climate Change Science Program Chapter 2

40 have moved to different locations, intro

ducing inconsistencies into the record (Gaffen, 1994).
Two additional observing systems were added to the existing system in the 1970s. Aircraft observations increasedin 1973, along with some early satellitebased temperature observations. In 1978, the number of observations increased dramatically in preparation for the First GARP Global Experiment, known as FGGE. The increased observation coverage included three satellite-based vertical temperature sounder instru

ments (MSU/HIRS/SSU), cloud-tracked winds, and the expansion of aircraft observations and surface observations from ocean drifting buoys. The impact of these additional observations (especially in the Southern Hemisphere) has been noted in the NCEP/NCAR and NCEP/ DOE reanalyses (Kalnay et al ., 1996; Kistler et al ., 2001).
Currently the global radiosonde network consists of about 900 stations, although most radiosondes are launched from continents in the Northern Hemisphere. Of these, there are approximately 600 sonde ascents at 00:00 UTC (Coordi

nated Universal Time) and 600 ascents at 12:00 UTC, with many from stations that launch the radiosondes only once per day. Most of these launches produce vertical profiles of variables that extend only into the lowest levels of the strato

sphere (about six miles above the Earth’s surface), at which height the balloons burst. A further troubling aspect of the radiosonde network is the recent closure of stations, especially in Africa, where the network is especially sparse.
As indicated above, the number of atmo

spheric observations increased dramati

cally in the 1970s with the introduction of remote sensed temperature retrievals, along with a succession of ancillary mea

surements ( e.g. , Figure 2.1). Temperature retrievals are made by observing the intensity of upwelling radiation in the microwave and infrared bands and then using physical models Figure 2.19 Distribution of temperature profile observations in the World Ocean Database extending from the surface of the ocean to 150 meter depth showing 40,000 profiles for 1960 (panel a), 105,000 profiles for 1980 (panel b), and 106,000 profiles for 2004 (panel c) (<http://www.nodc.noaa.gov/OC5/indprod.html>).41 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
to relate these intensity measurements to a particular temperature profile. The issue of unknown systematic errors in the observations and the need for redundant observations has been highlighted in recent years by a false cool

ing trend detected in microwave tropospheric temperature retrievals. This false cooling trend has recently been corrected by properly ac

counting for the effects of orbital decay (Mears et al ., 2003).
The ocean observing system has also under

gone a gradual expansion of in situ observa

tions ( i.e. , measurements obtained through direct contact with the ocean), followed by a dramatic increase of satellite-based observa

tions (Figures 2.19 and 2.20).
Prior to 1970, the main instrument for mea

suring subsurface ocean temperature was the mechanical bathythermograph, an instrument primarily deployed along trade shipping routes in the Northern Hemisphere, which recorded temperature only in the upper 280 meters, well above the oceanic thermocline (a thin layer in which temperature changes more rapidly with depth than it does in the layers above or below) at most locations. In the late 1960s the expendable bathythermograph (XBT) was introduced. In addition to being much easier to deploy, the XBT typically records temperature to a depth of 450 meters or 700 meters. Since the late 1980s, moored thermistor arrays have been deployed in the tropical oceans, begin

ning with the TAO/Triton array of the tropical Pacific, expanding into the Atlantic (PIRATA) in 1997, and most recently into the tropical In

dian Ocean. These surface moorings typically measure temperature and, less often, salinity at depths to 500 meters.
Two major problems have been discovered in the historical ocean temperature sampling record. First, much of the data were missing from the oceanographic centers; however, this problem is improving. The 1974 version of the World Ocean Atlas contained 1.5 million profiles. Thanks to great efforts by Global Oceanographic Data Archaeology and Res

cue (GODAR) the latest release of the World Ocean Database (WOD2005) contains nearly 8 million profiles (Boyer et al ., 2006). Such data archaeology and rescue work needs to be continued. Second, similar to the atmospheric radiosonde, the XBT instrument was not de

signed for climate monitoring. It is now known that XBT profiles underestimate the depth of the measurement by 1 to 2.5 percent of the actual depth (Hanawa et al ., 1995). Unfortu

nately, the compensating drop-rate correction differs for different varieties of XBTs, and less than half of the XBT observations identify the variety used. Some of the XBT observationscollected since the late 1990s have had a droprate correction applied without accompanying documentation, while there is evidence that the drop-rate error has changed over time, being higher in the 1970s compared with other time periods (AchutaRao et al ., 2007).
For the last half of the twentieth century the main instrument for collecting deep ocean temperature and salinity profiles was the Salinity Temperature Depth or Conductivity Temperature Depth (CTD) sensor. The CTD profiles are accurate, but there are five times fewer CTD profiles compared to the number Scientists can to al large extent only speculate about the

historical changes in deep ocean circulation for the last half of the twentieth century. Figure 2.20 Distribution of salinity observations as a function of depth and time in the upper 1000 meters from the World Ocean Database 2001 (Carton and Giese, 2008). The decrease in salinity observations in 1974 resulted from the closure of ocean weather stations, while the decrease in the mid 1990s resulted from the end of the World Ocean Circulation Experiment and from the effects of the time delay in transferring salinity observations into the data archives. The recent increase in salinity observations is due to the deployment of the Argo array. Argo is a global array of free-drifting profiling floats that measures the temperature and salinity of the upper 2000 meters of the ocean.The U.S. Climate Change Science Program Chapter 2

42 of XTB profiles. As a result, scientists can to a large extent only speculate about the historical changes in deep circulation.
Since 2003 a new international observing program called Argo (Roemmich and Owens, 2000) has revolutionized ocean observation. Argo consists of a set of several thousand au

tonomous drifting platforms that are mainly located at about 1000 meter depth. At regular intervals, generally ten days, the Argo drifters sink and then rise to the surface, recording a profile of temperature and salinity, which is then transmitted via satellite to data archival centers. The introduction of Argo has greatly increased ocean coverage in the Southern Hemisphere as a whole and at mid-depths everywhere, and also greatly increased the number of salinity obser

vations. Argo is gradually being expanded to measure variables such as oxygen levels, which are important for understanding the movement of greenhouse gases

. Satellite remote sensing has further expanded the ocean observing system. This process began in the 1980s with the introduction of infrared and microwave sensing of sea surface temperature, followed by the introduction of continuous radar observations of sea level in the early 1990s, and then by regular surface wind observations from satellite-based scat

terometers in the late 1990s. Scatterometers use the radar backscatter from wind-driven ripples on the ocean surface to provide information on wind speed and direction.
The availability of ocean datasets as well as general circulation models of the ocean has led to considerable interest in the development of ocean reanalyses (see Table 2.3). The tech

niques used are analogous to those used for the atmosphere. One example is the Simple Ocean Data Assimilation (SODA) ocean reanalysis by Carton et al . (2000). Like its atmospheric counterpart, this reanalysis shows distinctly different climate variability when satellite data is included.
It is important to address issues regarding thecollection and interpretation of reanalysisrelevant land surface data. First, global in situ measurements of land states ( e.g. , soil moisture,snow, ground temperature) are essentially nonexistent. Scattered measurements of soil mois

ture data are available in Asia (Robock et al ., 2000), and snow measurement networks provide useful snow information in certain regions ( e.g. , SNOTEL, <www.wcc.nrcs.usda.gov/snotel/>), but grid-scale in situ averages that span the globe are unavailable. Satellite data provide global coverage; however, they have limitations. Even the most advanced satellite-based obser

vations can only measure soil moisture several centimeters into the soil, and not at all under

The NASA/Global Modeling and Assimilation Office (GMAO) atmospheric global reanalysis project is called the Modern Era Retrospective-Analysis for Research and Applications (MERRA). MERRA (Bosilovich et al. , 2006) is based on a major new version of the Goddard Earth Observing System Data Assimilation System (GEOS-5), that includes the Earth System Modeling Framework (ESMF)-based GEOS-5 AGCM and the new NCEP unified grid-point statistical interpolation (GSI) analysis scheme developed as a collaborative effort between NCEP and the GMAO.
MERRA supports NASA Earth science by synthesizing the current suite of research satellite observations in a climate data context (covering the period 1979 to present), and by providing the science and applications communities with of a broad range of weather and climate data, with an emphasis on improved estimates of the hydrological cycle.
MERRA products consist of a host of prognostic and diagnostic fields including comprehensive sets of cloud, radia

tion, hydrological cycle, ozone, and land surface diagnostics. A special collection of data files are designed to facili

tate off-line forcing of chemistry/aerosol models. The model or native resolution of MERRA is 0.67° longitude by 0.5° latitude with 72 levels extending to a pressure of 0.01 hectoPascals (hPa). Analysis states and two-dimensional diagnostics will be made available at the native resolution, while many of the three-dimensional diagnostics will be made available on a coarser 1.25° latitude, 1.25° longitude grid. Further information about MERRA and its status may be found at <http://gmao.gsfc.nasa.gov/research/merra/>. BOX 2.2: Modern Era Retrospective-Analysis for Research and Applications ( MERRA)43 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
dense vegetation (Entekhabi et al ., 2004). Also, existing satellite-based estimates of surface soil moisture, as produced from different sensors and algorithms, are not consistent (Reichle et al. , 2007), implying the need for bias correction. Time-dependent gravity measurements may provide soil moisture at deeper levels, but only at spatial scales much coarser than those needed for reanalysis (Rodell et al. , 2007). Snow cover data from satellite are readily available, but the estimation of total snow amount from satellite data is subject to significant uncertainty (Foster et al. , 2005).

There are now a number of recommendations that have been put forth by the scientific com

munity ( e.g. , Schubert et al. , 2006) in order to make progress on issues regarding data quality and improvement of the world’s inventories of atmospheric, ocean, and land observations. These include the need for all major data centers to prepare inventories of observations needed for reanalysis, to form collaborations that can sustain frequent data upgrades and create high quality datasets from all instruments useful for reanalyses, to develop improved record tracking control for observations, and to fur

ther improve the use of information about the quality of the reanalyses targeted especially for data providers/developers. Furthermore, the observational, reanalysis, and climate com

munities should take a coordinated approach to further optimizing the usefulness of reanalysis for climate. These recommendations have now been considered by the WCRP Observations and Assimilation Panel (WOAP) and the Global Climate Observing System (GCOS)/WCRP Atmospheric Observations Panel for Climate.
2.5.2 Modeling and Data Assimilation Issues False trends may be introduced into the re

analyses by systematic errors in the models used to provide background estimates for data assimilation and by incomplete modeling of those systematic errors in the data assimilation algorithm. Atmospheric models include numeri

cal representations of the primitive equations ofmotion along with parameterizations of smallscale processes such as radiation, turbulent fluxes, and precipitation

. Model integrations begin with some estimate of the initial state, along with boundary values of solar radiation and sea surface temperature, and are integrated forward in time. While initial global reanaly

ses (Table 2.1) had resolutions of about 100 to 200 kilometers, the latest reanalysis efforts, NASA’s Modern Era Retrospective-Analysis for Research and Applications, MERRA, (see Box 2.2), and NOAA’s Reanalysis and Reforecasts of the NCEP Climate Forecast System, CFSRR, (see Box 2.3) have horizontal resolutions of about 50 kilometers or less. Regional models have much finer resolution, currently approach

ing one kilometer, and time steps of seconds. Improvements in resolution have improved representation of physical processes such as the strength and position of storm tracks and thus have improved simulation of local climate variability and reduced model bias.
Despite these increases in resolution, many important physical processes still cannot be explicitly resolved in current global models, such as convection, cloud formation, and pre

cipitation in the form of both water and ice. Therefore, these processes must be parameter

ized, or estimated from other, presumably more accurately simulated, model variables. Inac

curacies in these parameterizations are a major source of uncertainty in numerical simulation of the atmosphere and are a cause of false trends, or bias, in atmospheric models. In addition, the presence of atmospheric instabilities ( e.g. , Farrell, 1989; Palmer, 1988) will lead to model forecast errors.
Ocean models also include representations of primitive equations, with parameterizations for processes such as mixing and sea ice phys

ics. Ocean models exchange thermodynamic, radiative, and momentum fluxes with the atmo

sphere. Horizontal resolution of current global ocean models is approaching 10 kilometers in order to resolve the complex geometry of the ocean basins and the oceanic mesoscale. Despite this fine resolution, such models still exhibit systematic errors, suggesting that the small horizontal and vertical scales upon which key processes such as vertical mixing, convec

tion, and sea ice formation are still not being resolved (Smith et al. , 2000).
In most analyses, the fluxes between ocean and atmosphere are one way because the ocean reanalysis is controlled partly by atmospheric Improvements in model resolution have improved representation of physical processes such as the strength and position of storm tracks and thus have improved

simulation of local climate variability and

reduced model bias.The U.S. Climate Change Science Program Chapter 2

44 fluxes, while the atmospheric reanalysis is controlled partly by sea surface temperatures that are specified from observations. Thus, the fluxes in the reanalyses computed for the ocean and for the atmosphere, which should be identical, are in practice substantially different. Carrying out both reanalyses in a fully intercon

nected atmosphere/ocean model would ensure consistency; however, the surface exchanges are less constrained and thus, initial efforts at a combined analysis have been found to con

tain considerable systematic errors in both the atmosphere and the ocean (Collins et al ., 2006; Delworth et al ., 2006). A major challenge in the future will be to correct these systematic errors and subsequently develop consistent and accurate atmosphere/ocean reanalyses. NCEP is currently carrying out the first weakly coupled ocean-atmosphere reanalysis; results are encouraging but it is too early to know the extent to which the fluxes and trends are reli

able (Box 2.3).
The land surface component of an atmospheric model also provides fluxes of heat, water, and radiation at the Earth’s surface. The major dif

ficulty in producing realistic land fluxes is the large amount of variability ( e.g. , in topography, vegetation character, soil type, and soil mois

ture content) across areas (relative to that found in the atmosphere or ocean) in the properties that control these fluxes. These variabilities are difficult to accurately model for two rea

sons. First, given the area resolutions used for global reanalyses (now and in the foreseeable future), the physical processes that control the land surface fluxes cannot be properly resolved and therefore the small-scale processes must be parameterized. Second, there are few high reso

lution global measurements, which are required for many of the relevant land properties.
Despite these limitations, land models have been used in numerous Land Data Assimilation System (LDAS) projects. The current LDAS approach is to drive regional or global arrays of land surface models with observations-based meteorological forcing ( e.g. , precipitation, radiation) rather than with forcing from an at

mospheric model. This allows the land models to evolve their soil moisture and temperature states to presumably realistic values and to produce surface moisture and heat fluxes for diagnostic studies (Figure 2.21).
A list of some current LDAS projects is provided in Table 2.4. The LDAS framework is amenable to true assimilation, in which satellite-derived fields of soil moisture, snow, and temperature are incorporated into the gridded model integra

tions using new techniques ( e.g. , Reichle and Koster, 2005; Sun et al ., 2004).
Data assimilation provides a general way to correct a background estimate of the state of the atmosphere, ocean, and land surface that is consistent with available observations (Kalnay, 2003; Wunsch, 2006). However, most current data assimilation algorithms make several as

sumptions either for efficiency or from lack of information, limiting their effectiveness. These assumptions include: (1) that any systematic Figure 2.21 Schematic showing the inputs and outputs of a typical Land Data Assimilation System (LDAS) project.

Data assimilation provides a general way to correct a background estimate of the state of the atmosphere, ocean, and land surface that is consistent with available observations.45 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

trends or biases in the observation measure

ments or sampling have been identified and cor

rected; (2) that the forecast model is unbiased; and (3) that the error statistics, such as the model forecast error, have linear, Gaussian (normally distributed) characteristics.
Several changes can be made to improve these assumptions. Systematic errors introduced by expansions of the observing system can be reduced by repeating the reanalysis with a re

duced, but more consistent dataset, excluding, for example, satellite observations. An extreme version of this approach is to use only surface observations (Compo et al ., 2006). In this case, atmospheric reanalysis methods would need to make better use of historical surface observa

tions from land stations and marine platforms. These records include existing climate datasets, such as daily or monthly air temperature, pres

sure, humidity, precipitation, and cloudiness, which have already undergone extensive quality control for the purpose of climate variability and trend applications. Project Sponsor(s) Spatial Domain Unique Aspects Reference Project website

GSWP-2 GEWEX Global, 1° Separate datasets produced by at least 15 land models for the period 1986 to 1995 Dirmeyer et al. (2006) <http://www.iges.org/gswp2/>

GLDAS NASA, NOAA Global, .25° to ~2° Multiple land models; near-real-time data generation Rodell et al. (2004) <http://ldas.gsfc.nasa.gov/>

NLDAS Multiple Institutions Continental U.S., 0.125° Multiple land models; near-real-time data generation Mitchell et al. (2004) <http://ldas.gsfc.nasa.gov/>

ELDAS and ECMWF follow-on European Commission Europe, 0.2° True data assimilation of air temperature and humidity in some versions Van den Hurk (2002); Van den Hurk et al. (2008) <http://www.knmi.nl/samenw/ eldas/> Table 2.4 A partial list of current Land Data Assimilation System (LDAS) projects.

The New Reanalysis and Reforecasts of the NCEP Climate Forecast System (CFSRR) is a major upgrade to the coupled atmosphere/ocean/land Climate Forecast System (CFS; Saha et al. , 2006). This upgrade is planned for Janu

ary 2010 and involves changes to all components of the CFS, including the NCEP atmospheric Gridded Statistical Interpolation scheme (GSI), the NCEP atmospheric Global Forecast System (GFS), the NCEP Global Ocean Data Assimilation System (GODAS), which includes the use of the new GFDL MOM4 Ocean Model, and the NCEP Global Land Data Assimilation System (GLDAS), which includes the use of a new NCEP NOAA Land model.
There are two essential components to this upgrade: a new reanalysis of atmosphere, ocean, land, and sea ice, and a complete reforecast of the new CFS. The new reanalysis will be conducted for the 31-year period (1979 to 2009). The reanalysis system includes an atmosphere with high horizontal (spectral T382, about 38 km) and vertical (64 sigma-pressure hybrid levels) resolution, an ocean with 40 levels in the vertical to a depth of 4737 meters and a horizontal resolution of 0.25° at the tropics, tapering to a global resolution of 0.5° northwards and southwards of 10°N and 10°S, respectively, an interactive sea ice model, and an interactive land model with four soil levels.
In addition to the higher horizontal and vertical resolution of the atmosphere, the key differences from the previousNCEP global reanalysis are that the guess forecast will be generated from an interconnected atmosphere-oceanland-sea ice system, and that radiance measurements from the historical satellites will be assimilated. BOX 2.3: Climate Forecast System Reanalysis and Reforecast Project (CFSRR)The U.S. Climate Change Science Program Chapter 2

46 Systematic errors in the models may be ex

- plicitly accounted for and thus potentially cor

- rected in the data assimilation algorithm ( e.g. , Dee and da Silva, 1998; Danforth et al ., 2007). However, additional work is needed to improve bias modeling. In addition to estimating and reducing bias, there is a need to improve the representation of error covariances, and to provide improved estimates of the uncertain

- ties in all reanalysis products. New techniques ( e.g. , the Ensemble Kalman Filter) are being developed that are both economical and able to provide such estimates ( e.g. , Tippett et al ., 2003; Ott et al ., 2004).
Looking ahead, a promising pathway for improved reanalyses is the development of coupled data assimilation systems, along with methods to correct for the tendency of coupled models to develop bias. In this case, the ob

- served atmosphere, ocean, and land states are assimilated jointly into the atmosphere, ocean, and land components of a fully coupled climate system model; however, the substantial bias in current coupled models makes this a sig

- nificant challenge. Nevertheless, as scientists continue to improve coupled models, this joint assimilation should ensure greater consistency of model states across the components because the states would be allowed to evolve together. For example, a satellite-based correction to a soil moisture value would be able to impact and thereby potentially improve overlying atmospheric moisture and temperature states. The overall result of coupled assimilation would presumably be a more reliable and more useful reanalysis product. Several efforts are mov

- ing toward coupled data assimilation in the United States. These are focused primarily on developing more balanced initial conditions for the seasonal and longer forecast problem, and include the Climate Forecast System Reanalysis and Reforecast (CFSRR, see Box 2.3) project at NCEP and an ensemble-based approach be

- ing developed at NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL) (Zhang et al ., 2007). Also, the GMAO is utilizing both the MERRA product (Box 2.2) and an ocean data assimilation system to explore data assimilation in a fully coupled climate model. As scientists continue to improve coupled models, joint assimilation between atmosphere, ocean, and land components should ensure greater consistency of model states across the components because the states of the systems would be allowed to evolve together.CHAPTER 

# 3

47 

# A ttribution of the Causes of Climate

Variations and Trends over North America during the Modern Reanalysis Period

Convening Lead Author: Martin Hoerling, NOAA/ESRL

Lead Authors: Gabriele Hegerl, Edinburgh Univ.; David Karoly, Univ.

of Melbourne; Arun Kumar, NOAA; David Rind, NASA
Contributing Author: Randall Dole, NOAA/ESRL



### KEY FINDINGS Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

Significant advances have occurred over the past decade in capabilities to attribute causes for observed • climate variations and change. Methods now exist for establishing attribution for the causes of North American climate variations • and trends due to internal climate variations and/or changes in external climate forcing.
Annual, area-averaged change since 1951 across North America shows: Seven of the warmest ten years for annual surface temperatures since 1951 have occurred in the last • decade (1997 to 2006). The 56-year linear trend (1951 to 2006) of annual surface temperature is +0.90°C ±0.1°C (1.6°F ± • 0.2°F). Virtually all of the warming since 1951 has occurred after 1970. • More than half of the warming is • likely the result of anthropogenic greenhouse gas forcing of climate change. Changes in ocean temperatures • likely explain a substantial fraction of the anthropogenic warming of North America. There is no discernible trend in average precipitation since 1951, in contrast to trends observed in • extreme precipitation events (CCSP, 2008).
Spatial variations in annually-averaged change for the period 1951 to 2006 across North America show: Observed surface temperature change has been largest over northern and western North America, • with up to +2°C (3.6°F) warming in 56 years over Alaska, the Yukon Territories, Alberta, and Sas

katchewan. Observed surface temperature change has been smallest over the southern United States and eastern • Canada, where no significant trends have occurred. There is • very high confidence that changes in atmospheric wind patterns have occurred, based upon reanalysis data, and that these wind pattern changes are the likely physical basis for much of the spatial variations in surface temperature change over North America, especially during winter. The spatial variations in surface temperature change over North America are • unlikely to be the result of anthropogenic forcing alone. The spatial variations in surface temperature change over North America are • very likely influenced by variations in global sea surface temperatures through the effects of the latter on atmospheric circula

tion, especially during winter.The U.S. Climate Change Science Program Chapter 3

48 Spatial variations of seasonal average change for the period 1951 to 2006 across the United States show: Six of the warmest 10 summers and winters for the contiguous United States average surface • temperatures from 1951 to 2006 have occurred in the last decade (1997 to 2006). During summer, surface temperatures have warmed most over western states, with in

• significant change between the Rocky Mountains and the Appalachian Mountains. During winter, surface temperatures have warmed most over northern and western states, with insignificant change over the central Gulf of Mexico and Maine. The spatial variations in summertime surface temperature change are • unlikely to be the result of anthropogenic greenhouse forcings alone. The spatial variations and seasonal differences in precipitation change are • unlikely to be the result of anthropogenic greenhouse forcings alone. Some of the spatial variations and seasonal differences in precipitation change and variations •
are likely the result of regional variations in sea surface temperatures.
An assessment to identify and attribute the causes of abrupt climate change over North America for the period 1951 to 2006 shows: There are limitations for detecting rapid climate shifts and distinguishing these shifts from • quasi-cyclical variations because current reanalysis data only extends back to the midtwentieth century. Reanalysis over a longer time period is needed to distinguish between these possibilities with scientific confidence.
An assessment to determine trends and attribute causes for droughts for the period 1951 to 2006 shows: It is • unlikely that a systematic change has occurred in either the frequency or area cover

age of severe drought over the contiguous United States from the mid-twentieth century to the present. It is • very likely that short-term (monthly-to-seasonal) severe droughts that have impacted North America during the past half-century are mostly due to atmospheric variability, in some cases amplified by local soil moisture conditions. It is • likelythat sea surface temperature anomalies have been important in forcing longterm (multi-year) severe droughts that have impacted North America during the past half-century. It is • likely that anthropogenic warming has increased drought impacts over North America in recent decades through increased water stresses associated with warmer conditions, but the magnitude of the effect is uncertain.49 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change



### INTRODUCTION
Increasingly, climate scientists are being asked to go beyond descriptions of what the current climate conditions are and how they compare with the past, to also explain why climate is evolving as observed; that is, to provide at

tribution of the causes for observed climate variations and change. Today, a fundamental concern for policy makers is to understand the extent to which anthropo

genic factors and natural climate variations are responsible for the observed evolution of climate. A central focus for such efforts, as articulated in the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports (IPCC, 2007a) has been to establish the cause, or causes, for globally averaged temperature in

creases over roughly the past century. However, requests for climate attribution far transcend

Figure 3.1 Schematic illustration of the datasets and modeling strategies for performing attribution. The map of North America on the right side displays a climate condition whose origin is in question. Various candidate causal mechanisms are illustrated in the right-to-left sequences of figures, together with the attribution tool. Listed above each in maroon boxes is a plausible cause that could be assigned to the demonstrated mechanism depending upon the diagnosis of forcing-response relationships derived from attribution methods. The efficacy of the first mechanism is tested, often empirically, by determin

ing consistency with patterns of atmospheric variability, such as the teleconnection processes (climate anomalies over different geographical regions that are linked by a common cause) identifiable from reanalysis data. This step places the current condition within a global and historical context. The efficacy of the second mechanism tests the role of boundary forcings, most often with atmospheric models ( e.g. , Atmospheric Model Intercomparison Project, AMIP). The efficacy of the third mechanism tests the role of natural or anthropogenic influences, most often with linked ocean-atmosphere models. The processes responsible for the climate condition in question may, or may not, involve teleconnections, but may result from local changes in direct radiative effect on climate change or other near-surface forcing such as from land surface anomalies. The lower panels illustrate the representative processes: from left-to-right; time-evolving atmospheric carbon dioxide at Mauna Loa, Hawaii, the warming trend over several decades in tropical Indian Ocean/West Pacific warm pool sea surface temperatures (SSTs), the yearly SST variability over the tropical east Pacific due to the El Niño-Southern Oscillation (ENSO), the atmospheric pattern over the North Pacific/North America referred to as the Pacific North American (PNA) teleconnection. Today, a fundamental concern for policy makers is to understand the extent to which

anthropogenic factors and natural

climate variations

are responsible for the observed evolution of climate.The U.S. Climate Change Science Program Chapter 3

50 global temperature change alone, with notable interest in explaining regional temperature vari

ations and the causes for high-impact climate events, such as the recent multi-year drought in the western United States and the record set

ting U.S. warmth in 2006. For many decision makers who must assess potential impacts and management options, a particularly important question is: What are the causes for regional and seasonal differences in climate variations and trends, and how well do we understand them? For example, is the recent drought in the western United States due mainly to fac

tors internal to the climate system (e.g. , the sea surface temperature variations associated with ENSO), in which case a return toward previous climate conditions might be anticipated, or is it a manifestation of a longer-term trend toward increasing aridity in the region that is driven primarily by anthropogenic forcing? Why do some droughts last longer than others? Such examples illustrate that, in order to support informed decision making, the capability to attribute causes for past and current climate conditions can be a major consideration.
The recently completed IPCC Fourth Assess

ment Report (AR4) from Working Group I con

tains a full chapter (Chapter 9) devoted to the topic “Understanding and Attributing Climate Change” (IPCC, 2007a). This Chapter attempts to minimize overlap with the IPCC Report by focusing on a subset of questions of particular interest to the U.S. public, decision makers, and policy makers that may not have been covered in detail (or in some cases, at all) in the IPCC Report. The specific emphasis here is on present scientific capabilities to attribute the causes for observed climate variations and change over North America. For a more detailed discussion of attribution, especially for other regions and at the global scale, the interested reader is re

ferred to Chapter 9 of the AR4 Working Group I Report (IPCC, 2007a).
Figure 3.1 illustrates methods and tools used in climate attribution. The North American map (right side) shows an observed surface condition, the causes of which are sought. A roadmap for attribution involves the systematic probing of cause-effect relationships. Plausible factors that contribute to the change are identi

fied along the top of Figure 3.1 (maroon boxes), and arrows illustrate connections among these as well as pathways for explaining the observed condition.
The attribution process begins by examining conditions of atmospheric wind patterns (also called circulation patterns) that coincide with the North American surface climate anomaly. It is possible, for instance, that the surface condition evolved concurrently with a change in the tropospheric jet stream, such as accom

panies the Pacific-North American pattern (see Chapter 2). Reanalysis data are essential for this purpose because they provide a global description of the state of the troposphere (the lowest region of the atmosphere which extends from the Earth’s surface to around 10 kilome

ters, or about 6 miles, in altitude) that is physi

cally consistent in space and time. Although reanalysis can illuminate a connection between atmospheric circulation patterns and surface climate, it may not directly implicate the causes, that is, provide attribution.
Additional tools are often needed to explain the atmospheric circulation pattern itself. Is it, for instance, due to chaotic internal atmospheric variations, or is it related to forcing external to the atmosphere ( e.g. , changes in sea surface temperatures or solar forcing)? The middle column in Figure 3.1 illustrates the common approach used to assess the forcing-response associated with Earth’s surface boundary condi

tions (physical conditions at a given boundary), in particular sea surface temperatures. The principal tool is atmospheric general circulation models that are forced, that is, are subjected to a specific influence (see Box 3.2), for example, a specified history of sea surface temperatures as boundary conditions (Gates, 1992). Reanalysis would continue to be important in this stage of attribution in order to evaluate the suitability of the models as an attribution tool, including the realism of simulated circulation variability (Box 3.1).
In the event that diagnosis of the Atmospheric Model Intercomparison Project (AMIP) simu

lation fails to confirm a role for Earth’s lower boundary conditions, then two plausible expla

nations for the atmospheric circulation (and its associated North American surface condition) remain. One explanation is that it was due to For many decision makers who must assess potential impacts and management options, a particularly important question is: What are the causes for regional and seasonal differences in climate variations and trends, and how well do we understand them?51 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
chaotic atmospheric variability rather than natural or anthropogenic influences. Reanaly

sis data would be useful to determine whether the circulation state was within the scope of known variations during the reanalysis record. The other possible explanation is that external natural ( e.g. , volcanic and solar) or external anthropogenic perturbations may directly have caused the responsible circulation pattern. Coupled ocean-atmosphere climate models would be used to explore the forcing-response relationships involving such external forcings. As illustrated by the left column, coupled models have been widely employed in the Reports of the IPCC. Here again, reanalysis is important for assessing the suitability of this attribution tool, including the realism of simulated ocean-atmosphere variations such as the El Niño-Southern Oscillation (ENSO) and accompanying atmospheric teleconnections (climate anomalies over different geographical regions that are linked by a common cause) that influence North American surface climate (Box 3.1).
If diagnosis of the AMIP simulations confirms a role for Earth’s lower boundary conditions, it becomes important to explain the cause for the boundary condition itself. Comparison of the observed sea surface temperatures with coupled model simulations would be the prin

cipal approach. If externally-forced models that consider human influences on climate change fail to yield the observed boundary conditions, then the boundary condition may be attributed to chaotic intrinsic coupled ocean-atmosphere variations. If coupled models instead replicate the observed boundary conditions, this estab

lishes a consistency with external forcing as an ultimate cause. (In addition, it is necessary to confirm that the coupled models also generate the atmospheric circulation patterns; that is, to demonstrate that the model result is obtained for the correct physical reason.)
Figure 3.1 illustrates basic approaches applied in the following sections of Chapter 3. It is evident that a physically-based scientific interpretation for the causes of a climate condition requires ac

curately measured and analyzed features of the time and space characteristics of atmospheric circulation and surface conditions. In addition, the interpretation relies heavily upon the use of climate models to test candidate cause-effect relations. Reanalysis is essential for both com

ponents of such attribution science.
While this Chapter considers the approximate period covered by modern reanalyses (roughly 1950 to the present), datasets other than reanaly

ses, such as gridded surface station analyses of temperature and precipitation, are also used. The surface conditions illustrated in Figure 3.1 are generally derived from such datasets, and these are extensively used to describe various key features of the recent North American climate variability in Chapter 3. These, to

gether with modern reanalysis data, provide a necessary historical context against which the uniqueness of current climate conditions both at Earth’s surface and in the free atmosphere can be assessed.



### 3.1 CLIMATE ATTRIBUTION AND SCIENTIFIC METHODS USED FOR ESTABLISHING ATTRIBUTION

3.1.1 What is Attribution? Climate attribution is a scientific process for establishing the principal causes or physical explanation for observed climate conditions and phenomena. Within its Reports, the IPCC states that “attribution of causes of climate change is the process of establishing the most likely causes for the detected change with some level of confidence” (IPCC 2007). As noted in the Introduction, the definition is expanded in this Product to include attribution of the causes of observed climate variations that may not be unusual in a statistical sense but for which great public interest exists because they produce major societal impacts.
It is useful to outline some general classes of mechanisms that may produce climate varia

tions or change. One important class is exter

nal forcing , which contains both natural and anthropogenic sources. Examples of natural external forcing include solar variability and volcanic eruptions. Examples of anthropo

genic forcing are changing concentrations of greenhouse gases and aerosols and land cover changes produced by human activities. A sec

ond class involves internal mechanisms within the climate system that can produce climate Climate attribution is a scientific process

for establishing the principal causes or physical explanation for observed climate conditions

and phenomena.The U.S. Climate Change Science Program Chapter 3

52 variations manifesting themselves over seasons, decades, and longer. Internal mechanisms in

clude processes that are due primarily to inter

actions within the atmosphere as well as those that involve coupling the atmosphere with vari

ous components of the climate system. Climate variability due to purely internal mechanisms is often called internal variability

. For attribution to be established, the relation

ship between the observed climate state and the proposed causal mechanism needs to be demonstrated, and alternative explanations need to be determined as unlikely. In the case of attributing the cause of a climate condition to internal variations, for example, due to ENSO-related tropical east Pacific sea surface conditions, the influence of alternative modes of internal climate variability must also be as

sessed. Before attributing a climate condition to anthropogenic forcing, it is important to determine whether the climate condition was 

### BOX 3.1: Assessing Model Suitability
A principal tool for attributing the causes of climate variations and change involves climate models. For instance, atmospheric models using specified sea surface temperatures are widely used to assess the impact of El Niño on seasonal climate variations. Coupled ocean-atmosphere models using specified atmospheric chemical constituents are widely used to assess the impact of greenhouse gases on detected changes in climate conditions. One prerequisite for the use of models as tools is their capacity to simulate the known leading patterns of atmospheric (and for the coupled models, oceanic) modes of variations. Realism of the models enhances confidence in their use for probing forcing-response relationships, and it is for this reason that an entire chapter of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) is devoted to evaluation of the models for simulating known features of large-scale climate variability. That report emphasizes the considerable scrutiny and evaluations under which these models are being placed, making it “less likely that significant model errors are being overlooked”. Reanalysis data of global climate variability of the past half-century provide valuable benchmarks against which key features of model simulations can be meaningfully assessed.
The box figure illustrates a simple use of reanalysis for validation of models that are employed for attribution else

where in this report. Chapter 8 of the Working Group I report of IPCC AR4 and the references therein provide numerous additional examples of validation studies of the IPCC coupled models that are used in this SAP. Shown are the leading winter patterns of atmospheric variability, discussed previously in Chapter 2 (Figures 2.8 and 2.9), that have strong influence on North American climate. These are the Pacific-North American pattern (left), the North Atlantic Oscillation pattern (middle), and the El Niño-Southern Oscillation pattern (right). The spatial expressions of these patterns is depicted using correlations between observed (simulated) indices of the PNA, NAO, and ENSO with wintertime 500 hectoPascals geopotential heights derived from reanalysis (simulation) data for 1951 to 2006. Both atmospheric (middle) and coupled ocean-atmospheric (bottom) models realistically simulate the phase and spatial scales of the observed (top) patterns over the Pacific-North American domain. The correlations within the PNA and NAO centers of action are close to those observed indicating the fidelity of the models in generating these atmospheric teleconnections. The ENSO correlations are appreciably weaker in the models than in reanalysis. This is in part due to averaging over multiple models and multiple realizations of the same model. It perhaps also indicates that the tropical-extratropical interactions in these models is weaker than observed, and for the CMIP runs it may also indicate weaker ENSO sea surface temperature variability. These circulation patterns are less pronounced dur

ing summer, at which time climate variations become more dependant upon local processes ( e.g. , convection and land-surface interaction) which poses a greater challenge to climate models.
More advanced applications of reanalysis data to evaluate models include budget diagnoses that test the realism ofphysical processes associated with climate variations, frequency analysis of the time scales of variations, and multivariate analysis to assess the realism of coupling between surface and atmospheric fields. It should be noted that despite the exhaustive evaluations that can be conducted, model assessments are not always conclusive about their suitability as an attribution tool. First, the tolerance to biases in models needed to produce reliable assessment of cause-effect relationships is not well understood. It is partly for this reason that large multi-model ensemble methods are employed for attribution studies in order to reduce the random component of biases that exist across individual models. Second, even when known features of the climate system are judged to be realistically simulated in models, there is no assurance that the modeled response to increased greenhouse gas emissions will likewise be realistic under future scenarios. Therefore attribution studies (IPCC, Chapter 9) compare observed with climate model simulated change because such sensitivity is difficult to evaluate from historical observations.53 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

unlikely to have resulted from natural external forcing or internal variations alone.
Attribution is associated with the process of explaining the cause of a detected change. In particular, attribution of anthropogenic cli

mate change—the focus of the IPCC Reports (Houghton et al. , 1996; IPCC, 2001; IPCC, 2007a)—has the specific objective of explaining a detected climate change that is significantly different from that which could be expected from natural external forcing or internal varia

tions of the climate system. According to the IPCC Third Assessment Report, the attribution requirements for a detected change are: (1) a demonstrated consistency with a combination of anthropogenic and natural external forcings, and (2) an inconsistency with “alternative, phys

ically plausible explanations of recent climate change that exclude important elements of the given combination of forcings” (IPCC, 2001).

3.1.2 How is Attribution Performed? The methods used for attributing the causes for observed climate conditions depend on the spe

cific problem or context. To establish the cause, it is necessary to identify possible forcings, determine the responses produced by such forc

ings, and determine the agreement between the forced response and the observed condition. It is also necessary to demonstrate that the observed climate condition is unlikely to have originated from other forcing mechanisms. 

### BOX 3.1: Assessing Model Suitability Cont’d

Figure Box 3.1 Temporal correlation between winter season (December, January, February) 500 hectoPascals (hPa) geopotential heights and indices of the leading patterns of Northern Hemisphere climate variability: Pacific-North American (PNA, left), North Atlantic Oscillation (middle), and El Niño-Southern Oscillation (ENSO, right) circulation patterns. The ENSO index is based on equatorial Pacific sea surface temperatures averaged 170°W to 120°W, 5°N to 5°S, and the PNA and NAO indices based on averaging heights within centers of maximum observed height variability following Wallace and Gutzler (1981). Assessment period is 1951 to 2006: observations based on reanalysis data (top), simulations based onatmospheric climate models forced by observed specified sea surface temperature variability (middle), and coupled oceanatmosphere models forced by observed greenhouse gas, atmospheric aerosols, solar and volcanic variability (bottom). AMIP comprised of 2 models and 33 total simulations. CMIP comprised of 19 models and 41 total simulations. Positive (negative) correlations in red (blue) contours.The U.S. Climate Change Science Program Chapter 3

54 The methods for signal identification, as discussed in more detail below, involve both empirical analysis of past climate relation

ships and experiments with climate models in which forcing-response relations are evaluated. Similarly, estimates of internal variability can be derived from the instrumental records of historical data including reanalyses and from simulations performed by climate models in the absence of the candidate forcings. Both empiri

cal and modeling approaches have limitations. Empirical approaches are hampered by the relatively short duration of the climate record, the confounding of influences from various forcing mechanisms, and possible non-physical inconsistencies in the climate record that can result from changing monitoring techniques and analysis procedures (see Chapter 2 for examples of non-physical trends in precipita

tion due to shifts in reanalysis methods). The climate models are hampered by uncertainties in the representation of physical processes and by coarse spatial resolution, meaning that each grid cell in a global climate model generally covers an area of several hundred kilometers, which can lead to model biases.
In each case, the identified signal (forcingresponse relationship) must be robust to these uncertainties, and requires demonstrating that an empirical analysis is both physically mean

ingful, is insensitive to sample size, and is re

producible when using different climate models. Best attribution practices employ combinations of empirical and numerical approaches using multiple climate models to minimize the effects of possible biases resulting from a single line of approach. Following this approach, Table 3.1 and Table 3.2 lists the observational and model datasets used to generate analyses in Chapter 3.
The specific attribution method can also differ according to the forcing-response relation being probed. As discussed below, three methods have been widely employed. These methods consider different hierarchical links in causal relation

ships as illustrated in the Figure 3.1 schematic and discussed in Section 3.1.2.1: (1) climate conditions arising from mechanisms internal to the atmosphere; (2) climate conditions forced from changes in atmospheric lower boundary conditions (for example, changes in ocean or Model Acronym Country Institution ES 1 CCCmaCGCM3.1(T47) Canada Canadian Centre for Climate Modelling and Analysis 1

2 CCSM3 United States National Center for Atmospheric Research 6

3 CNRM-CM3 France Météo-France/Centre National de Recher

ches Météorologiques 1

4 CSIRO-Mk3.0 Australia CSIRO a Marine and Atmospheric Research 1

5 ECHAM5/MPI-OM Germany Max-Planck Institute for Meteorology 3

6 FGOALS-g1.0 China Institute for Atmospheric Physics 1

7 GFDL-CM2.0 United States Geophysical Fluid Dynamics Laboratory 1

8 GFDL-CM2.1 United States Geophysical Fluid Dynamics Laboratory 1

9 GISS-AOM United States Goddard Institute for Space Studies 2

10 GISS-EH United States Goddard Institute for Space Studies 3

11 GISS-ER United States Goddard Institute for Space Studies 2

12 INM-CM3.0 Russia Institute for Numerical Mathematics 1

13 IPSL-CM4 France Institute Pierre Simon Laplace 1

14 MIROC3.2(medres) Japan Center for Climate System Research/ NIES b /JAMSTEC c 3

15 MIROC3.2(hires) Japan Center for Climate System Research/ NIES b /JAMSTEC c 1

16 MRI-CGCM2.3.2 Japan Meteorological Research Institute 5

17 PCM United States National Center for Atmospheric Research 4

18 UKMO-HadCM3 United Kingdom Hadley Centre for Climate Prediction and Research 1

19 UKMO-HadGEM1 United Kingdom Hadley Centre for Climate Prediction and Research 1

a CSIRO is the Commonwealth Scientific and Industrial Research Organization.

b NIES is the National Institute for Environmental Studies.

c JAMSTEC is the Frontier Research Center for Global Change in Japan. Table 3.1 Acronyms of climate models referenced in this Chapter. All 19 models performed simulations of twentieth century climate change (“20CEN”) as well as the 720 parts per million (ppm) stabilization scenario (SRESA1B) in support of the IPCC Fourth Assessment Report (IPCC, 2007a). The ensemble size (ES) is the number of independent realizations of the 20CEN experiment that were analyzed here.55 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
land surface conditions); and (3) climate con

ditions forced externally, whether natural or anthropogenic. In some cases, more than one of these links, or pathways, can be involved. For example, changes in greenhouse gas forcing may induce changes in the ocean component of the climate system. These changing ocean conditions can then force a response in the atmosphere that leads to regional temperature or precipitation changes.

3.1.2.1 S ignal determination 1) Attribution to internal atmospheric varia

tions Pioneering empirical research, based only on surface information, discovered statistical link

ages between anomalous climate conditions that were separated by continents and oceans (Walker and Bliss, 1932), structures that are referred to today as teleconnection patterns. The North Atlantic Oscillation (NAO), which is a see-saw in anomalous pressure between the subtropical North Atlantic and the Arctic, and the Pacific-North American (PNA) pattern, which is a wave pattern of anomalous climate conditions arching across the North Pacific and North American regions, are particularly relevant to understanding North American climate variations. Chapter 2 illustrates the use of reanalysis data to diagnose the tropospheric wintertime atmospheric circulations associated with a specific phase of the PNA and NAO pat

terns, respectively. They each have widespread impacts on North American climate conditions as shown by station-based analyses of surface temperature and precipitation anomalies, and the reanalysis data of free atmospheric condi

tions provides the foundation for a physical explanation of the origins of those fingerprints (physical patterns), (see Section 3.1.2.2). The reanalysis data are also used to validate the realism of atmospheric circulation in climate models, as illustrated in Box. 3.1.
Observations of atmospheric circulation pat

terns in the free atmosphere fueled theories of the dynamics of these teleconnections, clarifying the origins for their regional surface impacts (Rossby, 1939). The relevant atmo

spheric circulations represent fluctuations in the semi-permanent positions of high and low pressure centers, their displacements being induced by a variety of mechanisms including URL Link Information for Data Sets CRU HadCRUT3v Climatic Research Unit of the University of East Anglia and the Hadley Centre of the UK Met Office <http://www.cru.uea.uk/cru/data/temperature/>

NOAA Land/Sea Merged Temperature NOAA’s National Climatic Data Center (NCDC) <http://www.ncdc.noaa.gov/oa/climate/research/anomalies/>

NASA Land+Ocean Temperature NASA’s Goddard Institute for Space Studies (GISS) <http://data.giss.noaa.gov/gistemp/>

NCDC Gridded Land Temperature NOAA’s National Climatic Data Center (NCDC) Gridded Land Precipitation <http://www.ncdc.noaa.gov/oa/climate/research/ghcn/>

NCDCdiv Contiguous U.S. Climate Division Data (temperature and precipitation) <http://www.ncdc.noaa.gov/oa/climate/onlineprod/>

PRISM Spatial Climate Gridded Data Sets (temperature and precipitation) Oregon State University’s Oregon Climate Service (OCS) <http://prism.oregonstate.edu>

CHEN Global Land Precipitation NOAA’s Climate Prediction Center (CPC) <http://www.cpc.noaa.gov/products/precip/>

GPCC Global Gridded Precipitation Analysis Global Precipitation Clima

tology Centre (GPCC) <http://www/dwd/de/en/FundE/Klima/KLIS/int/GPCC/>

CMIP3 CMIP3 World Climate Research Programme's (WCRP's) Cou

pled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset <http://www-pcmdi.llnl.gov/ipcc/>

Reanalysis NCEP50 National Centers for Environmental Prediction (NCEP), NOAA, and the National Center for Atmospheric Research (NCAR) <http://dss.ucar.edu/pub/reanalysis/data_usr.html/>

ECHAM4.5 ECHAM4.5 <http://iridl.ldeo.columbia.edu/SOURCES/.IRI/.FD/.ECHAM4p5/.History/. MONTHLY>

NASA/NSIPP Runs Table 3.2 Datasets utilized in the Product. The versions of these data used in this Product include data through December 2006. The web sites listed below provide URLs to the latest versions of these data
sets, which may incorporate changes made after December 2006.The U.S. Climate Change Science Program Chapter 3

56 anomalous atmospheric heating ( e.g. , due to changes in tropical rainfall patterns), changes in wind flow over mountains, the movement and development of weather systems ( e.g. , along their storm tracks across the oceans), and other processes (Wallace and Guzzler, 1981; Horel and Wallace, 1981; see Glantz et al ., 1991 for a review of the various mechanisms linking worldwide climate anomalies). The PNA and NAO patterns are now recognized as repre

senting preferred structures of extratropical climate variations that are readily triggered by internal atmospheric mechanisms and also by surface boundary variations, especially from ocean sea surface temperatures (Hoskins and Karoly, 1981; Horel and Wallace, 1981; Sim

mons et al ., 1983).
As indicated in Chapter 2, these and other teleconnection patterns can be readily identi

fied in the monthly and seasonal averages of atmospheric circulation anomalies in the free atmosphere using reanalysis data. Reanalysis data has also been instrumental in understand

ing the causes of teleconnection patterns and their North American surface climate impact (Feldstein 2000, 2002; Thompson and Wal

lace, 1998, 2000a,b). The ability to assess the relationships between teleconnections and their surface impacts provides an important foundation for attribution—North American climate variations are often due to particular atmospheric circulation patterns that connect climate anomalies over distant regions of the globe. Such a connection is illustrated schemati

cally in Figure 3.1.
2) Attribution to surface boundary forcing In some situations, teleconnections, including those described above, are a forced response to anomalous conditions at the Earth’s surface. Under such circumstances, attribution state

ments that go beyond the statement of how recurrent features of the atmospheric circula

tion affect North American surface climate are feasible, and provide an explanation of the cause for the circulation itself. For instance, the atmospheric response to tropical Pacific sea surface temperature anomalies takes the form of a PNA-like pattern having significant impacts on North American climate, especially in the winter and spring seasons. However, other surface forcings, such as those related to sea ice and soil moisture conditions, can also cause appreciable climate anomalies, although their influence is more local and does not usu

ally involve teleconnections.
Bjerknes (1966, 1969) demonstrated that a surface pressure see-saw between the western and eastern tropical Pacific (now known as the Southern Oscillation) was linked with the occurrence of equatorial Pacific sea surface temperature (SST) anomalies, referred to as El Niño. This so-called El Niño-Southern Oscil

lation (ENSO) phenomenon was discovered to be an important source for year-to-year North American climate variation, with recent ex

amples being the strong El Niño events of 1982 to 1983 and 1997 to 1998, whose major me

teorological consequences over North America included flooding and storm damage over a wide portion of the western and southern United States and unusually warm winter temperatures over the northern United States (Rasmusson and Wallace, 1983). The cold phase of the cycle, referred to as La Niña, also has major impacts on North America, in particular, an enhanced drought risk across the southern and western United States (Ropelewski and Halpert, 1986; Cole et al. , 2002).
The impacts of ENSO on North American cli

mate have been extensively documented using both historical data and sensitivity experiments in which the SST conditions associated with ENSO are specified in atmospheric climate models (see review by Trenberth et al. , 1998). Figure 3.2 illustrates the observed winter

time tropospheric circulation pattern during El Niño events of the last half century based on reanalysis data, and the associated North American surface signatures in temperature and precipitation. Reanalysis data is accurate enough to distinguish between the characteris

tic circulation pattern of the PNA (Figure 2.8) and that induced by ENSO—the latter having more widespread high pressure over Canada. Surface temperature features consist more of a north-south juxtaposition of warm-cold over North America during ENSO, as compared to the west-east structure associated with the PNA. The capacity to observe such distinctions is im

portant when determining attribution because particular climate signatures indicate different possible causes. North American climate variations are often due to particular atmospheric circulation patterns that connect climate anomalies over distant regions of the globe.57 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

The use of climate models subjected to speci

fied SSTs has been essential for determining the role of oceans in climate, and such tools are now extensively used in seasonal climate forecast practices. The atmospheric models are often subjected to realistic globally com

plete, monthly evolving SSTs (so-called AMIP experiments [Gates, 1992]) or to regionally confined idealized SST anomalies in order to explore specific cause-effect relations. These same models have also been used to assess the role of sea ice and soil moisture conditions on climate. The process of forcing a climate model is dis

cussed further in Box 3.2.
3) Attribution to external forcing Explaining the origins for the surface bound

ary conditions themselves is another stage in attribution. El Niño, for example, is a known internal variation of the coupled ocean-atmo

sphere. On the other hand, a warming trend of ocean SST, as seen in recent decades over the tropical warm pool of the Indian Ocean/West Pacific, is recognized to result in part from changes in greenhouse gas forcing (Santer et Figure 3.2 The correlation between a sea surface temperature index of El Niño-Southern Oscillation (ENSO) and 500 millibar (mb) pressure height field (contours). The shading indi

cates the correlations between ENSO index and the surface temperature (top panel) and the precipitation (bottom panel). The 500mb height is from the NCEP/NCAR R1 reanalysis. The surface temperature and precipitation are from independent observational datasets. The correlations are based on seasonal mean winter (December-January-February) data for the period 1951 to 2006. The contours with negative correlation are dashed. El Niño is a known internal variation ofthe coupled oceanatmosphere.The U.S. Climate Change Science Program Chapter 3

58 Figure Box 3.2 Schematic view of the components of the climate system, their processes and interactions (From IPCC, 2007a). 

### BOX 3.2: Forcing a Climate Model
The term “forcing” as used in Chapter 3 refers to a process for subjecting a climate model to a specified influence, often with the intention to probe cause-effect relationships. The imposed conditions could be “fixed” in time, such as a might be used to represent a sudden emission of aerosols by volcanic activity. It may be “time evolving” such as by specifying the history of sea surface temperature variations in an atmospheric model. The purpose of forcing a model is to study the Earth system response, and the degrees of freedom sensitivity of that response to both the model and the forcing employed. The schematic of the climate system helps to better understand the forcings used in various models of Chapter 3.
For atmospheric model simulations used in this SAP, the forcing consists of specified monthly evolving global sea surface temperatures during 1951 to 2006. By so restricting the lower boundary condition of the simulations, the response of unconstrained features of the climate system can be probed. In this SAP, the atmosphere and land surface are free to respond. Included in the former are the atmospheric hydrologic cycle involving clouds, precipitation, wa

ter vapor, temperature, and free atmospheric circulation. Included in the latter is soil moisture and snow cover, and changes in these can further feedback upon the atmosphere. Sea ice has been specified to climatological conditions in the simulations of this report, as has the chemical composition of the atmosphere including greenhouse gases, aerosols, and solar output.
For coupled ocean-atmosphere model simulations used in this SAP, the forcing consists of specified variations in atmospheric chemical composition ( e.g, , carbon dioxide, methane, nitrous oxide), solar radiation, volcanic and an

thropogenic aerosols. These are estimated from observations during 1951 to 2000, and then based upon a emissions scenario for 2001 to 2006. The atmosphere, land surface, ocean, and sea ice are free to respond to these specified conditions. The atmospheric response to those external forcings could result from the altered radiative forcing directly, though interactions and feedbacks involving the responses of the lower boundary conditions ( e.g. , oceans and cryosphere) are often of leading importance. For instance, much of the high-latitude amplification of surface air temperature warming due to greenhouse gas emissions is believed to result from such sea ice and snow cover feedback processes. Neither the coupled ocean-atmospheric models nor the atmospheric models used in this SAP include changes in land surface, vegetation, or ecosystems. Nor does the oceanic response in the coupled models include changes in biogeochemistry.
Multiple realizations of the climate models subjected to the same forcings are required in order to effectively sepa

rate the climate model’s response from low-frequency climate variability. Ensemble methods are therefore used in Chapter 3. In the case of the atmospheric models, 33 total simulations (derived from two different models) forced as discussed above are studied. In the case of the coupled ocean-atmosphere models, 41 total simulations (derived from 19 different models) forced as discussed above are studied59 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

al. , 2006; Knutson et al. , 2006). Figure 3.1 highlights the differences in how SSTs vary over the eastern versus western tropical Pacific as a consequence of different processes occur

ring in those regions. Thus, the remote effects of recent sea surface warming of the tropical ocean’s warmest waters (the so-called warm pool) on North American climate might bejudged to be of external origins to the oceanatmosphere system, tied in part to changes in the atmosphere’s chemical composition.

The third link in the attribution chain involves attribution of observed climate conditions to external forcing. The external forcing could be natural, for instance originating from volcanic aerosol effects or solar fluctuations, or the external forcing could be anthropogenic. As discussed extensively in the IPCC Reports, the attribution of climate conditions to external radiative forcing (greenhouse gases, solar, and volcanic forcing) can be done directly by specifying the natural and anthropogenic forc

ings within coupled ocean-atmosphere-land models. An indirect approach can also be used to attribute climate conditions to external forc

ing, for instance, probing the response of an atmospheric model to SST conditions believed to have been externally forced (Hoerling et al. , 2004). However, if an indirect approach is used, it can only be qualitatively determined that external forcing contributed to the event—an accurate quantification of the magnitude of the impact by external forcing can only be deter

mined in a direct approach.
The tool used for attribution of external forcing, either to test the signal (see Section 3.1.2.2) due to anthropogenic greenhouse gas and atmo

spheric aerosol changes or land use changes, or natural external forcing due to volcanicand solar forcing, involves coupled oceanatmosphere-land models forced by observed external forcing variations. As illustrated in Figure 3.1, this methodology has been widely used in the IPCC Reports to date. Several studies have used reanalysis data to first detect change in atmospheric circulation, and then test with models whether such change resulted from human influences. (Chapter 2 also discusses the use of reanalysis data in establishing the suit

ability of climate models used for attribution.) For instance, a trend in wintertime sea level pressure has been observed and confirmed in reanalysis data that resembles the positive po

larity of the NAO (high surface pressure over the midlatitude North Atlantic and low pressure over the Arctic), and greenhouse gas and sulfate aerosol changes due to human activities have been implicated as a contributing factor (Gillett et al. , 2003; Figure 3.7). Reanalysis data have been used to detect an increase in the height of the tropopause—a boundary separating the troposphere and stratosphere—and modeling results have established anthropogenic changes in stratospheric ozone and greenhouse gases as the primary cause (Santer et al. , 2003).

3.1.2.2 F ingerprinting Many studies use climate models to predict the expected pattern of response to a forcing, re

ferred to as “fingerprints” in the classic climate change literature, or more generally referred to as the “signal” (Mitchell et al. , 2001; IDAG, 2005; Hegerl et al. , 2007). The space and time scales used to analyze climate conditions are typically chosen so as to focus on the space and time scale of the signal itself, filtering out structure that is believed to be unrelated to forcing. For example, changes in greenhouse gas forcing are expected to cause a large-scale (global) pattern of warming that evolves slowly over time, and thus scientists often smooth data to remove small-scale variations in both time and space. On the other hand, it is expected that ENSO-related SST forcing yields a regionally focused pattern over the Pacific North Ameri

can sector, having several centers of opposite signed anomalies ( i.e. , warming or cooling), and therefore averaging over a large region such as this is inappropriate. To ensure that a strong signal has been derived from climate models, individual realizations of an ensemble, in which each member has been identically forced, are averaged. Ensemble methods are thus essential in separating the model’s forced signal from its internal variability so as to minimize the mix of signal and noise, which results from unforced climatic fluctuations.
The consistency between an observed climate condition and the estimated response to a hypothesized key forcing is determined by (1) estimating the amplitude of the expected fingerprint empirically from observations; (2) assessing whether this estimate is statistically Many studies use climate models to predict the expected pattern of response to

a forcing, referred to as “fingerprints” in the classic climate change literature, or more generally referred to as the “signal”.The U.S. Climate Change Science Program Chapter 3

60 consistent with the expected amplitude derived from forced model experiments; and then (3) inquiring whether the fingerprint related to the key forcing is distinguishable from that due to other forcings. The capability to do this also depends on the amplitude of the expected fingerprint relative to the noise.
In order to separate contributions by different forcings and to investigate if other combinations of forcing can also explain an observed event, the simultaneous effect of multiple forcings are also examined, typically using a statistical mul

tiple regression analysis of observations onto several fingerprints representing climate re

sponses to each forcing that, ideally, are clearly distinct from one another (Hasselmann, 1979; 1997; Allen and Tett, 1999; IDAG, 2005; Hegerl et al. , 2007). Examples include the known unique sign and global patterns of temperature response to increased sulfate aerosols (cooling of the troposphere, warming of the stratosphere)

versus increased carbon dioxide (warming of the troposphere but cooling of the stratosphere). Another example is the known different spatial patterns of atmospheric circulation response over the North American region to SST forc

ing from the Indian Ocean compared to the tropical eastern Pacific Ocean (Simmons et al. , 1983; Barsugli and Sardeshmukh, 2002). If the climate responses to these key forcings can be distinguished, and if rescaled combinations of the responses to other forcings fail to explain the observed change, then the evidence for a causal connection is substantially increased. Thus, the attribution of recent large-scale warm

ing to greenhouse gas forcing becomes more reliable if influences of other natural external forcings, such as solar variability, are explicitly accounted for in the analysis.

The confidence in attribution will thus be sub

ject to the uncertainty in the fingerprints both estimated empirically from observations and numerically from forced model simulations. The effects of forcing uncertainties, which can be considerable for some forcing variables such as solar irradiance and aerosols, also remain difficult to evaluate despite recent advances in research.

Satellite and in situ observations during the reanalysis period yield reliable estimates of SST conditions over the world oceans, thus increas

ing the reliability of attribution based on SST forced atmospheric models. Estimates of other land surface conditions, including soil moisture and snow cover, are less reliable. Attribution re

sults based on several models or several forcing observation histories also provide information on the effects of model and forcing uncertainty. Likewise, empirical estimates of fingerprints derived from various observational datasets provide information of uncertainty.
Finally, attribution requires knowledge of the internal climate variability on the time scales considered—the noise within the system against which the signal is to be detected and explained. The residual (remaining) variabil

ity in instrumental observations of the Earth system after the estimated effects of external forcing ( e.g. , greenhouse gases and aerosols) have been removed is sometimes used to esti

mate internal variability of the coupled system. However, these observational estimates are uncertain because the instrumental records are too short to give a well-constrained estimate of internal variability, and because of uncertainties in the forcings and the corresponding estimates of responses. Thus, internal climate variability is usually estimated from long control simula

tions from climate models. Subsequently, an assessment is usually made of the consistency between the residual variability referred to above and the model-based estimates of internal variability; and analyses that yield implausibly large residuals are not considered credible. Confidence is further increased by comparisons between variability in observations and climate model data, by the ability of models to simulate modes of climate variability, and by compari

sons between proxy reconstructions and climate simulations of the last millennium.
Sections 3.2, 3.3, 3.4, and 3.5 summarize cur

rent understanding on the causes of detected changes in North American climate. These sections will illustrate uses of reanalysis data in combination with surface temperature and precipitation measurements to examine the nature of North American climate variations, and compare with forced model experiments that test attributable causes. In addition, these sections also assess the current understanding of causes for other variations of significance The attributionof recent largescale warming to greenhouse gas forcing becomes more reliable if influences of other natural external forcings, such as solar variability, are explicitly accounted for in the analysis.61 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
in North America’s recent climate history, focusing especially on major North American droughts. In the mid-1930s, Congress requested that the Weather Bureau explain the causes for the 1930s Dust Bowl drought, with a key concern being to understand whether this event was more likely a multi-year occurrence or an indication of longer-term change. Similar to 70 years earlier, fundamental challenges in at

tribution science today are to distinguish quasicyclical variations from long-term trends, and natural from anthropogenic origins.



### 3.2 PRESENT UNDERSTANDING OF NORTH AMERICAN ANNUAL TEMPERATURE AND PRECIPITATION CLIMATE TRENDS FROM 1951 TO 2006
3.2.1 Summary of IPCC Fourth Assessment Report Among the major findings of the IPCC Fourth Assessment Report (IPCC, 2007b) is that “it is likely that there has been significant anthropo

genic warming over the past 50 years averaged over each continent except Antarctica”. Thisconclusion was based on recent fingerprintbased studies on the attribution of annual sur

face temperature involving space-time patterns of temperature variations and trends. Model studies using only natural external forcings were unable to explain the warming over North America in recent decades, and only experi

ments including the effects of anthropogenic forcings reproduced the recent upward trend. The IPCC Report also stated that, for precipita

tion, there was low confidence in detecting and attributing a change, especially at the regional level.
This assessment focuses in greater detail on North American temperature and precipitation variability during the period 1951 to 2006.
The origins for the North American fluctua

tions are assessed by examining the impacts on North America from time-evolving sea surface conditions (including ENSO and decadal ocean variations), in addition to time evolving anthro

pogenic effects. The use of reanalysis data to aid in the attribution of surface climate conditions is illustrated. 3.2.2 North American Annual Mean Temperature 3.2.2.1 D e s cription o f the ob s erved variability Seven of the warmest ten years since 1951 have occurred in the last decade (1997 to 2006). The manner in which North American annual temperatures have risen since 1951, however, has been neither smooth nor consistent, being characterized by occasional peaks and valleys (Figure 3.3, top). The coldest year since 1951 occurred in 1972, and below average annual temperatures occurred as recently as 1996. Explanations for such substantial variability are no less important than explanations for the warming trend.
Virtually all of the warming averaged over North America since 1951 has occurred after 1970. It is noteworthy that North American tem

peratures cooled during the period 1951 through the early 1970s. In the 1970s, the general public and policy makers were interested in finding the reason for this cooling, with concerns about food production and societal disruptions. They turned to the meteorological community for expert assessment. Unfortunately, climate science was in its early stages and attribution was considerably more art than science. The essential tools for performing rigorous attribu

tion such as global climate models were not yet available, nor was much known then about the range of historical climate variations such as those that have been subsequently revealed by paleoclimate studies. A consistent climate analysis of the historical instrumental record that included descriptions of the free atmo

sphere was also unavailable.
Barring an explanation of the cause for the cooling, and with no comprehensive climate models available, some scientists responded to the public inquiries on what would happen by merely extrapolating recent trends, thereby portraying an increased risk for a cooling world (Kukla and Mathews, 1972). Others suggested in the mid-1970s that we might be at the brink of a pronounced global warming, arguing that in

ternal variations of the climate were then mask

ing an anthropogenic signal (Broecker, 1975). The 1975 National Academy of Sciences report (NRC, 1975) on understanding climate change emphasized the fragmentary state of knowledge Seven of the warmest ten years since 1951 have occurred in the last decade (1997 to 2006). Virtually all of the warming averaged over North America since 1951 has occurred after 1970.The U.S. Climate Change Science Program Chapter 3

62 

### BOX 3.3: Choosing the Assessment Period
The authors of this Product were asked to examine the strengths and limitations of current reanalysis products, and to assess capabilities for attributing the causes for climate variations and trends during the reanalysis period. The scope of this assessment is thus bounded by the reanalysis record (1948 to present). An important further consideration is the availability of sufficient, quality controlled surface observations to define key climate variations accurately. For precipitation, a high quality global gridded analysis is available beginning in 1951, thereby focusing the attribution to the period from 1951 to 2006.
It is reasonable to ask whether such a 56-year as

sessment period adequately samples the principal features of climate variability. Does it, for example, capture the major climate events, such as droughts, that may be of particular concern to decision makers? Is it a sufficiently long period to permit the distinction between fluctuations in climate conditions that are transient, or cyclical, from trends that are related to a changing climate? How well do scientists understand the climate conditions prior to 1951, and what insight does analysis of those conditions provide toward ex

plaining post-1950 conditions? These are all important questions to bear in mind when reading this Product, especially if one wishes to generalize conclusions about the nature of and causes for climate conditions during 1951 to 2006 to earlier or future periods.
As a case in point, the U.S. surface temperature re

cord since 1895 is remarkable for its multi-decadal fluctuations (top panel). A simple linear trend fails to describe all features of U.S. climate variations, and furthermore, a trend analysis for any subset of this 112-year period may be problematic since it may capture merely a segment of a transient oscillation. The 1930s was a particularly warm period, one only recently eclipsed. The United States has undergone two major swings between cold epochs (beginning in the 1890s and 1960s) and warm epochs (1930s and 2000s). It is reasonable to wonder whether the current warmth will also revert to colder conditions in coming decades akin to events following the 1930s peak, and attribution sci

ence is therefore important for determining whether the same factors are responsible for both warmings or not. Some studies reveal that the earlier warming may have resulted from a combination of anthropogenic forcing and an unusually large natural multi-decadal fluctuation of climate (Delworth and Knutson, 2000). Other work indicates a contribution to the early twentieth century warming by natural forcing of climate, such as changes in solar radiation or volcanic activity ( e.g. , Hegerl et al. , 2006). The 1930s warming was part of a warming focused mainly in the northern high latitudes, a pattern reminiscent of an increase in poleward ocean heat transport (Rind and Chandler, 1991), which can itself be looked upon as due to “natural variability”. In contrast, the recent warming is part of a global increase in temperatures, and the IPCC Fourth Assessment Report, Chapter 9 states that it is likely that a significant part of warming over the past 50 years over North America may be human related (IPCC, 2007a), thus contrasting causes of the warming that occurred in this period from that in 1930s. The physical processes related to this recent warming are further examined in this Chapter.
The year 1934 continues to stand out as one the warmest years in the United States’ 112-year record, while averaged over the entire globe, 1934 is considerably cooler than the recent decade. The warmth of the United States in the 1930s coincided with the Dust Bowl (lower panel), and drought conditions likely played a major role in increasing land surface temperatures. Prior studies suggest that the low precipitation during the Dust Bowl was related in part to sea surface temperature conditions over the tropical oceans (Schubert et al. , 2004; Seager et al. , 2005). Current understanding of severe U.S. droughts that have occurred during the reanalysis period as described in this Chapter builds upon such studies of the Dust Bowl. Figure Box 3.3 Time series of U.S. area-averaged and annually averaged surface air temperature (top) and the Palmer Drought Severity Index (bottom) for the period 1895 to 2006. Curves are smoothed annual values using a nine-point Gaussian filter. The Gaussian filter is a weighted time averaging applied to the raw annual values in order to highlight lower frequency variations. “Nine-point” refers to the use of nine annual values in the weighting process. Data source is the contiguous U.S. climate division data of NOAA’s National Climatic Data Center.63 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
of the mechanisms causing climate variations and change, and posed the question of whether scientists would be able to recognize the first phases of a truly significant climate change when it does occur (NRC, 1975). Perhaps the single most important attribution chal

lenge today regarding the time series of Figure 3.3 is whether the reversal of the cooling trend after 1975 represents such a change, and one for which a causal explanation can be offered.
There is very high confidence in the detection that the observed tem

perature trend reversed after the early 1970s. The shaded area in Figure 3.3 (top right panel) illustrates the spread among four different analyses of sur

face measurements (see Table 3.2 for descriptions of these data), and the analysis uncertainty as revealed by their range is small compared to the amplitude of the trend and principal variations. Also shown is the surface temperature time series derived from the reanalysis. Despite the fact that the assimilating model used in producing the NCEP/NCAR reanalysis does not incorporate observations of surface temperature (Kalnay et al. , 1996), the agreement with the in situ observations is strong. This indicates that the surface temperature averaged over the large do

main of North America is constrained by and is consistent with climate con

ditions in the free atmosphere. Both for the emergent warming trend in the 1970s, and for the variations about it, this excellent agreement among time series based on different observational datasets and the reanalysis increases confidence that they are not artifacts of analysis procedure.
The total 1951 to 2006 change in observed North American annual surface temperatures is +0.90°C ± 0.1°C (about +1.6°F ± 0.2°F), with the uncertainty estimated from the range between trends derived from four different observational analyses. Has a significant North American warming been detected? Answers to this ques

tion require knowledge of the plausible range in 56-year trends that can occur naturally in the absence of any time varying anthropogenic forcing. The length of the observational record does not permit such an assessment, but an analysis of such variations in coupled model simulations that exclude variations in anthro

pogenic forcing provides an indirect estimate. To estimate the confidence that a change in Figure 3.3 The 1951 to 2006 trend in annually averaged North American surface tem

perature from observations (top), CMIP simulations (middle), AMIP simulations (bottom). Maps (left side) show the linear trend in annual temperatures for 1951 to 2006 (units, °C change over 56 years). Time series (right side) show the annual values from 1951 to 2006 of surface temperatures averaged over the whole of North America. Curves are smoothed annual values using a five-point Gaussian filter, based on the average of four gridded surface observational analyses, and the ensemble mean of climate simulations. The Gaussian filter is a weighted time-averaging applied to the raw annual values in order to highlight lower frequency variations. “Five-point” refers to the use of five annual values in the weighting process. Unsmoothed annual observed temperatures are shown by red circles, with filled circles denoting the ten warmest years since 1951. Plotted values are the total 56-year change (°C), with the double asterisks denoting very high confidence that an observed change was detected. For observations, the gray band denotes the range among four surface temperature analyses. The blue curve is the NCEP/NCAR reanalysis surface temperature time series. For simulations, the gray band contains the 5 to 95 percent occurrence of individual model simulations.The U.S. Climate Change Science Program Chapter 3

64 North American temperatures has been de

tected, a non-parametric test, which makes no assumptions about the statistical distribution of the data, has been applied that estimates the range of 56-year trends attributable to natural variability alone (see Appendix B for method

ological details). A diagnosis of 56-year trends from the suite of “naturally forced” Coupled Model Intercomparison Project (CMIP) runs is performed, from which a sample of 76 such trends were generated for annual North Ameri

can average surface temperatures. Of these 76 “trends estimates” consistent with natural vari

ability, no single estimate was found to generate a 56-year trend as large as observed.

It is thus very likely that a change in North American annual mean surface temperature has been detected. That assessment takes into account the realization that the climate models have biases that can affect statistics of their simulated internal climate variability. 3.2.2.2 A ttribution o f the ob s erved variation s 3.2.2.2.1 External Forcing The IPCC Fourth Assessment Report provided strong attribution evidence for a significant anthropogenic greenhouse gas forced warm

ing of North American surface temperatures (IPCC, 2007a). Figure 3.4 is drawn from that Report, and compares continental-averaged surface temperature changes observed with those simulated using the CMIP coupled models having both natural and anthropogenic forcing. It is clear that only experiments using observed time varying anthropogenic forcing explain the warming in recent decades. Numerous detection and attribution studies, as reviewed by Hegerl et al. (2007), have shown that the observed warming of North American surface temperature since 1950 cannot be explained by natural climate variations alone and is consistent with the response to anthropogenic climate forcing, particularly increases in at

mospheric greenhouse gases (Karoly et al. , 2003; Stott, 2003; Zwiers and Zhang, 2003; Knutson et al. , 2006; Zhang et al. , 2006). The suitability of these coupled climate models for attribution is indicated by the fact that they are able to simulate variability on time scales of decades and longer that is consistent with reanalysis data of the free atmosphere and surface observations over North America (Hegerl et al. , 2007).

A more detailed examination of the human influence on North America is provided in Figure 3.3 (middle) that shows the spatial map of the 1951 to 2006 model-simulated surface temperature trend, in addition to the trend over time. There are several key agreements between the CMIP simu

lations and observations that support the argument for an anthropogenic effect. First, both indicate that most of the warming has occurred in the past 30 years. The North American warming after 1970 is thus likely the result of the region’s response to anthropogenic forcing. Second, the total 1951 to 2006 change in observed North American annual surface tem

Figure 3.4 Temperature changes relative to the corresponding temperature average for 1901 to 1950 (°C) from decade to decade for the period 1906 to 2005 over the Earth’s continents, as well as the entire globe, global land area, and the global ocean (lower graphs). The black line indicates observed temperature change and the colored bands show the combined range covered by 90 percent of recent model simulations. Red indicates simula

tions that include natural and human factors, while blue indicates simulations that include only natural factors. Dashed black lines indicate decades and continental regions for which there are substantially fewer observations compared with other continents during that time. Detailed descriptions of this figure and the methodology used in its production are given in Hegerl et al. (2007). Numerous detection and attribution studies have shown that the observed warming of North American surface temperature since 1950 cannot be explained by natural climate variations. alone and is consistent with the response to anthropogenic climate forcing, particularly increases in atmospheric greenhouse gases65 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
peratures of +0.90°C (about +1.6°F) compares well to the simulated ensemble averaged warm

ing of +1.03°C (almost +1.9°F). Whereas the observed 56-year trend was shown in Section 3.2.2.1 to be inconsistent with the population of trends drawn from a state of natural climate variability, the observed warming is found to be consistent with the population of trends drawn from a state that includes observed changes in the anthropogenic greenhouse gas forcing dur

ing 1951 to 2006.
Further, the observed low frequency variations of annual temperature fall within the 5 to 95 percent uncertainty range of the individual model simulations. All CMIP runs that in

clude anthropogenic forcing produce a North American warming during 1951 to 2006. For some simulations, the trend is less than that observed and for some it is greater than that observed. This range results from two main factors. One is the uncertainty in anthropogenic signals; namely that the individual 19 models subjected to identical forcing exhibit somewhat different sensitivities. The other results from the internal variability of the models; namely that individual runs of the same model generate a range of anomalies owing to natural coupledocean atmosphere fluctuations.
Each of the 41 anthropogenic forced simulations produces a 56-year North American warming (1951 to 2006) that accounts for more than half of the observed warming. Our assessment of the origin for the observed North American surface temperature trend is that more than half of the warming during 1951 to 2006 is likely the result of human influences. It is exceptionally unlikely that the observed warming has resulted from natural variability alone because there is a clear separation between the ensembles of climate model simulations that include only natural forcings and those that contain both an

thropogenic and natural forcings (Hegerl et al. , 2007). These confidence statements reflect the uncertainty of the role played by model biases in their sensitivity to external forcing, and also the unknown impact of biases on the range of their unforced natural variability.
From Figure 3.3, it is evident that the yearly fluctuations in observed North American tem

perature are of greater amplitude than those occurring in the ensemble average of externally


### BOX 3.4: Use of Expert Assessment
The use of expert assessment is a necessary element in attribution as a means to treat the complexities that generate uncertainties. Expert assessment is used to define levels of confidence, and the terms used in this Product (see Preface) follow those of the IPCC Fourth Assessment Report. The attribution statements used in Chapter 3 of this SAP also employ probabilistic language (for example, “virtually certain”) to indicate a likelihood of occurrence.
To appreciate the need for expert assessment, it is useful to highlight the sources of uncertainty that arise in seeking the cause for climate conditions. The scientific process of attribution involves various tools to probe cause-effect rela

tionships such as historical observations, climate system models, and mechanistic theoretical models. Despite ongoing improvements in reanalysis and models, these and other tools have inherent biases rendering explanations of the cause for a climate condition uncertain. Uncertainty can arise in determining a forced signal ( i.e. , fingerprint identification). For instance, the aerosol-induced climate signal involves direct radiative effects that require on accurate knowledgeof the amount and distribution of aerosols in the atmosphere. These are not well observed quantities, leading to socalled “value uncertainties” (IPCC, 2007a) because the forcing itself is poorly known. The aerosol-induced signal also involves an indirect radiative forcing, the latter depending on cloud properties and water droplet distributions. These cloud radiative interactions are poorly represented in current generation climate models (Kiehl, 1999), contributing to so-called “structural uncertainties” (IPCC, 2007a). Even if the forcing is known precisely and the model includes the relevant processes and relationships, the induced signal may be difficult to distinguish from other patterns of climate variability thereby confounding the attribution.
The scientific peer-reviewed literature provides a valuable guide to the author team of Chapter 3 for determining attribution confidence. In addition, new analyses in this Product are also examined in order to provide additional information. These employ methods and techniques that have been extensively tested and used in the scientific lit

erature. In most cases, new analyses involve observational data and model simulations that have merely updated to include recent years through 2006. More than half of the warming over North America during 1951 to

2006 is likely the result of human influences. It is exceptionally

unlikely that the observed warming has

resulted from natural variability alone.The U.S. Climate Change Science Program Chapter 3

66 forced runs. This is consistent with the fact that the observations blend the effects of internal and external influences while the model esti

mates only the time-evolving impact of external forcings. Nonetheless, several of these observed fluctuations align well with those in the CMIP data. In particular, the model warming trend is at times punctuated by short periods of cooling, and these episodes coincide with major tropical volcanic eruptions ( e.g. , Aguang in 1963; Mt. Pi

natubo in 1991). These natural externally forced cooling episodes correspond well with periods of observed cooling, as will be discussed further in Section 3.4. 3.2.2.2.2 Sea Surface Temperature Forcing The oceans play a major role in climate, not only for determining its average conditions and seasonal cycle, but also for determining itsanomalous conditions including interannualto-decadal fluctuations. Section 3.1 discussed modes of anomalous sea surface temperature (SST) variations that impact North America, in particular those associated with ENSO. Figure 3.5 illustrates the variations in time of SSTs over the global oceans and over various ocean basins during 1951 to 2006. Three characteristic features of the observed SST fluctuations are noteworthy. First, SSTs in the eastern tropical Pacific (top panel) vary strongly from year to year, as warm events alternate with cold events, which is indicative of the ENSO cycle. Second, extratropical North Pacific and North Atlantic SSTs have strong year-to-year persistence, with decadal periods of cold conditions followed by decadal periods of warm conditions. Third, the warm pool of the tropical Indian Ocean/West Pacific, the tropically averaged SSTs, and glob

ally averaged SSTs are dominated by a warming trend. In many ways, these resemble the North American surface temperature changes over time, including a fairly rapid emergence of warmth after the 1970s.
A common tool for determining the SST effects on climate is the use of atmospheric general circulation models (AGCM) forced with the specified time evolution of the observed SSTs, in addition to empirical methodologies (see Figure 3.2 for the El Niño impact inferred from reanalysis data, and Box 3.1 for an assessment of model simulated ENSO teleconnections). Such numerical modeling approaches are gen

erally referred to as AMIP simulations (Gates, 1992), and that term is adapted in this Product to refer to model runs spanning the period 1951 to 2006.

Much of the known effect of SSTs has focused on the boreal winter season, a time when ENSO and its impacts on North America are at their peak. However, the influence of SSTs on annual average variability over North America is not yet documented in the peer-reviewed literature. Therefore, an expert assessment is presented in this Section based on the analysis of two AGCMs (Table 3.1). It is important to note that the AMIP simulations used in this analysis do Figure 3.5 Observed annual mean sea surface temperature (SST) time series for 1951 to 2006. The oceanic regions used to compute the indices are 5°N to 5°S, 90°W to 150°W for El Niño, 10°S to 10°N, 60°E to 150°E for the warm pool, 30°S to 30°N for the tropics, 30°N to 60°N for the North Atlantic, 30°N to 60°N for the North Pacific, and 40°S to 60°N for the global oceans. The dataset used is the HadiSST monthly gridded fields, and anomalies are calculated relative to a 1951 to 2006 climatological reference. The oceans play a major role in climate, not only for determining its average conditions and seasonal cycle, but also for determining its anomalous conditions includinginterannual-todecadal fluctuations.67 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

not include the observed evolution of external forcings ( e.g. , solar, volcanic aerosols, or anthro

pogenic greenhouse gases). The specified SSTs may, however, reflect the footprints of such external influences (see Section 3.4 and Figure 3.18 for a discussion of the same SST time series constructed from the CMIP simulations).
North American annual temperature trends and their evolution over time are well replicated in the AMIP simulations (Figure 3.3, bottom). There are several key agreements between the AMIP simulations and observations that sup

port the argument for an SST effect. First, most of the AMIP simulated warming occurs after 1970, in agreement with observations. The time evolution of simulated annual North American surface temperature fluctuations is very realis

tic, with a temporal correlation of 0.79 between the raw unsmoothed observed data and simulat

ed annual values. While slightly greater than the observed correlation with CMIP of 0.68, much of the positive year-over-year correlation is due to the warming trend. Second, the AMIP pat

tern correlation of 0.87 with the observed trend map highlights the remarkable spatial agree

ment and exceeds the 0.79 spatial correlation for the CMIP simulated trend. Several other notable features of the AMIP simulations include the greater warming over western North America and slight cooling over eastern and southern regions of the United States. The total 1951 to 2006 change in observed North American an

nual surface temperatures of +0.90°C (about 1.6°F) compares well to the AMIP simulated warming of +0.59°C (almost 1.1°F).
A strong agreement exists between the AMIP and CMIP simulated North American surface temperature trend patterns and their time evo

lutions during 1951 to 2006. This comparison of the CMIP and AMIP simulations indicates that a substantial fraction of the area-averaged anthropogenic warming over North America has likely occurred as a consequence of sea sur

face temperature forcing. However, the physical processes by which the oceans have led to North American warming is not currently known.
An important attribution challenge is determin

ing which aspects of regional SST variability during 1951 to 2006 have been important in contributing to the signals shown in Figure 3.3. Idealized studies linking regional SST anomalies to atmospheric variability have been conducted (Hoerling et al. , 2001; Robertson et al. , 2003; Barsugli et al. , 2002; Kushnir et al. , 2002); however, a comprehensive suite of model simulations to address variability in North American surface temperatures during 1951 to 2006 has not yet been undertaken. Whereas the North American sensitivity to SST forcing from the ENSO region is well understood, the effect of the progressive tropical-wide SST warming, a condition that has been the major driver of globally averaged SST behavior during the last half century, is less well known (Figure 3.5). A further question is the effect that recent decadal warming of the North Pacific and North Atlantic Oceans have had on North American climate, either in explaining the spatial varia

tions in North American temperature trends or as a factor in the accelerated pace of North American warming since 1970. Although the desired simulation suite have yet to be con

ducted, some attribution evidence for regional SST effects can be learned empirically from the reanalysis data itself, which are capable of describing changes in tropospheric circulation patterns, elements of which are known to have regional SST sources. This will be the subject of further discussion in Section 3.3, where ob

served changes in PNA and NAO circulation patterns since 1950 are described and their role in North American climate trends is assessed.
3.2.2.2.3 Analysis of Annual Average Rainfall Variability Over North America North American precipitation exhibits con

siderably greater variability in both space and time compared with temperature. The annual cycle of precipitation varies greatly across the continent, with maximum winter amounts along western North America, maximum sum

mertime amounts over Mexico and Central America, and comparatively little seasonality over eastern North America. Therefore, it is not surprising that the 1951 to 2006 trends in annual precipitation are mainly regional in nature (Figure 3.6, top). Several of these trends are discussed further in Section 3.3.
For area-averaged North America as a whole, there is no coherent trend in observed precipita

tion since 1951. The time series of annual values has varied within 10 percent of the 56-year North American precipitation exhibits considerably greater variability in both space and time compared

with temperature.The U.S. Climate Change Science Program Chapter 3

68 climatological precipitation average, with the most notable feature being the cluster of dry years from the late 1990s to the early 2000s. However, even these annual variations for North American averaged precipitation as a whole are of uncertain physical significance because of the regional focus of precipitation fluctuations and the considerable cancellation between different types of anomalies when averaging across the continent, as is done in Figure 3.6. For instance, above average precipitation due to excess rain in one region can offset below average precipitation due to drought in another region.
Neither externally forced nor SST forced simulations show a significant change in North American-wide pre

cipitation since 1951. In addition, the area-averaged annual fluctuations in the simulations are generally within a few percent of the 56-year clima

tological average (Figure 3.6, middle and bottom panels). The comparison of the observed and CMIP simulated North America precipitation indicates that the anthropogenic signal is small relative to the observed variability over years and decades. As a note of caution regarding the suitability of the CMIP models for precipitation, the time series of North American precipitation in the individual CMIP simulations show much weaker dec

adal variability than is observed. Note especially that the recent observed dry anomalies reside well outside the range of outcomes produced by all available CMIP runs, suggesting that the models may underestimate the observed variability, at least for North American annual and area averages.

A small number of detection and attri

bution studies of average precipitation over land have identified a signal due to volcanic aerosols in low frequency variations of precipitation (Gillett et al. , 2004; Lambert et al. , 2004). Climate models appear to underes

timate both the variation of average precipitation over land compared to observations and the observed precipitation changes in response to volcanic eruptions (Gil

lett et al. , 2004; Lambert et al. , 2004). Zhang et al. (2007) examined the human influence on precipitation trends over land within latitudinal bands during 1950 to 1999, finding evidence for anthropogenic drying in the subtropics and increased precipitation over sub-polar latitudes, Figure 3.6 The 1951 to 2006 trend in annually averaged North American precipitation from observations (top), CMIP simulations (middle), AMIP simulations (bottom). Maps (left side) show the linear trend in annual precipitations for 1951 to 2006 (units, total 56-year change as percent of the climatological average). Time series (right side) show the annual values from 1951 to 2006 compared as a percentage of the 56-year climatological precipita

tion average. Curves are smoothed annual values using a five-point Gaussian filter, based on the Global Precipitation Climatology Center observational analysis, and the ensemble mean of climate simulations. The Gaussian filter is a weighted time averaging applied to the raw annual values in order to highlight lower frequency variations. “Five-point” refers to the use of five annual values in the weighting process. Unsmoothed annual observed precipitation is shown by red circles. The blue curve is the NCEP/NCAR reanalysis precipitation over time. For simulations, the gray band contains the 5 to 95 percent occurrence of individual model simulations.69 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
though observed and greenhouse gas forced simulations disagreed over much of North America.
The time series of North America precipita

tion from the AMIP simulations shows bet

ter agreement with the observations than the CMIP simulations, including marked negative anomalies ( e.g. , droughts) over the last decade. This suggests that a part of the observed low frequency variations stems from observed variations of global SST. A connection be

tween ENSO-related tropical SST anomalies and rainfall variability over North America has been well documented, particularly for the boreal winter, as mentioned earlier. In addition, the recent years of dryness are consistent with the multi-year occurrence of La Niña (Figure 3.5). The influence of tropical-wide SSTs and droughts in the midlatitudes and North America has also been documented in previous studies (Hoerling and Kumar, 2003; Schubert et al. , 2004; Lau et al. , 2006; Seager et al. , 2005; Her

weijer et al. , 2006). Such causal links do provide an explanation for the success of AMIP integra

tions in simulating and explaining some aspects of the observed variability in North American area-averaged precipitation, although it is again important to recognize the limited value of such an area average for describing moisture related climate variations.



### 3.3 PRESENT UNDERSTANDING OF UNITED STATES SEASONAL AND REGIONAL DIFFERENCES IN TEMPERATURE AND PRECIPITATION TRENDS FROM 1951 TO 2006

3.3.1 Introduction As noted in the recent IPCC Fourth Assessment Report, identification of human causes for vari

ations or trends in temperature and precipitation at regional and seasonal scales is more difficult than for larger area and annual averages (IPCC, 2007a). The primary reason is that internal climate variability is greater at these scales— averaging over larger space-time scales reduces the magnitude of the internal climate variations (Hegerl et al. , 2007). Early idealized studies (Stott and Tett, 1998) indicated that the spatial variations of surface temperature changes due to changes in external forcing, such as green

house gas related forcings, would be detectable only at scales of 5000 kilometers (about 3100 miles) or more. However, these signals will be more easily detectable as the magnitude of the expected forced response increases with time. The IPCC Fourth Assessment Report highlights the acceleration of the warming response in recent decades (IPCC, 2007a).
Consistent with increased external forcing in recent decades, several studies (Karoly and Wu, 2005; Knutson et al. , 2006; Wu and Karoly, 2007; Hoerling et al. , 2007) have shown that the warming trends over the second half of the twentieth century at many individual cells, which are 5° latitude by 5° longitude in area (about 556 by 417 kilometers or 345 by 259 miles), across the globe can now be detected in observations. Further, these are also consistent with the modeled response to anthropogenic cli

mate forcing and cannot be explained by inter

nal variability and response to natural external forcing alone. However, there are a number of regions that do not show significant warming, including the southeast United States, although modeling results have yet to consider a range of other possible forcing factors that may be more important at regional scales, including changes in carbonaceous aerosols (IPCC, 2007a) and changes in land use and land cover (Pielke et al. , 2002; McPherson, 2007).
What is the current capability to explain spatial variations and seasonal differences in NorthAmerican climate trends over the past halfcentury? Can various differences in space and time be accounted for by the climate system’s sensitivity to time evolving anthropogenic forcing? To what extent can the influences of natural processes be identified? Recent studies have linked some regional and seasonal varia

tions in temperature and precipitation over the United States to variations in SST ( e.g. , Livezey et al. , 1997; Kumar et al. , 2001; Hoerling and Kumar 2002; Schubert et al. , 2004; Seager et al. , 2005). These published results have either focused on annually averaged or winter-only conditions. This Product will assess both the winter and summer origins change over North America, the contiguous United States, and various sub-regions of the United States. Recent studies have linked some regional and seasonal variations in temperature and precipitation over the United States to variations in sea surface temperature.The U.S. Climate Change Science Program Chapter 3

70 3.3.2 Temperature Trends 3.3.2.1 N orth A merica The observed annually averaged temperature trends over North America in Figure 3.3 show considerable variation in space, with the larg

est warming over northern and western North America and least warming over the south

eastern United States. The ensemble-averaged model response to anthropogenic and natural forcing since 1951 (CMIP runs in Figure 3.3) shows a more uniform warming pattern, with larger values in higher latitudes and in the interior of the continent. While the spatial correlation of the CMIP simu

lations with observations for the 1951 to 2006 North American surface tem

perature trend is 0.79, that agreement is almost entirely due to the agreement in the area-averaged temperature trend. Upon removing the area-averaged warm

ing, a process that highlights the spatial variations, the resulting pattern correla

tion between trends in CMIP and ob

servations is only 0.13. Thus, the spatial variations in observed North American surface temperature change since 1951

are unlikely to be due to anthropogenic forcing alone.

An assessment of AMIP simulations indicates that key features of the spatial variations of annually averaged tempera

ture trends are more consistent with a response to SST variations during 1951 to 2006. The ensemble-averaged model response to observed SST variations (CMIP runs in Figure 3.3) shows a spatial pattern of North American surface tem

perature trends that agrees well with the observed pattern, with a correlation of 0.87. Upon removing the area-averaged warming, the resulting correlation is still 0.57. This indicates that the spatial varia

tion of the observed warming over North America is likely influenced by observed regional SST variations, which is consis

tent with the previously published results of Robinson et al. (2002) and Kunkel et al. (2006).
A diagnosis of observed trends in free atmospheric circulation, using the re

analysis data of 500 millibar (mb) pres

sure heights, provides a physical basis for the observed regionality in North American surface temperature trends. Figure 3.7 illustrates the 1951 to 2006 November to April surface tem

perature trends together with the superimposed 500 mb height trends. It is during the cold half of the year that many of the spatial features in the annual trend originate, a time during which teleconnection patterns are also best developed and exert their strongest impacts. The reanaly

sis data captures two prominent features of

Figure 3.7 The 1951 to 2006 November to April trend of 500 millibars heights (contours, units meters total change over 56 year period, contour interval 10 meters) and North American surface temperature (color shading, units °C total change over 56 year period) for observations (top), CMIP ensemble mean (middle), AMIP ensemble mean (bottom). Anomalous High and Low Pressure regions are highlighted. Arrows indicate the anomalous wind direction, which circulates around the High (Low) Pressure centers in a clockwise (counterclockwise) direction. The spatial variations in observed North American surface temperature change since 1951 are unlikely to be due to anthropogenic forcing alone. The spatial variation of the observed warming over North America is likely influenced by observed regional sea surface temperature variations.71 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
circulation change since 1951, one that projects upon the positive phase of the Pacific North American pattern and the other that projects upon the positive phase of the North Atlantic Oscillation pattern. Recalling from Chapter 2 the surface temperature fingerprints attribut

able to the PNA and NAO, the diagnosis in Figure 3.7 reveals that the pattern of observed surface temperature trend can be understood as a linear combination of two separate physical patterns, consistent with prior published results of Hurrell (1995) and Hurrell (1996).
The historical reanalysis data thus proves in

valuable for providing a physically consistent description of the regional structure of North American climate trends. A reason for the inability of the CMIP simulations to replicate key features of the observed spatial variations is revealed by diagnosing their simulated free atmospheric circulation trends, and comparing to the reanalysis data. The CMIP 500 mb height trends (Figure 3.7, middle panel) have little spatial structure, instead being dominated by a nearly uniform increase in heights. Given the strong thermodynamic relation between 500 mb heights and air temperature in the troposphere, the relative uniformity of North American surface warming in the CMIP simulations is consistent with the uniformity in its circulation change (there are additional factors that can influence surface temperature patterns, such as local soil moisture, snow cover and sea ice al

bedo [amount of short wave radiation reflected] effects on surface energy balances, that may have little influence in 500 mb heights).
In contrast, the ability of the AMIP simulations to produce key features of the observed spatial variations in surface temperature stems from the fact that SST variations during 1951 to 2006 force a trend in atmospheric circulation that projects upon the positive phases of both the PNA and NAO patterns (Figure 3.7, bottompanel). Although the amplitude of the ensembleaveraged AMIP 500 mb height trends is weaker than the observed 500 mb height trends, their spatial agreement is high. It is this spatially varying pattern of the the tropospheric circula

tion trend since 1951 that permits the reorgani

zation of air mass movements and storm track shifts that is an important factor for explaining key regional details of North American surface climate trends.

3.3.2.2 C ontiguou s U nited S tate s For the U.S. area-averaged temperature varia

tions, six of the warmest ten summers (Figure 3.8, top) and six of the warmest ten winters (Fig

ure 3.9, top) during 1951 to 2006 occurred in the last decade (1997 to 2006). This recent cluster

Figure 3.8 Spatial maps of the linear temperature trend (°C total change over 56 year period) in summer (June-July-August) (left side) and time series of the variations over time of United States area-averaged temperatures in summer from observations, CMIP model simulations, and AMIP model simulations. Plot

ted values are the total 56-year change (°C), with the single asterisk denoting high confidence that an observed change was detected. Gray band in top panel denotes the range of observed temperatures based on five different analyses, gray band in middle panel denotes the 5 to 95 percent range among 41 CMIP model simulations, and gray band in lower panel denotes the 5 to 95 percent range among 33 AMIP model simulations. Curves are smoothed annual values using a five-point Gaussian filter. The Gaussian filter is a weighted time averaging applied to the raw annual values in order to highlight lower frequency variations. “Five-point” refers to the use of five annual values in the weighting process. Unsmoothed observed annual temperature anomalies are shown in open red circles, with warmest ten years shown in closed red circles.The U.S. Climate Change Science Program Chapter 3

72 ing of record warm occurrences is consistent with the increasing signal of anthropogenic greenhouse gas warming, as evidenced from the CMIP simulations (Figures 3.8 and 3.9, middle panels) that indicate accelerated warming over the United States during the past decade during both summer and winter. During summer since 1951, some regions of the United States have observed strong warming while other regions experienced no significant change. The lack of mid-continent warming is a particularly striking feature of the observed trends since 1951, especially compared with the strong warming in the West. This overall pattern of U.S. temperature change is unlikely due to anthropogenic forcing alone, an as

sessment that is supported by several pieces of evidence. First, the spatial variations of the CMIP simulated U.S. temperature trend (Figure 3.8, middle) are not correlated with those observed—the pattern correlation is -0.10 (low and negative correlation) when removing the area-averaged warming. The ensemble CMIP area-averaged summer warming trend of +0.99°C (+1.78°F) is also three times higher than the observed area-averaged warming of +0.33°C (+0.59°F). In other words, there has been much less summertime warming observed for the United States as a whole than expected, based on changes in the external forcing. There is reason to believe, as discussed further below, that internal variations have been masking the anthropogenic greenhouse gas warming signal in summer to date, although the possibility that the simulated signal is too strong cannot be entirely ruled out.
Second, the spatial variations of the AMIP sim

ulations for the U.S. temperature trend (Figure 3.8, bottom) are positively correlated with the observed observations, with a pattern correla

tion of +0.43 when the area-averaged warming is removed. The cooling of the southern Plains in the AMIP simulations agrees particularly well with observations. The reduced ensemble AMIP area-averaged U.S. summer warming trend of only +0.34°C (+0.61°F) is similar to observations. It thus appears that regional SST variability has played an important role in U.S. summer temperature trends since 1951. The nature of these important SST variations re

mains unknown. The extent to which they are due to internal coupled system variations and the contribution from anthropogenic forcing are among the vital questions awaiting future attribution research.
During winter, the pattern of observed surface temperature trends (Figure 3.9, top) consists of strong and significant warming over the western Figure 3.9 Spatial maps of the linear temperature trend (°C total over 56 year period) in winter (December-January-February) (left side) and time series of the variations over time of U.S. area-averaged temperatures in summer from observations, CMIP model simulations, and AMIP model simulations. Plotted values are the total 56-year change (°C), with the double asterisks denoting very high confidence that an observed change was detected. Gray band in top panel denotes the range of observed temperatures based on five different analyses, gray band in middle panel denotes the 5 to 95 percent range among 41 CMIP model simulations, and gray band in lower panel denotes the 5 to 95 percent range among 33 AMIP model simulations. Curves smoothed with five-point Gaussian filter. The Gaussian filter is a weighted time averaging applied to the raw annual values in order to highlight lower frequency variations. “Five-point” refers to the use of five annual values in the weighting process. Unsmoothed observed annual temperature anomalies are shown in open red circles, with warmest ten years shown in closed red circles.73 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
and northern United States, and insignificant change along the Gulf Coast in the South. Both CMIP and AMIP simulations produce key features of the U.S. temperature trend pattern (spatial correlations of 0.70 and 0.57, respec

tively, upon removing the U.S. area-averaged warming trend), although the cooling along the Gulf Coast appears inconsistent with external forcing, but consistent with SST forcing. The observed U.S. winter warming trend of +0.75°C (1.35°F) has been stronger than that occurring in summer, and compares to an area-averaged warming of +0.85°C (+1.53°F) in the ensemble of CMIP and +0.41°C (+0.74°F) in the ensemble of AMIP simulations.
It is worth noting that the United States alsoexperienced warm conditions during the midtwentieth century—the early years of available reanalyses (see also Box 3.3 for discussion of the warmth in the United States in the early twentieth century). This is an indication as to how sensitive trends are to the beginning and ending years selected for diagnosis, thus requir

ing that the trend analysis be accompanied by an assessment of the full evolution over time during 1951 to 2006. Regarding confidence levels for the observed U.S. temperature trends for 1951 to 2006, a non-parametric test has been applied that es

timates the probability distribution of 56-year trends attributable to natural variability alone (see Appendix B for methodological details). As in Section 3.2, this involves diagnosis of 56-year trends from the suite of “naturally forced” CMIP runs, from which a sample of 76 such trends were generated for the contiguous United States for winter and summer seasons. The observed area-averaged U.S. summer trend of +0.33°C (+0.59°F) is found to exceed the 80 percent level of trend occurrences in those natural forced runs, indicating a high level of confidence that warming has been detected. For winter, the observed trend of +0.75°C (+1.35°F) is found to exceed the 95 percent level of trends in the natural forced runs indicating a very high level of confidence. These diagnoses supportthis assessment that a warming of U.S. areaaveraged temperatures during 1951 to 2006 has likely been detected for summer and very likely been detected for winter.

Figure 3.10 Ten-year running-mean area-averaged time series of surface temperature anomalies (°C) relative to 1881 to 1920 for observations and models for various regions: (a) through (c) rest of the contiguous United States, and (j) through (l) U.S. Southeast. The left column and middle columns are based on all-forcing historical runs 1871 to 2000 and observations 1871 to 2004 for GFDL coupled climate model CM2.0 (n=3) and CM2.1 (n=5), respectively. The right column is based on observed and model data through 2000, with ±2 standard error ranges (shading) obtained by sam

pling several model runs according to observed missing data. The red, blue, and green curves in the right-hand-column diagrams are ensemble mean results for the CM2.1 all-forcing (n=5), natural-only (n=3), and anthropogenic-only (n=3) forcing historical runs. Model data were masked according to observed data coverage. From Knutson et al. (2006). A warming of U.S. area-averaged temperatures during 1951 to 2006 has likely been detected for summer and

very likely been detected for winter.The U.S. Climate Change Science Program Chapter 3

74 The causes of the reduced warming in the U.S. Southeast compared to the remainder of the country, seen during both winter and sum

mer seasons, have been considered in several studies. Knutson et al. (2006) contrasted the area-averaged temperature variations for the Southeast with variations for the remainder of the United States (as shown in Figure 3.10) for both observations and model simulations with the GFDL CM2 coupled model. While the observed and simulated warming due to anthropogenic forcing agrees well for the remainder of the United States, the observed cooling was outside the range of temperature variations that occurred among the small num

ber of individual model simulations performed. For a larger ensemble size, such as provided by the whole CMIP multi-model ensemble as considered by Kunkel et al. (2006), the cooling in the Southeast is within the range of model simulated temperature variations but would have to be associated with a very large case of natural cooling superimposed on anthropogenic forced larger scale warming. Robinson et al. (2002) and Kunkel et al. (2006) have shown that this regional cooling in the central and southeastern United States is associated with the model response to observed SST varia

tions, particularly in the tropical Pacific and North Atlantic oceans, and is consistent with the additional assessment of AMIP simulations presented in this Section.
For the cold half of the year in particular, the Southeast cooling is also consistent with the trends in teleconnection patterns that were diagnosed from the reanalysis data.
Other studies have argued that land use and land cover changes are additional possible fac

tors for explaining the observed spatial varia

tions of warming over the United States since 1951. The marked increase of irrigation in the Central Valley of California and the northern Great Plains is likely to have lead to an increase (warming) in minimum temperatures and a re

duced increase (lesser warming) in maximum temperatures in summer (Christy et al. , 2006;

Figure 3.11 Regional U.S. surface temperature changes in summer (June-July-August) from 1951 to 2006. The observations are shown in bold red, ensemble-averaged CMIP in blue, and ensemble-averaged AMIP in green. A five-point Gaussian filter has been applied to the time series to emphasize multi-annual scale time variations. The Gaussian filter is a weighted time averaging applied to the raw annual values in order to highlight lower frequency variations. “Five-point” refers to the use of five annual values in the weighting process. Plotted values in each graph indicate the total 1951 to 2006 temperature change averaged for the sub-region. Double (single) asterisks denote regions where confidence of having detected a change is very high (high). Urbanization, land clearing, deforestation, and reforestation are likely to have contributed to some of the spatial patterns of warming over the United States.75 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

Kueppers et al. , 2007; Mahmood et al. , 2006). Urbanization, land clearing, deforestation, and reforestation are likely to have contributed to some of the spatial patterns of warming over the United States, though a quantification of these factors is lacking (Hale et al. , 2006; Kalnay and Cai, 2003; Trenberth, 2004; Vose et al ., 2004; Kalnay et al. , 2006).
As a further assessment of the spatial struc

ture of temperature variations, the summer and winter surface temperature changes from 1951 to 2006 for nine U.S. subregions are shown in Figure 3.11 and 3.12, respectively. The observed temperature change is shown by the red bold curve, and the CMIP and AMIP ensemble-averaged temperature changes are given by blue and green curves, respectively. No attribution of recent climate variations and trends at these scales has been published, aside from the aforementioned Knutson et al. (2006) and Kunkel et al. (2006) studies that examined conditions over the U.S. Southeast. For deci

sion making at these regional scales, as well as smaller local scales, a systematic explanation of such climate conditions is needed. In this Product, several salient features of the observed and simulated changes are discussed; however, a complete synthesis has yet to be undertaken. For each region of the United States, the total 1951 to 2006 observed surface temperature change and its significance is plotted beneath the time series. Single asterisks denote high confidence and double asterisks denote very high confidence that a change has been detected using the methods described above.
During summer (Figure 3.11), there is very high confidence that warming has been observed over Pacific Northwest and Southwest regions. For these regions, the net warming since 1951 has been about +0.9°C (+1.6°F), exceeding the 95 percent level of trends in the natural forced runs at these regional levels. High confidence of a detected warming also exists for the North

east, where the observed 56-year change is not as large, but occurs in a region of reduced variability, thereby increasing detectability of a change. These three warming regions also exhibit the best temporal agreement with the warming simulated in the CMIP models. In addition, the comparatively weaker observed Figure 3.12 Regional U.S. surface temperature changes in winter (December-January-February) from 1951 to 2001. The observations are shown in bold red, ensemble-averaged CMIP in blue, and ensemble-averaged AMIP in green. A five-point Gaussian filter has been applied to the time series to emphasize multi-annual scale time variations. The Gaussian filter is a weighted time averaging applied to the raw annual values in order to highlight lower frequency variations. “Five-point” refers to the use of five annual values in the weighting process. Plotted values in each graph indicate the total 1951 to 2006 temperature change averaged for the sub-region. Single (double) asterisks denote regions where confidence of having detected a change is high (very high).

During summer, there is very high confidence that warming has been observed over Pacific Northwest and Southwest regions. For these regions, the net warming since 1951 has been about +0.9°C (+1.6°F).The U.S. Climate Change Science Program Chapter 3

76 summertime trends during 1951 to 2006 in the interior West, the southern Great Plains, the Ohio Valley, and the Southeast may be influenced by the very warm conditions at the beginning of the reanalysis record, a period of widespread drought in those regions of the country.
During winter (Figure 3.12), there is very high confidence that warming has been detected over the northern Great Plains and the Great Lakes region. Confidence is high that warm

ing during 1951 to 2006 has been detected in the remaining regions, except along the Gulf Coast in the South, where no detectable change in temperature has occurred. In the northern regions, most of the overall warming of about +1.5°C (+2.7°F) has happened in the last two decades. The CMIP simulations also produce accelerated winter warming over the northern United States in the past 20 years, suggesting that this regional and seasonal feature may have been influenced by anthropogenic forcing.
The 1950s produced some of the warmest win

ters during the 1951 to 2006 period for several regions of the U.S. The latest decade of warmth in the four southern and eastern United States regions still fails to exceed that earlier decadal warmth. The source for the warm winters in those regions in mid-century is not currently known, and it is unclear whether it is related to a widespread warm period across the Northern Hemisphere during the 1930s and 1940s that was attributed primarily to internal variability (Delworth and Knutson, 2000). The fact that neither CMIP nor AMIP ensemble-averaged responses produce 1950s warmth supports an interpretation that this warmth was likely un

related to external or the SST forcing.
3.3.3 Precipitation Trends 3.3.3.1 N orth A merica The observed annual North American precipita

tion trends during 1951 to 2006 in Figure 3.6 are dominated by regional scale features. The prominent identifiable features of change are the annual drying of Mexico and the greater Ca

ribbean region, and the increase over northern Canada. However, due to the strong and differ

ing seasonal cycles of precipitation across the continent, a diagnosis of the annually averaged trends is of limited value. Therefore, this Sec

tion focuses further discussion on the seasonal and regional analyses. Figure 3.13 The 1951 to 2006 November to April trend of 500 millibar heights (contours, units meters total over 56 year period, contour interval 10 meters) and North American precipitation (color shading, units 56-year change as percent of the 1951 to 2006 climatological average) for observations (top), CMIP ensemble-averaged (middle), AMIP ensemble-averaged (bottom). Anomalous High and Low Pressure regions are high

lighted. Arrows indicate the anomalous wind direction, which circulates around the High (Low) Pressure centers in a clockwise (counterclockwise) direction.77 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
The cold-season (November to April) North American observed precipitation change is shown in Figure 3.13 (top), with superimposed contours of the tropospheric circulation change (identical to Figure 3.7). The reanalysis data of circulation change provides physical insights on the origins of the observed regional precipita

tion change. The band of drying that extends from British Columbia across much of southern Canada and part of the northern United States corresponds to upper level high pressure from which one can infer reduced storminess. In contrast, increased precipitation across the southern United States and northern Mexico in winter is consistent with the deeper southeast

ward shifted Aleutian low, a semi-permanent low pressure system situated over the Aleutian Islands in winter, that is conducive for increased winter storminess across the southern region of the United States. Further south, drying again appears across southern Mexico and Central America. This regional pattern is unrelated to external forcing alone, as revealed by the lack of spatial agreement with the CMIP trend pattern (middle panel), and the lack of a wavy tropospheric circulation response in the CMIP simulations. However, many key features of the observed regional precipitation change are consistent with the forced response to global SST variations during 1951 to 2006, as is evi

dent from the AMIP trend pattern (bottom). In particular, the AMIP simulations generate the zonal band of enhanced high latitude precipita

tion, the band of reduced precipitation centered along 45°N, wetness in the southern United States and northern Mexico, and dryness over Central America. These appear to be consistent with the SST forced change in tropospheric circulation. Thus, in future attribution research it is important to determine the responsible re

gional SST variations, and to assess the origin of the SSTs anomalies themselves.

3.3.3.2 C ontiguou s U nited S tate s The observed seasonally-averaged precipitation trends over the period 1951 to 2006 are com

pared with the ensemble-averaged responses of the CMIP and AMIP simulations for summer in Figure 3.14 and for winter in Figure 3.15. In general, during all seasons there are smaller scale spatial variations of the observed precipi

tation trends across the United States than for the temperature trends, and larger interannual and decadal variability. These factors under

mine the detectability of any physical change in precipitation since 1951.
During summer (Figure 3.14), there is a gen

eral pattern of observed rainfall reductions in the U.S. West and Southwest and increases in the East. There is some indication of similar patterns in the CMIP and AMIP simulations, however, the amplitudes are so weak that the ensemble model anomalies are themselves un

likely to be significant. The time series of U.S. summer rainfall is most striking for a recent

Figure 3.14 Spatial maps of the linear trend in precipitation (percentchange of seasonally averaged 1951 to 2006 climatology) in summer (JuneJuly-August) (left side) and the variations over time of U.S. area-averaged precipitation in summer from observations, CMIP model simulations, and AMIP model simulations. Gray band in middle panel denotes the 5 to 95 percent range among 41 CMIP model simulations, and gray band in lower panel denotes the 5 to 95 percent range among 33 AMIP model simulations. Curves smoothed using a five-point Gaussian filter. The Gaussian filter is a weighted time averaging applied to the raw annual values in order to high

light lower frequency variations. “Five-point” refers to the use of five annual values in the weighting process. Unsmoothed observed annual precipitation anomalies are shown in open red circles.The U.S. Climate Change Science Program Chapter 3

78 fluctuation between wet conditions in the 1990s, followed by dry conditions in the late 1990s and early 2000s. This prominent variation is well explained by the region’s summertime response to SST variations, as seen by the remarkable correspondence of observations with the time evolving AMIP rainfall (lower panel). For the 56-year period as a whole, the temporal correla

tion of AMIP simulated and observed summer U.S. average rainfall is +0.64. During winter (Figure 3.15), there is little agree

ment between the observed and CMIP modeled spatial patterns of trends, though considerably better agreement exists with the AMIP modeled spatial pattern. Again, the ensemble-averaged CMIP model simulations shows no significant long term trends during 1951 to 2006, and they also exhibit weak variability (middle), suggest

ing that changes in external forcing have had no appreciable influence on area-averaged pre

cipitation in the United States. This is consistent with the published results of Zhang et al. (2007) who find disagreement between observed and CMIP simulated trends over the United States. In contrast, several key decadal variations are captured by the ensemble mean AMIP simula

tions including again the swing from wet 1990s to dry late 1990s early 2000 conditions. For the 56-year period as a whole, the temporal correla

tion of AMIP simulated and observed winter U.S. average rainfall is +0.59.
For the nine separate U.S. regions, Figures 3.16 and 3.17 illustrate the variations over time of observed, ensemble CMIP, and ensemble AMIP precipitation for summer and winter seasons, re

spectively. These highlight the strong temporal swings in observed regional precipitation be

tween wet and dry periods, such that no single region has a detectable change in precipitation during 1951 to 2006. These observed fluctua

tions are nonetheless of great societal relevance, being associated with floods and droughts hav

ing catastrophic local impacts. Yet, comparing to CMIP simulations indicates that it is excep
tionally unlikely that these events are related to external forcing. There is some indication from the AMIP simulations that their occurrence is somewhat determined by SST events, especially in the South and West, during winter presum

ably related to the ENSO cycle.
Other statistical properties of rainfall, including extremes in daily amounts and the fraction of annual rainfall due to individual wet days have exhibited a detectable change over the United States in recent decades, and such changes have been attributed to anthropogenic forc

ing in the companion CCSP SAP 3.3 Product (CCSP, 2008). Figure 3.15 Spatial maps of the linear trend in precipitation (percent change of seasonal climatology) in winter (December-January-February) (left side) and the variations over time of U.S. area-averaged precipitation in winter from observations, CMIP model simulations, and AMIP model simulations. Gray band in middle panel denotes the 5 to 95 percent range among 41 CMIP model simulations, and gray band in lower panel denotes the 5 to 95 percent range among 33 AMIP model simulations. Curves smoothed using a five-point Gaussian filter. The Gaussian filter is a weighted time averaging applied to the raw annual values in order to highlight lower frequency variations. “Five-point” refers to the use of five annual values in the weighting process. Unsmoothed observed annual precipitation anomalies are shown in open red circles.79 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

Figure 3.16 The 1951 to 2006 regional U.S. precipitation changes over time in summer (June-JulyAugust). The observations are shown in bold red, ensemble-averaged CMIP in blue, and ensembleaveraged AMIP in green. A five-point Gaussian filter has been applied to the time series to emphasize multi-annual scale time variations. The Gaussian filter is a weighted time averaging applied to the raw annual values in order to highlight lower frequency variations. “Five-point” refers to the use of five annual values in the weighting process.

Figure 3.17 The 1951 to 2006 regional U.S. precipitation changes over time in winter (DecemberJanuary-February). The observations are shown in bold red, ensemble-averaged CMIP in blue, and ensemble-averaged AMIP in green. A five-point Gaussian filter has been applied to the time series to emphasize multi-annual scale time variations. The Gaussian filter is a weighted time averaging applied to the raw annual values in order to highlight lower frequency variations. “Five-point” refers to the use of five annual values in the weighting process.The U.S. Climate Change Science Program Chapter 3

80 

### 3.4 NATURE AND CAUSE OF APPARENT RAPID CLIMATE SHIFTS FROM 1951 TO 2006

3.4.1 Introduction Rapid climate shifts are of scientific interest and of public concern because of the expecta

tion that such occurrences may be particularly effective in exposing the vulnerabilities of soci

eties and ecosystems (Smith et al. , 2001). Such abrupt shifts are typically distinguished from the gradual pace of climate change associated, for instance, with anthropogenic greenhouse gas forcing. However, through non-linear feed

backs, gradual forcing could also trigger rapid shifts in some parts of the climate system, a frequently cited example being a possible col

lapse of the global ocean’s principal conveyor of heat between the tropics and high latitudes known as the thermohaline circulation (Clarke et al. , 2002).
By their very nature, abrupt shifts are unex

pected events—climate surprises—and thus offer particular challenges to policy makers in planning for their impacts. A retrospective assessment of such “rare” events may offer insights on mitigation strategies that are con

sistent with the severity of impacts related to rapid climate shifts. Such an assessment would also consider impacts of abrupt climate shifts on societies and ecosystems and would also prepare decision makers to anticipate conse

quences of gradual changes in climate, insofar as they may be no less severe than those related to rapid climate shifts.
3.4.2 Defining Rapid Climate Shifts A precise definition for a climate shift that is either “rapid” or “abrupt” does not exist because there is limited knowledge about the full sensi

tivity of the climate system. For instance, due to nonlinearity, changes in external forcing may not lead to a proportionate climate response. It is conceivable that a gradual change in external forcing could yield an abrupt response when applied near a tipping point (the point at which a slow gradual change becomes irreversible and then proceeds at a faster rate of change) of sensitivity in the climate system, whereas an abrupt change in forcing may not lead to any abrupt response when it is applied far from the system’s tipping point. To date, little is known about the threshold tipping points of the climate system (Alley et al. , 2003).
In its broadest sense, a “rapid” shift is a transi

tion between two climatic states that individu

ally have much longer duration than the transi

tion period itself. From an impacts viewpoint, a rapid climate shift is one occurring so fast that societies and ecosystems have difficulty adapting to it.

3.4.3 Mechanisms for Rapid Climate Shifts The National Research Council (NRC, 2002) has undertaken a comprehensive assessment of rapid climate change, summarizing evidence of such changes occurring before the instrumen

tal and reanalysis records, and understanding abrupt changes in the modern era. The NRC (2002) report on abrupt climate change draws attention to evidence for severe swings in climate proxies of temperature (so-called pa

leoreconstructions) during both the last ice age and the subsequent interglacial period known as the Holocene. Ice core data indicate that abrupt shifts in climate have often occurred during Earth’s climate history, indicating that gradual and smooth movements do not always charac

terize climate variations. Identification of such shifts is usually empirical, based upon expert assessment of long time series of the relevant climate records, and in this regard, their recog

nition is retrospective. Against this background of abundant evidence for the magnitude of rapid climate shifts, there is a lack of information about the mechanisms that can lead to climate shifts and of the processes by which climate is maintained in various altered states (Broecker, 2003). Understanding the causes of such shifts is a prerequisite to any early warning system that is, among other purposes, needed for plan

ning the scope and pace of mitigation.
The National Academy report (NRC, 2002) also highlights three possible mechanisms for abrupt change: (1) an abrupt forcing, such as may occur through meteorite impacts or volcanic eruptions; (2) a threshold-like sensitivity of the climate system in which sudden changes can occur even when subjected to gradual changes in forcing; and (3) an unforced behavior of the climate system resulting purely from chaotic internal variations. A rapid climate shift is one occurring so fast that societies and ecosystems have difficulty adapting to it.81 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
3.4.4 Rapid Climate Shifts since 1950 Although changes in external forcing, whether natural or anthropogenic, are not yet directly assimilated in the current genera

tion of reanalysis products, abrupt changes in external forcings can still influence the reanalyses indirectly through their effect on other assimilated variables. Observa

tional analyses of the recent instrumental record give some clues of sudden climate shifts, characterized as those that have had known societal consequences. These are summarized below according to the current understanding of the potential mechanism in

volved. For several reasons, the sustainability of these apparent shifts is not entirely known. First, since 1950, multi-decadal fluctuations are readily seen in North American tempera

tures (Figure 3.3) and precipitation (Figure 3.6). Although the post-1950 period is the most accurately observed period of Earth’s climate history, the semi-permanency of any change cannot be readily judged from merely 50 years of data. This limited perspective of our brief modern climate record stands in contrast to proxy climate records, within which stable climate was punctuated by abrupt change leading to new climate states lasting centuries to millennia. Second, it is not known whether any recent rapid transi

tions have involved threshold exceedences in a manner that would forewarn of their permanence.

3.4.4.1 A brupt natural external f orcing s s ince 1950 The period of the reanalysis record was a volcanically active one, particularly com

pared with the first half of the twentieth century. Three major volcanic eruptions included the Agung in 1963, El Chichon in 1982, and Mt. Pinatubo in 1991. Each erup

tion injected aerosols into the stratosphere (about 10 kilometers, or 6 miles, above the Earth’s surface), acting to significantly increase the stratospheric aerosol optical depth that led to an increase in the reflectance of incoming solar radiation (Santer et al. , 2006).
Each of these abrupt volcanic forcings has been found to exert a discernable impact on climate conditions. Observed sea surface temperatures cooled in the wake of the eruptions, the detect

ability of which was largest in oceans having small unforced, internal variability (Santer et al. , 2006). Surface-based observational analyses of these and other historical volcanoes indicate that North American surface temperatures tend to experience warming in the winters following strong eruptions, but cooling in the subsequent summer (Kirchner et al. , 1999). However, these abrupt forcings have not led to sustained changes in climate conditions, namely because Figure 3.18 CMIP simulated annually-averaged SST changes over time for 1951 to 2006. The oceanic regions used to compute the indices are 5°N to 5°S, 90°W to 150°W for El Niño, 10°S to 10°N, 60°E to 150°E for the warm pool, 30°S to 30°N for the tropics, 30°N to 60°N for the North Atlantic, 30°N to 60°N for the North Pacific, and 40°S to 60°N for the global oceans. Dataset is the ensemble average of 19 CMIP models subjected to the combination of external anthropo

genic and natural forcing, and anomalies are calculated relative to each model’s 1951 to 2006 reference. Green curve is the surface temperature change based on the ensemble average of four CMIP models forced only by time evolving natural forcing (volcanic and solar).The U.S. Climate Change Science Program Chapter 3

82 the residence time for the stratospheric aerosol increases due to volcano eruption is less than a few years (depending on the particle sizes and the geographical location of the volcanic erup

tion), and the fact that major volcanic events since 1950 have been well separated in time.
The impact of the volcanic events is readily seen in Figure 3.18 (green curve) which plots annual SST changes over time in various ocean basins derived from the ensemble-averaged CMIP simulations forced externally by esti

mates of the time evolving volcanic and solar forcings (so-called “natural forcing” runs). The SST cooling in the wake of each event is evident. Furthermore, in the comparison with SST evolutions in the fully forced natural and anthropogenic CMIP runs (Figure 3.18, bars), the lull in ocean warming in the early 1980s and early 1990s was likely the result of the vol

canic aerosol effects. Similar lulls in warming rates are evident in the observed SSTs at these times (Figure 3.5). They are also evident in the observed and CMIP simulated North American surface temperature changes over time (Figure 3.3). Yet, while having detected the climate system’s response to abrupt forcing, and while some model simulations detect decade-long reductions in oceanic heat content following volcanic eruptions (Church et al. , 2005), their impacts on surface temperature have been relatively brief and transitory.

3.4.4.2 A bruptne ss related to gradual increa s e o f greenhou s e ga s e s s ince 1950 Has the gradual increase in greenhouse gas ex

ternal forcing triggered threshold-like behavior in climate, and what has been the relevance for North America? There is evidence of abrupt changes of ecosystems in response to anthro

pogenic forcing that is consistent with tipping point behavior over North America (Adger et al. , 2007). Some elements of the physical climate system including sea ice, snow cover, mountainous snowpack, and streamflow have also exhibited rapid change in recent decades (IPCC, 2007a).
There is also some suggestion of abrupt change in ocean surface temperatures. Whereas the overall global radiative forcing due to increasing greenhouse gases has increased steadily since 1950 (IPCC, 2007a), observed sea surface tem

perature over the warmest regions of the world ocean—the so-called warm pool—experienced a rapid shift to warm conditions in the late 1970s (Figure 3.5). In this region covering the tropi

cal Indian Ocean/West Pacific where surface temperatures can exceed 30°C (86°F), the noise of internal SST variability is weak, increasing the confidence in the detection of change. While there is some temporal correspondence between the rapid 1970s emergent warm pool warming in observations and CMIP simulations (Figure 3.18), further research is required to confirm that a threshold-like response of the ocean surface heat balance to steady anthropogenic forcing occurred.
The matter of the relevance of abrupt oceanic warming for North American climate is even less clear. On one hand, North American sur

face temperatures also warmed primarily after the 1970s, although not in an abrupt manner. The fact that the AMIP simulations yield a similar behavior suggests some cause-effect link to the oceans. On the other hand, the CMIP simulations generate a steadier rate of North American warming during the reanalysis pe

riod, punctuated by brief pauses due to volcanic aerosol-induced cooling events.

3.4.4.3 A bruptne ss due to un f orced chaotic behavior s ince 1950 Some rapid climate transitions in recent de

cades appear attributable to chaotic natural fluctuations. One focus of studies has been the consequence of an apparent shift in the charac

ter of ENSO events after the 1970s, with more frequent El Niño warming in recent decades (Trenberth and Hoar, 1996).
Abrupt decreases in rainfall occurred over the U.S. Southwest and Mexico in the 1950s and 1960s (Narisma et al. , 2007), with a period of enhanced La Niña conditions during that decade being a likely cause (Schubert et al. , 2004; Seager et al. , 2005). However, this dry period, and the decadal period of the Dust Bowl that preceded it over the Great Plains, did not constitute permanent declines in those regions’ rainfall, despite meeting some criteria for detecting abrupt rainfall changes (Narisma et al. , 2007). In part, the ocean conditions that Some rapid climate transitions in recent decades appear attributable to chaotic natural fluctuations.83 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
contributed to these droughts did not persist in their cold La Niña state.
An apparent rapid transition of the atmo

sphere-ocean system over the North Pacific was observed to occur in the period from 1976 to 1977. From an oceanographic per

spective, changes in ocean heat content and SSTs that happened suddenly over the Pacific basin north of 30°N were caused by atmospheric circulation anomalies (Miller et al. , 1994). These consisted of an unusually strong Aleutian Low that developed in the fall season of 1976, a feature that recurred during many successive winters for the next decade (Trenberth, 1990). These sur

face features were linked with a persistent positive phase of the PNA teleconnection pattern in the free atmosphere as revealed by reanalysis data. The time series of winter

time Alaskan surface temperatures (Figure 3.19) reveals the mild conditions that sud

denly emerged after 1976. This transition in climate was accompanied by significant shifts in marine ecosystems throughout the Pacific Basin (Mantua et al. , 1997). It is now evident that this Pacific Basin-North American event, while perhaps meeting some criteria for a rapid transition, was mostly due to a large scale coupled-ocean atmosphere variation over mul

tiple decades (Latif and Barnett, 1996). Thus, it is best viewed as a climate “variation” ratherthan as an abrupt change in the coupled oceanatmosphere system (Miller et al. , 1994). Such multidecadal variations are readily seen in the observed index of both the North Pacific and the North Atlantic SSTs. However, the Alaskan temperature time series also indicates that there has been no return to cooler surface conditions in recent years. While the pace of anthropogenic warming alone during the last half-century has been more gradual than the rapid warming observed over Alaska, the superposition of an internal decadal fluctuation can lend the ap

pearance of an abrupt warming, as Figure 3.19 indicates occurred over western North America in the mid-1970s. It is plausible that the perma

nency of the shifted surface warmth is rendered by the progressive increase in the strength of the external anthropogenic signal relative to the amplitude of internal decadal variability. 

### 3.5 UNDERSTANDING OF THE CAUSES FOR NORTH AMERICAN HIGH-IMPACT DROUGHT EVENTS FOR 1951 TO 2006

3.5.1. Introduction Climate science has made considerable prog

ress in understanding the processes leading to drought, due in large part to the emergence of global observing systems. The analysis of the observational data reveals relationships with large-scale atmospheric circulation pat

terns, and illustrates linkages with sea surface temperature patterns as far away from North America as the equatorial Pacific and Indian Ocean. Computing capabilities to perform ex

tensive experimentation—only recently avail

able—are permitting first ever quantifications of the sensitivity of North American climate to various forcings, including ocean temperatures and atmospheric chemical composition.
Such progress, together with the recognition that the U.S. economy suffers during severe droughts, has led to the launch of a National In

tegrated Drought Information System (NIDIS, 2004), whose ultimate purpose is to develop a timely and useful early warning system for drought. Figure 3.19 Observed Alaska annual surface temperature departures for 1951 to 2006. Anomalies are calculated relative to a 1951 to 2006 reference. Smoothed curve is a five-point Gaussian filter applied to the annual departures to emphasize multi-annual variations. The Gaussian filter is a weighted time averaging applied to the raw annual values in order to highlight lower frequency variations. “Five-point” refers to the use of five annual values in the weighting process.

Credible prediction systems are always improved when supported by knowledge of

the underlying mechanisms and

causes for the phenomenon’s variability.The U.S. Climate Change Science Program Chapter 3

84 Credible prediction systems are always im

proved when supported by knowledge of the underlying mechanisms and causes for the phenomenon’s variability. In this Section, current understanding of the origins of North American drought is assessed, focusing on events during the period of abundant global observations since about 1950. Assessments of earlier known droughts (such as the Dust Bowl) serve to identify potential cause-effect relationships that may apply to more recent and future North American regional droughts, and this perspective is provided as well (see Box 3.3 for discussion of the Dust Bowl).
3.5.2 Definition of Drought Many definitions for drought appear in the literature, each reflecting its own unique social and economic context in which drought infor

mation is desired. In this Product, the focus is on meteorological drought as opposed to the numerous impacts (and measures) that could be used to characterize drought ( e.g. , the hydro

logic drought, indicated by low river flow and reservoir storage, or the agricultural drought, indicated by low soil moisture and deficient plant yield).
Meteorological drought has been defined as “a period of abnormally dry weather sufficiently prolonged for the lack of water to cause seri

ous hydrologic imbalance in the affected area” (Huschke, 1959). The policy statement of the American Meteorological Society defines meteorological and climatological drought in terms of the magnitude of a precpitation shortfall and the duration of this shortfall event (AMS, 2004).
The Palmer Drought Severity Index (PDSI) (Palmer, 1965) measures the deficit in moisture supply relative to its demand at the Earth’s surface, and is used in this Chapter to illus

trate some of the major temporal variations of drought witnessed over North America. The Palmer Drought Index is also useful when in

tercomparing historical droughts over different geographical regions ( e.g. , Karl, 1983; Diaz, 1983), and it has been found to be a useful proxy of soil moisture and streamflow deficits thatrelate to the drought impacts having decisionmaking relevance ( e.g. , Dai et al. , 2004).
3.5.3 Drought Causes 3.5.3.1 D rought s tati s tic s , mechani s m s and proce ss e s The North American continent has experi

enced numerous periods of drought during the reanalysis period. Figure 3.20 illustrates the time variability of areal coverage of severe drought since 1951, and on average, 10 percent (14 percent) of the area of the contiguous (west

ern) United States experiences severe drought each year. The average PDSI for the western states during this time period is shown in the bottom panel; while it is very likely dominated by internal variability, the severity of the recent drought compared with others since 1950 is also apparent.
The middle of the twentieth century began with severe drought that covered much of the United



### BOX 3.5: Drought Attribution and Use of Reanalysis Data The indications for drought itself, such as the Palmer Drought Severity Index (PDSI) or precipitation, are not derived from reanalysis data, but from the network of surface observations. The strength of reanalysis data lies in its depiction of the primary variables of the free atmospheric circulation and linking them with the variability in the PDSI. As discussed in Chapter 3, the development and maintenance of atmospheric ridges is the prime ingredient for drought conditions, and reanalysis data is useful for understanding the etymology of such events: their relationship to initial atmospheric conditions, potential downstream and upstream linkages, and the circulation response to soil moisture deficits and SST anomalies. Many drought studies compare model simulations of hypothetical causes to observed atmospheric circulation parameters; reanalysis data can help differentiate among the different possible causes by depicting key physical processes by which drought events evolved.
For final attribution, the drought mechanism must be related to either a specific forcing or internal variability. Reanalysis data, available only since about 1950, is of too short a length to provide a firm indication of internal variability. It also does not indicate (or utilize) direct impact of changing climate forcings, such as increased greenhouse gases or varying solar irradiance. The relationship of atmospheric circulation changes to these forcings must be provided by empirical correlation or, better yet, General Circulation Model (GCM) studies where cause and effect can be directly related. The North American continent has experienced numerous periods of drought during the reanalysis period, 1951 to 2006.85 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

States. Figure 3.21 illustrates the observed surface temperature (top) and precipitation anomalies (bottom) during the early 1950s drought. The superimposed contours are of the 500 mb height from reanalysis data that indi

cates one of the primary causal mechanisms for drought: high pressure over and upstream that steers moisture-bearing storms away from the drought-affected region. The northeastern United States had severe drought from about 1962 to 1966, with dry conditions extending southwestward into Texas. The 1970s were relatively free from severe drought, and since 1980 there has been an in

creased frequency of what the National Climatic Data Center (NCDC) refers to as “billion dol

lar United States weather disasters”, including several major drought events: (1) Summer 1980, central/eastern United States; (2) Summer 1986, Figure 3.20 Percentage of contiguous United States (top) and western United States (middle) covered by severe or extreme drought, as defined by Palmer Drought Severity Index (PDSI) as less than -3. Time series of the western United States area-averaged PDSI. Positive (Negative) PDSI indicative of above (below) average surface moisture conditions. The western United States consists of the 11 western-most contiguous U.S. states. Red lines depict the time series smoothed using a nine-point Gaussian filter in order to emphasize lower frequency variations. The Gaussian filter is a weighted time averaging applied to the raw annual values. “Five-point” refers to the use of five annual values in the weighting process.The U.S. Climate Change Science Program Chapter 3

86 southeastern United States; (3) Summer 1988, central/eastern United States; (4) Fall 1995 to Summer 1996, U.S. southern plains; (5) Sum

mer 1998, U.S. southern plains; (6) Summer 1999, eastern United States; (7) 2000 to 2002 western United States/U.S. Great Plains; (8) Spring/summer 2006, centered in Great Plains but widespread.
The droughts discussed above cover various parts of the United States, but droughts are most common in the central and southern Great Plains. Shown in Figure 3.22 is the aver

age summer precipitation for the United States (top) and the seasonal standard deviation for the period 1951 to 2006 (bottom). The largest vari

ability occurs along the 95°W meridian, while the lowest variability relative to the average precipitation is in the northeast, a distribution that parallels the occurrence of summertime droughts. This picture is somewhat less rep

resentative of droughts in the western United States, a region which receives most of its precipitation during winter.
It is natural to ask whether the plethora of recent severe drought conditions identified by NCDC is associated with human effects, particularly greenhouse gas emissions. Figure 3.20 shows that the United States area covered by recent droughts (lower panel) is similar to that which prevailed in the 1950s, and also similar to conditions before the reanalysis period such as the “Dust Bowl” era of the 1930s (Box 3.3). Paleoreconstructions of drought conditions for the western United States (upper panel) indicate that recent droughts are considerably less severe and protracted than those that have been estimated for time periods in the twelfth and thirteenth centuries from tree ring data (Cook et al. , 2004). Hence, from a frequency/ area standpoint, droughts in the recent decades are not particularly outstanding. The causes for these droughts need to be better understood in order to better assess human influences on drought.
While drought can have many definitions, all of the episodes discussed relate to a specific weather pattern that resulted in reduced rain

fall, generally to amounts less than 50 percent of average precipitation values. The specific weather pattern in question features an ampli

fied broad-scale high pressure area (ridge) in the troposphere over the affected region (Figure 3.21). Sinking air motion associated with a ridge reduces summertime convective rainfall, results in clear skies with abundant sunshine reaching the surface, and provides for a low-level wind flow that generally prevents substantial mois

ture advection into the region.
The establishment of a stationary wave pattern in the atmosphere is thus essential for generat

ing severe drought. Such stationary, or blocked atmospheric flow patterns can arise due to mechanisms internal to the atmosphere, and the ensuing droughts can be thought of as due to internal atmospheric processes—so-called Figure 3.21 Observed climate conditions averaged for 1951 to 1956 dur

ing a period of severe U.S. Southwest drought. The 500 millibar height field (contours, units\meters) is from the NCEP/NCAR R1 reanalysis. The shading indicates the five-year average anomaly of the surface temperature (top) and precipitation (bottom). The surface temperature and precipita

tion are from independent observational datasets. Anomalous High and Low Pressure regions are highlighted. Arrows indicate the anomalous wind direction, which circulates around the High (Low) Pressure centers in a clockwise (counterclockwise) direction.87 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
unforced variability. However, the longer the anomalous weather conditions persist, the more likely it is to have some stationary forcing act

ing as a flywheel ( i.e. , as a source for inertia) to maintain the anomalies.
The droughts discussed above can be distin

guished by their duration, with longer lasting events more likely involving forcing of the atmosphere. The atmosphere does not have much heat capacity, and its “memory” of past conditions is relatively short (on the order of a few weeks). Hence, the forcing required to sustain a drought over seasons or years would be expected to lie outside of the atmospheric domain; an obvious possibility with greater heat capacity (and hence a longer “memory”) is the ocean. Therefore, most studies have assessed the ability of particular ocean sea surface tem

perature patterns to generate the atmospheric wave pattern that would result in tropospheric ridges in the observed locations during drought episodes.
Namias (1983) pointed out that the flow pattern responsible for Great Plains droughts, with a ridge over the central United States, also in

cludes other regions of ridging, one in the East Central Pacific and the other in the East Central Atlantic. As described in Chapter 2 and Section 3.1, these teleconnections represent a standing Rossby wave pattern. Using 30 years of data, Namias showed that if the “tropospheric high pressure center in the Central Pacific is strong, there is a good probability of low heights along the West Coast and high heights over the Plains” (Namias, 1983). This further suggests that the cause for the stationary ridge is not completely local, and may have its origins in the Pacific.
Droughts in the western United States are also associated with an amplified tropospheric ridge, which is further west than for Great Plains droughts and in winter displaces storm tracks north of the United States/Canadian border. In winter, the ridge is also associated with an amplified Aleutian Low in the North Pacific, and this has been associated with forcing from the tropical eastern Pacific in conjunction with El Niño events ( e.g. , Namias, 1978), whose tele

connection and resulting U.S. climate pattern has been discussed in Section 3.1. Could ENSO also be responsible for warmseason droughts? Trenberth et al. (1988) and Trenberth and Branstator (1992) suggested, on the basis of observations and a simplified linear model of atmospheric wave propagation, that colder sea surface temperatures in the tropical eastern Pacific (equatorward of 10°N), the La Niña phase of ENSO, in conjunction with the displacement of warmer water and the Inter

tropical Convergence Zone (ITCZ) northward Figure 3.22 Climatological average (top) and standard deviation (bottom) of summer (June-July-August) seasonally-averaged pre

cipitation over the continental United States for the period 1951 to 2006. Contour intervals are (a) 15 millimeters per month and (b) 3 millimeters per day (adopted from Ting and Wang, 1997). Data is the NOAA Climate Division dataset.The U.S. Climate Change Science Program Chapter 3

88 in that same region (15° to 20°N), led to the amplified ridging over the United States in the spring of 1988. While this was the leading theory at the time, the general opinion now is that most of the short-term summer droughts are more a product of initial atmospheric conditions (Namias, 1991; Lyon and Dole, 1995; Liu et al. , 1998; Bates et al. , 2001; Hong and Kalnay, 2002) amplified by the soil moisture deficits that arise in response to lack of precipitation (Wolfson et al. , 1987; Atlas et al. , 1993; Hong and Kalnay, 2002).
For droughts that occur for longer periods of time, various possibilities have been empirically related to dry conditions over specific regions of the United States and Canada. Broadly speaking, they are associated with the eastern tropical Pacific (La Niñas in particular); the Indian Ocean/West Pacific; the North Pacific; and (for the eastern United States) the western Atlantic Ocean. Cool conditions in the eastern tropical Pacific have been related to annual U.S. droughts in various studies (Barlow et al. , 2001; Schubert et al. , 2004, Seager et al. , 2005), although they are more capable of influencing the U.S. climate in late winter when the average atmospheric state is more conducive to allow

ing an extratropical influence (Newman and Sardeshmukh, 1998; Lau et al. , 2006). Warm conditions in the Indian Ocean/West Pacific region are capable of instigating drought in the United States year round (Lau et al. , 2006) but especially in spring (Chen and Newman, 1998). Warmer conditions in the North Pacific have been correlated with drought in the Great Plains (Ting and Wang, 1997) and the U.S. Northeast (Barlow et al. , 2001), although modeling studies often fail to show a causal influence (Wolfson et al. , 1987; Trenberth and Branstator, 1992; Atlas et al. , 1993). The North Pacific SST changes appear to be the result of atmospheric forc

ing, rather than the reverse; therefore, if they are contributing to drought conditions, they may not be the cause of the initial circulation anomalies. Alexander et al. (2002) concluded from Global Circulation Model (GCM) ex

periments that roughly one-quarter to one-half of the change in the dominant pattern of low frequency variability in the North Pacific sea surface temperatures during winter was itself the result of ENSO, which helps intensify the Aleutian Low and increases surface heat fluxes (promoting cooling).
Sea surface temperature perturbations down

stream of North America, in the North Atlantic, have occasionally been suggested as influenc

ing some aspects of U.S. drought. For example, Namias (1983) noted that the wintertime drought in the western United States in 1977, one of the most extensive far western droughts in recent history, appeared to be responsive to a downstream deep trough over the eastern United States. Warmer sea surface temperatures in the western North Atlantic have the potential to intensify storms in that region. Conversely, colder sea surface temperatures in summer can help intensify the ridge ( i.e. , the “Bermuda High”) that exists in that region. Namias (1966) suggested that such a cold water regime played an integral part in the U.S. Northeast spring and summer drought of 1962 to 1965, and Schubert et al. (2004) find Atlantic SST effects on the Dust Bowl, while multi-decadal swings between wet and dry periods over the United States as a whole have been statistically linkedwith Atlantic SST variations of similar timescale (McCabe et al. , 2004; Figure 3.5).
In Mexico, severe droughts during the reanaly

sis period were noted primarily in the 1950s, and again in the 1990s. The 1990s time period featured seven consecutive years of drought (1994 to 2000). Similar to the United States, droughts in Mexico have been linked to tropo

spheric ridges that can affect northern Mexico, and also to ENSO. However, there are additional Warm conditions in the Indian Ocean/ West Pacific region are capable of instigating drought in the United States year round but especially in spring.89 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
factors tied to Mexico’s complex terrain and its strong seasonal monsoon rains. Mexican rainfall in the warm season is associated with the North American Monsoon System (NAMS), which is driven by solar heating from mid-May into July. Deficient warm season rainfall over much of the country is typically associated with El Niño events. La Niña conditions often produce increased rainfall in southern and northeastern Mexico, but have been associated with drought in northwestern Mexico (Higgins et al. , 1999). During winter and early spring, there is a clear association with the ENSO cycle ( e.g. , Stahle et al. , 1998), with enhanced precipi

tation during El Niño events associated with a strengthened subtropical jet that steers storms to lower latitudes and reduced rainfall with La Niñas when the jet moves poleward.
Therefore, the occurrence of drought in Mexico is heavily dependent on the state of the ENSO cycle, or its teleconnection to the extratropics, and on solar heating variations. In the warm season there is often an out-of-phase relation

ship between southern and northern Mexico, and between spring and summer, dependent on the phasing of the NAMS (Therrell et al. , 2002). These aspects make attribution of recent droughts difficult. For example, the consecutive drought years from 1994 to 2000 occurred over several different phases of ENSO, suggesting multiple causes including El Niño conditions for warm season drought through 1998, the possible influence of Indian Ocean/West Pacific warm

ing during the subsequent La Niña phase, and internal atmospheric variability.
Because a large proportion of the variance of drought conditions over North America is unre

lated to sea surface temperature perturbations, it is conceivable that when a severe drought occurs it is because numerous mechanisms are acting in tandem. This was the conclusion reached in association with the recent U.S. drought (1999 to 2005) that affected large areas of the south

ern, western and central United States. During this time, warm conditions prevailed over the Indian Ocean/West Pacific region along with La Niña conditions in the eastern tropical Pacific— influences from both regions working together may have helped intensify and/or prolong the annual droughts (Hoerling and Kumar, 2003; Lau et al. , 2006). 3.5.3.2 H uman in f luence s on N orth A merican drought s ince 1951 To the extent that ENSO cycle variations (La Niñas in particular) are the cause of drought in the United States, it is difficult to show that they are related to greenhouse gas forcing. While some studies ( e.g. , Clement et al. , 1996) have suggested that La Niña conditions will be favored as climate warms, in fact more intense El Niño events have occurred since the late 1970s, perhaps due at least in part to anthropo

genic warming of the eastern equatorial Pacific (Mendelssohn et al. , 2005). There is a tendencyin model projections for the future greenhousegas warmed climate to indicate an average shift towards more El Niño-like conditions in the tropical eastern Pacific Ocean, including the overlying atmospheric circulation; this latter aspect may already be occurring (Vecchi and Soden, 2007). With respect to the human influ

ence on ENSO variability, Merryfield (2006) surveyed 15 coupled atmosphere-ocean models and found that for future projections, almost half exhibited no change, five showed reduced variability, and three increased variability. Hence, to the extent that La Niña conditions are associated with drought in the United States, there is no indication that they have been or will obviously be influenced by anthropogenic forcing.
However, given that SST changes in the Indian Ocean/West Pacific are a factor for long-term U.S. drought, a somewhat different story emerges. Shown in Figure 3.23 are the SST anomalies in this region, as well as the tropical central-eastern Pacific (Lau et al. , 2006). As noted with respect to the recent droughts, the Indian Ocean/West Pacific region has been con

sistently warm when compared with the 1971 to 2000 sea surface temperature climatology. What has caused this recent warming?
The effect of more frequent El Niños alone results in increased temperatures in the Indian Ocean, acting through an atmospheric bridge that alters the wind and perhaps the cloud field in the Indian Ocean region (Klein et al. , 1999; Yu and Rienecker, 1999; Alexander et al. , 2002; Lau and Nath, 2003); an oceanic bridge between the Pacific and the Indian Ocean has also been modeled (Bracco et al. , 2007). This effect could then influence droughts over the Because a large proportion of the variance of drought conditions over North America is

unrelated to sea surface temperature

perturbations, it is conceivable that when a severe drought occurs it is because numerous mechanisms

are acting in tandem.The U.S. Climate Change Science Program Chapter 3

90 United States in the summer after an El Niño, as opposed to the direct influence of La Niña (Lau et al. , 2005).
Nevertheless, as shown in Figure 3.23, the warming in the Indian Ocean/West Pacific region has occurred over different phases of the ENSO cycle, making it less likely that the overall effect is associated with it. Hoerling and Kumar (2003) note that “the warmth of the tropical Indian Ocean and the western Pacific Ocean was unsurpassed during the twentieth century”; the region has warmed about 1°C (1.8°F) since 1950. That is within the range of warming projected by models due to anthro

pogenic forcing for this region and is outside the range expected from natural variability, as judged by coupled atmo

sphere-ocean model output of the CMIP simulations (Hegerl et al. , 2007). The comparison of the observed warm pool SST time series with those of the CMIP simulations in Section 3.2.2 indicates that it is very likely that the recent warm

ing of SSTs over the Indian Ocean/West Pacific region is of human origins.
The possible poleward expansion of the subtropical region of descent of the Hadley Circulation is an outcome that is favored by models in response to a warming climate (IPCC, 2007a). It would transfer the dry conditions of northern Mexico to the U.S. Southwest and southern Great Plains; Seager et al. (2007) suggest that may already be hap

pening, and is associated with drought in the southwestern United States. Ad

ditional observations and modeling im

provements will be required to assess the likelihood of its occurrence with greater confidence.
An additional impact of greenhouse warming is a likely increase in evapo

transpiration during drought episodes because of warmer land surface tem

peratures. It was noted in the discussion of potential causes that reduced soil moisture from precipitation deficits helped sustain and amplify drought conditions, as the surface radiation im

balance increased with less cloud cover, and sensible heat fluxes increased in lieu of latent heat fluxes. This effect would not have initiated drought conditions but would be an additional factor, one that is likely to grow as climate warms. For example, drier conditions have been noted in the northeast United States despite increased annual precipitation, due to a century-long warming (Groisman et al. , 2004); this appears to be true for Alaska and southern and western Canada as well (Dai et al. , 2004). Droughts in the western United States also appear to have been influenced by increas

ing temperature (Andreadis and Lettenmaier, 2006; Easterling et al. , 2007). The areal extent of forest fires in Canada has been high since Figure 3.23 Top panel: Sea surface temperature anomalies relative to the period 1970through 2000 as a function of year in the Indian Ocean/West Pacific (left) and centraleastern Pacific (right) (from Lau et al. , 2006). Bottom panel: Number of 12-month periods in June 1997 to May 2003 with SST anomalies at individual 5° latitude by 5° longitude rectangles being above normal (red shading) or below normal (blue shading) by more than one-half of a standard deviation ( i.e. one-half the strength of the expected variability).91 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
1980 compared with the previous 30 years and Alaska experienced record forest fire years in 2004 and 2005 (Soja et al. , 2007). Hence, by adding additional water stress global warming can exacerbate naturally occurring droughts, in addition to influencing the meteorological conditions responsible for drought.
A further suggestion of the increasing role played by warm surface temperatures on drought is given in Figure 3.24. A diagnosis of conditions during the recent U.S. Southwest drought is shown, with contours depicting the atmospheric circulation pattern based on reanal

ysis data, and shading illustrating the surface temperature anomaly (top) and precipitation anomaly (bottom). High pressure conditions prevailed across the entire continent during the period, acting to redirect storms far away from the region. Continental-scale warmth during 1999 to 2004 was also consistent with the anthropogenic signal. It is plausible that the regional maximum in warmth seen over the Southwest during this period was in part a feedback from the persistently below normal precipitation, together with the anthropogenic signal. Overall, the warmth associated with this recent drought has been greater than the warmth observed during the 1950s drought in the Southwest (Figure 3.21), likely augmenting its negative impacts on water resource and eco

logic systems compared to the earlier drought.

Breshears et al. (2005) estimated the vegetation die-off extent across southwestern North Amer

ica during the recent drought. The combination of drought with pine bark beetle infestation resulted in more than a 90 percent loss in Piñon pine trees in some areas. They noted that such a response was much more severe than during the 1950s drought, arguing that the recent drought’s greater warmth was the material factor explain

ing this difference.
Current understanding is far from complete concerning the origin of individual droughts. While the assessment discussed here has emphasized the apparently random nature of short-term droughts, a product of initial condi

tions which then sometimes develop rapidly into strong tropospheric ridges, the relationships of such phenomena to sea surface temperature patterns, including the ENSO cycle, are still being debated. The ability of North Atlantic sea surface temperatures to influence the upstream circulation still needs further examination in certain circumstances, especially with respect to droughts in the eastern United States. The exact mechanisms for influencing Rossby wave

development downstream, including the role of transient waves relative to stationary wave patterns, will undoubtedly be the subject of continued research. The Hadley Cell response to climate change, as noted above, is still un

certain. Also, while some modeling studies have emphasized the role played by surface Figure 3.24 Observed climate conditions averaged for 1999 to 2004 dur

ing a period of severe southwestern U.S. drought. The 500 millibar height field (contours, units meters) is from the NCEP/NCAR R1 reanalysis. The shading indicates the five-year average anomaly of the surface temperature (top) and precipitation (bottom). The surface temperature and precipita

tion are from independent observational datasets. Anomalous High and Low Pressure regions are highlighted. Arrows indicate the anomalous wind direction, which circulates around the High (Low) Pressure centers in a clockwise (counterclockwise) direction.The U.S. Climate Change Science Program Chapter 3

92 soil moisture deficits in exacerbating these droughts, the magnitude of the effect is some

what model-dependent, and future generations of land-vegetation models may act somewhat differently.
Given these uncertainties, it is concluded from the above analysis that, of the severe droughts that have impacted North America over thepast five decades, the short-term (monthly-toseasonal) events are most likely to be primarily the result of initial atmospheric conditions, subsequently amplified by local soil moisture conditions, and in some cases initiated by teleconnection patterns driven in part by SST anomalies. For the longer-term events, the effect of steady forcing through sea surface tempera

ture anomalies becomes more important. Also, the accumulating greenhouse gases and global warming have increasingly been felt as a caus

ative factor, primarily through their influence on Indian Ocean/West Pacific temperatures, conditions to which North American climate issensitive. The severity of both short- and longterm droughts has likely been amplified by local greenhouse gas warming in recent decades. The severity of both short- and long-term droughts has likely been amplified by local greenhouse gas warming in recent decades.

# R ecommendationsCHAPTER 

# 4

93 Convening Lead Author: Randall Dole, NOAA/ESRL

Lead Authors: Martin Hoerling, NOAA/ESRL; Siegfried Schubert,

NASA/GMAO Contributing Authors: Phil Arkin, Univ. of Maryland; James Carton, Univ. of Maryland; Gabi Hegerl, Univ. of Edinburgh; David Karoly, Univ. of Melbourne; Eugenia Kalnay, Univ. of Maryland; Randal Koster, NASA/GMAO; Arun Kumar, NOAA/CPC; Roger Pulwarty, NOAA/ CPO; David Rind, NASA/GISS



### RECOMMENDATIONS Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

This Chapter discusses steps needed to improve national capabilities in climate analysis, reanalysis, and attribution in order to better address key issues in climate science and to increase the value of this information for applications and decision making. Limitations, gaps in current capabilities, and opportunities for improvement that have been identified in earlier chapters, together with several related studies and reports, provide the primary foundations for the find

ings and recommendations provided here. The overarching goal is to provide high-level recommendations to improve national capabilities in climate analyses, reanalyses, and attribution in order to increase their value for scientific and practical applications.The U.S. Climate Change Science Program

94 Chapter 4



### 4.1 need for a systematic approach to climate analysis and reanalysis As discussed throughout this report, reanalysis products have played a major role in advancing climate science and have supported numerous applications, including: monitoring climate and comparing current conditions with those of the past; providing initial conditions required for climate model predictions; supporting research on climate variability and change; enabling more reliable climate attribution; and provid

ing benchmarks for evaluating climate models. Climate analyses and reanalyses are also being used in an increasing range of practical applica

tions in sectors such as energy, agriculture, wa

ter resources, and insurance ( e.g., Schwartz and George, 1998; Pryor et al. , 2006; Challinor et al ., 2005; Pulwarty, 2003; Pinto et al ., 2007).
These are important benefits. However, there are limitations in climate analysis and re

analysis products that presently constrain their value. Perhaps the largest constraint for climate applications is that, while the model and data assimilation system remain the same over the reanalysis period, the observing system does not, and this can lead to false trends, jumps, and other uncertainties in climate records (Arkin et al. , 2004; Simmons et al. , 2006; Bengtsson et al. , 2007).
Another constraint is the limited length of present reanalysis records, which now extend back to 1948, at most. Extending reanalysis back over a century or longer would improve descriptions and attribution of causes of impor

tant climate variations, such as the pronounced warm interval in the 1930s and 1940s, the Dust Bowl drought over much of the United States in the 1930s, and multi-decadal climate varia

tions. International efforts such as the Global Climate Observing System (GCOS, 2004) and Global Earth Observation Systems of Systems (GEOSS, 2005) have identified the need for reanalysis datasets extending as far back as pos

sible in order to compare recent and projected climate changes with those of the past.
The development of current climate analysis and reanalysis activities, while encouraging and beneficial, is occurring without clear coordina

tion at national interagency levels, which may result in less than optimal progress and the in

ability to ensure a focus on problems of greatest scientific and public interest. Currently, no U.S. agency is charged with primary responsibility to ensure that the Nation has an ongoing capa

bility in climate analysis or reanalysis, putting the sustainability of national capabilities at some risk.
The following recommendations focus on the value, needs, and opportunities for climate analysis and reanalysis in providing consistent descriptions and attribution of past climate vari

ability and change, and in supporting applica

tions and decision making at relevant national, regional and local levels. The recommendations point to the necessity for improved coordination between U.S. agencies and with international partners to develop an ongoing climate analysis and systematic reanalysis capacity that would address an increasing range of scientific and practical needs.



### 4.2 R ecommendations for improving future climate analyses and reanalyses
To better detect changes in the climate • system, improve the qualit y and consistency of the observational data and reduce effects of observing system changes.
As discussed in this Product, changes in ob

serving systems (for example, the advent of comprehensive satellite coverage in the late 1970s), create significant uncertainties in the detection of true climate variations and trends over multiple decades. To reduce these uncertainties, closer collaborations are needed between observational and reanalysis commu

nities to improve the existing global database of Earth system observations (Schubert et al. , 2006). Priorities include improving quality control, identification and correction of obser

vational bias and other errors, the merging of various datasets, data recovery, improving the handling of metadata (that is, information de

scribing how, when, and by whom a particular set of data was collected, content and structure of records, and their management through time), and developing and testing techniques to more Climate analyses and reanalyses are being used in an increasing range of practical applications in sectors such as energy, agriculture, water resources, and insurance.95 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
effectively adjust to changes in observing sys

tems (Dee, 2005).

This recommendation resonates with recom

mendations from other reports, including CCSP Synthesis and Assessment Product 1.1, which focuses on steps for understanding and reconciling differences in temperature trends in the lower atmosphere (CCSP, 2006). That report stated: Consistent with Key Action 24 of GCOS (2004) and a 10 Year Climate Target of GEOSS (2005), efforts should be made to create several homogeneous atmospheric reanalyses. Particular care needs to be taken to identify and homogenize critical input climate data, and to more effectively manage large-scale changes in theglobal observing system to avoid nonclimatic influences (CCSP, 2006).
Recent World Meteorological Organization (WMO) reports emphasize the need for ongo

ing climate analyses and periodic reanalyses as critical parts of the Global Climate Observing System (GCOS) , e.g. , GCOS (2003, 2004), Simmons et al. (2006), Trenberth et al. (2006). GCOS (2004) states that “Parties are urged to give high priority to establishing a sustained capacity for global climate reanalysis, and to develop improved methods for such reanalysis, and to ensure coordination and collaboration among Centers in conducting reanalyses”.
Data quality control and expanding the use of available observations will be crucial to this effort. Significant gains are possible for both satellite and conventional observations (Ar

kin et al. , 2004). More research is required to understand biases in individual satellite data collections, to account for different resolutions and sensor measurements, and to minimize the impact of transitions between satellite missions, which may lead to data gaps or to apparent dis

continuities if the satellite measurements are not cross-calibrated, e.g. , by comparing measure

ments obtained over an overlapping time period for the missions. In addition, early satellite data from the late 1960s and 1970s need further quality control and processing before they can be used effectively in reanalyses. Dedicated ef

forts are required to determine the full effects of changes in the observing systems, to focus on bias-corrected observations, and to assess remaining uncertainties in trends and estimates of variability. Observing System Experiments (OSEs) that consider the effects of inclusion or removal of particular data can be helpful in identifying and reducing possible harmful impacts of changes in observing systems.
Develop analysis methods that are • optimized for climate research and applications. These methods should include uncertainty estimates for all reanalysis products.
As discussed in Chapter 2, data assimilation techniques used in initial climate reanalyses were developed from methods optimized for use in numerical weather prediction. The primary goal of numerical weather prediction is to pro

duce the best forecast. True four-dimensional data assimilation methods (using data that includes observations from before and after the analysis time, which is the start time of the forecast) have been developed for numerical weather prediction. However, the requirements for weather forecasts to be ready within a short time frame (typically within a few hours after the analysis time) result in observational data obtained after the beginning of the forecast cycle either not being assimilat

ed at all or treated differently from ob

servations obtained before or at the analysis time. The strong constraints placed by the needs for timely forecasts also substantially limit the capability of analyses to use the full historical observational data

base, which may not be collected until long after the fore

cast is completed.

These constraints are not relevant forThe U.S. Climate Change Science Program

96 Chapter 4

climate analyses, and modification of current data assimilation methods is needed to im

prove representations of long-term trends and variability (Arkin et al. , 2004). Further, many potentially available observations, including numerous satellite, surface temperature, and precipitation observations, could not be effec

tively assimilated within the first atmospheric reanalyses due to limitations of the models and assimilation techniques, and because some data were not available when the reanalyses were conducted (Kalnay et al. , 1996). Advances in data assimilation that have occurred since the pioneering reanalysis projects enable bet

ter and more complete use of these additional observations.
To produce reanalyses that better serve climate research and applications, it will be essential to develop methods to more effectively use the wealth of information provided by diverse Earth observations, reduce the sensitivity of the data assimilation to changes in the observing system, and provide estimates of remaining uncertain

ties in reanalysis products. A major emphasis for efforts should be on the modern satellite era, essentially 1979 to present, during which time the number and diversity of observational data have expanded greatly but have yet to be fully utilized. An important development that should help to achieve this goal is the national Earth System Modeling Framework (ESMF, <http://www.esmf.ucar.edu>). The ESMF is a collaborative effort between NASA, NOAA, the National Science Foundation, and the Depart

ment of Energy that is developing the overall organization, infrastructure, and low-level utili

ties required to allow the interchange of models, model sub-components, and analysis systems. ESMF should greatly expand the ability of scientists outside the main data assimilation centers ( e.g. , in universities and other organiza

tions) to accelerate progress in addressing key challenges toward improving the analyses.
There are many applications of reanalyses, and it is likely that different scientific approaches will be required to optimally address particular problems. For example, if the primary goal is to optimize the detection of climate trends, partic

ular care must be given to minimizing effects of changing observing systems so as to ensure the highest quality analysis over an extended time period. In this case, an appropriate reanalysis strategy may be to use a subset of very high quality data that is available continuously, or nearly continuously, over as long a period as feasible. Conversely, if the primary goal is to perform detailed studies of processes at high space and time resolutions, the most accurate analysis at any given time may be preferred. Here, the best strategy may be to take advantage of all available observations. In any case, uncer

tainties in the analyses and their implications should be appropriately documented.

Alternative data assimilation methods should be explored for their potential benefits. One alternative that is being examined intensively, ensemble data assimilation, shows considerable promise in addressing a wide range of problems. This technique uses multiple model predictions (called an “ensemble”) to estimate where errors may be particularly large or small at a given time. This time- and location-dependent un

certainty information is then incorporated into the analysis (Houtekamer and Mitchell, 1998; Whitaker and Hamill, 2002). This approach pro

vides estimates of uncertainties in the full range of reanalysis products (including, for example, the components of the water cycle). Ensemble data assimilation is becoming more economical with the development of innovative methods to take advantage of massively parallel computing (Ott et al. , 2004). In addition, ensemble-based approaches are being developed that explicitly account for model error (Zupanski and Zu

panski, 2006), thereby providing a potentially important step toward better estimating analysis uncertainties.
To improve the descr ipt ion and • understanding of major cl imate variations that occurred prior to the mid-twentieth century, develop the longest possible consistent record of past climate conditions

. For many applications, the relatively short period encompassed by initial reanalyses is a very important constraint. Current reanalysis datasets extend back to the mid-twentieth cen

tury at most. As a consequence, many climate variations of great societal interest, such as the prolonged Dust Bowl drought of the 1930s, are not included in present reanalyses, increasing There are many applications of reanalyses, and it is likely that different scientific approaches will be required to optimally address particular problems.97 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
uncertainties in both their descriptions and

causes.

Recent research has demonstrated that a reanal

ysis through the entire twentieth century, and perhaps earlier, is feasible using only surface pressure observations (Whitaker et al. , 2004; Compo et al. , 2006). Extending reanalysis back over a century or longer would be of great value in improving descriptions and attribution of causes of important climate variations such as the pronounced warm interval in the 1930s and1940s, the Dust Bowl drought, and other multidecadal climate variations. International efforts such as the GCOS and GEOSS have identified the need for reanalysis datasets extending as far back as possible to compare the patterns and magnitudes of recent and projected cli

mate changes with past changes (GCOS, 2004; GEOSS, 2005). Such reanalysis datasets should also enable researchers to better address issues on the range of natural variability of weather and climate and increase understanding of how El Niño-Southern Oscillation and other climate patterns alter the behavior of extreme events.

Alternative assimilation methods should also be evaluated for obtaining maximum information for estimating climate variability and trends from very sparse observations and from surface observations alone, where observational records are available over much longer periods than other data sources. Ensemble data assimila

tion methods have already shown considerable promise in this area (Ott et al. , 2004; Whitaker et al. , 2004; Compo et al. , 2006; Simmons et al. , 2006), and, as mentioned previously, also provide estimates of analysis uncertainty. Im

proved methods of estimating and correcting observational and model errors, recovery of historical observations, and the development of optimal, consistent observational datasets will also be required in this effort.
To improve decision support, produce • future climate reanalysis products at finer space scales ( e.g. , resolutions of 10 miles rather than 100 miles) and emphasize products that are most relevant for applications, such as surface temperatures, winds, cloudiness, and precipitation. For many applications, the value of the initial reanalysis products has been constrained by their relatively coarse horizontal resolution (200 kilometers or approximately 120 miles). For many users, improved representation of the water cycle (inputs, storage, outputs) is a key need. In addition, land-surface processes are important for both surface energy (temperature) and water balance, with land cover and land use becoming increasingly important at smaller scales. These processes should be research focus areas for future improvements.
Within the United States, one step forward in addressing these issues is the implementation of NASA’s new reanalysis project (MERRA, see Box 2.2 for a detailed description), which will provide global reanalyses at approximately 50-kilometer (about 30-mile) resolution and has a focus on providing improved estimates of the water cycle <http://gmao.gsfc.nasa.gov/ research/merra/>. Another important step for

ward is the completion of the North American Regional Reanalysis, or NARR (Mesinger et al. , 2006). While this is a regional analysis for North America and adjacent areas, rather than global reanalysis, it is at considerably higher resolution than the global reanalyses with a grid spacing of 32 kilometers (about 20 miles). Importantly, NARR also incorporates signifi

cant advances in modeling and data assimila

tion that were made following the initial global reanalysis by NOAA and the National Center for Atmospheric Re

search (Kalnay et al. , 1996), including the ability to assimilate precipitation obser

vations. This has re

sulted in substantial improvements in pre

cipitation analyses over the contiguous United States as well as improvements in near-surface tem

peratures and wind fields (Mesinger et al. , 2006). While ad

vances are impres

sive, early studies show that further improvements are Extending reanalysis back over a century or longer would be of great value in improving descriptions and attribution of causes of important climate variations such as the pronounced warm interval in the 1930s and 1940s, the Dust

Bowl drought, and other multi-decadal

climate variations.The U.S. Climate Change Science Program

98 Chapter 4

needed to accurately represent the complete water cycle ( e.g. , Nigam and Ruiz-Barradas, 2006). The ability to improve analyses of key surface variables and the water cycle therefore remain as important challenges.
Develop new national capabilities in • analysis and reanalysis that focus on variables that are of high relevance to policy and decision support. Such variables include those required to monitor changes in the carbon cycle and to understand interactions among Earth system components (atmosphere, ocean, land, cryosphere, and biosphere) that may lead to accelerated or diminished rates of climate change.
Initial reanalyses focused on reconstructing past atmospheric conditions. For both scientific and practical purposes, there is a strong need to consider other Earth system components, such as the ocean, land, cryosphere, and biosphere, as well as variables that are of interest for climate but are of less immediate relevance for weather prediction ( e.g. , related to the carbon cycle). As discussed in Chapter 2, such efforts are ongoing for ocean and land data assimilation but are still in relatively early stages. The long-term goal should be to move toward ongoing analyses and periodic reanalyses of major Earth system components relevant to climate variability and change.
Future climate analyses and reanalyses should incorporate additional climate system com

ponents that are relevant for decision making and policy de

velopment, for example, a car

bon cycle to aid in identifying changes in carbon emissions sources and removal processes. A reanalysis of the chemical state of the atmosphere would improve monitoring and un

derstanding of air quality ina changing climate, aerosolclimate interactions, and other key policy-relevant issues. Ini

tial attempts at coupling of climate system components, e.g. , ocean-atmosphere reanaly

sis, should be fostered, with a long-term goal being to develop an integrated Earth system analysis (IESA) capability that includes inter

actions among the Earth system components (atmosphere, ocean, land, snow and ice, and biological systems).
An IESA would provide the scientific com

munity, resource managers, decision makers, and policy makers with a high quality, inter

nally consistent, continuous record of the Earth system that can be used to identify, monitor, and assess changes in the system over time. Developing an IESA would also contribute to improved descriptions and understanding of the coupled processes that may produce rapid or accelerated climate changes, for example, from high-latitude feedbacks related to changes in sea ice or melting of permafrost that may amplify an initial warming due to natural or anthropogenic causes. Key processes include: atmosphere-ocean interactions for physical and biogeochemical processes; climate feedbacks from snow and ice processes; carbon cycle feedbacks; and atmosphere-land-biosphere interactions.
To achieve an IESA will require a sustained ca

pacity to assimilate current and planned future observations from diverse platforms into Earth system models. This approach will be essential for realizing the full value of investments in current and proposed future observing systems within GEOSS, as it provides the means of integrating diverse datasets together to obtain a unified, physically consistent description of the Earth system. It would also take advantage of rapid advances in Earth system modeling, while providing the ability to evaluate models used for attribution and climate predictions and projections.
Recent efforts have shown the feasibility of extending initial atmospheric analyses beyond traditional weather variables. For example, the European Union has funded a project, the Global Environment Monitoring System (GEMS), that is incorporating satellite and in situ data (data collected at its original location) to develop an analysis and forecast capability for atmospheric aerosols, greenhouse gases, and reactive gases (Hollingsworth et al. , 2005). The GEMS operational system will be an extension Future climate analyses and reanalyses should incorporate additional climate system components that are relevant for decision making and policy development, for example, a carbon cycle to aid in identifying changes in carbon emissions sources and removal processes.99 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
of current weather data assimilation capabili

ties, with implementation planned for 2009. The main users of the GEMS Project are intended to be policy-makers, operational regional air quality and environmental forecasters, and the scientific community. GEMS will support operational regional air-quality and “chemical weather” forecast systems across Europe. Part of the motivation for this project is to provide improved alerts for events such as the 2003 heat waves in Western Europe that led to at least 22,000 deaths, mostly due to heat stress, but also connected to poor air quality (Kosatsky, 2005). GEMS will generate a reanalysis of atmospheric dynamics and composition, and state-of-the-art estimates of the emissions sources and removal processes as well as how gases and aerosols are transported across con

tinents. These estimates are designed to meet key information requirements of policy-makers, and to be relevant to the Kyoto and Montreal Protocols and the United Nation Convention on long-range trans-boundary air pollution (Hol

lingsworth et al. , 2005).
Within the United States, NOAA has developedplans to use a fully coupled atmosphere-landocean-sea ice model for its next generation of global reanalysis, extending over the period 1979 to 2008 (S. Saha, personal communica

tion, 2007). This reanalysis is based on the NOAA-NCEP Climate Forecast System (CFS) model (Saha et al. , 2006). While the compo

nent analyses will be performed separately through independent atmosphere, land, ocean, and sea ice data assimilation systems, the use of a coupled model provides consistent initial estimates for all variables that is an important step toward a fully coupled Earth system analysis. Current plans are to begin production and evaluation of this reanalysis in 2008. This global atmosphere-ocean reanalysis would provide important advances on a number of fronts, taking advantage of improvements in modeling, data assimilation, and computingthat have occurred since the pioneering NCEPNCAR reanalysis. Atmospheric resolution will also be greatly increased, from approximately 200 kilometers (120 miles) in the earlier version to 30 to 40 kilometers (around 20 to 25 miles) in the new version. In addition to atmospheric, ocean, and land data assimilation, significant new efforts are examining the use of data as

similation techniques to analyze other aspects of the Earth system, with one important focus being to better represent and identify gas and aerosol emissions sources and removal pro

cesses in the atmospheric carbon cycle (Peters et al. , 2005).
Develop a more coordinated, effective, • and sustained national capability in analysis and reanalysis to support climate research and applications.
Without a clear and systematic institutional commitment, future efforts in climate analysis and reanalysis are likely to be ad hoc , and are unlikely to result in high quality, sustained, cost-effective products. Developing a national capability in climate (and Earth system) analysis and reanalysis will be essential to achieving key CCSP objectives and, in particular, CCSP Goal 1: “Improve knowledge of the Earth’s past and present climate and environment, including its natural variability, and improve understanding of the causes of climate variability and change” (CCSP, 2003).
This idea was first highlighted over 15 years ago in a National Research Council report (NRC, 1991) that outlined a strategy for a fo

cused national program on data assimilation for the Earth system. A key recommendation of that report was that “A coordinated national program should be implemented and funded to develop consistent, long term assimilated datasets ... for the study of climate and global change”. This recommendation has been reiter

Without a clear and systematic institutional commitment, future efforts in climate analysis and reanalysis are likely to be ad hoc , and are unlikely to result in high quality,sustained, costeffective products.The U.S. Climate Change Science Program

100 Chapter 4

ated frequently in several subsequent studies and reports; for example, in a recent interagen

cy-sponsored workshop whose participants included scientists and managers from several federal agencies, the academic community, and international organizations (Arkin et al. , 2004). As discussed in Hollingsworth et al. (2005), such an activity is also essential to realizing the full benefits of GEOSS, because of its crucial role in transforming Earth system observations into the status-assessment and predictive prod

ucts required by GEOSS across many areas of socioeconomic interest (Figure 4.1).
To be truly successful such a program must include multiple agencies, since it requires resources and expertise in a broad range of scientific disciplines and technologies beyond that of any single agency ( e.g. , atmosphere, ocean, land surface and biology, observations and modeling, measurements, computing, data visualization and delivery). It also will need strong ties with the Earth science user commu

nity, to ensure that the analysis and reanalysis products satisfy the requirements of a broad spectrum of users and provide increasing value over time.



### 4.3 need for improved climate attribution

Recent events underscore the socioeconomic significance of credible and timely climate attribution. For instance, the recent extremely warm year of 2006 in the United States raises questions over whether the probability of oc

currence of such warm years has changed, what the factors are contributing to the changes, and how such factors might alter future prob

abilities of similar or warmer years. Policy and decision makers want to know the answers to these questions because this information can be used within planning and response strategies. What climate processes are responsible for the persistent western U.S. drought and what impli

cations does this have for the future? Planners are assessing the sustainability and capacity of the region for further growth, and the resilience Figure 4.1 From Hollingsworth (2005), based on the GEOSS Implementation Plan (GEOSS, 2005), illustrating the transformation of observations into predictive and current-status information. On the right-hand side are products from an Earth system forecasting system and associated specialized models organized in GEOSS categories of socioeconomic benefits,stratified by the lead time required for products (current status assessments, forecast timerange, long-term studies of reanalysis). On the left-hand side are observational requirements for a comprehensive Earth system model, including in situ data as well as current and projected satellite data. In the center are “tool boxes” needed to achieve the transformation from ob

servations into information.101 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

of water resources to climate variations and change is an important factor that they must consider. What processes contributed to the extremely active 2004 and 2005 North Atlantic hurricane seasons as well as to the general in

crease in hurricane activity in this region since the mid-1990s? Emergency managers want to know the answers to such questions and related implications for the coming years, in order to better prepare for the future.

This Product has identified several outstand

ing challenges in attribution research that are motivated by observed North American climate variations that occurred during the reanalysis period but have yet to be fully explained. For instance, what is the cause of the so-called summertime “warming hole” over the cen

tral United States? The results of Chapter 3 indicate that this pattern is inconsistent with an expected anthropogenic warming obtained from coupled model simulations, although model simulations with specified sea surface temperature variations over the period are able to represent aspects of this pattern. Other forc

ings resulting from human activities, including by atmospheric aerosols and land use and land cover changes, may play significant roles but their effects have yet to be quantified. From a decision-making perspective, it is important to know whether the absence of summertime warming in the primary grain producing re

gion of the United States is a natural climate variation that may be temporarily offsetting long-term human-induced warming, or whether current climate models contain specific errors that are leading to systematic overestimates of projected warming for this region.
As emphasized in Hegerl et al. (2006), to bet

ter serve societal interests there is a need to go beyond detecting and attributing the causes of global average surface temperature trends to consider the causes of other climate variations and changes. As detection and attribution stud

ies move toward smaller scales of space and time and consider a broader range of variables, important challenges must be addressed. 

### 4.4 R ecommendations for improving climate attribution capabilities
Develop a national capability in climate • attribution to provide regular and reliable explanations of evolving climate conditions relevant to decision making.
Similar to the present status of U.S. efforts in climate analysis and reanalysis, attribution re

search is presently supported through a range of agency research programs without clear national coordination (Trenberth et al. , 2006). This may limit abilities to address attribution problems of highest scientific or public interest. There are also no clearly designated responsibilities to communicate state-of-science findings on attribution. Therefore, the public and media are often exposed to an array of opinions on causes for observed climate events, with diametrically opposed views sometimes expressed by differ

ent scientists from within the same agency. In many cases, these statements are made without any formal attribution studies, and in some cases subsequent attribution research has shown that public statements on probable causes are extremely unlikely (Hoerling et al. , 2007).
The ability to attribute observed climate variations and change provides an essential component within a comprehensive climate information system designed to serve a broad range of public needs (Trenberth et al. , 2006; NIDIS, 2007). Reliable attribution provides a scientific underpinning for improving climate predictions and climate change projections and information useful for evaluating policy options and responses and managing resources. This capability is also vital for assessing climate model performance and identifying where fu

ture model improvements are most needed. The associated scientific capacity should include providing coordination of and access to critical observational and reanalysis datasets as well as output from model experiments in which dif

ferent forcings are systematically included or excluded. Without a clear and systematic insti

tutional commitment, future efforts in climate attribution are likely to continue to be ad hoc , and unlikely to be conducted as efficiently and effectively as possible. Reliable attribution provides a scientific underpinning for improving climate predictions and climate change projections and information useful for evaluating

policy options and responses and managing resources.The U.S. Climate Change Science Program

102 Chapter 4

In order to develop this capacity, there is a need to improve coordination of, and access to, climate model and observational data relevant for climate attribution. Compared with earlier climate change assessments, a major advance in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report was the much larger number of simulations obtained from a broader range of models (IPCC, 2007). Taken together with additional observations, these more extensive simulations helped pro

vide for the first time quantitative estimates of the likelihoods of certain aspects of future climate change. This work was facilitated substantially through the Program for Climate Model Diagnosis and Intercomparison (PC

MDI), which provided facilities for storing and distributing the very large datasets that were generated from the numerous climate model simulations of past climate and climate change projections that were generated for the IPCC re

port. Other basic infrastructure tasks provided through PCMDI included: the development of software for data management; visualization and computation; the assembly and organiza

tion of observational datasets for model valida

tion; and consistent documentation of climate model features. Providing similar infrastruc

ture support for a broader range of necessary model simulations will be vital to continuing advances in research on climate attribution. In addition to fundamental data management responsibilities, advances in scientific visual

ization and diagnostic and statistical methods for intercomparing and evaluating results from model simulations would substantially facilitate future research.
The continual interplay between observations and models for climate analysis and reanalysis that occurs in attribution studies is fundamental to achieving the long-term objectives of the CCSP (CCSP, 2003). Detection and attribution research provide a rigorous comparison be

tween model-simulated and observed climate changes. Climate variations and changes that can be detected and attributed to factors exter

nal to the climate system, such as from solar variations, greenhouse gas increases produced by human activities, or aerosols ejected into the atmosphere from volcanic eruptions, help to constrain uncertainties in future predictions and projections of climate variations and change. Climate variations that can be attributed to factors that are internal to the climate system, such as sea surface temperature or soil moisture conditions, can also help constrain uncertainties in future predictions of climate variations over time periods of seasons to decades. At the same time, where there are significant discrepancies between model simulations and observations that are outside the range of natural climate variability, the information provided through detection and attribution studies helps identify model deficiencies and areas where additional effort will be required to reduce uncertain

ties in climate predictions and climate change projections.
Focus research to better explain causes of • climate conditions at regional and local levels, including the roles of changes in land cover, land use, atmospheric aerosols, greenhouse gases, sea surface temperatures, and other factors that contribute to climate change.
While significant advances have occurred over the past decade in attributing causes for observed climate variations and change, there remain important sources for uncertainties. These sources become increasingly important in going from global to regional and local scales. They include: uncertainties in observed magnitudes and distributions of forcing from various processes; uncertainties in responses to various forcings; uncertainties in natural vari

ability in the climate system, that is, variability that would occur even in the absence of changes in external forcing.
To address these uncertainties, further research is needed to improve observational estimates of changes in radiative forcing factors over a ref

erence time period, for example, the twentieth century to the present. In addition to greenhouse gas changes, such factors include variations in solar forcing, effects of atmospheric aerosols, and land use and land cover changes. The rela

tive importance of these factors varies among climate variables, and space and time scales. For instance, land use changes are likely to have a relatively small effect in changing global aver

age temperature ( e.g. , Matthews et al. , 2004) but may have more substantial effects on local weather ( e.g. , Pielke et al. , 1999; Chase et al. , There is a need to improve coordination of, and access to, climate model and observational data relevant for climate attribution.103 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
2000; Baidya and Avissar, 2002; Pielke, 2001). Aerosol variations are also likely to be increas

ingly important in forcing climate variations at regional to local levels (Kunkel et al. , 2006). Detection and attribution results are sensitive to forcing uncertainties, which can be seen when results from models are compared with different forcing assumptions ( e.g. , Santer et al. , 1996; Hegerl et al. , 2000; Allen et al. , 2006).
More comprehensive and systematic investiga

tions are also required of the climate response to individual forcing factors, as well as to com

binations of factors. Parallel efforts are neces

sary to estimate the range of unforced natural variability and model climate drift. Ensemble model experiments should be performed with a diverse set of coupled climate models over a common reference period, such as the twentieth century to present, in which different factors are systematically included or excluded. For example, model simulations including and excluding changes in observed land cover are needed to better quantify the potential influence of anthropogenic land cover change, especially at regional or smaller levels. Extended control simulations are required with the same models to estimate natural internal variability and as

sess model climate drifts. The ability to carry out the extensive simulations that are required will depend strongly on the availability of high performance computing capabilities.

A first estimate of combined model errors and forcing uncertainties can be determined by combining data from simulations forced with different estimates of radiative forcings and simulated with different models (Hegerl et al. , 2006). Such multi-model fingerprints provide an increased level of confidence in at

tribution of observed warming from increases in greenhouse gases and cooling from sulfate aerosols (Gillett et al. , 2002). Both forcing and model uncertainties need to be explored more completely in order to better understand the effects of forcing and model uncertainty, and their representation in detection and attribu

tion (Hasselmann, 1997). Because the use of a single model may lead to underestimates of the true uncertainty, it is important that such experiments reflect a diversity of responses as obtained from a broad range of models (Hegerl et al. , 2006). As discussed in Chapter 3, atmospheric mod

els forced by observed changes in sea-surface temperatures have shown considerable ability to reproduce aspects of climate variability and change over North America and surrounding regions since 1950. A growing body of evidence indicates that changes in the oceans are central to understanding the causes of other major climate anomalies. Additional assessments are required to better determine the atmospheric response to sea surface temperature variations and, in particular, the extent to which changing ocean conditions may account for past and on

going climate variations and change. As part of this assessment, ensemble experiments should be conducted with atmospheric models forced by observed sea-surface temperatures over the same baseline time period and in parallel with the experiments recommended earlier.
Explore a range of methods to better • quantify and communicate findings from attribution research.
There is a need to develop alternative approach

es to more effectively communicate knowledge on the causes of observed climate variability and change, and potential implications for de

cision makers ( e.g. , for risk assessment). New methods will become increasingly important in considering variability and changes at smaller space and time scales than in traditional global change studies, as well as for assessments of factors contributing to the likelihood of extreme weather and climate events. There is a strong need to go beyond present communication methods to approaches that include specific More comprehensive and systematic investigations are

required of the climate response to individual forcing

factors, as well as

to combinations of factors. Parallel efforts are necessary to estimate the range of unforced natural variability and

model climate drift.The U.S. Climate Change Science Program

104 Chapter 4

responsibilities for addressing questions of public interest.

Much of the climate attribution research to date has focused on identifying the causes for long-term climate trends. An important new challenge is quantifying the impact of various factors that influence the probability of specific weather or short-term climate events (CCSP, 2008). An often-stated assertion is that it is impossible to attribute a single event in a chaotic system to external forcing, although it is through such events that society experiences many of the impacts of climate variability and change. As discussed in Hegerl et al. (2006), this statement is based in part on an underlying statistical model that assumes that what is ob

served at any time is a deterministic response to forcing upon which is superposed random “cli

mate noise”. From such a model, it is possible to estimate underlying deterministic changes in certain statistical properties, for example, expected changes in event frequency over time, but not to attribute causes for individual events themselves.
However, several recent studies demonstrate that quantitative probabilistic attribution state

ments are possible for individual weather and climate events, if the statements are framed in terms of the contribution of the external forcing to changes in the relative likelihood of occur

rence of the event (Allen, 2003; Stone and Allen, 2005; Stott et al. , 2004). Changes in likelihood in response to a forcing can be stated in terms of the “fraction of attributable risk” (FAR) due to that forcing. The FAR has a long-established use in fields such as epidemiology, for example, in determining the contribution of a given risk factor ( e.g. , tobacco smoking) to disease occur

rence ( e.g. , lung cancer). This approach has been applied to attribute a fraction of the probability of an extreme heat wave observed in Europe in 2003 to anthropogenic forcing (Stott et al. , 2004) and more recently, to the extreme annual U.S. warmth of 2006 (Hoerling et al. , 2007). These probabilistic attribution findings related to risk assessment should be explored further, as this information may be more readily interpre

table and usable by many decision makers. There is also a strong need to go beyond present limited efforts at communicating knowledge on the causes of observed climate variations and change. In order to be more responsive to ques

tions from government, media, and the public, a coordinated, ongoing activity in climate at

tribution should include specific responsibilities for addressing questions of public and private interests on the causes of observed climate variations and change. This capability will form a necessary collaborative component within a national climate information system designed to meet the core CCSP objective of providing science-based information for improved deci

sion support (CCSP, 2003).

In order to be more responsive to questions from government, media, and the public, a coordinated, ongoing activity in climate attribution should include specific responsibilities for addressing questions of public and private interests on the causes of observed climate variations and change.APPENDIX A

105 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

Data assimilation is an exercise in the calculation of conditional probabilities in which short-term model forecasts are combined with observations to best estimate the state of, for example, the atmosphere. Since there are limitations in model resolution and er

rors associated with parameterization of unresolved physical processes, and the behavior of the atmo

sphere is chaotic, forecast accuracy is described by a probability distribution, as is observation accuracy. These probability distributions are combined to form conditional probabilities, which are simplified by assuming these distributions are Gaussian (normally distributed). The conditional probabilities are used to create a more accurate analysis than can be obtained solely from either the forecasts or the observations. The same approach can be applied to the ocean, land surface, or cryosphere.
Atmospheric data assimilation proceeds through a succession of (typically) six hour analysis cycles

. At the beginning of each cycle, a six-hour model forecast is carried out starting from initial conditions of atmospheric pressure, temperature, humidity, and winds provided by the previous analysis cycle, with observed boundary conditions such as sea surface temperature and snow cover. At the end of each cycle all available current observations are quality con

trolled, and the differences between the observations and the model forecast of the same variables, referred to as observational increments or innovations, are computed. The observations may include the same variables observed with different systems ( e.g. , winds measured from airplanes or by following the move

ment of clouds). They may also include observations of variables that do not directly enter the forecast such as satellite radiances, which contain information about both temperature and moisture. If the evolving probability distributions of the model forecasts and observations are known, then it is pos

sible to construct an analysis that is optimal because the expected error variance, which is the difference between the analysis of a variable and its true value, is minimized. In practice, the probability distributions are unknown. In addition, it is not possible to solve the computational problem of minimizing the error variance for realistic complex systems. In order to address these problems, several simplifying assumptions are needed. The observational increments are generally assumed to be Gaussian. With this assumption a cost function can be constructed whose minimization, which provides us with the optimal analysis, leads to the Kalman Filter equations. A bigger assumption that the probability distribution of the forecast errors does not depend on time, gives rise to the widely used and more simplistic three-dimensionalvariational type of data assimilation (3DVAR). Fourdimensional variational data assimilation (4DVAR) is a generalization of the cost function approach that allows the forecast initial conditions (or other control variables such as diffusive parameters) to be modified based on observations within a time window.
Despite the use of simplifying assumptions, the Kalman Filter and 4DVAR approaches still lead to challenging computational problems. Efforts to reduce the magnitude of the computational problems and exploit physical under

standing of the physical system have led to the develop

ment of Monte Carlo approaches known as Ensemble Kal

man Filter (EnKF). EnKF methods, like 4DVAR, can be posed in such a way that the analysis at a given time can be influenced by past, present, and future observations. This property of time symmetry is especially desirable in reanalyses since it allows the analysis at past times to benefit to some extent from future enhancements of the observing system. 

# Data Assimilation

Coordinating Lead Authors: James Carton, University of Maryland; Eugenia Kalnay, University of MarylandThe U.S. Climate Change Science Program Appendix A

106 Organization/System Model Analysis Method Time Period Weblinks

NCEP, Météo France, CERFACS OPA8.2,2 ° x2 ° x31 Lev (~0.5 ° x2 ° tropics) ERA40 forcing Multivariate 3DVar (OPAVAR) for T & S profiles 1962 to 2001 <.fr/globc/overview.html>

ECMWF HOPE, 1 ° x1 ° x29 Lev (1/3 ° x1 ° tropics) OI 1959 to 2006 <ecmwf.int/products/ forecasts/d/charts/ocean/ reanalysis/> ECCO-GODAE MITgcm 1 ° x1 ° 4DVAR 1992 to 2004 <www.ecco-group.org> ECCO-JPL MITgcm and MOM4 1 ° x1 ° x50 lev Kalman filter and RTS smoother 1993 to present <ecco.jpl.nasa.gov/external/>

ECCO-SIO 1 ° x1 ° 4DVAR 1992 to 2002 <ecco.ucsd.edu> ECCO2 MITgcm, 18kmx50 Lev Green’s functions 1992 to present

ENACT consortium 1962 to 2006 <www.ecmwf.int/research/ EU_projects/ENACT/> FNMOC/GODAE <www.usgodae.org>

GECCO 1950 to 2000 <www.ecco-group.org>

GFDL 1960 to 2006 <www.gfdl.noaa.gov/>

UK Met Office GloSea GloSea OGCM 1.25 ° x1.25 ° x40 Lev (0.3 ° x1.25 ° tropics) daily ERA40 fluxes with cor

rected precipitation OI 1962 to 1998 <www.metoffice.gov.uk/re

search/seasonal/glosea.html>

NASA Goddard GMAO Poseidon, 1/3 ° x5/8 ° MVOI, Ensemble KF 1993 to present <gmao.gsfc.nasa.gov>

INGV OPA8.2 2 ° x2 ° x31 Lev (0.5 ° x2 ° tropics) ERA40 and operational ECMWF fluxes Reduced Order MVOI with bivari

ate T and S EOFs 1962 to present

MEXT K-7 MOMv3 1 ° x1 ° x36 Lev NCEP2 reanalysis, ISCCP data 4DVAR 1990 to 2000 <www.jamstec.go.jp/frcgc/ k7-dbase2/eng/>

MERCATOR-3 OPA8.2 2 ° x2 ° x31 Lev (~0.5 ° meridional at the tropics) Single Evolutive Extended Kalman (SEEK) filter 1993 to 2001 <www.mercator-ocean. fr.html/systemes_ops/psy3/ index_en.html>

JMA MOVE/MRI.COM 1949 to 2005 <www.mri-jma.go.jp/Dep/oc/ oc.html>

NOAA/NCEP GODAS MOMv3 1 ° x1 ° x40 Lev (1/3 ° x1 ° tropics) NCEP Reanalysis2 3DVAR 1980 to present <www.bom.gov.au/bmrc/ ocean/JAFOOS/POAMA/>

BoM, CSIRO, POAMA ACOM2 (based on MOM2), 2 ° x2 ° x27 Lev (0.5 ° x2 ° at high latitudes) ERA40 MVOI, ensemble KF 1980 to 2006 <www.atmos.umd. edu/~ocean/>

SODA POP1.4, POP2.01, global

ave MVO1 with evolv

ing error 1958 to 2005 Table A.1 Characteristics of some existing global ocean model-based reanalyses of ocean climate (extracted from: <http://www.clivar.org/data/synthesis/directory.php>)APPENDIX B

107 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change



# Data and Methods Used for Attribution

Convening Lead Author: Martin Hoerling, NOAA Lead Authors: Gabriele Hegerl, University of Edinburgh; David Karoly, University of Melbourne;

Arun Kumar, NOAA; David Rind, NASA



### B.1 OBSERVATIONAL DATA North American surface temperatures during the assessment period of 1951 to 2006 are derived from four data sources, which include: the U.K. Hadley Centre’s HadCRUT3v (Brohan et al. , 2006); NOAA’s land/ocean merged data (Smith and Reynolds, 2005); NOAA’s global land gridded data (Peterson et al. , 1998); and NASA’s gridded data (Hansen et al. , 2001). For analysis of U.S. surface temperatures, two addi

tional datasets used are NOAA’s U.S. Climate Division data (NCDC, 1994) and PRISM data.
Spatial maps of the surface temperature trends shown in Chapter 3 are based on combining all the above datasets. For example, the North American and U.S. surface temperature trends during 1951 to 2006 were computed for each dataset, and the trend map is based on equal-weighted averages of the individual trends. The uncertainty in observations is displayed by plot

ting the extreme range among the time series of the 1951 to 2006 trends from individual datasets.
North American precipitation data are derived from the Global Precipitation Climatology Project (GPCC) (Rudolf and Schneider, 2005); the NOAA gridded precipitation data has also been consulted (Chen et al. , 2002). However, the North American analysis shown in Chapter 3 is based on the GPCC data alone which is judged to be superior, owing to its greater volume of input stations over Canada and Alaska in particu

lar. For analysis of U.S. precipitation, two additional datasets used are NOAA’s U.S. Climate Division data and PRISM data. Spatial maps of U.S. precipitation trends during 1951 to 2006 were computed for each of these three datasets, and the U.S. trend map is based on equal-weighted averages of the individual trends.
Free atmospheric conditions during 1951 to 2006, in

cluding 500 hPa geopotential heights, are derived from the NCEP/NCAR reanalysis (Kalnay et al. , 1996). A comparison of various reanalysis data is provided in Chapter 2, but only the NCEP/NCAR version is avail

able for the entire 1951 to 2006 assessment period. 

### B.2 CLIMATE MODEL SIMULATION DATA
Two configurations of climate models are used in this Report: atmospheric general circulation models (AMIP), and coupled ocean-atmosphere general circulation mod

els (CMIP). For the former, the data from two different atmospheric models are studied; the European Center/ Hamburg model (ECHAM4.5) (Roeckner et al. , 1996) whose simulations were performed by the International Research Institute for Climate and Society at LaMont Doherty (L. Goddard, personal communication), and the NASA Seasonal-to-Interannual Prediction Project (NSIPP) model (Schubert et al. , 2004) whose simula

tions were conducted at NASA/Goddard. The models were subjected to specified monthly varying observed global sea surface temperatures during 1951 to 2006. In a procedure that is commonly used in climate science, multiple realizations of the 1951 to 2006 period were conducted with each model in which the separate runs started from different atmospheric initial conditions but were subjected to identically evolving SST conditions. A total of 33 AMIP runs (24 ECHAM and 9 NASA) were available.
The coupled models are those used in the IPCC Fourth Assessment Report (IPCC, 2007a). These are forced with estimated greenhouse gases, atmospheric aerosols, solar irradiance, and the radiative effects of volcanic activity for 1951 to 1999, and with the IPCC Special Emissions Scenario (SRES) A1B (IPCC, 2007a) for 2000 to 2006. The model data are available from the Program for Cli

mate Model Diagnosis and Intercomparison (PCMDI) archive as part of the Coupled Model Intercomparison Project (CMIP3). Table 3.1 lists the 19 different models used and the number of realizations conducted with each model. A total of 41 runs were available.
The SST-forced (externally-forced) signal of North Amer

ican and U.S. surface temperature and precipitation vari

ability during 1951 to 2006 is estimated by averaging the total of 33 AMIP (41 CMIP) simulations. Trends during 1951 to 2006 were computed for each model run in a man

ner identical to the observational method; the trend mapThe U.S. Climate Change Science Program Appendix B

108 shown in Chapter 3 is based on an equal-weighted ensemble average of the individual trends. The uncertainty in these simulated trends is displayed graphically by plotting the 5 to 95 percent range amongst the individual model runs.

All the observational and model data used in this Product are available in the public domain (see Table 3.2 for website information). Further, these data have been widely used for a variety of climate analysis studies as reported in the refereed scientific literature.



### B.3 DATA ANALYSIS AND ASSESSMENT
Analysis of observational and model data is based on standard statistical procedures used extensively in climate research and the physical sciences (von Storch and Zwiers, 1999). Trends for 1951 to 2006 are computed using a linear methodology based on least squares which is a mathematical method of finding a best fitting curve by minimizing the sums of the squares of the residuals. Statistical estimates of the significance of the observed trends are based on a non-parametric test in which the 56-year trends are ranked against those computed from CMIP simulations subjected to only natural forcing (solar irradiance and volcanic aerosol). The principal uncertainty in such an analysis is knowing the population (number) of 56-year trends that are expected in the absence of anthropogenic forcing. Chapter 3 uses four different coupled models, and a total of sixteen 100-year simulations to estimate the statistical population of naturally occurring 56-year trends, though the existence of model biases is taken into account in making expert assessments.
Observed and modeled data are compared using routine lin

ear statistical methods. Time series are intercompared using standard temporal correlations. Spatial maps of observed and simulated trends over North America are compared using standard spatial correlation and congruence calculations. Similar empirical methods have been applied for pattern analysis of climate change signals in the published literature (Santer et al. , 1994).
Expert judgment is used in Chapter 3 to arrive at probabi

listic attribution statements. The analyses described above are only a small part of the information available to theauthors, who also make extensive use of the scientific peerreviewed literature. For more details on the use of expert assessment in this Product, the reader is referred to Box 3.4 and the Preface.GLOSSARY AND ACRONYMS

109 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change



### GLOSSARY This glossary defines some specific terms within the context of this Product. Most terms below are adapted directly from definitions provided in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report Glossary. Those terms not included in the IPCC report or whose definitions are not identical to the usage in the IPCC Glossary are marked with an asterisk.
abrupt climate change The non-linearity of the climate system may lead to abrupt climate change, sometimes called rapid climate change , abrupt events or even surprises . The term “abrupt” often refers to changes that occur on time scales faster than the typical time scale of the responsible forcing. However, abrupt climate changes need not be externally forced, and rapid transitions can result simply from physical or dynamical processes internal to the climate system.

aerosols A collection of airborne solid or liquid particles, with a typical size between 0.01 and 10 micrometers (μm) and residing in the atmosphere for at least several hours. Aerosols may be of either natural or anthro

- pogenic origin.
analysis* A detailed representation of the state of the atmosphere and, more generally, other components of the climate system, such as oceans or land surfaces, that is based on observations.

annular modes Preferred patterns of change in atmospheric circula

- tion corresponding to changes in the zonally averaged midlatitude westerlies. The Northern Annular Mode has a bias to the North Atlantic and has a large correla

- tion with the North Atlantic Oscillation. The Southern Annular Mode occurs in the Southern hemisphere.
anthropogenic Resulting from or produced by the activities of hu

- man beings.

attribution* The process of establishing the most likely causes for a detected climate variation or change with some defined level of confidence. climate The statistical description in terms of the mean and vari

- ability of relevant atmospheric variables over a period of time ranging from months out to decades, centuries, and beyond. Climate conditions are often described in terms of surface variables such as temperature, precipitation, and wind. Climate in a wider sense is a description of the full climate system, including: the atmosphere, the oceans, the cryosphere, the land surface, and the bio

- sphere, as well as their interactions.
climate change A change in the state of the climate that can be identified ( e.g ., using statistical tests) by changes in the mean and/ or the variability of its properties, and that persists for an extended period, typically decades or longer. Climate change may be due to natural internal processes or exter

nal forcings, or to persistent anthropogenic changes in the composition of the atmosphere or in land use.
climate system The climate system is the highly complex system consist

- ing of five major components: the atmosphere, the hydro

- sphere, the cryosphere, the land surface and the biosphere, and the interactions between them. The climate system evolves in time under the influence of its own internal dy

- namics and because of external forcings such as volcanic eruptions, solar variations and human-induced forcings such as the changing composition of the atmosphere and changes in land cover and land use.
climate variability Variations in the mean state and other statistics (such as standard deviations, the occurrence of extremes, etc .) of the climate on all temporal and spatial scales beyond that of individual weather events. Variability may be due to natural internal processes within the climate system (internal variability), or to variations in natural or anthro

- pogenic external forcing (external variability).

confidence The likelihood of the correctness of a result as expressed in this Product, using a standard terminology defined in the Preface.

data assimilation* The combining of diverse observations, possibly sampled at different times and intervals and different locations, into a unified and consistent description of a physical sys

- tem, such as the state of the atmosphere. This combination is obtained by integrating the observations together in aThe U.S. Climate Change Science Program Glossary and Acronyms

110 numerical prediction model that provides an initial estimate of the state of the system, or “first guess”.
drought In general terms, drought is a “prolonged absence or marked deficiency of precipitation”, a “deficiency that results in water shortage for some activity or for some group”, or a “period of abnormally dry weather sufficiently prolonged for the lack of precipitation to cause a serious hydrological imbalance” (Heim, 2002). Related terms include the fol

lowing: Agricultural drought relates to moisture deficits in the topmost meter or so of soil (the root zone) that impacts crops , meteorological drought is mainly a prolonged deficit of precipitation, and hydrologic drought is related to below normal streamflow, lake and groundwater levels. A mega
drought is a long, drawn-out, and pervasive drought, lasting much longer than normal, usually a decade or more.
El Niño-Southern Oscillation (ENSO) El Niño , in its original sense, is a warm water current that periodically flows along the coast of Ecuador and Perú, disrupting the local fishery. It has since become identified with a basin-wide warming of the tropical Pacific east of the dateline. This oceanic event is associated with a fluc

tuation of a global scale tropical and subtropical surface pressure pattern, called the Southern Oscillation. This coupled atmosphere-ocean phenomenon, with preferred time scales of two to about seven years, is collectively known as El Niño-Southern Oscillation, or ENSO. ENSO is often measured by the surface pressure anomaly difference between Darwin, Australia and Tahiti, and the sea surface temperatures in the central and eastern equatorial Pacific. During an ENSO event the prevailing trade winds weaken, reducing upwelling and altering ocean currents such that the sea surface temperatures warm, further weakening the trade winds. This event has great impact on the wind, sea surface temperature and precipitation patterns in the tropical Pacific. It has climatic effects throughout the Pacific region and in many other parts of the world, through global teleconnec

tions with fluctuations elsewhere. The cold phase of ENSO is called La Niña

.
ensemble A group of parallel model simulations. Typical ensemble sizes in many studies range from 10 to 100 members, al

though this number is often considerably smaller for long runs with the most complex climate models. Variation of the results across the ensemble members gives an estimate of uncertainty. Ensembles made with the same model but different initial conditions characterize the uncertainty asso

ciated with internal climate variability, whereas multi-model ensembles including simulations by several models also include effects of model differences. Perturbed-parameter ensembles, in which model parameters are varied in a sys

tematic manner, aim to produce a more objective estimate of modeling uncertainty than is possible with traditional multi-model ensembles.
evapotranspiration The combined process of evaporation from the Earth’s sur

face and transpiration from vegetation.
fingerprint The climate response pattern in space and/or time to a spe

cific forcing. Fingerprints are used to detect the presence of this response in observations and are typically estimated using forced climate model simulations.
geostrophic wind (or current) A wind or current that represents a balance between the horizontal pressure gradient and the Coriolis force. The geostrophic wind or current flows directly parallel to iso

bars with a speed inversely proportional to the spacing of the isobaric contours ( i.e ., tighter spacing implies stronger geostrophic winds). This is one example of an important balance relationship between two fundamental fields, mass (represented by pressure) and momentum (represented by winds), and implies that information about one of those two fields also implies information on the other.

land use and land-use change Land use refers to the total of arrangements, activities and inputs undertaken in a certain land cover type (a set of hu

man actions). The term “land use” is also used in the sense of the social and economic purposes for which land is managed ( e.g ., grazing, timber extraction, and conservation). Land-use change refers to a change in the use or manage

ment of land by humans, which may lead to a change in land cover. Land cover and land-use change may have an impact on the surface albedo, evapotranspiration, sources and sinks of greenhouse gases, or other properties of the climate system and may thus have a radiative forcing and/ or other impacts on climate, locally or globally.

likelihood The probability of an occurrence, an outcome or a result. This is expressed in this Product using a standard terminol

ogy, as defined in the Preface.
modes of climate variability Natural variability of the climate system, in particular on seasonal and longer timescales, predominantly occurs with preferred spatial patterns and timescales, through the dy

namical characteristics of the atmospheric circulation and through interactions with the land and ocean surfaces. Such patterns are often called regimes or modes or Pacific North American pattern (PNA), the El Niño-Southern Oscillation (ENSO), the Northern Annular Mode (NAM; previously111 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change
called Arctic Oscillation, AO) and the Southern Annular Mode (SAM; previously called Antarctic Oscillation, AAO). Many of the prominent modes of climate variability are discussed in Chapter 2.
non-linearity A process where there is no simple proportional relation between cause and effect. The climate system contains many such non-linear processes, resulting in a system with a potentially very complex behavior. Such complexity may lead to abrupt climate change.
North Atlantic Oscillation (NAO) The North Atlantic Oscillation is defined by opposing variations of barometric pressure near Iceland and near the Azores. Through the geostrophic wind relationship, it also corresponds to fluctuations in the strength of the main westerly winds across the Atlantic into Europe, and thus also influences storm tracks that influence these regions.
Northern Annular Mode (NAM) A winter-time fluctuation in the amplitude of a pattern characterized by low surface pressure in the Arctic and strong middle latitude westerlies. The NAM has links with the northern polar vortex into the stratosphere. Its pattern has a bias to the North Atlantic and has a large correlation with the North Atlantic Oscillation.
numerical prediction model* A model that predicts the evolution of the atmosphere (and more generally, other components of the climate system, such as the ocean) through numerical methods that repre

sent the governing physical and dynamical equations for the system. Such approaches are fundamental to almost all dynamical weather prediction schemes, since the complexity of the governing equations do not allow exact solutions.
Pacific Decadal Variability Coupled decadal-to-interdecadal variability of the atmo

spheric circulation and underlying ocean in the Pacific basin. It is most prominent in the North Pacific, where fluctuations in the strength of the wintertime Aleutian Low pressure sys

tem co-vary with North Pacific sea surface temperature, and are linked to decadal variations in atmospheric circulation, sea surface temperature and ocean circulation throughout the whole Pacific Basin.
Pacific North American (PNA) pattern An atmospheric large-scale wave pattern featuring a se

quence of tropospheric high and low pressure anomalies stretching from the subtropical west Pacific to the east coast of North America. paleoclimate Climate during periods prior to the development of measur

ing instruments, including historic and geologic time, for which only proxy climate records are available.
parameterization The technique of representing processes that cannot be ex

plicitly resolved at the spatial or temporal resolution of the model (sub-grid scale processes), by relationships between model-resolved larger scale flow and the area or time aver

aged effect of such sub-grid scale processes.
patterns of climate variability Natural variability of the climate system, in particular on seasonal and longer time-scales, predominantly occurs with preferred spatial patterns and timescales, through the dynamical characteristics of the atmospheric circulation and through interactions with the land and ocean surfaces. Such patterns are often called regimes, modes or teleconnections. Examples are the North Atlantic Oscillation (NAO), the Pa

cific-North American pattern (PNA), the El Niño-Southern Oscillation (ENSO), and the Northern and Southern Annual Mode (NAM and SAM). Many of the prominent modes of climate variability are discussed in Chapter 2.
predictability The extent to which future states of a system may be pre

dicted based on knowledge of current and past states of the system.
probability density function (PDF) A probability density function is a function that indicates the relative chances of occurrence of different outcomes of a variable.
reanalysis* An objective, quantitative method for representing past weather and climate conditions and, more generally, condi

tions of other components of the Earth’s climate system such as the oceans or land surface. An important goal of most reanalysis efforts to date has been to reconstruct a detailed, accurate, and continuous record of past global atmospheric conditions, typically at time intervals of every 6 to 12 hours, over periods of decades or longer. This reconstruction is accomplished by integrating observations obtained from numerous data sources together within a numerical predic

tion model through a process called data assimilation.
sea surface temperature The bulk temperature in the top few meters of the ocean. Measurements are made by ships, buoys and drifters.The U.S. Climate Change Science Program Glossary and Acronyms

112 

### A cronyms

AGCM Atmospheric General Circulation Model AMIP Atmospheric Model Intercomparison Project AMO Atlantic Multi-decadal Oscillation AMS American Meteorological Society AR4 IPCC Fourth Assessment Report BC black carbonCCCmaCGCM3.1(T47) a Canadian Centre for Climate Modelling and Analysis model CCSM3 a National Center for Atmospheric Research model CCSP Climate Change Science Program CFS Climate Forecast System CFSRR Climate Forecast System Reanalysis and Reforecast Project CMIP Coupled Model Intercomparison Project CNRM-CM3 aMétéo-France/Centre National de Recherches Météorologiques model CRU Climate Research Unit CRUTEM Climate Research Unit Land Temperature Record CSIRO Commonwealth Scientific and Industrial Organization CSIRO-Mk3.0 a CSIRO Marine and Atmospheric Research model CTD Conductivity Temperature Depth DJF December-January-February DOE Department of Energy ECHAM5/MPI-OM a Max-Planck Institute for Meteorology model ECMWF European Center for Medium-Range Weather Forecasting ENSO El Niño-Southern Oscillation ESMF Earth System Modeling Framework EU European Union FAR fraction of attributable risk FGGE First GARP Global Experiment FGOALS-g1.0 an Institute for Atmospheric Physics model GARP GEMPAK Analysis and Rendering Program GCHN Global Historical Climatology Network GCM Global Circulation Model GCOS Global Climate Observing System GEMPAK General Meteorology Package GEMS Global Environment Monitoring System GEOS Goddard Earth Observing System GEOSS Global Earth Observing System of Systems storm tracks Originally a term referring to the tracks of individual cyclonic weather systems, but now often generalized to refer to the regions where the main tracks of extratropical disturbances occur as sequences of low (cyclonic) and high (anticyclonic) pressure systems.
stratosphere The highly stratified region of the atmosphere above the troposphere extending from about 10 kilometers (km) (rang

ing from 9 km in high latitudes to 16 km in the tropics on average) to about 50 km altitude.

teleconnection A connection between climate variations over widely separated parts of the world. In physical terms, teleconnec

tions are often a consequence of large-scale wave motions, whereby energy is dispersed from source regions along preferred paths in the atmosphere.
troposphere The lowest part of the atmosphere from the surface to about 10 kilometers (km) in altitude in midlatitudes (ranging from 9 km in high latitudes to 16 km in the tropics on average) where clouds and weather phenomena occur. In the tropo

sphere temperatures generally decrease with height.113 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

GFDL G e o p h y s i c a l F l u i d D y n a m i c s Laboratory GFDL-CM2.0 a G e o p hy s ic a l F l u id D y n a m ic s Laboratory model GFDL-CM2.1 a G e o p hy s ic a l F l u id D y n a m ic s Laboratory model GISS Goddard Institute for Space Studies GISS-EH a Goddard Institute for Space Studies model GISS-ER a Goddard Institute for Space Studies model GMAO Global Modeling and Assimilation Office GODAR Global Oceanographic Data Archaeol

ogy and Rescue GPCC Global Precipitation Climatology Project GRIPS GCM-Reality Intercomparison Project for SPARC GSI grid-point statistical interpolation HIRS High-resolution Infrared Radiation Sounder ICOADS International Comprehensive Ocean- Atmosphere Data Set IDAG International Ad Hoc Detection and Attribution Group IESA integrated Earth system analysis INM-CM3.0 an Institute for Numerical Mathematics model IPCC Intergovernmental Panel on Climate Change IPSL-CM4 Institute Pierre Simon Laplace model ITCZ Intertropical Convergence Zone JAMSTEC Frontier Research Center for Global Change in Japan JJA June-July-August LDAS Land Data Assimilation System LLJ low-level jet MERRA Modern Era Retrospective-Analysis for Research and Applications MIROC3.2(medres) a Center for Climate System Research model MIROC3.2(hires) a Center for Climate System Research model MJO Madden-Julian Oscillation MRI Meteorological Research Institute MRI-CGCM2.3.2 a Meteorological Research Institute model MSU Microwave Sounding Unit NAM Northern Annular Mode NAMS North American Monsoon System NAO North Atlantic Oscillation NARR North American Regional Reanalysis NASA Nat ional Aeronaut ics a nd Space Administration NCAR National Center for Atmospheric Research NCDC National Climatic Data Center NCEP National Centers for Environmental Prediction NIDIS National Integrated Drought Information System NIES National Institute for Environmental Studies NOAA National Oceanic and Atmospheric Administration 

### NRC National Research Council NSIPP NASA Seasonal-to-Interannual Prediction Project OSE Observing System Experiments PCM National Center for Atmospheric Research model PCMDI Program for Climate Model Diagnosis and Intercomparison PDO Pacific Decadal Oscillation PDSI Palmer Drought Severity Index PIRATA Pilot Research Moored Array in the Atlantic PNA Pacific North American Pattern PRISM Precipitation-elevation Regressions on Independent Slopes Model QBO Quasi-Biennial Oscillation SAP Synthesis and Assessment Product SNOTEL Snowpack Telemetry SODA Simple Ocean Data Assimilation SPARC Stratospheric Processes and their Role in Climate SRES (IPCC) Special Emissions Scenario SST sea surface temperature SSU Stratospheric Sounding Unit TAO Tropical Atmosphere Ocean TAR IPCC Third Assessment Report T 2m 

### two meter height temperature UKMO-HadCM3 a Hadley Centre for Climate Prediction and Research model UKMO-HadGEM1 a Hadley Centre for Climate Prediction and Research model WCRP World Climate Research Programme WOAP WCRP Observations and Assimilation Panel WOD World Ocean Database XBT expendable bathythermographThe U.S. Climate Change Science Program Glossary and Acronyms

114REFERENCES

115 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change



## CHAPTER 1 REFERENCES

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## P hotographyC redits Cover/Title Page/Table of Contents Image for Chapter 3, page 47, (Doppler), NOOA photo Image for Chapter 4, page 93, (Polar researcher), NOAA, Micahel Van Woert
Chapter 2 Page 28, (Polar stratospheric clouds), Mark Schoeberl, GSFC/ NASA Page 39, (Weather balloon launch), Mike Dunn, NOAA/CPO
Chapter 4 Page 93, (Polar research team), NOAA/POPS Page 95, (Research ship workers recovering equipment), NOAA Page 99, (Polar researchers), NOAA Office of Ocean Exploration, J. Potter Page 103, (Setting up equipment), NOAA/CPO, Mike Dunn133 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed ChangeThe U.S. Climate Change Science Program References

134135 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed ChangeThe U.S. Climate Change Science Program References

136137 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change



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