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 Change
4

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 Integration
I

TABLE 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 Introduction......................................................................................................................................... 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..................................................................................... 43
II

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 Report.........................................................................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..............................101

TABLE OF CONTENTS
III

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 ...................................................................................................................................................117

TABLE OF CONTENTS
IV

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

IV
V

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. 

V
VI
VII

ABSTRACT

This 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. 

This 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. 

The 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.

The 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. 

The 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. 

In 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.

VII
The 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 Administration, 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 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. 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 Schubert (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 Administration, 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 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. 47–92 

Chapter 4:

Dole, R., M. Hoerling, and S. Schubert, 2008: Recommendations. 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. 93–104 
IX

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 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. 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 [Randall Dole, Martin Hoerling, and Siegfried Schubert (eds.)]. National Oceanic and Atmospheric Administration, National 

Climatic Data Center, Asheville, NC, pp. 107–114
The U.S. Climate Change Science Program Preface

X
XI

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/ESRL

PREFACE

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 identified 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 environment, 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 reanalysis. 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 climate 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 comprehensive, 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, typically 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 applications, 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 insurance. 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 conditions 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 climate 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 current 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 during the reanalysis period (from the mid-twentieth century 

to the present), including the uses, limitations, and opportunities for improvement of reanalysis data applied for this 

purpose. 

The Product is intended to be of value to the following users:

• 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 required 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 discussions 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 issues 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 longest 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 reanalyses 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 reanalysis play within a comprehensive climate observing 

system? 

• What can reanalysis tell us about climate processes and 

their representation in models used for climate predictions and climate change projections? 

• What is the capacity of current reanalyses to help 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 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 conditions? Specifically, what contributions could be made 
XIII

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 priorities 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 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? 

• What are the nature and causes 

of apparent rapid climate shifts 

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 provide policy makers with an assessment of current scientific 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 assessment.

Finally, Chapter 4 discusses steps needed to improve national 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 likelihood 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 complete; 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 findings 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 Assessment Report and other synthesis and assessment reports being 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 Primary Results

and Findings

ES.1.1 Strengths and Limitations of 

Current Reanalysis Datasets for Representing 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 atmosphere, oceans and land surface.

Among the most common questions that climate scientists 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, focusing 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 nature, 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 affect the United States, including climate 

variations associated with major modes of 

climate 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 temperature datasets constructed from surface 

observations not included in the reanalyses, 

particularly since the late 1970s. However, in some regions (e.g., Australia) the 

reanalysis trends show major differences 

with observations. 

• Reanalysis precipitation trends are less 

consistent with those calculated from observational 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 valuable 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 temperature, winds or precipitation.

• Current global reanalysis data are most 

reliable in Northern Hemisphere midlatitudes, 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 studies 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 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.

• 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 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-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 explain 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 Saskatchewan. 

• 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 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 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 changes in regional patterns of sea surface temperatures 

through the effects of sea surface temperatures 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 winter, 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 surface temperature change are unlikely to be 

the result of anthropogenic forcing alone. 

• The spatial variations and seasonal differences 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 because current reanalysis data only extends 

back until to the mid-twentieth 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 that:

• It is unlikely that a systematic change 

has occurred in either the frequency or 

area coverage of severe drought over the 

contiguous 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 local 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 the 
The U.S. Climate Change Science Program Executive Summary

4

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.

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 system, improve the quality and consistency 

of the observational data and reduce effects 

of observing system changes.

• Develop analysis methods that are optimized for climate research and applications. These methods should include 

uncertainty estimates for all reanalysis 

products.

• To improve the description and understanding of major climate variations that occurred 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 applications, such as surface temperatures, winds, 

cloudiness, and precipitation. 

• 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. 

• 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 temperatures, and other factors that contribute to 

climate change. 

• Explore a range of methods to better 

quantify and communicate findings from 

attribution research. 
Introduction

CHAPTER

1

5

Convening Lead Author: Randall Dole, NOAA/ESRL

Lead Author: 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 climate 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 representation of the state of the atmosphere that is based on observations (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 multiple 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 atmospheric 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 to 

date 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). However, 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 extended 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 data 

sources (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 elevations 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 reconstructing past weather and climate conditions, unless 

stated otherwise. 

Chapter 2 describes reanalysis methods and assesses 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 comprehensive climate observing system? 

• What can reanalysis tell us about climate 

processes and their representation in models 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 observation 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 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 

conditions through improved 

reanalysis methods? Specifically, what contributions could 

be made 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 priorities for improving the scientific and practical value of future 

climate reanalyses.

1.2 ATTRIBUTION

The term attribute has as a common use definition “to assign to a cause or source” (Webster’s 

II Dictionary, 1988). The Intergovernmental 

Panel on Climate Change (IPCC) has specifically 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 variations: (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 approximately 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 apparent 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 assessing present scientific 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 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 reanalysis 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 irregularly both in time and space over the Earth. 

These issues and others pose significant challenges 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 applying the same analysis method to carefully 

reconstruct the past climate history. Extending 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 volcanic 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 relationships helps illuminate potential applications of, 

and connections between, climate reanalysis 

and attribution. 

1.3.2 Further comments

Reanalysis can be considered as playing a central 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, observations serve as the fundamental starting point for 

climate reanalysis; observations themselves are 

generally not sufficient to establish attribution; 

models incorporating fundamental understanding of key physical processes and their relationships 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. Physical consistency enables identification of the 

roles of various processes in producing climate 

variations and change along with corresponding linked patterns of variability. Thus, the 

method of reanalysis can contribute to more 

confident attribution of the processes that produce 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 attribution 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 estimated responses to particular climate forcings 

are consistent with the observed climate features 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 establishing 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 current knowledge, and expert judgment is often 

required to assess the evidence regarding 

particular processes (see Chapter 3). The language 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 varied 

since 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 different 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 identifying 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; Pulwarty, 2003; Pinto et al., 2007). For example, 

a recently completed high-resolution regional 

reanalysis, the North American Regional Reanalysis (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 climate 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.
Reanalysis of Historical Climate Data 

for Key Atmospheric Features

CHAPTER

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 comprehensive, 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; however, 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 reanalysis 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 and 
The 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 pressure. 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 developing 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 effects on the economy. Climate change, whether 

natural or anthropogenic, can profoundly influence social and natural environments throughout 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 phenomena, 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 fundamental level, an analysis is a detailed representation of the state of the atmosphere and, more 

generally, of other Earth climate system components, 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 numerical 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 observed (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 generally not produce an identical analysis when 

given multiple opportunities.

Thus, by the early 1950s the need for an automatic, objective analysis of atmospheric conditions had become apparent. The important 

technological advance provided by the early 

computers of that time, while primitive by today’s standards, could still perform calculations 

far faster than previously possible, making this 

a feasible goal. 

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. Such techniques are still 

widely employed today to produce many types 

of analyses, such as global maps of surface temperatures, 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, statistical 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 observations together within a numerical prediction 

model that mathematically represents the physical and dynamical 

processes operating 

within the system. 

This observationsmodel integration is 

achieved through a 

technique called data 

assimilation. One 

important aspect of 

a comprehensive climate observing system 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 numerical 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 impacted by a multitude of practical decisions and 

compromises, involving the analysis methodology, quality control, the choice of observations 

and how they are used, and the model (see Appendix A and the discussion below). Figure 2.3 

compares three different reanalyses produced 

from the observations available for 5 September 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 Environmental Prediction (NCEP)/National Center 

Figure 2.2 A schematic of data assimilation.

Figure 2.3 The global distribution of the mid-tropospheric pressure field (contours are of the 500 millibars [mb] geopotential 

height field) and total water vapor (shaded color; units are in millimeters) 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 distribution; however, there is substantial disagreement 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 relationships 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 Interpolation (OI) with 

incremental analysis 

update

No longer available Schubert et al. (1993)

NOAA NCEP 

and NCAR (R1)

1948 to present

T62 - ∆x~200km 

L28 (σ, top at 

about 3mb)

Spectral Statistical

Interpolation (SSI)

<http://www.cpc.

ncep.noaa.gov/products/wesley/reanalysis.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/products/wesley/reanalysis2/>

Kanamitsu et al. (2002) 

(Fixes errors found in 

R1 including fixes to 

PAOBS, snow, 

humidity, etc.)

European 

Centre for 

Medium-Range 

Weather Forecasts (ECMWF) Reanalysis 

(ERA-15)

1979 to 1993

T106 - ∆x~125km 

L31(σ-p, top at 

10mb)

Optimal Interpolation (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 CRIEPI (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 Regional Reanalysis (NARR)

1979 to present ∆x= 32km L45 3D-Var, precipitation 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 understanding 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 introduced 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) numerical prediction model and data assimilation 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 fundamentally 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 reanalyses (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), especially 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 methods 

developed 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 foreseeable 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 observations 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 atmosphere’s evolution together with all available 

observations to estimate the state of the atmosphere 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 observations 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 TB 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 observational 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 incompletely 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 physical 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 residual 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 document 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 within 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) variability 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 reanalyses 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 observations 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 addressing specific questions defined in the Preface 

are discussed. 

2.2 rOLE OF REANALYSIS IN 

UNDERSTANDING CLIMATE 

PROCESSES AND evaluating

climate MODELs

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 climate 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 differently 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 behavior 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 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.

The above model-centric view of data assimilation 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 parameterizations 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 assessing 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 seasonal 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 useful 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 obtaining 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 reanalysis data are now routine and critical aspects of 

any model development and evaluation effort. 

(e.g., Atmospheric Model Intercomparison 

Project [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 European Centre for Medium-Range Weather 

Forecasts (ECMWF) 

reanalysis (ERA-15, 

Table 2.1). 

The comparison shows 

considerable differences 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 General Circulation Model 

(AGCM) simulations 

forced by observed sea 

surface temperatures to 

reproduce that part of 

the variability associated with ENSO. 

In this case the comparison is made with the 

NCEP/NCAR R1 reanalysis 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 produces and maintains climate anomalies. This involves 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 (m2

) 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 evaporation). 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 assimilation (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 ultimately 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 applied 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 various 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 precipitation 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 factors? 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 description 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 cannot be addressed by reanalysis data alone. Going back to the previous example, how can the 

role of local evaporative changes and changes 

in the storm tracks be separated? Model experimentation 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 identifying 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 adjustments 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 forecast 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 major

seasonal-to-decadal

climate variations

In this Section the strengths and weaknesses 

of current reanalyses for identifying and understanding 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 connections between reanalysis, models, and attribution 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 affecting 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 pronounced impacts on the North American climate (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 the 

free-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, implying 

the 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 Oscillation (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 patterns, can shift weather patterns and disrupt 

local climate features (e.g., Gutzler et al., 1988; 

Hurrell, 1996). 

As discussed in the following Sections, the quality 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 assessment of the consistency of the atmospheric 

manifestations of these modes (and their impacts on regional climate) in current reanalyses 

based on such general considerations.

Figures 2.8 and 2.9 show examples of the connection between the PNA and NAO patterns and 

North American surface temperature and precipitation 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 changes in the climate modes and regional changes 

in surface temperature and precipitation. 

During the positive phase of the PNA pattern, 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-toSeasonalWeak 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-todecadalModerate on long 

time scales

East coast winters Good

Northern Annular 

Mode (NAM)

Thompson and 

Wallace (2000); 

Wallace (2000)

Subseasonal-todecadalModerate on long 

time scales

East coast winters Good to fair in 

stratosphere

El Niño-Southern 

Oscillation 

(ENSO)

Philander (1990) Seasonal-to-interannualStrong Winter in west 

coast and southern 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 pluvials over North 

America 

Fair to poor

Atlantic Multidecadal Oscillation (AMO)Folland et al.

(1986) 

Decadal Strong Drought or pluvials 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 relatively 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 regime 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 atmospheric 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 ocean 

variations. 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 predicting atmospheric variability over seasonal and 

longer time periods. Observations of each of 

these climate system components, while improving 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 understanding.

Among all components of the climate system, 

the atmospheric component possesses the most 

advanced observational capabilities. This system 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 observing system provided by satellite measurements 

beginning in the late 1970s. The present observing 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 wintertime 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 observational 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 observational record was that the investment in operational weather prediction could be leveraged 

by harnessing the prediction infrastructure 

(the global models and data assimilation methods 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 understanding of seasonal to decadal variability of 

climate (Table 2.1).

The reanalysis data provide the most comprehensive 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 formation, 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 assimilation 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 secondary 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 climate 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 characteristics, 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 wintertime 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 observational 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 provided by reanalyses, along with estimates of the 

physical processes that drive the atmosphere, 

has greatly facilitated diagnostic studies that attempt to identify the causes of large-scale atmospheric 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 perspective 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 differences between the PNA and a similar pattern 

of variability in the Pacific/North American region 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 connections 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 propagation 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 evaluation of large-scale climate variability and of the 

physical mechanisms that produce high impact 

regional climate anomalies.

While reanalysis data have played a fundamental role in diagnostic studies of the leading 

middle- and high-latitude variability and of 

regional climate anomalies, there are inadequacies in the stratosphere—a region of the atmosphere 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 inadequate 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 extratropical 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 simulation experiments, and the reanalyses are used 

to both characterize the atmospheric behavior 

and to validate the model results. This is an important 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 respects 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 structure and evolution (e.g., Lin et al., 2004)—an 

interaction that has not yet been well represented 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 century 

varies 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 particular. 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 available 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 making it difficult to separate true climate variations 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 tropical 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 particularly 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 atmospheric variability, particularly if used in conjunction 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 with 

independent 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 system 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 (discussed 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 representing 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 understanding 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 observations 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 reanalysis 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 currently 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 observing 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 correction 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 mentioned 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 reanalysis 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 reanalyses 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 understanding 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 accurately 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 physical 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 fundamental errors in the model forcing of hydrological 

climate anomalies (e.g., Ruiz-Barradas and 

Nigam, 2005). 

On global scales, the limitations in the assimilation models are shown as biases in, for example, 

monthly averaged heat and moisture budgets, 

introducing uncertainties in the physical processes 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 exA 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) evaluated 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., precipitation, 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 development 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 averages 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 climate, as reflected in changes in long-term average 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 calculating 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 “underlying climatic average” of rainfall is increasing 

with time over the period of interest. Such a 

trend calculation is limited by the overall noisiness 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 T2m, and precipitation. 

Simmons et al. (2004) provide the most comprehensive evaluation to date of reanalysis-based 

trends in surface temperature, T2m. Figure 2.13, 

reproduced from that work which uses linear regression techniques, shows comparison of T2m

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 Northern 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 averages are 0.30°C for the observations, 0.27°C for 

ERA-40, and 0.19°C for NCEP/NCAR. Simmons 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 T2m 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 T2m 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 10oS-equator, 55o-45oW 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 particular does not adequately reproduce trends in 

southern South America, a problem also noted 

by Rusticucci and Kousky (2002). 

A similarly comprehensive evaluation of precipitation 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 observational data and four reanalyses. 

The biggest discrepancy between the observations 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 positive 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 precipitaCompared 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 positive (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 extent 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 inadequacies 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 constrained 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, leading to an artificial positive temperature trend 

(Figure 2.17). 

Bengtsson et al. (2004a) use this reasoning to 

explain an apparently false trend in lower troposphere 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 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 (Pielke 

et al., 1999; Kalnay et al., 2006). 

The ERA-40 reanalysis, which assimilates 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 difference 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 observational 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 important 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 reanalysis 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 elevations, and are therefore not representative of 

total precipitation in that region. Simmons et al. 

(2004) note that the gridded observational values 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 different record lengths and other inhomogeneities 

over space and time (Hamlet and Lettenmaier, 

2005). Jones et al. (1999) note that urban development 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 decades are also subject to changes in measurement systems. Takahashi et al. (2006) suggest 

that the use of a new satellite data product 

(introduced in 1987) in an observational precipitation dataset led to a change in the character of 

the data. Kalnay et al. (2006) found an artificial 

trend in observational temperature data induced 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 computing 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 different observational datasets from 1951 to 2006, 

and the bottom map shows the trend computed 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 Climatic 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 for 

both 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 identical, 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 statistical 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 estimation of trends from either reanalyses or observational datasets, it is the authors’ assessment 

that it is likely that most of the trend differences 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 quantifying 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 temperature (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 inadequacies 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 understanding 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 precipitation 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 (Trenberth and Shea, 2005); therefore, their trends 

should not be studied in isolation. 

Given these and other examples of trend complexity, one advantage of a reanalysis dataset 

becomes clear: 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. 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 observational 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 estimates 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 reasons 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 introduce 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 features, 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 meteorological forcing will be an additional source 

of false trends. The following Section address 

issues of systematic instrument and data sampling errors as well as model and data assimilation 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 Comprehensive 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 (Whitaker et al., 2004; Compo et al., 2006). 

In 1936, the U.S. Weather Bureau began operational 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 International Geophysical Year of 1958, the radiosonde network expanded globally to include 

Antarctica and became recognized as a central 

component of the historical observation network 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 internal 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, introducing inconsistencies into the record 

(Gaffen, 1994). 

Two additional observing systems were 

added to the existing system in the 

1970s. Aircraft observations increased 

in 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 instruments (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 (Coordinated 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 stratosphere (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 atmospheric observations increased dramatically in the 1970s with the introduction 

of remote sensed temperature retrievals, 

along with a succession of ancillary measurements (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 cooling trend detected in microwave tropospheric 

temperature retrievals. This false cooling trend 

has recently been corrected by properly accounting for the effects of orbital decay (Mears 

et al., 2003).

The ocean observing system has also undergone a gradual expansion of in situ observations (i.e., measurements obtained through 

direct contact with the ocean), followed by a 

dramatic increase of satellite-based observations (Figures 2.19 and 2.20). 

Prior to 1970, the main instrument for measuring 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, beginning with the TAO/Triton array of the tropical 

Pacific, expanding into the Atlantic (PIRATA) 

in 1997, and most recently into the tropical Indian 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 Rescue (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 designed 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). Unfortunately, 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 observations 

collected 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 autonomous 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 observations. 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 scatterometers 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 techniques 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 the 

collection 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 moisture 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 observations 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, radiation, hydrological cycle, ozone, and land surface diagnostics. A special collection of data files are designed to facilitate 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 community (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 further improve the use of information about the 

quality of the reanalyses targeted especially 

for data providers/developers. Furthermore, 

the observational, reanalysis, and climate communities 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 reanalyses 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 numerical representations of the primitive equations of 

motion 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 reanalyses (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 approaching 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 precipitation in the form of both water and ice. 

Therefore, these processes must be parameterized, or estimated from other, presumably more 

accurately simulated, model variables. Inaccuracies 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 physics. Ocean models exchange thermodynamic, 

radiative, and momentum fluxes with the atmosphere. 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, convection, 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 interconnected 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 contain 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 reliable (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 difficulty in producing realistic land fluxes is the 

large amount of variability (e.g., in topography, 

vegetation character, soil type, and soil moisture 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 reasons. 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 resolution 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 atmospheric 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 integrations 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 assumptions 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 measurements or sampling have been identified and corrected; (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 reduced, 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 observations from land stations and marine platforms. 

These records include existing climate datasets, 

such as daily or monthly air temperature, pressure, 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 January 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 previous 

NCEP 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

 Attribution 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 Saskatchewan. 

• 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 circulation, 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 insignificant 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 coverage 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 likely that 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 attribution of the causes for observed climate 

variations and change. 

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. 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 increases 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 determining 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 variations and the causes for high-impact climate 

events, such as the recent multi-year drought in 

the western United States and the record setting 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 factors 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 Assessment Report (AR4) from Working Group I contains 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 referred 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 identified 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 accompanies 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 kilometers, or about 6 miles, in altitude) that is physically 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 conditions (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) simulation fails to confirm a role for Earth’s lower 

boundary conditions, then two plausible explanations 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. Reanalysis 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 principal 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 establishes 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 accurately 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 components of such attribution science.

While this Chapter considers the approximate 

period covered by modern reanalyses (roughly 

1950 to the present), datasets other than reanalyses, 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, together 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 variations or change. One important class is external forcing, which contains both natural and 

anthropogenic sources. Examples of natural 

external forcing include solar variability and 

volcanic eruptions. Examples of anthropogenic forcing are changing concentrations of 

greenhouse gases and aerosols and land cover 

changes produced by human activities. A second 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 include processes that are due primarily to interactions within the atmosphere as well as those 

that involve coupling the atmosphere with various components of the climate system. Climate 

variability due to purely internal mechanisms 

is often called internal variability.

For attribution to be established, the relationship 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 assessed. 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 elsewhere 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 during 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 of 

physical 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 climate 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 variations 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, physically 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 specific problem or context. To establish the cause, 

it is necessary to identify possible forcings, 

determine the responses produced by such forcings, 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 on 

atmospheric 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 relationships 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 empirical 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 precipitation 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 meaningful, is insensitive to sample size, and is reproducible 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 relationships 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 Recherches Météorologiques

1

4 CSIRO-Mk3.0 Australia CSIROa

 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/

NIESb

/JAMSTECc

3

15 MIROC3.2(hires) Japan Center for Climate 

System Research/

NIESb

/JAMSTECc

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 conditions 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 Signal determination

1) Attribution to internal atmospheric variations

Pioneering empirical research, based only on 

surface information, discovered statistical linkages 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 patterns, 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 conditions 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 patterns in the free atmosphere fueled theories 

of the dynamics of these teleconnections, 

clarifying the origins for their regional surface 

impacts (Rossby, 1939). The relevant atmospheric 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 Climatology Centre (GPCC)

<http://www/dwd/de/en/FundE/Klima/KLIS/int/GPCC/>

CMIP3 CMIP3 World Climate Research Programme's (WCRP's) Coupled 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 datasets, 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 representing 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; Simmons et al., 1983). 

As indicated in Chapter 2, these and other 

teleconnection patterns can be readily identified in the monthly and seasonal averages of 

atmospheric circulation anomalies in the free 

atmosphere using reanalysis data. Reanalysis 

data has also been instrumental in understanding the causes of teleconnection patterns and 

their North American surface climate impact 

(Feldstein 2000, 2002; Thompson and Wallace, 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 schematically 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 statements that go beyond the statement of how 

recurrent features of the atmospheric circulation 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 usually 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 Oscillation (ENSO) phenomenon was discovered to 

be an important source for year-to-year North 

American climate variation, with recent examples being the strong El Niño events of 1982 

to 1983 and 1997 to 1998, whose major meteorological 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 climate 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 wintertime 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 characteristic 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 important 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.
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Reanalysis of Historical Climate Data for Key Atmospheric Features: 

Implications for Attribution of Causes of Observed Change

The use of climate models subjected to specified 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 complete, 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 discussed further in Box 3.2.

3) Attribution to external forcing

Explaining the origins for the surface boundary conditions themselves is another stage in 

attribution. El Niño, for example, is a known 

internal variation of the coupled ocean-atmosphere. 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 indicates 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 of 

the 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, water 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 anthropogenic 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 separate 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 studied
59

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 occurring 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 be 

judged 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 forcings within coupled ocean-atmosphere-land 

models. An indirect approach can also be used 

to attribute climate conditions to external forcing, 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 determined 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 atmospheric aerosol changes or land use changes, 

or natural external forcing due to volcanic 

and 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 suitability 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 polarity 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 Fingerprinting

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” (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 American 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 multiple regression analysis of observations onto 

several fingerprints representing climate responses 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 forcing 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 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. 

The confidence in attribution will thus be subject 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 increasing 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 results 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) variability 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 estimate 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 simulations 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 comparisons between proxy reconstructions and climate 

simulations of the last millennium.

Sections 3.2, 3.3, 3.4, and 3.5 summarize current 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 attribution 

of 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 attribution 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 anthropogenic warming over the past 50 years averaged 

over each continent except Antarctica”. This 

conclusion was based on recent fingerprintbased studies on the attribution of annual surface 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 experiments including the effects of anthropogenic 

forcings reproduced the recent upward trend. 

The IPCC Report also stated that, for precipitation, 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 fluctuations 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 anthropogenic 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 Description of the observed

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 temperatures 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 attribution 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 atmosphere 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 internal variations of the climate were then masking 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 assessment 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 explaining 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 record 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 science 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 challenge 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 temperature trend reversed after the early 

1970s. The shaded area in Figure 3.3 

(top right panel) illustrates the spread 

among four different analyses of surface 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 domain of North America is constrained 

by and is consistent with climate conditions 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 question 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 anthropogenic 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 temperature 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 detected, 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 methodological 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 American average surface temperatures. Of these 76 

“trends estimates” consistent with natural variability, 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 Attribution of the observed

variations

3.2.2.2.1 External Forcing

The IPCC Fourth Assessment Report provided 

strong attribution evidence for a significant 

anthropogenic greenhouse gas forced warming 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 atmospheric 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 simulations 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 temFigure 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 simulations 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 gases
65

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 warming 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 during 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 include 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 anthropogenic 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 temperature 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 relationships 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 knowledge 

of 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 literature. 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 estimates 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. Pinatubo 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 its 

anomalous 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 globally 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 generally 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 including 

interannual-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 anthropogenic 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 support 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 realistic, with a temporal correlation of 0.79 between 

the raw unsmoothed observed data and simulated 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 pattern correlation of 0.87 with the observed trend 

map highlights the remarkable spatial agreement 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 annual 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 evolutions 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 surface 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 determining 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 variations 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 conducted, 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 observed 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 considerably 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 summertime 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 precipitation 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 precipitation since 1951. In addition, the 

area-averaged annual fluctuations in 

the simulations are generally within 

a few percent of the 56-year climatological 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 decadal 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 attribution 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 underestimate both the variation of average 

precipitation over land compared to 

observations and the observed precipitation 

changes in response to volcanic eruptions (Gillett 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 precipitation 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 precipitation from the AMIP simulations shows better 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 between 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; Herweijer et al., 2006). Such causal links do provide 

an explanation for the success of AMIP integrations 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 variations 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 greenhouse 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 climate forcing and cannot be explained by internal 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 North 

American 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 variations 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 North America

The observed annually averaged temperature 

trends over North America in Figure 3.3 show 

considerable variation in space, with the largest warming over northern and western North 

America and least warming over the southeastern 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 simulations with observations for the 1951 

to 2006 North American surface temperature trend is 0.79, that agreement 

is almost entirely due to the agreement 

in the area-averaged temperature trend. 

Upon removing the area-averaged warming, a process that highlights the spatial 

variations, the resulting pattern correlation between trends in CMIP and observations 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 temperature 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 temperature 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 variation of the observed warming over North 

America is likely influenced by observed 

regional SST variations, which is consistent 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 reanalysis data of 500 millibar (mb) pressure 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 temperature 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 reanalysis 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 attributable 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 invaluable 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 albedo [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, bottom 

panel). 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 circulation trend since 1951 that permits the reorganization 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 Contiguous United States

For the U.S. area-averaged temperature variations, six of the warmest ten summers (Figure 

3.8, top) and six of the warmest ten winters (Figure 3.9, top) during 1951 to 2006 occurred in the 

last decade (1997 to 2006). This recent clusterFigure 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. Plotted 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 assessment 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 simulations for the U.S. temperature trend (Figure 

3.8, bottom) are positively correlated with the 

observed observations, with a pattern correlation 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 remains 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, respectively, 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 also 

experienced 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 requiring 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 estimates 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 support 

this 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 sampling 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 summer 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 number 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 variations, 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 factors for explaining the observed spatial variations 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 reduced 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 structure 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 decision 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 Northeast, 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 warming 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 winters 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 unrelated to external or the SST forcing. 

3.3.3 Precipitation Trends

3.3.3.1 North America

The observed annual North American precipitation 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 Caribbean region, and the increase over northern 

Canada. However, due to the strong and differing seasonal cycles of precipitation across the 

continent, a diagnosis of the annually averaged 

trends is of limited value. Therefore, this Section 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 highlighted. 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 precipitation 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 southeastward 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 evident from the AMIP trend pattern (bottom). In 

particular, the AMIP simulations generate the 

zonal band of enhanced high latitude precipitation, 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 regional SST variations, and to assess the origin 

of the SSTs anomalies themselves. 

3.3.3.2 Contiguous United States

The observed seasonally-averaged precipitation 

trends over the period 1951 to 2006 are compared 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 precipitation trends across the United States than for 

the temperature trends, and larger interannual 

and decadal variability. These factors undermine the detectability of any physical change 

in precipitation since 1951.

During summer (Figure 3.14), there is a general 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 unlikely 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 (percent 

change 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 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.
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 correlation of AMIP simulated and observed summer 

U.S. average rainfall is +0.64. 

During winter (Figure 3.15), there is little agreement 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), suggesting that changes in external forcing have had 

no appreciable influence on area-averaged precipitation 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 simulations including again the swing from wet 1990s 

to dry late 1990s early 2000 conditions. For the 

56-year period as a whole, the temporal correlation 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, respectively. These highlight the strong temporal 

swings in observed regional precipitation between wet and dry periods, such that no single 

region has a detectable change in precipitation 

during 1951 to 2006. These observed fluctuations are nonetheless of great societal relevance, 

being associated with floods and droughts having catastrophic local impacts. Yet, comparing 

to CMIP simulations indicates that it is exceptionally 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 presumably 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 forcing 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.
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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 expectation that such occurrences may be particularly 

effective in exposing the vulnerabilities of societies 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 feedbacks, gradual forcing could also trigger rapid 

shifts in some parts of the climate system, a 

frequently cited example being a possible collapse 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 unexpected 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 consistent 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 consequences 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 sensitivity 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 transition between two climatic states that individually have much longer duration than the transition 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 instrumental 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 paleoreconstructions) 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 characterize 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 recognition 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 planning 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. 
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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 generation of reanalysis products, abrupt changes 

in external forcings can still influence the 

reanalyses indirectly through their effect 

on other assimilated variables. Observational 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 involved. 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 temperatures (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 transitions have involved threshold exceedences 

in a manner that would forewarn of their 

permanence. 

3.4.4.1 Abrupt natural external

forcings since 1950

The period of the reanalysis record was a 

volcanically active one, particularly compared 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 eruption 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 detectability 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 anthropogenic 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 eruption), 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 estimates 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 volcanic 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 Abruptness related to gradual

increase of greenhouse gases since

1950

Has the gradual increase in greenhouse gas external 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 anthropogenic 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 temperature 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 tropical 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 surface 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 period, punctuated by brief pauses due to volcanic 

aerosol-induced cooling events. 

3.4.4.3 Abruptness due to unforced

chaotic behavior since 1950

Some rapid climate transitions in recent decades appear attributable to chaotic natural 

fluctuations. One focus of studies has been the 

consequence of an apparent shift in the character 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. 
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Reanalysis of Historical Climate Data for Key Atmospheric Features: 

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contributed to these droughts did not persist 

in their cold La Niña state. 

An apparent rapid transition of the atmosphere-ocean system over the North Pacific 

was observed to occur in the period from 

1976 to 1977. From an oceanographic perspective, 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 surface 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 wintertime Alaskan surface temperatures (Figure 

3.19) reveals the mild conditions that suddenly 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 multiple decades (Latif and Barnett, 1996). Thus, 

it is best viewed as a climate “variation” rather 

than 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 appearance of an abrupt warming, as Figure 3.19 

indicates occurred over western North America 

in the mid-1970s. It is plausible that the permanency 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 progress 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 patterns, 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 extensive experimentation—only recently available—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 Integrated 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

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Credible prediction systems are always improved 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 information 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 hydrologic 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 serious 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 illustrate some of the major temporal variations of 

drought witnessed over North America. The 

Palmer Drought Index is also useful when intercomparing 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 that 

relate to the drought impacts having decisionmaking relevance (e.g., Dai et al., 2004).

3.5.3 Drought Causes

3.5.3.1 Drought statistics, mechanisms

and processes

The North American continent has experienced 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 (western) 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. 
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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 indicates 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 increased frequency of what the National Climatic 

Data Center (NCDC) refers to as “billion dollar 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

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southeastern United States; (3) Summer 1988, 

central/eastern United States; (4) Fall 1995 to 

Summer 1996, U.S. southern plains; (5) Summer 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 average summer precipitation for the United States 

(top) and the seasonal standard deviation for the 

period 1951 to 2006 (bottom). The largest variability 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 representative 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 rainfall, generally to amounts less than 50 percent 

of average precipitation values. The specific 

weather pattern in question features an amplified 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 moisture advection into the region. 

The establishment of a stationary wave pattern 

in the atmosphere is thus essential for generating 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 during 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 precipitation 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.
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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 acting as a flywheel (i.e., as a source for inertia) 

to maintain the anomalies. 

The droughts discussed above can be distinguished 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 temperature 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 includes 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 teleconnection 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 Intertropical Convergence Zone (ITCZ) northward 

Figure 3.22 Climatological average (top) and standard deviation 

(bottom) of summer (June-July-August) seasonally-averaged precipitation 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

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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 allowing 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 forcing, 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) experiments 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 downstream of North America, in the North Atlantic, 

have occasionally been suggested as influencing 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 linked 

with Atlantic SST variations of similar timescale (McCabe et al., 2004; Figure 3.5). 

In Mexico, severe droughts during the reanalysis 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 tropospheric 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.
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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 precipitation 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 relationship 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 warming during the subsequent La Niña phase, and 

internal atmospheric variability. 

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. This was the conclusion reached in 

association with the recent U.S. drought (1999 

to 2005) that affected large areas of the southern, 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 Human influences on North

American drought since 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 anthropogenic warming of the eastern equatorial Pacific 

(Mendelssohn et al., 2005). There is a tendency 

in 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 influence 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 consistently 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.
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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 anthropogenic forcing for this region and is 

outside the range expected from natural 

variability, as judged by coupled atmosphere-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 warming 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 happening, and is associated with drought 

in the southwestern United States. Additional observations and modeling improvements will be required to assess the 

likelihood of its occurrence with greater 

confidence. 

An additional impact of greenhouse 

warming is a likely increase in evapotranspiration during drought episodes 

because of warmer land surface temperatures. 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 imbalance 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 increasing 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 1970 

through 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 reanalysis 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 ecologic systems compared to the earlier drought.

Breshears et al. (2005) estimated the vegetation 

die-off extent across southwestern North America 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 explaining 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 conditions 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 uncertain. Also, while some modeling studies 

have emphasized the role played by surface 

Figure 3.24 Observed climate conditions averaged for 1999 to 2004 during 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 precipitation 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 somewhat 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 the 

past 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 temperature anomalies becomes more important. Also, 

the accumulating greenhouse gases and global 

warming have increasingly been felt as a causative factor, primarily through their influence 

on Indian Ocean/West Pacific temperatures, 

conditions to which North American climate is 

sensitive. 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. 
Recommendations 

CHAPTER

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 findings 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

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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 providing benchmarks for evaluating climate models. 

Climate analyses and reanalyses are also being 

used in an increasing range of 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; Pulwarty, 2003; Pinto et al., 2007).

These are important benefits. However, there 

are limitations in climate analysis and reanalysis 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 important 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 variations. 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 possible 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 coordination at national interagency levels, which may 

result in less than optimal progress and the inability 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 capability 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 variability and change, and in supporting applications 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 Recommendations 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 observing 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 communities to improve the existing global database 

of Earth system observations (Schubert et al., 

2006). Priorities include improving quality 

control, identification and correction of observational bias and other errors, the merging of 

various datasets, data recovery, improving the 

handling of metadata (that is, information describing 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.
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Reanalysis of Historical Climate Data for Key Atmospheric Features: 

Implications for Attribution of Causes of Observed Change

effectively adjust to changes in observing systems (Dee, 2005). 

This recommendation resonates with recommendations 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 the 

global observing system to avoid nonclimatic influences (CCSP, 2006).

Recent World Meteorological Organization 

(WMO) reports emphasize the need for ongoing 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 (Arkin 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 discontinuities if the satellite measurements are not 

cross-calibrated, e.g., by comparing measurements 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 efforts 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 produce 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 assimilated at all or treated 

differently from observations 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 database, which may not 

be collected until 

long after the forecast is completed. 

These constraints 

are not relevant for 
The U.S. Climate Change Science Program

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Chapter 4

climate analyses, and modification of current 

data assimilation methods is needed to improve 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 effectively 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 better 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 uncertainties 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 Department of Energy that is developing the overall 

organization, infrastructure, and low-level utilities 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 organizations) 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, particular 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, uncertainties 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 uncertainty information is then incorporated into 

the analysis (Houtekamer and Mitchell, 1998; 

Whitaker and Hamill, 2002). This approach provides 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 Zupanski, 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 century 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.
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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 reanalysis 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 and 

1940s, 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 climate 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 assimilation 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. Improved 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 forward 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 significant advances in modeling and data assimilation that were made following the initial global 

reanalysis by NOAA and the National Center 

for Atmospheric Research (Kalnay et al., 

1996), including the 

ability to assimilate 

precipitation observations. This has resulted in substantial 

improvements in precipitation analyses 

over the contiguous 

United States as well 

as improvements in 

near-surface temperatures and wind 

fields (Mesinger et 

al., 2006). While advances are impressive, 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

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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 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. 

A reanalysis of the chemical 

state of the atmosphere would 

improve monitoring and understanding of air quality in 

a changing climate, aerosolclimate interactions, and other 

key policy-relevant issues. Initial attempts at coupling of 

climate system components, 

e.g., ocean-atmosphere reanalysis, should be fostered, with a long-term goal 

being to develop an integrated Earth system 

analysis (IESA) capability that includes interactions among the Earth system components 

(atmosphere, ocean, land, snow and ice, and 

biological systems). 

An IESA would provide the scientific community, resource managers, decision makers, 

and policy makers with a high quality, internally 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 capacity 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. 
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Reanalysis of Historical Climate Data for Key Atmospheric Features: 

Implications for Attribution of Causes of Observed Change

of current weather data assimilation capabilities, 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 continents. 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 (Hollingsworth et al., 2005).

Within the United States, NOAA has developed 

plans 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 communication, 2007). This reanalysis is based on the 

NOAA-NCEP Climate Forecast System (CFS) 

model (Saha et al., 2006). While the component 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 computing 

that 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 assimilation 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 processes 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 focused 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 reiterWithout 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 interagency-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 products 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 community, 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 occurrence of such warm years has changed, what 

the factors are contributing to the changes, 

and how such factors might alter future probabilities 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 implications 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 observations 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 increase 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 outstanding 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 central 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 forcings 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 region 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 better 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 studies move toward smaller scales of space and 

time and consider a broader range of variables, 

important challenges must be addressed. 

4.4 Recommendations

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 research 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 different 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 future 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 different forcings are systematically included or 

excluded. Without a clear and systematic institutional 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 provide 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 (PCMDI), 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 report. Other basic infrastructure tasks provided 

through PCMDI included: the development of 

software for data management; visualization 

and computation; the assembly and organization of observational datasets for model validation; and consistent documentation of climate 

model features. Providing similar infrastructure 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 visualization 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 between model-simulated and observed climate 

changes. Climate variations and changes that 

can be detected and attributed to factors external 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 uncertainties 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 variability 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 reference 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 relative 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 average 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 increasingly 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 investigations are also 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. 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 assess 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 attribution 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 attribution (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 models 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 ongoing 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 approaches to more effectively communicate knowledge 

on the causes of observed climate variability 

and change, and potential implications for decision 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 observed at any time is a deterministic response to 

forcing upon which is superposed random “climate 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 statements 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 occurrence 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 occurrence (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 interpretable 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 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. 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 decision 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 errors associated with parameterization of unresolved 

physical processes, and the behavior of the atmosphere 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 controlled, 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 movement 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 possible 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-dimensional 

variational 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 understanding of the physical system have led to the development of Monte Carlo approaches known as Ensemble Kalman 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 Maryland
The 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 corrected precipitation

OI 1962 to 1998 <www.metoffice.gov.uk/research/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 bivariate T and S EOFs1962 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 evolving 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 additional 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 plotting 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 particular. 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, including 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 available 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 models (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 simulations 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 Climate 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 American and U.S. surface temperature and precipitation variability 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 manner identical to the observational method; the trend map 
The 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 linear 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 probabilistic attribution statements. The analyses described above 

are only a small part of the information available to the 

authors, 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 external 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 a 
The 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 following: 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 megadrought 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 fluctuation 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 teleconnections 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, although 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 associated 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 systematic 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 surface and transpiration from vegetation.

fingerprint 

The climate response pattern in space and/or time to a specific 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 isobars 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 human 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 management 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 terminology, 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 dynamical 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; previously 
111

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 represent 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 atmospheric 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 system 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 sequence 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 measuring instruments, including historic and geologic time, for 

which only proxy climate records are available. 

parameterization 

The technique of representing processes that cannot be explicitly 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 averaged 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 Pacific-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 predicted 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, conditions 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 prediction 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

Acronyms

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 carbon

CCCmaCGCM3.1(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) (ranging 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, teleconnections 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 troposphere 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 Archaeology 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

T2m 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 bathythermograph
The U.S. Climate Change Science Program Glossary and Acronyms

114
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Reanalysis of Historical Climate Data for Key Atmospheric Features: 

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Photography Credits

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 Dunn
133

Reanalysis of Historical Climate Data for Key Atmospheric Features: 

Implications for Attribution of Causes of Observed Change
The U.S. Climate Change Science Program References

134
135

Reanalysis of Historical Climate Data for Key Atmospheric Features: 

Implications for Attribution of Causes of Observed Change
The U.S. Climate Change Science Program References

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137

Reanalysis of Historical Climate Data for Key Atmospheric Features: 

Implications for Attribution of Causes of Observed Change

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