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.