# U.S. Climate Change Science Program 

# Synthesis and Assessment Product 2.2

March 2007 

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

U.S. Climate Change Science Program 

# Synthesis and Assessment Product 1.3 

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

## FEDERAL EXECUTIVE TEAM

Director, Climate Change Science Program:

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

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

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

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

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



## EDITORIAL AND PRODUCTION TEAM

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

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

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

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

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

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

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

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

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

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

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

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

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

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

and the Subcommittee on Global Change Research

EDITED BY: Randall M. Dole 

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

### December, 2008

Members of Congress:

On behalf of the National Science and Technology Council, the U.S. Climate Change Science Program (CCSP) is pleased to transmit to the President and the Congress this Synthesis and Assessment Product (SAP) Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change . This is part of a series of 21 SAPs produced by the CCSP aimed at providing current assessments of climate change science to inform public debate, policy, and operational decisions. These reports are also intended to help the CCSP develop future program research priorities.

The CCSP’s guiding vision is to provide the Nation and the global community with the science-based knowledge needed to manage the risks and capture the opportunities associated with climate and related environmental changes. The SAPs are important steps toward achieving that vision and help to translate the CCSP’s extensive observational and research database into informational tools that directly address key questions being asked of the research community.

This SAP addresses current capabilities to integrate observations of the climate system into a consistent description of past and current conditions through the method of reanalysis. In addition, this report assesses present capabilities to attribute causes for climate variations and trends over North America during the reanalysis period, which extends from the mid-twentieth century to the present. It was developed with broad scientific input and in accordance with the Guidelines for Producing CCSP SAPs, the Information Quality Act (Section 515 of the Treasury and General Government Appropriations Act for Fiscal Year 2001 [Public Law 106-554]), and the guidelines issued by the Department of Commerce and the National Oceanic and Atmospheric Administration pursuant to Section 515.

We commend the report’s authors for both the thorough nature of their work and their adherence to an inclusive review process.

Sincerely,

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

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

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

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

Science and Technology IntegrationITABLE OF CONTENTS Abstract

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

Preface

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

Executive Summary

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



## CHAPTER



# 1

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

1.1 Reanalysis

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

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

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

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

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

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



# 2

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

Key Findings

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

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

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

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

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

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

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

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

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

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

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

Climate Variations

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

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

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

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

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

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

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

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

2.5.1 Instrument and Sampling Issues

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

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

# 3

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

Key Findings

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

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

.......... 51

3.1.1 What is Attribution?

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

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

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

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

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

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

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

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

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

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

3.4.1 Introduction

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

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

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

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

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

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

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

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

# 4

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

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

4.1 Need for a Systematic Approach to Climate Analysis and Reanalysis

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

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

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

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

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

................................................................................................................................... 105

Appendix B

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

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

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

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

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

Glossary and Acronyms

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

References

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

## AUTHOR TEAM FOR THIS REPORT

Preface Convening Lead Author: Randall Dole, NOAA

Executive Summary Convening Lead Author: Randall Dole, NOAA Lead Authors: Martin Hoerling, NOAA; Siegfried Schubert, NASA

Chapter 1 Convening Lead Author: Randall Dole, NOAA Lead Author: Martin Hoerling, NOAA

Chapter 2 Convening Lead Author: Siegfried Schubert, NASA Lead Authors: Phil Arkin, University of Maryland; James Carton, University of Maryland; Eugenia Kalnay, University of Maryland; Randal Koster, NASA Contributing Authors: Randall Dole, NOAA ; Roger Pulwarty, NOAA

Chapter 3 Convening Lead Author: Martin Hoerling, NOAA Lead Authors: Gabriele Hegerl, University of Edinburgh; David Karoly, University of Melbourne; Arun Kumar, NOAA; David Rind, NASA Contributing Author: Randall Dole, NOAA

Chapter 4 Convening Lead Author: Randall Dole, NOAA Lead Authors: Martin Hoerling, NOAA; Siegfried Schubert, NASA

Appendix A Convening Lead Authors: James Carton, University of Maryland; Eugenia Kalnay, University of Maryland

Appendix B Convening Lead Author: Martin Hoerling, NOAA Lead Authors: Gabriele Hegerl, University of Edinburgh; David Karoly, University of Melbourne; Arun Kumar, NOAA; David Rind, NASA

IVV 

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

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

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

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

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

VVIVIIABSTRACT



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

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

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

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

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

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

VIIThe U.S. Climate Change Science Program Preface

VIII 

### RECOMMENDED CITATIONS

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

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

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

bution of Causes of Observed Change. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research [Randall Dole, Martin Hoerling, and Siegfried Schubert (eds.)]. National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC, pp. XI–XIV

Executive Summary: Dole, R., M. Hoerling, and S. Schubert, 2008: Executive summary. In: Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research [Randall Dole, Martin Hoerling, and Siegfried Schu

bert (eds.)]. National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC, pp. 1–4

Chapter 1: Dole, R. and M. Hoerling, 2008: Introduction. In: Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research [Randall Dole, Martin Hoerling, and Siegfried Schubert (eds.)]. National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC, pp. 5–10

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

tion, National Climatic Data Center, Asheville, NC, pp. 11–46

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

gram and the Subcommittee on Global Change Research [Randall Dole, Martin Hoerling, and Siegfried Schubert (eds.)]. National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC, pp. 47–92

Chapter 4: Dole, R., M. Hoerling, and S. Schubert, 2008: Recommendations. In: Reanalysis of Historical Climate Data for Key At

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

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

ence Program and the Subcommittee on Global Change Research [Randall Dole, Martin Hoerling, and Siegfried Schubert (eds.)]. National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC, pp. 105–106

Appendix B: Hoerling, M., G. Hegerl, D. Karoly, A. Kumar, and D. Rind, 2008: Appendix B: Data and methods used for attribution. In: Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research [Ran

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

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



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

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

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

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

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



### P.1 OVERVIEW OF PRODUCT

New climate observations are most informative when they can be put in the context of what has occurred in the past. Are current conditions unusual or have they been observed frequently before? Are the current conditions part of a long-term trend or a manifestation of climate variability that may be expected to reverse over months, seasons, or years? Are similar or related changes occurring in other parts of the globe? What are the processes and mechanisms that can explain current conditions, and how are they similar to, or different from, what has occurred in the past?

The scientific methods of climate reanalysis and attribution are central to addressing such questions. In brief, reanalysis is a method for constructing a high-quality record of past cli

mate conditions. Attribution is the process of establishing the most likely cause (or causes) for an observed climate variation or change.

An important goal of the reanalysis efforts assessed in this Product is to provide com

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

cally at six-hour intervals over several decades, they play an important role in documenting how weather and climate conditions are changing over time. The comprehensive nature of climate reanalyses also makes such datasets of great value in helping scientists to better understand the often complex relationships among variables, for example, how changes in temperatures may be connected to changes in winds, and how these in turn may be related to changes in cloudiness and precipitation.

Reanalysis datasets provide a foundation for a broad range of weather and climate research. As one measure of their extraordinary research impact, an overview paper describing one of the initial reanalyses produced in the United States is now the most widely cited paper in the geophysical sciences. Beyond their research ap

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

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

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

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

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

This Product addresses the strengths and limitations of cur

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

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

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

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

quired to advance national and international capabilities in climate reanalysis and attribution.

Following guidance provided by the Climate Change Science Program, this Product is written primarily for the informed lay reader. For subject matter experts, more detailed discus

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



### P.2 PRIMARY FOCUS OF THE PRODUCT

Chapter 1 provides a brief, non-technical discussion of the fundamental concepts of reanalysis and attribution. Two is

sues of broad interest follow, within which specific questions are addressed: (1) the reanalysis of historical climate data for key atmospheric features, in particular, for past climate variations and trends over the reanalysis period from the mid-twentieth century to the present, and (2) attribution of the causes of climate variations and trends over North America during the same period. These topics are described in more detail below.

P.2.1 Reanalysis of Historical Climate Data for Key Atmospheric Features The availability and usefulness of reanalysis data have led to many important scientific advances as well as a broad range of new applications. However, limitations of past and current observations, models, and reanalysis methods have each contributed to uncertainties in describing climate system behavior. Chapter 2 focuses on the strengths and limitations of current reanalysis data for identifying and describing past climate variations and trends.

The first global atmospheric reanalyses were developed a little over a decade ago by NASA, NOAA (together with the National Center for Atmospheric Research [NCAR]), and the European Centre for Medium Range Weather Forecasts. These initial reanalyses were constructed by combining observations from diverse data sources within sophisticated models used for weather predictions through a process called data assimilation. Because of the origins in the use of weather models, the initial reanalyses and the majority of those conducted since that time have focused on reconstructing past atmospheric conditions. The lon

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

analyses for other components of the Earth’s climate system, such as the ocean and land surface; emerging capabilities in these areas will also be briefly discussed.

The key questions addressed in Chapter 2 are: What is a climate reanalysis? What role does reanaly

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

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

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

tions? Specifically, what contributions could be madeXIII Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change

through advances in data recovery or quality control, modeling, and/or data assimilation techniques?

The assessment of capabilities and limitations of current reanalysis datasets for various purposes will be of value for determining best uses of current reanalysis products for scientific and practical purposes. This Chapter will also be useful for science program managers in developing priori

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

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

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

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

• ing of the causes for high-impact drought events over North America during the reanalysis record?

This Chapter will provide policy mak

ers with an assessment of current sci

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

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

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



### P.3 TREATMENT OF UNCERTAINTY

Terms used in this Product to indicate the assessed likeli

hood of an outcome are consistent with those used in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report ( Climate Change 2007: The Physical Science Basis ) and summarized in Table P.1.

Terms denoting levels of confidence in findings are also consistent with the IPCC Fourth Assessment Report usage, as specified in Table P.2.

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

Terminology Degree of confidence in being correct
Very High Confidence At least nine out of ten chance of being correct High Confidence About eight out of ten chance

Medium Confidence About five out of ten chance

Low Confidence About two out of ten chance Very Low Confidence Less than one out of ten chance Table P.2 Terminology regarding degree of confidence according to IPCC AR4.The U.S. Climate Change Science Program Preface

XIV 

### P.4 SCOPE AND LIMITATIONS OF THIS PRODUCT

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

plete; therefore, it is not possible to assess the preliminary results in this Product.

The findings presented in this Product provide a snapshot of the current state of knowledge as of mid-2007. The fields of climate analysis, reanalysis, and attribution are cutting edge areas of climate research, with new results being obtained every month. Within the next few years new results are likely to appear that will supersede some of the key find

ings discussed in this Product; for example, with respect to the quality, types, and lengths of reanalysis records now available.

The scope of this Product was considered in light of other ongoing assessments, in particular the IPCC Fourth Assess

ment Report and other synthesis and assessment reports be

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

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

Convening Lead Author: Randall Dole, NOAA/ESRL

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



### ES.1 P rimaryR esults and F indings

ES.1.1 Strengths and Limitations of Current Reanalysis Datasets for Repre

senting Key Atmospheric Features

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

sphere, oceans and land surface. 

## A mong the most common questions that climate scien

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

cusing on advances in scientific understanding obtained through the methods of reanalysis and attribution.

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

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

ture, causes, and impacts of global-scale and regional-scale climate phenomena.

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

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

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

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

ever, in some regions ( e.g. , Australia) the reanalysis trends show major differences with observations.

Reanalysis precipitation trends are less • consistent with those calculated from ob

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

Current reanalysis data are extremely valu

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

perature, winds or precipitation.

Current global reanalysis data are most • reliable in Northern Hemisphere midlati

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

ies of midlatitude storms and short-term climate variability.

Present reanalyses are more limited in • their value for detecting long-term climate trends, although there are cases where re

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

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

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

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

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

Methods now exist for establishing attri

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

nal climate variations and/or changes in external climate forcing.

Annual, area-average change for the period 1951 to 2006 across North America shows the following: Seven of the warmest ten years for annual • surface temperatures from 1951 to 2006 have occurred between 1997 and 2006.

The 56-year linear trend (1951 to 2006) • of annual surface temperature is +0.90°C ±0.1°C (1.6°F ± 0.2°F).

Virtually all of the warming since 1951 has • occurred after 1970.

More than half of this warming is • likely the result of human-caused greenhouse gas forcing of climate change.

Changes in ocean temperatures • likely ex

plain a substantial fraction of the humancaused warming of North America.3 Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change There is no discernible trend in average • precipitation since 1951, in contrast to trends observed in extreme precipitation
events.

Spatial variations in annual average change for the period from 1951 to 2006 across North America show the following: Observed surface temperature change has • been largest over northern and western North America, with up to +2°C (3.6°F) warming in 56 years over Alaska, the Yukon Territories, Alberta, and Saskatch

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

nificant trends have occurred.

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

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

The spatial variations in surface tem

• perature change over North America are unlikely to be the result of anthropogenic forcing alone.

The spatial variations in surface tem

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

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

peratures on atmospheric wind patterns, especially during winter.

Spatial variations of seasonal average change for the period 1951 to 2006 across the United States show that: Six of the warmest ten summers and • winters for the contiguous United States average surface temperatures from 1951 to 2006 occurred recently (1997 to 2006).

During summer, surface temperatures • warmed most over western states, with insignificant change between the Rocky and Appalachian Mountains. During win

ter, surface temperatures warmed most over northern and western states, with insignificant changes over Maine and the central Gulf of Mexico.

The spatial variations in summertime sur

• face temperature change are unlikely to be the result of anthropogenic forcing alone.

The spatial variations and seasonal differ

• ences in precipitation change are unlikely to be the result of anthropogenic greenhouse gas forcing alone.

Some of the spatial variations and seasonal • differences in precipitation change and variations are likely the result of regional variations in sea surface temperatures.

An assessment to identify and attribute the causes of abrupt climate change over North America for the period 1951 to 2006 finds that: There are limitations for detecting rapid • climate shifts and distinguishing these shifts from quasi-cyclical variations be

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

sibilities with scientific confidence.

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

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

cal soil moisture conditions.

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

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

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



### ES.2 RECOMMENDATIONS

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

• tem, improve the quality and consistency of the observational data and reduce effects of observing system changes.

Develop analysis methods that are opti

• mized for climate research and applica

tions. These methods should include uncertainty estimates for all reanalysis products.

To improve the description and understand

• ing of major climate variations that oc

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

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

tions, such as surface temperatures, winds, cloudiness, and precipitation.

Develop new national capabilities in analy

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

tions among Earth system components (atmosphere, ocean, land, cryosphere, and biosphere) that may lead to accelerated or diminished rates of climate change.

Develop a more coordinated, effective, and • sustained national capability in analysis and reanalysis to support climate research and applications. The following priorities are recommended for reducing uncertainties in climate attribution and realizing the benefits of this information for decision support: Develop a national capability in climate • attribution to provide regular and reliable explanations of evolving climate conditions relevant to decision making.

Focus research to better explain causes of • climate conditions at regional and local levels, including the roles of changes in land cover, land use, atmospheric aerosols, greenhouse gases, sea surface tempera

tures, and other factors that contribute to climate change.

Explore a range of methods to better • quantify and communicate findings from attribution research.

# IntroductionCHAPTER 

# 1

5 Convening Lead Author: Randall Dole, NOAA/ESRL

Lead Autho r: Martin Hoerling, NOAA/ESRL

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



### FUNDAMENTAL CONCEPTS

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

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



### 1.1 REANALYSIS

In atmospheric science, an analysis is a detailed representa

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

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

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

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

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

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

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

The atmospheric reanalyses assessed in the Product provide values for all atmospheric variables over the entire globe, extending in height from the Earth’s surface up to eleva

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

ing past weather and climate conditions, unless stated otherwise.

Chapter 2 describes reanalysis methods and as

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

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

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

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

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

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

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

tion techniques?

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

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

ing the scientific and practical value of future climate reanalyses.



### 1.2 ATTRIBUTION

The term attribute has as a common use defini

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

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

tions: (1) climate variations often have large economic impacts on regions and communities in the United States, sometimes in the billions of dollars (NCDC, 2007); (2) there is strong public interest in explanations of the causes of major short-term climate variations, for example, related to the El Niño-Southern Oscillation (ENSO), severe droughts, and other extreme events; and (3) many impacts of climate change are likely to be experienced through changes in extreme weather and climate events; that is, through changes in variability as well as changes in average conditions (IPCC, 2007b).

Methods for attributing the causes of observed climate variations and trends are discussed in Chapter 3, including the use of reanalysis data for this purpose. This Chapter focuses on observed climate variations and changes over the North American region, extending from ap

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

uses of current reanalysis products

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

8 What are the nature and causes of appar

• ent rapid climate shifts that are relevant to North America over the reanalysis record? What is the present understanding of the • causes for high-impact drought events over North America during the reanalysis record?

This Chapter will aid policy makers in as

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

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



### 1.3 CONNECTIONS BETWEEN REANALYSIS AND ATTRIBUTION

This Product focuses on two major topics: climate reanalysis and attribution. Are there scientific connections between reanalysis and attribution and, specifically, why might reanaly

sis be useful for determining attribution? Figure 1.3 illustrates schematically some key steps commonly used in climate science including reanalysis and attribution.

1.3.1 Steps in climate science Observations provide the foundation for all of climate science. The observations are obtained from numerous disparate observing systems (see Figure 1.2) and are also distributed ir

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

lenges to scientists in evaluating present climate conditions and in comparing present conditions with those of the past.

As discussed previously, an analysis is a method for combining diverse observations to obtain a quantitative (numerical) depiction of the state of the atmosphere or, more generally, the state of the climate system at a given time (Figure 1.3). Reanalysis corresponds to the step of ap

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

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

canic aerosols) is either included or excluded in order to assess its potential effects.

Establishing attribution provides a scientific underpinning for predicting future climate and information useful for evaluating needs and options for adaptation and/or mitigation due to climate variability or change. Detailed discussions of climate prediction, adaptation, and mitigation are beyond the scope of this

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

Product; however, recognition of such relation

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

1.3.2 Further comments Reanalysis can be considered as playing a cen

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

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

ing of key physical processes and their relation

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

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

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

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

bution often depend on results obtained from a broad range of datasets, models, and analysis techniques.

In order to establish more definitive attribution, climate scientists perform controlled climate model experiments to determine whether esti

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

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

lishing attribution.

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

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

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



### 1.4 REANALYSIS APPLICATIONS AND USES

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

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

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

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

tifying phenomena, regions, and time periods for which confidence in features is relatively high or low (Santer et al. , 2005).

Reanalysis datasets are also increasingly used for practical applications in sectors such as energy, agriculture, water resources, and insurance ( e.g. , Schwartz and George, 1998; Pryor et al ., 2006; Challinor et al ., 2005; Pul

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

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

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

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

# R eanalysis of Historical Climate Data for Key Atmospheric FeaturesCHAPTER 

# 2

11 Convening Lead Author: Siegfried Schubert, NASA

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

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

NOAA



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

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

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

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

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

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

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

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



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

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

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

ence social and natural environments through

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

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

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

2.1.2 What is a Climate Analysis? As discussed in Chapter 1, at its most funda

mental level, an analysis is a detailed represen

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

ponents, such as oceans or land surface, that is based on observations. A number of techniques can be used to create an analysis from a given set of observations.

One common technique for creating an analysis is based on the expertise of human analysts, who apply their knowledge of phenomena and physical relationships to estimate values of variables between observation locations, a technique referred to as interpolation. Such subjective analysis methods were used almost exclusively before the onset of modern numeri

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

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

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

ally not produce an identical analysis when given multiple opportunities.

Thus, by the early 1950s the need for an auto

matic, objective analysis of atmospheric con

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

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

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

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

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

The first objective analyses used simple statistical techniques

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

14 variables, as in numerical weather prediction or in research to assess detailed mechanisms for climate variability and change.

An alternative objective analysis method, which is the principal focus for this Product, is to estimate the state of the climate system (or of one of its components) by combining observa

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

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

. One important aspect of a comprehensive cli

mate observing sys

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

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

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

In practice, the quality of a global analysis is im

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

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

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

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

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

Figure 2.3 The global distribution of the mid-tropospheric pres

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

limeters) for 5 September 2003 from three different analyses.