Climate Models

An Assessment of 

Strengths and Limitations

U.S. Climate Change Science Program

Synthesis and Assessment Product 3.1

July 2008
This document does not express any regulatory policies of the United States or any of its agencies, or provide recommendations for 

regulatory action. Further information on the process for preparing Synthesis and Assessment products and the CCSP itself can be found

at www.climatescience.gov.

FEDERAL EXECUTIVE TEAM

Director, U.S. Climate Change Science Program...........................................................William J. Brennan

Director, Climate Change Science Program Office.......................................................Peter A. Schultz

Lead Agency Principal Representative to CCSP;

Associate Director, Department of Energy, Office of Biological 

and Environmental Research .........................................................................................Anna Palmisano

Product Lead; Department of Energy,

Office of Biological and Environmental Research ........................................................Anjuli S. Bamzai

Synthesis and Assessment Product Advisory Group Chair;

Associate Director, EPA National Center for Environmental

Assessment.....................................................................................................................Michael W. Slimak

Synthesis and Assessment Product Coordinator,

Climate Change Science Program Office ......................................................................Fabien J.G. Laurier

OTHER AGENCY REPRESENTATIVES

National Aeronautics and Space Administration ...........................................................Donald Anderson

National Oceanic and Atmospheric Administration ......................................................Brian D. Gross

National Science Foundation .........................................................................................Jay S. Fein
Synthesis and Assessment Product 3.1

Report by the U.S. Climate Change Science Program

and the Subcommittee on Global Change Research

AUTHORS:

David C. Bader, Lawrence Livermore National Laboratory

Curt Covey, Lawrence Livermore National Laboratory

William J. Gutowski Jr., Iowa State University

Isaac M. Held, NOAA Geophysical Fluid Dynamics Laboratory

Kenneth E. Kunkel, Illinois State Water Survey

Ronald L. Miller, NASA Goddard Institute for Space Studies

Robin T. Tokmakian, Naval Postgraduate School

Minghua H. Zhang, State University of New York Stony Brook 

Climate Models

An Assessment of 

Strengths and Limitations
CLIMATE CHANGE SCIENCE PROGRAM PRODUCT DEVELOPMENT

ADVISORY COMMITTEE

Eight members of the Climate Change Science Progam Product Development Advisory Committee (CPDAC) wrote

this Climate Change Science Program Synthesis and Assessment Product at the request of the Department of Energy.

The entire CPDAC has accepted the contents of the product. Recommendations made in this report regarding

programmatic and organizational changes, and the adequacy of current budgets, reflect the judgment of the report’s

authors and the CPDAC and are not necessarly the views of the U.S. Government.

*David C. Bader

Lawrence Livermore National

Laboratory

Virginia R. Burkett

U.S. Geological Survey

Leon E. Clarke

Pacific Northwest National

Laboratory

*Curt Covey

Lawrence Livermore National

Laboratory

James A. Edmonds

Pacific Northwest National

Laboratory

Karen Fisher-Vanden

Dartmouth College

Brian P. Flannery

Exxon-Mobil Corporation

*William J. Gutowski Jr.

Iowa State University

David G. Hawkins

Natural Resources Defense Council

*Isaac M. Held

Geophysical Fluid Dynamics

Laboratory

Henry D. Jacoby

Massachusetts Institute of

Technology

David W. Keith

University of Calgary

*Kenneth E. Kunkel

Illinois State Water Survey

Richard S. Lindzen

Massachusetts Institute of

Technology

Linda O. Mearns

National Center for Atmospheric

Research

*Ronald L. Miller

National Aeronautics and Space

Administration

Edward A. Parson

University of Michigan

Hugh M. Pitcher

Pacific Northwest National

Laboratory

William A. Pizer

Resources for the Future

John M. Reilly

Massachusetts Institute of

Technology

Richard G. Richels

Electric Power Research Institute

Cynthia E. Rosenzweig

National Aeronautics and Space

Administration

*Robin T. Tokmakian

Naval Postgraduate School

Mort D. Webster

University of North Carolina

Julie A. Winkler

Michigan State University

Gary W. Yohe

Wesleyan University

*Minghua H. Zhang

State University of New York 

Stony Brook 

*Authors for Climate Models: An

Assessment of Strengths and Limitations

Chair

Soroosh Sorooshian, University of California, Irvine

Vice Chair

Antonio J. Busalacchi, University of Maryland

Designated Federal Officer

Anjuli S. Bamzai, Department of Energy

Office of Biological and Environmental Research

Members

ii
iii

July 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), Climate Models: An Assessment of Strengths and Limitations. 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 assesses the strengths and limitations of climate models. It was developed with broad scientific input and in accordance with the Guidelines for Producing CCSP SAPs, the Federal Advisory

Committee Act, 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 Energy 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,

Carlos M. Gutierrez

Secretary of Commerce

Vice-Chair, Committee on 

Climate Change 

Science and Technology Integration

Samuel W. Bodman

Secretary of Energy

Chair, Committee on 

Climate Change

Science and Technology Integration

John H. Marburger, III, Ph.D.

Director, Office of 

Science and Technology Policy 

Executive Director, Committee on 

Climate Change 

Science and Technology Integration
ACKNOWLEDGEMENT

This report has been peer reviewed in draft form by individuals chosen for their diverse perspectives and technical expertise. The expert review and selection of reviewers followed the OMB’s Information Quality Bulletin for Peer Review.

The purpose of this independent review is to provide candid and critical comments that will assist the Climate Change

Science Program in making this published report as sound as possible and to ensure that the report meets institutional

standards. The peer-review comments, draft manuscript, and response to the peer-review comments are publicly available at: www.climatescience.gov/Library/sap/sap3-1/default.php.

We wish to thank the following individuals for their peer review of this report:

Kerry H. Cook, University of Texas Austin

Carlos R. Mechoso, University of California Los Angeles

Gerald A. Meehl, National Center for Atmospheric Research

Phil Mote, University of Washington Seattle

Brad Udall, Western Water Assessment, Boulder, Colorado

John E. Walsh, International Arctic Research Center

We would also like to thank the following individuals who provided comments during the public comment period:

California Department of Water Resources: Michael Anderson

NOAA Research Council: Derek Parks, Tim Eichler, Michael Winton, Ron Stouffer, and Jiayu Zhou

NOAA Office of Federal Coordination of Meteorology: Samuel P. Williamson

NSF: Marta Cehelsky

The public review comments, draft manuscript, and response to the public comments are publicly available at: 

www.climatescience.gov/Library/sap/sap3-1/default.php 

Intellectual contributions from the following individuals are also acknowledged: John J. Cassano, Elizabeth N. Cassano,

Peter Gent, Bala Govindasamy, Xin-Zhong Liang, William Lipscomb, and Thomas J. Phillips.

EDITORIAL TEAM

Technical Editors ................................................................................Judy Wyrick, Anne Adamson, 

Oak Ridge National Laboratory

Report Coordinators ..........................................................................Judy Wyrick, Anne Adamson, Shirley Andrews, 

Oak Ridge National Laboratory

Technical Advisor ..............................................................................David Dokken, CCSPO

Graphic Production ............................................................................DesignConcept

Recommended Citation for the entire report

CCSP, 2008: Climate Models: An Assessment of Strengths and Limitations. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research [Bader D.C., C. Covey, W.J. Gutowski Jr., I.M. Held,

K.E. Kunkel, R.L. Miller, R.T. Tokmakian and M.H. Zhang (Authors)]. Department of Energy, Office of Biological and 

Environmental Research, Washington, D.C., USA, 124 pp.

iv
TABLE OF CONTENTS

Executive Summary............................................................................................1

CHAPTERS

1 ........................................................................................................................ 7

Introduction

2 ...................................................................................................................... 13

Description of Global Climate System Models

3 ...................................................................................................................... 31

Added Value of Regional Climate Model Simulations

4 ...................................................................................................................... 39

Model Climate Sensitivity

5 ...................................................................................................................... 51

Model Simulation of Major Climate Features

6 ...................................................................................................................... 85

Future Model Development

7 ...................................................................................................................... 91

Example Applications of Climate Model Results

References ........................................................................................................ 97

Climate Models: An Assessment of Strengths and Limitations

v
vi

The U.S. Climate Change Science Program
EXECUTIVE SUMMARY

1

Climate Models: An Assessment of Strengths and Limitations

Chapter 2 describes the four major components

of modern coupled climate models: atmosphere,

ocean, land surface, and sea ice. The development of each of these individual components

raises important questions as to how key physical processes are represented in models, and

some of these questions are discussed in this report. Furthermore, strategies used to couple the

components into a climate system model are detailed. Development paths for the three U.S.

modeling groups that contributed to the 2007

Intergovernmental Panel on Climate Change

(IPCC) Scientific Assessment of Climate

Change (IPCC 2007) serve as examples. Experience and expert judgment are essential in constructing and evaluating a climate modeling

system, so multiple modeling approaches are

still needed for full scientific evaluation of the

state of the science.

The set of most recent climate simulations, referred to as CMIP3 models and utilized heavily

in Working Group 1 and 2 reports of the Fourth

IPCC Assessment, have received unprecedented

scrutiny by hundreds of investigators in various

areas of expertise. Although a number of systematic biases are present across the set of models, more generally the simulation strengths and

weaknesses, when compared against the current

climate, vary substantially from model to

model. From many perspectives, an average

over the set of models clearly provides climate

simulation superior to any individual model,

thus justifying the multimodel approach in

many recent attribution and climate projection

studies.

Climate modeling has been steadily improving

over the past several decades, but the pace has

been uneven because several important aspects

of the climate system present especially severe

challenges to the goal of simulation.

What are the major components and

processes of the climate system that are

included in present state-of-the-science

climate models, and how do climate models represent these aspects of the climate

system? EXECUTIVE SUMMARY

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

on the most powerful computers available, to examine hypotheses about

past and present-day climates. Development of climate models is fully consistent with approaches being taken in many other fields of science dealing with very complex systems. These climate simulations provide a

framework within which enhanced understanding of climate-relevant

processes, along with improved observations, are merged into coherent

projections of future climate change. This report describes the models and

their ability to simulate current climate.

The science of climate modeling has matured through finer spatial resolution, the inclusion of a greater number of physical

processes, and comparison to a rapidly expanding array of observations. These models have important strengths and limitations. They successfully simulate a growing set of processes and phenomena; this set intersects with, but does not fully cover,

the set of processes and phenomena of central importance for attribution of past climate changes and the projection of future changes. Following is a concise summary of the information in this report, organized around questions from the “Prospectus,” which motivated its preparation, and focusing on these strengths and weaknesses.
2

The U.S. Climate Change Science Program Executive Summary

The Earth’s radiant energy balance at the top of

the atmosphere helps to determine its climate.

Chapter 2 contains a brief description of energytransfer simulation within models, particularly

within the atmospheric component. More important, Chapter 4 includes an extensive discussion about radiative forcing of climate

change and climate sensitivity. The response of

global mean temperature to a doubling of carbon dioxide remains a useful measure of climate

sensitivity. The equilibrium response—the response expected after waiting long enough

(many hundreds of years) for the system to

reequilibrate—is the most commonly quoted

measure. Remaining consistent for three

decades, the range of equilibrium climate sensitivity obtained from models is roughly consistent with estimates from observations of

recent and past climates. The canonical threefold range of uncertainty, 1.5 to 4.5°C, has

evolved very slowly. The lower limit has been

nearly unchanged over time, with very few recent models below 2°. Difficulties in simulating Earth’s clouds and their response to climate

change are the fundamental reasons preventing

a reduction in this range in model-generated climate sensitivity.

Other common measures of climate sensitivity

measure the climate response on time scales

shorter than 100 years. By these measures there

is considerably less spread among the models—

roughly a factor of two rather than three. The

range still is considerable and is not decreasing

rapidly, due in part to difficulties in cloud simulation but also to uncertainty in the rate of heat

uptake by the oceans. This uncertainty rises in

importance when considering the responses on

these shorter time scales.

Climate sensitivity in models is subjected to

tests using observational constraints. Tests include climate response to volcanic eruptions;

aspects of internal climate variability that provide information on the strength of climatic

“restoring forces”; the response to the 11-year

cycle in solar irradiance; paleoclimatic information, particularly from the peak of the last Ice

Age some 20,000 years ago; aspects of the seasonal cycle; and the magnitude of observed

warming over the past century. Because each

test is subject to limitations in data and complications from feedbacks in the system, they do

not provide definitive tests of models’ climate

sensitivity in isolation. Studies in which multiple tests of model climate responses are considered simultaneously are essential when

analyzing these constraints on sensitivity.

Improvements in our confidence in estimates of

climate sensitivity are most likely to arise from

new data streams such as the satellite platforms

now providing a first look at the three-dimensional global distributions of clouds. New and

very computationally intensive climate modeling strategies that explicitly resolve some of the

smaller scales of motion influencing cloud

cover and cloud radiative properties also promise to improve cloud simulations.

Chapter 1 provides an overview of improvement

in models in both completeness and in the ability to simulate observed climate. Climate models are compared to observations of the mean

climate in a multitude of ways, and their ability

to simulate observed climate changes, particularly those of the past century, have been examined extensively. A discussion of metrics that

may be used to evaluate model improvement

over time is included at the end of Chapter 2,

which cautions that no current model is superior to others in all respects, but rather that different models have differing strengths and

weaknesses.

As discussed in Chapter 5, climate models developed in the United States and around the

world show many consistent features in their

simulations and projections for the future. Accurate simulation of present-day climatology for

near-surface temperature and precipitation is

necessary for most practical applications of cliHow uncertain are climate model results?

In what ways has uncertainty in modelbased simulation and prediction changed

with increased knowledge about the climate system?

How are changes in the Earth’s energy

balance incorporated into climate models? How sensitive is the Earth’s (modeled) climate to changes in the factors

that affect the energy balance?
3

Climate Models: An Assessment of Strengths and Limitations

mate modeling. The seasonal cycle and largescale geographical variations of near-surface

temperature are indeed well simulated in recent

models, with typical correlations between models and observations of 95% or better.

Climate model simulation of precipitation has

improved over time but is still problematic. Correlation between models and observations is 50

to 60% for seasonal means on scales of a few

hundred kilometers. Comparing simulated and

observed latitude-longitude precipitation maps

reveals similarity of magnitudes and patterns in

most regions of the globe, with the most striking disagreements occurring in the tropics. In

most models, the appearance of the Inter-Tropical Convergence Zone of cloudiness and rainfall in the equatorial Pacific is distorted, and

rainfall in the Amazon Basin is substantially underestimated. These errors may prove consequential for a number of model predictions,

such as forest uptake of atmospheric CO2.

Simulation of storms and jet streams in middle

latitudes is considered one of the strengths of

atmospheric models because the dominant

scales involved are reasonably well resolved. As

a consequence, there is relatively high confidence in the models’ ability to simulate changes

in these extratropical storms and jet streams as

the climate changes. Deficiencies that still exist

may be due partly to insufficient resolution of

features such as fronts, to errors in the forcing

terms from moist physics, or to inadequacies in

simulated interactions between the tropics and

midlatitudes or between the stratosphere and the

troposphere. These deficiencies are still large

enough to impact ocean circulation and some

regional climate simulations and projections.

The quality of ocean climate simulations has

improved steadily in recent years, owing to better numerical algorithms and more realistic assumptions concerning the mixing occurring on

scales smaller than the models’ grid. Many of

the CMIP3 class of models are able to maintain

an overturning circulation in the Atlantic with

roughly the observed strength without the artificial correction to air-sea fluxes commonly

used in previous generations of models, thus

providing a much better foundation for analysis

of the circulation’s stability. Circulation in the

Southern Ocean, thought to be vitally important

for oceanic uptake of carbon dioxide from the

atmosphere, is sensitive to deficiencies in simulated winds and salinities, but a subset of models is producing realistic circulation in the

Southern Ocean as well.

Models forced by the observed well-mixed

greenhouse gas concentrations, volcanic

aerosols, estimates of variations in solar energy

incidence, and anthropogenic aerosol concentrations are able to simulate the recorded 20th

Century global mean temperature in a plausible

way. Solar variations, observed through direct

satellite measurements for the last few decades,

do not contribute significantly to warming during that period. Solar variations early in the 20th

Century are much less certain but are thought

to be a potential contributor to warming in that

period.

Uncertainties in the climatic effects of manmade aerosols (liquid and solid particles suspended in the atmosphere) constitute a major

stumbling block in quantitative attribution studies and in attempts to use the observational

record to constrain climate sensitivity. We do

not know how much warming due to greenhouse gases has been cancelled by cooling due

to aerosols. Uncertainties related to clouds increase the difficulty in simulating the climatic

effects of aerosols, since these aerosols are

known to interact with clouds and potentially

can change cloud radiative properties and cloud

cover.

The possibility that natural variability has been

a significant contributor to the detailed time

evolution seen in the global temperature record

is plausible but still difficult to address with

models, given the large differences in characteristics of the natural decadal variability between models. While natural variability may

very well be relevant to observed variations on

the scale of 10 to 30 years, no models show any

hint of generating large enough natural, unforced variability on the 100-year time scale to

compete with explanations that the observed

century-long warming trend has been predominantly forced.

The observed southward displacement of the

Southern Hemisphere storm track and jet

stream in recent decades is reasonably well simulated in current models, which show that the

displacement is due partly to greenhouse gases
4

The U.S. Climate Change Science Program Executive Summary

but also partly to the presence of the stratospheric ozone hole. Circulation changes in the

Northern Hemisphere over the past decades

have proven more difficult to capture in current

models, perhaps because of more complex interactions between the stratosphere and troposphere in the Northern Hemisphere.

Observations of ocean heat uptake are beginning to provide a direct test of aspects of the

ocean circulation directly relevant to climate

change simulations. Coupled models provide

reasonable simulations of observed heat uptake

in the oceans but underestimate the observed

sea-level rise over the past decades.

Model simulations of trends in extreme weather

typically produce global increases in extreme

precipitation and severe drought, with decreases

in extreme minimum temperatures and frost

days, in general agreement with observations.

Simulations from different state-of-the-science

models have not fully converged, however, since

different groups approach uncertain model aspects in distinctive ways. This absence of convergence is one useful measure of the state of

climate simulation; convergence is to be expected once all climate-relevant processes are

simulated in a convincing physically based

manner. However, measuring the quality of climate models so the metric used is directly relevant to our confidence in the models’

projections of future climate has proven difficult. The most appropriate ways to translate

simulation strengths and weaknesses into confidence in climate projections remain a subject

of active research.

Simulation of climate variations also is described in Chapter 5. Simulations of El Niño oscillations, which have improved substantially in

recent years, provide a significant success story

for climate models. Most current models spontaneously generate El Niño–Southern Oscillation variability, albeit with varying degrees of

realism. Oscillation spatial structure and duration are impressive in a model subset but with a

tendency toward too short a period. Bias in the

Inter-Tropical Convergence Zone (ITCZ) in

coupled models is a major factor preventing further improvement in these models. Projections

for future El Niño variability and the state of the

Pacific Ocean are centrally important for regional climate change projections throughout

the tropics and in North America.

Other aspects of the tropical simulations in current models remain inadequate. The MaddenJulian Oscillation, a feature of the tropics in

which precipitation is organized by large-scale

eastward-propagating features with periods of

roughly 30 to 60 days, is a useful test of simulation credibility. Model performance using this

measure is still unsatisfactory. The “double

ITCZ–cold tongue bias,” in which water is excessively cold near the equator and precipitation

splits artificially into two zones straddling the

equator, remains as a persistent bias in current

coupled atmosphere-ocean models. Projections

of tropical climate change are affected adversely

by these deficiencies in simulations of the organization of tropical convection. Models typically overpredict light precipitation and

underpredict heavy precipitation in both the

tropics and middle latitudes, creating potential

biases when studying extreme events. Tropical

cyclones are poorly resolved by the current generation of global models, but recent results with

high-resolution atmosphere-only models and

dynamical downscaling provide optimism that

the simulation of tropical cyclone climatology

will advance rapidly in coming years, as will

our understanding of observed variations and

trends.

The quality of simulations of low-frequency

variability on decadal to multidecadal time

scales varies regionally and also from model to

model. On average, models do reasonably well

in the North Pacific and North Atlantic. In other

oceanic regions, lack of data contributes to uncertainty in estimating simulation quality at

these low frequencies. A dominant mode of

low-frequency variability in the atmosphere,

known as northern and southern annular modes,

is very well captured in current models. These

modes involve north-south displacements of the

extratropical storm track and have dominated

observed atmospheric circulation trends in recent decades. Because of their ability to simulate annular modes, global climate models do

How well do climate models simulate

natural variability and how does variability change over time?
5

Climate Models: An Assessment of Strengths and Limitations

fairly well with interannual variability in polar

regions of both hemispheres. They are less successful with daily polar-weather variability, although finer-scale regional simulations do show

promise for improved global-model simulations

as their resolution increases.

Chapter 3 describes techniques to downscale

coarse-resolution global climate model output

to higher resolution for regional applications.

These downscaling methodologies fall primarily into two categories. In the first, a higherresolution, limited-area numerical

meteorological model is driven by global climate model output at its lateral boundaries.

These dynamical downscaling strategies are

beneficial when supplied with appropriate seasurface and atmospheric boundary conditions,

but their value is limited by uncertainties in information supplied by global models. Given the

value of multimodel ensembles for larger-scale

climate prediction, coordinated downscaling

clearly must be performed with a representative

set of global model simulations as input, rather

than focusing on results from one or two models. Relatively few such multimodel dynamical downscaling studies have been performed

to date.

In the second category, empirical relationships

between large- and small-scale observations are

developed, then applied to global climate model

output to provide regional detail. Statistical

techniques to produce appropriate small-scale

structures from climate simulations are referred

to as “statistical downscaling.” They can be as

effective as high-resolution numerical simulations in providing climate change information

to regions unresolved by most current global

models. Because of the computational efficiency of these techniques, they can much more

easily utilize a full suite of multimodel ensembles. The statistical methods, however, are completely dependent on the accuracy of regional

circulation patterns produced by global models.

Dynamical models, through higher resolution

or better representation of important physical

processes, often can improve the physical realism of simulated regional circulation. Thus,

the strengths and weaknesses of dynamical

modeling and statistical methods often are

complementary.

Regional trends in extreme events are not always captured by current models, but it is difficult to assess the significance of these

discrepancies and to distinguish between model

deficiencies and natural variability.

The use of climate model results to assess economic, social, and environmental impacts is becoming more sophisticated, albeit slowly.

Simple methods requiring only mean changes

in temperature and precipitation to estimate impacts remain popular, but an increasing number

of studies are using more detailed information

such as the entire distribution of daily or

monthly values and extreme outcomes. The

mismatch between models’ spatial resolution vs

the scale of impact-relevant climate features and

of impact models remains an impediment for

certain applications. Chapter 7 provides several

examples of applications using climate model

results and downscaling techniques.

Chapter 6 is devoted to trends in climate model

development. With increasing computer power

and observational understanding, future models

will include both higher resolution and more

processes.

Resolution increases most certainly will lead to

improved representations of atmospheric and

oceanic general circulations. Ocean components

of current climate models do not directly simulate the oceans’ very energetic motions referred

to as “mesoscale eddies.” Simulation of these

small-scale flow patterns requires horizontal

grid sizes of 10 km or smaller. Current oceanic

components of climate models are effectively

laminar rather than turbulent, and the effects of

these eddies must be approximated by imperfect theories. As computer power increases, new

models that resolve these eddies will be incorporated into climate models to explore their imWhat are the tradeoffs to be made in further climate model development (e.g., 

between increasing spatial/temporal resolution and representing additional 

physical/biological processes)?

How well do climate models simulate 

regional climate variability and change?
6

The U.S. Climate Change Science Program Executive Summary

pact on decadal variability as well as heat and

carbon uptake. Similarly, atmospheric general

circulation models will evolve to “cloud-resolving models” (CRMs) with spatial resolutions of less than a few kilometers. The hope is

that CRMs will provide better results through

explicit simulation of many cloud properties

now poorly represented on subgrid scales of

current atmospheric models. CRMs are not new

frameworks but rather are based on models designed for mesoscale storm and cumulus convection simulations.

Models of glacial ice are in their infancy. Glacial models directly coupled to atmosphereocean models typically account for only direct

melting and accumulation at the surface of ice

sheets and not the dynamic discharge due to glacial flow. More-detailed current models typically generate discharges that change only over

centuries and millennia. Recent evidence for

rapid variations in this glacial outflow indicates

that more-realistic glacial models are needed to

estimate the evolution of future sea level.

Inclusion of carbon-cycle processes and other

biogeochemical cycles is required to transform

physical climate models into full Earth system

models that incorporate feedbacks influencing

greenhouse gas and aerosol concentrations in

the atmosphere. Land models that predict vegetation patterns are being developed actively,

but the demands of these models on the quality

of simulated precipitation patterns ensures that

their evolution will be gradual and tied to improvements in the simulation of regional climate. Uncertainties about carbon-feedback

processes in the ocean as well as on land, however, must be reduced for more reliable future

estimates of climate change.
CHAPTER

1

Climate Models: An Assessment of Strengths and Limitations

Introduction

CHAPTER

1

7

The use of computers to simulate complex systems has grown in the past few decades to play a

central role in many areas of science. Climate modeling is one of the best examples of this trend

and one of the great success stories of scientific simulation. Building a laboratory analog of the

Earth’s climate system with all its complexity is impossible. Instead, the successes of climate modeling allow us to address many questions about climate by experimenting with simulations—that

is, with mathematical models of the climate system. Despite the success of the climate modeling

enterprise, the complexity of our Earth imposes important limitations on existing climate models. This report aims to help the reader understand the valid uses, as well as the limitations, of current climate models.

Climate modeling and forecasting grew from

the desire to predict weather. The distinction between climate and weather is not precise. Operational weather forecasting has focused

historically on time scales of a few days but

more recently has been extended to months and

seasons in attempts to predict the evolution of El

Niño episodes. The goal of climate modeling

can be thought of as the extension of forecasting

to longer and longer time periods. The focus is

not on individual weather events, which are unpredictable on long time scales, but on the statistics of these events and on the slow evolution

of oceans and ice sheets. Whether the forecasting of individual El Niño episodes is considered

weather or climate is a matter of convention. For

the purpose of this report, we will consider El

Niño forecasting as weather and will not address it directly. On the climate side we are concerned, for example, with the ability of models

to simulate the statistical characteristics of El

Niño variability or extratropical storms or Atlantic hurricanes, with an eye toward assessing

the ability of models to predict how variability

might change as the climate evolves in coming

decades and centuries.

An important constraint on climate models not

imposed on weather-forecast models is the requirement that the global system precisely and

accurately maintain the global energy balance

over very long periods of time. The Earth’s energy balance (or “budget”) is defined as the difference between absorbed solar energy and

emitted infrared radiation to space. It is affected

by many factors, including the accumulation of

greenhouse gases, such as carbon dioxide, in the

atmosphere. The decades-to-century changes in

the Earth’s energy budget, manifested as climate

changes, are just a few percent of the average

values of that budget’s largest terms. Many decisions about model construction described in
8

The U.S. Climate Change Science Program Chapter 1 - Introduction

Chapter 2 are based on the need to properly 

and accurately simulate the long-term energy

balance.

This report will focus primarily on comprehensive physical climate models used for the most

recent international Coupled Model Intercomparison Project (CMIP) coordinated experiments (Meehl et al. 2006) sponsored by the

World Climate Research Programme (WCRP).

These coupled atmosphere-ocean general circulation models (AOGCMs) incorporate detailed representations of the atmosphere, land

surface, oceans, and sea ice. Where practical,

we will emphasize and highlight results from

the three U.S. modeling projects that participated in the CMIP experiments. Additionally,

this report examines the use of regional climate

models (RCMs) for obtaining higher-resolution

details from AOGCM simulations over smaller

regions. Still, other types of climate models are

being developed and applied to climate simulation. The more-complete Earth system models,

which build carbon-cycle and ecosystem

processes on top of AOGCMs, are used primarily for studies of future climate change and paleoclimatology, neither of which is directly

relevant to this report. Another class of models

not discussed here but used extensively, particularly when computer resources are limited, is

Earth system models of intermediate complexity (EMICs). Although these models have many

more assumptions and simplifications than are

found in CMIP models (Claussen et al. 2002),

they are particularly useful in exploring a wide

range of mechanisms and obtaining broad estimates of future climate change projections that

can be further refined with AOGCM experiments.

1.1 BRIEF HISTORY OF CLIMATE

MODEL DEVELOPMENT

As numerical weather prediction was developing in the 1950s as one of the first computer applications, the possibility of also using

numerical simulation to study climate became

evident almost immediately. The feasibility of

generating stable integrations of atmospheric

equations for arbitrarily long time periods was

demonstrated by Norman Phillips in 1956.

About that time, Joseph Smagorinsky started a

program in climate modeling that ultimately became one of the most vigorous and longestlived GCM development programs at the 

National Oceanic and Atmospheric Administration’s Geophysical Fluid Dynamics Laboratory

(GFDL) at Princeton University. The University

of California at Los Angeles began producing

atmospheric general circulation models

(AGCMs) beginning in 1961 under the leadership of Yale Mintz and Akio Arakawa. This program influenced others in the 1960s and 1970s,

leading to modeling programs found today at

National Aeronautics and Space Administration

(NASA) laboratories and several universities.

At Lawrence Livermore National Laboratory,

Cecil E. Leith developed an early AGCM in

1964. The U.S. National Center for Atmospheric

Research (NCAR) initiated AGCM development in 1964 under Akira Kasahara and Warren

Washington. Leith moved to NCAR in the late

1960s and, in the early 1980s, oversaw construction of the Community Climate Model, a

predecessor to the present Community Climate

System Model (CCSM).

Early weather models focused on fluid dynamics rather than on radiative transfer and the atmosphere’s energy budget, which are centrally

important for climate simulations. Additions to

the original AGCMs used for weather analysis

and prediction were needed to make climate

simulations possible. Furthermore, because climate simulation focuses on time scales longer

than a season, oceans and sea ice must be included in the modeling system in addition to the

more rapidly evolving atmosphere. Thus, ocean

and ice models have been coupled with atmospheric models. The first ocean GCMs were developed at GFDL by Bryan and Cox in the

1960s and then coupled with the atmosphere by

Manabe and Bryan in the 1970s. Paralleling

events in the United States, the 1960s and 1970s

also were a period of climate- and weathermodel development throughout the world, with

major centers emerging in Europe and Asia.

Representatives of these groups gathered in

Stockholm in August 1974, under the sponsorship of the Global Atmospheric Research Programme to produce a seminal treatise on

climate modeling (GARP 1975). This meeting

established collaborations that still promote international cooperation today.
9

Climate Models: An Assessment of Strengths and Limitations

The use of climate models in research on carbon dioxide and climate began in the early

1970s. The important study, “Inadvertent Climate Modification” (SMIC 1971), endorsed the

use of GCM-based climate models to study the

possibility of anthropogenic climate change.

With continued improvements in both climate

observations and computer power, modeling

groups furthered their models through steady

but incremental improvements. By the

late1980s, several national and international organizations formed to assess and expand scientific research related to global climate change.

These developments spurred interest in accelerating the development of improved climate

models. The primary focus of Working Group 1

of the United Nations Intergovernmental Panel

on Climate Change (IPCC), which began in

1988, was the scientific inquiry into physical

processes governing climate change. IPCC’s

first Scientific Assessment (IPCC 1990) stated,

“Improved prediction of climate change depends on the development of climate models,

which is the objective of the climate modeling

programme of the World Climate Research Programme.” The United States Global Change Research Program (USGCRP), established in

1989, designated climate modeling and prediction as one of the four high-priority integrating

themes of the program (Our Changing Planet

1991). The combination of steadily increasing

computer power and research spurred by WCRP

and USGCRP has led to a steady improvement

in the completeness, accuracy, and resolution of

AOGCMS for climate simulation and prediction. An often-used illustration from the Third

IPCC Working Group 1 Scientific Assessment

of Climate Change in 2001 depicts this evolution (see Fig. 1.1). Even more comprehensive

climate models produced a series of coordinated

numerical simulations for the third international

Climate Model Intercomparison Project

(CMIP3), which were used extensively in research cited in the recent Fourth IPCC Assessment (IPCC 2007). Contributions came from

three groups in the United States (GFDL,

NCAR, and the NASA Goddard Institute for

Space Studies) and others in the United Kingdom, Germany, France, Japan, Australia,

Canada, Russia, China, Korea, and Norway. 

Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere

Mid-1970s Mid-1980s Early 1990s Late 1990s

Development of Climate Models: Past, Present, and Future

Present Day Early 2000s?

Land surface Land surface Land surface Land surface Land surface

Ocean and sea ice Ocean and sea ice Ocean and sea ice Ocean and sea ice

Sulphate aerosol Sulphate aerosol Sulphate aerosol

Nonsulphate

aerosol

Nonsulphate

aerosol

Carbon cycle Carbon cycle

Dynamic

vegetation

Ocean and

sea ice model

Sulphur

cycle model

Nonsulphate

aerosols

Carbon

cycle model Ocean carbon

cycle model

Land carbon

cycle model

Dynamic

vegetation

Dynamic

vegetation

Atmospheric

chemistry

Atmospheric

chemistry

Atmospheric

chemistry

Atmospheric

chemistry

Adapted from IPCC 2001

Figure 1.1. Historical

Development of

Climate Models. 

[Figure source: Climate

Change 2001: The Scientific

Basis, Contribution of

Working Group 1 to the

Assessment Report of the

Intergovernmental Panel on

Climate Change, p. 48.

Used with permission

from IPCC.]
10

The U.S. Climate Change Science Program Chapter 1 - Introduction

1.2 CLIMATE MODEL

CONSTRUCTION

Comprehensive climate models are constructed

using expert judgments to satisfy many constraints and requirements. Overarching considerations are the accurate simulation of the most

important climate features and the scientific understanding of the processes that control these

features. Typically, the basic requirement is that

models should simulate features important to

humans, particularly surface variables such as

temperature, precipitation, windiness, and

storminess. This is a less-straightforward requirement than it seems because a physically

based climate model also must simulate all

complex interactions in the coupled atmosphere–ocean–land surface–ice system manifested as climate variables of interest. For

example, jet streams at altitudes of 10 km above

the surface must be simulated accurately if

models are to generate midlatitude weather with

realistic characteristics. Midlatitude highs and

lows shown on surface weather maps are intimately associated with these high-altitude wind

patterns. As another example, the basic temperature decrease from the equator to the poles cannot be simulated without taking into account the

poleward transport of heat in the oceans, some

of this heat being carried by currents 2 or 3 km

deep into the ocean interior. Thus, comprehensive models should produce correctly not just

the means of variables of interest but also the

extremes and other measures of natural variability. Finally, our models should be capable of

simulating changes in statistics caused by relatively small changes in the Earth’s energy

budget that result from natural and human actions.

Climate processes operate on time scales ranging from several hours to millennia and on spatial scales ranging from a few centimeters to

thousands of kilometers. Principles of scale

analysis, fluid dynamical filtering, and numerical analysis are used for intelligent compromises and approximations to make possible the

formulation of mathematical representations of

processes and their interactions. These mathematical models are then translated into computer codes executed on some of the most

powerful computers in the world. Available

computer power helps determine the types of

approximations required. As a general rule,

growth of computational resources allows modelers to formulate algorithms less dependent on

approximations known to have limitations,

thereby producing simulations more solidly

founded on established physical principles.

These approximations are most often found in

“closure” or “parameterization” schemes that

take into account unresolved motions and

processes and are always required because climate simulations must be designed so they can

be completed and analyzed by scientists in a

timely manner, even if run on the most powerful computers.

Climate models have shown steady improvement over time as computer power has increased, our understanding of physical

processes of climatic relevance has grown,

datasets useful for model evaluation have been

developed, and our computational algorithms

have improved. Figure 1.2 shows one attempt at

quantifying this change. It compares a particular metric of climate model performance among

the CMIP1 (1995), CMIP2 (1997), and CMIP3

(2004) ensembles of AOGCMs. This particular

metric assesses model performance in simulating the mean climate of the late 20th Century as

measured by a basket of indicators focusing on

aspects of atmospheric climate for which observational counterparts are deemed adequate.

Model ranking according to individual members of this basket of indicators varies greatly, so

this aggregate ranking depends on how different

indicators are weighted in relative importance.

Nevertheless, the conclusion that models have

improved over time is not dependent on the relative weighting factors, as nearly all models

have improved in most respects. The construction of metrics for evaluating climate models is

itself a subject of intensive research and will be

covered in more detail in Chapter 2.
11

Climate Models: An Assessment of Strengths and Limitations

Also shown in Fig. 1.2 is the same metric evaluated from climate simulation results obtained

by averaging over all AOGCMs in the CMIP1,

CMIP2, and CMIP3 archives. The CMIP3 “ensemble-mean” model performs better than any

individual model by this metric and by many

others. This kind of result has convinced the

community of the value of a multimodel approach to climate change projection. Our understanding of climate is still insufficient to

justify proclaiming any one model “best” or

even showing metrics of model performance

that imply skill in predicting the future. More

appropriate in any assessments focusing on

adaptation or mitigation strategies is to take into

account, in a pertinently informed manner, the

products of distinct models built using different

expert judgments at centers around the world.

1.3 SUMMARY OF SAP 3.1

CHAPTERS

The remaining sections of this report describe

climate model development, evaluation, and applications in more detail. Chapter 2 describes

the development and construction of models

and how they are employed for climate research.

Chapter 3 discusses regional climate models

Figure 1.2. Performance Index I2 for Individual Models (circles) and Model

Generations (rows). 

Best performing models have low I

2 values and are located toward the left. Circle sizes indicate the length

of the 95% confidence intervals. Letters and numbers identify individual models; flux corrected models are

labeled in red. Grey circles show the average I

2 of all models within one model group. Black circles indicate

the I

2 of the multimodel mean taken over one model group. The green circle (REA) corresponds to the I2

of the NCEP/NCAR Reanalysis (Kalnay et al. 1996), conducted by the National Weather Service’s National

Centers for Environmental Prediction and the National Center for Atmospheric Research. Last row

(PICTRL) shows I

2 for the preindustrial control experiment of the CMIP3 project. [Adapted from Fig. 1 in

T. Reichler and J. Kim 2008: How well do coupled models simulate today’s climate? Bulletin American

Meteorological Society, 89(3), doi:10.1175/BAMS-89-3-303. Reproduced by permission of the American

Meteorological Society.]

18 15 10 12 4 14 7 5 11

6 3 17 2 89 1 13 16

0.4 0.5 1 2 3 4 5 67

CMIP-1

CMIP-2

CMIP-3

REA

PICtrl

I2

q

LX W U C

S WL

X Y U CD H GJ OV I

T K FQRM P N

K

S TG H Y D Q R MV

I F OP J N

p l b ci f n

e od k a j h

g

m

B
12

The U.S. Climate Change Science Program Chapter 1 - Introduction

and their use in “downscaling” global model results to specific geographic regions, particularly

North America. The concept of climate sensitivity—the response of a surface temperature to

a specified change in the energy budget at the

top of the model’s atmosphere—is described in

Chapter 4. A survey of how well important climate features are simulated by modern models

is found in Chapter 5, while Chapter 6 depicts

near-term development priorities for future

model development. Finally, Chapter 7 illustrates a few examples of how climate model

simulations are used for practical applications.

A detailed Reference section follows Chapter 7.
CHAPTER

2

Climate Models: An Assessment of Strengths and Limitations

Description of 

Global Climate 

Systems Models

CHAPTER

2

13

Modern climate models are composed of a system of interacting model components, each of

which simulates a different part of the climate system. The individual parts often can be run independently for certain applications. Nearly all the Coupled Model Intercomparison Project 3

(CMIP3) class of models include four primary components: atmosphere, land surface, ocean, and

sea ice. The atmospheric and ocean components are known as “general circulation models” or

GCMs because they explicitly simulate the large-scale global circulation of the atmosphere and

ocean. Climate models sometimes are referred to as coupled atmosphere-ocean GCMs. This name

may be misleading because coupled GCMs can be employed to simulate aspects of weather and

ocean dynamics without being able to maintain a realistic climate projection over centuries of simulated time, as required of a climate model used for studying anthropogenic climate change. What

follows in this chapter is a description of a modern climate model’s major components and how

they are coupled and tested for climate simulation.

2.1 ATMOSPHERIC GENERAL

CIRCULATION MODELS

Atmospheric general circulation models

(AGCMs) are computer programs that evolve

the atmosphere’s three-dimensional state forward in time. This atmospheric state is described by such variables as temperature,

pressure, humidity, winds, and water and ice

condensate in clouds. These variables are defined on a spatial grid, with grid spacing determined in large part by available computational

resources. Some processes governing this atmospheric state’s evolution are relatively well

resolved by model grids and some are not. The

latter are incorporated into models through 

approximations often referred to as parameterizations. Processes that transport heat, water,

and momentum horizontally are relatively well

resolved by the grid in current atmospheric

models, but processes that redistribute these

quantities vertically have a significant part 

that is controlled by subgrid-scale parameterizations.

The model’s grid-scale evolution is determined

by equations describing the thermodynamics

and fluid dynamics of an ideal gas. The atmosphere is a thin spherical shell of air that envelops the Earth. For climate simulation,

emphasis is placed on the atmosphere’s lowest

20 to 30 km (i.e., the troposphere and the lower

stratosphere). This layer contains over 95% of

the atmosphere’s mass and virtually all of its

water vapor, and it produces nearly all weather

although current research suggests possible in-
The U.S. Climate Change Science Program Chapter 2 - Description of Global Climate System Models

14

teractions between this layer and higher atmospheric levels (e.g., Pawson et al. 2000). Because

of the disparity between scales of horizontal and

vertical motions governing global and regional

climate, the two motions are treated differently

by model algorithms. The resulting set of equations is often referred to as the primitive equations (Haltiner and Williams 1980).

Although nearly all AGCMs use this same set

of primitive dynamical equations, they use different numerical algorithms to solve them. In all

cases, the atmosphere is divided into discrete

vertical layers, which are then overlaid with a

two-dimensional horizontal grid, producing a

three-dimensional mesh of grid elements. The

equations are solved as a function of time on

this mesh. The portion of the model code governing the fluid dynamics explicitly simulated

on this mesh often is referred to as the model’s

“dynamical core.” Even with the same numerical approach, AGCMs differ in spatial resolutions and configuration of model grids. Some

models use a “spectral” representation of winds

and temperatures, in which these fields are written as linear combinations of predefined patterns on the sphere (spherical harmonics) and

are then mapped to a grid when local values are

required. Some models have few layers above

the tropopause (the moving boundary between

the troposphere and stratosphere (e.g., GFDL

2004)), while others have as many layers above

the troposphere as in it (e.g., Schmidt et al. 2006).

All AGCMs use a coordinate system in which

the Earth’s surface is a coordinate surface, simplifying exchanges of heat, moisture, trace substances, and momentum between the Earth’s

surface and the atmosphere. Numerical algorithms of AGCMs should precisely conserve the

atmosphere’s mass and energy. Typical AGCMs

have spatial resolution of 200 km in the horizontal and 20 levels in the volume below the altitude of 15 km. Because numerical errors often

depend on flow patterns, there are no simple

ways to assess the accuracy of numerical discretizations in AGCMs. Models use idealized

cases testing the model’s long-term stability and

efficiency (e.g., Held and Suarez 1994), as well

as tests focusing on accuracy using short integrations (e.g., Polvani, Scott, and Thomas 2005).

All AGCMs must incorporate the effects of radiant-energy transfer. The radiative-transfer

code computes the absorption and emission of

electromagnetic waves by air molecules and atmospheric particles. Atmospheric gases absorb

and emit radiation in “spectral lines” centered

at discrete wavelengths, but the computational

costs are too high in a climate model to perform

this calculation for each individual spectral line.

AGCMs use approximations, which differ

among models, to group bands of wavelengths

together in a more efficient calculation. Most

models have separate radiation codes to treat

solar (visible) radiation and the much-longerwavelength terrestrial (infrared) radiation. Radiation calculation includes the effects of water

vapor, carbon dioxide, ozone, and clouds. Models used in climate change experiments also include aerosols and additional trace gases such

as methane, nitrous oxide, and the cloroflourocarbons. Validation of AGCM radiation codes

often is done offline (separate from other

AGCM components) by comparison with lineby-line model calculations that, in turn, are

compared against laboratory and field observations (e.g., Ellingson and Fouquart 1991;

Clough, Iacono, and Moncet 1992; Collins et al.

2006b).

All GCMs use subgrid-scale parameterizations

to simulate processes that are too small or operate on time scales too fast to be resolved on

the model grid. The most important parameterizations are those involving cirrus and stratus

cloud formation and dissipation, cumulus convection (thunderstorms and fair-weather cumulus clouds), and turbulence and subgrid-scale

mixing. For cloud calculations, most AGCMs

treat ice and liquid water as atmospheric state

variables. Some models also separate cloud particles into ice crystals, snow, graupel (snow pellets), cloud water, and rainwater. Empirical

relationships are used to calculate conversions

among different particle types. Representing

these processes on the scale of model grids is

particularly difficult and involves calculation of

fractional cloud cover within a grid box, which

greatly affects radiative transfer and model sensitivity. Models either predict cloud amounts

from the instantaneous thermodynamical and

hydrological state of a grid box or they treat

cloud fraction as a time-evolving model vari-
able. In higher-resolution models, one can attempt to explicitly simulate the size distribution

of cloud particles and the “habit” or nonspherical shape of ice particles, but no current global

AGCMs attempt this.

Cumulus convective transports, which are important in the atmosphere but cannot be explicitly resolved at GCM scale, are calculated using

convective parameterization algorithms. Most

current models use a cumulus mass flux scheme

patterned after that proposed by Arakawa and

Schubert (1974), in which convection’s upward

motion occurs in very narrow plumes that take

up a negligible fraction of a grid box’s area.

Schemes differ in techniques used to determine

the amount of mass flowing through these

plumes and the manner in which air is entrained

and detrained by the rising plume. Most models

do not calculate separately the area and vertical

velocity of convection but try to predict only the

product of mass and area, or convective mass

flux. Prediction of convective velocities, however, is needed for new models of interactions

between aerosols and clouds. Most current

schemes do not account for differences between

organized mesoscale convective systems and

simple plumes. The turbulent mixing rate of updrafts and downdrafts with environments and

the phase changes of water vapor within convective systems are treated with a mix of empiricism and constraints based on the moist

thermodynamics of rising air parcels. Some

models also include a separate parameterization

of shallow, nonprecipitating convection (fairweather cumulus clouds). In short, clouds generated by cumulus convection in climate models

should be thought of as based in large part on

empirical relationships.

All AGCMs parameterize the turbulent transport of momentum, moisture, and energy in the

atmospheric boundary layer near the surface. A

long-standing theoretical framework, MoninObukhov similarity theory, is used to calculate

the vertical distribution of turbulent fluxes and

state variables in a thin (typically less than 10

m) layer of air adjacent to the surface. Above

the surface layer, turbulent fluxes are calculated

based on closure assumptions that provide a

complete set of equations for subgrid-scale variations. Closure assumptions differ among

AGCMs; some models use high-order closures

in which the fluxes or second-order moments

are calculated prognostically (with memory in

these higher-order moments from one time step

to the next). Turbulent fluxes near the surface

depend on surface conditions such as roughness, soil moisture, and vegetation. In addition,

all models use diffusion schemes or dissipative

numerical algorithms to simulate kinetic energy

dissipation from turbulence far from the surface

and to damp small-scale unresolved structures

produced from resolved scales by turbulent atmospheric flow.

The realization that a significant fraction of momentum transfer between atmosphere and surface takes place through nonturbulent pressure

forces on small-scale “hills” has resulted in a

substantial effort to understand and model this

transfer (e.g., McFarlane 1987; Kim and Lee

2003). This process is often referred to as gravity wave drag because it is intimately related to

atmospheric wave generation. The variety of

gravity wave drag parameterizations is a significant source of differences in mean wind fields

generated by AGCMs. Accounting for both surface-generated and convectively generated gravity waves are difficult aspects of modeling the

stratosphere and mesosphere (≥ 20 km altitude),

since winds in those regions are affected

strongly by transfer of momentum and energy

from these unresolved waves.

Extensive field programs have been designed to

evaluate parameterizations in GCMs, ranging

from tests of gravity wave drag schemes

[Mesoscale Alpine Program (called MAP), e.g.,

Bougeault et al. 2001] to tests of radiative transfer and cloud parameterizations [Atmospheric

Radiation Measurement Program (called

ARM), Ackerman and Stokes 2003]. Running

an AGCM coupled to a land model as a numerical weather prediction model for a few days—

starting with best estimates of the atmosphere

and land’s instantaneous state at any given

time—is a valuable test of the entire package of

atmospheric parameterizations and dynamical

core (e.g., Xie et al. 2004). Atmosphere-land

models also are routinely tested by running

them with boundary conditions taken from observed sea-surface temperatures and sea-ice distributions (Gates 1992) and examining the

resulting climate.

Climate Models: An Assessment of Strengths and Limitations

15
16

The U.S. Climate Change Science Program Chapter 2 - Description of Global Climate System Models

2.2 OCEAN GENERAL

CIRCULATION MODELS

Ocean general circulation models (OGCMs)

solve the primitive equations for global incompressible fluid flow analogous to the ideal-gas

primitive equations solved by atmospheric

GCMs. In climate models, OGCMs are coupled

to the atmosphere and ice models through the

exchange of heat, salinity, and momentum at the

boundary among components. Like the atmosphere, the ocean’s horizontal dimensions are

much larger than its vertical dimension, resulting in separation between processes that control

horizontal and vertical fluxes. With continents,

enclosed basins, narrow straits, and submarine

basins and ridges, the ocean has a more complex three-dimensional boundary than does the

atmosphere.. Furthermore, the thermodynamics

of sea water is very different from that of air, so

an empirical equation of state must be used in

place of the ideal gas law.

An important distinction among ocean models

is the choice of vertical discretization. Many

models use vertical levels that are fixed distances below the surface (Z-level models) based

on the early efforts of Bryan and Cox (1967)

and Bryan (1969a, b). The General Fluid Dynamics Laboratory (GFDL) and Community

Climate System Model (CCSM) ocean components fall into this category (Griffies et al. 2005;

Maltrud et al. 1998). Two Goddard Institute for

Space Studies (GISS) models (R and AOM) use

a variant of this approach in which mass rather

than height is used as the vertical coordinate

(Russell, Miller, and Rind 1995; Russell et al.

2000). A more fundamental alternative uses

density as a vertical coordinate. Motivating this

choice is the desire to control as precisely as

possible the exchange of heat between layers of

differing density, which is very small in much of

the ocean yet centrally important for simulation

of climate. The GISS EH model utilizes a hybrid scheme that transitions from a Z-coordinate

near the surface to density layers in the ocean

interior (Sun and Bleck 2001; Bleck 2002; Sun

and Hansen 2003).

Horizontal grids used by most ocean models in

the CMIP3 archive are comparable to or somewhat finer than grids in the atmospheric models

to which they are coupled, typically on the order

of 100 km (~ 1o spacing in latitude and longitude) for most of Earth. In many OGCMs the

north-south resolution is enhanced within 5o latitude of the equator to improve the ability to

simulate important equatorial processes.

OGCM grids usually are designed to avoid coordinate singularities caused by the convergence

of meridians at the poles. For example, the

CCSM OGCM grid is rotated to place its North

Pole over a continent, while the GFDL models

use a grid with three poles, all of which are

placed over land (Murray 1996). Such a grid results in having all ocean grid points at numerically viable locations.

Processes that control ocean mixing near the

surface are complex and take place on small

scales (order of centimeters). To parameterize

turbulent mixing near the surface, the current

generation of OGCMs uses several different approaches (Large, McWilliams, and Doney

1994) similar to those developed for atmospheric near-surface turbulence. Within the

ocean’s stratified, adiabatic interior, vertical

mixing takes place on scales from meters to

kilometers (Fig. 2.1); the smaller scales also

must be parameterized in ocean components.

Ocean mixing contributes to its heat uptake and

stratification, which in turn affects circulation

patterns over time scales of decades and longer.

Experts generally feel (e.g., Schopf et al. 2003)

that subgrid-scale mixing parameterizations in

OGCMs contribute significantly to uncertainty

in estimates of the ocean’s contribution to climate change.

Very energetic eddy motions occur in the ocean

on the scale of a few tens of kilometers. These

so-called mesoscale eddies are not present in the

ocean simulations of CMIP3 climate models.

Ocean models used for climate simulation cannot afford the computational cost of explicitly

resolving ocean mesoscale eddies. Instead, they

must parameterize mixing by the eddies. Treatment of these mesoscale eddy effects is an important factor distinguishing one ocean model

from another. Most real ocean mixing is along

rather than across surfaces of constant density.

Development of parameterizations that account

for this essential feature of mesoscale eddy mixing (Gent and McWilliams 1990; Griffies 1998)
17

Climate Models: An Assessment of Strengths and Limitations

is a major advance in recent ocean and climate

modeling. Inclusion of higher-resolution,

mesoscale eddy–resolving ocean models in future climate models would reduce uncertainties

associated with these parameterizations.

Other mixing processes that may be important

in the ocean include tidal mixing and turbulence

generated by interactions with the ocean’s bottom, both of which are included in some models. Lee, Rosati, and Spellman (2006) describe

some effects of tidal mixing in a climate model.

Some OGCMs also explicitly treat the bottom

boundary and sill overflows (Beckman and

Dosher 1997; Roberts and Wood 1997; Griffies

et al. 2005). Furthermore, sunlight penetration

into the ocean is controlled by chlorophyll distributions (e.g., Paulson and Simpson 1977;

Morel and Antoine 1994; Ohlmann 2003), and

the depth of penetration can affect surface temperatures. All U.S. CMIP3 models include some

treatment of this effect, but they prescribe rather

than attempt to simulate the upper ocean biology controlling water opacity. Finally, the inclusion of fresh water input by rivers is essential

to close the global hydrological cycle; it affects

ocean mixing locally and is handled by models

in a variety of ways.

The relatively crude resolution of OGCMs used

in climate models results in isolation of the

smaller seas from large ocean basins. This requires models to perform ad hoc exchanges of

water between the isolated seas and the ocean

to simulate what in nature involves a channel or

strait. (The Strait of Gibraltar is an excellent example.) Various modeling groups have chosen

different methods to handle water mixing between smaller seas and larger ocean basins.

OGCM components of climate models are often

evaluated in isolation—analogous to the evaluation of AGCMs with prescribed ocean and seaice boundary conditions—in addition to being

evaluated as components of fully coupled

ocean-atmosphere GCMs. (Results of full

AOGCM evaluation are discussed in Chapter

5.) Evaluation of ocean models in isolation requires input of boundary conditions at the airsea interface. To compare simulations with

observed data, boundary conditions or surface

forcing are from the same period as the data.

These surface fluxes also have uncertainties

and, as a result, the evaluation of OGCMs with

specified sea-surface boundary conditions must

take these uncertainties into account.

2.3 LAND-SURFACE MODELS

Interaction of Earth’s surface with its atmosphere is an integral aspect of the climate system. Exchanges (fluxes) of mass and energy,

water vapor, and momentum occur at the interface. Feedbacks between atmosphere and surFigure 2.1. Schematic Showing Interaction of a WellMixed Surface Layer with Stratified Interior in a Region

with a Strong Temperature Gradient.

Mixing (dashed lines) is occurring both across temperature (T)

gradients and along the temperature gradient with increasing depth.

This process is poorly observed and not well understood. It must be

parameterized in large-scale models. [Adapted from Fig. 1, p. 18, in

Coupling Process and Model Studies of Ocean Mixing to Improve Climate

Models—A Pilot Climate Process Modeling and Science Team, a U.S.

CLIVAR white paper by Schopf et al. (2003). Figure originated by John

Marshall, Massachusetts Institute of Technology.] 

T+

Heat

T T–
18

The U.S. Climate Change Science Program Chapter 2 - Description of Global Climate System Models

face affecting these fluxes have important effects on the climate system (Seneviratne et al.

2006). Modeling the processes taking place over

land is particularly challenging because the land

surface is very heterogeneous and biological

mechanisms in plants are important. Climate

model simulations are very sensitive to the

choice of land models (Irannejad, HendersonSellers, and Sharmeen 2003).

In the earliest global climate models, land-surface modeling occurred in large measure to provide a lower boundary to the atmosphere that

was consistent with energy, momentum, and

moisture balances (e.g., Manabe 1969). The

land surface was represented by a balance

among incoming and outgoing energy fluxes

and a “bucket” that received precipitation from

the atmosphere and evaporated moisture into

the atmosphere, with a portion of the bucket’s

water draining away from the model as a type

of runoff. The bucket’s depth equaled soil field

capacity. Little attention was paid to the detailed

set of biological, chemical, and physical

processes linked together in the climate system’s

terrestrial portion. From this simple starting

point, land surface modeling for climate simulation has increased markedly in sophistication,

with increasing realism and inclusiveness of terrestrial surface and subsurface processes.

Although these developments have increased

the physical basis of land modeling, greater

complexity has at times contributed to more differences among climate models (Gates et al.

1999). However, the advent of systematic programs comparing land models, such as the Project for Intercomparison of Land Surface

Parameterization Schemes (PILPS, HendersonSellers et al. 1995; Henderson-Sellers 2006) has

led gradually to more agreement with observations and among land models (Overgaard, Rosbjerg, and Butts 2006), in part because

additional observations have been used to constrain their behavior. However, choices for

adding processes and increasing realism have

varied among land-surface models (e.g., Randall et al. 2007).

Figure 2.2 shows schematically the types of

physical processes included in typical land

models. Note that the schematic in the figure

describes a land model used for both weather

forecasting and climate simulation, an indication of the increasing sophistication demanded

by both. The figure also hints at important biophysical and biogeochemical processes that

gradually have been added and continue to be

added to land models used for climate simulation, such as biophysical controls on transpiration and carbon uptake.

Figure 2.2. Schematic

of Physical Processes in

a Contemporary Land

Model. 

[Adapted from Fig. 6 in F.

Chen and J. Dudhia 2001:

Coupling an advanced land

surface–hydrology model

with the Penn State–NCAR

MM5 modeling system. Part I:

Model implementation and

sensitivity, Monthly Weather

Review, 129, 569–585.

Reproduced by permission of

the American Meteorological

Society.]
19

Climate Models: An Assessment of Strengths and Limitations

Some of the most extensive increases in complexity and sophistication have occurred with

vegetation modeling in land models. An early

generation of land models (Wilson et al. 1987;

Sellers et al. 1986) introduced biophysical controls on plant transpiration by adding a vegetation canopy over the surface, thereby

implementing vegetative control on the terrestrial water cycle. These models included exchanges of energy and moisture among the

surface, canopy, and atmosphere, along with

momentum loss to the surface. Further developments included improved plant physiology

that allowed simulation of carbon dioxide fluxes

(e.g., Bonan 1995; Sellers et al. 1996). This

method lets the model treat the flow of water

and carbon dioxide as an optimization problem,

balancing carbon uptake for photosynthesis

against water loss through transpiration. Improvements also included implementation of

model parameters that could be calibrated with

satellite observation (Sellers et al. 1996),

thereby allowing global-scale calibration.

Continued development has included more realistic parameterization of roots (Arora and

Boer 2003; Kleidon 2004) and the addition of

multiple canopy layers (e.g., Gu et al. 1999;

Baldocchi and Harley 1995; Wilson et al. 2003).

The latter method, however, has not been used

in climate models because the added complexity of multicanopy models renders unambiguous calibration very difficult. An important

ongoing advance is the incorporation of biological processes that produce carbon sources and

sinks through vegetation growth and decay and

the cycling of carbon in the soil (e.g., Li et al.

2006), although considerable work is needed to

determine observed magnitudes of carbon uptake and depletion.

Most land models assume soil with properties

that correspond to inorganic soils, generally

consistent with mixtures of loam, sand, and clay.

High-latitude regions, however, may have extensive zones of organic soils (peat bogs), and

some models have included organic soils topped

by mosses, which has led to decreased soil heat

flux and increased surface-sensible and latentheat fluxes (Beringer et al. 2001).

Climate models initially treated snow as a single

layer that could grow through snowfall or deplete though melt (e.g., Dickinson, HendersonSellers, and Kennedy 1993). Some recent land

models for climate simulation include subgrid

distributions of snow depth (Liston 2004) and

blowing (Essery and Pomeroy 2004). Snow

models now may use multiple layers to represent fluxes through the snow (Oleson et al.

2004). Effort also has gone into including and

improving effects of soil freezing and thawing

(Koren et al. 1999; Boone et al. 2000; Warrach,

Mengelkamp, and Raschke 2001; Li and Koike

2003; Boisserie et al. 2006), although permafrost modeling is more limited (MalevskyMalevich et al. 1999; Yamaguchi, Noda, and

Kitoh 2005).

Vegetation interacts with snow by covering it,

thereby masking snow’s higher albedo (Betts

and Ball 1997) and retarding spring snowmelt

(Sturm et al. 2005). The net effect is to maintain warmer temperatures than would occur

without vegetation masking (Bonan, Pollard,

and Thompson 1992). Vegetation also traps

drifting snow (Sturm et al. 2001), insulating the

soil from subfreezing winter air temperatures

and potentially increasing nutrient release and

enhancing vegetation growth (Sturm et al.

2001). Albedo masking is included in some

land-surface models, but it requires accurate

simulations of snow depth to produce accurate

simulation of surface-atmosphere energy exchanges (Strack, Pielke, and Adegoke 2003).

Time-evolving ice sheets and mountain glaciers

are not included in most climate models. Ice

sheets once were thought to be too sluggish to

respond to climate change in less than a century.

However, observations via satellite altimetry,

synthetic aperture radar interferometry, and

gravimetry all suggest rapid dynamic variability

of ice sheets, possibly in response to climatic

warming (Rignot and Kanagaratnam 2006;

Velicogna and Wahr 2006). Most global climate

models to date have been run with prescribed,

immovable ice sheets. Several modeling groups

are now experimenting with the incorporation

of dynamic ice sheet models. Substantial physical, numerical, and computational improvements, however, are needed to provide reliable

projections of 21st Century ice sheet changes.

Among major challenges are incorporation of a

unified treatment of stresses within ice sheets,

improved methods of downscaling atmospheric
20

The U.S. Climate Change Science Program Chapter 2 - Description of Global Climate System Models

fields to the finer ice sheet grid, realistic parameterizations of surface and subglacial hydrology (fast dynamic processes controlled largely

by water pressure and extent at the base of the

ice sheet), and models of ice shelf interactions

with ocean circulation. Ocean models, which

usually assume fixed topography, may need to

be modified to include flow beneath advancing

and retreating ice. Meeting these challenges will

require increased interaction between the

glaciological and climate modeling communities, which until recently have been largely isolated from each another.

The initial focus of land models was vertical

coupling of the surface with the overlying atmosphere. However, horizontal water flow

through river routing has been available in some

models for some time (e.g., Sausen, Schubert,

and Dümenil et al. 1994; Hagemann and Dümenil 1998), with spatial resolution of routing

in climate models increasing in more recent versions (Ducharne et al. 2003). Freezing soil

poses additional challenges for modeling runoff

(Pitman et al. 1999), with more recent work

showing some skill in representing its effects

(Luo et al. 2003; Rawlins et al. 2003; Niu and

Yang 2006).

Work also is under way to couple groundwater

models into land models (e.g., Gutowski et al.

2002; York et al. 2002; Liang, Xie, and Huang

2003; Maxwell and Miller 2005; Yeh and Eltahir

2005). Groundwater potentially introduces

longer time scales of interaction in the climate

system in places where it has contact with vegetation roots or emerges through the surface.

Land models encompass spatial scales ranging

from model grid-box size down to biophysical

and turbulence processes operating on scales

the size of leaves. Explicit representation of all

these scales in a climate model is beyond the

scope of current computing systems and the observing systems that would be needed to provide adequate model calibration for global and

regional climate. Model fluxes do not represent

a single point but rather the behavior in a grid

box that may be many tens or hundreds of kilometers across. Initially, these grid boxes were

treated as homogeneous units but, starting with

the pioneering work of Avissar and Pielke

(1989), many land models have tiled a grid box

with patches of different land-use and vegetation types. Although these patches may not interact directly with their neighbors, they are

linked by their coupling to the grid box’s atmospheric column. This coupling does not

allow for possible small-scale circulations that

might occur because of differences in surfaceatmosphere energy exchanges among patches

(Segal and Arritt 1992; Segal et al. 1997). Under

most conditions, however, the imprint of such

spatial heterogeneity on the overlying atmospheric column appears to be limited to a few

meters above the surface (e.g., Gutowski, Ötles,

and Chen 1998).

Vertical fluxes linking the surface, canopy, and

near-surface atmosphere generally assume some

form of down-gradient diffusion, although

counter-gradient fluxes can exist in this region

much as in the overlying atmospheric boundary

layer. Some attempts have been made to replace

diffusion with more advanced Lagrangian random-walk approaches (Gu et al. 1999; Baldocchi and Harley 1995; Wilson et al. 2003).

Topographic variation within a grid box usually

is ignored in land modeling. Nevertheless, implementing detailed river-routing schemes requires accurate digital elevation models (e.g.,

Hirano, Welch, and Lang 2003; Saraf et al.

2005). In addition, some soil water schemes include effects of land slope on water distribution

(Choi, Kumar, and Liang 2007) and surface radiative fluxes (Zhang et al. 2006).

Validation of land models, especially globally,

remains a problem due to lack of measurements

for relevant quantities such as soil moisture and

energy, momentum, moisture flux, and carbon

flux. The PILPS project (Henderson-Sellers et

al. 1995) has allowed detailed comparisons of

multiple models with observations at points

around the world having different climates, thus

providing some constraint on the behavior of

land models. Global participation in PILPS has

led to more understanding of differences among

schemes and improvements. Compared to previous generations, the latest land surface models exhibit relatively smaller differences from

current observation-based estimates of the

global distribution of surface fluxes, but the reliability of such estimates remains elusive (Henderson-Sellers et al. 2003). River routing can
21

Climate Models: An Assessment of Strengths and Limitations

provide a diagnosis vs observations of a land

model’s spatially distributed behavior (Kattsov

et al. 2000). Remote sensing has been useful for

calibrating models developed to exploit it but

generally has not been used for model validation. Regional observing networks that aspire to

give Earth system observations, such as some

mesonets in the United States, offer promise of

data from spatially distributed observations of

important fields for land models.

Land modeling has developed in other disciplines roughly concurrently with advances in

climate models. Applications are wide ranging

and include detailed models used for planning

water resources (Andersson et al. 2006), managing ecosystems (e.g., Tenhunen et al. 1999),

estimating crop yields (e.g., Jones and Kiniry

1986; Hoogenboom, Jones, and Boote 1992),

simulating ice-sheet behavior (Peltier 2004),

and projecting land use such as transportation

planning (e.g., Schweitzer 2006). As suggested

by this list, widely disparate applications have

developed from differing scales of interest and

focus. Development in some other applications

has informed advances in land models for climate simulation, as in representation of vegetation and hydrologic processes. Because land

models do not include all climate system features, they can be expected in future to engage

other disciplines and encompass a wider range

of processes, especially as resolution increases.

2.4 SEA-ICE MODELS

Most climate models include sea-ice components that have both dynamic and thermodynamic elements. That is, models include the

physics governing ice movement as well as that

related to heat and salt transfer within the ice.

While sea ice in the real world appears as ice

floes on the scale of meters, in climate models

sea ice is treated as a continuum with an effective large-scale rheology describing the relationship between stress and flow.

Rheologies commonly in use are the standard

Hibler viscous-plastic (VP) rheology (Hibler

1979; Zhang and Rothrock 2000) and the morecomplex elastic-viscous-plastic (EVP) rheology

of Hunke and Dukowicz (1997), designed primarily to improve the computational efficiency

of ice models. The EVP method explicitly

solves for the ice-stress tensor, while the VP solution uses an implicit iterative approach. As examples, the GFDL models (Delworth et al. 2006

) and Community Climate System Model, Version 3 (CCSM3) (Collins et al. 2006a) use the

EVP rheology, while the GISS models use the

VP implementation.

The thermodynamic portions of sea ice models

also vary. Earlier generations of climate models

generally used the sea ice thermodynamics of

Semtner (1976), which includes one snow layer

and two ice layers with constant heat conductivities together with a simple parameterization

of brine (salt) content. The GFDL climate models continue to use this but also include the interactions between brine content and heat

capacity (Winton 2000). The CCSM3 and GISS

models use variations (Bitz and Lipscomb 1999,

Briegleb et al. 2002) incorporating additional

physical processes within the ice, such as the

melting of internal brine regions. Different

models define snow and ice layers and ice categories differently, but all include an open water

category. Typically, ice models share the grid

structure of the underlying ocean model.

The albedo (proportion of incident sunlight reflected from a surface) of snow and ice plays a

significant role in the climate system. Sea-ice

models parameterize the albedo using expressions based on a mix of radiative transfer theory and empiricism. Figure 2.3 from Curry,

Schramm, and Ebert (1995) illustrates sea-ice

system interrelations and how the albedo is a

function of snow or ice thickness, ice extent,

open water, and surface temperature, and other

factors. Models treat these factors in similar

ways but vary on details. For example, the

CCSM3 sea-ice component does not include dependence on solar elevation angle (Briegleb et

al. 2002), but the GISS model does (Schmidt et

al. 2006). Both models include the contribution

of melt ponds (Ebert and Curry 1993; Schramm

et al. 1997). The GFDL model follows Briegleb

et al. (2002) but accounts for different effects of

the different wavelengths comprising sunlight

(Delworth et al. 2006). 
22

The U.S. Climate Change Science Program Chapter 2 - Description of Global Climate System Models

2.5 COMPONENT COUPLING

AND COUPLED MODEL

EVALUATION

The climate system’s complexity and our inability to resolve all relevant processes in models result in a host of choices for development

teams. Differing expertise, experience, and interests result in distinct pathways for each climate model. While we eventually expect to see

model convergence forced by increasing insights into the climate system’s workings, we

are still far from that limit today in several important areas. Given this level of uncertainty,

multiple modeling approaches clearly are

needed. Models vary in details primarily because development teams have different ideas

concerning underlying physical mechanisms

relevant to the system’s less-understood features. In the following, we describe some key

aspects of model development by the three U.S.

groups that contributed models to the IPCC

Fourth Assessment (IPCC 2007). Particular

focus is on points most relevant for simulating

the 20th Century global mean temperature and

on the model’s climate sensitivity.

2.5.1 NOAA GFDL ModelDevelopment Path

NOAA’s GFDL conducted a thorough restructuring of its atmospheric and climate models for

more than 5 years prior to its delivery of models to the CMIP3 database in 2004. This was

done partly in response to the need for modernizing software engineering and partly in response to new ideas in modeling the

atmosphere, ocean, and sea ice. Differences between the resulting models and the previous

generation of climate models at GFDL are varied and substantial. Mapping out exactly why

climate sensitivity and other considerations of

climate simulations differ between these two

generations of models would be very difficult

and has not been attempted. Unlike the earlier

generation, however, the new models do not use

flux adjustments; some other improvements are

discussed below.

The new atmospheric models developed at

GFDL for global warming studies are referred

to as AM2.0 and AM2.1 (GFDL Atmospheric

Model Development Team 2004). Key points of

departure from previous GFDL models are the

adoption of a new numerical core for solving

+/–

+/–

– –

– –

–

–

– – +

+

+ +

+

Snow cover

Melt ponds

Ice extent Ice thickness

Surface

albedo

Lead fraction Surface

Temperature

Figure 2.3. Schematic

Diagram of Sea Ice–

Albedo Feedback

Mechanism. 

Arrow direction indicates

the interaction direction.

The “+” signs indicate

positive interaction (i.e.,

increase in the first

quantity leads to increase

in the second quantity),

and the “–” signs indicate

negative interaction (i.e.,

increase in the first

quantity leads to decrease

in the second quantity).

The “+/–” signs indicate

either that the interaction

sign is uncertain or that

the sign changes over the

annual cycle. [From Fig. 6

in J.A. Curry, J. Schramm,

and E.E. Ebert 1995: On

the sea ice albedo climate

feedback mechanism, J.

Climate, 8, 240–247.

Reproduced by permission

of the American

Meteorological Society.]
23

Climate Models: An Assessment of Strengths and Limitations

fluid dynamical equations for the atmosphere,

the inclusion of liquid and ice concentrations as

prognostic variables, and new parameterizations

for moist convection and cloud formation.

Much atmospheric development was based on

running the model over observed sea-surface

temperature and sea-ice boundary conditions

from 1980 to 2000, with a focus on both the

mean climate and the atmospheric response to

El Niño–Southern Oscillation (ENSO) variability in the tropical Pacific. Given the basic model

configuration, several subgrid closures were

varied to optimize climate features. Modest improvements in the midlatitude wind field were

obtained by adjusting the “orographic gravity

wave drag,” which accounts for the effects of

force exerted on the atmosphere by unresolved

topographic features. Substantial improvements

in simulating tropical rainfall and its response

to ENSO were the result of parameter optimization as well, especially the treatment of vertical transport of horizontal momentum by

moist convection.

The ocean model chosen for this development is

the latest version of the modular ocean model

(MOM) developed over several decades at

GFDL. Notable new features in this version are

a grid structure better suited to simulating the

Arctic Ocean and a framework for subgrid-scale

mixing that avoids unphysical mixing among

oceanic layers of differing densities (Gent and

McWilliams 1990; Griffies 1998). A new seaice model includes an EVP large-scale effective

rheology that has proven itself in the past

decade in several models and multiple ice thicknesses in each grid box. The land model chosen

is relatively simple, with vertically resolved soil

temperature but retaining the “bucket hydrology” from the earlier generation of models.

The resulting climate model was studied, restructured, and tuned for an extended period,

with particular interest in optimizing the structure and frequency of the model’s spontaneously

generated El Niño events, minimizing surface

temperature biases, and maintaining an Atlantic

overturning circulation of sufficient strength.

During this development phase, climate sensitivity was monitored by integrating the model

to equilibrium with doubled CO2 when coupled

to a “flux-adjusted” slab ocean model. A single

model modification reduced the model’s sensitivity range from 4.0 to 4.5 K to between 2.5

and 3.0 K, as discussed further in Chapter 4.

The change responsible for this reduction was

inclusion of a new model of mixing in the planetary boundary near the Earth’s surface. GFDL

included the mixing model because it generated

more-realistic boundary-layer depths and nearsurface relative humidities. Sensitivity reduction resulted from modifications to the

low-level cloud field; the size of this reduction

was not anticipated.

Aerosol distributions used by the model were

computed offline from the MOZART II model

as described in Horowitz et al. (2003). No attempt was made to simulate indirect aerosol effects (interactions between clouds and aerosols),

as confidence in the schemes tested was

deemed insufficient. In 20th Century simulations, solar variations followed the prescription

of Lean, Beer, and Bradley (1995), while volcanic forcing was based on Sato et al. (1993).

Stratospheric ozone was prescribed, with the

Southern Hemisphere ozone hole prescribed in

particular, in 20th Century simulations. A new

detailed land-use history provided a time history of vegetation types.

Final tuning of the model’s global energy balance, using two parameters in the cloud prediction scheme, was conducted by examining

control simulations of the fully coupled model

using fixed 1860 and 1990 forcings (see box,

Tuning the Global Mean Energy Balance). The

resulting model is described in Delworth et al.

(2006) and Gnanadesikan et al. (2006). IPCCrelevant runs of this model (CM2.0) were provided to the CMIP3–IPCC archive. Simulations

of the 20th Century with time-varying forcings

provided to the database and described in Knutson et al. (2006) were the first of this kind generated with this model. The model was not

retuned, and no iteration of the aerosol or any

other time-varying forcings followed these initial simulations.

Model development proceeded in the interim,

and a new version emerged rather quickly in

which the atmospheric model’s numerical core

was replaced by a “finite-volume” code (Lin

and Rood 1996). Treatment of wind fields near

the surface improved substantially, which in

turn resulted in enhanced extratropical ocean
24

The U.S. Climate Change Science Program Chapter 2 - Description of Global Climate System Models

circulation and temperatures. ENSO variability

increased in this model to unrealistically large

values; however, the ocean code’s efficiency

also improved substantially. With retuning of

the clouds for global energy balance, the new

model CM2.1 was deemed to be an improved

model over CM2.0 in several respects, warranting the generation of a new set of database runs.

CM2.1, when run with a slab-ocean model, was

found to have somewhat increased sensitivity.

However, transient climate sensitivity—the

global mean warming at the time of CO2 doubling in a fully coupled model with 1% a year

increase in CO2—actually is slightly smaller

than in CM2.0. Solar, aerosol, volcanic, and

greenhouse gas forcings are identical in the two

models.

2.5.2 Community Climate System

Model-Development Path

CCSM3 was released to the climate community

in June 2004. CCSM3 is a coupled climate

model with components representing the atmosphere, ocean, sea ice, and land surface connected by a flux coupler. CCSM3 is designed to

produce realistic simulations over a wide range

of spatial resolutions, enabling inexpensive simulations lasting several millennia or detailed

studies of continental-scale dynamics, variability, and climate change. Twenty-six papers documenting all aspects of CCSM3 and runs

performed with it were published in a special

issue of the Journal of Climate 19(11) (June

2006). The atmospheric component of CCSM3

is a spectral model. Three different resolutions

of CCSM3 are supported. The highest resolution is the configuration used for climatechange simulations, with a T85 grid for

atmosphere and land and a grid with around 1o

resolution for ocean and sea ice but finer meridional resolution near the equator. The second

resolution is a T42 grid for atmosphere and land

with 1o ocean and sea-ice resolution. A lowerresolution version, designed for paleoclimate

studies, has T31 resolution for atmosphere and

land and a 3o version of ocean and sea ice.

The new CCSM3 version incorporates several

significant improvements in physical parameterizations. Enhancements in model physics are

designed to reduce several systematic biases in

A procedure common to all comprehensive climate models is tuning the global mean energy balance. A climate model must be in balance at top of atmosphere (TOA) and globally averaged to

within a few tenths of a W/m2 in its control (pre-1860) climate if it is to avoid temperature drifts

in 20th and 21st century simulations that would obscure response to imposed changes in greenhouse, aerosol, volcanic, and solar forcings. Especially because of difficulty in modeling clouds but

also even in clear sky, untuned models do not currently possess this level of accuracy in their radiative fluxes. Untuned imbalances more typically range up to 5 W/m2

. Parameters in the cloud

scheme are altered to create a balanced state, often taking care that individual components of this

balance—the absorbed solar flux and emitted infrared flux—are individually in agreement with observations, since these help ensure the correct distribution of heating between atmosphere and

ocean. This occasionally is referred to as “final tuning” the model to distinguish it from various

choices made for other reasons while the model is being configured.

The need for final tuning does not preclude the use of these models for global warming simulations

in which radiative forcing itself is on the order of several W/m2

. Consider, for example, the Ramaswamy et al. (2001) study on the effects of modifying the “water vapor continuum” treatment in

a climate model. This is an aspect of the radiative transfer algorithm in which there is significant uncertainty. While modifying continuum treatment can change the TOA balance by more than 1 W/m2

,

the effect on climate sensitivity is found to be insignificant. The change in radiative transfer in this

instance alters the outgoing infrared flux by roughly 1%, and it affects the sensitivity (by changing

the flux derivative with respect to temperature) by roughly the same percentage. A sensitivity change

of this magnitude, say from 3 K to 3.03 K, is of little consequence given uncertainties in cloud feedbacks. The strength of temperature-dependent feedbacks, not errors in mean fluxes per se, is of particular concern in estimating climatic responses.

Tuning the Global Mean Energy Balance
25

Climate Models: An Assessment of Strengths and Limitations

mean climate produced by previous CCSM versions. These enhancements include new treatments of cloud processes, aerosol radiative

forcing, land-atmosphere fluxes, ocean mixedlayer processes, and sea-ice dynamics. Significant improvements are shown in sea-ice

thickness, polar radiation budgets, tropical seasurface temperatures, and cloud radiative effects. CCSM3 produces stable climate

simulations of millennial duration without ad

hoc adjustments to fluxes exchanged among

component models. Nonetheless, there are still

systematic biases in ocean-atmosphere fluxes in

coastal regions west of continents, the spectrum

of ENSO variability, spatial distribution of precipitation in tropical oceans, and continental

precipitation and surface air temperatures. Work

is under way to produce the next version of

CCSM, which will reduce these biases further,

and to extend CCSM to a more accurate and

comprehensive model of the complete Earth climate system.

CCSM3’s climate sensitivity is weakly dependent on the resolution used. Equilibrium temperature increase due to doubling carbon dioxide,

using a slab-ocean model, is 2.71°C, 2.47°C,

and 2.32°C, respectively, for the T85, T42, and

T31 atmosphere resolutions. The transient climate temperature response to doubling carbon

dioxide in fully coupled integrations is much

less dependent on resolution, being 1.50°C,

1.48°C, and 1.43°C, respectively, for the T85,

T42, and T31 atmosphere resolutions (Kiehl et

al. 2006).

The following CCSM3 runs were submitted for

evaluation for the IPCC Fourth Assessment Report and to the Program for Climate Model Diagnosis and Intercomparison (called PCMDI)

for dissemination to the climate scientific community: long, present day, and 1870 control

runs; an ensemble of eight 20th Century runs;

and smaller ensembles of future scenario runs

for the A2, A1B, and B1 scenarios and for the

20th Century commitment run where carbon

dioxide levels were kept at their 2000 values.

The control and 20th Century runs have been

documented and analyzed in several papers in

the Journal of Climate special issue, and future

climate change projections using CCSM3 have

been documented by Meehl et al. (2006).

2.5.3 GISS Development Path

The most recent version of the GISS atmospheric GCM, ModelE, resulted from a substantial reworking of the previous version, Model

II′. Although model physics has become more

complex, execution by the user is simplified as

a result of modern software engineering and improved model documentation embedded within

the code and accompanying web pages. The

model, which can be downloaded from the

GISS website by outside users, is designed to

run on myriad platforms ranging from laptops

to a variety of multiprocessor computers, partly

because of NASA’s rapidly shifting computing

environment. The most recent (post-AR4) version can be run on an arbitrarily large number of

processors.

Historically, GISS has eschewed flux adjustment. Nonetheless, the net energy flux at the top

of atmosphere (TOA) and surface has been reduced to near zero by adjusting threshold relative humidity for water and ice cloud formation,

two parameters that otherwise are weakly constrained by observations. Near-zero fluxes at

these levels are necessary to minimize drift of

either the ocean or the coupled climate.

To assess climate-response sensitivity to treatment of the ocean, ModelE has been coupled to

a slab-ocean model with prescribed horizontal

heat transport, along with two ocean GCMs.

One GCM, the Russell ocean (Russell, Miller,

and Rind 1995), has 13 vertical layers and horizontal resolution of 4o latitude by 5o longitude

and is mass conserving (rather than volume

conserving like the GFDL MOM). Alternatively, ModelE is coupled to the Hybrid Coordinate Ocean Model (HYCOM), an isopycnal

model developed originally at the University of

Miami (Bleck et al. 1992). HYCOM has 2o latitude by 2o longitude resolution at the equator,

with latitudinal spacing decreasing poleward

with the cosine of latitude. A separate rectilinear grid is used in the Arctic to avoid polar singularity and joins the spherical grid around

60°N.

Climate sensitivity to CO2 doubling depends

upon the ocean model due to differences in sea

ice. Climate sensitivity is 2.7°C for the slabocean model and 2.9°C for the Russell ocean
26

The U.S. Climate Change Science Program Chapter 2 - Description of Global Climate System Models

GCM (Hansen et al. 2005). As at GFDL and

CCSM, no effort is made to match a particular

sensitivity, nor is the sensitivity or forcing adjusted to match 20th Century climate trends

(Hansen et al. 2007). Aerosol forcing is calculated from prescribed concentration, computed

offline by a physical model of the aerosol life

cycle. In contrast to GFDL and NCAR models,

ModelE includes a representation of the aerosol

indirect effect. Cloud droplet formation is related empirically to the availability of cloud

condensation nuclei, which depends upon the

prescribed aerosol concentration (Hansen et al.

2005; Menon and Del Genio 2007).

Flexibility is emphasized in model development

(Schmidt et al. 2006). ModelE is designed for a

variety of applications ranging from simulation

of stratospheric dynamics and middle-atmosphere response to solar forcing to projection of

21st Century trends in surface climate. Horizontal resolution typically is 4o latitude by 5o longitude, although twice that resolution is used

more often for studies of cloud processes. The

model top has been raised from 10 mb (as in the

previous Model II') to 0.1 mb, so the top has less

influence on stratospheric circulation. Coding

emphasizes “plug-and-play” structure, so the

model can be adapted easily for future needs such

as fully interactive carbon and nitrogen cycles.

Model development is devoted to improving the

realism of individual model parameterizations,

such as the planetary boundary layer or sea-ice

dynamics. Because of the variety of applications, relatively little emphasis is placed on optimizing the simulation of specific phenomena

such as El Niño or the Atlantic thermohaline

circulation; as noted above, successful reproduction of one phenomenon usually results in a

suboptimal simulation of another. Nonetheless,

some effort was made to reduce biases in previous model versions that emerged from the interaction of various model features such as

subtropical low clouds, tropical rainfall, and

variability of stratospheric winds. Some model

adjustments were structural, as opposed to the

adjustment of a particular parameter—for example, introduction of a new planetary boundary layer parameterization that reduced

unrealistic cloud formation in the lowest model

level (Schmidt et al. 2006).

Because of their uniform horizontal coverage,

satellite retrievals are emphasized for model

evaluation like Earth Radiation Budget Experiment fluxes at TOA, Microwave Sounding Unit

channels 2 (troposphere) and 4 (stratosphere)

temperatures, and International Satellite Cloud

Climatology Project (ISCCP) diagnostics. Comparison to ISCCP is through a special algorithm

that samples GCM output to mimic data collection by an orbiting satellite. For example, high

clouds may include contributions from lower

levels in both the model and the downwardlooking satellite instrument. This satellite perspective within the model allows a rigorous

comparison to observations. In addition to satellite retrievals, some GCM fields like zonal wind

are compared to in situ observations adjusted by

European Center for Medium Range Weather

Forecasts’ 40-year reanalysis data (Uppala et al.

2005). Surface air temperature is taken from the

Climate Research Unit gridded global surface

temperature dataset (Jones et al. 1999).

2.5.4 Common Problems

The CCSM and GFDL development teams met

several times to compare experiences and discuss common problems in the two models. A

subject of considerable discussion and concern

was the tendency for an overly strong “cold

tongue” to develop in the eastern equatorial Pacific Ocean and for associated errors to appear

in the pattern of precipitation in the Inter-Tropical Convergence Zone (often referred to as the

“double-ITCZ problem”). Meeting attendees

noted that the equilibrium climate sensitivities

of the two models to doubled atmospheric carbon dioxide (see Chapter 4) had converged from

earlier generations in which the NCAR model

was on the low end of the canonical sensitivity

range of 1.5 to 4.5 K, while the GFDL model

was near the high end. This convergence in

global mean sensitivity was considered coincidental because no specific actions were taken to

engineer convergence. It was not accompanied

by any noticeable convergence in cloud-feedback specifics or in the regional temperature

changes that make up global mean values.
27

Climate Models: An Assessment of Strengths and Limitations

2.6 REDUCTIVE VS HOLISTIC

EVALUATION OF MODELS

To evaluate models, appreciation of their structure is necessary. For example, discussion of climatic response to increasing greenhouse gases

is intimately related to the question of how infrared radiation escaping to space is controlled.

When summarizing results from climate models, modelers often speak and think in terms of

a simple energy balance model in which the

global mean infrared energy escaping to space

has a simple dependence on global mean surface temperature. Water vapor or cloud feedbacks often are incorporated into such global

mean energy balance models with simple relationships that can be tailored easily to generate

a desired result. In contrast, Fig. 2.4 shows a

snapshot at an instant when infrared radiation is

escaping to space in the kind of AGCM discussed in this report. Detailed distributions of

clouds and water vapor simulated by the model

and transported by the model’s evolving wind

fields create complex patterns in space and time

that, if the simulation is sufficiently realistic, resemble images seen from satellites viewing

Earth at infrared wavelengths.

As described above, AGCMs evolve the state of

atmosphere and land system forward in time,

starting from some initial condition. They consist of rules that generate the state of a variable

(e.g., temperature, wind, water vapor, clouds,

rainfall rate, water storage in the land, and landsurface temperature) from its preceding state

roughly a half-hour earlier. By this process a

model simulates the weather over the Earth. To

change the way the model’s infrared radiation

reacts to increasing temperatures, the rules

would have to be modified.

One goal of climate modeling is to decrease empiricism and base models as much as possible

on well-established physical principles. This

goal is pursued primarily by decomposing the

climate system into a number of relatively simple processes and interactions. Modelers focus

on rules governing the evolution of these individual processes rather than working with more

holistic concepts such as global mean infrared

radiation escaping to space, average summertime rainfall over Africa, and average wintertime surface pressure over the Arctic. These are

all outcomes of the model, determined by the

set of reductive rules that govern the model’s

evolution.

Suppose the topic under study is how ocean

temperatures affect rainfall over Africa. An empirical statistical model could be developed

Figure 2.4. A Snapshot

in Time of Infrared

Radiation Escaping to

Space in a Version of

Atmospheric Model

AM2 Constructed at

NOAA’s Geophysical

Fluid Dynamics

Laboratory (GFDL

2004). 

The largest amount of energy

emitted is in the darkest

areas, and the least is in the

brightest areas. This version of

the atmospheric model has

higher resolution than that

used for simulations in the

CMIP3 archive (50 km rather

than 200 km), but, other than

resolution, it uses the same

numerical algorithm. The

resolution is typical in many

current studies with

atmosphere-only simulations.
28

The U.S. Climate Change Science Program Chapter 2 - Description of Global Climate System Models

using observations and standard statistical techniques in which the model is tuned to these observations. Alternatively, one can use an AGCM

giving results like those pictured in Fig. 2.4. An

AGCM does not deal directly with high-level

climate output such as African rainfall averaged

over some period. Rather, it attempts to simulate the climate system’s inner workings or dynamics at a much finer level of granularity. To

the extent that the simulation is successful and

convincing, the model can be analyzed and manipulated to uncover the detailed physical mechanisms underlying the connection between

ocean temperatures and rainfall over Africa. The

AGCM-simulated connection may or may not

be as good as the fit obtained with the explicitly

tuned statistical model, but a reductive model

ideally provides a different level of confidence

in its explanatory and predictive power. See, for

example, Hoerling et al. (2006) for an analysis

of African rainfall and ocean temperature relationships in a set of AGCMs.

Our confidence in the explanatory and predictive power of climate models grows with their

ability to simulate many climate system features

simultaneously with the same set of physically

based rules. When a model’s ability to simulate

the evolution of global mean temperature over

the 20th Century is evaluated, it is important to

try to make this evaluation in the context of the

model’s ability to spontaneously generate El

Niño variability of the correct frequency and

spatial structure, for example, and to capture the

effects of El Niño on rainfall and clouds. Simulation quality adds confidence in the reductive

rules being used to generate simultaneous simulation of all these phenomena.

A difficulty to which we will return frequently

in this report is that of relating climate-simulation qualities to a level of confidence in the

model’s ability to predict climate change.

2.7 USE OF MODEL METRICS

Recently, objective evaluation has exploded

with the wide availability of model simulation

results in the CMIP3 database (Meehl et al.

2006). One important area of research is in the

design of metrics to test the ability of models to

simulate well-observed climate features (Reichler and Kim 2008; Gleckler, Taylor, and Doutriaux 2008). Aspects of observed climate that

must be simulated to ensure reliable future predictions are unclear. For example, models that

simulate the most realistic present-day temperatures for North America may not generate the

most reliable projections of future temperature

changes. Projected climate changes in North

America may depend strongly on temperature

changes in the tropical Pacific Ocean and the

manner in which the jet stream responds to

them. The quality of a model’s simulation of airsea coupling over the Pacific might be a more

relevant metric. However, metrics can provide

guidance about overall strengths and weaknesses of individual models, as well as the general state of modeling.

The use of metrics also can explain why the

“best” climate model cannot be chosen at this

time. In Fig. 2.5 below, each colored triangle

represents a different metric for which each

model was evaluated (e.g., “ts” represents surface temperature). The figure displays the relative error value for a variety of metrics for each

model, represented by a vertical column above

each tick mark on the horizontal axis. Values

less than zero represent a better-than-average

simulation of a particular field measured by the

metric, while values greater than zero show

models with errors greater than the average. The

black triangles connected by the dashed line

represent the normalized sum from the errors of

all 23 fields. The models were ranked from left

to right based on the value of this total error. As

can be seen, models with the lowest total errors

tend to score better than average in most individual metrics but not in all. For an individual

application, the model with the lowest total errors may not be the best choice.

2.8 CLIMATE SIMULATIONS

DISCUSSED IN THIS REPORT

Three types of climate simulation discussed in

this report are described below. They differ according to which climate-forcing factors are

used as model input.

Control runs use constant forcing. The sun’s

energy output and the atmospheric concentrations of carbon dioxide and other gases and

aerosols do not change in control runs. As with

other types of climate simulation, day-night and
29

Climate Models: An Assessment of Strengths and Limitations

seasonal variations occur, along with internal

“oscillations” such as ENSO. Other than these

variations, the control run of a well-behaved climate model is expected eventually to reach a

steady state.

Values of control-run forcing factors often are

set to match present-day conditions, and model

output is compared with present-day observations. Actually, today’s climate is affected not

only by current forcing but also by the history of

forcing over time—in particular, past emissions

of greenhouse gases. Nevertheless, present-day

control-run output and present-day observations

are expected to agree fairly closely if models are

reasonably accurate. We compare model control

runs with observations in Chapter 5.

Idealized climate simulations are aimed at understanding important processes in models and

in the real world. They include experiments in

which the amount of atmospheric carbon dioxide increases at precisely 1% per year (about

twice the current rate) or doubles instantaneously. Carbon dioxide doubling experiments

typically are run until the simulated climate

reaches a steady state of equilibrium with the

enhanced greenhouse effect. Until the mid1990s, idealized simulations often were employed to assess possible future climate changes

including human-induced global warming. Recently, however, more realistic time-evolving

simulations (defined immediately below) have

been used for making climate predictions. We

discuss idealized simulations and their implications for climate sensitivity in Chapter 4.

Figure 2.5. Model Metrics for 23 Different Climate Fields. 

Values less than 0 indicate an error less than the average CMIP3 model, while values greater than 0 are

more than the average. The black triangles connected by the black line show a total score obtained by

averaging all 23 fields. Each tick mark represents a different model. [Figure adapted from P.J. Gleckler, K.E.

Taylor, and C. Doutriaux 2008: Performance metrics for climate models. J. Geophysical Research, 113, D06104,

doi:10.1029/2007JD008972. Reproduced by permission of the American Geophysical Union (AGU).]
30

The U.S. Climate Change Science Program Chapter 2 - Description of Global Climate System Models

Time-dependent climate-forcing simulations

are the most realistic, especially for eras in

which climate forcing is changing rapidly, such

as the 20th and 21st centuries. Input for 20th Century simulations includes observed time-varying values of solar energy output, atmospheric

carbon dioxide, and other climate-relevant gases

and aerosols, including those produced in volcanic eruptions. Each modeling group uses its

own best estimate of these factors. Significant

uncertainties occur in many of them, especially

atmospheric aerosols, so different models use

different input for their 20th Century simulations. We discuss uncertainties in climateforcing factors in Chapter 4 and 20th Century

simulations in Chapter 5 after comparing control runs with observations.

Time-evolving climate forcing also is used as

input for modeling future climate change. This

subject is discussed in CCSP Synthesis and Assessment Product 3.2. Finally, we mention for

the record simulations of the distant past (various time periods ranging from early Earth up to

the 19th Century). These simulations are not

discussed in this report, but some of them have

been used to loosely “paleocalibrate” simulations of the more recent past and the future

(Hoffert and Covey 1992; Hansen et al. 2006;

Hegerl et al. 2006).
CHAPTER

3

Climate Models: An Assessment of Strengths and Limitations

Added Value of 

Regional Climate 

Model Simulations

CHAPTER

3

31

3.1 TYPES OF DOWNSCALING SIMULATIONS

This section focuses on downscaling using three-dimensional models based on fundamental conservation laws [i.e., numerical models with foundations similar to general circulation models

(GCMs)]. A later section of the chapter discusses an alternative method, statistical downscaling.

There are three primary approaches to numerical downscaling:

• Limited-area models (Giorgi and Mearns

1991, 1999; McGregor 1997; Wang et al.

2004).

• Stretched-grid models (e.g., Déqué and

Piedelievre 1995; Fox-Rabinovitz et al.

2001, 2006).

• Uniformly high resolution atmospheric

GCMs (AGCMs) (e.g., Brankovic and Gregory 2001; May and Roeckner 2001; Duffy

et al. 2003; Coppola and Giorgi 2005).

Limited-area models, also known as regional

climate models (RCMs), have the most widespread use. The third method sometimes is

called “time-slice” climate simulation because

the AGCM simulates a portion of the period

represented by the coarser-resolution parent

GCM that supplies the model’s boundary conditions. All three methods use interactive land

models, but sea-surface temperatures and sea

ice generally are specified from observations or

an atmosphere-ocean GCM (AOGCM). All

three also are used for purposes beyond downscaling global simulations, most especially for

studying climatic processes and interactions on

scales too fine for typical GCM resolutions.

As limited-area models, RCMs cover only a

portion of the planet, typically a continental domain or smaller. They require lateral boundary

conditions (LBCs), obtained from observations

such as atmospheric analyses (e.g., Kanamitsu

et al. 2002; Uppala et al. 2005) or a global simulation. There has been limited two-way coupling wherein an RCM supplies part of its

output back to the parent GCM (Lorenz and

Jacob 2005). Simulations with observationbased boundary conditions are used not only to

study fine-scale climatic behavior but also to

help segregate GCM errors from those intrinsic

to the RCM when performing climate change

simulations (Pan et al. 2001). RCMs also may

use grids nested inside a coarser RCM simulation to achieve higher resolution in subregions

(e.g., Liang, Kunkel, and Samel 2001; Hay et

al. 2006).

Stretched-grid models, like high-resolution

AGCMs, are global simulations but with spatial

resolution varying horizontally. The highest res-
The U.S. Climate Change Science Program Chapter 3 - Added Value of Regional Climate Model Simulations

32

olution may focus on one (e.g., Déqué and

Piedelievre 1995; Hope, Nicholls, and McGregor 2004) or a few regions (e.g., Fox-Rabinovitz, Takacs, and Govindaraju 2002). In some

sense, the uniformly high resolution AGCMs

are the upper limit of stretched-grid simulations

in which the grid is uniformly high everywhere.

Highest spatial resolutions are most often several tens of kilometers, although some (e.g.,

Grell et al. 2000a, b; Hay et al. 2006) have simulated climate with resolutions as small as a few

kilometers using multiple nested grids. Duffy et

al. (2003) have performed multiple AGCM

time-slice computations using the same model

to simulate resolutions from 310 km down to 55

km. Higher resolution generally yields improved climate simulation, especially for fields

such as precipitation that have high spatial variability. For example, some studies show that

higher resolution does not have a statistically

significant advantage in simulating large-scale

circulation patterns but does yield better monsoon precipitation forecasts and interannual

variability (Mo et al. 2005) and precipitation intensity (Roads, Chen, and Kanamitsu 2003).

Improvement in results, however, is not guaranteed: Hay et al. (2006) find deteriorating timing

and intensity of simulated precipitation vs observations in their inner, high-resolution nests,

even though the inner nest improves topography

resolution. Extratropical storm tracks in a timeslice AGCM may shift poleward relative to the

coarser parent GCM (Stratton 1999; Roeckner

et al. 2006) or to lower-resolution versions of

the same AGCM (Brankovic and Gregory

2001); thus these AGCMs yield an altered climate with the same sea-surface temperature distribution as the parent model.

Spatial resolution affects the length of simulation periods because higher resolution requires

shorter time steps for numerical stability and accuracy. Required time steps scale with the inverse of resolution and can be much smaller

than AOGCM time steps. Increases in resolution most often are applied to both horizontal

directions, meaning that computational demand

varies inversely with the cube of resolution.

Several RCM simulations have lasted 20 to 30

years (Christensen, Carter, and Giorgi 2002;

Leung et al. 2004; Plummer et al. 2006) and

even as long as 140 years (McGregor 1999)

with no serious drift away from reality. Even so,

the RCM, stretched-grid, and time-slice AGCM

simulations typically last only months to a few

years. Vertical resolution usually does not change

with horizontal resolution, although Lindzen and

Fox-Rabinovitz (1989) and Fox-Rabinovitz and

Lindzen (1993) have expressed concerns about

the adequacy of vertical resolution relative to

horizontal resolution in climate models.

Higher resolution in RCMs and stretched-grid

models also must satisfy numerical constraints.

Stretched-grid models whose ratio of coarsestto-finest resolution exceeds a factor of roughly

3 are likely to produce inaccurate simulation

due to truncation error (Qian, Giorgi, and FoxRabinovitz 1999). Similarly, RCMs will suffer

from incompletely simulated energy spectra and

thus loss of accuracy if their resolution is about

12 times or more finer than the resolution of the

LBC source, which may be coarser RCM grids

(Denis et al. 2002; Denis, Laprise, and Caya

2003; Antic et al. 2004, 2006; Dimitrijevic and

Laprise 2005). In addition, these same studies

indicate that LBCs should be updated more frequently than twice per day.

Additional factors also govern ingestion of

LBCs by RCMs. LBCs are most often ingested

in RCMs by damping the model’s state toward

LBC fields in a buffer zone surrounding the domain of interest (Davies 1976; Davies and

Turner 1977). If the buffer zone is only a few

grid points wide, the interior region may suffer

phase errors in simulating synoptic-scale waves

(storm systems), with resulting error in the overall regional simulation (Giorgi, Marinucci, and

Bates 1993). Spurious reflections also may

occur in boundary regions (e.g., MiguezMacho, Stenchikov, and Robock 2005). RCM

boundaries should be where the driving data are

of optimum accuracy (Liang, Kunkel, and

Samel 2001), but placing the buffer zone in a

region of rapidly varying topography can induce

surface-pressure errors. These errors result from

mismatch between the smooth topography implicit in the coarse resolution driving the data

and the varying topography resolved by the

model (Hong and Juang 1998). Domain size

also may influence RCM results. If a domain is
too large, the model’s interior flow may drift

from the large-scale flow of the driving dataset

(Jones, Murphy, and Noguer 1995). However,

too small a domain overly constrains interior

dynamics, preventing the model from generating appropriate response to interior mesoscalecirculation and surface conditions (Seth and

Giorgi 1998). RCMs appear to perform well for

domains roughly the size of the contiguous

United States. Figure 3.1 shows that the daily,

root-mean-square difference (RMSD) between

simulated and observed (reanalysis) 500-hPa

heights generally is within observational noise

levels (about 20 m).

Because simulations from the downscaling

models may be analyzed for periods as short as

a month, model spinup is important (e.g., Giorgi

and Bi 2000). During spinup, the model evolves

to conditions representative of its own climatology, which may differ from the sources of initial conditions. The atmosphere spins up in a

matter of days, so the key factor is spinup of soil

moisture and temperature, which evolve more

slowly. Equally important, data for initial conditions often are lacking or have low spatial resolution, so initial conditions may be only a poor

approximation of the model’s climatology.

Spinup is especially relevant for downscaling

because these models presumably are resolving

finer surface features than coarser models, with

the expectation that the downscaling models are

providing added value through proper representation of these surface features. Deep-soil temperature and moisture, at depths of 1 to 2

meters, may require several years of spinup.

However, these deep layers generally interact

weakly with the rest of the model, so shorter

spinup times are used. For multiyear simulations, a period of 3 to 4 years appears to be the

minimal requirement (Christensen 1999; Roads

et al. 1999). This ensures that the upper meter of

soil has a climatology in further simulations that

is consistent with the evolving atmosphere.

Many downscaling simulations, especially with

RCMs, are for periods much shorter than 2

years. Such simulations probably will not use

Climate Models: An Assessment of Strengths and Limitations

33

Figure 3.1. Daily Root-Mean-Square Differences (RMSD) in 500-hPa Heights Between Observations

(Reanalysis) and Seven Models Participating in the PIRCS 1a Experiment, for May 15 to July 15, 1988.

RMSD values were averaged over the simulation domain inside the boundary-forcing zone. [Adapted from Fig. 4 in E.S. Takle et al. 1999:

Project to Intercompare Regional Climate Simulations (PIRCS): Description and initial results. J. Geophysical Research, 104, 19443–

19461. Used with permission of the American Geophysical Union.] 
34

The U.S. Climate Change Science Program Chapter 3 - Added Value of Regional Climate Model Simulations

multiyear spinup. Rather, these studies may

focus on more rapidly evolving atmospheric behavior governed by LBCs, including extreme

periods such as drought (Takle et al. 1999) or

flood (Giorgi et al. 1996; Liang, Kunkel, and

Samel 2001; Anderson, C. J., et al. 2003). Thus,

they assume that interaction with the surface,

while not negligible, is not strong enough to

skew the atmospheric behavior studied. Alternatively, relatively short regional simulations

may specify, for sensitivity study, substantial

changes in surface evaporation (e.g., Paegle,

Mo, and Nogués-Paegle 1996), soil moisture

(e.g., Xue et al. 2001), or horizontal moisture

flux at lateral boundaries (e.g., Qian, Tao, and

Lau 2004).

3.1.1 Parameterization Issues

Even with higher resolution than standard

GCMs, models simulating regional climate still

need parameterizations for subgrid-scale

processes, most notably boundary-layer dynamics, surface-atmosphere coupling, radiative

transfer, and cloud microphysics. Most regional

simulations also require a convection parameterization, although a few have used sufficiently

fine grid spacing (a few kilometers) to allow acceptable simulation without it (e.g., Grell et al.

2000). Often, these parameterizations are the

same or nearly the same as those used in GCMs.

All parameterizations, however, make assumptions that they are representing the statistics of

subgrid processes. Implicitly or explicitly, they

require that the grid box area in the real world

has sufficient samples to justify stochastic modeling. For some parameterizations such as convection, this assumption becomes doubtful

when grid boxes are only a few kilometers in

size (Emanuel 1994).

In addition, models simulating regional climate

may include circulation characteristics, such as

rapid mesoscale circulations (jets) whose interaction with subgrid processes like convection

and cloud cover differs from larger-scale circulations resolved by typical GCMs. This factor is

part of a larger issue, that parameterizations

may have regime dependence, performing better for some conditions than for others. For example, the Grell (1993) convection scheme is

responsive to large-scale tropospheric forcing,

whereas the Kain and Fritsch (1993) scheme is

heavily influenced by boundary-layer forcing.

As a result, the Grell scheme better simulates

the propagation of precipitation over the U.S.

Great Plains that is controlled by large-scale tropospheric forcing, while the Kain–Fritsch

scheme better simulates late-afternoon convection peaks in the southeastern United States that

are governed by boundary-layer processes

(Liang et al. 2004). As a consequence, parameterizations for regional simulation may differ

from their GCM counterparts, especially for

convection and cloud microphysics. As noted

earlier, regional simulation in some cases may

have resolution of only a few kilometers, and

the convection parameterization may be discarded (Grell et al. 2000). A variety of parameterizations exist for each subgrid process, with

multiple choices often available in a single

model (e.g., Grell, Dudhia, and Stanfler 1994;

Skamarock et al. 2005).

3.1.2 Regional Simulation vs

Computational Costs

The chief reason for performing regional simulation, whether by an RCM, a stretched-grid

model, or a time-slice AGCM, is to resolve behavior considered important for a region’s climate that a global model does not resolve. Thus,

regional simulation should have clearly defined

regional-scale (mesoscale) phenomena targeted

for simulation. These include tropical storms

(e.g., Oouchi et al. 2006), effects of mountains

(e.g., Leung and Wigmosta 1999; Grell et al.

2000; Zhu and Liang 2007), jet circulations

(e.g., Takle et al. 1999; Anderson et al. 2001;

Anderson, C. J., et al. 2003; Byerle and Paegle

2003; Pan et al. 2004), and regional ocean-land

interaction (e.g., Kim et al. 2005; Diffenbaugh,

Snyder, and Sloan 2004). The most immediate

value of regional simulation, then, is to explore

how such phenomena operate in the climate system, an understanding of which becomes a justification for the expense of performing regional

simulation. Phenomena and computational

costs together influence the design of regional

simulations. Simulation periods and resolution

are balanced between sufficient length and

number of simulations for climate statistics vs

computational cost. For RCMs and stretchedgrid models, the sizes of regions targeted for
35

Climate Models: An Assessment of Strengths and Limitations

high-resolution simulation are determined in

part by where the phenomenon occurs.

In the context of downscaling, regional simulation offers the potential to include phenomena

affecting regional climate change that are not

explicitly resolved in the global simulation.

When incorporating boundary conditions corresponding to future climate, regional simulation can then indicate how these phenomena

contribute to climate change. Results, of course,

are dependent on the quality of the boundarycondition source (Pan et al. 2001; de Elía,

Laprise, and Denis 2002), although use of multiple sources of future climate may lessen this

vulnerability and offer opportunity for probabilistic estimates of regional climate change

(Raisanen and Palmer 2001; Giorgi and Mearns

2003; Tebaldi et al. 2005). Results also depend

on the physical parameterizations used in the

simulation (Yang and Arritt 2002; Vidale et al.

2003; Déqué et al. 2005; Liang et al. 2006).

Advances in computing power suggest that typical GCMs eventually will operate at resolutions

of most current regional simulations (a few tens

of kilometers), so understanding and modeling

improvements gained for regional simulation

can promote appropriate adaptation of GCMs to

higher resolution. For example, interaction between mesoscale jets and convection appears to

require parameterized representation of convective downdrafts and their influence on the

jets (Anderson, Arritt, and Kain 2007), parameterized behavior not required for resolutions that

do not resolve mesoscale circulations.

Because of the variety of numerical techniques

and parameterizations employed in regional

simulation, many models and versions of models exist. Generally in side-by-side comparisons

(e.g., Takle et al. 1999; Anderson, C. J., et al.

2003; Fu et al. 2005; Frei et al. 2006; Rinke et

al. 2006), no single model appears best vs observations, with different models showing superior performance depending on the field

examined. Indeed, the best results for downscaling climate simulations and estimating climate-change uncertainty may come from

assessing an ensemble of simulations (Giorgi

and Bi 2000; Yang and Arritt 2002; Vidale et al.

2003; Déqué et al. 2005). Such an ensemble

may capture much of the uncertainty in climate

simulation, offering an opportunity for physically based analysis of climate changes and also

the uncertainty of the changes. Several regional

models have performed simulations of climate

change for parts of North America, but at present no regional projections have used an ensemble of regional models to simulate the same

time periods with the same boundary conditions. Such systematic evaluation has occurred

in Europe in the PRUDENCE (Christensen,

Carter, and Giorgi 2002) and ENSEMBLES

(Hewitt and Griggs 2004) projects and is starting in North America with the North American

Regional Climate Change Assessment Program

(NARCCAP 2007).

3.2 EMPIRICAL DOWNSCALING

Empirical or statistical downscaling is an alternative approach to obtaining regional-scale climate information (Kattenberg et al. 1996;

Hewitson and Crane 1996; Giorgi et al. 2001;

Wilby et al. 2004, and references therein). It

uses statistical relationships to link resolved behavior in GCMs with climate in a targeted area.

The targeted area’s size can be as small as a single point. As long as significant statistical relationships occur, empirical downscaling can

yield regional information for any desired variable such as precipitation and temperature, as

well as variables not typically simulated in climate models, such as zooplankton populations

(Heyen, Fock, and Greve 1998) and initiation of

flowering (Maak and von Storch 1997). This approach encompasses a range of statistical techniques from simple linear regression (e.g.,

Wilby et al. 2000) to more-complex applications such as those based on weather generators

(Wilks and Wilby 1999), canonical correlation

analysis (e.g., von Storch, Zorita, and Cubasch

1993), or artificial neural networks (e.g., Crane

and Hewitson 1998). Empirical downscaling

can be very inexpensive compared to numerical

simulation when applied to just a few locations

or when simple techniques are used. Lower

costs, together with flexibility in targeted variables, have led to a wide variety of applications

for assessing impacts of climate change.

Some methods have been compared side by side

(Wilby and Wigley 1997; Wilby et al. 1998;
36

The U.S. Climate Change Science Program Chapter 3 - Added Value of Regional Climate Model Simulations

Zorita and von Storch 1999; Widman, Bretherton, and Salathe 2003). These studies have

tended to show fairly good performance of relatively simple vs more-complex techniques and

to highlight the importance of including moisture and circulation variables when assessing

climate change. Statistical downscaling and regional climate simulation also have been compared (Kidson and Thompson 1998; Mearns et

al. 1999; Wilby et al. 2000; Hellstrom et al.

2001; Wood et al. 2004; Haylock et al. 2006),

with no approach distinctly better or worse than

any other. Statistical methods, though computationally efficient, are highly dependent on the

accuracy of regional temperature, humidity, and

circulation patterns produced by their parent

global models. In contrast, regional climate simulation, though computationally more demanding, can improve the physical realism of

simulated regional climate through higher resolution and better representation of important regional processes. The strengths and weaknesses

of statistical downscaling and regional modeling thus are complementary.

3.3 STRENGTHS AND

LIMITATIONS OF REGIONAL

MODELS

We focus here on numerical models simulating

regional climate but do not discuss empirical

downscaling because the wide range of applications using the latter makes difficult a general

assessment of strengths and limitations.

The higher resolution in regional-scale simulations provides quantitative value to climate simulation. With finer resolution, scientists can

resolve mesoscale phenomena contributing to

intense precipitation, such as stronger upward

motions (Jones, Murphy, and Noguer 1995) and

coupling between regional circulations and convection (e.g., Anderson, Arritt, and Kain 2007).

Time-slice AGCMs show intensified storm

tracks relative to their parent model (Solman,

Nunez, and Rowntree 2003; Roeckner et al.

2006). Thus, although regional models may still

miss the most extreme precipitation (Gutowski

et al. 2003, 2007a), they can give more intense

events that will be smoothed in coarser-resolution GCMs. The higher resolution also includes

other types of scale-dependent variability, especially short-term variability such as extreme

winds and locally extreme temperature that

coarser-resolution models will smooth and thus

inhibit.

Mean fields also appear to be simulated somewhat better on average than are those in coarser

GCMs because spatial variations potentially are

better resolved. Thus, Giorgi et al. (2001) report

typical errors in RCMs of less than 2 ̊C temperature and 50% for precipitation in regions 105 to

106 km2. Large-scale circulation fields tend to

be well simulated, at least in the extratropics.

As alluded to above, regional-scale simulations

also have phenomenological value, simulating

processes that GCMs either cannot resolve or

can resolve only poorly. These include internal

circulation features such as the nocturnal jet that

imports substantial moisture to the center of the

United States and couples with convection (e.g.,

Byerle and Paegle 2003; Anderson, Arritt, and

Kain 2007). These processes often have substantial diurnal variation and thus are important

to proper simulation of regional diurnal cycles

of energy fluxes and precipitation. Some

processes require the resolution of surface features too coarse for typical GCM resolution.

These include rapid topographic variation and

its influence on precipitation (e.g., Leung and

Wigmosta 1999; Hay et al. 2006) and the climatic influences of bodies of water such as the

Gulf of California (e.g., Anderson et al. 2001)

and the North American Great Lakes (Lofgren

2004) and their downstream influences. In addition, regional simulations resolve land-surface

features that may be important for climatechange impact assessments such as distributions

of crops and other vegetation (Mearns 2003;

Mearns et al. 2003), although care is needed to

obtain useful information at higher resolution

(Adams, McCarl, and Mearns 2003).

An important limitation for regional simulations

is that they are dependent on boundary conditions supplied from some other source. This applies to all three forms of numerical simulation

(RCMs, stretched-grid models, and time-slice

AGCMs), since they all typically require input

of sea-surface temperature and ocean ice. Some

RCM simulations have been coupled to a regional ocean-ice model, with mixed-layer ocean
37

Climate Models: An Assessment of Strengths and Limitations

(Lynch et al. 1995; Lynch, Maslanic, and Wu

2001) and a regional ocean-circulation model

(Rummukainen et al. 2004), but this is not common. In addition, of course, RCMs require

LBCs. Thus, regional simulations by these models are dependent on the model quality or on observations supplying boundary conditions. This

is especially true for projections of future climate, suggesting value in performing an ensemble of simulations using multiple

atmosphere-ocean global models to supply

boundary conditions, thus including some of the

uncertainty involved in constructing climate

models and projecting future changes in boundary conditions.

Careful evaluation also is necessary to show differences, if any, between the regional simulation’s large-scale circulation and its driving

dataset. Generally, any tendency for the regional

simulation to alter biases in the parent GCM’s

large-scale circulation should be viewed with

caution (Jones, Murphy, and Noguer 1995). An

RCM normally should not be expected to correct large-scale circulation problems of the parent model unless the physical basis for the

improvement is clearly understood. Clear physical reasons for the correction due to higher resolution, such as better rendition of physical

processes like topographic circulation (e.g.,

Leung and Qian 2003), surface-atmosphere interaction (Han and Roads 2004), and convection (Liang et al. 2006) must be established.

Otherwise, the regional simulation may simply

have errors that counteract the parent GCM’s errors, thus undermining confidence in projected

future climate.

RCMs also may exhibit difficulty in outflow regions of domains, especially regions with relatively strong cross-boundary flow, which may

occur in extratropical domains covering a single continent or less. The difficulty appears to

arise because storm systems may track across

the RCM’s domain at a different speed from

their movement in the driving-data source, resulting in a mismatch of circulations at boundaries where storms would be moving out of the

domain. Also, unresolved scales of behavior are

always present, so regional simulations are still

dependent on parameterization quality for the

scales explicitly resolved. Finally, higher computational demand due to shorter time steps limits the length of typical simulations to 2 to 3

decades or less (e.g., Christensen, Carter, and

Giorgi 2002; NARCCAP 2007), with few ensemble simulations to date.
38

The U.S. Climate Change Science Program Chapter 3 - Added Value of Regional Climate Model Simulations
CHAPTER

4

Climate Models: An Assessment of Strengths and Limitations

Model Climate 

Sensitivity

CHAPTER

4

39

The response of climate to a perturbation such as a change in carbon dioxide concentration, or

in the flux of energy from the sun, can be divided into two factors: “radiative forcing” due to the

perturbation in question and “climate sensitivity,” characterizing the response of the climate per

unit change in radiative forcing. Climate response is then the product of radiative forcing and climate sensitivity. This distinction is useful because of two approximations: radiative forcing often

can be thought of as independent of the resulting climate response, and climate sensitivity can

often be thought of as independent of the agent responsible for perturbation to the energy balance. When two or more perturbations are present simultaneously, their cumulative effect can be

approximated by adding their respective radiative forcings (Hansen et al. 2006).

Climate sensitivity as traditionally defined

refers to the global mean temperature, but a

model’s global mean temperature response is

very relevant to its regional temperature responses as well. This “pattern scaling” effect is

discussed at the end of this chapter.

Radiative forcing typically is calculated by

changing the atmospheric composition or external forcing and computing the net trapping

of heat that occurs before the climate system has

had time to adjust.1 These direct heat-trapping

properties are well characterized for the most

significant greenhouse gases. As a result, uncertainty in climate responses to greenhouse

gases typically is dominated by uncertainties in

climate sensitivity rather than in radiative forcing (Ramaswamy et al. 2001). For example,

suddenly doubling the atmospheric amount of

carbon dioxide would add energy to the surface

and the troposphere at the rate of about 4 W/m2

for the first few months after the doubling

(Forster et al. 2007). Eventually, lower tropospheric temperatures would increase (and climate would change in other ways) in response

to this forcing, Earth would radiate more energy

to space, and the imbalance would diminish as

the system returned to equilibrium.

4.1 CHARACTERIZING CLIMATE

RESPONSE

4.1.1 Equilibrium Sensitivity and

Transient Climate Response

The idea of characterizing climate response

using a single number represented by climate

sensitivity appeared early in the development of

1 Because the stratosphere cools rapidly in response to increasing carbon dioxide and this cooling affects the net

warming of the lower atmosphere and surface, it has become standard to include the effects of this stratospheric cooling in estimating radiative forcing due to carbon dioxide.
climate models (e.g., Schneider and Mass

1975). Today, two different numbers are in common use. Both are based on changes in global

and annual mean-surface or near-surface temperature. Equilibrium sensitivity is defined as

the long-term near-surface temperature increase

after atmospheric carbon dioxide has been doubled from preindustrial levels but thereafter held

constant until the Earth reaches a new steady

state, as described in the preceding paragraph.

Transient climate response or TCR is defined

by assuming that carbon dioxide increases by

1% per year and then recording the temperature

increase at the time carbon dioxide doubles

(about 70 years after the increase begins). TCR

depends on how quickly the climate adjusts to

forcing, as well as on equilibrium sensitivity.

The climate’s adjustment time itself depends on

equilibrium sensitivity and on the rate and depth

to which heat is mixed into the ocean, because

the depth of heat penetration tends to be greater

in models with greater sensitivity (Hansen et al.

1985; Wigley and Schlesinger 1985). Accounting for ocean heat uptake complicates many attempts at estimating sensitivity from observations,

as outlined below.

Equilibrium sensitivity depends on the strengths

of feedback processes involving water vapor,

clouds, and snow or ice extents (see, e.g.,

Hansen et al. 1984; Roe and Baker 2007). Small

changes in the strengths of feedback processes

can create large changes in sensitivity, making it

difficult to tightly constrain climate sensitivity

by restricting the strength of each relevant feedback process. As a result, research aimed at constraining climate sensitivity—and evaluating the

sensitivities generated by models—is not limited to studies of these individual feedback

processes. Studies of observed climate responses on short time scales (e.g., the response

to volcanic eruptions or the 11-year solar cycle)

and on long time scales (e.g., the climate of last

glacial maximum 20,000 years ago) also play

central roles in the continuing effort to constrain

sensitivity. The quantitative value of each of

these observational constraints is limited by the

quality and length of relevant observational

records, as well as the necessity in several cases

to simultaneously restrict ocean heat uptake and

equilibrium sensitivity. Equilibrium warming is

directly relevant when considering paleoclimates, where observations represent periods

that are very long compared to the climate’s adjustment time. The transient climate response is

more directly relevant to the attribution of recent warming and projections for the next century. For example, Stott et al. (2006) show that

global mean warming due to well-mixed greenhouse gases over the 20th Century, in the set of

models they consider, is closely proportional to

the model’s TCR. In the following, we discuss individual feedback processes as well as these additional observational constraints on sensitivity.

Equilibrium warming in an AOGCM is difficult

to obtain because the deep ocean takes a great

deal of time to respond to changes in climate

forcing. To avoid unacceptably lengthy computer simulations, equilibrium warming usually

is estimated from a modified climate model in

which the ocean component is replaced by a

simplified, fast-responding “slab ocean model.”

This procedure makes the assumption that horizontal redistribution of heat in the ocean does

not change as the climate responds to the perturbation. Current climate models generate a

range of equilibrium and transient climate sensitivities. For the models in the CMIP3 archive

utilized in the Fourth Assessment of the IPCC,

the range of equilibrium sensitivity is 2.1 to

4.4°C with a median of 3.2°C. This ensemble of

models was not constructed to systematically

span the plausible range of uncertainty in climate sensitivity; rather, each development team

simply provided its best attempt at climate simulation. Complementary to this approach is one

in which a single climate model is modified in

a host of ways to explore more systematically

the sensitivity variations associated with the

range of uncertainty in various key parameters.

Results with a Hadley Centre model give a 5 to

95 percentile range of ~2 to 6°C for equilibrium

sensitivity (Piani et al. 2005; Knutti et al. 2006).

Charney (1979) provided a range of equilibrium

sensitivities to CO2 doubling of 1.5 to 4.5°C,

based on the two model simulations available at

the time. Evidently, the range of model-implied

climate sensitivity has not contracted significantly over three decades. The current range,

however, is based on a much larger number of

models subjected to a far more comprehensive

comparison to observations and containing

40

The U.S. Climate Change Science Program Chapter 4 - Model Climate Sensitivity
41

Climate Models: An Assessment of Strengths and Limitations

more detailed treatments of clouds and other

processes that are fundamental to climate sensitivity. We understand in much more detail why

models differ in their equilibrium climate sensitivities: the source of much of this spread lies

in differences in how clouds are modeled in

AOGCMs. Questions remain as to whether or

not the substantial spread among models is a

good indication of the uncertainty in climate

sensitivity, given all the constraints on this

quantity of which we are aware. There also is a

desire to know the prospects for constraining

equilibrium climate sensitivity more sharply in

the near future.

The variation among models is less for TCR

than for equilibrium warming, a consequence of

the interrelationship between the climate’s adjustment time and its sensitivity to forcing noted

above (Covey et al. 2003). The full range for

TCR in the CMIP3 archive is 1.3 to 2.6°C, with

a median of 1.6°C and 25 to 75% quartiles of

1.5 to 2.0°C (Randall et al. 2007). Systematic

exploration of model input parameters in one

Hadley Centre model gives a range of 1.5 to

2.6°C (Collins, M., et al. 2006).

The equilibrium and transient sensitivities in

some models developed by U.S. centers contributing to CMIP3 are listed in Table 4.1. In the

last column, the larger of the two GISS ModelE

values is obtained using a full ocean model in

which the circulation is allowed to adjust. All

other values of equilibrium warming in the table

are obtained with the ocean component replaced

by a slab ocean model. The close agreement in

transient climate sensitivity among models in

this subset should not be overinterpreted, given

the larger range among the full set of CMIP3

models.

Climate sensitivity is not a model input. It

emerges from explicitly resolved physics, subgrid-scale parameterizations, and numerical approximations used by the models—many of

which differ from model to model—particularly

those related to clouds and ocean mixing. The

climate sensitivity of a model can be changed

by modifying parameters that are poorly constrained by observations or theory. Influential

early papers by Senior and Mitchell (1993,

1996) demonstrated how a seemingly minor

modification to the cloud-prediction scheme alters climate sensitivity. In the standard version

of the model, the effective size of cloud drops

was fixed. In two other versions, this cloud-drop

size was tied to the total amount of liquid-water

cloud through two different empirical relationships. The equilibrium sensitivity ranged from

1.9 to 5.5°C in these three models. In general,

the nonlinear dependence of equilibrium sensitivity on the strength of feedback processes allows relatively small changes in feedbacks to

generate large changes in sensitivity (see, e.g.,

Hansen et al. 1984; Roe and Baker 2007).

Studies of the CCSM family of models provide

another example of this problem. Kiehl et al.

(2006) found that a variety of factors is responsible for differences in climate sensitivity

among the models of this family. However, the

lower TCR of CCSM2 (relative to CSM1.4 and

CCSM3), evident in Table 4.1, results primarily

from a single change in the model’s algorithm

for simulating convective clouds. Table 4.2

shows how equilibrium sensitivity varied during development of the most recent GFDL models. The dramatic drop in sensitivity between

model versions p10 and p12.5.1 was unexMODEL TCR

(oC)

Equilibrium

Warming (oC)*

CSM1.4 1.4 2.0

CCSM2 1.1 2.3

CCSM3 1.5 2.5

GFDL CM2.0 1.6 2.9

GFDL CM2.1 1.5 3.4

GISS Model E 2.7 to 2.9

*Equilibrium warming was assessed by joining a

simplified slab ocean model to the atmosphere,

land, and sea-ice AOGCM components. 

[Sources of Information in table. First three

lines – J.T. Kiehl et al. 2006: The climate

sensitivity of the Community Climate System

Model: CCSM3. J. Climate, 19, 2584–2596. Next

two lines – R.J. Stouffer et al. 2006: GFDL’s CM2

global coupled climate models. Part IV: Idealized

climate response. J. Climate, 19, 723–740. Last

line – J. Hansen et al. 2007: Climate simulations

for 1880–2003 with GISS ModelE. Climate

Dynamics, 29(7–8), 661–696.]

Table 4.1

Equilibrium and

Transient

Sensitivities in

Some U.S. Models

Contributing to

CMIP3
42

The U.S. Climate Change Science Program Chapter 4 - Model Climate Sensitivity

pected. It followed a reformulation of the

model’s treatment of processes in the lower atmospheric boundary layer, which, in turn, affected how low-level clouds in the model

respond to climate change.

4.1.2 Observational Constraints on

Sensitivity

Climate models in isolation have not yet converged on a robust value of climate sensitivity.

Furthermore, the actual climate sensitivity in

nature might not be found in the models’ range

of sensitivities, since all the models may share

common deficiencies. However, observations

can be combined with models to constrain climate sensitivity. The observational constraints

include the response to volcanic eruptions; aspects of the internal variability of climate that

provide information on the strength of climatic

“restoring forces”; the response to the 11-year

cycle in solar irradiance; paleoclimatic information, particularly from the peak of the last Ice

Age some 20,000 years ago; aspects of the seasonal cycle; and, needless to say, the magnitude

of observed warming over the past century.

4.1.2.1 VOLCANIC ERUPTIONS

Volcanoes provide a rapid change in radiative

forcing due to the scattering and absorption of

solar radiation by stratospheric volcanic aerosol.

Of special importance, recovery time after the

eruption contains information about climate

sensitivity that is independent of uncertainties

in the magnitude of the radiative forcing perturbation (e.g., Lindzen and Giannitsis 1998).

Larger climate sensitivity implies weaker restoring forces on Earth’s temperature, and, therefore, a slower relaxation back toward the

unperturbed climate. However, this time scale

also is affected by the pathways through which

heat anomalies propagate into the ocean depths,

with deeper penetration increasing the relaxation time. Several modeling studies have confirmed that this relaxation time after an eruption

increases as climate sensitivity increases in

GCMs when holding the ocean model fixed

(Soden et al. 2002; Yokohata et al. 2005), encouraging the use of volcanic responses to constrain sensitivity. On the other hand, Boer,

Stowasser, and Hamilton (2007) study two models with differing climate sensitivity and different ocean models; they highlight the difficulty

in determining which model has the higher sensitivity from the surface-temperature responses

to volcanic forcing in isolation, without quantitative information on ocean heat uptake.

Some studies have argued that observations of

responses to volcanoes imply that models are

overestimating climate sensitivity (e.g., Douglass and Knox 2005; Lindzen and Giannitsis

1998). These studies argue that observed relaxation times are shorter than those expected if

climate sensitivity is as large as in typical

AOGCMs. Studies that directly examine the

volcanic responses in AOGCMs, however, find

no such gross disagreement with observations

(Wigley et al. 2005; Boer, Stowasser, and

Hamilton 2007; Frame et al. 2005) consistent

with earlier studies (e.g., Hansen et al. 1996;

Santer et al. 2001). They nevertheless consistently suggest (Frame et al. 2005; Yokohata et

al. 2005) that climate sensitivities as large as

MODEL VERSION Equilibrium 

Warming (oC)*

p7 3.87

p9 4.28

p10 4.58

p12.5.1 2.56

p12.7 2.65

p12.10b 2.87

p12b 2.83

CM 2.0 2.90

CM 2.1 3.43

*Equilibrium warming was assessed by joining a

simplified slab ocean model to the atmosphere,

land, and sea-ice AOGCM components. 

[Source of information for table: Personal

communication with Thomas Knutson, NOAA

GFDL laboratory.]

Table 4.2

Equilibrium Global

Mean Near-Surface

Warming Due to

Doubled

Atmospheric

Carbon Dioxide

from Intermediate

(“p”) Model

Versions Leading to

GFDL’s CM2.0 and

CM2.1
43

Climate Models: An Assessment of Strengths and Limitations

6oC are inconsistent with observed relaxation

times. It is important to note that these “observational” studies of climate sensitivity that do

not utilize GCMs still make use of models, but

they use simple energy balance “box” models

rather than GCMs. The value of these studies

depends on the relevance of the simple models

as well as on the techniques for estimating parameters in models that control climate sensitivity. From these analyses, one can infer that

further research isolating changes in ocean heat

content after eruptions, such as that of Church,

White, and Arblaster (2005), will be needed to

strengthen constraints on climate sensitivity

provided by responses to volcanic eruptions.

4.1.2.2 NATURAL CLIMATEVARIABILITY

Natural variability of climate also provides a

way of estimating the strength of the restoring

forces that determine climate sensitivity. Just as

investigators learn something about sensitivity

by watching the climate recover from a volcanic

eruption, they can hope to obtain similar information by watching the climate relax from an

unforced period of unusual global warmth or

cold. This approach to constraining the response

to a perturbation by examining the character of

a system’s natural variability, discussed by Leith

(1975) in the context of climate sensitivity, is

referred to as “fluctuation-dissipation” analysis

in other branches of physics. In the case of equilibrium statistical mechanics, this relationship

between characteristics of natural variability

and response to an external force has been

placed on a firm theoretical footing, but application to the climate is more heuristic, generally depending on approximation of the climate

system by a linear stochastically forced model.

The power of the approach is illustrated by Gritsun and Branstator (2007) in a study of the extratropical atmosphere’s response to a

perturbation in tropical heating. A recent attempt

to apply this approach to climate sensitivity can

be found in Schwartz (2007). This technique deserves more attention, with careful analysis of

uncertainties. Its value likely will be determined

by its ability to infer an AOGCM’s sensitivity

from an analysis of its internal variability.

4.1.2.3 SOLAR VARIATIONS

The 11-year solar cycle has potential for providing very useful information on climate sensitivity. Total solar irradiance is known to vary

by roughly 0.1% over this cycle (Frölich 2002).

The expected response in global mean temperature is only ~0.1oC, so the technique is limited

in value by the quality and length of the observational record, both of which restrict our ability to isolate this small signal. Recent results

show promise in more cleanly identifying the

climatic response to this cyclic perturbation

(Camp and Tung 2007). Since ultraviolet wavelengths play a disproportionately larger role in

these cyclic variations, detailed representations

of the stratosphere and mesosphere, where ultraviolet radiation is absorbed, along with ozone

chemistry are required for quantitative analysis

of climatic response to the solar cycle (e.g.,

Shindell et al. 2006). Solar variations also have

been invoked repeatedly to explain early 20th

Century warming and to connect the Little Ice

Age to the Maunder Minimum in sunspot number. While these connections may very well

have a valid basis, using them to constrain climate sensitivity remains difficult as long as

variations in insolation on time scales longer

than the 11-yr cycle are not better quantified.

To illustrate the difficulty, we note the substantial reduction in estimated insolation variations

in the 20th Century between the Third and

Fourth IPCC Assessments (Forster et al. 2007).

Further analyses of responses to the sunspot

cycle in models and observations seem likelier

to lead to stronger constraints on climate sensitivity in the near term.

4.1.2.4 GLACIAL-INTERGLACIALVARIATIONS

The glacial-interglacial fluctuations of the Pleistocene (the Ice Ages) are thought to be forced

by changes in the Earth’s orbit on time scales of

20,000 years and longer—the astronomical theory of the Ice Ages. Since this theory assumes

that the mean temperature of the Earth can be

altered by changing the distribution of the incoming solar flux without changing its global

mean, it suggests important limitations to simple models based solely on global mean radiative forcing. For the limited purpose of

constraining climate sensitivity, we need not understand how glacial-interglacial variations of

ice sheets and of carbon dioxide are forced by

changes in the Earth’s orbit. Since we have

knowledge from ice cores of greenhouse gas

concentrations at the peak of the last major gla-
44

The U.S. Climate Change Science Program Chapter 4 - Model Climate Sensitivity

cial advance 20,000 years ago as well as considerable information on the extent of continental ice sheets, one may ask if climate models

can simulate the ocean-surface temperatures inferred from a variety of proxies, given these

greenhouse gas concentrations and ice sheets

(Manabe and Broccoli 1985). A logical assumption is that models that are more sensitive

to doubling of carbon dioxide would also simulate larger cooling during the low carbon dioxide levels 20,000 years ago. Crucifix (2006)

describes some of the difficulties with this simple picture. Annan and Hargreaves (2006) argue

that the tropics and Antarctica are regions where

this connection may be the strongest. Model results generated in the Paleoclimate Modelling

Intercomparison Project (Braconnet et al.

2007a, b; Crucifix et al. 2006)) provide a valuable resource for analyzing these relationships.

Despite these complications, several studies

agree that past climates are difficult to reconcile with the low end of the equilibrium-sensitivity range generated by models (e.g., Hansen

et al. 1993; Covey, Sloan, and Hoffert 1996).

Models of the last glacial maximum also provide some of the strongest evidence that climate

sensitivity is very unlikely to be larger than 6°C

(Annan et al. 2005; Annan and Hargreaves

2006). As paleoclimatic reconstructions for this

period improve, these simulations will become

of greater quantitative value. Uncertainty in Ice

Age aerosol concentrations may be the most difficult obstacle to overcome.

4.1.2.5 SEASONALVARIATION

The seasonal cycle is a familiar forced climate

response to changes in the Earth-sun geometry

and, therefore, should yield information on climate sensitivity. Although the seasonal cycles

of global (Lindzen 1994) and hemispheric

(Covey et al. 2000) mean temperature are not

themselves strongly related to equilibrium climate sensitivity, regional variations and other

aspects of the seasonal cycle may constrain sensitivity. Knutti et al. (2006) provide an example

of a methodology using ensembles of climate

model simulations to search for variables, or

combinations of variables, that correlate with

climate sensitivity (see also Shukla et al. 2006).

If such a variable that predicts climate sensitivity in models is found, investigators can then examine its value in observations and hope

thereby to constrain climate sensitivity. Knutti

et al. (2006) use a neural network to look for aspects of the seasonal cycle with this predictive

capability, with some success. Their study favors sensitivity in the middle of the typical

model range (near 3 ̊C).

The work of Qu and Hall (2006) provides an especially straightforward example of this approach. They do not address climate sensitivity

directly but only the strength of one feedback

mechanism that contributes to sensitivity: snowalbedo feedback (the decrease in reflection of

solar radiation by snow as the snowcover retreats in a warming climate). Qu and Hall

demonstrate that the strength of this feedback

in models is strongly correlated to the seasonal

cycle of the snow cover simulated by the models. Comparison of observed and simulated seasonal cycles of snow cover then suggest which

model simulations of snow albedo feedback are

the most reliable. These studies suggest that detailed comparisons of modeled and observed

seasonal cycles should provide valuable information on climate sensitivity in the future.

The observed 20th Century warming is a fundamental constraint on climate models, but it is

less useful than one might think in constraining

sensitivity because of the large uncertainty in

forcing due to anthropogenic aerosols. Twentieth Century simulations are important in

demonstrating the consistency of certain combinations of sensitivity, aerosol forcing, and

ocean-heat uptake, but they do not provide a

sharp constraint on sensitivity in isolation

(Kiehl 2007). Further discussion of 20th Century

simulations can be found in Chapter 5.

Rather than focusing on one particular observational constraint or on models in isolation, attempts to combine some or all of these

observational constraints with model simulations are recognized as the most productive approaches to constraining climate sensitivity

(Bierbaum et al. 2003; Randall et al. 2007; Stott

and Forest 2007). As an example, while model

ensembles in which parameters are varied sys2 Estimating the probability of very high climate sensitivities above the high end of the CMIP3 model range, even if

these probabilities are low, can be relevant for analyses of unlikely but potentially catastrophic climate change. It is

not within the scope of this report to attempt to quantify these probabilities.
45

Climate Models: An Assessment of Strengths and Limitations

tematically can include models with sensitivities larger than 6oC (Stainforth et al. 2005; Roe

and Baker 2007), these very high values can be

excluded with high confidence through comparisons with observations of volcanic relaxation

times and simulations of the last glacial maximum. As summarized by Randall et al. (2007) in

the Fourth IPCC Assessment, these multiconstraint studies are broadly consistent with the

spread of sensitivity in the CMIP3 models.2

4.2 FEEDBACKS

Better understanding of Earth’s climate sensitivity, with potential reduction in its uncertainty,

will require better understanding of a variety of

climate feedback processes (Bony et al. 2006).

We discuss some of these processes in more detail below.

4.2.1 Cloud Feedbacks

Clouds reflect solar radiation to space, cooling

the Earth-atmosphere system. Clouds also trap

infrared radiation, keeping the Earth warm. The

integrated net effect of clouds on climate depends on their height, location, microphysical

structure, and evolution through the seasonal

and diurnal cycles. Cloud feedback refers to

changes in cloud amounts and properties that

can either amplify or moderate a climate

change. Differences in cloud feedbacks in climate models have been identified repeatedly as

the leading source of spread in model-derived

estimates of climate sensitivity (beginning with

Cess et al. 1990). The fidelity of cloud feedbacks in climate models therefore is important

to the reliability of their prediction of future climate change.

Soden and Held (2006) evaluated cloud feedbacks in 12 CMIP3 AOGCMs and found

weakly to strongly positive cloud feedback in

the various models. The highest values of cloud

feedback raise the equilibrium climate sensitivity (for CO2 doubling) from values of about 2 K

to roughly 4 K. In comparison with the earlier

studies of Cess (1990) and Colman (2003), the

spread of cloud feedbacks among GCMs has

become somewhat smaller over the years but is

still very substantial. Indeed, intermodel differences in cloud feedback are the primary reason

that models disagree in their estimates of equilibrium climate sensitivity; which (if any) models give accurate cloud simulations remains

unclear (Randall et al. 2007) as debate over specific processes continues (Spencer et al. 2007)

Examples of competing hypotheses concerning

high clouds (for which the infrared trapping effects are large) are the IRIS hypothesis of

Lindzen, Chou, and Hou (2001) and the FAT

(Fixed Anvil Temperature Hypothesis) of Hartmann and Larsson (2002). The IRIS hypothesis

asserts that warmer temperatures cause the area

coverage of clouds in the tropical upper troposphere to decrease, a negative feedback since

these clouds are infrared absorbers. The FAT hypothesis asserts that the altitude of these tropical high clouds tends to increase with warming,

minimizing the temperature change at the cloud

tops—a positive feedback since the lack of

warming at cloud top prevents the increase in

outgoing radiation needed to balance the heat

trapping of greenhouse gases. Observational

studies aimed at evaluating these mechanisms

are difficult because clouds in the tropics are

strongly forced by circulations that are, in turn,

driven by temperature gradients and not by the

local temperature in isolation. These circulation

effects must be eliminated to isolate effects relevant to global warming. Very high resolution

simulations in localized regions have some potential to address these questions. The FAT hypothesis, in particular, has received some

support from high-resolution modeling (Kuang

and Hartmann 2007).

Although these studies focus on high clouds, the

intermodel differences in model responses of

low-level clouds are responsible for most of the

spread of cloud feedback values in climate models (Bony et al. 2006). While tempting, assuming that this implies that low-cloud feedbacks

are more uncertain than high-cloud feedbacks

probably is premature. The strengths and weaknesses of cloud-cover simulations for presentday climate are described in Chapter 5.

As discussed in Chapter 6, a new class of much

higher resolution global atmospheric simulations promises fundamental improvements in

cloud simulation. Using the surrogate climate

change framework of Cess (1990) in which
46

The U.S. Climate Change Science Program Chapter 4 - Model Climate Sensitivity

ocean temperatures are warmed uniformly,

Miura et al. (2005) carried out experiments

using a global model with 7-km resolution, obtaining results suggestive of negative cloud

feedback outside the tropics, and Wyant et al.

(2006) describe results from a multigrid technique in which high-resolution cloud models are

embedded in each grid box of a traditional

GCM. Much work will be required with these

new types of models before they can be given

substantial weight in discussions of the most

probable value for cloud feedback, but they suggest that real-world feedback is less positive

than the typical CMIP3 AGCMs and that midlatitude cloud feedbacks may be more important than hitherto assumed. Results from this

new generation of models will be of considerable interest in the coming years.

Several questions remain to be answered about

cloud feedbacks in GCMs. Physical mechanisms underlying cloud feedbacks in different

models must be better characterized. How best

to judge the importance of model biases in simulations of current climate and in simulations of

cloud changes in different modes of observed

variability is not clear. In particular, how to

translate these biases into levels of confidence

in simulations of cloud feedback processes in

climate change scenarios is unclear. New satellite products such as those from active radar and

lidar systems should play a central role in cloud

research in coming years by providing more

comprehensive space-time cloud datasets.

4.2.2 Water-Vapor Feedbacks

Analysis of radiative feedbacks in the CMIP3

models (Soden and Held 2006) reaffirms that

water-vapor feedback—the increase in heat

trapping due to the increase in water vapor as

the lower atmosphere warms—is fundamental

to the models’ climate sensitivity. The strength

of their water-vapor feedback typically is close

in magnitude to but slightly weaker than that obtained by assuming that relative humidity remains unchanged as the atmosphere warms.

A trend toward increasing column water vapor

in the atmosphere consistent with model predictions has been documented from microwave

satellite measurements (Trenberth, Fasullo, and

Smith 2005), and excellent agreement for this

quantity has been found between satellite observations and climate models constrained by

the observed ocean-surface temperatures

(Soden 2000). These studies increase confidence in the models’ vapor distributions more

generally, but column water vapor is dominated

by changes in the lower troposphere, whereas

water-vapor feedback is strongest in the upper

troposphere where most outgoing terrestrial radiation to space originates. The results of Soden

and Held (2000) imply that at least half the

global water-vapor feedback arises from the

tropical upper troposphere in models in which

relative humidity changes are small. Studies of

vapor trends in this region are therefore of central importance. Soden et al. (2005) present

analysis of radiance measurements, implying

that relative humidity has remained unchanged

in the upper tropical troposphere over the past

few years, which, combined with temperature

measurements, provides evidence that water

vapor in this region is increasing.

Observations of interannual variability in water

vapor can help to judge the quality of model

simulations. Soden et al. (2002) concluded that

a GCM appropriately simulates water-vapor

variations in the tropical upper troposphere during cooling associated with the Pinatubo volcanic eruption. Minschwaner, Essler, and

Sawaengphokhai (2006) compared the interannual variability of humidity measured in the

highest altitudes of the tropical troposphere with

CMIP3 20th Century simulations. Both models

and observations show a small negative correlation between relative humidity and tropical

temperatures, due in large part to lower relative

humidity in warm El Niño years and higher relative humidity in cold La Niña years. However,

there is a suggestion that the magnitude of this

covariation is underestimated in most models.

There also is a tendency for models with larger

interannual variations in relative humidity to

produce larger reductions in this region in response to global warming, suggesting that this

deficiency in interannual variability might be

relevant for climate sensitivity. (This is another

example, analogous to the Qu and Hall (2006)

analysis of snow feedback, in which the strength

of a feedback in models is correlated with a

more readily observed aspect of climatic variability.) In short, the study of Minschwaner,

Essler, and Sawaengphokhai (2006) suggests
47

Climate Models: An Assessment of Strengths and Limitations

that water-vapor feedback in the very highest

levels of the tropical troposphere may be overestimated in models, but it does not imply that

a significant correction is needed to the overall

magnitude of the feedback.

Positive water-vapor feedback, resulting from

increases in vapor that keep the relative humidity from changing substantially as the climate

warms, has been present in all GCMs since the

first simulations of greenhouse gas–induced

warming (Manabe and Wetherald 1975). It represents perhaps the single most robust aspect of

global warming simulations. Despite the fact

that the distribution of water vapor in the atmosphere is complex, we are aware of no observational or modeling evidence that casts

doubt of any significance on this basic result,

and we consider the increase in equilibrium sensitivity to roughly 2oC from this feedback to be

a solid starting point from which the more uncertain cloud feedbacks then operate.

4.3 TWENTIETH CENTURY

RADIATIVE FORCING

Radiative forcing is defined as a change that affects the Earth’s radiation balance at the top of

the tropopause between absorbed energy received in the form of solar energy and emitted

infrared energy to space, typically expressed in

terms of changes to the equilibrium preindustrial climate. Uncertainties in 20th Century radiative forcing limit the precision with which

climate sensitivity can be inferred from observed temperature changes. In this section, we

briefly discuss the extent to which models provide consistent and reliable estimates of radiative forcing over the 20th Century. Further

information is provided by Forster et al. (2007).

Radiative forcing in models can be quantified

in different ways, as outlined by Hansen et al.

(2005). For example, the radiative forcing for

the idealized case of CO2 doubling can be computed by (1) holding all atmospheric and surface temperatures fixed, (2) allowing the

stratospheric temperatures to adjust to the new

CO2 levels, (3) fixing surface temperatures over

both land and ocean and allowing the atmosphere to equilibrate, or (4) fixing ocean temperatures only and allowing the atmosphere and

land to equilibrate. Comparing model forcings

in the literature is complex because of differing

calculations in different papers. An important

objective for the climate modeling community

is to improve the consistency of its reporting of

radiative forcing in models.

4.3.1 Greenhouse Gases

Greenhouse gases like carbon dioxide and

methane have atmospheric lifetimes that are

long, compared to the time required for these

gases to be thoroughly mixed throughout the atmosphere. Trends in concentration, consistent

throughout the world, have been measured routinely since the International Geophysical Year

in 1958. Measurements of gas bubbles trapped

in ice cores give the concentration prior to that

date (with less time resolution). Nevertheless,

the associated radiative forcing varies somewhat

among climate models because GCM radiative

calculations must be computationally efficient,

necessitating approximations that make them

less accurate than the best laboratory spectroscopic data and radiation algorithms. Using

changes in well-mixed greenhouse gases measured between 1860 and 2000, Collins et al.

(2006b) compared the radiative forcing of climate models (including CCSM, GFDL, and

GISS) with line-by-line (LBL) calculations in

which fewer approximations are made. The median LBL forcing at the top of the model by

greenhouse gases is 2.1 W/m2, and the corresponding median among the climate models is

higher by only 0.1 W/m2. However, the standard

deviation among model estimates is 0.30 W/m2

(compared to 0.13 for the LBL calculations).

Based on these most-recent comparisons with

LBL computations, we can reasonably assume

that radiative forcing due to carbon dioxide doubling in individual climate models may be in

error by roughly 10%.

4.3.2 Other Forcings

While increases in the concentration of greenhouse gases provide the largest radiative forcing during the 20th Century, other smaller

forcings must be considered to quantitatively

model the observed change in surface air temperature. The burning of fossil fuels that release

greenhouse gases into the atmosphere also produces an increase in atmospheric aerosols

(small liquid droplets or solid particles that are
48

The U.S. Climate Change Science Program Chapter 4 - Model Climate Sensitivity

temporarily suspended in the atmosphere).

Aerosols cool the planet by reflecting sunlight

back to space. In addition, among other forcings

are changes in land use that alter the reflectivity

of the Earth’s surface, as well as variations in

sunlight impinging on the Earth.

4.3.2.1 AEROSOLS

Aerosols have short lifetimes (on the order of a

week) that prevent them from dispersing uniformly throughout the atmosphere, in contrast

to well-mixed greenhouse gases. Consequently,

aerosol concentrations have large spatial variations that depend on the size and location of

sources as well as changing weather that disperses and transports the aerosol particles.

Satellites can provide the global spatial coverage needed to observe these variations, but

satellite instruments cannot distinguish between

natural and anthropogenic contributions to total

aerosol forcing. The anthropogenic component

can be estimated using physical models of

aerosol creation and dispersal constrained by

available observations.

Satellites increasingly are used to provide observational estimates of the “direct effect” of

aerosols on the scattering and absorption of radiation. These estimates range from –0.35 +/–

0.25 W/m2 (Chung et al. 2005) to –0.5 +/– 0.33

W/m2 (Yu et al. 2006) to –0.8 +/– 0.1 W/m2

(Bellouin et al. 2005). The fact that two of these

three estimates do not overlap suggests incomplete uncertainty analysis in these studies. In

particular, each calculation must decide how to

extract the anthropogenic fraction of aerosol.

Global direct forcing by aerosols is estimated

by the IPCC AR4 as –0.2 +/– 0.2 W/m2, according to models, and –0.5 +/– 0.4 W/m2,

based upon satellite estimates and models. This

central estimate is smaller in magnitude than the

2001 IPCC estimate of –0.9 +/– 0.5 W/m2.

In addition to their direct radiative forcing,

aerosols also act as cloud condensation nuclei.

Through this and other mechanisms, they alter

the radiative forcing of clouds (Twomey 1977;

Albrecht 1989; Ackerman et al. 2004). Complex

interactions among aerosols and cloud physics

make this “aerosol indirect effect” very difficult

to measure, and model estimates of it vary

widely. This effect was generally omitted from

the IPCC AR4 models, although, among the

U.S. CMIP3 models, it was included in GISS

ModelE where increased cloud cover due to

aerosols results in a 20th Century forcing of –

0.8 W/m2 (Hansen et al. 2007).

4.3.2.2 VARIABILITY OF SOLAR IRRADIANCE AND

VOLCANIC AEROSOLS

Other climate forcings include variability of

solar irradiance and volcanic aerosols. Satellites

provide the only direct measurements of these

quantities at the top of the atmosphere. Satellite

measurements of solar irradiance are available

from the late 1970s and now span about 3 of the

sun’s 11-year magnetic or sunspot cycles. Extracting a long-term trend from this relatively

brief record (Wilson et al. 2003) is difficult.

Prior to the satellite era, solar variations are inferred using records of sunspot area and number

and cosmic ray–generated isotopes in ice cores

(Foukal et al. 2006), which are converted into

irradiance variations using empirical relations

The U.S. CMIP3 models all use the solar reconstruction by Lean, Beer, and Bradley (1995)

with subsequent updates.

Volcanic aerosols prior to the satellite era are inferred from surface estimates of aerosol optical

depth. The radiative calculation requires aerosol

amount and particle size, which is inferred

using empirical relationships with optical depth

derived from recent eruptions. The GFDL and

GISS models use updated versions of the Sato

et al. (1993) eruption history, while the CCSM

uses Ammann et al. (2003). As with solar variability, different reconstructions of volcanic

forcing differ substantially (see, e.g., Lindzen

and Giannitsis 1998). Land-use changes also are

uncertain, and they can be of considerable significance locally. Global models, however, typically show very modest global responses, as

discussed in Hegerl et al. (2007).

Studies attributing 20th Century global warming

to various natural and human-induced forcing

changes clearly are hindered by these uncertainties in radiative forcing, especially in the

solar and aerosol components. The trend in total

solar irradiance during the last few decades (averaging over the sun’s 11-year cycle) apparently

is negative and thus cannot explain recent global

warming (Lockwood and Fröhlich 2007). The
49

Climate Models: An Assessment of Strengths and Limitations

connection between solar energy output

changes and the warming earlier in the 20th Century is more uncertain. With the solar reconstructions assumed in the CMIP3 models, much

of the early 20th Century warming is driven by

solar variations, but uncertainties in these reconstructions do not allow confident attribution

statements concerning this early-century warming. The large uncertainties in aerosol forcing

are a more important reason that the observed

late 20th Century warming cannot be used to

provide a sharp constraint on climate sensitivity.

We do not have good estimates of the fraction of

greenhouse gas forcing that has been offset by

aerosols.

4.4 OCEAN HEAT UPTAKE AND

CLIMATE SENSITIVITY

As noted above, the rate of heat uptake by the

ocean is a primary factor determining transient

climate response (TCR): the larger the heat uptake by the oceans, the smaller the initial response of Earth’s surface temperature to

radiative forcing (e.g., Sun and Hansen 2003).

Studies show (e.g., Völker, Wallace, and WolfGladrow 2002) that CO2 uptake by the ocean

also is linked to certain factors that control heat

uptake, albeit not in a simple fashion. In an

AOGCM, the ocean component’s ability to take

up heat depends on vertical mixing of heat and

salt and how the model transports heat between

low latitudes (where heat is taken up by the

ocean) and high latitudes (where heat is given

up by the ocean). The models make use of several subgrid-scale parameterizations (see Chapter 2), which have their own uncertainties. Thus,

as part of understanding a model’s climate-sensitivity value, we must assess its ability to represent the ocean’s mixing processes and the

transport of its heat, as well as feedbacks among

the ocean, ice, and atmosphere.

The reasons for differing model estimates of

ocean uptake are incompletely understood. Assessments typically compare runs of the same

model or output from different AOGCMs.

Raper, Gregory, and Stouffer (2002) examined

climate sensitivity and ocean heat uptake in a

suite of then-current AOGCMs. They calculated

the ratio of the change in heat flux (from the

surface to the deep ocean) to the change in temperature (Gregory and Mitchell 1997) and

found in general that models with lower oceanuptake efficiency had lower climate sensitivity,

as expected (Hansen et al. 1985; Wigley and

Schlesinger 1985). Uptake efficiency can be

thought of as the amount of heat the ocean absorbs through mixing relative to the change in

surface temperature (e.g., to reproduce the observed 20th Century warming despite a high climate sensitivity, a model needs large heat export

to the deep ocean). Comparing the current generation of AOGCMs with the previous generation, however, Kiehl et al. (2006) found that the

atmospheric component of the models is the primary reason for different transient climate sensitivities, and the ocean component’s ability to

uptake heat is of secondary importance. Ocean

heat-uptake efficiency values calculated in this

study differ substantially from those in Raper et

al. (2002).

Despite these complexities, modern ocean

GCMs are able to transport both heat

(AchutaRao et al. 2006) and passive tracers

such as chlorofluorocarbons and radiocarbon

(Gent et al. 2006; Dutay et al. 2002) consistent

with the limited observations available for these

quantities. Better observations in the future—

particularly of the enhanced ocean warming expected from the anthropogenic greenhouse

effect—should provide stronger constraints on

modeled ocean transports.

4.5 IMPACT OF CLIMATE

SENSITIVITY ON USING MODEL

PROJECTIONS OF FUTURE

CLIMATES

This chapter has emphasized the global and annual mean of surface temperature change even

though practical applications of climate change

science involve particular seasons and locations. The underlying assumption is that local

climate impacts scale with changes in global

mean surface temperature (Santer et al. 1990).

In that case, time histories of global mean temperature—obtained from a simple model of

global mean temperature, run under a variety of

forcing scenarios—could be combined with a

single AOGCM-produced map of climate

change normalized to the global mean surface

temperature change. In that way, the regional

changes expected for many different climateforcing scenarios could be obtained from just
50

The U.S. Climate Change Science Program Chapter 4 - Model Climate Sensitivity

one AOGCM simulation using one idealized

forcing scenario such as atmospheric CO2 doubling (Oglesby and Saltzman 1992) or 1% per

year increasing CO2 (Mitchell et al. 1999). This

“pattern scaling” assumption also permits the

gauging of effects on regional climate change

that arise from different estimates of global climate sensitivity. For example, if an AOGCM

with TCR = 1.5 K predicts temperature and precipitation changes ΔT and ΔP as a function of

season and location in a 21st Century climate

simulation, and if investigators believe that TCR

= 1.0 K is a better estimate of the real world’s

climate sensitivity, then, under the pattern-scaling assumption, they would reduce the local ΔT

and ΔP values by 50%.

Although it introduces its own uncertainties, the

pattern-scaling assumption increasingly is used

in climate impact assessments (e.g., Mitchell

2003; Ruosteenoja, Tuomenvirta, and Jylha

2007). For example, the annual mean temperature change averaged over the central United

States during the 21st Century for any of the

projections in the IPCC Special Report on

Emissions Scenarios shows that about 75% of

the variance among the CMIP3 models is explained by their differing global mean warming

(B. Wyman, personal communication). (The

central United States is defined in this context

following Table 11.1 in Christensen et al. 2007.)

Precipitation patterns, in contrast, do not scale

as well as temperature patterns due to sharp

variations between locally decreasing and locally increasing precipitation in conjunction

with global warming.
CHAPTER

5

Climate Models: An Assessment of Strengths and Limitations

Model Simulation 

of Major Climate Features

CHAPTER

5

51

Although a typical use of atmosphere-ocean general circulation model (AOGCM) output for climate impact assessment focuses on one particular region such as a river basin or one of the 50

United States, knowing model simulation overall accuracy on continental to global scales is important. Fidelity in simulating climate on the largest scales is a necessary condition for credible

predictions of future climate on smaller scales. Model developers devote great effort to assessing the level of agreement between simulated and observed large-scale climate, both for the present day and for the two centuries since the Industrial Era began. Unlike physical theories of such

fundamentally simple systems as the hydrogen atom, AOGCMs cannot promise precise accuracy

for every simulated variable on all relevant space and time scales. Nevertheless, before applying a

model to a practical question, users should demand reasonable overall agreement with observations, with the definition of “reasonable” in part subjective and dependent on the problem at

hand. Here we provide an overview of how well modern AOGCMs satisfy this criterion.

5.1 MEAN SURFACE

TEMPERATURE AND

PRECIPITATION

Simulations of monthly near-surface air temperature and precipitation provide a standard

starting point for model evaluation since these

fields are central to many applications. The two

fields also illustrate the difficulty in designing

appropriate metrics for measuring model quality.

By most measures, modern AOGCMs simulate

the basic structure of monthly mean near-surface temperatures quite well. The globally averaged annual mean value generally lies within

the observed range (~286 to 287 K) of modern

and preindustrial values; this agreement, however, is in part a consequence of the “final tuning” of the models’ energy balance as described

in Chapter 2 and by itself is not a stringent test

of model quality. More relevant is consideration

of space and time variations about the global annual mean (including the seasonal cycle). The

overall correlation pattern between simulations

and observations typically is 95 to 98%, and

variation magnitudes typically agree within

±25% (Covey et al. 2003). This level of success

has been retained in the latest generation of

models that allow ocean and atmosphere to exchange heat and water without artificial adjustments (Randall et al. 2007). Nevertheless, as

shown below, local errors in surface temperature that are clearly outside the bounds of observational uncertainty persist in the latest

generation of models.
The U.S. Climate Change Science Program Chapter 5 - Model Simulation of Major Climate Features

52

AOGCM simulations are considerably less accurate for monthly mean precipitation than for

temperature. The space-time correlation between models and observations typically is only

about 50 to 60% (Covey et al. 2003). As we discuss below, these poor correlations originate

mainly in the tropics, where precipitation varies

greatly over relatively small ranges of latitude

and longitude. Strong horizontal gradients in the

field lead to a significant drop in correlations

with observations, even with only slight shifts

in the modeled precipitation distribution. These

modest correlations are relevant for precipitation at a particular location, but AOGCMs generally reproduce the observed broad patterns of

precipitation amount and year-to-year variability (see Fig. 5.1 and Dai 2006). One prominent

error is that models without flux adjustment

typically fail to simulate the observed northwest-to-southeast orientation of a large region

of particularly heavy cloudiness and precipitation in the southwest Pacific Ocean. Instead,

these models tend to rotate this convergence

zone into an east-west orientation, producing an

unrealistic pair of distinct, parallel convection

bands straddling the equator instead of a continuous Inter-Tropical Convergence Zone

(ITCZ). The double-ITCZ error has been frustratingly persistent in climate models despite

much effort to correct it.

Another discrepancy between models and observations appears in the average day-night

cycle of precipitation. While the model’s diurnal

temperature cycle exhibits general agreement

with observations, simulated cloud formation

and precipitation tend to start too early in the

day. Also, when precipitation is sorted into light,

moderate, and heavy categories, models reproduce the observed extent of moderate precipitation (10 to 20 mm/day) but underestimate that

of heavy precipitation and overestimate the extent of light precipitation (Dai 2006). Additional

model errors appear when precipitation is studied in detail for particular regions [e.g., within

the United States (Ruiz-Barradas and Nigam

2006)].

For illustration, we show examples from two of

the U.S. models discussed in Chapter 4. In Fig.

5.1 (Delworth et al. 2006) and Fig. 5.2 (Collins

et al. 2006a), simulated and observed maps of

surface temperature and even precipitation appear rather similar at first glance. Constructing

simulated-minus-observed difference maps,

however, reveals monthly and seasonal mean

temperature and precipitation errors up to 10°C

and 7 mm/day, respectively, at some points.

CCSM3 temperature-difference maps exhibit

the largest errors in the Arctic (note scale

change in Fig. 5.2d), where continental wintertime near-surface temperature is overestimated.

AOGCMs find this quantity particularly difficult to simulate because, for land areas near the

poles in winter, models must resolve a strong

temperature inversion above the surface (warm

air overlying cold air). For precipitation, GFDL

difference maps reveal significant widespread

errors in the tropics, most notably in the ITCZ

region discussed above and in the Amazon

River basin, where precipitation is underestimated by several millimeters per day. Similar

precipitation errors appear in CCSM3 results

(e.g., a 28% underestimate of Amazon annual

mean). AOGCM precipitation errors have serious implications for Earth system models with

interactive vegetation, because such models use

simulated precipitation to calculate plant growth

(see Chapter 6). Errors of this magnitude would

produce an unrealistic distribution of vegetation

in an Earth system model, for example, by spuriously deforesting the Amazon basin.

In summary, modern AOGCMs generally simulate continental and larger-scale mean surface

temperature and precipitation with considerable

accuracy, but the models often are not reliable

for smaller regions, particularly for precipitation.
Climate Models: An Assessment of Strengths and Limitations

53

Figure 5.1a–e. Observed and GFDL Model-Simulated Precipitation (mm/day).

Observed image from P. Xie and P.A. Arkin 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite

estimates, and numerical model outputs. Bulletin American Meteorological Society, 78, 2539–2558. [Other images from Fig. 17 in T.L.

Delworth et al. 2006: GFDL’s CM2 global coupled climate models. Part I: Formulation and simulation characteristics. J. Climate, 19, 643–

674. Images reproduced with permission of the American Meteorological Society.]

(a)

(b)

(c)

(d)

(e)

Observed

CM2.0 CM2.1

CM2.0 minus observed CM2.1 minus observed

150°E 110°W 10°W 90°E

150°E 110°W 10°W 90°E 150°E 110°W 10°W 90°E

150°E 110°W 10°W 90°E 150°E 110°W 10°W 90°E

80°N

40°N

0°

40°S

80°S

80°N

40°N

0°

40°S

80°S

80°N

40°N

0°

40°S

80°S

0 4 1 2 3 1 5 10 5 20

–7 –3 –5 –1.75 –1.25 –0.25–0.75 0.25 0.75 1.75 5 1.25 3 7
54

The U.S. Climate Change Science Program Chapter 5 - Model Simulation of Major Climate Features

Figure 5.2a–c. CCSM3 December-January-February Simulated (top panel), Observed (middle panel), and

Simulated-Minus-Observed (bottom panel) Near-Surface Air Temperature for Land Areas (°C).

Note change in scale from 5.2a to 5.2c. [Figures from W. Collins et al. 2006: The Community Climate System Model Version 3 (CCSM3).

J. Climate, 19(11), 2122–2143. Reproduced with permission of the American Meteorological Society.]

CCSM3 – WILLMOTT

mean = 0.84 rmse = 3.58 K

CCSM3 (yrs 400–499) DJF 2-meter Temp (land) mean = 276.91 K

WILLMOTT

2-meter Temp (land) mean = 276.31 K

Min = 236.83 Max = 305.62

Min = 220.88 Max = 305.82

Min = –16.12 Max = 20.98

310

305

300

295

290

285

280

275

270

260

250

240

230

220

210

310

305

300

295

290

285

280

275

270

260

250

240

230

220

210

12

10

8

6

4

2

0

–2

–4

–6

–8

–10

–12

–14
55

Climate Models: An Assessment of Strengths and Limitations

5.2 TWENTIETH CENTURY

TRENDS

Modern AOGCMs are able to simulate not only

the time-average climate but also changes

(trends) in climate over the past 140 years. For

example, Fig. 5.3 shows results from the three

U.S. models and the “average” CMIP3 model.

Plotted in the figure are curves of globally averaged annual mean near-surface temperature

from model simulations and the observational

value as determined from the U.K. Climatic Research Unit (CRU) gridded observational database. Two curves are plotted for the CMIP3

models. The first shows the average over all

CMIP3 models, and the second, the average

over only CMIP3 models that included the effects of volcanic eruptions. Results from individual U.S. models are shown for separate

ensemble members (dotted lines) and for the average over all ensemble members (continuous

lines). Individual members of a particular model

ensemble differ from each other because they

were run from different initial conditions. Precise initial conditions, especially deep-ocean

temperature and salinity, are not known for

1860. The spread among individual simulations

from the same model (the dotted-line curves)

thus indicates uncertainty in model-simulated

temperature arising from lack of knowledge

about initial conditions.

These results demonstrate that modern climate

models exhibit agreement with observed global

mean near-surface temperature trends to within

observational uncertainty, despite imprecise initial conditions and uncertain climate forcing

and heat uptake by the deep ocean (Min and

Hense 2006). Models achieve this agreement

only if they include anthropogenic emissions of

greenhouse gases and aerosols. No plausible

combination of natural climate-forcing factors

allows models to explain the global warming

observed over the last several decades. Indirect

solar effects [e.g., involving cosmic rays and

clouds (Svensmark 2007)] are not generally included in AOGCM simulations. These effects

have been proposed occasionally as causes of

global warming, although over the past 20 years

their trends would, if anything, lead to cooling

Figure 5.2d. CCSM3 Annual Mean Simulated-Minus-Observed Sea Surface

Temperature (°C). 

[Figure from W. Collins et al. 2006: The Community Climate System Model Version 3 (CCSM3). J. Climate,

19(11), 2122–2143. Reproduced with permission of the American Meteorological Society.]
56

The U.S. Climate Change Science Program Chapter 5 - Model Simulation of Major Climate Features

(Lockwood and Fröhlich 2007). Unless the

models grossly underestimate the climate system’s natural internally generated variability or

are all missing a large unknown forcing agent,

the conclusion is that most recent warming is

anthropogenic (IPCC 2007b).

Nevertheless, total climate forcing during the

20th Century is not accurately known, especially

the aerosol component (see Chapter 2). Aerosol

forcing used in these simulations, however, is

derived from aerosol parameterizations constrained by satellite and ground-based measurements of the aerosols themselves and was not

designed to obtain a fit to observed global mean

temperature trends. The observed trend in surface temperature can result from models with

different aerosol forcing (Schwartz 2007). Thus,

20th Century temperature records cannot distinguish models that would warm by differing

amounts for the same total forcing.

Note that climate sensitivity is not prescribed in

AOGCMs. Instead, this sensitivity emerges as a

result of a variety of lower-level modeling

choices. In contrast to simple energy-balance

models that predict only the global mean temperature using a limited representation of climate physics, an AOGCM’s climate sensitivity

is difficult to specify a priori. More fundamentally, AOGCMs, unlike simpler climate models,

have far fewer adjustable parameters than the

number of observations available for model

evaluation (Randall et al. 2007). Thus, an

AOGCM’s multidimensional output can be

compared to observations independent of this

adjustment (e.g., using observed trends in regional temperature). Agreement between modeled and observed trends has been described for

temperature trends on each inhabited continent

(Min and Hense 2007); for trends in climate extremes, such as heat-wave frequency and frostday occurrence (Tebaldi et al. 2006); and for

trends in surface pressure and Arctic sea ice (see

Chapter 9 in IPCC 2007), all of which complement comparisons between modeled and observed time-averaged climate discussed in the

following sections.

Figure 5.3a. Simulation

of 20th Century

Globally Averaged

Surface Temperature

from GFDL CM2.1.

“CRU” is the value based on

the Climate Research Unit

gridded observational

dataset, “IPCC Mean” is the

average value of all CMIP3

models, and “IPCC Mean

Volc” is the average of all

CMIP3 models that included

volcanic forcing. Individual

realizations of the CMIP3 20th

Century experiment are

denoted by the dotted

curves labeled “run(1–3),”

and the ensemble mean is

marked “Mean.”

1860

1870

1880

1890

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

Time

-0.5

0

0.5

1

tas

IPCC Mean Volc

IPCC Mean

run1

run2

run3

Mean

CRU

Global Warming relative to 1900 for gfdl_cm2_1

 (10 years running average smoothed)
57

Climate Models: An Assessment of Strengths and Limitations

Figure 5.3b. Simulation

of 20th Century

Globally Averaged

Surface Temperature

from GISS Model E-R.

Curve labels are the same as

in Fig. 5.3a.

1860

1870

1880

1890

1900

1910

1920

1930

1940

1950

1960

1970

1980

19902000

Time

-0.5

0

0.5

1

tas

IPCC Mean Volc

IPCC Mean

run1

run2

run3

run4

run5

run6

run7

run8

run9

Mean

CRU

Global Warming relative to 1900 for giss_model_e_r

 (10 years running average smoothed)

Figure 5.3c. Simulation

of 20th Century

Globally Averaged

Surface Temperature

from CCSM3.

Curve labels are the same as

in Fig. 5.3a.

1860

1870

1880

1890

1900

1910

1920

1930

19401950

1960

1970

1980

1990

2000

Time

-0.5

0

0.5

1

tas

IPCC Mean Volc

IPCC Mean

run1

run2

run3

run4

run5

run6

run7

run9

Mean

CRU

Global Warming relative to 1900 for ncar_ccsm3_0

 (10 years running average smoothed)

Figure 5.3d.

Comparison of

Simulations of 20th

Century Globally

Averaged Surface

Temperature from the

Three U.S. CMIP3

Models. 

Model curves represent

ensemble means for CCSM3

(ncar_ccsm3_0), GISS Model

E-R (giss_e_r), and GFDL

CM2.1 (gfdl_cm2_1). “CRU,”

“IPCC Mean,” and “IPCC

Mean Volc” labels are the

same as in Fig. 5.3a.

1860

1870

1880

1890

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

Time

-0.5

0

0.5

1

tas

IPCC Mean Volc

IPCC Mean

gfdl_cm2_1

giss_model_e_r

ncar_ccsm3_0

CRU

Global Warming Relative to 1900 for American Models

 (10 years running average smoothed)
58

The U.S. Climate Change Science Program Chapter 5 - Model Simulation of Major Climate Features

As an example of 20th Century temperature

trends on continental-to-global spatial scales

and multidecadal time scales, Fig. 5.4 shows

global maps for different time periods between

1880 and 2003 as observed and simulated by

GISS ModelE (Hansen et al. 2006; also see

Knutson et al. 2006). The figure shows general

agreement between model and observations not

only for the overall period but also for segments

1880 to 1940 and 1979 to 2003, which encompass periods of early and late 20th Century

warming. For 1940 to 1979, the model simulates only a small change in global mean temperature in agreement with observations, but it

fails to simulate the strong north polar cooling

observed for this period. As a result, the modelsimulated global mean-temperature change

(upper right corner of each frame) is slightly

positive rather than slightly negative as observed. Part of this discrepancy may result from

chaotic fluctuations within observed climate

that the model cannot synchronize correctly due

to inprecise knowledge of the initial conditions

in the 19th Century period. These chaotic fluctuations generally are more important in regional trends than in the global average, where

uncorrelated fluctuations in different regions

tend to cancel. For both 20th Century warming

periods, the model simulates, but underestimates, the high-latitude amplification of global

warming. Additional discrepancies between

AOGCMs and observations appear at smaller

scales. For example, model-simulated trends do

not consistently match the observed lack of 20th

Century warming in the central United States

(Kunkel et al. 2006).

5.2.1 Trends in Vertical Temperature

While models simulate the 20th Century warming observed at the surface, agreement is less

obvious with tropospheric observations from

satellites and weather balloons. This issue was

the focus of CCSP SAP 1.1 (CCSP 2006). Since

1979 (beginning of the satellite record), globally averaged warming in the troposphere according to climate models is within the range of

available observations. Within the tropics, the

model-simulated troposphere warms more rapidly than observed (see CCSP 2006, Fig. 5.4 F–

Figure 5.4. Near-Surface Temperature Changes as Observed (top panels) and as

Simulated by GISS ModelE (bottom panels) for Selected Time Periods Between 1880

and 2003. 

Numbers above upper right panel corners are global means. [Images from Fig. 9 in J. Hansen et al. 2007:

Climate simulations for 1880–2003 with GISS ModelE. Climate Dynamics, 29(7–8), 661–696. Reproduced

with kind permission of Springer Science and Business Media.]

Surface Temperature Change Based on Local Linear Trends (°C)

1880-2003

Observations .60

1880-1940

.24

1940-1979

.06

1979-2003

.39

All Forcings 55 26 07 32

–2 –1 –.1 .1 .3 .6 1 1.5 3 –.6
59

Climate Models: An Assessment of Strengths and Limitations

G). SAP 1.1 noted, however, that “Large structural uncertainties in the observations . . . make

it difficult to reach more definitive conclusions

regarding the significance and importance of

model-data discrepancies” (CCSP 2006, p. 112

and Section 5.4).

Research since publication of SAP1.1 has continued to highlight uncertainties implicit in

measuring the difference between surface and

lower-atmospheric warming. For example,

Thorne et al. (2007) found that the tropical atmosphere-to-surface warming ratio in both observations and model simulations is sensitive to

the time period analyzed. Meanwhile, debate

continues over the best way to process data from

satellites (Christy et al. 2007) and weather balloons (Christy and Spencer 2005). AOGCMs

continue to differ from most published observations on the ratio of atmosphere-to-surface

warming in the tropics since the beginning of

satellite observations (e.g., as shown by Thorne

et al. 2007, Fig. 3), with the ratio being larger in

the models than is seen in decadal observational

trends.

Paradoxically, trends are more consistent between models and observations on interannual

time scales. AOGCM simulation of tropical atmospheric warming involves mainly subgridscale parameterizations. As discussed in

Chapter 2, these are not as trustworthy as explicitly computed processes, but internal variability [primarily due to El Niño–Southern

Oscillation (ENSO)] provides a useful test of

the models’ ability to redistribute heat realistically. AOGCMs simulate very well the portion

of tropical temperature trends due to interannual

variability (Santer et al. 2005). In addition, explaining how atmospheric water vapor increases

coincidentally with surface temperature is difficult (Trenberth, Fasullo, and Smith 2005; Santer et al. 2007; Wentz et al. 2007) unless lower

tropospheric temperature also increases coincidentally with surface temperature. While deficiencies in model subgrid-scale parameterizations are certainly possible, trends in poorly

documented forcing agents (see Chapter 4) may

prove important in explaining the discrepancy

over the longer time scales. Future research is

required to resolve the issue because tropospheric observations at face value suggest a trend

toward greater tropical instability, which has implications for many aspects of model projections in the tropics.

5.2.2 Model Simulation of Observed

Climate Variability

The following sections discuss a number of specific climate phenomena directly or indirectly

related to near-surface temperature, precipitation, and sea level. Numerous studies of climate

change have focused on one or two of these phenomena, so a great deal of information (and occasional debate) has accumulated for each of

them. Here we attempt to summarize the points

that would best give users of AOGCM model

output a general sense of model reliability or

unreliability. Although the following sections

individually note different types of climate variation, the reader should recognize that the total

amount of natural climate variability forms

background “noise” that must be correctly assessed to identify the “signal” of anthropogenic

climate change. Natural variability in turn separates into an externally forced part (e.g., from

solar energy output and volcanic eruptions) and

internally generated variability just as weather

varies on shorter time scales because of the system’s intrinsic chaotic character. As noted

above, long-term trends in both solar and volcanic forcing during the past few decades have

had a cooling rather than warming effect. It follows that if global warming during this period is

not anthropogenic, then the climate system’s internal variation is the most likely alternative explanation.

Control runs of AOGCMs (in which no changes

in external climate forcing are included) provide

estimates of the level of internally generated climate variability. Control runs generally obtain

realistic near-surface temperature variability on

annual-to-decadal time scales, although they

typically underestimate variability in areas of

the Pacific and Indian Ocean where ENSO and

the Pacific Decadal Oscillation (PDO) (see

below) predominate (Stouffer, Hegerl, and Tett

2000). Unfortunately, the longest time periods

that are directly relevant to separating natural

from anthropogenic climate change are the least

observed. Assessing variations of surface temperature for time periods longer than 50 to 100

years depends on paleodata such as ice-core

composition and tree-ring thickness. Interpreta-
60

The U.S. Climate Change Science Program Chapter 5 - Model Simulation of Major Climate Features

tion of these data is made difficult by sparse geographical coverage and also is complicated by

natural variations in external climate forcing.

5.2.2.1 EXTRA-TROPICAL STORMS

Climate models have developed from numerical weather-prediction models whose performance has been judged primarily on their ability

to forecast midlatitude weather. The success of

forecast models in their simulation of midlatitude cyclones and anticyclones has resulted in

continuous growth in the value of numerical

weather prediction. The ability of GCMs to generate realistic statistics of midlatitude weather

also has been central in climate model development. This is true not only because midlatitude

weather is important in its own right, but also

because these storms are the primary mechanism by which heat, momentum, and water

vapor are transported by the atmosphere, making their simulation crucial for simulation of

global climate. Indeed, a defining feature of atmospheric general circulation models

(AGCMs) is that they compute midlatitude eddy

statistics and associated eddy fluxes through explicit computation of the life cycles of individual weather systems and not through some

turbulence or parameterization theory. Computing the evolution of individual eddies may seem

very inefficient when primary interest is in

long-term eddy statistics, but the community

clearly has judged for decades that explicit eddy

simulation in climate models is far superior to

attempts to develop closure theories for eddy

statistics. The latter theories typically form the

basis for Earth system models of intermediate

complexity (EMICs), which are far more efficient computationally than GCMs but provide

less convincing simulations.

Two figures illustrate the quality of simulated

midlatitude eddy statistics from coupled

AOGCMs used in IPCC AR4. Shown for the

GFDL CM2.1 in Fig. 5.5a is wintertime variance of the north-south velocity component at

300 hPa (in the upper troposphere). This quantity represents the magnitude of variability in

the upper troposphere associated with day-today weather. In Fig. 5.5b, the wintertime poleward eddy heat flux or covariance between

temperature and north-south velocity is shown

at 850 mb (in the lower troposphere). For these

calculations, the monthly means were subtracted before computing variances. In each

case, eddy statistics are compared to estimates

of observed statistics obtained from

NCEP/NCAR Reanalysis (B.Wyman, personal

communication). When analyzing eddy statistics, the data are typically filtered to retain only

those time scales, roughly 2 to 10 days, associated with midlatitude weather systems. The two

quantities chosen here, however, are sufficiently

dominated by these time scales that they are relatively insensitive to the monthly filtering used

here. In winter, Northern Hemisphere storms

are organized into two major oceanic storm

tracks over the Pacific and Atlantic oceans. Historically, atmospheric models of horizontal resolutions of 200 to 300 km typically are capable

of simulating midlatitude storm tracks with realism comparable to that shown in the figure.

Eddy amplitudes often are a bit weak and often

displaced slightly equatorward. In spectral models with resolution coarser than 200 to 300 km,

simulation of midlatitude storm tracks typically

deteriorates significantly (see, e.g., Boyle

1993). General improvements in most models

in the CMIP3 database over previous generations of models, as described in Chapter 1, are

thought to be partly related to the fact that most

of these models now have grid sizes of 100 to

300 km or smaller. Although even-finer resolution results in better simulations of midlatitudestorm structure, including that of warm and cold

fronts and interactions among these storms and

coastlines and mountain ranges, improvements

in midlatitude climate on large scales tend to be

less dramatic and systematic. Other factors besides horizontal resolution are considered important for details of storm track structure. Such

factors include distribution of tropical rainfall,

which is sensitive to parameterization schemes

used for moist convection, and interactions between stratosphere and troposphere, which are

sensitive to vertical resolution. Roeckner et al.

(2006), for example, illustrate the importance

of vertical resolution for midlatitude circulation

and storm track simulation.

Lucarini et al. (2006) provide a more detailed

look at the ability of CMIP3 models to simulate

the space-time spectra of observed eddy statistics. These authors view the deficiencies noted,

which vary in detail from model to model, as

serious limitations to model credibility. As indicated in Chapter 1, however, our ability is lim-
61

Climate Models: An Assessment of Strengths and Limitations

ited in translating measures of model biases into

useful measures of model credibility for 21st

Century projections, and the implications of

these biases in eddy space-time spectra are not

self-evident. Indeed, in the context of simulating

eddy characteristics generated in complex turbulent flows in the laboratory (e.g., Pitsch

2006), the quality of atmospheric simulations,

based closely on fluid dynamical first principles, probably should be thought of as one of

the most impressive characteristics of current

models. As an example of a significant model

deficiency that plausibly can be linked to limitations in climate projection credibility, note

that the Atlantic storm track, as indicated by the

maximum velocity variance in Fig. 5.5a, follows

a latitude circle too closely and the observed

storm track has more of a southwest-northeast

tilt. This particular deficiency is common in

CMIP3 models (van Ulden and van Oldenborgh

2006) and is related to difficulty in simulating

the blocking phenomenon in the North Atlantic

with correct frequency and amplitude. Van

Ulden and van Oldenborgh make the case that

this bias is significant for the quality of regional

climate projections over Europe.

max = 337.5

min = 2.989

avg = 88.05

std = 72.62

max = 85.53

min = –118

avg = –7.245

rms = 24.45

max = 358.4

min = 5.79

avg = 95.3 NCEP (1981 – 2000) std = 78.99

CM2.1_AllForc_H2 – NCEP

80°N

40°N

0°

40°S

80°S

400

350

300

250

200

150

100

50

0

80°N

40°N

0°

40°S

80°S

400

350

300

250

200

150

100

50

0

80°N

40°N

0°

40°S

80°S

125

100

75

50

25

0

–25

–50

–75

–100

–125

50°E 150°E 110°W 10°W

50°E 150°E 110°W 10°W

50°E 150°E 110°W 10°W

DJF 300 hPa v' v' (m2s–2)

CM2.1_AllForc_H2 (1981 – 2000)

Figure 5.5a. Top:

Variance of NorthSouth Velocity at 300

hPa as Simulated by

GFDL CM2.1 Model in

Years 1981 to 2000 of

One Realization of

20C3M Simulation, as

Contributed to the

CMIP3 Database. 

Units are m2/s2. Middle:

Same quantity as obtained

from NCEP/NCAR

Reanalysis (Kalnay et al.

1996). Bottom: Model minus

observations. 
62

The U.S. Climate Change Science Program Chapter 5 - Model Simulation of Major Climate Features

5.2.2.2 TROPICAL STORMS

Tropical storms (hurricanes in the Atlantic and

typhoons in the Pacific and Indian oceans) are

too small to be simulated reliably in the class of

global-climate models currently used for climate projections. There is hope for simulating

regional climate aspects that control the genesis of tropical depressions, however. Vitart and

Anderson (2001), for example, identified tropical storm-like vortices in simulations with

models of this type, demonstrating some skill

in simulating the effects of El Niño on Atlantic

storm frequency.

Simulations with atmospheric models are

steadily moving to higher resolutions (e.g.,

Bengtsson, Hodges, and Esch 2007). The recent

20-km–resolution simulation with an atmospheric model over prescribed ocean temperatures by Oouchi et al. (2006) is indicative of the

kinds of modeling that will be brought to bear

on this problem in the next few years. Experimax = 32.27

min = 14.85

avg = 2.3

std = 8.24

max = 18.4

min = –11.78

avg = 0.9021

rms = 2.588

max = 32.38

min = 17.59

avg = 1.398

std = 7.577 NCEP (1981 – 2000)

CM2.1_AllForc_H2 – NCEP

80°N

40°N

0°

40°S

80°S

40

35

30

25

20

15

10

5

0

–5

–10

–15

–20

–25

–30

–35

–40

80°N

40°N

0°

40°S

80°S

40

35

30

25

20

15

10

5

0

–5

–10

–15

–20

–25

–30

–35

–40

80°N

40°N

0°

40°S

80°S

21

18

12

9

6

3

0

–3

–6

–9

–12

–15

–18

–21

50°E 150°E 110°W 10°W

50°E 150°E 110°W 10°W

50°E 150°E 110°W 10°W

DJF 850 hPa v' T' (m° K s–1)

CM2.1_AllForc_H2 (1981 – 2000)

Figure 5.5b. Top:

Covariance of NorthSouth Velocity and

Temperature at 850

hPa as Simulated by

GFDL CM2.1 Model in

Years 1981 to 2000 of

One Realization of

20C3M Simulation, as

Contributed to the

CMIP3 Database. 

Units are K-m/s. Middle:

Same quantity as obtained

from NCEP/NCAR

Reanalysis (Kalnay et al.

1996). Bottom: Model minus

observations.
63

Climate Models: An Assessment of Strengths and Limitations

ence with tropical storm forecasting suggests

that this resolution should be adequate for describing many aspects of the evolution of mature tropical storms and possibly the generation

of storms from incipient disturbances, but probably not tropical storm intensity. A promising

alternative approach is described by Knutson et

al. (2007), in which a regional model of comparable resolution (18 km) is used in a downscaling framework (see Chapter 3) to simulate

the Atlantic hurricane season. Given observed

year-to-year variations in the large-scale atmosphere structure over the Atlantic Ocean, the

model is capable of simulating year-to-year

variations in hurricane frequency over a 30-year

period with a correlation of 0.7 to 0.8. It also

captures the observed trend toward greater hurricane frequency in the Atlantic during this period. These results suggest that downscaling

using models of this resolution may be able to

provide a convincing capability for tropical

storm frequency projections into the future, although these projections still will rely on the

quality of global model projections for changes

in sea-surface temperature, atmospheric stability, and vertical shear.

5.2.2.3 MONSOONS

A monsoonal circulation is distinguished by its

seasonal reversal after the sun crosses the equator into the new summer hemisphere. Rain is

most plentiful in, if not entirely restricted to,

summer within monsoonal climates, when continental rainfall is supplied mainly by evaporation from the nearby ocean. This limits the reach

of monsoon rains to the distance over which

moisture can be transported onshore (Privé and

Plumb 2007). Variations in the monsoon’s spatial extent from year to year determine which

inland regions experience drought.

Over a billion people are dependent on the arrival of monsoon rains for water and irrigation

for agriculture. The Asian monsoon during boreal summer is the most prominent example of

a monsoon circulation dominating global rainfall during this season. However, the summer

rainfall maximum and seasonal reversal of

winds also indicate monsoon circulations in

West Africa and the Amazon basin. In addition,

during boreal summer, air flows off the eastern

Pacific Ocean toward Mexico and the American

Southwest while, over the Great Plains, moisture from the Gulf of Mexico brings an annual

peak in rainfall. Thus, the climate in these regions also is described as monsoonal.

Because of the Asian monsoon’s geographical

extent, measures of the fidelity of Asian monsoonal simulations can differ depending on specific regional focus and the metrics being used.

Kripalani et al. (2007) judged that 3/4 of the 18

analyzed coupled models match the timing and

magnitude of the summertime peak in precipitation over East Asia between 100 and 145°E

and 20 to 40°N evident in the NOAA-NCEP

Climate Prediction Center’s Merged Analysis of

Precipitation (CMAP, Xie, and Arkin 1997).

However, only half of these models were able to

reproduce the gross observed spatial distribution of monsoon rainfall and its migration along

the coast of China toward the Korean peninsula

and Japan. Considering a broader range of longitude (40 to 180°E) that includes the Indian

subcontinent, Annamalai, Hamilton, and Sperber (2007) found that 6 of 18 AOGCMs significantly correlated with the observed spatial

pattern of CMAP precipitation from June

through September. (These six models also produced relatively realistic simulation of ENSO

variability, which is known to influence interannual variations in the Asian summer monsoon.) Kitoh and Uchiyama (2006) computed

the spatial correlation and root-mean-square

error of simulated precipitation over a similar

region and found, for example, the GFDL models in the top tercile with a spatial correlation

exceeding 0.8.

During boreal winter, Asian surface winds are

directed offshore: from the northeast over India

and the northwest over East Asia. Hori and

Ueda (2006) provide correlations between observed spatial distributions of surface pressures

and 850-mb zonal winds during the East Asian

winter monsoon with winds and pressures simulated by nine CMIP3 models. Correlations for

zonal winds, for example, vary from 0.96 to

0.75. Monsoonal simulations in these models

clearly vary considerably in quality, more so

perhaps than other circulation features. Observed year-to-year variability of the West

African monsoon is related to remote ocean

temperatures in the North and South Atlantic

and Indian oceans (Rowell et al. 1992; Zhang

and Delworth 2006) as well as to temperatures
64

The U.S. Climate Change Science Program Chapter 5 - Model Simulation of Major Climate Features

in the nearby Gulf of Guinea. Cook and Vizy

(2006) found that slightly more than half of 18

analyzed coupled models reproduced the observed precipitation maximum over land from

June through August. Of these models, only six

(including GISS ModelE-H and both GFDL

models) reproduced the observed anticorrelation between Gulf of Guinea ocean temperature

and Sahel rainfall.

The late 20th Century Sahel drought was a dramatic change in the Earth’s hydrological cycle

that plausibly must be simulated by climate

models if we are to have any confidence in their

ability to project future climate in this region.

Atmospheric models, when run over observed

oceanic temperatures, simulate this drought reasonably well (Hoerling et al. 2006). In these

models, the drought is at least partly forced by

warming of the Northern Hemisphere oceans,

particularly the North Atlantic, with respect to

Southern Hemisphere oceans, especially the Indian Ocean and Gulf of Guinea. Although the

consensus is that these variations in ocean temperature gradients are at least partly due to natural variability, they may have been partly

anthropogenically forced. Analysis of CMIP3

simulations of the 20th Century by Biasutti and

Giannini (2006), supporting the earlier modeling study of Rotstayn and Lohmann (2002),

suggests that aerosol forcing in these models

played a part in generating this drought by cooling the North Atlantic with respect to other

ocean basins. A small number of coupled models simulate droughts of the observed magnitude, including GFDL models (Held and Soden

2006), but why some models are more realistic

in this regard than others is not understood.

Rainfall over the Sahel and Amazon are anticorrelated: when the Gulf of Guinea warms,

rainfall generally is reduced over the Sahel but

increases over South America. Amazon rainfall

also depends on the eastern equatorial Pacific,

and, during an El Niño, rainfall is reduced in the

Nordeste region of the Amazon. Li et al. (2006)

compare the hydrological cycle of 11 CGCMs

over the Amazon during the late 20th and 21st

centuries. Based on a comparison to CMAP

rainfall, the GISS ModelE-R is among the best.

The ability of climate models to simulate Northern Hemisphere summer rainfall over the U.S.

Great Plains and Mexico was summarized by

Ruiz-Barradas and Nigam (2006). Models generally have more difficulty in simulating summer rainfall in the Great Plains than winter

rainfall, and this disparity probably should be

thought of as reflecting the quality of future

rainfall projections as well. Strengths and weaknesses vary considerably across the models. As

an example, GISS ModelE-H closely matches

the annual precipitation cycle over the Great

Plains and Mexico and is one of two models to

simulate interannual precipitation variations

significantly correlated with observed variability during the second half of the 20th Century.

Initial monsoon evaluations simulated by the

most recent generation of climate models have

emphasized the seasonal time scale. However,

subseasonal variations, such as break periods

when the monsoon rains are interrupted temporarily, are crucial to forecasting the monsoon’s impact on water supply. Simulating the

diurnal cycle and the local hour of rainfall also

is important to partitioning rainfall between

runoff and transpiration, and these are important topics for future model evaluation. Transports of moisture by regional circulations

beneath model resolution (such as low-level jets

along the Rockies and Andes and tropical cyclones) contribute to the onshore transport of

moisture. In general, models show some success

at simulating gross seasonal features of various

monsoon circulations, but studies are limited on

variations of the smaller spatial and time scales

important to specific watersheds and hydrological projections.

5.2.2.4 MADDEN-JULIAN OSCILLATIONS

The Madden-Julian Oscillation (MJO) consists

of large-scale eastward-propagating patterns in

humidity, temperature, and atmospheric circulation that strengthen and weaken tropical rainfall as they propagate around the Earth in

roughly 30 to 60 days. This pattern often dominates tropical precipitation variability on time

scales longer than a few days and less than a

season, creating such phenomena as 1- to 2-

week breaks in Asian monsoonal rainfall and

weeks with enhanced hurricane activity in the

eastern North Pacific and the Gulf of Mexico.

Inadequate prediction of the evolution of these

propagating structures is considered a main impediment to more useful extended-range
65

Climate Models: An Assessment of Strengths and Limitations

weather forecasts in the tropics, and improved

simulation of this phenomenon is considered an

important metric for the credibility of climate

models in the tropics.

Nearly all models capture the pattern’s essential

feature, with large-scale eastward propagation

and with roughly the correct vertical structure.

But propagation often is too rapid and amplitudes too weak. Recent surveys of model performance indicate that simulations of MJO

remain inadequate. For example, Lin et al.

(2006), in a study of many CMIP3 models, conclude that “... current GCMs still have significant problems and display a wide range of skill

in simulating the tropical intraseasonal variability,” while Zhang et al. (2005) in another

multimodel comparison study, state that “...

commendable progress has been made in MJO

simulations in the past decade, but the models

still suffer from severe deficiencies ....” As an

example of recent work, Boyle et al. (2008) attempted, with limited success, to determine

whether two U.S. CMIP3 models could maintain a preexisting strong MJO pattern when initialized with observations [from the Tropical

Ocean Global Atmosphere–Coupled Ocean Atmosphere Response Experiment (called TOGACOARE) field experiment].

The difficulty in simulating MJO is related to

the phenomenon’s multiscale nature: the propagating pattern itself is large enough to be resolvable by climate models, but the convection

and rainfall modulated by this pattern, which

feed back on the large-scale environment, occur

on much smaller, unresolved scales. In addition

to this dependence on parameterization of tropical convection, a long list of other effects has

been shown by models and observational studies to be important for MJO. These effects include the pattern of evaporation generated as

MJO propagates through convecting regions,

feedback from cloud-radiative interactions, intraseasonal ocean temperature changes, the diurnal cycle of convection over the ocean, and

the vertical structure of latent heating , especially the proportion of shallow cumulus congestus clouds and deep convective cores in

different phases of oscillation (Lin et al. 2004)].

A picture seems to be emerging that simulation

difficulty may not be due to a single model deficiency but is a result of the phenomenon’s

complexity, given the long list of factors thought

to be significant. In several multimodel studies

such as Lin et al. (2006), a few models do perform well. However, without a clearer understanding of how these factors combine to

generate the observed characteristics of MJO,

maintaining a good simulation when the model

is modified for other reasons is difficult, as is

applying the understanding gained from one

model’s successful simulation to other models.

Whether models with superior MJO simulations

should be given extra weight in multimodel

studies of tropical climate change is unclear.

5.2.2.5 EL NIÑO–SOUTHERN OSCILLATION

By the mid-20th Century, scientists recognized

that a local anomaly. in rainfall and oceanic upwelling near the coast of Peru was in fact part of

a disruption to atmospheric and ocean circulations across the entire Pacific basin. During El

Niño, atmospheric mass migrates west of the

dateline as part of the Southern Oscillation, reducing surface pressure and drawing rainfall

into the central and eastern Pacific (Rasmussen

and Wallace 1983). Together, El Niño and the

Southern Oscillation, abbreviated in combination as ENSO, are the largest source of tropical

variability observed during recent decades. Because of the Earth’s rotation, easterly winds

along the equator cool the surface by raising

cold water from below, which offsets heating by

sunlight absorption (e.g., Clement et al. 1996).

Cold water is especially close to the surface in

the east Pacific, while warm water extends

deeper in the west Pacific so upwelling has little effect on surface temperature there. The

westward increase in temperature along the

equator is associated with a decrease in atmospheric pressure, reinforcing the easterly trade

winds. El Niño occurs when easterly trade

winds slacken, reducing upwelling and warming the ocean surface in the central and east Pacific.

Changes along the equatorial Pacific have been

linked to global disruptions of climate (Ropelewski and Halpert 1987). During an El Niño

event, the Asian monsoon typically is weakened,

along with rainfall over eastern Africa, while

precipitation increases over the American

Southwest. El Niño raises the surface temperature as far poleward as Canada, while changes
66

The U.S. Climate Change Science Program Chapter 5 - Model Simulation of Major Climate Features

in the north Pacific Ocean are linked to decadal

variations in ENSO (Trenberth and Hurrell

1994). In many regions far from eastern equatorial Pacific, accurate projections of climate

change in the 21st Century depend upon the accurate projection of changes to El Niño. Moreover, the demonstration that ENSO alters

climate across the globe indicates that even

changes to the time-averaged equatorial Pacific

during the 21st Century will influence climate

far beyond the tropical ocean. For example,

long-term warming of the eastern equatorial Pacific relative to the surrounding ocean will favor

a weaker Asian monsoon year after year, even

in the absence of changes to the size and frequency of El Niño events.

In general, coupled models developed for

CMIP3 are far more realistic than those of a

decade ago, when ENSO variability was comparatively weak and some models lapsed into

permanent El Niño states (Neelin et al. 1992).

Even compared to models assessed more recently by the El Niño Simulation Intercomparison Project (called ENSIP) and CMIP2 (Latif

et al. 2001; AchutaRao and Sperber 2002),

ENSO variability of ocean surface temperature

is more realistic in CMIP3 simulations, although sea-level pressure and precipitation

anomalies show little recent improvement

(AchutaRao and Sperber 2006). Part of this

progress is the result of increased resolution of

equatorial ocean circulation that has accompanied increases in computing speed. Table 5.1

shows horizontal and vertical resolution near

the equator in oceanic components of the seven

American coupled models whose output was

submitted to CMIP3.

Along the equator, oceanic waves that adjust the

equatorial temperature and currents to changes

in the wind are confined tightly to within a few

degrees of latitude. To simulate this adjustment,

the ocean state is calculated at points as closely

spaced as 0.27 degrees of latitude in the NCAR

CCSM3. NCAR PCM has a half-degree resolution, while both GFDL models have equatorial

resolution of a third of a degree. This degree of

detail is a substantial improvement compared to

previous generations of models. In contrast, the

GISS AOM and ModelE-R calculate equatorial

temperatures at grid points separated by four degrees of latitude. This is broad compared to the

latitudinal extent of cold temperatures observed

within the eastern Pacific. The cooling effect of

upwelling is spread over a larger area, so the

amplitude of the resulting surface temperature

fluctuation is weakened. In fact, both the GISS

AOM models and ModelE-R have unrealistic

ENSO variations that are much smaller than observed (Hansen et al. 2007). This minimizes the

influence of their simulated El Niño and La

Niña events on climate outside the equatorial

Pacific, and we will not discuss these two models further in this section.

In comparison to previous generations of global

models, where ENSO variability was typically

weak (Neelin et al. 1992), the AR4 coupled

models generally simulate El Niño near the observed amplitude or even above (AchutaRao

and Sperber 2006). The latter study compared

sea-surface temperature (SST) variability within

the tropical Pacific, calculated under preindustrial conditions. Despite its comparatively low

two-degree latitudinal grid spacing, the GISS

ModelE-H (among American models) most

closely matches observed SST variability since

the mid-19th Century, according to the HadISST

v1.1 dataset (Rayner et al. 2003). The NCAR

PCM also exhibits El Niño warming close to the

observed magnitude. This comparison is based

on spatial averages within three longitudinal

bands, and GISS ModelE-H, along with NCAR

models, exhibits its largest variability in the

eastern band as observed. However, GISS ModelE-H underestimates variability since 1950,

when the NCAR CCSM3 is closest to observaMODEL Longitude Latitude Vertical

Levels

GFDL CM2.0 1 1/3 50

GFDL CM2.1 1 1/3 50

GISS AOM 5 4 13

GISS ModelE-H 2 2 16

GISS ModelE-R 5 4 13

NCAR CCSM3 1.125 0.27 27

NCAR PCM 0.94 0.5 32

*Except for GISS models, spacing of grid points

generally increases away from the equator outside

the ENSO domain, so resolution is highest at the

equator.

Table 5.1. Spacing

of Grid Points at

the Equator in the

American

Coupled Models

Developed for

AR4*
67

Climate Models: An Assessment of Strengths and Limitations

tions (Joseph and Nigam 2006). Although the

fidelity of each model’s ENSO variability depends on the specific dataset and period of comparison (c.f. Capotondi, Wittenberg, and Masina

2006; Merryfield 2006; van Oldenborgh, Philip,

and Collins 2005), the general consensus is that

GISS ModelE-H, both NCAR models, and

GFDL CM2.0 have roughly the correct amplitude, while variability is too large by roughly

one-third in GFDL CM2.1. Most models (including GISS ModelE-H and both NCAR models but excluding GFDL models) exhibit the

largest variability in the eastern band of longitude, but none of the CMIP3 models matches

the observed variability at the South American

coast where El Niño was identified originally

(AchutaRao and Sperber 2006; Capotondi, Wittenberg, and Masina 2006). This possibly is because the longitudinal spacing of model grids is

too large to resolve coastal upwelling and its interruption during El Niño (Philander and

Pacanowski 1981). Biases in atmospheric models (e.g., underestimating persistent stratus

cloud decks along the coast) also may contribute (Mechoso et al. 1995).

El Niño occurs every few years, albeit irregularly. The spectrum of anomalous ocean temperature shows a broad peak between 2 and 7

years, and multidecadal variations occur in

event frequency and amplitude. Almost all AR4

models have spectral peaks within this range of

time scales. Interannual power is distributed

broadly within the American models, as observed, with the exception of NCAR CCSM3,

which exhibits strong biennial oscillations

(Guilyardi 2006).

Although models generally simulate the observed magnitude and frequency of events, reproducing their seasonality is more elusive.

Anomalous warming typically peaks late in the

calendar year, as originally noted by South

American fisherman. Among American models, this seasonal dependence is simulated only

by NCAR CCSM3 (Joseph and Nigam 2006).

Warming in GFDL CM2.1 and GISS ModelEH is nearly uniform throughout the year, while

warming in NCAR PCM is largest in December but exhibits a secondary peak in early summer. The mean seasonal cycle along the

equatorial Pacific also remains a challenge for

the models. Each year, the east Pacific cold

tongue is observed to warm during boreal

spring and cool again late in the calendar year.

GFDL CM2.1 and NCAR PCM1 have the

weakest seasonal cycle among American models, while GISS ModelE-H, GFDL 2.0, and

NCAR CCSM3 are closest to the observed amplitude (Guilyardi 2006). Among the worldwide

suite of CMIP3 models, amplitude of the seasonal cycle of equatorial ocean temperature

generally varies inversely with the ENSO

cycle’s strength.

Several studies have compared mechanisms

generating ENSO variability in CMIP3 models

to those inferred from observations (e.g., van

Oldenborgh, Philip, and Collins 2005; Guilyardi

2006; Merryfield 2006; Capotondi, Wittenberg,

and Masina 2006). Models must simulate the

change in ocean upwelling driven by changes in

surface winds, which in turn are driven by regional contrasts in ocean temperature. In general, GFDL2.1 is ranked consistently among

American models as providing the most realistic simulation of El Niño. This is not based primarily on its surface-temperature variability

(which is slightly too large) but on its faithful

simulation of the observed relationship between

ocean temperature and surface wind, along with

wind-driven ocean response. While SST variability in CMIP3 models is controlled by anomalies of either upwelling rate or temperature,

these processes alternate in importance over

several decades within GFDL CM2.1 as observed (Guilyardi 2006). Since the 1970s the

upwelling temperature, rather than the rate, has

been the predominant driver of SST variability

(Wang 1995). A confident prediction of future

El Niño amplitude requires both the upwelling

rate and temperature, along with their relative

amplitude, to be simulated correctly. This remains a challenge.

El Niño events are related to climate anomalies

throughout the globe. Models with more realistic ENSO variability generally exhibit an anticorrelation with the strength of the Asian

summer monsoon (e.g., Annamalai, Hamilton,

and Spencer 2007), while 21st Century changes

to Amazon rainfall have been shown to depend

on projected trends in the tropical Pacific (Li et

al. 2006). El Niño has a long-established relation to North American climate (Horel and Wallace 1981), assessed in CMIP3 models by
68

The U.S. Climate Change Science Program Chapter 5 - Model Simulation of Major Climate Features

Joseph and Nigam (2006). This relation is

strongest during boreal winter, when tropical

anomalies are largest. Anomalous circulations

driven by rainfall over the warming equatorial

Central Pacific radiate atmospheric disturbances into midlatitudes amplified within the

north Pacific storm track (Sardeshmukh and

Hoskins 1988; Held, Lyons, and Nigam 1989;

Trenberth et al. 1998). To simulate ENSO’s influence on North America, models must represent realistic rainfall anomalies in the correct

season so the connection is amplified by wintertime storm tracks. The connection between

equatorial Pacific and North American climate

is simulated most accurately by the NCAR

PCM model (Joseph and Nigam 2006). In

GFDL CM2.1, North American anomalies are

too large, consistent with the model’s excessive

El Niño variability within the equatorial Pacific.

The connection between the two regions is realistic if the model’s tropical amplitude is accounted for. In the GISS model, anomalous

rainfall during ENSO is small, consistent with

the weak tropical wind stress anomaly cited

above. The influence of El Niño over North

America is nearly negligible in this model. The

weak rainfall anomaly presumably is a result of

unrealistic coupling between atmospheric and

ocean physics. When SST instead is prescribed

in this model, rainfall calculated by the GISS

ModelE AGCM over the American Southwest

is significantly correlated with El Niño as observed.

Realistic simulation of El Niño and its global

influence remains a challenge for coupled models because of myriad contributing processes

and their changing importance in the observational record. Key aspects of coupling between

ocean and atmosphere—the relation between

SST and wind stress anomalies, for example—

are the result of complicated interactions among

resolved model circulations, along with parameterizations of ocean and atmospheric boundary layers and moist convection. Simple models

identify parameters controlling the magnitude

and frequency of El Niño, such as the wind

anomaly resulting from a change in SST (e.g.,

Zebiak and Cane 1987; Fedorov and Philander

2000), offering guidance to improve the realism

of fully coupled GCMs. However, in a GCM,

the coupling strength is emergent rather than

prescribed, and it is often unclear a priori how to

change the coupling. Nonetheless, improved

simulations of the ENSO cycle compared to

previous generations (AchutaRao and Sperber

2006) suggest that additional realism can be expected in the future.

5.2.2.6 ANNULAR MODES

The primary mode of Arctic interannual variability is the Arctic Oscillation (Thompson and

Wallace 1998), which also is referred to as the

northern annular mode (NAM) and is related to

the North Atlantic Oscillation (Hurrell 1995).

The primary mode of Antarctic interannual variability is the southern annular mode (SAM)

(Thompson and Wallace 2000), also known as

Antarctic Oscillation. The variability modes are

particularly important for attributing and projecting climate change; observed circulation

changes in the past few decades (especially in

the Southern Hemisphere) and model-projected

changes in future circulation strongly resemble

these structures.

Coupled climate models have shown skill in

simulating NAM (Fyfe, Boer, and Flato 1999;

Shindell et al. 1999; Miller, Schmidt, and Shindell 2006). In some cases, too much variability

in the simulation of sea-level pressure is associated with NAM (Miller, Schmidt, and Shindell 2006). Global climate models also

realistically simulate SAM (Fyfe, Boer, and

Flato 1999; Cai, Whetton, and Karoly 2003;

Miller, Schmidt, and Shindell 2006), although

some details of SAM (e.g., amplitude and zonal

structure) show disagreement among global climate model simulations and reanalysis data

(Raphael and Holland 2006; Miller, Schmidt,

and Shindell 2006).

In response to increasing concentrations of

greenhouse gases and tropospheric sulfate

aerosols in the 20th Century, the multimodel average exhibits a positive trend in the annular

mode index in both hemispheres, with decreasing sea-level pressure over the poles and a compensating increase in midlatitudes most

apparent in the Southern Hemisphere (Miller,

Schmidt, and Shindell 2006). A variety of modeling studies also have shown that trends in

stratospheric climate can affect the troposphere’s annular modes (Shindell et al. 1999). Indeed, an important result from atmospheric

modeling in recent years is the realization that
69

Climate Models: An Assessment of Strengths and Limitations

the stratospheric ozone hole has contributed significantly to observed trends in surface winds

and sea-level pressure distribution in the Southern Hemisphere (Thompson and Solomon

2002; Gillett and Thompson 2003). The models, however, may not be trustworthy in their

simulation of the relative magnitude of greenhouse gas and stratospheric ozone effects on the

annular mode. They also may underestimate the

coupling of stratospheric changes due to volcanic aerosols with annular surface variations

(Miller, Schmidt, and Shindell 2006; Arblaster

and Meehl 2006).

5.2.2.7 OTHER MODES OF MULTIDECADAL

VARIABILITY

In the Arctic during the last century, two longperiod warm events occurred, one between 1920

and 1950 and another beginning in the late

1970s. Wang et al. (2007) evaluated a set of

CMIP3 models for their ability to reproduce the

amplitudes of air temperature variability of this

character. As examples, CCSM3 and GFDLCM2 models contain variance similar to that observed in the Arctic region.

Multidecadal variability in the North Atlantic is

characterized by the Atlantic Multidecadal Oscillation (AMO) index, which represents a spatial average of SST (Enfield, Mestas-Nuñez,

and Trimble 2001). Kravtsov and Spannagle

(2007) analyzed SST from a set of current generation climate models. Their analysis attempts

to separate variability associated with internal

ocean fluctuations from that associated with

changes by anthropogenic contributions. By

isolating the multidecadal period of several regions in the ensemble SST series through statistical methods, they found that models obtain

the observed magnitude of the AMO (Kravtsov

and Spannagle 2007).

In the midlatitude Pacific region, decadal variability generally is underrepresented in the

ocean (e.g., volume transports as described by

Zhang and McPhaden 2006), with some models approaching amplitudes seen in observations. Examination of complicated feedbacks

between atmosphere and ocean at decadal and

longer scales shows that, while climate models

generally reproduce the SST pattern related to

the Pacific Decadal Oscillation (PDO), observed correlations between PDO and tropical

SST are not seen in the models (e.g., Alexander

et al. 2006).

One of the most difficult areas to simulate is the

Indian Ocean because of the competing effects

of warm water inflow through the Indonesian

archipelago, ENSO, and monsoons. The

processes interact to varying degrees, challenging a model’s ability to simulate all system aspects with observed relative emphasis. An index

used to understand variability is the Indian

Ocean Dipole pattern that combines information about SST and wind stress fields (Saji et al.

1999). While most models evaluated by Saji,

Xie, and Yamagata (2005) were able to simulate

the Indian Ocean’s response to local atmospheric forcing in short time periods (semiannual), longer-period events such as the ocean’s

response to ENSO changes in the Pacific were

not simulated well.

5.2.3 Polar Climates

Changes in polar snow and ice cover affect the

Earth’s albedo and thus the amount of insolation heating the planet (e.g., Holland and Bitz

2003; Hall 2004; Dethloff et al. 2006). Melting

glaciers and ice sheets in Greenland and western

Antarctica could produce substantial sea-level

rise (Arendt et al. 2002; Braithwaite and Raper

2002; Alley et al. 2005). Polar regions thus require accurate simulation for projecting future

climate change and its impacts.

Polar regions present unique environments and,

consequently, challenges for climate modeling.

Key processes include sea ice, seasonally frozen

ground, and permafrost (Lawrence and Slater

2005; Yamaguchi, Noda, and Kitoh 2005).

Processes also include seasonal snow cover

(Slater et al. 2001), which can have significant

subgrid heterogeneity (Liston 2004), and clearsky precipitation, especially in the Antarctic

(King and Turner 1997; Guo, Bromwich, and

Cassano 2003). Polar regions test the ability of

models to handle extreme geophysical behavior

such as longwave radiation in clear, cold environments (Hines et al. 1999; Chiacchio, Francis, and Stackhouse 2002; Pavolonis, Key, and

Cassano 2004) and cloud microphysics in the

relatively clean polar atmosphere (Curry et al.

1996; Pinto, Curry, and Intrieri 2001; Morrison

and Pinto 2005). In addition, polar atmospheric
70

The U.S. Climate Change Science Program Chapter 5 - Model Simulation of Major Climate Features

boundary layers can be very stable (Duynkerke

and de Roode 2001; Tjernström, Zagar, and

Svensson 2004; Mirocha, Kosovic, and Curry

2005), and their simulation remains an important area for model improvement.

For polar regions, much of simulated-variability

assessment has focused on primary modes of

polar interannual variability, along with the

northern and southern annular modes. Less attention has been given to the ability of global

climate-system models to simulate shorter-duration climate and weather variability in polar

regions. Uotila et al. (2007) and Cassano et al.

(2007) evaluated the ability of an ensemble of

15 global climate-system models to simulate

daily variability in sea-level pressure in the

Antarctic and Arctic. In both polar regions, they

found that the ensemble was not able to reproduce many features of daily synoptic climatology, with only a small subset of models

accurately simulating the frequency of primary

synoptic weather patterns identified in global

reanalysis datasets. U.S. models discussed in detail in Chapter 2 of this report spanned the same

range of accuracy as non-U.S. models, with

GFDL and CCSM models part of a small, accurate subset. More encouraging results were

obtained by Vavrus et al. (2006), who assessed

the ability of seven global climate models to

simulate extreme cold-air outbreaks in the

Northern Hemisphere.

Attention also has been given to the ability of

regional climate models to simulate polar climate. In particular, the Arctic Regional Climate

Model Intercomparison Project (ARCMIP) engaged a suite of Arctic regional atmospheric

models to simulate a common domain and period over the western Arctic (Curry and Lynch

2002). Rinke et al. (2006) evaluated spatial and

temporal patterns simulated by eight ARCMIP

models and found that the model ensemble

agreed well with global reanalyses, despite

some large errors for individual models. Tjernstrom et al. (2005) evaluated near-surface properties simulated by six ARCMP models. In

general, surface pressure, air temperature, humidity, and wind speed all were well simulated,

as were radiative fluxes and turbulent momentum flux. The research group also found that

turbulent heat flux was poorly simulated and

that, over an entire annual cycle, the accumulated turbulent heat flux simulated by models

was many times larger than the observed turbulent heat flux (Fig. 5.6).

In global models, polar climate may be affected

by errors in simulating other planetary regions,

but much of the difference from observations

and the uncertainty about projected climate

change stem from current limitations in polar

simulation. These limitations include missing or

incompletely represented processes and poor

resolution of spatial distributions.

As with other regions, model resolution affects

simulation of important processes. In polar regions, surface distributions of snow depth vary

markedly, especially when snow drifting occurs.

Improved snow models are needed to represent

such spatial heterogeneity (e.g., Liston 2004),

which will continue to involve scales smaller

than resolved for the foreseeable future. Frozen

ground, whether seasonally frozen or occurring

as permafrost, presents additional challenges.

Models for permafrost and seasonal soil freezing and thawing are being implemented in land

surface models (see Chapter 2). Modeling soil

freeze and thaw continues to be a challenging

problem as characteristics of energy and water

flowing through soil affect temperature

changes. Such fluxes are poorly understood (Yamaguchi, Noda, and Kitoh 2005).

Frozen soil affects surface and subsurface hydrology, which influences the surface water’s

spatial distribution with attendant effects on

other parts of the polar climate system such as

carbon cycling (e.g., Gorham 1991; Aurela,

Laurila, Tuovinen 2004), surface temperature

(Krinner 2003), and atmospheric circulation

(Gutowski et al. 2007). The flow of fresh water

into polar oceans potentially alters their circulation, too. Surface hydrology modeling typically includes, at best, limited representation of

subsurface water reservoirs (aquifers) and horizontal flow of water both at and below the surface. These features limit the ability of climate

models to represent changes in polar hydrology,

especially in the Arctic.

Vegetation has been changing in the Arctic

(Callaghan et al. 2004), and projected warming,

which may be largest in regions where snow and

ice cover retreat, may produce further changes
71

Climate Models: An Assessment of Strengths and Limitations

in vegetation (e.g., Lawrence and Slater 2005).

Current models use static distributions of vegetation, but dynamic vegetation models will be

needed to account for changes in land-atmosphere interactions influenced by vegetation.

A key concern in climate simulations is how

projected anthropogenic warming may alter

land ice sheets, whose melting could raise sea

levels substantially. At present, climate models

do not include ice-sheet dynamics (see Chapter

2), and thus cannot account directly for ways in

which ice sheets might change, possibly changing heat absorption from the sun and atmospheric circulation in the vicinity of ice sheets.

Distributions of snow, ice sheets, surface water,

frozen ground, and vegetation have important

spatial variation on scales smaller than the resolutions of typical contemporary climate models. This need for finer resolution may be

satisfied by regional models simulating just a

polar region. Because both northern and southern polar regions are within circumpolar atmospheric circulations (cf. Giorgi and Bi 2000

and Gutowski et al. 2007b), their coupling with

other regions is more limited than in the case of

midlatitude regions, which could allow polarspecific models that focus on Antarctic and Arctic processes, in part, to improve modeling of

surface-atmosphere exchange processes (Fig.

5.6). Although each process above has been

simulated in finer-scale, stand-alone models,

their interactions as part of a climate system

also need to be simulated and understood.

5.2.3.1 SEA ICE

Sea ice plays a critical role in the exchange of

heat, mass, and momentum between ocean and

atmosphere, and any errors in the sea-ice system will contribute to errors in other components. Two recent papers (Holland and Raphael

2006; Parkinson, Vinnikov, and Cavalieri 2006a,

b) quantify how current models simulate the climate system’s sea-ice process. Very limited observations make any evaluation of sea ice

difficult. The primary observation available is

sea-ice areal concentration. In some comparisons, sea-ice extent (the area where local ice

concentration is greater than 15%) is used. For

the past few decades, satellites have made it

possible to produce a more complete dataset of

observations. Observations of ice extent were

fewer before that. Other quantities that might be

evaluated include ice thickness, but, due to limited observations, comparisons with models are

difficult and will not be discussed further here.

The seasonal pattern in ice growth and decay in

polar regions for all the models is reasonable

(Holland and Raphael 2006; see Fig. 5.7). However, a large amount of variability between models occurs in their representation of sea-ice

extent in both Northern and Southern hemispheres. Generally, models do better in simulating the Arctic than the Antarctic region, as

shown with Fig. 5.8. An example of the complex nature of reproducing the ice field is given

in Parkinson, Vinnikov, and Cavalieri (2006a,b),

which found that all models showed an ice-free

region in winter to the west of Norway, as seen

in observational data, but all also produced too

much ice north of Norway. The authors suggest

that this is because the North Atlantic Current is

not being simulated correctly. In a qualitative

comparison, Hudson Bay is ice covered in winter in all models correctly reproducing the observations. The set of models having the most

fidelity in the Arctic is not the same as the set

having the most fidelity in the Antarctic. This

difference may be due to distinctive ice regimes

in the north and south or to differences in simulations of oceanic or meteorological circulations in those regions.

Holland and Raphael (2006) examined carefully

the variability in Southern Ocean sea-ice extent.

As an indicator of ice response to large-scale atmospheric events, they compared data from a

set of IPCC AR4 climate models to the atmospheric index SAM for the April–June (AMJ) period (see Table 5.2). The models show that ice

variability does respond modestly to large-scale

atmosphere forcing but less than the limited observations show. Table 5.2 uses the U.S. models

to examine whether models exhibit the observed

out-of-phase buildup of ice between the Atlantic

and Pacific sectors (referred to as the Antarctic

Dipole).
72

The U.S. Climate Change Science Program Chapter 5 - Model Simulation of Major Climate Features

AMJ SAM and High-Pass

Filtered Fields

AMJ SAM and Detrended

Fields 

Observations 0.47 0.47

CCSM3 0.40 0.44

GFDL-CM2.1 0.39 0.19

GISS-ER 0.30 0.20

The leading mode of sea-ice variability represents a shift of ice from the Atlantic to the Pacific

sector. Bold values are significant at the 95% level, accounting for autocorrelation of the time series. 

[Table modified from Table 1, p. 19, in M.M. Holland and M.N. Raphael 2006: Twentieth Century

simulations of the Southern Hemisphere climate in coupled models. Part II: Sea ice conditions

and variability. Climate Dynamics, 26, 229–245. Reproduced with kind permission of Springer

Science and Business Media.]

Table 5.2. Correlations

of the Leading Mode of

Sea-Ice Variability and

Southern Annular

Mode (SAM) for

Observations and

Model Simulations

Figure 5.6. Cumulative

Fluxes of Surface

Sensible Heat (top

panel) and Latent Heat

(bottom) at the

SHEBA Site. 

Data are from six models

simulating a western Arctic

domain for Sept. 1997

through Sept. 1998 for

ARCMIP. SHEBA

observations are gray shaded

regions; model results are

shown by the individual

curves identified in the key at

the lower left of the upper

panel. [Figure adapted from

Fig. 10(c and d) in M.

Tjernstrom et al. 2005:

Modelling the Arctic

boundary layer: An evaluation

of six ARCMIP regional-scale

models with data from the

SHEBA project. BoundaryLayer Meteorology, 117,

337–381. Reproduced with

kind permission of Springer

Science and Business Media.]

ARCSYM

COAMPS

HIRHAM

PMMS

RCA

REMO

50

0

–50

–100

–150

–200

–250

Accum. Sens. Heat Flux (MJ 

m–2)

1 Sep 1 Dec 1 Mar 1 Jun 1 Sep

200

150

100

50

0

–250

Accum. Sens. Heat Flux (MJ 

m–2)

1 Sep 1 Dec 1 Mar

Date

1 Jun 1 Sep
73

Climate Models: An Assessment of Strengths and Limitations

Figure 5.7. Annual Cycle

of Southern Hemisphere

Ice Extent. 

It is defined as the area of ice

with concentrations greater

than 15%. Observations are

identified by the black curve

labeled “Obs,” while the results

from individual models are

identified by the six colored

curves. [From Fig. 1 in M.M.

Holland and M.N. Raphael 2006:

Twentieth Century simulations

of the Southern Hemisphere

climate in coupled models. Part

II: Sea ice conditions and

variability. Climate Dynamics, 26,

229–245. Reproduced with kind

permission of Springer Science

and Business Media.]

25

20

15

10

5

0

106 k

m3

obs

ccsm

giss_model_e_r

gfd_cm2_1

csiro_mk3_0

ukmo_hodcm3

miroc3_2_hires

SH Total Extent

J FMAM J

Month

J A S OND

Figure 5.8. Difference

Between Modeled 1979

to 2004 Monthly Average

Sea-Ice Extents and

Satellite-Based

Observations (modeled

minus observed). 

Data are shown for each of 11

major GCMs for both (a)

Northern Hemisphere and (b)

Southern Hemisphere. [From

Fig. 4 in C.L. Parkinson, K.Y.

Vinnnikov, and D.J. Cavalieri

2006: Correction to evaluation

of the simulation of the annual

cycle of Arctic and Antarctic.

J. Geophysical Research, 111,

C07012. Reproduced by

permission of the American

Geophysical Union (AGU).]

Ice Extension Differences (106 k

m2) Ice Extension Differences (106 k

m2) 10

5

0

–5

–10

10

5

0

–5

–10

J F MAM J J A S OND

J F MAM J J A S OND

(a) Northern Hemisphere

(b) Southern Hemisphere

HadCM3

HadGEM1

ECHAM5

CGCM3

CSIRO Mk3

MIROC3

BCCR BCM2

GISS ER

IPSL CM4

INM CM3

GFDL CM2.1
74

The U.S. Climate Change Science Program Chapter 5 - Model Simulation of Major Climate Features

5.2.4 Ocean Structure and

Circulation

Unlike the atmosphere, the amount of observational data available to evaluate ocean simulations is very limited for long time periods.

Nevertheless, sufficient data exist to identify a

set of ocean characteristics or metrics to evaluate ocean models for their climate simulation

properties. The most important is sea-surface

temperature, but other quantities that serve as

good indicators of ocean realism in climate

models are ocean heat uptake, meridional overturning and ventilation, sea-level variability, and

global sea-level rise.

5.2.4.1 SEA-SURFACE TEMPERATURE

Sea-surface temperature (SST) plays a critical

role in determining climate and the predictability of climate changes. Because of interactions

in atmospheric and ocean circulations at the surface, errors in SSTs typically originate with deficiencies in both atmospheric and ocean model

components. In general, more recent model versions show improvement over previous models

when simulated SST fields are compared to observations. Figure 5.9 (Delworth et al. 2006)

shows comparisons of simulated and observed

mean SST fields of both the older GFDL

CM2.0 and newer CM2.1 averaged over a 100-

year period. The new model reduced a cold bias

in the Northern Hemisphere from earlier simulations, resulting in both a more-realistic representation of atmospheric wind stress at the

ocean surface and a modified treatment of subgrid-scale oceanic mixing. The CCSM3.0

model’s improved SST simulation over

CCSM2.0 results mainly from changes in representing processes associated with the mixed

layer of upper ocean waters (Danabasoglu et al.

2006).

–10 –8 8 –6 –3 –4 10 –2 1 –1 2 3 6 4

(a) CM2.0

(b) CM2.1

–1

–2

–4

–1

–2

–1

–3

–1

–2

1 2

1

2

Sea Surface Temperature: Model minus Observations

80°N

40°N

0°

40°S

80°S

80°N

40°N

0°

40°S

80°S

150°E 110°W 10°W 90°E

Figure 5.9. Maps of

Simulation Errors in

Annual Mean SST. 

Units are Kelvin (K). Errors

are computed as model

minus observations from

Reynolds SST data

(provided by NOAA-CIRES

Climate Diagnostics Center,

Boulder, Colorado, from

their Web site,

www.cdc.noaa.gov). (a)

CM2.0 (using model years

101 to 200). (b) CM2.1

(using model years 101 to

200). Contour interval is 

1 K, except for no shading

of values between 1 K and

+1 K. [Images from T.L.

Delworth et al. 2006:

GFDL’s CM2 global coupled

climate models. Part 1:

Formulation and simulation

characteristics. J. Climate, 19,

643–684. Reproduced by

permission of the American

Meteorological Society.]
75

Climate Models: An Assessment of Strengths and Limitations

In addition to SST mean values, 20th Century

trends of SST changes also are significant for

model evaluation, since ocean SST contributes

the dominant signal to the observed global surface temperature trend. An intermodel comparison of 50-year tropical SST trends is shown in

Fig. 5.10. Trends range from a low of 0.1°C/50

yrs to a high of about 0.6°C/50 yrs, with the observational trend estimate given as about

0.43°C/50 yrs. The figure also shows some randomness within a group of simulations run by

the same model. For example, the two different

GFDL model versions discussed above were

each run for multiple realizations of the 20th

Century. CM 2.0 simulations are noted by

GFDL201, GFDL202, and GFDL203, and CM

2.1 simulations are noted by GFDL211,

GFDL212, and GFDL213.

5.2.4.2 MERIDIONAL OVERTURNING

CIRCULATION AND VENTILATION

The planetary-scale circulation transporting

heat and freshwater throughout global oceans is

referred to as global thermohaline circulation.

The Atlantic portion is called the Atlantic

meridional overturning circulation (AMOC).

Tropical and warm waters flow northward via

the Gulf Stream and North Atlantic Current.

Southward flow occurs when water is subducted

in regions around Labrador and Greenland; surface waters freshen, become denser, and flow

down the slope to deeper depths. Similar

processes occur at locations in the Southern

Ocean. “Ventilation” is the name given to the

process by which these dense surface waters are

carried into the ocean interior. An important climate parameter is the rate at which this process

occurs. The pattern of circulation may weaken,

0

0.2

0.4

0.6

0.8

1

1.2

Obs.

cnrm

csiro

gfdl201gfdl202gfdl203gfdl211gfdl212gfdl213

gissaom

gisseh

gisser

fgoals

miroch

mirocm

mri

ccsm3

pcm

hadcm3

(°C / 50 Yrs)

SST Trend

0

0.04

0.08

0.12

0.16

0.2

0.24

0.28

0.32

Obs.

cnrm

csiro

gfdl201

gfdl202

gfdl203

gfdl211

gfdl212

gfdl213

gissaom

gisseh

gisser

fgoals

miroch

mirocm

mri

ccsm3

pcm

hadcm3

(°C)

SST Standard Deviation

Figure 5.10. Trends

and Standard

Deviations of

Tropical SST

Between 1950 and

1999.

Observations are shown

by the leftmost bar in

each figure. All others are

model results. Error bars

show 95% significance

levels for trends. [Images

from Fig. 9 in D. Zhang and

M.J. McPhaden 2006:

Decadal variability of the

shallow Pacific meridional

overturning circulation:

Relation to tropical seasurface temperatures in

observations and climate

change models. Ocean

Modelling, 15, 250–273.

Used with permission

from Elsevier.]
76

The U.S. Climate Change Science Program Chapter 5 - Model Simulation of Major Climate Features

affecting the climate in the region surrounding

the North Atlantic. Schmittner, Latif, and

Schneider (2005) examined a small ensemble

set of simulations to quantify uncertainty in

model representation of 20th Century AMOC

transports. To make their estimate, they evaluated global temperature, global salinity, pycnocline depth, surface temperature, surface

salinity in the Atlantic (SST, SSS), and the overturning calculations at three Atlantic locations.

Their results suggest that temperature is simulated most successfully on a large scale and that

the overturning transports at 24°N are close

(~18 Sv) to observed measurements (~15.8 Sv).

However, the maximum mean overturning

transports in these models are too high, between

21.2 and 31.7 Sv, when compared to the observed value (17.7 Sv). Several other CMIP3

models underestimated maximum transport.

The authors do not attempt to explain why models are different from each other and from observations.

Another aspect of planetary-scale ocean circulation of interest is transport of mass by the

Antarctic Circumpolar Current through the

Drake Passage. The passage, between the tip of

South America and the Antarctic Peninsula, provides a constrained passage to measure the flow

between two large ocean basins. Observed mean

transport is around 135 Sv (Cunningham et al.

2003). Russell, Stouffer, and Dixon (2006,

2007) estimate passage flow for a subset of climate models. Simulated mean values show a

wide range. For example, GFDL and GISS-EH

models do fairly well in reproducing the observed average transport with values between

113 and 175 Sv. Once again, the interaction between the atmospheric and ocean component

models appears to be important in reproducing

the observed transport. The strength and location of the zonal wind stress provided by the atmosphere correlate with how well the transport

reflects observed values.

5.2.4.3 NORTHWARD HEAT TRANSPORT

A common metric used to quantify the realism

in ocean models is the northward transport of

heat. This integrated quantity (from top to bottom and across latitude bands) gives an estimate

of how heat moves within the ocean and is important in balancing the overall heat exchange

between the tropics and the extratropical regions

of the Earth. The calculations for the ocean’s

northward heat transport in the current generation of climate models show that the models

reasonably represent the observations (Delworth

et al. 2006; Collins et al. 2006a; Schmidt et al.

2006). The current models have significantly

improved over the last generation in the Northern Hemisphere. Comparisons of simulated values to observed values for the North Atlantic are

within the uncertainty of the observations. In

the Southern Hemisphere, the comparisons in

all the models are not as good, with the Indian

Ocean transport estimates contributing to a significant part of the mismatch. In coupled oceanatmosphere simulations, erroneous ocean heat

transport is compensated by changes in atmospheric heat transport that give a more realistic

total heat transport (Covey and Thompson

1989).

Heat Content. The global mean mass-weighted

ocean temperature is called the ocean’s heat

content. Its time evolution is centrally important

in determining how realistically the models reproduce heat uptake. The seasonal cycle and

longer-term trends of heat content provide useful model metrics, although the seasonal cycle

does not affect the deep ocean. An evaluation of

temporally evolving ocean-heat content in the

CMIP3 suite of climate models shows the models’ abilities to simulate the zonally integrated

annual and semiannual cycle in heat content. In

the middle latitudes (Gleckler, Sperber, and

AchutaRao 2006), the models do a reasonable

job, although a broad spread of values is apparent for tropical and polar regions. This analysis

showed that the models replicate the annual

cycle’s dominant amplitude along with its phasing in the midlatitudes (Figs. 5.11 a–b and 5.12

a–f). At high latitudes, comparisons with observations are not as consistent. Although the annual cycle and global trend are reproduced,

model analyses (e.g., Hansen et al. 2005a, b)

show they do not simulate decadal changes in

estimates made from observations (Levitus et

al. 2001). Part of the difficulty of comparisons

at high latitudes and long periods is the paucity

of observational data (Gregory et al. 2004).
77

Climate Models: An Assessment of Strengths and Limitations

20

15

10

5

0

A

mplitude (108J/m)

Latitude

90N 75 60 45 30 15 15 Eq 30 45 60 75 90

cnrm_cm3

csiro_mk3_0

gfdl_cm2_1

giss_aom

giss_model_e_h

giss_model_e_r

iap_fgoals1_0_g

miroc3_2_hires

miroc3_2_medres

ncar_ccsm3_0

ukmo_hadcm3

WOA04

a)

Latitude

90N 75 60 45 30 15 15 Eq 30 45 60 75 90S

cnrm_cm3

csiro_mk3_0

gfdl_cm2_1

giss_aom

giss_model_e_h

giss_model_e_r

iap_fgoals1_0_g

miroc3_2_hires

miroc3_2_medres

ncar_ccsm3_0

ukmo_hadcm3

WOA04

1.5

1.0

0.5

0

A

mplitude (108J/m)

b)

Figure 5.11a–b.

Observed and

Simulated Zonally

Integrated Ocean

Heat Content 

(0–250 m).

Observations are

represented by the curve

labeled “WOA04.” All

other curves are model

results. (a) annual cycle

amplitude (108 J/m2) and

(b) semiannual/annual

(A2/A1). [From Fig. 1 in P.J.

Gleckler, K.R. Sperber, and

K. AchutaRao 2006: Annual

cycle of global ocean heat

content: Observed and

simulated. J. Geophysical

Research, 111, C06008.

Reproduced by

permission of the

American Geophysical

Union (AGU).] 
78

The U.S. Climate Change Science Program Chapter 5 - Model Simulation of Major Climate Features

a) Global d) Tropics (20S-20N)

b) Northern Hemisphere e) Southern Ocean

c) Southern Hemisphere

Heat Co

nte

nt (10**22J)

f) Arctic Ocean

Month Month

J F MAM J J A S OND J F MAM J J A S OND

J F MAM J J A S OND J F MAM J J A S OND

J F MAM J J A S OND J F MAM J J A S OND

5

4

3

2

1

0

–1

–2

–3

–4

–5

12

10

8

6

4

2

0

–2

–4

–6

–8

–10

–12

14

12

10

8

6

4

2

0

–2

–4

–6

–8

–10

–12

–14

2

1

0

–1

–2

2

1

0

–1

–2

2

1

0

–1

–2

cnrm_cm3

csiro_mk3_0

gfdl_cm2_1

giss_aom

giss_model_e_h

giss_model_e_r

iap_fgoals1_0_g

miroc3_2_hires

miroc3_2_medres

ncar_ccsm3_0

ukmo_hadcm3

WOA01

WOA04

Figure 5.12a–f. Annual Cycle of Observed and Simulated Basin Average Global Ocean Heat Content (0–250 m). 

Observations are represented by the curves labeled “WOA01” and “WOA04.” Units are 1022 J. Arctic Ocean is defined as north of 60°N,

and Southern Ocean is south of 60°S. [From Fig. 3 in P.J. Gleckler, K.R. Sperber, and K. AchutaRao 2006: Annual cycle of global ocean heat

content: Observed and simulated. J. Geophysical Research, 111, C06008. Reproduced by permission of the American Geophysical Union

(AGU).]
79

Climate Models: An Assessment of Strengths and Limitations

5.2.5 Global Mean Sea-Level Rise

Two separate physical processes contribute to

sea-level rising: (1) ocean thermal expansion

from an increase in ocean heat uptake (steric

component) and (2) addition of freshwater from

precipitation, continental ice melt, and river

runoff (eustatic component). Various ocean

models handle freshwater fluxes in different

ways. With the addition of a free surface in the

current generation of ocean models, freshwater

flux into oceans can be included directly

(Griffies et al. 2001). The freshwater contribution is computed in quantities estimated by the

climate model’s atmosphere and ice-sheet components (e.g., Church, White, and Arblaster

2005; Gregory, Lowe, and Tett 2006). In general, state-of-the-art climate models underestimate the combined global mean sea-level rise

as compared to tide gauge and satellite altimeter estimates, while the rise for each separate

component is within the observed values’ uncertainty. The reason for this is an open research

question and may relate either to observational

sampling or to incorrectly accounting for all eustatic contributions. The steric component to

global mean sea-level rise is estimated at 0.40 ±

0.05 mm/yr from observations (Antonov, Levitus, and Boyer 2005). Models simulate a similar

but somewhat smaller rise (Gregory, Lowe, and

Tett 2006; Meehl et al. 2005). Significant differences also occur in the magnitudes of

decadal variability between observed and simulated sea level. Progress is being made, however,

over the previous generation of climate models.

When atmospheric effects from volcanic eruptions are included, for example, current-generation ocean models capture the volcanoes’

observed impact (a decrease in the global mean

sea level). Figure 5.13 from Church, White, and

Arblaster (2005) gives an example of a few

models and their detrended estimate of the historic global mean sea level. It shows the influence of including additional atmospheric

forcing agents in changing the ocean’s steric

height.

5.3 EXTREME EVENTS

Flood-producing precipitation, drought, heat

waves, and cold waves have severe impacts on

North America. Flooding resulted in average

annual losses of $3.7 billion between 1983 and

2003 (www.flooddamagedata.org). Losses from

the 1988 drought were estimated at $40 billion

and the 2002 drought at $11 billion. Heat waves

in 1995 resulted in 739 additional deaths in

Chicago alone (Whitman et al. 1997). A large

component of overall climate change impacts

probably will arise from changes in the intensity and frequency of extreme events.

Modeling of extreme events poses special challenges since they are, by definition, rare. Although the intensity and frequency of extreme

events are modulated by ocean and land surface

state and by trends in the mean climate state, internal atmospheric variability plays a very large

role, and the most extreme events arise from

chance confluence of unlikely conditions. The

very rarity of extreme events makes statistical

evaluation of model performance less robust

than for mean climate. For example, in evaluating a model’s ability to simulate heat waves as

intense as that in 1995, only a few episodes in

the entire 20th Century approach or exceed that

intensity (Kunkel et al. 1996). For such rare

events, estimates of the real risk are highly uncertain, varying from once every 30 years to

once every 100 years or more. Thus, a model

that simulates these occurrences at a frequency

of once every 30 years may be performing adequately, but its performance cannot be distinguished from that of the model that simulates a

frequency of once every 100 years.

Although it might be expected that a change in

mean climate conditions will apply equally to

changes in extremes, this is not necessarily the

case. Using as an example the 50-state recordlow temperatures, the decade with the largest

number of records is the 1930s, yet winters during that decade averaged third warmest since

1890; in fact, no significant correlation is

shown between the number of records and U.S.

wintertime temperature (Vavrus et al. 2006).

Thus, the severest cold air outbreaks in the past

do not necessarily coincide with cold winters.

Another examination of model data showed that

future changes in extreme temperatures differ

from changes in mean temperature in many regions (Hegerl et al. 2004). This means that climate model output must be analyzed explicitly

for extremes by examining daily (or even finer–

resolution) data, a resource-intensive effort.
80

The U.S. Climate Change Science Program Chapter 5 - Model Simulation of Major Climate Features

1960 1965 1970 1975 1980 1985 1990 1995 2000

−8x10 22

−4x10 22

0

4x10 22

8x10 22

Heat content Levitus Model (PCM)

Ishii

Heat content (J)

(a)

Agung El Chichon

Pinatubo

1960 1965 1970 1975 1980 1985 1990 1995 2000

−10

−5

0

5

10 GMS L Levitus Model (PCM)

Ishii

GMSL (mm)

(b)

Agung El Chichon

Pinatubo

1960 1965 1970 1975 1980 1985 1990 1995 2000

−10

−5

0

5

10

MIROC3.2(hires)

MIROC3.2(medres)

GISS−ER

PCM

GMS L

Agung El Chichon

Pinatubo

(c)

GMSL (mm)

Figure 5.13. Observed and Modeled Global Ocean Heat Content (GOHC) and Global Mean Sea Level (GMSL)

for 1960 to 2000. 

The response to volcanic forcing, as indicated by differences between pairs of PCM simulations for GOHC (a) and GMSL (b) is shown for

the ensemble mean (bold line) and the three ensemble members (light lines). Observational estimates of GOHC and GMSL are shown by

the black and blue bold lines. For a and b, all results are for the upper 300 m only and have been detrended over the period 1960 to 2000.

For c, the ensemble mean (full-depth) GMSL for GISS-ER, MIROC3.2(hires), MIROC3.2(medres), and PCM models (after subtracting a

quadratic) are shown. [From Fig. 2 in J.A. Church, N.J. White, and M. Arblaster 2005: Significant decadal-scale impact volcanic eruptions on

sea level and ocean heat content. Nature, 438(7064), 74–77. Used with permission from Nature Publishing Group.] 
81

Climate Models: An Assessment of Strengths and Limitations

Evaluation of model performance with respect

to extremes is hampered by incomplete data on

historical frequency and severity of extremes.

Frich et al. (2002) analyzed ten indicators of climate extremes and presented global results.

However, many areas were missing due to lack

of suitable station data, particularly in the tropics. Using some of these indices for comparisons between models and observations has

become common. Another challenge for model

evaluation is the spatially averaged nature of

model data, representing an entire grid cell,

while station data represent point observations.

For some comparisons, averaging station data

over areas representing a grid cell is necessary.

Several approaches are used to evaluate model

performance for simulation of extremes. One

approach examines whether a model reproduces

the magnitude of extremes. For example, a daily

rainfall amount of 100 mm or more is expected

to occur about once every year in Miami, every

6 years in New York City, every 13 years in

Chicago, and every 200 years in Phoenix. A

useful metric would be the extent to which a

model is able to reproduce absolute magnitudes

and spatial variations of such extremes. A second approach examines whether a model reproduces observed trends in extremes. Perhaps the

most prominent observed global trend is an increase in the frequency of heavy precipitation,

particularly during the last 20 to 30 years of the

20th Century. This trend is significant at the 95%

confidence level for the period 1979 to 2003

and at the 99% confidence level for the period

1951 to 2003 (Trenberth et al. 2007). Another

notable observed trend is an increase in the

length of the frost-free season.

In some key respects, model simulation of temperature extremes probably is less challenging

than simulating precipitation extremes, in large

part due to the scales of these phenomena. The

typical heat wave or cold wave covers a relatively large region, on the order of several hundred miles or more or a number of grid cells in

a modern climate model. By contrast, heavy

precipitation can be much more localized, often

extending over regions of much less than 150

km, or less than the size of a grid cell. Thus, the

modern climate model can simulate directly the

major processes causing temperature extremes

while heavy precipitation is sensitive to parameterization of subgrid-scale processes, particularly convection (Chapter 2; Emori and Brown

2005; Iorio et al. 2004).

5.3.1 DROUGHTS AND EXCESSIVE RAINFALL

LEADING TO FLOODS

Recent analysis indicates a globally averaged

trend toward greater areal coverage of drought

since 1972 (Dai et al. 2004). A simulation by

the HadCM3 model reproduces this dry trend

(Burke, Brown, and Christidis 2006) only if anthropogenic forcing is included. A control simulation indicates that the observed drying trend

is outside the range of natural variability. The

model, however, does not always correctly simulate the regional distributions of areas of increasing wetness and dryness. The relationship

between droughts and variability was covered

above in Section 5.2.2.3 Monsoons.

Several different measures of excessive rainfall

have been used in analyses of model simulations. A common one is the annual maximum

5-day precipitation amount, one of the Frich et

al. (2002) indices. This has been analyzed in

several recent studies (Kiktev et al. 2003;

Hegerl et al. 2004; Tebaldi et al. 2006). Other

analyses have examined thresholds of daily precipitation, either absolute (e.g., 50 mm/day in

Dai 2006) or percentile (e.g., 4th-largest precipitation event equivalent to 99th percentile of 365

daily values as in Emori et al. 2005). Recent

studies of model simulations produced for

CMIP3 provide information on the performance

of the latest model generation.

Models generally tend to underestimate very

heavy precipitation. This is shown in a comparison between satellite (TRMM) estimates of

daily precipitation and model-simulated values

within the 50°S–50°N latitude belt (Dai 2006).

TRMM observations derive 7% of total precipitation from very heavy rainfall of 50 mm or

more per day, in contrast to only 0 to 2% for the

models. For the frequency of very heavy precipitation of 50 mm or more per day, TRMM

data show a frequency of 0.35% (about once

every 300 days), whereas it is 0.02 to 0.11%

(once every 900 to 5000 days) for the models. A

global analysis of model simulations showed

that models produced too little precipitation in

events exceeding 10 mm/day (Sun et al. 2006).

Examining how many days it takes to accumu-
82

The U.S. Climate Change Science Program Chapter 5 - Model Simulation of Major Climate Features

late two-thirds of annual precipitation, models

generally show too many days compared to observations over North America, although a few

models are close to reality. In contrast to the

general finding of a tendency toward underestimation, a study (Hegerl et al. 2004) of two models indicates generally good agreement with

observed annual maximum 5-day precipitation

amounts over North America for HadCM3 and

even somewhat of an overestimation for

CGCM2.

This model tendency to produce rainfall events

less intense than observed appears to be due in

part to global models’ low spatial resolution.

Experiments with individual models show that

increasing resolution improves the simulation

of heavy events. For example, the fourth-largest

precipitation event in a model simulation with a

resolution of about 300 km averaged 40 mm

over the conterminous United States, compared

to an observed value of about 80 mm. When the

resolution was increased to 75 km and 50 km,

the fourth-largest event was still smaller than

observed but by a much smaller amount (Iorio

et al. 2004). A second important factor is the parameterization of convection. Thunderstorms

are responsible for many intense events, but

their scale is smaller than the size of model

grids and thus must be indirectly represented in

models (Chapter 2). One experiment showed

that changes to this representation improve

model performance and, when combined with

high resolution of about 1.1° latitude, can produce quite-accurate simulations of the fourthlargest precipitation event on a globally

averaged basis (Emori et al. 2005). Another experiment found that the use of a cloud-resolving model imbedded in a global model

eliminated underestimation of heavy events

(Iorio et al. 2004). A cloud-resolving model

eliminates the need for convection parameterization but is very expensive to run. These sets of

experiments indicate that the problem of heavyevent underestimation may be reduced significantly in future as increases in computer power

allow simulations at higher spatial resolution

and perhaps eventually the use of cloud-resolving models.

Improved model performance at higher spatial

resolutions provides motivation for use of regional climate models when only a limited area,

such as North America, is of interest. These

models have spatial resolution sufficient to resolve major mountain chains, and some thus

display considerable skill in areas where topography plays a major role in spatial patterns. For

example, they are able to reproduce rather well

the spatial distribution of the magnitude or extent of precipitation in the 95th percentile (Leung

and Qian 2003), frequency of days with more

than 50 mm and 100 mm (Kim and Lee 2003),

frequency of days over 25 mm (Bell, Sloan, and

Snyder 2004), and annual maximum daily precipitation amount (Bell, Sloan, and Snyder

2004) over the western United States. Kunkel et

al. (2002) found that an RCM’s simulation of

extreme-event magnitude over the United States

varied spatially and depended on event duration.

There was a tendency for overestimation in

western United States and good agreement or

underestimation in central and eastern United

States.

Most studies of observed precipitation extremes

suggest that they have increased in frequency

and intensity during the latter half of the 20th

Century. A study by Tebaldi et al. (2006) indicates that models generally simulate a trend toward a world characterized by intensified

precipitation, with a greater frequency of heavyprecipitation and high-quantile events, although

with substantial geographical variability. This is

in agreement with observations. Wang and Lau

(2006) find that CGCMs simulate an increasing

trend in heavy rain over the tropical ocean.

5.3.2 Heat and Cold Waves

Analyses of simulations for IPCC AR4 by seven

climate models indicate that they reproduce the

primary features of cold air outbreaks (CAOs),

with respect to location and magnitude (Vavrus

et al. 2006). In the analyses, a CAO is an

episode of at least 2 days duration during which

the daily mean winter (December-JanuaryFebruary) surface temperature at a gridpoint is

two standard deviations below the gridpoint’s

winter mean temperature. Maximum frequencies of about four CAO days per winter are simulated over western North America and Europe,

while minimal occurrences of less than one day

per winter exist over the Arctic, northern Africa,

and parts of the North Pacific. GCMs generally

are accurate in their simulation of primary fea-
83

Climate Models: An Assessment of Strengths and Limitations

tures, with high pattern correlation to observations and maximum number of days meeting

CAO criteria around 4 per winter. One favored

region for CAOs is in western North America,

extending from southern Alaska into the upper

Midwest. Here, models simulate a frequency of

about 4 CAO days per year, in general agreement with the observed values of 3 to 4 days.

Models underestimate frequency in the southeastern United States (mean simulated values

range from 0.5 to 2 days vs 2 to 2.5 days in observations). This regional bias occurs in all

models and reflects the inability of GCMs to

penetrate Arctic air masses far enough southeastward over North America.

CMIP3 model simulations show a positive trend

for growing season, heat waves, and warm

nights and a negative trend for frost days and

daily temperature range (maximum minus minimum) (Tebaldi et al. 2006). The simulations indicate that this is in general agreement with

observations, except that there is no observed

trend in heat waves. The modeled spatial patterns generally have larger positive trends in

western North America than in eastern sections.

For the United States, this is in qualitative

agreement with observations showing that decreases in frost-free season and frost days are

largest in the western United States (Kunkel et

al. 2004; Easterling 2002).

Analysis of individual models provides a more

detailed picture of model performance. In a simulation from PCM (Meehl, Tebaldi, and Nychka

2004), the largest trends for decreasing frost

days occur in the western and southwestern

United States (values greater than –2 days per

decade). Trends near zero in the upper Midwest

and northeastern United States show good

agreement with observations. The biggest discrepancy between model and observations is

over parts of the southeastern United States,

where the model shows trends for decreasing

frost days and observations show slight increases. This is thought to be a partial consequence of two large El Niño events in

observations during this time period (1982–

1983 and 1997–1998) when anomalously cool

and wet conditions occurred over the southeastern United States and contributed to slight increases of frost days. The model’s ensemble

mean averages out effects from individual El

Niño events, and thus frost-day trends reflect a

more general response to forcings that occurred

during the latter part of the 20th Century. An

analysis of short-duration heat waves simulated

by PCM (Meehl and Tebaldi 2004) indicates

good agreement with observed heat waves for

North America. In that study, heat waves were

defined by daily minimum temperature. The

most intense events occurred in the southeastern

United States for both model simulation and observations. The overall spatial pattern of heatwave intensity in the model matched closely

with the observed pattern. In a four-member ensemble of simulations from HadCM3 (Christidis et al. 2005), the model showed a rather

uniform pattern of increases in the warmest

night for 1950 to 1999. Observations also show

a global mean increase, but with considerable

regional variations. In North America, observed

trends in the warmest night vary from negative

in the south-central sections to strongly positive

in Alaska and western Canada, compared to a

rather uniform pattern in the model. However,

this discrepancy might be expected, since the

observations probably reflect a strong imprint

of internal climate variability that is reduced by

ensemble averaging of the model simulations.

Analysis of the magnitude of temperature extremes for California in a regional climate

model simulation (Bell, Sloan, and Snyder

2004) shows mixed results. The hottest maximum in the model is 4°C less than observations,

while the coldest minimum is 2.3°C warmer.

The number of days >32°C is 44 in the model

compared to an observed value of 71. This

could result from the lower diurnal temperature

range in the model (15.4°C observed vs 9.7°C

simulated). While these results are better than

the driving GCM, RCM results are still somewhat deficient, perhaps reflecting the study region’s very complex topography.

Models display some capability to simulate extreme temperature and precipitation events, but

there are differences from observed characteristics. Models typically produce global increases in extreme precipitation and severe

drought and decreases in extreme minimum

temperatures and frost days, in general agreement with observations. Models have a general,

though not universal, tendency to underestimate

the magnitude of heavy precipitation events.
84

The U.S. Climate Change Science Program Chapter 5 - Model Simulation of Major Climate Features

Regional-trend features are not always captured.

Since the causes of observed regional-trend

variations are not known in general and such

trends could be due in part to the climate system’s stochastic variability, assessing the significance of these discrepancies is difficult.
6CHAPTER

Climate Models: An Assessment of Strengths and Limitations

Future Model 

Development

CHAPTER

6

85

Climate models are evolving toward greater comprehensiveness, incorporating such aspects of the

chemical and biological environment as active vegetation on land and oceanic biogeochemistry that

affect and are affected by the physical climate. Climate models are simultaneously evolving toward finer spatial resolution.

Improvements in climate simulations as resolution increases can be both incremental and fundamental. Incremental improvements are expected in treatment of the atmosphere due to better

simulation of atmospheric fronts, interactions among extratropical storms and sharp topographic

features, and, especially, tropical storms. In the ocean, finer resolution incrementally improves the

simulation of narrow boundary currents and the circulation in relatively small basins, such as the

Labrador Sea, that play key roles in oceanic circulation.

More fundamental changes also happen in both the atmosphere and the ocean as resolution improves. In the ocean a key transition occurs at grid scales of tens of kilometers, at which point

mesoscale eddies (see Chapter 2) begin to be explicitly resolved. In the atmosphere, a fundamental transition takes place when the grid scale drops to a few kilometers, where direct simulation of dominant deep convective circulations begins to be feasible and the model’s dependence

on uncertain subgrid-scale parameterization of deep moist convection diminishes.

In the following, we discuss these more fundamental oceanic and atmospheric transitions and

then describe some examples of increased comprehensiveness in climate modeling (see also

Chapter 2 for glacial modeling, another important future development).

The climate modeling enterprise is evolving

along additional paths (apart from evolution of

the models themselves) that are not discussed

here. One path is the creation of large ensembles of model simulations by varying uncertain

physical parameters so as to better estimate the

associated uncertainties [quantifying uncertainty in model predictions (called QUMP);

Murphy et al. 2004; climateprediction.net]. Others include the movement toward initializing climate models with estimates of observed

climatic states, particularly the observed

oceanic state, so as to optimize the realism of

decadal forecasts, which marks an evolution toward the merging of seasonal-interannual and

decadal forecasting (Troccoli and Palmer 2007).
The U.S. Climate Change Science Program Chapter 6 - Future Model Development

86

6.1 HIGH-RESOLUTION MODELS

6.1.1 Mesoscale Eddy-Resolving

Ocean Models

The distinction between laminar and turbulent

flow in the ocean is fundamental. Simulations

of the more realistic turbulent regime promise

to substantially raise the level of realism in

oceanic climate simulations. For example, Fig.

6.1 shows two simulations of the Southern

Ocean by an ocean model developed at the Geophysical Fluid Dynamics Laboratory (GFDL)

(Hallberg and Gnanadesikan 2006). The field

shown is an instantaneous snapshot of the surface current speed. Resolution of the model on

the left is about 1° latitude. The result is a relatively laminar (nonturbulent) flow with a gently meandering circumpolar current. The figure

on the right is obtained by reducing the grid size

to 1/6 of a degree. A much more turbulent flow

is simulated by the model with abundant vortex

generation. This model is beginning to resolve

the spectrum of mesoscale eddies that populate

the Southern Ocean and many other oceanic regions. As discussed in Chapter 2, the effects on

ocean circulation of mesoscale eddy-induced

mixing are parameterized in current ocean models, which can be thought of as essentially laminar.

While progress has been made in recent years,

explicit simulation of these eddies undoubtedly

is more reliable than mixing parameterizations.

In the Southern Ocean, eddies are thought to

control the circumpolar current’s response to

wind changes (Hallberg and Gnanadesikam

2006) and the way carbon dioxide is taken up

by the Southern Ocean.

Figure 6.1. Surface-Current Speed in Two Simulations of the Southern Ocean in Lowand High-Resolution Ocean Models. 

[From Fig. 6 in R. Hallberg and A. Gnanadesikam 2006: The role of eddies in determining the structure and

response of the wind-driven Southern Hemisphere overturning: Results from the modeling eddies in the

Southern Ocean (MESO) project. J. Physical Oceanography, 36, 2232–2252. Reproduced by permission of

the American Meteorological Society (AMS).]
Global mesoscale eddy-resolving ocean models

are beginning to be examined in various modeling centers in the United States and around the

world, even though exploiting such models will

require substantial increases in computational

resources. Challenges that may arise when these

models are integrated for long time periods include maintaining realistically small amounts of

mixing across constant-density surfaces in the

more turbulent flows to avoid distortion of

much slower thermohaline circulations.

As noted in Chapter 5, models provide estimates of the climate system’s centennial-scale

variability that underlies attribution studies of

climatic trends. Seeing if eddy-resolving

OGCMs increase the variability level on long

time scales in climate models will be of great

interest.

6.1.2 Cloud-Resolved Atmospheric

Models

As atmospheric models attain higher resolution

and more detailed representation of physical

processes, short-range weather prediction and

longer-range climate prediction become more

synergistic (Phillips et al. 2004). This is particularly evident in “cloud-resolving models”

(CRMs) with spatial resolutions of less than a

few kilometers. CRMs can explicitly simulate

atmospheric systems that exist on scales much

smaller than the grid resolution of conventional

atmospheric general circulation models

(AGCMs) (Randall et al. 2003; Khairoutdinov,

Randall, and DeMott 2005). These systems include mesoscale organizations in squall lines,

deep updrafts and downdrafts, and cirrus anvils.

CRMs also allow calculation of cloud properties and amounts based on more realistic smallscale structure in the flow field. The desired

result is not only better simulations of regional

climates, especially in the tropics, but also more

reliable estimates of cloud feedbacks and climate sensitivity.

CRMs are variations of models designed for

mesoscale storm and cumulus convection simulations. At CRM grid scales, hydrostatic balance is no longer universally valid. CRMs are

therefore formulated with nonhydrostatic equations in which vertical accelerations are calculated explicitly (Tripoli 1992).

Like AGCMs, CRMs must employ empirical

parameterizations to calculate the impact of

subgrid scale processes, but CRMs explicitly

represent a larger portion of the size spectrum

of meteorological systems, so the parameterizations’ impact on large-scale circulation and

climate may be less severe. Most important, cumulus parameterizations for deep tropical convection are not needed in CRMs. CRMs can

accommodate more realistic microphysical

processes, including those by which aerosols

nucleate cloud drops, allowing more convincing treatment of aerosol and cloud interactions

involved in indirect aerosol radiative forcing.

However, shallow nonprecipitating convection

(which produces fair-weather cumulus clouds)

is dominated by flows on scales less than 1 km

and will probably still require subgrid-scale parameterization in foreseeable global CRMs.

Cloud feedbacks in regions of shallow convection are an important source of disparity in climate sensitivity in CMIP3 models (Bony et al.

2006). Furthermore, most cloud microphysical

processes take place on CRM subgrid scales

and so must be parameterized. Thus, uncertainty

in cloud feedbacks will not disappear when

global CRMs begin to play a role in climate assessments, but modelers hope that uncertainty

will be reduced substantially.

Global models with CRM resolution have been

attempted to date only at the Japanese Earth

Simulator, but, with continued increase in computer power, global CRMs are expected to become centrally important in climate (as well as

weather) research. Nevertheless, as noted above,

major uncertainties in cloud microphysics will

remain, especially in the prediction of ice-particle concentrations, fall speed of cloud particles,

hydrometeorological spectra evolution, and entrainment rates into convective plumes (Cotton

2003). At CRM resolutions, more sophisticated

algorithms of radiative-transfer calculation than

those in current GCMs may be required because

the plane parallel assumption for convergence of

radiant energy may not be valid. Validation of

CRMs probably will continue to take place in regional models and short-range forecasts, followed by their incorporation into global models.

Several observational programs such as the

DOE Atmospheric Radiation Measurement

Climate Models: An Assessment of Strengths and Limitations

87
88

The U.S. Climate Change Science Program Chapter 6 - Future Model Development

(ARM) Program have collected data essential to

evaluate CRMs (M.H. Zhang et al. 2001; Tao et

al. 2004). Results from such programs will facilitate improvement of CRM subgrid-scale

physics. Extensive parameter-sensitivity tests

with global models will still be needed to reduce

uncertainties in microphysics and the treatment

of shallow convection for climate sensitivity and

regional climate-change simulation.

6.2 BIOGEOCHEMISTRY AND

CLIMATE MODELS

6.2.1 Carbon Cycle

The physical climate system and biogeochemical processes are tightly coupled. Changes in

climate affect the exchange of atmospheric CO2

between land surface and ocean, and changes in

CO2 fluxes affect Earth’s radiative forcing and

thus the physical climate system. Some recently

developed atmosphere-ocean general circulation models (AOGCMs) include the carbon

cycle and have confirmed the potential for

strong feedback between it and global climate

(Cox et al. 2000; Friedlingstein et al. 2001;

Govindasamy et al. 2005). The next generation of

AOGCMs may include the carbon cycle as well as

interactive atmospheric aerosols and chemistry.

Models that include the carbon cycle are able to

predict time-evolving atmospheric CO2 concentrations using, as input, anthropogenic emissions

rather than assumed concentrations.

Simulation of the global carbon cycle must account for the processes shown in Fig. 6.2. As the

figure shows, the present-day global carbon cycle

is not in equilibrium because of fossil-fuel burning and other anthropogenic carbon emissions.

These carbon sources must, of course, be included in models of climate change. Such a calculation is not easy because human-induced

changes to the carbon cycle are small compared to

large natural fluxes, as shown in the figure. In addition, although the globally and annually averaged carbon reservoirs and fluxes shown in the

figure are consistent with estimates from a variety

of sources, substantial uncertainties are attached

to the numbers (e.g., often a factor >2 uncertainty

for fluxes; see Prentice et al. 2001). Additional

uncertainty applies to regional, seasonal, and interannual variations in the carbon cycle.

Feedbacks between the physical climate system

and the carbon cycle are represented plausibly

but with substantial differences in various

AOGCM carbon-cycle models. Cox et al.

(2000) obtained a very large positive feedback,

with global warming reducing the fraction of

anthropogenic carbon absorbed by the biosphere, thus boosting the model’s simulated atmospheric CO2. Friedlingstein et al. (2001)

obtained much weaker feedback. Thompson et

al. (2004) demonstrated that making different

assumptions about the land biosphere within a

single model gave markedly different feedback

values. Using the same model, Govindasamy et

al. (2005) noted a positive correlation between

the magnitude of carbon-cycle feedback and the

sensitivity of the physical climate system.

A recent study examined carbon-cycle feedbacks in 11 coupled AOGCM carbon-cycle

models using the same forcing (Friedlingstein

et al. 2006). The models unanimously agreed

that global warming will reduce the fraction of

anthropogenic carbon absorbed by the biosphere—a positive feedback—but the magnitude

of this feedback varied widely among models

(Fig. 6.3). When models included an interactive

carbon cycle, predictions of the additional

global warming due to carbon-cycle feedback

ranged between 0.1 and 1.5°C. Eight models attributed most of the feedback to the land biosphere, while three attributed it to the ocean.

These results demonstrate the large sensitivity

of climate model output to assumptions about

carbon-cycle processes. Future carbon-cycle

models, coupled to physical climate models and

constrained by new global remote-sensing

datasets and in situ measurements, may allow

more definitive projection of CO2 concentrations in the atmosphere for given emission scenarios. CCSP SAP 2.2 contains more

information on the carbon cycle and climate

change (CCSP 2007).

6.2.2 Other Biogeochemical Issues

Methane (CH4) is a potent greenhouse gas

whose atmospheric concentration is controlled

by its emission rate and the atmosphere’s oxidative capacity (especially hydroxyl radical concentration). Methane concentrations are now

much higher than in preindustrial times but have
89

Climate Models: An Assessment of Strengths and Limitations

not increased in the past decade, for reasons that

continue to be debated. Whether or not this trend

carries into the future has substantial implications

for radiative forcing. To resolve this question,

AOGCMs would need to include atmospheric

chemistry models incorporating a number of different trace gases and reaction rates.

Another emerging issue is the interactive evolution of climate with the storage of water and

carbon by plants. To address this process, dynamic vegetation models (in which plant growth

is calculated rather than specified a priori) are

under development at modeling centers in the

United States and elsewhere. This inclusion of a

wider range of processes poses challenges [e.g.,

it amplifies errors in rainfall prediction (Bonan

and Levis 2006)]. In addition, ecosystems fertilized with CO2 are limited by the availability

of nutrients such as nitrogen and phosphorous

Figure 6.2. Global

Carbon Cycle from 

the Point of View of

Existing Physical

Climate System

Models (Coupled

AOGCMs). 

The four boxes represent

atmosphere, land surface,

ocean, and sea ice—major

components of AOGCMs.

Earth system models will

evolve from AOGCMs by

incorporating relevant

biogeochemical cycles into

the four-box framework

(with sea ice not acting as a

carbon reservoir). Numbers

shown are average values for

the 1990s. Small (≤1

PgC/year) fluxes such as

those involving methane are

not shown, except for burial

of 0.2 PgC/year in oceanbottom sediments, assuming

a 50-50 split between plant

and microbial respiration. 

Global Carbon Cycle as Seen by an AOGCM

surface dissolved

inorganic

Ocean

Atmosphere

Land surface

Sea ice

760

920

3

37000

150

marine biota

deep ocean

ocean sediments

vegetation

and soils

2300

3500

fossil fuels

123

photosynthesis

GPP

}NPP

92

0.2

Circled numbers =

 storage in gigatonnes

 (or petagrams) carbon

Other numbers =

fluxes in gigatonnes

 carbon/year

11

92 101 92 101

deep-surface ocean

exchange

photosy photosynthesis 50 thesis 50 39 remineralizatio eralization

6

fossil-fuel burning and cement production

60

microbial icrobial

respiratio respiration

60

plant

resp.

2

land-cover d-cover change

91 ocean-atm

exchange

92 101

deep-surface ocean

exchange

photosynthesis 50 39 remineralization

6

fossil-fuel burning

and cement production

60

microbial

respiration

60

plant

resp.

2

land-cover

change

91

ocean-atm exchange

rivers

1

Figure 6.3. Time Series

of Atmospheric CO2

from 11 Different

AOGCM CarbonCycle Models. 

[From Fig. 1(a) of P.

Friedlingstein et al. 2006:

Climate-carbon cycle

feedback analysis: Results

from the C4MIP model

intercomparison. J. Climate,

19, 3337–3353. Reproduced

by permission of the

American Meteorological

Society (AMS).]

At

mospheric CO2 (pp

m)

1000

900

800

700

600

500

400

300

1850 1900 1950 2000 2050 2100

Year
90

The U.S. Climate Change Science Program Chapter 6 - Future Model Development

that are important to the carbon cycle (Field,

Jackson, and Mooney 1995; Schimel 1998;

Nadelhoffer et al. 1999; Shaw et al. 2002; Hungate et al. 2003). Future climate-carbon models

probably will need to include these nutrients.

The few models that do so now show less plant

growth in response to increasing atmospheric

CO2 (Cramer et al. 2001; Oren et al. 2001;

Nowak, Ellsworth, and Smith 2004). Incorporation of other known limiting factors such as

acclimation of soil microbiology to higher temperatures (Kirschbaum 2000; Tjoelker, Oleksyn,

and Reich 2001) will be important in developing comprehensive Earth system models.

Aerosol modeling also will be a central element

in future models (this subject will be covered by

CCSP SAP 2.3, whose estimated publication

date is June 2008).

Often, climate-carbon simulations include natural ecosystems but do not include the effects

of human land-cover and land-management

changes (e.g., deforestation and reforestation).

Land-cover change often is accounted for simply by prescribing estimates for the historical

period (e.g., Houghton 2003) and for future scenarios from the IPCC Special Report on Emissions Scenarios (IPCC 2000). These estimates

do not include practices such as crop irrigation

and fertilization. Many models with “dynamic

vegetation” do not actually simulate crops; they

only allow natural vegetation to grow. Deforestation, land cultivation, and related human activities probably will be included in at least

some future AOGCMs, enabling more complete

assessment of total anthropogenic effects on the

global climate and environment (Ramankutty et

al. 2002; Root and Schneider 1993).

6.2.3 Ocean Biogeochemistry

Climate change impacts on the marine environment—including changes in the ocean’s biota

and carbon content due to modified ocean temperature, salinity, and circulation patterns—

must be accounted for, along with terrestrial

biogeochemistry, in a complete Earth system

model. Implementation of ocean biogeochemistry processes into AOGCMs is under way 

to improve simulation of the ocean carbon 

cycle under various scenarios [e.g., “CCSM

Biogeochemistry Working Group Meeting 

Report,” March 2006 (www.ccsm.ucar.edu/ 

working_groups/Biogeo/reports/060328_BGC

WGrpt.pdf); GFDL’s Earth system model

(gfdl.noaa.gov/~jpd/ esmdt.html); Doney et al.

2004]. One issue receiving particular attention

in recent years is that ocean productivity may

be increased through iron fertilization via dust

particles, potentially reducing atmospheric CO2

(Martin 1991). This effect is being assessed by

both observational programs (e.g., Bishop,

Davis, and Sherman 2002) and climate-carbon

models (Jickells et al. 2005).

An important challenge to these efforts is the

complexity of ocean ecosystems. Adding to this

complexity are organisms that fix nitrogen and

denitrify, calcify, or silicify; accounting for each

adds parameterizations and variables to the system (Hood et al. 2006). Biological models need

to be sufficiently complex to capture the observed variability on various time scales, since

this variability provides essential tests for the

models. As in many aspects of climate modeling, however, complexity that outgrows the ability to constrain models with available data

should be avoided (Hood et al. 2006).

Modeling groups have undertaken systematic

comparison of different models in the Ocean

Carbon Cycle Model Intercomparison Project

(OCMIP) under the auspices of the International Geosphere-Biosphere Programme.

OCMIP’s most recent phase involved 13

groups—including several from the United

States—implementing a common biological

model in their different OGCMs (Najjar et al.

2007). The common biological model includes

five prognostic variables: inorganic phosphate

(PO4

2–

), dissolved organic phosphorus (DOP),

dissolved oxygen (O2), dissolved inorganic carbon (CO2 + HCO3

– + CO32–), and total alkalinity (the system’s acid- and base-buffering

capacity). Model intercomparison revealed significant differences in simulated biogeochemical fluxes and reservoirs. A biogeochemistry

model’s realism was found to be tied closely to

the dynamics of the simulation’s ocean circulation. Just as for land vegetation modeling, a serious challenge to climate models is presented

by the quality of the physical climate simulation

required for realistic biogeochemical modeling.
CHAPTER

7

Climate Models: An Assessment of Strengths and Limitations

Example Applications 

of Climate Model Results

CHAPTER

7

91

In this chapter we present several cases where climate model simulation results were used for

studies involving actual and potential end-user applications. With the increased availability of climate model simulation output through the CMIP3 multimodel archive, impacts and applications

users are rapidly applying the model results for their needs. Just as quickly, the breadth and diversity

of applications will continue to grow in the future as climate statistics are no longer considered

stationary. The examples discussed in this chapter are meant for illustration and do not constitute

a complete accounting of all published instances of applications from model results. The influence

of climate, and therefore climate change, on different natural and societal systems is quite varied.

Some impacts of climate change result primarily from changes in mean conditions. Other impacts

are sensitive to climate variability—the sequence, frequency, and intensity of specific weather

events. Note that the climate simulations described below are not offering predictions of 21st

Century climate but simply projections of possible climate scenarios. Prediction requires knowing in advance how climatic forcings, including those produced by humans, would change in the future. SAP 3.2 examines climate projections by CMIP3 models in greater detail.

7.1 APPLYING MODEL RESULTS

TO IMPACTS

As shown in previous chapters, climate models

give approximate renditions of real climate.

Consequently, applications of climate model results to impact studies require consideration of

several limitations that characterize model output. In principle, using the direct output of climate models is desirable because these results

represent a physically consistent picture of future climate, including changes in climate variability and the occurrence of such various

weather phenomena as extreme events. In practice, this is rarely done for applications like

those presented below because of simulation biases and the coarse spatial resolution of typical

global simulations. Although the use of climate

projections for impacts is beyond the scope of

this report, aspects of the methodology for using

the projections are based on the models’ abilities to simulate observed climate. Employing

coarse-resolution global model output for regional and local impact studies requires two additional steps—downscaling, as discussed in

Chapter 3, and bias removal, or the adjustment

of future projections for known systematic

model errors, described in Chapters 2 and 5.
The U.S. Climate Change Science Program Chapter 7 - Example Applications of Climate Model Results

92

7.1.1 Downscaling

Downscaling is required because of the limitations of coarse spatial resolution in the global

models. In mountainous terrain, a set of model

values for a single grid box will represent conditions at the mean elevation level of that grid

box. In reality, however, conditions at mountaintop and valley locations will be much different. Such processes as local snowpack

accumulation and melting cannot be studied accurately with direct model output. Resolution

also limits the accuracy of representation of

small-scale processes. A prominent example is

precipitation. The occurrence of heavy downpours is an important climate feature for certain

impacts, but these events are often localized on

a scale smaller than a grid box. In many actual

situations, an area the size of a grid box may experience flooding rains at some points while

others receive no rain at all. As a result, gridbox precipitation tends to be more frequent, and

the largest values typically are smaller than

those observed at the local scale. Chapter 3 covered both dynamical downscaling with nested

regional models and statistical downscaling

methods that include diverse techniques such as

weather generators, transfer functions, and

weather typing.

7.1.2 Bias Removal

A simple approach developed for bias removal

during the early days of climate change assessments and still widely used today is sometimes

dubbed the “delta” method. Climate model output is used to determine future change in climate with respect to the model’s present-day

climate, typically a difference for temperature

and a percentage change for precipitation. Then,

these changes are applied to observed historical

climate data for input to an impacts model. The

delta method assumes that future model biases

for both mean and variability will be the same

as those in present-day simulations. One highly

questionable consequence of this assumption is

that the future frequency and magnitude of extreme weather events are the same relative to the

mean climate of the future as they are in present-day climate. Other bias-removal methods

have been developed, but none are nearly so

widespread, or they are versions of the delta

method.

7.2 CALIFORNIA CLIMATE

CHANGE ASSESSMENTS

One of the most comprehensive uses of climate

model simulation output for applications is

overseen by the California Climate Change

Center. The center was established by a state

agency, the California Energy Commission

(CEC), through its Public Interest Energy Research program (CEC 2006). The center wanted

to determine possible impacts of climate change

on California and utilized the CMIP3 model

simulation database as its starting point for climate change projections.

To generate future California scenarios, researchers selected three climate models from

the CMIP3 multimodel archive: the National

Center for Atmospheric Research–U.S. Department of Energy PCM, the NOAA GFDL

CM2.1, and the Hadley Centre HadCM3 (Hayhoe et al. 2004; Cayan et al. 2006). The models

were chosen in large part because of their ability to simulate both large-scale global climate

features and California’s multiple climatic regions when simulations of the 20th Century were

compared with high-resolution observations. Of

particular importance was the correct simulation of the state’s precipitation climatology, with

a pronounced wet season from November to

March, during which nearly all annual precipitation falls. Further, these three models offered

a range of sensitivities, with transient climate

responses of 1.3 K for PCM, 1.5 K for CM2.1,

and 2.0 K for HadCM3. Following model selection, projections from three scenarios with

low, medium, and high future greenhouse gas

emissions were chosen to span the range of possible future California climate states in the 21st

Century. The California scenarios employed a

statistical downscaling technique that, used observationally, derived probability density functions for surface temperature and precipitation

to produce corrected model-simulated distribution functions (Cayan et al. 2006). Corrections

were then applied to future scenario simulation

results. Once the scenarios were generated, they

were used to quantify possible climate change

impacts on public health, water resources, agriculture, forests, and coastal regions (CEC

2006).
7.3 DRYLAND CROP YIELDS

The effects of weather and climate on crops are

complex. Despite the fact that many details of

weather interactions with plant physiology are

poorly understood, numerous realistic cropgrowth simulation models have been developed.

Current-generation crop models typically step

through the growth process with daily frequency and use a number of meteorological

variables as input, typically maximum and minimum temperature, precipitation, solar radiation, and potential evapotranspiration. A key

characteristic of these models is that they have

been developed for application at a single location and have been validated based on point

data, including meteorological inputs. Thus,

their use in assessing climate change impacts on

crop yields confronts a mismatch between the

spatially averaged climate model grid-box data

and the point data expected by crop models.

Also, biases in climate model data can have unknown effects on crop model results because the

dependence of crop yields on meteorological

variables is highly nonlinear. The typical application study circumvents these difficulties by

avoiding the direct use of climate model output.

The delta method continues to be a common approach in contemporary crop studies. In the U.S.

National Assessment of the Consequences of

Climate Variability and Change, monthly

changes (model future – model control) were

applied to observed data, and a weather generator was used to produce daily weather data for

input to impacts models. For example, Winkler

et al. (2002) found a longer growing season and

greater seasonal heat accumulation in fruitgrowing regions of the Great Lakes but uncertainty about future susceptibility to freezes.

Olesen et al. (2007) investigated the potential

impacts of climate change on several European

crops. Crop models were driven by direct output

of regional climate models and also baseline

(present-day) observed daily climate data adjusted by GCM changes using the delta method.

Thomson et al. (2005) adjusted current daily climate data with monthly change values derived

from GCM projections (Smith et al. 2005) and

then used them as input to models to study future yields of dryland crops in the United States.

National yield changes were found to be up to ±

25%, depending on the climate scenario. These

applications of the delta method produce daily

climate unchanged in many respects from present-day observed data. The number of precipitation days and the time between them remains

the same. Also, relative changes in intensity are

the same for light and heavy days. Likewise, the

length of extended periods of extreme heat and

cold and the intensity of such extremes with respect to the new climate mean do not change.

In a recent study, Zhang (2005) used statistical

downscaling to estimate Oklahoma wheat yields

for a future simulation from HadCM3. In this

study, mean monthly changes of the means and

variances of temperature and precipitation between the HadCM3 control and future simulations were used to adjust the parameters of a

weather generator model. Weather generator parameters include mean precipitation, precipitation variance, the probability of a wet day

following a wet day, the probability of a dry day

following a wet day, mean temperature, and

temperature variance. The observed data were

used to determine a relationship between the

wet-wet and wet-dry day probabilities and total

monthly precipitation. This relationship was

used to assign future values of those probabilities based on the GCM-simulated precipitation

changes. With the new set of parameters, the

weather generator simulated multiple years of

daily weather variables for input to the yield

model. This approach is logical and consistent

and produces different variability characteristics depending on whether future climate is wetter or drier than the present, unlike the simple

delta method applied to daily climate data.

However, these changes are assumed to be similar to what occurs in the present-day climate

between wet and dry periods. Thus, more subtle

climate model–simulated changes that might affect yields (e.g., a change to longer wet and dry

spells without a change in total precipitation)

are not transmitted.

7.4 SMALL WATERSHED

FLOODING

This application faces many of the same issues

as applying model output to estimate changes

in dryland crop yields. For example, models

used for simulating runoff in small watersheds

have been validated using point station data. In

addition, runoff is a highly nonlinear function

Climate Models: An Assessment of Strengths and Limitations

93
94

The U.S. Climate Change Science Program Chapter 7 - Example Applications of Climate Model Results

of precipitation, and flooding occurrence is particularly sensitive to the exact frequency and

amount of precipitation for the most extreme

events. As noted in the “Extreme Events” section of Chapter 5, climate models often underestimate the magnitude of extremes. Again, the

delta method is frequently applied to estimate

the changes in flooding that may result from

global climate change. Recently, Cameron

(2006) determined percentage changes in precipitation from climate model simulations and

applied them to a stochastic rainfall model to

produce precipitation time series for input to a

hydrologic model. Flood magnitudes were estimated for return periods of 10 to 200 years and

for several climate changes scenarios. In most

cases, flood flows increased, but one scenario

produced a decrease.

Dibike and Coulibaly (2005) applied two statistical downscaling techniques to an analysis of

flow on a small watershed in northern Quebec.

One technique used the model of Wilby, Dawson, and Barrow (2002) to identify a set of

large-scale variables (i.e., pressure, flow, temperature, and humidity) related to surface temperature and precipitation in the watershed. The

resulting statistical relationships were applied

to the output of a Canadian GCM climate

change simulation to generate future surface

temperature and precipitation time series. The

second technique used a weather generator requiring various statistical parameters, estimated

by comparing surface temperature and precipitation data between GCM control and future

scenario simulations. The fundamental difference between these two statistical downscaling

techniques is that the Wilby, Dawson, and Barrow (2002) model uses a more complete set of

atmospheric data from the GCM output data

while the weather generator uses only surface

temperature and precipitation. The resulting

time series from both methods provided input

for a hydrologic model. In both cases, peak

flows are higher in the spring and lower in the

early summer in future warmer climates, reflecting changes in snowmelt timing. A major

difference is that the Wilby, Dawson, and Barrow (2002) model produces a trend of increasing daily precipitation not seen in the weather

generator data, resulting in larger spring increases in peak flow.

7.5 URBAN HEAT WAVES

This estimation of changes in heat-wave frequency and intensity can be accomplished using

only near-surface temperature. Because heat

waves are large-scale phenomena and near-surface temperature is rather highly correlated over

the scales of GCM grid-boxes, downscaling is

not usually required for their analysis. Biases,

while remaining an issue, can be accounted for

by using percentile-based definitions of heat

waves. Meehl and Tebaldi (2004) used output

from the PCM for 2080 to 2099 to calculate percentile-based measures of extreme heat; they

found that heat waves will increase in intensity,

frequency, and duration. If mortality estimates

are desired, then biases are an issue because existing models (Kalkstein and Greene 1997) used

location-specific absolute magnitudes of temperature to estimate mortality.

7.6 WATER RESOURCES IN THE

WESTERN UNITED STATES

The possibility that climate change may adversely affect limited water resources in the

mostly arid and semiarid western United States

poses a threat to the prosperity of that region. A

group of university and government scientists,

under the auspices of the U.S Department of Energy–sponsored Accelerated Climate Prediction

Initiative Pilot Project, conducted a coordinated

set of studies that represented an end-to-end assessment of this issue (Barnett et al. 2004). This

project is noteworthy because of close coordination between production of GCM simulations

and the needs of impacts modeling. It also is a

good example of more-sophisticated downscaling approaches.

A suite of carefully selected PCM climate simulations was executed (Dai et al. 2004; Pierce

2004) and then used to drive a regional climate

model to provide higher-resolution data (Leung

et al. 2004), both for direct assessment of effects

on water resources and for use in impacts models. A careful statistical downscaling approach

(Wood et al. 2004) also was used to produce an

alternate dataset for input to impacts models.

Using the observationally based 1/8° latitudeby-longitude resolution gridded dataset developed by Maurer et al. (2002), an empirical

mapping function was developed to relate quan-
95

Climate Models: An Assessment of Strengths and Limitations

tiles of the simulated monthly temperature and

precipitation frequency distributions from control runs to the observed climatological monthly

distributions at the GCM grid scale. This empirical mapping was then applied to simulated

future monthly temperature and precipitation

data and spatially disaggregated to the 1/8° resolution grid through a procedure that added

small-scale structure. Daily time series of future

climate on the 1/8° grid subsequently were produced by randomly sampling from historical

data and adding in the changes resulting from

the empirical mapping and disaggregation.

The daily time series were used in a set of studies to assess water resource impacts (Stewart,

Cayan, and Dettinger 2004; Payne et al. 2004;

VanRheenen et al. 2004; Christensen et al.

2004). The studies, which assumed the IPCC

business-as-usual emissions scenario for the climate change GCM simulation, indicate that

warmer temperatures will melt the snowpack

about a month earlier throughout western North

America by the end of the 21st Century. The

shift in snowmelt will decrease flows and increase competition for water during the summer

in the Columbia River Basin (Payne et al. 2004).

In the Sacramento River and San Joaquin River

basins, the average April 1 snowpack is projected to decrease by half. In the Colorado River

basin, a decrease in total precipitation would

mean that total system demand would exceed

river inflows.
96

The U.S. Climate Change Science Program Chapter 7 - Example Applications of Climate Model Results
REFERENCES

97

AchutaRao, K.M., et al., 2006: Variability of ocean heat uptake:

Reconciling observations and models. J. Geophysical Research, 111, doi:10.1029/2005JC003136.

AchutaRao, K.M., and K.R. Sperber, 2006: ENSO simulation in

coupled ocean-atmosphere models: Are the current models

better? Climate Dynamics, doi:10.1007/s00382-006-0119-7.

AchutaRao, K., and K.R. Sperber, 2002: Simulation of the El

Niño Southern Oscillation: Results from the Coupled Model

Intercomparison Project. Climate Dynamics, 19, 191–209.

Ackerman, A.S., et al., 2004: The impact of humidity above stratiform clouds on indirect aerosol climate forcing. Nature, 432,

1014–1017.

Ackerman, T. P., and G.M. Stokes, 2003: The Atmospheric Radiation Measurement Program. Physics Today, 56(1), 38–44.

Adams, R.M., B.A. McCarl, and O. Mearns, 2003: The effects of

spatial scale climate scenarios on economic assessments: An

example from U.S. agriculture. Climate Change, 60, 131–148.

Albrecht, B.A., 1989: Aerosols, cloud microphysics, and fractional cloudiness. Science, 245, 1227–1230.

Alexander, M., et al., 2006: Extratropical atmosphere-ocean variability in CCSM3. J. Climate, 19, 2496–2525.

Alley, R.B., et al., 2005: Ice-sheet and sea-level changes. Science,

310, 456–460.

Ammann, C.M., et al., 2003: A monthly and latitudinally varying

volcanic forcing dataset in simulations of 20th Century climate. Geophysical Research Letters, 30(12), 1657.

Anderson, B.T., et al., 2001: Model dynamics of summertime

low-level jets over northwestern Mexico. J. Geophysical Research, 106(D4), 3401–3413.

Anderson, C.J., R.W. Arritt, and J.S. Kain, 2007: An alternative

mass flux profile in the Kain-Fritsch convective parameterization and its effects in seasonal precipitation. J. Hydrometeorology, 8(5), 1128–1140, doi:10.1175/JHM624.1.

Anderson, C.J., et al., 2003: Hydrologic processes in regional climate model simulations of the central United States flood of

June–July 1993. J. Hydrometeorology, 4, 584–598.

Anderson, T.L., et al., 2003: Climate forcing by aerosols—a hazy

picture. Science, 300, 1103–1104.

Andersson, L., et al., 2006: Impact of climate change and development scenarios on flow patterns in the Okavango River. J.

Hydrology, 331, 43–57.

Annamalai, H., K. Hamilton, and K.R. Sperber, 2007: South

Asian summer monsoon and its relationship with ENSO in

the IPCC AR4 simulations. J. Climate, 20(6), 1071–1092.

Annan, J.D., and J.C. Hargreaves, 2006: Using multiple observationally based constraints to estimate climate sensitivity. Geophysical Research Letters, 33(6), doi:10.1029/2005GL025259.

Annan, J.D., et al., 2005: Efficiently constraining climate sensitivity with paleoclimate simulations. SOLA, 1, 181–184.

Antic, S., et al., 2006: Testing the downscaling ability of a one-way

nested regional climate model in regions of complex topography. Climate Dynamics, 26, 305–325.

Antic, S., et al., 2004: Testing the downscaling ability of a one-way

nested regional climate model in regions of complex topography. Climate Dynamics, 23(5), 473–493.

Antonov, J.I., S. Levitus, and T.P. Boyer, 2005: Thermosteric sea level

rise, 1955–2003. Geophysical Research Letters, 32, L12602.

Arakawa, A., and W.H. Schubert, 1974: Interaction of a cumulus

cloud ensemble with the large-scale environment. Part I. J.

Atmospheric Sciences, 31, 674–701.

Arblaster, J.M., and G.A. Meehl, 2006: Contribution of various

external forcings to trends in the Southern Annular Mode. J.

Climate, 19, 2896–2905.

Arendt, A.A., et al., 2002: Rapid wastage of Alaska glaciers and

their contribution to rising sea level. Science, 297, 382–386.

Arora, V.K., and G.J. Boer, 2003: A representation of variable root distribution in dynamic vegetation models. Earth Interactions, 7, 1–19.

Arzel, O., T. Fichefet, and H. Goosse, 2006: Sea ice evolution over

the 20th and 21st centuries as simulated by current AOGCMs.

Ocean Modelling, 12, 401–415.

Aurela, M., T. Laurila, and J.P. Tuovinen, 2004: The timing of

snow melt controls the annual CO2 balance in a subarctic fen.

Geophysical Research Letters, 31, L16119.

Climate Models: An Assessment of Strengths and Limitations
98

The U.S. Climate Change Science Program References

Avissar, R., and R.A. Pielke, 1989: A parameterization of heterogeneous

land-surface for atmospheric numerical models and its impact on regional meteorology. Monthly Weather Review, 117, 2113–2136.

Baldocchi, D., and P. Harley, 1995: Scaling carbon dioxide and water

vapour exchange from leaf to canopy in a deciduous forest. I. Leaf

model parameterization. Plant, Cell, Environment, 18, 1146–1156.

Bard, E., B. Hamelin, and R.G. Fairbanks, 1990: U-Th ages obtained by

mass spectrometry in corals from Barbados: Sea level during the past

130,000 years. Nature, 346, 456–458.

Barnett, T.P., et al., 2005: Penetration of human-induced warming into

the world’s oceans. Science, 309, 284.

Barnett, T., et al., 2004: The effects of climate change on water resources

in the West: Introduction and overview. Climatic Change, 62, 1–11.

Beckman, A., and R. Doscher, 1997: A method for improved representation of dense water spreading over topography in geopotential-coordinate models. J. Physical Oceanography, 27, 581–591.

Bell, J.L., L.C. Sloan, and M.A. Snyder, 2004: Regional changes in extreme climatic events: A future climate scenario. J. Climate, 17(1),

81–87.

Bellouin, N., et al., 2005: Global estimate of aerosol direct radiative forcing from satellite measurements. Nature, 438, 1138–1141.

Bengtsson, L., K.I. Hodges, and M. Esch, 2007: Tropical cyclones in a

T159 resolution global climate model: Comparison with observations and re-analyses. Tellus A, 59(4), 396–416, doi:10.1111/j.1600-

0870.2007.00236.x.

Beringer, J., et al., 2001: The representation of arctic soils in the land

surface model: The importance of mosses. J. Climate, 14, 3324–

3335.

Betts, A.K., and J.H. Ball, 1997: Albedo over the boreal forest. J. Geophysical Research–Atmosphere, 102, 28901–28909.

Biasutti, M., and A. Giannini, 2006: Robust Sahel drying in response to

late 20th Century forcings. Geophysical Research Letters, 33,

L11706.

Bierbaum, R.M., et al., 2003: Estimating Climate Sensitivity: Report of

a Workshop. National Academy of Sciences, Washington, D.C.

(books.nap.edu/catalog/10787.html).

Bishop, J.K.B., R.E. Davis, and J.T. Sherman, 2002: Robotic observations of dust storm enhancement of carbon biomass in the North Pacific. Science, 298, 817–821.

Bitz, C.M., and W.H. Lipscomb, 1999: An energy-conserving thermodynamic model of sea ice. J. Geophysical Research, 104, 15669–

12677.

Bjerknes, J., 1969: Atmospheric teleconnections from the equatorial Pacific. Monthly Weather Review, 97, 163–172.

Bleck, R., 2002: An oceanic general circulation model framed in hybrid

isopycnic-Cartesian coordinates. Ocean Modelling, 4, 55–88.

Bleck, R., et al., 1992: Salinity-driven thermocline transients in a windand thermohaline-forced isopycnic coordinate model of the North

Atlantic. J. Physical Oceanography, 22(12), 1485–1505.

Boer, G., J. Stowasser, and K. Hamilton, 2007: Inferring climate sensitivity from volcanic events. Climate Dynamics, 28(5), 481–502.

Boisserie, M., et al., 2006: Evaluation of soil moisture in the Florida

State University climate model: National Center for Atmospheric

Research community land model (FSU-CLM) using two reanalyses

(R2 and ERA40) and in situ observations. J. Geophysical Research,

111(D8), Art. No. D08103.

Bonan, G. B., and S. Levis, 2006: Evaluating aspects of the community

land and atmosphere models (CLM3 and CAM3) using a dynamic

global vegetation model. J. Climate, 19, 2290–2301.

Bonan, G.B., 1995: Sensitivity of a GCM simulation to inclusion of inland water surfaces. J. Climate, 8, 2691–2704.

Bonan, G.B., D. Pollard, and S.L. Thompson, 1992: Effects of boreal forest vegetation on global climate. Nature, 359, 716–718.

Bony, S., et al., 2006: How well do we understand and evaluate climate

change feedback processes? J. Climate, 19(15), 3445–3482.

Bony, S., and J.-L. Dufresne, 2005: Marine boundary layer clouds at the

heart of tropical cloud feedback uncertainties in climate models.

Geophysical Research Letters, 32, L20806.

Bony, S., et al., 2004: On dynamic and thermodynamic components of

cloud changes. Climate Dynamics, 22, 71–86.

Bony, S., and K.A. Emanuel, 2001: A parameterization of the cloudiness

associated with cumulus convection: Evaluation using TOGA

COARE data. J. Atmospheric Sciences, 58, 3158–3183.

Boone, A., et al., 2000: The influence of the inclusion of soil freezing on

simulations by a soil-vegetation-atmosphere transfer scheme. J. Applied Meteorology, 39, 1544–1569.

Boyle, J.S., et al., 2008: Climate model forecast experiments for TOGACOARE. Monthly Weather Review, in press.
99

Climate Models: An Assessment of Strengths and Limitations

Boyle, J.S., 1993: Sensitivity of dynamical quantities to horizontal resolution for a climate simulation using the ECMWF (cycle 33) model.

J. Climate, 6, 796–815.

Bougeault, P., et al., 2001: The MAP special observing period. Bulletin

American Meteorological Society, 82(3), 433–462.

Braconnot, P., et al., 2007a: Results of PMIP2 coupled simulations of the

mid-Holocene and Last Glacial Maximum. Part 1: Experiments and

large-scale features. Climate Past, 3(2), 261–277.

Braconnot, P., et al., 2007b: Results of PMIP2 coupled simulations of the

Mid-Holocene and Last Glacial Maximum. Part 2: Feedbacks with

emphasis on the location of the ITCZ and mid- and high-latitudes

heat budget. Climate Past 3(2), 279–296.

Braithwaite, R.J., and S.C.B. Raper, 2002: Glaciers and their contribution to sea level change. Physics Chemistry Earth, 27, 1445–1454.

Brankovic, T., and D. Gregory, 2001: Impact of horizontal resolution on

seasonal integrations. Climate Dynamics, 18, 123–143.

Breugem, W.-P., W. Hazeleger, and R.J. Haarsma, 2006: Multi-model

study of tropical Atlantic variability and change. Geophysical Research Letters, 33, L23706.

Briegleb, B.P., et al., 2002: Description of the Community Climate 

System Model Version 2 Sea Ice Model, 60 pp.

(www.ccsm.ucar.edu/models/ccsm2.0/csim/).

Brinkop, S., and E. Roeckner, 1995: Sensitivity of a general circulation

model to parameterizations of cloud-turbulence interactions in the

atmospheric boundary layer. Tellus, 47A, 197–220.

Brown, T.J., B.L. Hall, and A.L. Westerling, 2004: The impact of TwentyFirst Century climate change on wildland fire danger in the western

United States: An applications perspective. Climatic Change, 62,

365–388.

Bryan, F.O., et al., 2006: Changes in ocean ventilation during the 21st

Century in the CCSM3. Ocean Modelling, 15, 141–156.

Bryan, K., 1969a: A numerical method for the study of the circulation of

the world ocean. J. Computational Physics, 4, 347–376.

Bryan, K., 1969b: Climate and the ocean circulation. III: The ocean

model. Monthly Weather Review, 97, 806–824.

Bryan, K., and M.D. Cox, 1967: A numerical investigation of the oceanic

general circulation. Tellus, 19, 54–80.

Bryden, H.L., and S. Imawaki, 2001: Ocean heat transport, 455–474 in

Siedler, G., J. Church, and J. Gould, eds. Ocean Circulation and Climate: Observing and Modelling the Global Ocean, Academic Press,

San Diego, p.109.

Burke, E.J., S.J. Brown, and N. Christidis, 2006: Modeling the recent evolution of global drought and projections for the 21st Century with the

Hadley Centre climate model. J. Hydrometeorology, 7, 1113–1125.

Byerle, L.A., and J. Paegle, 2003: Modulation of the Great Plains lowlevel jet and moisture transports by orography and large-scale circulations. J. Geophysical Research, 108(D16), Art. No. 8611.

Cai, W.J., P. Whetton, and D.J. Karoly, 2003: The response of the Antarctic Oscillation to increasing and stabilized atmospheric CO2. J. Climate, 16, 1525–1538.

California Energy Commission (CEC), 2006: Our Changing Climate:

Assessing the Risks to California, A Summary Report from the California Climate Change Center, 16 pp. (www.climatechange.ca.gov/

biennial_reports/2006report/index.html).

Callaghan, T.V., et al., 2004: Responses to projected changes in climate

and UV-B at the species level. Ambio, 33, 418–435.

Cameron, D., 2006: An application of the UKCIP02 climate change scenarios to flood estimation by continuous simulation for a gauged

catchment in the northeast of Scotland, U.K. (with uncertainty). J.

Hydrology, 328, 212–226.

Camp, C.D., and K.K. Tung, 2007: Surface warming by the solar cycle

as revealed by the composite mean difference projection. Geophysical Research Letters, 34, L14703, doi:10.1029/2007GL030207.

Cane, M.A., et al., 1997: Twentieth Century sea surface temperature

trends. Science, 275, 957–960.

Capotondi, A., A. Wittenberg, and S. Masina, 2006: Spatial and temporal structure of ENSO in 20th Century coupled simulations. Ocean

Modelling, 15, 274–298.

Carril, A.F., C.G. Menéndez, and A. Navarra, 2005: Climate response associated with the Southern Annular Mode in the surroundings of

Antarctic Peninsula: A multimodel ensemble analysis. Geophysical

Research Letters, 32, L16713.

Cassano, J.J., et al., 2007: Predicted changes in synoptic forcing of net

precipitation in large Arctic river basins during the 21st Century. J.

Geophysical Research, 112, G04S49, doi:10.1029/2006JG000332.

Cassano, J.J., P. Uotilla, and A.H. Lynch, 2006: Changes in synoptic

weather patterns in the polar regions in the 20th and 21st centuries.

Part 1: Arctic. International J. Climatology, 26(9), 1181–1199.
100

The U.S. Climate Change Science Program References

Cayan, D., et al., 2006: Climate Scenarios from California: A Report

from California Climate Change Center, White Paper, 52 pp.

(www.energy.ca.gov/2005publications/CEC-500-2005-203/CEC500-2005-203.SF.PDF).

Cazenave, A., and R.S. Nerem, 2004: Present-day sea level change: Observations and causes. Reviews Geophysics, 42(3), RG3001.

CCSP, 2007: The First State of the Carbon Cycle Report (SOCCR): The

North American Carbon Budget and Implications for the Global

Carbon Cycle, ed. A.W. King, et al. Synthesis and Assessment Product 2.2, U.S. Climate Change Science Program and Subcommittee on

Global Change Research, Washington, D.C. (www.climatescience.gov/Library/sap/sap2-2/final-report/default.htm).

CCSP, 2006: Temperature Trends in the Lower Atmosphere: Steps for

Understanding and Reconciling Differences, ed. T.R. Karl et al. Synthesis and Assessment Product 1.1, U.S. Climate Change Science

Program and Subcommittee on Global Change Research, Washington, D.C. (www.climatescience.gov/Library/sap/sap1-1/finalreport/default.htm).

Cess, R.D., et al., 1990: Intercomparison and interpretation of climate

feedback processes in 19 atmospheric general circulation models. J.

Geophysical Research, 95(D10), 16601–16615.

Cess, R.D., and G.L. Potter, 1988: Exploratory studies of cloud radiative

forcing with a general circulation model. Tellus, 39A, 460–473.

Charney, J.G., 1979: Carbon Dioxide and Climate: A Scientific Assessment. National Academy of Sciences, Washington, D.C., 22 pp.

Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system.

Part I: Model implementation and sensitivity. Monthly Weather Review, 129, 569–585.

Cheng, A., and K.-M. Xu, 2006: Simulation of shallow cumuli and their

transition to deep convective clouds by cloud-resolving models with

different third-order turbulence closures. Quarterly J. Royal Meteorological Society, 132, 359–382.

Cheng, Y., V.M. Canuto, and A.M. Howard, 2002: An improved model for

the turbulent BBL. J. Atmospheric Sciences, 59, 1550–1565.

Chiacchio, M., J. Francis, and P. Stackhouse, Jr., 2002: Evaluation of

methods to estimate the surface downwelling longwave flux during

Arctic winter. J. Applied Meteorology, 41, 306–318.

Choi, H.I., P. Kumar, and X.-Z. Liang, 2007: Three-dimensional averaged soil-moisture transport model with a scalable parameterization

of subgrid topographic variability. Water Resources Research, 43,

WO4414.

Christensen, J.H., et al., 2007: Regional Climate Projections in Climate

Change 2007: The Physical Science Basis. Contribution of Working

Group 1 to the Fourth Assessment Report of the Intergovernmental

panel on Climate Change, ed. S. Solomon et al. Cambridge University Press, Cambridge, U.K., and New York, N.Y.

Christensen, J.H., T. Carter, and F. Giorgi, 2002: PRUDENCE employs

new methods to assess European climate change. Eos, 83, 147.

Christensen, N.S., et al., 2004: The effects of climate change on the hydrology and water resources of the Colorado River basin. Climatic

Change, 62, 337–363.

Christensen, O.B., 1999: Relaxation of soil variables in a regional climate model. Tellus, 51A, 674–685.

Christidis, N., et al., 2005: Detection of changes in temperature extremes

during the second half of the 20th Century. Geophysical Research

Letters, 32, L20716.

Christy, J.R., et al., 2007: Tropospheric temperature change since 1979

from tropical radiosonde and satellite measurements. J. Geophysical Research, 112, D06102, doi:10.1029/2005JD006881.

Christy, J. R., and R. W. Spencer, 2005: Correcting temperature data

sets. Science, 310, 972.

Chung, C.E., et al., 2005: Global anthropogenic aerosol direct forcing derived from satellite and ground-based observations. J. Geophysical

Research, 110, D24207.

Church, J.A., and N.J. White, 2006: A 20th Century acceleration in global

sea-level rise. Geophysical Research Letters, 33, L01602.

Church, J.A., N.J. White, and M. Arblaster, 2005: Significant decadalscale impact of volcanic eruptions on sea level and ocean heat content. Nature, 438(7064), 74–77, doi:10.1038/nature04237.

Claussen, M., et al., 2002: Earth system models of intermediate complexity: Closing the gap in the spectrum of climate system models.

Climate Dynamics, 18, 579–586.

Clement, A.C., and B. Soden, 2005: The sensitivity of the tropical-mean

radiation budget. J. Climate, 18, 3189–3203.

Clement, A.C., et al., 1996: An ocean dynamical thermostat. J. Climate,

9, 2190–2196.

Clough, S.A., M.J. Iacono, and J.-L. Moncet, 1992: Line-by-line calculations of atmospheric fluxes and cooling rates: Application to water

vapor. J. Geophysical Research, 97, 15761–15785.
101

Climate Models: An Assessment of Strengths and Limitations

Collins, M., et al., 2006: Towards quantifying uncertainty in transient

climate change. Climate Dynamics, 27(2–3), 127–147.

Collins, W.D., et al., 2006(a): The Community Climate System Model

Version 3: CCSM3. J. Climate, 19(11), 2122–2143.

Collins, W.D., et al., 2006(b): Radiative forcing by well-mixed greenhouse gases: Estimates from climate models in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report

(AR4). J. Geophysical Research, 111, D14317.

Collins, W.D., et al., 2004: Description of the NCAR Community Atmospheric Model (CAM3). NCAR Technical Note, National Center

for Atmospheric Research, NCAR/TN-464+STR, 226 pp.

Colman, R., 2003: A comparison: Climate feedbacks in general circulation models. Climate Dynamics, 20, 865–873.

Cook, K.H., and E.K. Vizy, 2006: Coupled model simulations of the

West African monsoon system: 20th Century simulations and 21st

Century predictions. J. Climate, 19, 3681–3703.

Coppola, E., and F. Giorgi, 2005: Climate change in tropical regions

from high-resolution time-slice AGCM experiments. Quarterly J.

Royal Meteorological Society, 131, 3123–3145.

Cotton, W.R., 2003: Cloud modeling from days of EML to the present—

Have we made progress? 95–106 in AMS Meteorological Monographs—Symposium on Cloud Systems, Hurricanes, and TRMM.

Covey, C., et al., 2003: An overview of results from the Coupled Model

Intercomparison Project, Global Planetary Change, 37(1–2), 103–

133.

Covey, C., et al., 2000: The seasonal cycle in coupled ocean-atmosphere

general circulation models. Climate Dynamics, 16, 775–787.

Covey, C., L.C. Sloan, and M.I. Hoffert, 1996: Paleoclimate data constraints on climate sensitivity: The paleocalibration method. Climatic

Change, 32, 165–184.

Cox, P.M., et al., 2000: Acceleration of global warming due to carboncycle feedbacks in a coupled model. Nature, 408, 184–187.

Cramer, W., et al., 2001: Global response of terrestrial ecosystem and

function to CO2 and climate change: Results from six dynamic

global vegetation models. Global Change Biology, 7, 357–373.

Crane, R.G., and B.C. Hewitson, 1998: Doubled CO2 precipitation

changes for the Susquehanna basin: Down-scaling from the GENESIS general circulation model. International J. Climatology, 18, 65–

76.

Crucifix, M., 2006: Does the last glacial maximum constrain climate

sensitivity? Geophysical Research Letters, 33, L18701,

doi:10.1029/2006GL027137.

Crucifix, M., et al., 2006: Second phase of Paleoclimate Modelling Intercomparison Project. EOS, Transactions American Geophysical

Union, 86(28), 264.

Cubasch, U., et al., 2001: Projections of future climate change, 525–582

in Climate Change 2001: The Scientific Basis, ed. J.T. Houghton, et

al. Cambridge University Press, Cambridge, U.K.

Cunningham, S.A., et al., 2003: Transport and variability of the Antarctic Circumpolar Current in Drake Passage. J. Geophysical Research,

108, doi:10.1029/2001JC001147.

Curry, J.A., and A.H. Lynch, 2002: Comparing Arctic regional climate

models. Eos, 83, 87.

Curry, J.A., et al., 1996: Overview of Arctic cloud and radiation characteristics. J. Climate, 9, 1731–1764.

Curry, J.A., J. Schramm, and E.E. Ebert, 1995: Sea ice-albedo climate

feedback mechanism. J. Climate, 8, 240–247.

Cusack, S., J.M. Edwards, and R. Kershaw, 1999: Estimating subgrid

variance of saturation and its parameterization for use in a GCM

cloud scheme. Quarterly J. Royal Meteorological Society, 125,

3057–3076.

Dai, A., 2006: Precipitation characteristics in eighteen coupled climate

models. J. Climate, 19, 4605–4630.

Dai, A., et al., 2004: The ACPI climate change simulations. Climatic

Change, 62, 29–43.

Danabasoglu, G., et al., 2006: Diurnal coupling in the tropical oceans of

CCSM3. J. Climate, 19, 2347–2365, doi:10.1175/JCLI3739.1.

Davies, H.C., and R.E. Turner, 1977: Updating prediction models by dynamical relaxation: An examination of the technique. International

J. Climatology, 103, 225–245.

Davies, H.C., 1976: Lateral boundary formulation for multilevel prediction models. Quarterly J. Royal Meteorological Society, 102, 405–

418.

DeCaria, A.J., et al., 2005: Lightning-generated NOx and its impact on

tropospheric ozone production: A three-dimensional modeling study

of a stratosphere–troposphere experiment: Radiation, aerosols, and

ozone (STERAO-A) thunderstorm. J. Geophysical Research, 110,

D14303, doi:10.1029/2004JD00556.
102

The U.S. Climate Change Science Program References

de Elía, R., R. Laprise, and B. Denis, 2002: Forcasting skill limits of

nested, limited-area models: A perfect-model approach. Monthly

Weather Review, 130, 2006–2023.

Del Genio, A.D., et al., 2005: Cumulus microphysics and climate sensitivity. J. Climate, 18, 2376–2387, doi:10.1175/JCLl3413.1.

Del Genio, A.D., A. Wolf, and M.-S. Yao, 2005: Evaluation of regional

cloud feedbacks using single-column models. J. Geophysical Research, 110, D15S13, doi:10.1029/2004JD005011.

Del Genio, A.D., et al., 1996: A prognostic cloud water parameterization

for global climate models. J. Climate, 9, 270–304.

Del Genio, A.D., and M.-S. Yao, 1993: Efficient cumulus parameterization for long-term climate studies: The GISS scheme. The Representation of Cumulus Convection in Numeric Models.

Meteorological Monograph, No. 46, American Meteorological Society, 181–184.

Delworth, T.L., et al., 2006: GFDL’s CM2 global coupled climate models. Part 1: Formulation and simulation characteristics. J. Climate,

19, 643–674.

Denis, B., R. Laprise, and D. Caya, 2003: Sensitivity of a regional climate

model to the resolution of the lateral boundary conditions. Climate

Dynamics, 20, 107–126.

Denis, B., et al., 2002: Downscaling ability of one-way, nested regional

climate models: The big-brother experiments. Climate Dynamics,

18, 627–646.

Déqué, M., et al., 2005: Global high resolution versus Limited Area

Model climate change projections over Europe: Quantifying confidence level from PRUDENCE results. Climate Dynamics, 25, 653–

670.

Déqué, M., and J.P. Piedelievre, 1995: High-resolution climate simulation over Europe. Climate Dynamics, 11, 321–339.

Dethloff, K., et al., 2006: A dynamical link between the Arctic and the

global climate system. Geophysical Research Letters, 33, L03703,

doi:10.1029/2005GL025245.

Dettinger, M.D., et al., 2004: Simulated hydrologic responses to climate

variations and change in the Merced, Carson, and American River

basins, Sierra Nevada, California, 1900–2099. Climatic Change, 62,

283–317.

Dibike, Y.B., and P. Coulibaly, 2005: Hydrologic impact of climate

change in the Saguenay watershed: Comparison of downscaling

methods and hydrologic models. J. Hydrology, 307, 145–163.

Dickinson, R.E., A. Henderson-Sellers, and P.J. Kennedy, 1993: Biosphere-Atmosphere Transfer Scheme (BATS) version 1e as coupled

to the NCAR Community Climate Model. NCAR Technical Note,

NCAR/TN-387+STR, National Center for Atmospheric Research,

Boulder, Colo., 72 pp. [Available from NCAR; P.O. Box 3000; Boulder, CO 80305]

Diffenbaugh, N.S., M.A. Snyder, and L.C. Sloan, 2004: Could CO2-induced land-cover feedbacks alter near-shore upwelling regimes? Proceedings National Academy Sciences, 101, 27–32.

Dimitrijevic, M., and R. Laprise, 2005: Validation of the nesting technique in a regional climate model and sensitivity tests to the resolution of the lateral boundary conditions during summer. Climate

Dynamics, 25, 555–580.

Dimotakis, P.E., 2005: Turbulent mixing. Annual Review Fluid Mechanics, 37, 329–356.

Doney, S.C., et al., 2004: Ocean Carbon and Climate Change (OCCC): An

Implementation Strategy for U.S. Ocean Carbon Cycle Science. University Corporation for Atmospheric Research, Boulder, Colo., 108 pp.

Douglass, D.H., and R.S. Knox, 2005: Climate forcing by the volcanic

eruption of Mount Pinatubo. Geophysical Research Letters, 32,

L05710, doi:10.1029/2004GL022119.

Ducharne, A., et al., 2003: Development of a high resolution runoff routing model, calibration, and application to assess runoff from the

LMD GCM. J. Hydrology, 280, 207–228.

Duffy, P.B., et al., 2003: High-resolution simulations of global climate.

Part 1: Present climate. Climate Dynamics, 21, 371–390.

Dutay, J.-C., et al., 2002: Evaluation of ocean model ventilation with

CFC-11: Comparison of 13 global ocean models. Ocean Modelling,

4, 89–120.

Duynkerke, P.G., and S.R. de Roode, 2001: Surface energy balance and

turbulence characteristics observed at the SHEBA ice camp during

FIRE III. J. Geophysical Research, 106, 15313–15322.

Easterling, D.R., 2002: Recent changes in frost days and the frost-free

season in the United States. Bulletin American Meteorological Society, 83, 1327–1332.

Ebert, E.E., J.L. Schramm, and J.A. Curry, 1995: Disposition of solar

radiation in sea ice and the upper ocean. J. Geophysical Research,

100, 15965–15975.

Ebert, E.E., and J.A. Curry, 1993: An intermediate one-dimensional thermodynamic sea ice model for investigating ice-atmosphere interactions. J. Geophysical Research, 98, 10085–10109.
103

Climate Models: An Assessment of Strengths and Limitations

Ellingson, R., and Y. Fouquart, 1991: The intercomparison of radiation

codes in climate models: An overview. J. Geophysical Research,

96(D5), 8925–8927.

Emanuel, K.A., 1994: Atmospheric Convection. Oxford University

Press, Oxford, U.K., 580 pp.

Emanuel, K.A., 1991: A scheme for representing cumulus convection

in large-scale models. J. Atmospheric Sciences, 48, 2313–2335.

Emori, S., and S.J. Brown, 2005: Dynamic and thermodynamic changes

in mean and extreme precipitation under changed climate. Geophysical Research Letters, 32, L17706.

Emori, S., et al., 2005: Validation, parameterization dependence, and future projection of daily precipitation simulated with a high-resolution

atmospheric GCM. Geophysical Research Letters, 32(6), L06708.

Enfield, D.B., A.M. Mestas-Nuñez, and P.J. Trimble, 2001: The Atlantic

Multidecadal Oscillation and its relationship to rainfall and river

flows in the continental U.S., Geophysical Research Letters, 28,

2077–2080.

Essery, R., and J. Pomeroy, 2004: Implications of spatial distributions of

snow mass and melt rate for snow-cover depletion: Theoretical considerations. Annals Glaciology, 38, 261–265.

Fedorov, A., and S.G. Philander, 2000: Is El Niño changing? Science,

288, 1997–2002.

Field, C., R. Jackson, and H. Mooney, 1995: Stomatal responses to increased CO2: Implications from the plant to the global scale. Plant,

Cell, Environment, 18, 1214–1225.

Forest, C.E., P.H. Stone, and A.P. Sokolov, 2006: Estimated PDFs of climate system properties including natural and anthropogenic forcings. Geophysical Research Letters, 33, L01705,

doi:10.1029/2005GL023977.

Forster, P., et al., 2007: Changes in Atmospheric Constituents and in Radiative Forcing. In Climate Change 2007: The Physical Science

Basis. Contribution of Working Group 1 to the Fourth Assessment of

the Intergovernmental Panel on Climate Change, ed. S. Solomon et

al. Cambridge University Press, Cambridge, U.K., and New York,

N.Y.

Foukal, P., et al., 2006: Variations in solar luminosity and their effect on

the Earth’s climate. Nature, 443, 161–166.

Fox-Rabinovitz, M.S., et al., 2006: Variable resolution general circulation models: Stretched-grid model intercomparison project

(SGMIP). J. Geophysical Research, 111(D16), Art. No. D16104.

Fox-Rabinovitz, M.S., L.L. Takacs, and R.C. Govindaraju, 2002: A variable-resolution stretched-grid general circulation model and data assimilation system with multiple areas of interest: Studying the

anomalous regional climate events of 1998. J. Geophysical Research,

107(D24), Art. No. 4768.

Fox-Rabinovitz, M.S., et al., 2001: A variable-resolution stretched-grid

general circulation model: Regional climate simulation. Monthly

Weather Review, 129, 453–469.

Fox-Rabinovitz, M.S., and R.S. Lindzen, 1993: Numerical experiments

on consistent horizontal and vertical resolution for atmospheric models and observing systems. Monthly Weather Review, 121, 264–271.

Frame, D.J., et al., 2005: Constraining climate forecasts: The role of prior

assumptions. Geophysical Research Letters, 32, L09702,

doi:10.1029/2004GL022241.

Frei, C., et al., 2006: Future change of precipitation extremes in Europe:

An intercomparison of scenarios from regional climate models. J.

Geophysical Research, 111(D6), Art. No. D06105.

Frich, P., et al., 2002: Observed coherent changes in climatic extremes

during the second half of the Twentieth Century. Climate Research,

19, 193–212.

Fridlind, A.M., et al., 2004: Evidence for the predominance of mid-tropospheric aerosols as subtropical anvil cloud nuclei. Science,

304(5671), 718–722.

Friedlingstein, P., et al., 2006: Climate-carbon cycle feedback analysis:

Results from the C4MIP model intercomparison. J. Climate, 19,

3337–3353.

Friedlingstein, P., et al., 2001: Positive feedback between future climate

change and the carbon cycle. Geophysical Research Letters, 28(8),

1543–1546.

Fröhlich, C., 2006: Solar irradiance variability since 1978. Space Science

Reviews, 125, 53–65, doi:10.1007/s11214-006-9046-5.

Fu, C.B., et al., 2005: Regional climate model intercomparison project

for Asia. Bulletin American Meteorological Society, 86, 257–266.

Fyfe, J.C., G.J. Boer, and G.M. Flato, 1999: The Arctic and Antarctic oscillations and their projected changes under global warming. Geophysical Research Letters, 11, 1601–1604.

Ganachaud, A., and C. Wunsch, 2000: Improved estimates of global

ocean circulation, heat transport and mixing from hydrographic data.

Nature, 408, 453–457.
104

The U.S. Climate Change Science Program References

Gao, S., L. Ran, and X. Li, 2006: Impacts of ice microphysics on rainfall and thermodynamic processes in the tropical deep convective

regime: A 2D cloud-resolving modeling study. Monthly Weather Review, 134(10), 3015–3024.

GARP, 1975: The Physical Basis of Climate Modeling, Global Atmospheric Research Program (GARP), Publication Series #16, April

1975.

Gates, W.L., et al., 1999: An Overview of the Atmospheric Model Intercomparison Project (AMIP). Bulletin American Meteorological Society, 80(1), 29–55, doi:10.1175/1520-0477(1999)080<0029:

AOOTRO.2.0.CO;2.

Gates, W.L., 1992: AMIP: The Atmospheric Model Intercomparison

Project. Bulletin American Meteorological Society, 73, 1962–1970.

Gent, P.R., et al., 2006: Ocean chlorofluorocarbon and heat uptake during the 20th Century in the CCSM3. J. Climate, 19, 2366–2381.

Gent, P., and J.C. McWilliams, 1990: Isopycnal mixing in ocean circulation models. J. Physical Oceanography, 20, 150–155.

GFDL Global Atmospheric Model Development Team (GAMDT),

2004: The new GFDL global atmosphere and land model

AM2/LM2: Evaluation with prescribed SST conditions. J. Climate,

17(24), 4641–4673.

Ghan, S. J., et al., 2000: An intercomparison of single column model

simulations of summertime midlatitude continental convection. J.

Geophysical Research, 105, 2091–2124.

Gillett, N.P., and D.W.J. Thompson, 2003: Simulation of recent Southern

Hemisphere climate change. Science, 302, 273–275,

doi:10.1126/science.1087440.

Giorgi, F., and L.O. Mearns, 2003: Probability of regional climate change

based on the reliability ensemble averaging (REA) method. Geophysical Research Letters, 30, Art. No. 1629.

Giorgi, F., et al., 2001: Regional climate change information—Evaluation and projections. Chapter 10, 583–638, in Climate Change 2001:

The Scientific Basis, ed. J.T. Houghton, et al. Cambridge University

Press, Cambridge, U.K.

Giorgi, F., and X. Bi, 2000: A study of internal variability of a regional

climate model. J. Geophysical Research, 105, 29503–29521.

Giorgi, F., and L.O. Mearns, 1999: Introduction to special section: Regional climate modeling revisited. J. Geophysical Research,

104(D6), 6335–6352.

Giorgi, F., et al., 1996: A regional model study of the importance of local

versus remote controls of the 1988 drought and the 1993 flood over

the central United States. J. Climate, 9, 1150–1162.

Giorgi, F., M.R. Marinucci, and G.T. Bates, 1993: Development of a second-generation regional climate model (RegCM2). Part II: Convective processes and assimilation of lateral boundary conditions.

Monthly Weather Review, 121, 2814–2832.

Giorgi, F., and L.O. Mearns, 1991: Approaches to the simulation of regional climate change—A review. Reviews Geophysics, 29, 191–

216.

Gleckler, P.J., K.E. Taylor, and C. Doutriaux, 2008: Performance metrics

for climate models, J. Geophysical Research, 113, D06104,

doi:10.1029/2007JD008972.

Gleckler, P.J., K.R. Sperber, and K. AchutaRao, 2006: Annual cycle of

global ocean heat content: Observed and simulated. J. Geophysical

Research, 111, C06008.

Gnanadesikan, A., et al., 2006: GFDL’s CM2 global coupled climate

models. Part II: The baseline ocean simulation. J. Climate, 19, 675–

697.

Gorham, E., 1991: Northern peatlands: Role in the carbon cycle and

probable responses to climatic warming. Ecological Applications, 1,

182–195.

Govindasamy, B., et al., 2005: Increase of carbon cycle feedback with

climate sensitivity: Results from a coupled climate and carbon cycle

model. Tellus, 57B, 153–163.

Grabowski, W.W., 2003: MJO-like coherent structures: Sensitivity simulations using the cloud-resolving convection parameterization

(CRCP). J. Atmospheric Sciences, 60, 847–864.

Grabowski, W.W., and M.W. Moncrieff, 2001: Large-scale organization

of tropical convection in two-dimensional explicit numerical simulations. International J. Climatology, 127, 445–468.

Gregory, J.M., J.A. Lowe, and S.F.B. Tett, 2006: Simulated global-mean

sea level changes over the last half-millennium. J. Climate, 19, 4576–

4592.

Gregory, J.M., et al., 2004: Simulated and observed decadal variability

in ocean heat content. Geophysical Research Letters, 31(15),

L15312.

Gregory, J.M., 1999: Representation of the radiative effect of convective anvils. Hadley Centre Technical Note 7, Hadley Centre for Climate Prediction and Research, Met Office, Fitzroy Road, Exeter,

EX1 3BP, U.K.
105

Climate Models: An Assessment of Strengths and Limitations

Gregory, J.M., and J.F.B. Mitchell, 1997: The climate response to CO2

of the Hadley Centre coupled AOGCM with and without flux adjustment. Geophysical Research Letters, 24, 1943–1946.

Gregory, D., and S. Allen, 1991: The Effect of Convective Scale Downdrafts upon NWP and Climate Simulations, 122-123 in Ninth Conference on Numerical Weather Prediction. American Meteorological

Society, Denver, Colo.

Gregory, D., and P.R. Rowntree, 1990: A mass flux convection scheme

with representation of cloud ensemble characteristics and stability

dependent closure. Monthly Weather Review, 118, 1483–1506.

Grell, G.A., et al., 2000a: Application of a multiscale, coupled

MM5/chemistry model to the complex terrain of the VOTALP valley campaign. Atmospheric Environment, 34, 1435–1453.

Grell, G.A., et al., 2000b: Nonhydrostatic climate simulations of precipitation over complex terrain. J. Geophysical Research–Atmospheres, 105 (D24), 29595–29608.

Grell, G.A., H. Dudhia, and D. S. Stanfler, 1994: A description of the

fifth generation Penn State–NCAR Mesoscale Model (MM5).

NCAR Technical Note. NCAR/TN-3981STR, National Center for

Atmospheric Research, Boulder, Colo., 122 pp. [Available from

NCAR; P.O. Box 3000; Boulder, CO 80305]

Grell, G.A., 1993: Prognostic evaluation of assumptions used by cumulus parameterizations. Monthly Weather Review, 121, 764–787.

Griffies, S.M., et al., 2005: Formulation of an ocean model for global climate simulations. Ocean Science, 1, 45–79.

Griffies, S.M., et al., 2001: Tracer conservation with an explicit free surface method for z-coordinate ocean models. Monthly Weather Review, 129, 1081–1098.

Griffies, S.M., 1998: The Gent-McWilliams skew-flux. J. Physical

Oceanography, 28, 831–841.

Gritsun, A., and G. Branstator, 2007: Climate response using a threedimensional operator based on the fluctuation–dissipation theorem.

J. Atmospheric Sciences, 64(7), 2558–2575

Gu, L., et al., 1999: Micrometeorology, biophysical exchanges, and NEE

decomposition in a two-storey boreal forest—Development and test

of an integrated model. Agricultural Forest Meteorology, 94, 123–

148.

Guilyardi, E., 2006: El Niño-mean state-seasonal cycle interactions in a

multi–model ensemble. Climate Dynamics, 26, 329–348.

Guo, Z., D.H. Bromwich, and J.J. Cassano, 2003: Evaluation of polar

MM5 simulations of Antarctic atmospheric circulation. Monthly

Weather Review, 131, 384–411.

Gutowski, W.J., et al., 2007a: A possible constraint on regional precipitation intensity changes under global warming. J. Hydrometeorology, 8, 1382–1396, doi: 10.1175/2007JHM817.1.

Gutowski, W.J., et al., 2007b: Influence of Arctic wetlands on Arctic atmospheric circulation. J. Climate, 20, 4243–4254, doi:10.1175/

JCL14243.1.

Gutowski, W.J., et al., 2003: Temporal-spatial scales of observed and simulated precipitation in central U.S. climate. J. Climate, 16, 3841–3847.

Gutowski, W.J., et al., 2002: A Coupled Land–Atmosphere Simulation

Program (CLASP). J. Geophysical Research, 107(D16), 4283,

doi:1029/2001JD000392.

Gutowski, W.J., Z. Ötles, and Y. Chen, 1998: Effect of ocean-surface heterogeneity on climate simulation. Monthly Weather Review, 126,

1419–1429.

Hack, J.J., 1994: Parameterization of moist convection in the National

Center for Atmospheric Research Community Climate Model

(CCM2). J. Geophysical Research, 99, 5551–5568.

Hagemann, S., and L. Dümenil, 1998: A parameterization of the lateral

water flow for the global scale. Climate Dynamics, 14, 17–31.

Hall, A., 2004: The role of surface albedo feedback in climate. J. Climate, 17, 1550–1568.

Hallberg, R., and A. Gnanadesikan, 2006: The role of eddies in determining the structure and response of the wind-driven Southern

Hemisphere overturning: Results from the Modeling Eddies in the

Southern Ocean (MESO) project. J. Physical Oceanography, 36,

2232–2252.

Haltiner, G.J., and R.T. Williams, 1980: Numerical Prediction and Dynamic Meteorology, Second Edition, John Wiley & Sons, New York,

477 pp.

Han, J., and J.O. Roads, 2004: U.S. climate sensitivity simulated with

the NCEP regional spectral model. Climate Change, 62, 115–154.

Hansen, J., et al., 2007: Climate simulations for 1880–2003 with 

GISS modelE. Climate Dynamics, 29(7-8), 661–696 (arxiv.org/

abs/physics/0610109).

Hansen, J., et al., 2006: Global temperature change. Proceedings 

National Academy Sciences, 103, 14288–14293, doi:10.1073/

pnas.0606291103.
106

The U.S. Climate Change Science Program References

Hansen, J., et al., 2005a: Earth’s energy imbalance: Confirmation and

implications. Science, 308, 1431–1435.

Hansen, J., et al., 2005b: Efficacy climate forcings. J. Geophysical Research, 110, D18104, doi:10.1029/2005JD005776.

Hansen, J., et al., 1996: Global surface air temperature in 1995: Return

to pre-Pinatubo level. Geophysical Research Letters, 23, 1665–1668,

doi:10.1029/96GL01040.

Hansen, J., et al., 1993: How sensitive is the world’s climate? National

Geographic Research Exploration, 9, 42–158.

Hansen, J., et al., 1985: Climate response times: Dependence on climate

sensitivity and ocean mixing. Science, 229, 857–859.

Hansen, J., et al., 1984: Climate Sensitivity: Analysis of Feedback Mechanisms, 130–163 in Climate Processes and Climate Sensitivity. Maurice Ewing Series, 5, ed. J.E. Hansen and T. Takahashi. American

Geophysical Union, Washington, D.C.

Hansen, J., et al., 1981: Climate impact of increasing atmospheric carbon dioxide. Science, 213, 957–966.

Harries, J.E., et al., 2001: Increases in greenhouse forcing inferred from

the outgoing longwave radiation spectra of the Earth in 1970 and

1997. Nature, 410, 355–357.

Hartmann, D.L., and K. Larson, 2002: An important constraint on tropical cloud–climate feedback. Geophysical Research Letters, 29,

1951, doi:10.1029/2002GL015835.

Hartmann, D.L., and M.L. Michelsen, 2002: No evidence for iris. Bulletin American Meteorological Society, 83(2), 249–254.

Hay, L.E., et al., 2006: One-way coupling of an atmospheric and a hydrologic model in Colorado. J. Hydrometeorology, 7, 569–589.

Hayhoe, K., et al., 2004: Emissions pathways, climate change, and impacts on California. Proceedings National Academy Sciences

101(34), 12422–12427.

Haylock, M.R., et al., 2006: Downscaling heavy precipitation over the

U.K.: A comparison of dynamical and statistical methods and their

future scenarios. International J. Climatology, 26, 1397–1415.

Hegerl, G., et al. 2007: Understanding and attributing climate change. In

Climate Change 2007: The Physical Science Basis. Contribution of

Working Group 1 to the Fourth Assessment of the Intergovernmental

Panel on Climate Change. Cambridge University Press, Cambridge,

U.K., and New York, N.Y.

Hegerl, G.C., et al., 2006: Climate sensitivity constrained by temperature

reconstructions over the past seven centuries. Nature, 440, 1029–1032.

Hegerl, G.C., et al., 2004: Detectability of anthropogenic changes in annual temperature and precipitation extremes. J. Climate, 17(19),

3683–3700.

Held, I.M., and B.J. Soden, 2006: Robust responses of the hydrological

cycle to global warming. J. Climate, 19, 5686–5699.

Held, I.M., et al., 2005: Simulation of Sahel drought in the 20th and 21st

centuries. Proceedings National Academy Sciences, 102, 17891–

17896.

Held, I.M., and B.J. Soden, 2000: Water vapor feedback and global

warming. Annual Review of Energy Environment, 25, 441–475.

Held, I., and M.J. Suarez, 1994: A proposal for the intercomparison of the

dynamical cores of atmospheric general circulation models. Bulletin

American Meteorological Society, 75(10), 1825–1830.

Held, I.M., R.S. Hemler, and V. Ramaswamy, 1993: Radiative-convective

equilibrium with explicit two-dimensional moist convection. J. Atmospheric Sciences, 50, 3909–3927.

Held, I.M., S.W. Lyons, and S. Nigam, 1989: Transients and the extratropical response to El Niño. J. Atmospheric Sciences, 46(1), 163–174.

Helfand, H.M., and J.C. Labraga, 1988: Design of a non-singular level

2.5 second order closure model for the prediction of atmospheric

turbulence. J. Atmospheric Sciences, 45, 113–132.

Hellstrom, C., et al., 2001: Comparison climate change scenarios for

Sweden based on statistical and dynamical downscaling of monthly

precipitation. Climate Research, 19, 45–55.

Henderson-Sellers, A., 2006: Improving land-surface parameterization

schemes using stable water isotopes: Introducing the “iPILPS” initiative. Global Planetary Change, 51, 3–24.

Henderson-Sellers, A., et al., 2003: Predicting land-surface climates—

Better skill or moving targets? Geophysical Research Letters, 30,

1777.

Henderson-Sellers, A., et al., 1995: The Project for Intercomparison of

Land-Surface Parameterization Schemes (PILPS) —Phase 2 and

Phase 3. Bulletin American Meteorological Society, 76, 489–503.

Hewitson, B.C., and R.G. Crane, 1996: Climate downscaling: Techniques

and application. Climate Research, 7, 85–95.

Hewitt, C.D., and D.J. Griggs, 2004: Ensembles-based predictions: Climate changes and their impacts. Eos, 85, 566.
107

Climate Models: An Assessment of Strengths and Limitations

Heyen, H., H. Fock, and W. Greve, 1998: Detecting relationships between the interannual variability in ecological time series and climate using a multivariate statistical approach—A case study on

Helgoland Roads zooplankton. Climate Research, 10, 179–191.

Hibler, W.D., 1979: A dynamic thermodynamic sea ice model. J. Physical Oceanography, 9, 815–846.

Hines, K.M., et al., 1999: Surface energy balance of the NCEP MRF and

NCEP-NCAR reanalysis in Antarctic latitudes during FROST.

Weather Forecasting, 14, 851–866.

Hirano, A., R. Welch, and H. Lang, 2003: Mapping from ASTER stereo

image data: DEM validation and accuracy assessment. ISPRS J.

Photogrammetry Remote Sensing, 57, 356–370.

Hoerling, M., et al., 2006: Detection and attribution of 20th Century

northern and southern African rainfall change. J. Climate, 19, 3989–

4008.

Hoffert, M.I., and C. Covey, 1992: Deriving global climate sensitivity

from paleoclimate reconstructions. Nature, 360, 573–576.

Holland, M.M., and M.N. Raphael, 2006: Twentieth Century simulation

of the Southern Hemisphere climate in coupled models. Part II: Sea

ice conditions and variability. Climate Dynamics, 26, 229–245.

Holland, M.M., and C.M. Bitz, 2003: Polar amplification: Climate

change in coupled models. Climate Dynamics, 21, 221–232.

Holtslag, A.A.M., and B.A. Boville, 1993: Local versus nonlocal boundary-layer diffusion in a global climate model. J. Climate, 6, 1825–

1842.

Hong, S.-Y., and H.-M.H. Juang, 1998: Orography blending in the lateral

boundary of a regional model. Monthly Weather Review, 126, 1714–

1718.

Hood, R.R., et al., 2006: Pelagic functional group modeling: Progress,

challenges and prospects. Deep-Sea Research II, 53, 459–512.

Hoogenboom, G., J.W. Jones, and K.J. Boote, 1992: Modeling growth,

development, and yield of grain legumes using SOYGRO, PNUTGRO, and BEANGRO—A review. Transactions ASAE, 35, 2043–

2056.

Hope, P.K., N. Nicholls, and J.L. McGregor, 2004: The rainfall response

to permanent inland water in Australia. Australian Meteorological

Magazine, 53, 251–262.

Horel, J.D., and J.M. Wallace, 1981: Planetary-scale atmospheric phenomena associated with the Southern Oscillation. Monthly Weather

Review, 109, 813–829.

Hori, M.E., and H. Ueda, 2006: Impact of global warming on the East

Asian winter monsoon as revealed by nine coupled atmosphere–

ocean GCMs. Geophysical Research Letters, 33,

doi:10.1029/2005GL024961.

Horowitz, L.W., et al., 2003: A global simulation of tropospheric ozone

and related tracers: Description and evaluation of MOZART, version

2. J. Geophysical Research–Atmospheres, 108(D24), 4784.

Houghton, R., 2003: Revised estimates of the annual net flux of carbon

to the atmosphere from changes in land use and land management

1850–2000. Tellus, 55B, 378–390.

Huang, B., P.H. Stone, and C. Hill, 2003: Sensitivities of deep-ocean

heat uptake and heat content to surface fluxes and subgridscale parameters in an ocean general circulation model with idealized geometry. J. Geophysical Research, 108(C1), 3015, doi:10.1029/

2001JC001218.

Hungate, B.A., et al., 2003: Nitrogen and climate change. Science, 302,

1512–1513.

Hunke, E.C., and Y. Zhang, 1999: A comparison of sea ice dynamics

models at high resolution. Monthly Weather Review, 127, 396–408.

Hunke, E.C., and J.K. Dukowicz, 1997: An elastic-viscous-plastic model

for sea ice dynamics. J. Physical Oceanography, 27, 1849–1867.

Hurrell, J.W., 1995: Decadal trends in the North Atlantic Oscillation and

relationships to regional temperature and precipitation. Science, 269,

676–679.

Iorio, J.P., et al., 2004: Effects of model resolution and subgrid-scale

physics on the simulation of precipitation in the continental United

States. Climate Dynamics, 23, 243–258.

IPCC, 2007a: Couplings Between Changes in the Climate System and

Biogeochemistry. In Climate Change 2007: The Physical Science

Basis. Contribution of Working Group 1 to the Fourth Assessment

Report of the Intergovernmental Panel on Climate Change, ed. S.

Solomon et al. Cambridge University Press, Cambridge, U.K., and

New York (www.ipcc.ch).

IPCC, 2007b: Summary for Policymakers. In Climate Change 2007:

The Physical Science Basis. Contribution of Working Group 1 to the

Fourth Assessment Report of the Intergovernmental Panel on Climate Change, ed. S. Solomon et al. Cambridge University Press,

Cambridge, U.K., and New York (www.ipcc.ch).

IPCC, 2001: Climate Change 2001: The Scientific Basis: Contribution

of Working Group 1 to the Third Assessment Report of the Intergovernmental Panel on Climate Change, ed. J.T. Houghton et al. Cambridge University Press, Cambridge, U.K., and New York.
108

The U.S. Climate Change Science Program References

IPCC, 2000: Special Report on Emissions Scenarios, ed. N. Nakicenovic and R. Swart (www.grida.no/climate/ipcc/emission/).

IPCC, 1990: Climate Change: The IPCC Scientific Assessment, ed. J.T.

Houghton, G.J. Jenkins, and J.J. Ephraums. Cambridge University

Press, Cambridge, U.K.

Irannejad, P., A. Henderson-Sellers, and S. Sharmeen, 2003: Importance

of land-surface parameterization for latent heat simulation. Geophysical Research Letters, 30, 1904.

Jakob, C., and G. Tselioudis, 2003: Objective identification of cloud

regimes in the tropical western Pacific. Geophysical Research Letters, 30, 2082.

Janowiak, J.E., 1988: An investigation of interannual rainfall variability

in Africa. J. Climate, 1, 240–255.

Jickells, T.D., et al., 2005: Global iron connections between desert dust,

ocean biogeochemistry, and climate. Science, 308, 67–71.

Jones, C.A., and J.R. Kiniry, 1986: CERES-Maize: A Simulation Model

of Maize Growth and Development. Texas A&M University Press,

College Station, Tex.

Jones, P.D., et al., 2006: Global and hemispheric temperature anomalies—land and marine instrumental records. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S.

DOE (cdiac.esd.ornl.gov/trends/temp/jonescru/jones.html).

Jones, P.D., et al., 1999: Surface air temperature and its changes over the

past 150 years. Reviews Geophysics, 37, 173–199.

Jones, P.D., T. Jónsson, and D. Wheeler, 1997: Extension to the North

Atlantic Oscillation using early instrumental pressure observations

from Gibraltar and south-west Iceland. International J. Climatology,

17, 1433–1450.

Jones, R.G., J.M. Murphy, and M. Noguer, 1995: Simulation of climate

change over Europe using a nested regional climate model. Part I:

Assessment of control climate, including sensitivity to location of

lateral boundaries. International J. Climatology, 121, 1413–1449.

Joseph, R., and S. Nigam, 2006: ENSO evolution and teleconnections in

IPCC’s 20th Century climate simulations: Realistic representation?

J. Climate 19(17), 4360–4377.

Kain, J.S., and J.M. Fritsch, 1993: Convective Parameterization in

Mesoscale Models: The Kain-Fritsch Scheme. In The Representation of Cumulus Convection in Numerical Models, Meteorological

Monographs. American Meteorological Society 46, 165–170.

Kalkstein, L.S., and J.S. Greene, 1997: An evaluation of climate and

mortality relationships in larger U.S. cities and the possible impacts

of a climate change. Environmental Health Perspectives, 105, 84–93.

Kalnay, E., et al., 1996: The NCEP/NCAR 40-year reanalysis project.

Bulletin American Meteorological Society, 77(3), 437–471.

Kanamitsu, M., et al., 2002: NCEP-DEOAMIP-II reanalysis (R-2). Bulletin American Meteorological Society, 83, 1631–1643.

Kattenberg, A., et al., 1996: Climate Models Projections of Future Climate. Chapter 6, Climate Models–Projections of Future Climate, 285–

358, in Climate Change 1995–The Science. Climate Change, ed. J.T.

Houghton, et al. Cambridge University Press, Cambridge, U.K.

Kattsov, V., and E. Källén, 2005: Future Changes of Climate: Modelling

and Scenarios for the Arctic Region in Arctic Climate Impact Assessment (ACIA). Cambridge University Press, Cambridge, U.K.,

1042 pp.

Kattsov, V.M., et al., 2000: Atmospheric Climate Models: Simulation of

the Arctic Ocean Fresh Water Budget Components, 209–247 in The

Freshwater Budget of the Arctic Ocean, ed. E.L. Lewis. Kluwer Academic Publishers, Dordrecht, The Netherlands.

Khain, A., and A. Pokrovsky, 2004: Simulation of effects of atmospheric

aerosols on deep turbulent convective clouds using a spectral microphysics mixed-phase cumulus cloud model. Part II: Sensitivity

study. J. Atmospheric Sciences, 61, 2963–2982.

Khairoutdinov, M., D. Randall, and C. DeMott: 2005: Simulations of the

Atmospheric General Circulation Using a Cloud-Resolving Model

as a Superparameterization of Physical Processes, J. Atmospheric

Sciences, 62(7), 2136–2154, doi:10.1175/JAS3453.1.

Kidson, J.W., and C.S. Thompson, 1998: Comparison of statistical and

model-based downscaling techniques for estimating local climate

variations. J. Climate, 11, 735–753.

Kiehl, J.T., 2007: Twentieth Century climate model response and climate

sensitivity. Geophysical Research Letters, 34, L22710,

doi:10.1029/2007GL031383.

Kiehl, J.T., et al., 2006: The climate sensitivity of the Community Climate

System Model: CCSM3. J. Climate, 19, 2584–2596.

Kiehl, J.T., et al., 1996: Description of the NCAR Community Climate

Model (CCM3). NCAR Technical Note. NCAR/TN-420+STR, National Center for Atmospheric Research, Boulder, Colo., 152 pp.

[Available from NCAR; P.O. Box 3000; Boulder, CO 80305]

Kiktev, D., et al., 2003: Comparison of modeled and observed trends in

indices of daily climate extremes. J. Climate, 16, 3560–3571.
109

Climate Models: An Assessment of Strengths and Limitations

Kim, J., et al., 2005: The effects of the Gulf of California SSTs on warmseason rainfall in the southwestern United States and northwestern

Mexico: A regional model study. J. Climate, 18, 4970–4992.

Kim, J., and J.E. Lee, 2003: A multiyear regional climate hindcast for

the western United States using the mesoscale atmospheric simulation model. J. Hydrometeorology, 4(5), 878–890.

Kimoto, M., 2005: Simulated change of the East Asian circulation under

global warming scenario. Geophysical Research Letters, 32, L16701.

King, J.C., and J. Turner, 1997: Antarctic Meteorology and Climatology.

Cambridge University Press, Cambridge, U.K., 256 pp.

Kirschbaum, M.U.F., 2000: Will changes in soil organic carbon act as a

positive or negative feedback on global warming? Biogeochemistry,

48, 21–51.

Kitoh, A., and T. Uchiyama, 2006: Changes in onset and withdrawal of

the East Asian summer rainy season by multi-model global warming

experiments. J. Meteorological Society Japan, 84, 247–258.

Kleidon, A., 2004: Global datasets of rooting zone depth inferred from

inverse methods. J. Climate, 17, 2714–2722.

Klein, S.A., and C. Jakob, 1999: Validation and sensitivities of frontal

clouds simulated by the ECMWF model. Monthly Weather Review,

127, 2514–2531.

Klemp, J.B., and R. Wilhelmson, 1978: The simulation of three-dimensional convective storm dynamics. J. Atmospheric Sciences, 35,

1070–1096.

Knowles, N., and D.R. Cayan, 2004: Elevational dependence of projected

hydrologic changes in the San Francisco estuary and watershed. Climatic Change, 62, 319–336.

Knutson, T.R., et al., 2007: Simulation of recent multidecadal increase

of Atlantic hurricane activity using an 18-km regional model. Bulletin American Meteorological Society, 88, 1549–1565.

Knutson, T.R., et al., 2006: Assessment of Twentieth Century regional

surface temperature trends using the GFDL CM2 coupled models. J.

Climate, 19(9), 1624–1651.

Knutson, T.R., and S. Manabe, 1998: Model assessment of decadal variability and trends in the tropical Pacific Ocean. J. Climate, 11, 2273–

2296.

Knutti, R., et al., 2006: Constraining climate sensitivity from the seasonal cycle in surface temperature. J. Climate, 19(17), 4224–4233.

Koren, V., et al., 1999: A parameterization of snowpack and frozen

ground intended for NCEP weather and climate models. J. Geophysical Research, 104(D16), 19569–19585.

Kraus, E.B., and J.S. Turner, 1967: A one-dimensional model of the seasonal thermocline. II: The general theory and its consequences. Tellus, 19, 98–105.

Kravtsov, S.V., and C. Spannagle, 2007: Multi-decadal climate variability in observed and modeled surface temperatures. J. Climate, submitted.

Krinner, G., 2003: Impact of lakes and wetlands on boreal climate. J.

Geophysical Research, 108(D16), 4520.

Krinner, E., 1991: Northern peatlands: Role in the carbon cycle and

probable responses to climatic warming. Ecological Applications, 1,

182–195.

Kripalani, R.H., J.H. Oh, and H.S. Chaudhari, 2007: Response of the

East Asian summer monsoon to doubled atmospheric CO2: Coupled

climate models simulations and projections under IPCC AR4. Theoretical Applied Climatology, 87, 1–28.

Kripalani, R.H., et al., 2007: South Asian summer monsoon precipitation variability: Coupled climate simulations and projections under

IPCC AR4. Theoretical Applied Climatology, doi:10.1007/s00704-

006-0282-0.

Krueger, S.K., 1988: Numerical simulation of tropical cumulus clouds

and their interaction with the subcloud layer. J. Atmospheric Sciences, 45, 2221–2250.

Kuang, Z., and D.L. Hartmann, 2007: Testing the fixed anvil temperature

(FAT) hypothesis in a cloud-resolving model. J. Climate, 20, 2051–

2057.

Kunkel, K.E., et al., 2006: Can CGCMs simulate the Twentieth Century

“warming hole” in the central United States? J. Climate, 19, 4137–

4153.

Kunkel, K.E., et al., 2004: Temporal variations in frost-free season in

the United States: 1895–2000. Geophysical Research Letters, 31,

L03201.

Kunkel, K.E., et al., 2002: Observations and regional climate model simulations of heavy precipitation events and seasonal anomalies: A

comparison. J. Hydrometeorology, 3, 322–334.

Kunkel, K.E., et al., 1996: The July 1995 heat wave in the Midwest: A

climatic perspective and critical weather factors. Bulletin American

Meteorological Society, 77, 1507–1518.
110

The U.S. Climate Change Science Program References

Kuo, H.L., 1974: Further studies of the parameterization of the influence

of cumulus convection on large-scale flow. J. Atmospheric Sciences,

31, 1232–1240.

Laprise, R., 2003: Resolved scales and nonlinear interactions in limitedarea models. J. Atmospheric Sciences, 60, 768–779.

Large, W., J.C. McWilliams, and S.C. Doney, 1994: Oceanic vertical

mixing: A review and a model with a nonlocal boundary mixing parameterization. Reviews Geophysics, 32, 363–403.

Latif, M., et al., 2001: ENSIP: The El Niño simulation intercomparison

project. Climate Dynamics, 18, 255–272.

Lau, K.-M., et al., 2006: A multi-model study of the 20th Century simulations of Sahel drought from the 1970s to 1990s. J. Geophysical Research, 111(D0711).

Lawrence, D.M., and A.G. Slater, 2005: A projection of severe near-surface permafrost degradation during the 21st Century. Geophysical

Research Letters, 32, L24401.

Le Treut, H., Z.X. Li, and M. Forichon, 1994: Sensitivity of the LMD

general circulation model to greenhouse forcing associated with two

different cloud water parameterizations. J. Climate, 7, 1827–1841.

Le Treut, H., and Z.X. Li, 1991: Sensitivity of an atmospheric general

circulation model to prescribed SST changes: Feedback effects associated with the simulation of cloud optical properties. Climate Dynamics, 5, 175–187.

Leaman, K., R. Molinari, and P. Vertes, 1987: Structure and variability

of the Florida current at 27N: April 1982–July 1984. J. Physical

Oceanography, 17, 565–583.

Lean, J., J. Beer, and R. Bradley, 1995: Reconstruction of solar irradiance

since 1610: Implications for climate change. Geophysical Research

Letters, 22, 3195–3198.

Lee, H.-C., A. Rosati, and M.J. Spelman, 2006: Barotropic tidal mixing

effects in a coupled climate model: Ocean conditions in the northern

Atlantic. Ocean Modelling, 11, 464–477.

Leith, C., 1975: Climate response and fluctuation dissipation. J. Atmospheric Sciences, 32, 2022–2026.

Leung, L.R., et al., 2004: Mid-century ensemble regional climate change

scenarios for the western United States. Climate Change, 62, 75–

113.

Leung, L.R., and Y. Qian, 2003: The sensitivity of precipitation and

snowpack simulations to model resolution via nesting in regions of

complex terrain. J. Hydrometeorology, 4, 1025–1043.

Leung, L.R., Y. Qian, and X. Bian, 2003: Hydroclimate of the western

United States based on observations and regional climate simulation

of 1981–2000. Part I: Seasonal statistics. J. Climate, 16(12), 1892–

1911.

Leung, L.R., and M.S. Wigmosta, 1999: Potential climate change impacts on mountain watersheds in the Pacific Northwest. J. American

Water Resources Association, 35, 1463–1471.

Levitus, S., et al., 2001: Anthropogenic warming of Earth’s climate system. Science, 292, 267–270.

Li, K.Y., et al., 2006: Root-water-uptake based upon a new water stress

reduction and an asymptotic root distribution function. Earth Interactions, 10, Art. No. 14.

Li, W., R. Fu, and R.E. Dickinson, 2006: Rainfall and its seasonality over

the Amazon in the 21st Century as assessed by the coupled models

for the IPCC AR4. J. Geophysical Research, 11, D02111.

Li, X., and T. Koike, 2003: Frozen soil parameterization in SiB2 and its

validation with GAME-Tibet observations. Cold Regions Science

Technology, 36, 165–182.

Li, X., et al., 2001: A comparison of two vertical-mixing schemes in a Pacific Ocean general circulation model. J. Climate, 14, 1377–1398.

Li, Z.X., 1999: Ensemble atmospheric GCM simulation climate interannual variability from 1979 to 1994. J. Climate, 12, 986–1001.

Liang, X.-Z., et al., 2006: Regional climate model downscaling of the

U.S. summer climate and future change. J. Geophysical Research,

111, D10108.

Liang, X.-Z., et al., 2004: Regional climate model simulation of summer

precipitation diurnal cycle over the United States. Geophysical Research Letters, 31, L24208.

Liang, X., Z. Xie, and M. Huang, 2003: A new parameterization for surface and groundwater interactions and its impact on water budgets

with the variable infiltration capacity (VIC) land surface model. J.

Geophysical Research, 108, 8613.

Liang, X.-Z., K.E. Kunkel, and A.N. Samel, 2001: Development of a regional climate model for U.S. midwest applications. Part 1: Sensitivity to buffer zone treatment. J. Climate, 14, 4363–4378.

Libes, S.M., 1992: An Introduction to Marine Biogeochemistry. New

York, Wiley, 734 pp.

Lin, J.L., et al., 2006: Tropical intraseasonal variability in 14 IPCC AR4

climate models. Part I: Convective signals. J. Climate, 19(12), 2665–

2690.
111

Climate Models: An Assessment of Strengths and Limitations

Lin, J.L., et al., 2004: Stratiform precipitation, vertical heating profiles,

and the Madden–Julian Oscillation. J. Atmospheric Sciences, 61,

296–309.

Lin, S.J., and R.B. Rood, 1996: Multidimensional flux-form semi-lagrangian transport schemes. Monthly Weather Review, 124, 2046–

2070.

Lin, W.Y., and M.H. Zhang, 2004: Evaluation of clouds and their radiative effects simulated by the NCAR Community Atmospheric Model

CAM2 against satellite observations. J. Climate, 17, 3302–3318.

Lindzen, R.S., M.-D. Chou, and A. Y. Hou, 2001: Does Earth have an

adaptive infrared iris? Bulletin American Meteorological Society, 82,

417–432.

Lindzen, R.S., and C. Giannitsis, 1998: On the climatic implications of

volcanic cooling. J. Geophysical Research, 103(D6), 5929–5941.

Lindzen, R.S., 1994: Climate dynamics and global change. Annual Review Fluid Mechanics, 26, 353–378, doi:10.1146/annurev.fl.

26.010194.002033.

Lindzen, R.S., and M.S. Fox-Rabinovitz, 1989: Consistent vertical and

horizontal resolution. Monthly Weather Review, 117, 2575–2583.

Liston, G.E., 2004: Representing subgrid snow cover heterogeneities in

regional and global models. J. Climate, 17, 1381–1397.

Liu, Z., 1998: On the role of ocean in the transient response of tropical

climatology to global warming. J. Climate, 11, 864–875.

Lock, A.P., et al., 2000: A new boundary layer mixing scheme. Part I:

Scheme description and single-column model tests. Monthly Weather

Review, 128, 3187–3199.

Lock, A., 1998: The parameterization of entrainment in cloudy boundary layers. Quarterly J. Royal Meteorological Society, 124, 2729–

2753.

Lockwood, M., and C. Fröhlich, 2007: Recent oppositely directed trends

in solar climate forcings and the global mean surface air temperature.

Proceedings Royal Society A, 463, 2447–2460,

doi:10.1098/rspa.2007.1880.

Lofgren, B.M., 2004: A model for simulation of the climate and hydrology of the Great Lakes basin. J. Geophysical Research, 109(D18),

Art. No. D18108.

Lohmann, U., and E. Roeckner, 1996: Design and performance of a new

cloud microphysics scheme developed for the ECHAM4 general circulation model. Climate Dynamics, 12, 557–572.

Lorenz, P., and D. Jacob, 2005: Influence of regional scale information

on the global circulation: A two-way nesting climate simulation.

Geophysical Research Letters, 32, Art. No. L18706.

Lucarini, V., et al., 2006: Intercomparison of the Northern Hemisphere

winter mid-latitude atmospheric variability of the IPCC models. Climate Dynamics, 28(7–8), 829–848.

Lumpkin, R., and K. Speer, 2003: Large-scale vertical and horizontal

circulation in the North Atlantic Ocean. J. Physical Oceanography,

33, 1902–1920.

Luo, L.F., et al., 2003: Effects of frozen soil on soil temperature, spring

infiltration, and runoff: Results from the PILPS 2(d) experiment at

Valdai, Russia. J. Hydrometeorology, 4, 334–351.

Lynch, A.H., J.A. Maslanik, and W.L. Wu, 2001: Mechanisms in the development of anomalous sea ice extent in the western Arctic: A case

study. J. Geophysical Research, 106(D22), 28097–28105.

Lynch, A.H., et al., 1995: Development of a regional climate model of

the western Arctic. J. Climate, 8, 1555–1570.

Lynn, B.H., et al., 2005: Spectral (bin) microphysics coupled with a

mesoscale model (MM5). Part II: Simulation of a CaPE rain event

with a squall line. Monthly Weather Review, 133, 59–71.

Maak, K., and H. von Storch, 1997: Statistical downscaling of monthly

mean temperature to the beginning of flowering of Galanthus nivalis

L. in Northern Germany. International J. Biometeorology, 

41(1), 5–12.

Malevsky–Malevich, S.P., et al., 1999: The evaluation of climate change

influence on the permafrost season soil thawing regime. Contemporary Investigation Main Geophysical Observatory, 1, 33–50 (in

Russian).

Maltrud, M.E., et al., 1998: Global eddy-resolving ocean simulations

driven by 1985–1995 atmospheric winds. J. Geophysical Research,

103(C13), 30, 825–30, 853.

Manabe, S., et al., 1991: Transient responses of a coupled ocean-atmosphere model to gradual changes of atmospheric CO2. Part I: Annual mean response. J. Climate, 4, 785–818.

Manabe, S., and A. Broccoli, 1985: A comparison of climate model sensitivity with data from the last glacial maximum. J. Atmospheric Sciences, 42, 2643–2651.

Manabe, S., and R.T. Wetherald, 1975: The effects of doubling the CO2

concentration on the climate of a general circulation model. J. Atmospheric Sciences, 32, 3–15.
112

The U.S. Climate Change Science Program References

Manabe, S., 1969: Climate and the ocean circulation. 1: The atmospheric

circulation and hydrology of the Earth’s surface. Monthly Weather

Review, 97, 739–774.

Manabe, S., and R.T. Wetherald, 1967: Thermal equilibrium of the atmosphere with a given distribution of relative humidity. J. Atmospheric Sciences, 24(3), 241–259.

Manabe, S., J. Smagorinsky, and R.F. Strickler, 1965: Simulated climatology of a general circulation model with a hydrological cycle.

Monthly Weather Review, 93, 769–798.

Martin, G.M., et al., 2000: A new boundary layer mixing scheme. Part

II: Tests in climate and mesoscale models. Monthly Weather Review,

128, 3200–3217.

Martin, J.H., et al., 1994: Testing the iron hypothesis in ecosystems of the

equatorial Pacific Ocean. Nature, 371, 123–129.

Martin, J.H., 1991: Iron as a limiting factor in oceanic productivity. In

Primary Productivity and Biogeochemical Cycles in the Sea, ed. P.G.

Falkowski and A.D. Woodhead, 123–137. Plenum Press, New York.

Maurer, E. P., et al., 2002: A long-term hydrologically-based data set of

land surface fluxes and states for the conterminous United States. J.

Climate, 15, 3237–3251.

Maxwell, R.M., and N.L. Miller, 2005: Development of a coupled land

surface and groundwater model. J. Hydrometeorology, 6, 233–247.

May, W., and E. Roeckner, 2001: A time-slice experiment with the

ECHAM4 AGCM at high resolution: The impact of horizontal resolution on annual mean climate change. Climate Dynamics 17(5–

6), 407–420.

McCreary, J., and P. Lu, 1994: Interaction between the subtropical and

equatorial ocean circulation—The subtropical cell. J. Physical

Oceanography, 24, 466–497.

McCulloch, M.T., and T. Ezat, 2000: The coral record of the last interglacial sea levels and sea surface temperatures. Chemical Geology,

169, 107–129.

McFarlane, N.A., 1987: The effect of orographically excited gravity

wave drag on the general circulation of the lower stratosphere and

troposphere. J. Atmospheric Sciences, 44, 1775–1800.

McGregor, J.L., 1999: Regional Modelling at CAR: Recent Developments, 43–48 in Parallel Computing in Meteorology and Oceanography. BMRC Research Report No. 75, Bureau of Meteorology,

Melbourne, Australia.

McGregor, J.L., 1997: Regional climate modelling. Meteorology Atmospheric Physics, 63, 105–117.

McPhaden, M.J., et al., 1998: The Tropical Ocean Global Atmosphere

(TOGA) observing system: A decade of progress. J. Geophysical

Research, 103, 14169–14240.

Mearns, L.O., 2003: Issues in the impacts of climate variability and

change on agriculture—Applications to the southeastern United

States. Climate Change, 60, 1–6.

Mearns, L.O., et al., 2003: Climate scenarios for the southeastern U.S.

based on GCM and regional model simulations. Climate Change, 60,

7–35.

Mearns, L.O., et al., 1999: Comparison of climate change scenarios generated from regional climate model experiments and statistical downscaling. J. Geophysical Research, 104, 6603–6621.

Mechoso, C.R., et al., 1995: The seasonal cycle over the tropical Pacific

in coupled ocean-atmosphere general circulation models. Monthly

Weather Review, 123, 2825–2838.

Meehl, G.A., et al., 2006: Climate change projections for the TwentyFirst Century and climate change commitment in the CCSM3. J. Climate, 19, 2597–2616.

Meehl, G.A., et al., 2005: How much more will global warming and sea

level rise? Science, 307, 1769–1772.

Meehl, G.A., and C. Tebaldi, 2004: More intense, more frequent, and

longer lasting heat waves in the 21st Century. Science, 305, 994–997.

Meehl, G.A., C. Tebaldi, and D. Nychka, 2004: Changes in frost days in

simulations of Twenty-First Century climate. Climate Dynamics, 23,

495–511.

Mellor, G.L., and T. Yamada, 1982: Development of a turbulence closure model for geophysical fluid problems. Reviews Geophysics

Space Physics, 20, 851–875.

Mellor, G.L., and T. Yamada, 1974: A hierarchy of turbulent closure

models for planetary boundary layers. J. Atmospheric Sciences, 31,

1791–1806.

Menon, S., and A. Del Genio, 2007: Evaluating the Impacts of Carbonaceous Aerosols on Clouds and Climate, 34–48 in Human-Induced Climate Change: An Interdisciplinary Assessment, ed. M.E.

Schlesinger et al. Cambridge University Press, Cambridge, U.K., and

New York, N.Y.

Merryfield, W.J., 2006: Changes to ENSO under CO2 doubling in a

multi-model ensemble. J. Climate, 19, 4009–4027.
113

Climate Models: An Assessment of Strengths and Limitations

Miguez-Macho, G., G.L. Stenchikov, and A. Robock, 2005: Regional

climate simulations over North America: Interaction of local

processes with improved large-scale flow. J. Climate, 18, 1227–1246.

Miller, D.A., and R.A. White, 1998: A coterminous United States multilayer soil characteristics dataset for regional climate and hydrology

modeling. Earth Interactions, 2, 1–26.

Miller, R.L., G.A. Schmidt, and D.T. Shindell, 2006: Forced variations

of annular modes in the 20th Century IPCC AR4 simulations. J. Geophysical Research, 111, D18101, doi:10.1029/2005JD006323.

Min, S.-K., and A. Hense, 2007: A Bayesian assessment of climate

change using multi-model ensembles. Part II: Regional and seasonal

mean surface temperature. J. Climate, 20(12), 1769–1790.

Min, S.-K., and A. Hense, 2006: A Bayesian assessment of climate

change using multi-model ensembles. Part I: Global mean surface

temperature. J. Climate, 19, 3237– 3256.

Minschwaner, K., E.D. Essler, and P. Sawaengphokhai, 2006: Multimodel analysis of the water vapor feedback in the tropical upper troposphere. J. Climate, 19, 5455–5464.

Minschwaner, K., and A.E. Dessler, 2004: Water vapor feedback in the

tropical upper troposphere: Model results and observations. J. Climate, 17(6), 272–1282.

Mirocha, J.D., B. Kosovic, and J.A. Curry, 2005: Vertical heat transfer in

the lower atmosphere over the Arctic Ocean during clear-sky periods.

Boundary-Layer Meteorology, 117, 37–71.

Mitchell, J.F.B., et al., 1999: Towards the construction of climate change

scenarios. Climatic Change, 41(3–4), 547–581. 

Mitchell, J.F.B., C.A. Senior, and W.J. Ingram, 1989: CO2 and climate:

A missing feedback? Nature, 341, 132–134.

Mitchell, T., 2003: Pattern scaling: An examination of the accuracy of the

technique for describing future climates. Climatic Change, 60, 217–

242.

Miura, H., et al., 2005: A climate sensitivity test using a global cloud

resolving model under an aqua planet condition. Geophysical Research Letters, 32, L19717.

Mo, K.C., et al., 2005: Impact of model resolution on the prediction of

summer precipitation over the United States and Mexico. J. Climate,

18, 3910–3927.

Moorthi, S., and M.J. Suarez, 1992: Relaxed Arakawa-Schubert: A parameterization of moist convection for general circulation models.

Monthly Weather Review, 120, 978–1002.

Morel, A., and D. Antoine, 1994: Heating rate within the upper ocean in

relation to its bio-optical state. J. Physical Oceanography, 24, 1652–

1665.

Morrison, H., and J.O. Pinto, 2005: Mesoscale modeling of springtime

Arctic mixed-phase stratiform clouds using two-moment bulk microphysics scheme. J. Atmospheric Sciences, 62, 3683–3704.

Murphy, J.M., et al., 2004: Quantification of modelling uncertainties in

a large ensemble of climate change simulations. Nature, 430(7001),

768–772.

Murray, R.J., 1996: Explicit generation of orthogonal grids for ocean

models. J. Computational Physics, 126, 251–273.

Nadelhoffer, K.J., et al., 1999: Nitrogen makes a minor contribution to

carbon sequestration in temperate forests. Nature, 398, 145–148.

Najjar, R.G., et al., 2007: Impact of circulation on export production,

dissolved organic matter, and dissolved oxygen in the ocean: Results

from OCMIP-2. Global Biogeochemistry Cycles, 21, GB3007,

doi:10.1029/2006GB002857.

NARCCAP (North American Regional Climate Change Assessment),

cited as 2007: www.narccap.ucar.edu/.

Neelin, J.D., et al., 1992: Tropical air-sea interaction in general circulation models. Climate Dynamics, 7, 73–104.

Niu, G.Y., and Z.L. Yang, 2006: Effects of frozen soil on snowmelt runoff

and soil water storage at a continental scale. J. Hydrometeorology, 7,

937–952.

Nordeng, T.E., 1994: Extended Versions of the Convective Parameterization Scheme at ECMWF and Their Impact on the Mean and Transient Activity of the Model in the Tropics. Technical Memorandum

206, European Center for Medium Range Weather Forecasting

(ECMWF), Reading, U.K.

Norris, J., and C.P. Weaver, 2001: Improved techniques for evaluating

GCM cloudiness applied to the NCAR CCM3. J. Climate, 14, 2540–

2550.

Nowak, R.S., D.S. Ellsworth, and S.D. Smith, 2004: Tansley review:

Functional responses of plants to elevated atmospheric CO2—Do

photosynthetic and productivity data from FACE experiments support early predictions? New Phytologist, 162, 253–280.

Oglesby, R.J., and B. Saltzman, 1992: Equilibrium climate statistics of

a general circulation model as a function of atmospheric carbon dioxide. Part I: Geographic distributions of primary variables. J. Climate,

5(1), 66–92.
114

The U.S. Climate Change Science Program References

Ohlmann, J.C., 2003: Ocean radiant heating in climate models. J. Climate, 16, 1337–1351.

Olesen, J.E., et al., 2007: Uncertainties in projected impacts of climate

change on European agriculture and terrestrial ecosystems based on

scenarios from regional climate models. Climatic Change, 81, 123–

143.

Oleson, K.W., et al., 2004: Technical Description of the Community Land

Model (CLM). NCAR Technical Note. NCAR/TN-461+STR, National Center for Atmospheric Research, Boulder, Colo., 173 pp.

[Available from NCAR; P.O. Box 3000; Boulder, CO 80305]

Oouchi, K., et al., 2006: Tropical cyclone climatology in a global-warming climate as simulated in a 20- km mesh global atmospheric model:

Frequency and wind intensity analyses. J. Meteorological Society

Japan, 84, 259–276.

Oren, R., et al., 2001: Soil fertility limits carbon sequestration by forest

ecosystems in a CO2–enriched atmosphere. Nature, 411, 469–477.

Ott, L.E., et al., 2007: The effects of lightning NOx production during the

21 July EULINOX storm studied with a 3-D cloud-scale chemical

transport mode. J. Geophysical Research, 112, D05307,

doi:10.1029/2006JD007365.

Our Changing Planet: The FY 1992 U.S. Global Change Research

Program. 1991. A Report by the Committee on Earth and Environmental Sciences. A Supplement to the U.S. President’s Fiscal Year

1992 Budget.

Overgaard, J., D. Rosbjerg, and M.B. Butts, 2006: Land-surface modelling in hydrological perspective–A review. Biogeoscience, 3, 229–

241.

Pacanowski, R.C., and S.G.H. Philander, 1981: Parameterization of vertical mixing in numerical models of tropical oceans. J. Physical

Oceanography, 11, 1443–1451.

Paegle, J., K.C. Mo, and J. Nogués-Paegle, 1996: Dependence of simulated precipitation on surface evaporation during the 1993 United

States summer floods. Monthly Weather Review, 124, 345–361.

Pan, Z., et al., 2004: Altered hydrologic feedback in a warming climate

introduces a “warming hole.” Geophysical Research Letters, 31,

L17109, doi:10.1029/2004GL020528.

Pan, Z., et al., 2001: Evaluation of uncertainties in regional climate

change simulations. J. Geophysical Research, 106, 17735–17752.

Parkinson, C.L., K.Y. Vinnikov, and D.J. Cavalieri, 2006a: Evaluation

of the simulation of the annual cycle of Arctic and Antarctic. J. Geophysical Research, 111, C07012.

Parkinson, C.L., K.Y. Vinnikov, and D.J. Cavalieri, 2006b: Correction to

evaluation of the simulation of the annual cycle of Arctic and Antarctic sea ice coverages by 11 major global climate models. J. Geophysical Research, 111, C12009, doi:10.1029/2006JC003949.

Paulson, C.A., and J.J. Simpson, 1977: Irradiance measurements in the

upper ocean. J. Applied Oceanography, 7, 952–956.

Pavolonis, M., J.R. Key, and J.J. Cassano, 2004: Study of the Antarctic

surface energy budget using a polar regional atmospheric model

forced with satellite-derived cloud properties. Monthly Weather Review, 132, 654–661.

Pawson, S., et al., 2000: The GCM-Reality Intercomparison Project for

SPARC GRIPS): Scientific issues and initial results. Bulletin American Meteorological Society, 81, 781–796.

Payne, A.J., et al., 2004: Recent dramatic thinning of largest West Antarctic ice stream triggered by oceans. Geophysical Research Letters,

31, L23401.

Payne, J.T., et al., 2004: Mitigating the effects of climate change on the

water resources of the Columbia River basin. Climatic Change, 62,

233–256.

Peltier, W.R., 2004: Global glacial isostasy and the surface of the iceage earth: The ice-5G (VM2) model and GRACE. Annual Review

Earth Planetary Science, 32, 111–149.

Philander, S.G.H., 1990: El Niño, La Niña, and the Southern Oscillation.

Academic Press, San Diego, Calif., 293 pp.

Philander, S.G.H., and R.C. Pacanowski, 1981: The oceanic response to

cross-equatorial winds (with application to coastal upwelling in low

latitudes). Tellus, 33, 201–210.

Philip, S.Y., and G. J. van Oldenborgh, 2006: Shifts in ENSO coupling

processes under global warming. Geophysical Research Letters, 33,

L11704.

Phillips, T.J., et al., 2004: Evaluating parameterizations in general circulation models: Climate simulation meets weather prediction. Bulletin American Meteorological Society, doi:10.1175/

BAMS-85-12-1903.

Piani, C., et al., 2005: Constraints on climate change from a multi-thousand member ensemble of simulations. Geophysical Research Letters, 32, L23825.

Pierce, D.W., et al., 2006: Three-dimensional tropospheric water vapor in

coupled climate models compared with observations from the AIRS

satellite system. Geophysical Research Letters, 33, L21701.
115

Climate Models: An Assessment of Strengths and Limitations

Pierce, D.W., 2004: Future change in biological activity in the north Pacific due to anthropogenic forcing of the physical environment. Climatic Change, 62, 389–418.

Pincus, R., H.W. Barker, and J.J. Morcrette, 2003: A fast, flexible, 

approximate technique for computing radiative transfer in 

inhomogeneous cloud fields. J. Geophysical Research,

doi:10.1209/2002JD0033222003.

Pinto, J.O., J.A. Curry, and J.M. Intrieri, 2001: Cloud-aerosol interactions during autumn over Beaufort Sea. J. Geophysical Research,

106, 15077–15097.

Pitman, A.J., et al., 1999: Uncertainty in the simulation of runoff due to

the parameterization of frozen soil moisture using the GSWP

methodology. J. Geophysical Research, 104, 16879–16888.

Pitsch, H., 2006: Large-eddy simulation of turbulent combustion. Annual Review Fluid Dynamics, 38, 453–482.

Plummer, D.A., et al., 2006: Climate and climate change over North

America as simulated by the Canadian Regional Climate Model. J.

Climate, 19, 3112–3132.

Polvani, L.M., R.K. Scott, and S.J. Thomas: 2004: Numerically converged solutions of the global primitive equations for testing the dynamical core of atmospheric GCMs. Monthly Weather Review, 132,

2539–2552.

Pope, V.D., et al., 2000: The impact of new physical parameterizations in

the Hadley Centre Climate Model—HadAM3. Climate Dynamics,

16, 123–146.

Prentice, I.C., et al., 2001: The Carbon Cycle and Atmospheric Carbon

Dioxide. Chapter 3 in Climate Change 2001: The Scientific Basis:

Contribution of Working Group 1 to the Third Assessment Report of

the Intergovernmental Panel on Climate Change, ed. J.T. Houghton et

al. Cambridge University Press, Cambridge, U.K., and New York, N.Y.

Privé, N.C., and R.A. Plumb, 2007: Monsoon dynamics with interactive

forcing. Part I: Axisymmetric studies. J. Atmospheric Sciences,

64(5), 1417–1430. 

Qian, J.-H., W.-K. Tao, and K.-M. Lau, 2004: Mechanisms for torrential

rain associated with the mei-yu development during SCSMEX 1998.

Monthly Weather Review, 132, 3–27.

Qian, J.-H., F. Giorgi, and M.S. Fox–Rabinovitz, 1999: Regional

stretched grid generation and its application to the NCAR RegCM.

J. Geophysical Research, 104(D6), 6501–6514.

Qu, X., and A. Hall, 2006: Assessing snow albedo feedback in simulated

climate change. J. Climate, 19(11), 2617–2630.

Raisanen, J., 2002: CO2–induced changes in interannual temperature

and precipitation variability in 19 CMIP experiments. J. Climate, 15,

2395–2411.

Raisanen, J., and T.N. Palmer, 2001: A probability and decision-model

analysis of a multimodel ensemble of climate change simulations. J.

Climate, 14, 3212–3226.

Rajagopalan, B., U. Lall, and M.A. Cane, 1997: Anomalous ENSO occurrences: An alternate view. J. Climate, 10(9), 2351–2357.

Ramankutty, N., et al., 2002: The global distribution of cultivable lands:

Current patterns and sensitivity to possible climate change. Global

Ecology Biogeography, 11, 377–392.

Ramaswamy, V., et al., 2001: Radiative Forcing Climate Change, 349–

416, in Climate Change 2001: The Scientific Basis, ed. J.T.

Houghton, et al. Cambridge University Press, Cambridge, U.K.

Randall, D.A., et al., 2007: Climate Models and Their Evaluation, Chapter 8, in Climate Change 2007. The Fourth Scientific Assessment, ed.

S. Solomon et al. Intergovernmental Panel on Climate Change

(IPCC) (www.ipcc.ch).

Randall, D.A., et al., 2003: Breaking the cloud parameterization deadlock. Bulletin American Meteorological Society, 84, 1547–1564,

doi:10.1175/BAMS-84-11-1547.

Randall, D.A., et al., 2000: Cloud Feedbacks in Frontiers in Science:

Climate Modeling, ed. J.T. Kiehl and V. Ramanathan. Proceedings

of a symposium in honor of Robert D. Cess.

Raper, S.C.B., J.M. Gregory, and R.J. Stouffer, 2002: The role of climate

sensitivity and ocean heat uptake on AOGCM transient temperature

response. J. Climate, 15, 124–130.

Raphael, M.N., and M.M. Holland, 2006: Twentieth Century simulation

of the Southern Hemisphere climate in coupled models. Part 1:

Large-scale circulation variability. Climate Dynamics, 26, 217–228.

Rasch, P.J., and J.E. Kristjánsson, 1998: A comparison of the CCM3

model climate using diagnosed and predicted condensate parameterizations. J. Climate, 11, 1587–1614.

Rasmussen, E.M., and J.M. Wallace, 1983: Meteorological aspects of

the El Niño/Southern Oscillation. Science, 222, 1195–2002.

Rawlins, M.A., et al., 2003: Simulating pan-Arctic runoff with a macroscale terrestrial water balance model. Hydrology Proceedings, 17,

2521–2539.
116

The U.S. Climate Change Science Program References

Rayner, N.A., et al., 2003: Global analyses of sea surface temperature,

sea ice, and night marine air temperature since the late Nineteenth

Century. J. Geophysical Research, 108(D14), 4407.

Reichler, T., and J. Kim, 2008: How well do coupled models simulate

today's climate? Bulletin American Meteorological Society, 89(3),

doi:10.1175/BAMS-89-3-303, (www.met.utah.edu/reichler/publications/papers/Reichler_07_BAMS_CMIP.pdf).

Rignot, E., and P. Kanagaratnam, 2006: Changes in the velocity structure

of the Greenland ice sheet. Science, 311, 986–990.

Ringer, M.A., and R.P. Allan, 2004: Evaluating climate model simulations of tropical clouds. Tellus, 56A, 308–327.

Rinke, A., et al., 2006: Evaluation of an ensemble of Arctic regional climate models: Spatiotemporal fields during the SHEBA year. Climate Dynamics, doi:10.1007/s00382-005-0095-3.

Roads, J., S.-C. Chen, and M. Kanamitsu, 2003: U.S. regional climate

simulations and seasonal forecasts. J. Geophysical Research,

108(D16), Art. No. 8606.

Roads, J.O., et al., 1999: Surface water characteristics in the NCEP

Global Spectral Model and reanalysis. J. Geophysical Research,

4(D16), 19307–19327.

Roberts, M.J., and R. Wood, 1997: Topographic sensitivity studies with a

Bryan-Cox-type ocean model. J. Physical Oceanography, 27, 823–

836.

Roe, G.H., and M.B. Baker, 2007: Why is climate sensitivity so unpredictable? Science, 318(5850), 629–632, doi:10.1126/science.1144735.

Roeckner, E., et al., 2006: Sensitivity of simulated climate to horizontal

and vertical resolution in the ECHAM5 atmosphere model. J. Climate, 19, 3771–3791.

Roeckner, E., et al., 1996: The atmospheric general circulation model

ECHAM-4: Model description and simulation of present-day climate. Report 128, Max-Planck-Institut für Meteorologie, Hamburg,

Germany.

Roeckner, E., et al., 1987: Cloud optical depth feedbacks and climate

modelling. Nature, 329, 138–140.

Root, T.L., and S.H. Schneider, 1993: Can large-scale climatic models be

linked with multiscale ecological studies? Conservation Biology,

7(2), 256–270.

Ropelewski, C.F., and M.S. Halpert, 1987: Global and regional scale precipitation patterns associated with the El Niño Southern Oscillation.

Monthly Weather Review, 115, 1606–1626.

Rosenzweig, C., et al., 2002: Increased crop damage in the U.S. from

excess precipitation under climate change. Global Environmental

Change, 12, 197–202.

Rothstein, L.M., et al., 2006: Modeling ocean ecosystems: The PARADIGM program. Oceanography, 19, 22–51.

Rotstayn, L.D., and U. Lohmann, 2002: Tropical rainfall trends and the

indirect aerosol effect. J. Climate, 15, 2103–2116.

Rotstayn, L.D., 1997: A physically based scheme for the treatment of

stratiform clouds and precipitation in large-scale models. Part I: Description and evaluation of microphysical processes. Quarterly J.

Royal Meteorological Society, 123, 1227–1282.

Rowell, D.P., et al., 1992: Modelling the influence of global sea surface

temperatures on the variability and predictability of seasonal Sahel

rainfall. Geophysical Research Letters, 19, 905–908.

Ruiz-Barradas, A., and S. Nigam, 2006: IPCC’s Twentieth Century climate simulations: Varied representations of North American hydroclimate variability. J. Climate, 19, 4041–4058.

Rummukainen, M., et al., 2004: The Swedish Regional Climate Modelling Programme, SWECLIM: A review. Ambio, 33, 176–182.

Ruosteenoja, K., H. Tuomenvirta, and K. Jylha, 2007: GCM-based regional temperature and precipitation change estimates for Europe

under four SRES scenarios applying a super-ensemble pattern-scaling method. Climatic Change, 81, Supplement 1,

doi:10.1007/s10584-006-9222-3.

Russell, G.L., et al., 2000: Comparison of model and observed regional

temperature changes during the past 40 years. J. Geophysical Research, 105, 14891–14898.

Russell, G.L., J.R. Miller, and D. Rind, 1995. A coupled atmosphereocean model for transient climate change studies. AtmosphereOcean, 33(4), 683–730.

Russell, J.L., R. Stouffer, and K.W. Dixon, 2007: Corrigendum. J. Climate, 20, 4287.

Russell, J.L., R.J. Stouffer, and K.W. Dixon, 2006: Intercomparison of the

Southern Ocean circulations in IPCC coupled model control simulations. J. Climate, 19, 4560–4575.

Ryan, B.F., et al., 2000: Simulations of a cold front by cloud-resolving,

limited-area, and large-scale models, and a model evaluation using

in situ and satellite observations. Monthly Weather Review, 128,

3218–3235.
117

Climate Models: An Assessment of Strengths and Limitations

Saji, N.H., S.-P. Xie, and T. Yamagata, 2005: Tropical Indian Ocean variability in the IPCC Twentieth Century climate simulations. J. Climate, 19(17), 4397.

Saji, N.H., et al., 1999: A dipole mode in the tropical Indian Ocean. Nature, 401, 360–363.

Santer, B.D., et al., 2007: Identification of human-induced changes in atmospheric moisture content. Proceedings National Academy Sciences, 104, 15248–15253.

Santer, B.D., et al., 2005: Amplification of surface temperature trends

and variability in the tropical atmosphere. Science, 309, 1551–1556.

Santer, B.D., et al., 2001: Accounting for the effects of volcanoes and

ENSO in comparisons of modeled and observed temperature trends.

J. Geophysical Research, 106, 28033–28059.

Santer, B.D., et al., 1990: Developing Climate Scenarios from Equilibrium GCM Results, Report No. 47, Max Planck Institute for Meteorology, Hamburg.

Saraf, A.K., et al., 2005: Digital Elevation Model (DEM) generation

from NOAA-AVHRR night-time data and its comparison with

USGS-DEM. International J. Remote Sensing, 26, 3879–3887.

Sardeshmukh, P.D., and B.J. Hoskins, 1988: The generation of global

rotational flow by steady idealized tropical divergence. J. Atmospheric Sciences, 45, 1228–1251.

Sato, M., et al., 1993: Stratospheric aerosol optical depth, 1850–1990. J.

Geophysical Research, 98, 22987–22994.

Sausen, R., S. Schubert, and L. Dumenil, 1994: A model of the river

runoff for use in coupled atmosphere-ocean models. J. Hydrology,

155, 337–352.

Schimel, D.S., 1998: The carbon equation. Nature, 393, 208–209.

Schmidt, G.A., et al., 2006: Present day atmospheric simulations using

GISS ModelE: Comparison to in-situ, satellite, and reanalysis data.

J. Climate, 19, 153–192, doi:10.1175/JCLI3612.1.

Schmittner, A., M. Latif, and B. Schneider, 2005: Model projections of

the North Atlantic thermohaline circulation for the 21st Century assessed by observations. Geophysical Research Letters, 32,

doi:10.1029/2005GL024368.

Schneider, S.H., and S.L. Thompson, 1981: Atmospheric CO2 and climate: Importance of the transient response. J. Geophysical Research,

86, 3135–3147.

Schneider, S.H., and C. Mass, 1975: Volcanic dust, sunspots, and temperature trends. Science, 190, 741–746.

Schopf, P., et al., 2003: Coupling Process and Model Studies of Ocean

Mixing to Improve Climate Models—A Pilot Climate Process Modeling and Science Team (a U.S. CLIVAR paper,

www.usclivar.org/CPT/Ocean_mixing_whitepaper.pdf).

Schramm, J.L., et al., 1997: Modeling the thermodynamics of a sea ice

thickness distribution. Part 1: Sensitivity to ice thickness resolution.

J. Geophysical Research, 102, 23079–23091.

Schwartz, S.E., 2007: Heat capacity, time constant, and sensitivity of

Earth's climate system. J. Geophysical Research, 112, D24S05,

doi:10.1029/2007JD008746, 2007

Schweitzer, L., 2006: Environmental justice and hazmat transport: A

spatial analysis in southern California. Transportation Research. Part

D–Transportation Environment, 11, 408–421.

Segal, M., et al., 1997: Small lake daytime breezes: Some observational

and conceptual evaluations. Bulletin American Meteorological Society, 78, 1135–1147.

Segal, M., and R.W. Arritt, 1992: Nonclassical mesoscale circulations

caused by surface sensible heat-flux gradients. Bulletin American

Meteorological Society, 73, 1593–1604.

Sellers, P.J., et al., 1996: A revised land surface parameterization (SiB2)

for atmospheric GCMs. Part 1: Model formulation. J. Climate, 9,

676–705.

Sellers, P.J., et al., 1986: A simple biosphere model (SiB) for use within

general-circulation models. J. Atmospheric Sciences, 43, 503–531.

Semtner, A.J., 1976: A model for the thermodynamic growth of sea ice

in numerical investigations of climate. J. Physical Oceanography, 6,

27–37.

Seneviratne, S.I., et al., 2006: Land-atmosphere coupling and climate

change in Europe. Nature, 443, 205–209.

Senior, C.A., and J.F.B. Mitchell, 1996: Cloud Feedbacks in the Unified

UKMO GCM, in Climate Sensitivity to Radiative Perturbations,

Physical Mechanism and Their Validation, ed. H. Le Treut. Springer,

331 pp.

Senior, C.A., and J.F.B. Mitchell, 1993: Carbon dioxide and climate: The

impact of cloud parameterization. J. Climate, 6, 5–21.

Seth, A., and F. Giorgi, 1998: The effects of domain choice on summer

precipitation simulation and sensitivity in a regional climate model.

J. Climate, 11, 2698–2712.
118

The U.S. Climate Change Science Program References

Shaw, M.R., et al., 2002: Grassland responses to global environmental

changes suppressed by elevated CO2. Science, 298, 1987–1990.

Shepherd, A., and D. Wingham, 2007: Recent sea-level contributions of

the Antarctica and Greenland ice sheets. Science, 315, 1529–1532.

Shindell, D.T., et al., 2006: Solar and anthropogenic forcing of tropical

hydrology. Geophysical Research Letters, 33, L24706,

doi:10.1029/2006GL027468.

Shindell, D.T., et al., 1999: Simulation of recent northern winter climate

trends by greenhouse-gas forcing. Nature, 399, 452–455.

Shukla, J., et al., 2006: Climate model fidelity and projections of climate change. Geophysical Research Letters, 33, L07702,

doi:10.1029/2005GL025579.

Siddall, M., et al., 2003: Sea-level fluctuations during the last glacial

cycle. Nature, 423, 853–858.

Skamarock, W.C., et al., 2005: A Description of the Advanced Research

WRF Version 2. NCAR Technical Note. NCAR/TN–468+STR, National Center for Atmospheric Research, Boulder, Colo., 88 pp.

[Available from NCAR; P.O. Box 3000; Boulder, CO 80305]

Slater, A.G., et al., 2001: The representation of snow in land surface

schemes: Results from PILPS 2(d). J. Hydrometeorology, 2, 7–25.

Small, C., V. Gornitz, and J.E. Cohen, 2000. Coastal hazards and the

global distribution of population. Environmental Geoscience, 7, 3–

12.

Smethie, W.M., Jr., and R.A. Fine, 2001: Rates of North Atlantic deep

water formation calculated from chlorofluorocarbon inventories.

Deep Sea Research, Part 1: Oceanographic Research Papers, 48,

189– 215.

SMIC, 1971: Inadvertent Climate Modification: Report of the Study of

Man’s Impact on Climate, Massachusetts Institute of Technology

Press, Cambridge, Mass., 308 pp.

Smith, R.D., and P.R. Gent, 2004: Reference Manual for the Parallel

Ocean Program (POP), Ocean Component of the Community Climate System Model (CCSM2.0 and 3.0). Technical Report LA-UR02-2484, Los Alamos National Laboratory, Los Alamos, New

Mexico (www.ccsm.ucar.edu/models/ccsm3.0/pop).

Smith, R.N.B., 1990: A scheme for predicting layer clouds and their

water content in a general circulation model. Quarterly J. Royal Meteorological Society, 116, 435–460.

Smith, S.J., et al., 2005: Climate change impacts for the conterminous

USA, Part 1: Scenarios and context. Climatic Change, 69, 7–25.

Soden, B.J., and I.M. Held, 2006: An assessment: Climate feedbacks in

coupled ocean–atmosphere models. J. Climate, 19, 3354–3360.

Soden, B.J., et al., 2005: The radiative signature of upper tropospheric moistening. Science, 310(5749), 841–844, doi:10.1126/science.1115602.

Soden, B.J., A.J. Broccoli, and R.S. Hemler, 2004: On the use of cloud

forcing to estimate cloud feedback. J. Climate, 17, 3661–3665.

Soden, B.J., et al., 2002: Global cooling after the eruption of Mount Pinatubo:

A test of climate feedback by water vapor. Science, 296, 727–730.

Soden, B.J., 2000: The diurnal cycle of convection, clouds, and water

vapor in the tropical upper troposphere. Geophysical Research Letters, 27, 2173–2176.

Soden, B.J., and I.M. Held, 2000: An assessment of climate feedbacks in

coupled ocean–atmosphere models. J. Climate, 19, 3354–3360.

Sokolov, A.P., and P.H. Stone, 1998: A flexible climate model for use in

integrated assessments. Climate Dynamics, 14, 291–303.

Solman, S.A., M.N. Nunez, and P.R. Rowntree, 2003: On the evaluation

of the representation of mid-latitude transients in the Southern Hemisphere by HadAM2B GCM and the impact of horizontal resolution.

Atmosfera, 16, 245–272.

Somerville, R.C.J., and L.A. Remer, 1984: Cloud optical thickness feedbacks in the CO2 climate problem. J. Geophysical Research, 89,

9668–9672.

Spencer, R.W., et al., 2007: Cloud and radiation budget changes associated with tropical intraseasonal oscillations. Geophysical Research

Letters, 34, L15707, doi:10.1029/2007GL029698.

Stainforth, D.A., et al., 2005: Uncertainty in predictions of the climate

response to rising levels of greenhouse gases. Nature, 443, 403–406.

Stephens, G.L., 2005: Cloud feedbacks in the climate system: A critical

review. J. Climate, 18, 237–273.

Stewart, I.T., D.R. Cayan, and M.D. Dettinger, 2004: Changes in

snowmelt runoff timing in western North America under a “business

as usual” climate change scenario. Climatic Change, 62, 217–232.

Stott, P.A., and C.E. Forest, 2007: Ensemble climate predictions using

climate models and observational constraints. Philosophical Transactions Royal Society A: Mathematical, Physical, and Engineering

Sciences, 365(1857), 2029–2052, doi: 10.1098/rsta.2007.2075.

Stott, P.A., et al., 2006: Observational constraints on past attributable

warming and predictions of future global warming. J. Climate,

19(13), 3055–3069, doi:10.1175/JCLI3802.1.
119

Climate Models: An Assessment of Strengths and Limitations

Stott, P.A., G.S. Jones, and J.F.B. Mitchell, 2003: Do models underestimate the solar contribution to recent climate change? J. Climate, 16,

4079–4093, doi:10.1175/1520-0442(2003)016!4079:

DMUTSCO2.0.CO;2.

Stouffer, R.J., et al., 2006: GFDL’s CM2 global coupled climate models.

Part IV: Idealized climate response. J. Climate, 19, 723–740.

Stouffer, R.J., J. Russell, and M.J. Spelman, 2006: Importance of oceanic

heat uptake in transient climate change. Geophysical Research Letters, 33(17), L17704, doi:10.1029/2006GL027242.

Stouffer, R.J., G. Hegerl, and S. Tett, 2000: A comparison of surface air

temperature variability in three 1000-yr coupled ocean-atmosphere

model integrations. J. Climate, 13, 513–537.

Strack, J.E., R.A. Pielke, and J. Adegoke, 2003: Sensitivity of modelgenerated daytime surface heat fluxes over snow to land-cover

changes. J. Hydrometeorology, 4, 24–42.

Stratton, R.A., 1999: A high resolution AMIP integration using the

Hadley Centre model HadAM2b. Climate Dynamics, 15, 9–28.

Sturm, M., et al., 2005: Changing snow and shrub conditions affect

albedo with global implications. J. Geophysical Research–Biogeosciences, 110, Art. No. G01004.

Sturm, M., et al., 2001: Snow-shrub interactions in Arctic tundra: A hypothesis with climatic implications. J. Climate, 14, 336–344.

Sud, Y.C., and G.K. Walker, 1999: Microphysics of clouds with the Relaxed Arakawa–Schubert Scheme (McRAS). Part II: Implementation and performance in GEOS II GCM. J. Atmospheric Sciences,

56(18), 3221–3240.

Sui, C.-H., X. Li, and K.-M. Lau, 1998: Radiative-convective processes

in simulated diurnal variations of tropical oceanic convection. J. Atmospheric Sciences, 55, 2345–2359.

Sun, S., and J. Hansen, 2003: Climate simulations for 1951–2050 with a

coupled atmosphere-ocean model. J. Climate, 16, 2807–2826,

doi:10.1175/1520–0442.

Sun, S., and R. Bleck, 2001: Atlantic thermohaline circulation and its response to increasing CO2 in a coupled atmosphere-ocean model.

Geophysical Research Letters, 28, 4223–4226. 

Sun, Y., et al., 2006: How often does it rain? J. Climate, 19, 916–934.

Svensmark, H., 2007: Cosmoclimatology: A new theory emerges. Astronomy Geophysics, 48, 118124, doi:10.1111/j.1468-4004.2007.

48118.x.

Takle, E.S., et al., 1999: Project to Intercompare Regional Climate Simulations (PIRCS): Description and initial results. J. Geophysical Research, 104(D16), 19443–19461.

Talley, L.D., J.L. Raid, and P.E. Robbins, 2003: Data-based meridional

overturning stream functions for the global ocean. J. Climate, 16,

3213–3226.

Tao, W.-K., 2007: Cloud-resolving modeling. J. Meteorological Society

Japan. 125th Anniversary Special Issue, 85B, 305-330.

Tao, W.-K., et al., 2004: Atmospheric energy budget and large-scale precipitation efficiency of convective systems during TOGA COARE,

GATE, SCSMEX and ARM: Cloud-resolving model simulations. J.

Atmospheric Sciences, 61, 2405–2423.

Tao, W.-K., 2003: Goddard Cumulus Ensemble (GCE) model: Application for understanding precipitation processes. Cloud systems, hurricanes, and the Tropical Rainfall Measuring Mission (TRMM): A

Tribute to Dr. Joanne Simpson, Meteorological Monograph. Bulletin

American Meteorological Society, 51, 107–138.

Tao, W.-K., et al., 1999: On equilibrium states simulated by cloud-resolving models. J. Atmospheric Sciences, 56, 3128–3139.

Tebaldi, C., et al., 2006: Going to the extremes: An intercomparison of

model-simulated historical and future changes in extreme events.

Climate Change 79(3–4), 185–211.

Tebaldi, C., et al., 2005: Quantifying uncertainty in projections of regional climate change: A Bayesian approach to the analysis of multimodel ensembles. J. Climate, 18, 1524–1540.

Tenhunen, J.D., et al., 1999: Ecosystem Studies, Land Use, and Resource

Management, 1–19 in Integrating Hydrology, Ecosystem Dynamics,

and Biochemistry in Complex Landscapes, ed. J.D. Tenhunen and P.

Kabat. Wiley, Chichester.

Thompson, D.W.J., and S. Solomon, 2002: Interpretation of recent

Southern Hemisphere climate change. Science, 296, 895–899,

doi:10.1126/ science.1069270.

Thompson, D.W.J., and J.M. Wallace, 2000: Annual modes in the extratopical circulation. Part I: Month-to-month variability. J. Climate,

13, 1000–1016.

Thompson, D.W.J., and J.M. Wallace, 1998: The Arctic Oscillation signature in the wintertime geopotential height and temperature fields.

Geophysical Research Letters, 25, 1297–1300.

Thompson, S., et al., 2004: Quantifying the effects of CO2-fertilized

vegetation on future global climate and carbon dynamics. Geophysical Research Letters, 31(23), L23211.
120

The U.S. Climate Change Science Program References

Thomson, A.M., et al., 2005: Climate change impacts for the conterminous USA, Part 3: Dryland production of grain and forage crops.

Climatic Change, 69, 43–65.

Thorne, P.W., et al., 2007: Tropical vertical temperature trends: A real

discrepancy? Geophysical Research Letters, 34, L16702,

doi:10.1029/2007GL029875.

Tiedtke, M., 1993: Representation of clouds in large-scale models.

Monthly Weather Review, 121, 3040–3061.

Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large scale models. Monthly Weather Review, 117,

1779–1800.

Tjernström, M., et al., 2005: Modelling the Arctic boundary layer: An

evaluation of six ARCMIP regional-scale models with data from the

SHEBA project. Boundary-Layer Meteorology, 117, 337–381.

Tjernström, M., M. Zagar, and G. Svensson, 2004: Model simulations

of the Arctic atmospheric boundary layer from the SHEBA year.

Ambio, 33, 221–227.

Tjoelker, M.G., J. Oleksyn, and P.B. Reich, 2001: Modelling respiration

of vegetation: Evidence for a general temperature-dependent Q10.

Global Change Biology, 7(2), 223–230.

Tompkins, A., 2002: A prognostic parameterization for the subgrid-scale

variability of water vapor and clouds in large-scale models and its use

to diagnose cloud cover. J. Atmospheric Sciences, 59, 1917–1942.

Trenberth, K.E., J. Fasullo, and L. Smith, 2005: Trends and variability

in column-integrated atmospheric water vapor. Climate Dynamics,

24 (7–8), 741–758, doi:10.1007/s00382-005-0017-4.

Trenberth, K.E., et al., 1998: Progress during TOGA in understanding

and modeling global teleconnections associated with tropical sea

surface temperatures. J. Geophysical Research, 103 (special TOGA

issue), 14291–14324.

Trenberth, K.E., and T.J. Hoar, 1997: El Niño and climate change. Geophysical Research Letters, 24, 3057–3060.

Trenberth, K.E., and J. Hurrell, 1994: Decadal atmosphere-ocean variations in the Pacific. Climate Dynamics, 9, 303–319.

Trier, S. B., et al., 1996: Structure and evolution of the 22 February 1993

TOGA COARE squall line: Numerical simulations. J. Atmospheric

Sciences, 53, 2861–2886.

Tripoli, G.J., 1992: A nonhydrostatic mesoscale model designed to simulate scale interaction. Monthly Weather Review, 120, 1342–1359.

Tripoli, G.J., and W.R. Cotton, 1989: Numerical study of an observed

orogenic mesoscale convective system. Part 2: Analysis of governing dynamics. Monthly Weather Review, 117, 305–328.

Troccoli, A., and T.N. Palmer, 2007: Ensemble decadal predictions from

analysed initial conditions. Philosophical Transactions Royal Society A, 365 (No. 1857).

Tselioudis, G., and C. Jakob, 2002: Evaluation of midlatitude cloud properties in a weather and a climate model: Dependence on dynamic

regime and spatial resolution. J. Geophysical Research, 107, 4781.

Tselioudis, G., Y.-C. Zhang, and W.R. Rossow, 2000: Cloud and radiation variations associated with northern midlatitude low and high sea

level pressure regimes. J. Climate, 13, 312–327, doi:10.1175/1520-

0442(2000).

Tsushima, Y., A. Abe-Ouchi, and S. Manabe, 2005: Radiative damping

of annual variation in global mean surface temperature: Comparison between observed and simulated feedback. Climate Dynamics,

24, 591–597.

Twomey, S., 1977: The influence of pollution on the short wave albedo

of clouds. J. Atmospheric Sciences, 34, 1149–1152.

Ueda, H., et al., 2006: Impact of anthropogenic forcing on the Asian summer monsoon as simulated by eight GCMs. Geophysical Research

Letters, 33, doi:10.1029/2005GL025336.

Uotila, P., et al., 2007: Changes in Antarctic net precipitation in the 21st

Century based on IPCC model scenarios. J. Geophysical Research,

112, D10107, doi:10.1029/2006JD007482.

Uppala, S.M., et al., 2005: The ERA-40 reanalysis. Quarterly J. Royal

Meteorological Society, 131(612), 2961–3012, doi:10.1256/

qj.04.176. Index is at www.ecmwf.int/research/era/ ERA-40_Atlas/

docs/index.html.

van Oldenborgh, G.J., S.Y. Philip, and M. Collins, 2005: El Niño in a

changing climate: A multi-model study. Ocean Science, 1, 81–95.

van Ulden, A.P., and G.J. van Oldenborgh, 2006: Large-scale atmospheric circulation biases and changes in global climate model simulations and their importance for climate change in Central Europe.

Atmospheric Chemistry Physics, 6(4), 863–881

VanRheenen, N.T., et al., 2004: Potential implications of PCM climate

change scenarios for Sacramento–San Joaquin River basin hydrology

and water resources. Climatic Change, 62, 257–281.

Vavrus, S., et al., 2006: The behavior of extreme cold air outbreaks under

greenhouse warming. International J. Climatology, 26, 1133–1147.
121

Climate Models: An Assessment of Strengths and Limitations

Velicogna, I., and J. Wahr, 2006: Acceleration of Greenland ice mass loss

in spring 2004. Nature, 443(7109), 329–331.

Vidale, P.L., et al., 2003: Predictability and uncertainty in a regional climate model. J. Geophysical Research, 108(D18), 4586.

Vinnikov, K.Y., D.J. Cavalieri, and C.L. Parkinson, 2006: A model assessment of satellite observed trends in polar sea ice extents. Geophysical Research Letters, 33, L05704.

Vitart, F., and J.L. Anderson, 2001: Sensitivity of Atlantic tropical storm

frequency to ENSO and interdecadal variability of SSTs in an ensemble of AGCM integrations. J. Climate, 14(4), 533–545.

Völker, C., D.W.R. Wallace, and D.A. Wolf-Gladrow, 2002: On the role

of heat fluxes in the uptake of anthropogenic carbon in the North Atlantic. Global Biogeochemical Cycles, 16(4), 1138, doi:10.1029/

2002GB001897.

Von Storch, H., E. Zorita, and U. Cubasch, 1993: Downscaling of global

climate change estimates to regional scales: An application to Iberian rainfall in wintertime. J. Climate, 6, 1161–1171.

Wang, B., 1995: Interdecadal changes in El Niño onset in the last four

decades. J. Climate, 8, 267–284.

Wang, C., 2005: A modeling study of the response of tropical deep convection to the increase of cloud condensation nuclei concentration: 1.

Dynamics and microphysics. J. Geophysical Research, 110, D21211.

Wang, H., and K.-M. Lau, 2006: Atmospheric hydrological cycle in the

tropics in Twentieth Century coupled climate simulations. J. Climate,

26, 655–678.

Wang, M., et al., 2007: Intrinsic versus forced variation in coupled climate model simulations over the Arctic during the Twentieth Century.

J. Climate, 20(6), 1093–1107.

Wang, W., and M.E. Schlesinger, 1999: The dependence on convection

parameterization of the tropical intraseasonal oscillation simulated

by the UIUC 11-layer atmospheric GCM. J. Climate, 12(5), 1423–

1457.

Wang, Y., et al., 2004: Regional climate modeling: Progress, challenges,

and prospects. J. Meteorological Society Japan, 82, 1599–1628.

Warrach, K., H.T. Mengelkamp, and E. Raschke, 2001: Treatment of

frozen soil and snow cover in the land surface model SEWAB. 

Theoretical Applied Climatology, 69, 23–37.

Webb, M.J., et al., 2006: On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles. 

Climate Dynamics, 27(1), 17–38, doi:10.1007/s00382-006-0111-2.

Webb, M.J., et al., 2001: Combining ERBE and ISCCP data to assess

clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models. Climate Dynamics, 17, 905–922.

Webster, P.J., et al., 1998: Monsoons: Processes, predictability, and the

prospects for prediction. J. Geophysical Research, 103(C7), 14451–

14510, doi:10.1029/97JC02719.

Wei, H., et al., 2002: Calibration and validation of a regional climate

model for pan-Arctic hydrologic simulation. J. Climate, 15, 3222–

3236.

Wentz, F.J., et al., 2007: How much more rain will global warming bring?

Science, 317, 233–235.

Wetherald, R.T., and S. Manabe, 1988: Cloud feedback processes in

general circulation models. J. Atmospheric Sciences, 45, 1397–1415.

Whitman, S., et al., 1997: Mortality in Chicago attributed to the July

1995 heat wave. American J. Public Health, 87, 1515–1518.

Widman, M., C.S. Bretherton, and E.P. Salathe, Jr., 2003: Statistical precipitation downscaling over the Northwestern United States using

numerically simulated precipitation as a predictor. J. Climate, 16,

799–816.

Wigley, T.M.L., et al., 2005: Effect of climate sensitivity on the response

to volcanic forcing. J. Geophysical Research, 110, D09107.1–

D09107.8, doi:10.1029/2004JD005557.

Wigley, T.M.L., and M.E. Schlesinger, 1985: Analytical solution for the effect

of increasing CO2 on global mean temperature. Nature, 315, 649–652.

Wilby, R.L., et al., 2004: Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods. IPCC Data Distribution Centre, University of East Anglia, U.K., 27 pp.

(ipcc-ddc.cru.uea.ac.uk/guidelines).

Wilby, R.L., R. Dawson, and E.M. Barrow, 2002: SDSM: A decision

support tool for the assessment of regional climate change assessments. Environmental Modelling Software, 17, 145–157.

Wilby, R.L., et al., 2000: Hydrological responses to dynamically and statistically downscaled general circulation model output. Geophysical

Research Letters, 27, 1199–1202.

Wilby, R.L., et al., 1998: Statistical downscaling of general circulation

model output: A comparison of methods. Water Resources Research,

34, 2995–3008.

Wilby, R.L., and T.M.L. Wigley, 1997: Downscaling general circulation

model output: A review of methods and limitations. Progress Physical Geography, 21, 530–548.
122

The U.S. Climate Change Science Program References

Wilks, D.S., and R.L. Wilby, 1999: The weather generation game: A review of stochastic weather models. Progress Physical Geography,

23, 329–357.

Williams, K.D., et al., 2006: Evaluation of a component of the cloud response to climate change in an intercomparison of climate models.

Climate Dynamics, 145, 145–165.

Williams, K.D., M.A. Ringer, and C.A. Senior, 2003: Evaluating the

cloud response to climate change and current climate variability. Climate Dynamics, 20, 705–721.

Willson, R.C., and A.V. Mordvinov, 2003: Secular total solar irradiance

trend during solar cycles 21–23. Geophysical Research Letters,

30(5), 1199–1202, doi:10.1029/2002GL016038, 2003.

Wilson, D.R., and S.P. Ballard, 1999: A microphysics-based precipitation scheme for the U.K. Meteorological Office numerical weather

prediction model. International J. Climatology, 125, 1607–1636.

Wilson, M.F., et al., 1987: Sensitivity of the Biosphere Atmosphere

Transfer Scheme (BATS) to the inclusion of variable soil characteristics. J. Climate Applied Meteorology, 26, 341–362.

Wilson, T.B., et al., 2003: Evaluation of the importance of Lagrangian

canopy turbulence formulations in a soil-plant-atmosphere model.

Agricultural Forest Meteorology, 115, 51–69.

Winkler, J.A., et al., 2002: Possible impacts of projected temperature

change on commercial fruit production in the Great Lakes Region.

J. Great Lakes Research, 28, 608–625.

Winton, M., 2000: A reformulated three-layer sea ice model. J. Atmospheric Oceanic Technology, 17, 525–531.

Wittenberg, A.T., et al., 2006: GFDL’s CM2 global coupled climate models. Part III: Tropical Pacific climate and ENSO. J. Climate, 19, 

698–722.

Wood, A.W., et al., 2004: Hydrological implications of dynamical and

statistical approaches to downscaling climate model outputs. Climate Change, 62, 189–216.

Woodward, F.I., and M.R. Lomas, 2004: Vegetation dynamics—simulating responses to climatic changes. Biological Reviews 79, 643–

670.

Wu, X., et al., 2007: Coupling of convective momentum transport with

convective heating in global climate simulations. J. Atmospheric Sciences, 64(4), 1334–1349.

Wu, X., and M.W. Moncrieff, 2001: Long-term behavior of cloud systems in TOGA COARE and their interactions with radiative and surface processes. Part III: Effects on the energy budget and SST. J.

Atmospheric Sciences, 58, 1155–1168.

Wyant, M.C., et al., 2006: A comparison of tropical cloud properties and

responses in GCMs using mid-tropospheric vertical velocity. Climate Dynamics, 27, 261–279.

Wyant, M.C., M. Khairoutdinov, and C.S. Bretherton, 2006: Climate

sensitivity and cloud response of a GCM with a superparameterization. Geophysical Research Letters, 33, L06714.

Wyrtki, K., 1975: El Niño—The dynamic response of the equatorial Pacific Ocean to atmospheric forcing. J. Physical Oceanography, 5,

572–584.

Xie, P., and P.A. Arkin, 1997: Global precipitation: A 17-year monthly

analysis based on gauge observations, satellite estimates, and numerical model outputs. Bulletin American Meteorological Society

78, 2539–2558.

Xie, S., et al., 2005: Simulations of midlatitude frontal clouds by singlecolumn and cloud-resolving models during the Atmospheric Radiation Measurement March 2000 cloud intensive operational period. J.

Geophysical Research, 110, D15S03.

Xie, S., et al., 2004: Impact of a revised convective triggering mechanism on Community Atmosphere Model, Version 2, simulations: Results from short-range weather forecasts. J. Geophysical Research,

109, D14102, doi:10.1029/2004JD004692.

Xu, K.-M., et al., 2005: Modeling springtime shallow frontal clouds with

cloud-resolving and single-column models. J. Geophysical Research,

110, D15S04, doi:10.1029/2004JD005153.

Xu, K.-M., and D.A. Randall, 1998: Influence of large-scale advective

cooling and moistening effects on the quasi-equilibrium behavior of

explicitly simulated cumulus ensembles. J. Atmospheric Sciences,

55, 896–909.

Xue, Y., et al., 2001: The impact of land surface processes on simulations of the U.S. hydrological cycle: A case study of the 1993 flood

using the SSiB land surface model in the NCEP ETA regional model.

Monthly Weather Review, 129(12), 2833–2860.

Yamaguchi, K., A. Noda, and A. Kitoh, 2005: The changes in permafrost

induced by greenhouse warming: A numerical study applying multiple-layer ground model. J. Meteorological Society Japan, 83, 799–

815.

Yang, Z.W., and R. Arritt, 2002: Tests of a perturbed physics ensemble

approach for regional climate modeling. J. Climate, 15, 2881–2896.
123

Climate Models: An Assessment of Strengths and Limitations

Yao, M.-S., and A.D. Del Genio, 2002: Effects of cloud parameterization on the simulation climate changes in the GISS GCM. Part II: Sea

surface temperature and cloud feedbacks. J. Climate, 15, 2491–2503.

Yeh, P.J.-F., and E.A.B. Eltahir, 2005: Representation of water table dynamics in a land surface scheme. Part 1: Model development. J. Climate, 18, 1861–1880.

Yokohata, T, et al., 2005: Climate response to volcanic forcing: Validation of climate sensitivity of a coupled atmosphere-ocean general

circulation model. Geophysical Research Letters, 32(21), L21710.1–

L21710.4.

York, J.P., et al., 2002: Putting aquifers into atmospheric simulation models: An example from the Mill Creek Watershed, northeastern

Kansas. Advances Water Resources, 25, 221–238.

Yu, H., et al., 2006: A review of measurement-based assessment of

aerosol direct radiative effect and forcing. Atmospheric Chemistry

Physics, 6, 613–666.

Yu, X., and M.J. McPhaden, 1999: Seasonal variability in the equatorial

Pacific. J. Physical Oceanography, 29, 925–947.

Zebiak, S.E., and M.A. Cane, 1987: A model El Niño-Southern Oscillation. Monthly Weather Review, 115, 2262–2278.

Zhang, C., et al., 2006: Simulations of the Madden-Julian Oscillation in

four pairs of coupled and uncoupled global models. Climate Dynamics 27(6), 573–592.

Zhang, D., and M.J. McPhaden, 2006: Decadal variability of the shallow

Pacific meridional overturning circulation: Relation to tropical sea

surface temperatures in observations and climate change models.

Ocean Modelling, 15, 250–273.

Zhang, G.J., and N.A. McFarlane, 1995: Sensitivity climate simulations

to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atmosphere–Ocean, 33,

407–446.

Zhang, J., and D. Rothrock, 2000: Modeling Arctic sea ice with an efficient plastic solution, J. Geophysical Research, 105, 3325–3338.

Zhang, M.H., et al., 2005: Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite

measurements. J. Geophysical Research, 110, D15S02, doi:10.1029/

2004JD005021.

Zhang, M., 2004: Cloud-climate feedback: How much do we know? In

Observation, Theory, and Modeling of Atmospheric Variability.

World Scientific Series on Meteorology of East Asia, Vol. 3, ed. Zhu

et al. World Scientific Publishing Co., Singapore, 632 pp.

Zhang, M.H., et al., 2001: Objective analysis of the ARM IOP data:

Method and sensitivity. Monthly Weather Review, 129, 295–311.

Zhang, M.H., et al., 1994: Diagnostic study of climate feedback

processes in atmospheric general circulation models. J. Geophysical

Research, 99, 5525–5537.

Zhang, R., and T.L. Delworth, 2006: Impact of Atlantic multidecadal oscillations on India/Sahel rainfall and Atlantic hurricanes. Geophysical Research Letters, 33, L17712, doi:10.1029/2006GL026267.

Zhang, X., and J.E. Walsh, 2006: Toward a seasonally ice-covered Arctic Ocean: Scenarios from the IPCC AR4 model simulations. J. Climate, 19, 1730–1747.

Zhang, X.-C., 2005: Spatial downscaling of global climate model output

for site-specific assessment of crop production and soil erosion.

Agricultural Forest Meteorology, 135, 215–229.

Zhang, Y.C., A.N. Huang, and X.S. Zhu, 2006: Parameterization of the

thermal impacts of sub-grid orography on numerical modeling of

the surface energy budget over East Asia. Theoretical Applied Climatology, 86, 201–214.

Zhu, J., and X.-Z. Liang, 2007: Regional climate model simulations of

U.S. precipitation and surface air temperature during 1982–2002: Interannual variation. J. Climate, 20(2), 218–232.

Zorita, E., and H. von Storch, 1999: The analog method as a simple statistical downscaling technique: Comparison with more complicated

methods. J. Climate, 12, 2474–2489.
124

The U.S. Climate Change Science Program Chapter 1 - Subtitle
CONTACT INFORMATION

CLIMATE CHANGE SCIENCE PROGRAM AND THE

SUBCOMMITTEE ON GLOBAL CHANGE RESEARCH

William J. Brennan, Chair

Department of Commerce

National Oceanic and Atmospheric Administration

Acting Director, Climate Change Science Program

Jack Kaye, Vice Chair

National Aeronautics and Space Administration

Allen Dearry

Department of Health and Human Services

Anna Palmisano

Department of Energy

Mary Glackin

National Oceanic and Atmospheric Administration

Patricia Gruber

Department of Defense

William Hohenstein

Department of Agriculture

Linda Lawson

Department of Transportation

Mark Myers

U.S. Geological Survey 

Jarvis Moyers

National Science Foundation 

Patrick Neale 

Smithsonian Institution

Jacqueline Schafer

U.S. Agency for International Development

Joel Scheraga

Environmental Protection Agency 

Harlan Watson

Department of State

EXECUTIVE OFFICE 

AND OTHER LIAISONS

George Banks

Council on Environmental Quality

Stuart Levenbach

Office of Management and Budget

Stephen Eule

Department of Energy

Director, Climate Change Technology Program

Howard Frumkin

Centers for Disease Control and Prevention

Katharine Gebbie

National Institute of Standards and Technology

Margaret R. McCalla

Office of the Federal Coordinator for Meteorology

Gene Whitney

Office of Science and Technology Policy

Global Change Research Information Office

c/o Climate Change Science Program Office

1717 Pennsylvania Avenue, NW, Suite 250

Washington, DC 20006

202-223-6262 (voice)

202-223-3065 (fax)

The Climate Change Science Program incorporates

the U.S. Global Change Research Program and the

Climate Change Research Initiative.

To obtain a copy of this document, place an order at

the Global Change Research Information Office

(GCRIO) web site: http://www.gcrio.org/orders
U.S. Climate Change Science Program

1717 Pennsylvania Avenue, NW • Suite 250 • Washington, D.C. 20006 USA

1-202-223-6262 (voice) • 1-202-223-3065 (fax)

http://www.climatescience.gov
An Assessment of Strengths and Limitations

Climate Models

CCSP

Goal

3.1