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A U.S. CLIVAR Project to Assess and Compare the Responses of Global Climate
Models to Drought-Related SST Forcing Patterns: Overview and Results
SIEGFRIED SCHUBERT,a DAVID GUTZLER,b HAILAN WANG,a,c AIGUO DAI,d TOM DELWORTH,e
CLARA DESER,d KIRSTEN FINDELL,e RONG FU,f WAYNE HIGGINS,g MARTIN HOERLING,hBEN KIRTMAN,i RANDAL KOSTER,a ARUN KUMAR,g DAVID LEGLER,j DENNIS LETTENMAIER,k
BRADFIELD LYON,l VICTOR MAGANA,m KINGTSE MO,g SUMANT NIGAM,n PHILIP PEGION,g
ADAM PHILLIPS,d ROGER PULWARTY,o DAVID RIND,p ALFREDO RUIZ-BARRADAS,n JAE SCHEMM,g
RICHARD SEAGER,q RONALD STEWART,r MAX SUAREZ,a JOZEF SYKTUS,s MINGFANG TING,qCHUNZAI WANG,t SCOTT WEAVER,a,c AND NING ZENGna Global Modeling and Assimilation Office, Science and Exploration Directorate, NASA GSFC, Greenbelt, Marylandb Department of Earth and Planetary Sciences, University of New Mexico, Albuquerque, New Mexicoc Goddard Earth Sciences and Technology Center, University of Maryland, Baltimore County, Baltimore, Marylandd National Center for Atmospheric Research, Boulder, Coloradoe National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory, Princeton University, Princeton,
New Jersey
f Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas
g NOAA/NWS/NCEP/Climate Prediction Center, Washington, D.C.
h NOAA/Earth System Research Laboratory, Boulder, Colorado

i Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida
j U.S. Climate Variability and Predictability Research Program, Washington, D.C.

k Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington
l International Research Institute for Climate and Society, Lamont-Doherty Earth Observatory, Columbia University, Palisades, New Yorkm Center for Atmospheric Sciences, National Autonomous University of Mexico, Mexico City, Mexicon Department of Atmospheric and Oceanic Science, University of Maryland, College Park, College Park, Maryland
o NOAA/National Integrated Drought Information System, Boulder, Colorado
p NASA Goddard Institute for Space Studies, New York, New Yorkq Lamont-Doherty Earth Observatory, Columbia University, Palisades, New Yorkr Department of Environment and Geography, University of Manitoba, Winnipeg, Manitoba, Canadas Environmental Protection Agency, Indooroopilly, Queensland, Australiat NOAA/Atlantic Oceanographic and Meteorological Laboratory/Physical Oceanography Division, Miami, Florida(Manuscript received 29 January 2009, in final form 24 April 2009)
ABSTRACTThe U.S. Climate Variability and Predictability (CLIVAR) working group on drought recently initiated aseries of global climate model simulations forced with idealized SST anomaly patterns, designed to address anumber of uncertainties regarding the impact of SST forcing and the role of land–atmosphere feedbacks onregional drought. The runs were carried out with five different atmospheric general circulation models(AGCMs) and one coupled atmosphere–ocean model in which the model was continuously nudged to theimposed SST forcing. This paper provides an overview of the experiments and some initial results focusing on
the responses to the leading patterns of annual mean SST variability consisting of a Pacific El Niño–Southern
Oscillation (ENSO)-like pattern, a pattern that resembles the Atlantic multidecadal oscillation (AMO), and a
global trend pattern.
One of the key findings is that all of the AGCMs produce broadly similar (though different in detail)precipitation responses to the Pacific forcing pattern, with a cold Pacific leading to reduced precipitation and awarm Pacific leading to enhanced precipitation over most of the United States. While the response to the
Atlantic pattern is less robust, there is general agreement among the models that the largest precipitation

Corresponding author address: Siegfried D. Schubert, Global Modeling and Assimilation Office, NASA GSFC, Greenbelt, MD 20771.
E-mail: siegfried.d.schubert@nasa.gov
DOI: 10.1175/2009JCLI3060.1
Ó 2009 American Meteorological Society

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response over the United States tends to occur when the two oceans have anomalies of opposite signs. Further
highlights of the response over the United States to the Pacific forcing include precipitation signal-to-noise
ratios that peak in spring, and surface temperature signal-to-noise ratios that are both lower and show lessagreement among the models than those found for the precipitation response. The response to the positive
SST trend forcing pattern is an overall surface warming over the world’s land areas, with substantial regionalvariations that are in part reproduced in runs forced with a globally uniform SST trend forcing. The precipitation response to the trend forcing is weak in all of the models.
It is hoped that these early results, as well as those reported in the other contributions to this special issue on
drought, will serve to stimulate further analysis of these simulations, as well as suggest new research on thephysical mechanisms contributing to hydroclimatic variability and change throughout the world.

1. Introduction
In recognition of the profound societal impact ofdrought in many regions of the world and the emerging
capabilities in simulating drought with global climate
models, the U.S. Climate Variability and Predictability(CLIVAR) program initiated a drought working groupin 2006 to ‘‘facilitate progress on the understanding andprediction of long-term (multi-year) drought over North
America and other drought-prone regions of the world,
including an assessment of the impact of global changeon drought processes’’ (Gutzler and Schubert 2007). The
specific tasks of the working group were to 1) propose aworking definition of drought and related model predictands of drought, 2) coordinate evaluations of existing
relevant model simulations, 3) suggest new experiments(coupled and uncoupled) designed to address outstanding
uncertainties in the nature of drought, 4) coordinate andencourage the analysis of observational datasets to reveal
antecedent linkages of multiyear droughts, and 5) organize
a community workshop to present and discuss the results.
This paper provides an overview and some results oftask 3 of the working group, involving the design, coordination, implementation, and initial evaluation of a
new set of model simulations that address the rolesof sea surface temperature forcing and land–atmosphere
feedbacks in the development and maintenance of drought.
This work extends and builds upon recent modelingstudies (e.g., Hoerling and Kumar 2003; Schubert et al.
2004a,b; Wang et al. 2008, 2009) as well as numerous observationally based studies (e.g., Trenberth andGuillemot 1996; Mo et al. 1997; Ting and Wang 1997;
Nigam et al. 1999; Koster et al. 2003; Ruiz-Barradas
and Nigam 2004; McCabe et al. 2004; Wang et al. 2006)
that have provided substantial insights into the natureof drought and the important role of both the oceans
and land–atmosphere interactions. In particular, thiswork addresses the remaining uncertainties regarding
the nature of the physical mechanisms linking remote
SST forcing to regional drought, the relative contributions of the different ocean basins and different
time scales of SST variability, and the strength of land–

atmosphere feedbacks, and thereby starts to frame
fundamental questions about the predictability of longterm drought. Specific questions addressed by the working group include the following: What are mechanismsthat maintain drought across the seasonal cycle and fromone year to the next? What is the role of the different
ocean basins, including the impact of El Niño–Southern
Oscillation (ENSO), the Pacific decadal oscillation (PDO),
the Atlantic multidecadal oscillation (AMO), and warming trends in the global oceans? What is the role of theland? To what extent can droughts develop independently of oceanic variability resulting from year-to-year
memory that may be inherent to the land?
To address these questions, the working group proposedthat a number of mechanistic experiments be performedthat are designed to address some of the key issues outlined
above using several different global climate models. A key
objective was to be able to assess the model dependence ofthe results unambiguously. To accomplish that, it was proposed that each model be forced with the same set of idealized SST forcing anomalies. In addition, it was proposedthat a control run be produced in which each model wasforced with the same climatological SSTs. To allow an assessment of land–atmosphere feedbacks, an additional setof runs was proposed in which the soil moisture was fixedusing a common approach that could be easily implemented
in each of the models. The main SST forcing patterns, theexperiments, and the models are described in section 2.
Section 3 presents some basic comparisons of the model
responses to the leading patterns of SST variability, with afocus on the United States. The summary and conclusions
are given in section 4. Information on auxiliary experiments and data availability are given in the appendix.

2. The SST forcing patterns, experiments,and models
a. SST forcing patterns
The basic SST data used in this study are the 1901–
2004 monthly SST data produced by Rayner et al. (2003).
The leading patterns of SST variability are isolated using

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FIG. 1. The (left) three leading rotated EOFS and (right) associated PCs of the annual mean SST based on the period of 1901–2004. The
values are scaled so that the product of the PCs and EOFs gives units of degrees Celsius. The Pacific and Atlantic patterns reflect thetwo standard deviation forcing amplitudes applied to the models, while the trend pattern must be divided by a factor of 2 to obtainthe forcing amplitude.

rotated empirical orthogonal functions (REOFs), where
varimax rotation (e.g., Richman 1986) is used to help
separate the leading patterns of Pacific and Atlantic
SST variability.
The REOFs are computed from global gridded valuesof annual mean SST for the period of 1901–2004. The useof annual means is meant to address the basic question of

the nature of the forcing of regional hydroclimates oninterannual time scales. This, of course, does not distinguish between, for example, ENSO and Pacific decadal
variability (e.g., Barlow et al. 2001); the responses toleading SST anomaly patterns that occur on these different time scales is addressed with other supplementary
REOFs described in the appendix.

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Figure 1 shows the three leading REOFs and associated principal components (PCs) of the annual mean
SST. The first is a global trend pattern, with warming
occurring over most of the global ocean. In fact, thisspatial pattern can essentially be reproduced by plotting
the slopes of the linear trends fit to the 1901–2004 annual
mean SST at each grid point (not shown). The associated
PC shows a somewhat more complicated (nonlinear)long-term time evolution, showing an almost step-likeincrease at about 1940, and a clear trend occurring only
after the mid-1960s1. The second REOF is a pan-Pacific
ENSO-like pattern that includes a weak Indian Ocean
component. The associated PC shows that this patternvaries on both interannual (ENSO) and decadal timescales, with the latter including the well-known shift that
occurred in the mid-1970s (e.g., Trenberth and Hurrell1994). The third REOF is confined for the most part to
the North Atlantic Ocean and resembles the Atlantic
multidecadal oscillation pattern (Enfield et al. 2001).
The associated PC shows that while this pattern has
clear decadal variability, it also exhibits considerable
interannual variations. These three leading rotated
EOFs will in the following be referred to as the trend,
Pacific, and Atlantic forcing patterns.

b. Proposed experiments
The proposed experiments consist of 50-yr2 simulations in which the model is forced with one or more of
the idealized SST anomaly patterns (the trend, Pacific,
and Atlantic forcing patterns described above3). The full
forcing patterns are produced by adding scaled versionsof the REOFs to the long-term monthly varying SST
climatology (defined for the period of 1901–2004). The
scaling factor for the Pacific and Atlantic consists of
62 standard deviations of the associated PCs. This rather
large amplitude was chosen to help isolate what in somecases may be rather subtle SST–drought linkages from
relatively short model integrations. In the case of thetrend, the pattern is scaled by 61 standard deviation,
which effectively forces the model with the 40-yr averages of the trend anomalies at the beginning (21 standard deviation) and end (11 standard deviation) of the
1901–2004 time period. An additional experiment was
proposed to force the models with a globally uniform1
We note that there is considerable sensitivity of the trend
pattern to the time period of interest and the datasets used particularly regarding the contribution from the Pacific (e.g., Vecchi
et al. 2008; Chen et al. 2008).
2
The NOAA/GFS experiments were somewhat shorter (35 yr).
3
Masks were applied to REOFs two and three to zero out any
small values that, for the Pacific pattern, fall outside the Pacific andIndian Oceans, and that, for the Atlantic pattern, fall outside theAtlantic Ocean.

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SST warming distribution equal to the global mean of
the positive trend pattern (0.168C).
It is important to note that the anomaly patterns are
fixed in time and therefore do not have an annual cycle.
The absence of a seasonal cycle in the forcing arguably
diminishes the importance of winter SST anomalies
relative to summer SST anomalies, especially for tropical Pacific variability, because observations indicate thatwinter anomalies tend to be much larger in magnitude
compared to summer anomalies (e.g., An and Choi2008). There is, however, an annual cycle in the full SST
forcing fields as a result of the annual cycle in the climatological SST on which the prescribed anomaly patterns are superimposed. The SST forcing is repeatedwith no interannual variability for each year of eachexperiment, but the models still generate interannual
variability resulting from unforced ‘‘weather noise’’ associated with the internal dynamics of the models. We
will assess the magnitude of the forced response to theprescribed SST anomalies relative to the magnitude of
the unforced interannual variability.
The nine baseline experiments of the project consistof the runs in which the models are forced with all eightcombinations of the Pacific and Atlantic patterns shownin Fig. 1, as well as the control run forced only with themonthly varying climatological SST. These are summarized in Table 1 according to the different combinations of patterns and phases of the forcing. For example,
PwAc indicates that a model is forced with the warm
phase of the Pacific and the cold phase of the Atlantic
patterns. In addition to those shown in Table 1, runs
were proposed in which the models are forced with either the positive or negative trend pattern, both alone,or superimposed on selected combinations of the Pacificand Atlantic patterns.
A number of other auxiliary experiments were proposed to isolate further various mechanisms and timescales of variability. Some isolate the role of the tropics,
while others attempt to separate the contributions fromENSO and lower-frequency Pacific variability. Anotherset of experiments were formulated to assess the impactof land–atmosphere feedbacks. These additional experiments are described in the appendix. Another related
and important set of experiments consist of Atmospheric
Model Intercomparison Project (AMIP)-style simulations (Gates et al. 1999). These are simulations (typicallyseveral decades long) in which the models are forced by
the historical record of observed SSTs. While the focusof the runs described here is on understanding mechanisms and model sensitivity to idealized SST forcing, theAMIP runs are important in that they facilitate model
validation by allowing more direct comparisons of theresults with observations.

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TABLE 1. The different combinations of the Pacific and Atlantic
SST anomaly patterns used to force the GCMs. Here w refers to thewarm phase of the pattern (with a 2 standard deviation weight) andc refers to the cold phase (with a 2 standard deviation weight).
Also, n denotes neutral, indicating that the pattern has zero weight.
In particular, the PnAn experiment denotes the control run forced
with the annually varying climatological SST.
Warm
Atlantic

NeutralAtlantic

Cold
Atlantic

PwAw
PnAw
PcAw

PwAn
PnAn
PcAn

PwAc
PnAc
PcAc

Warm Pacific
Neutral PacificCold Pacifictributed runs made with the Community Atmosphere
Model, version 3.5 (CAM3.5) AGCM. An additional set ofruns was made by Center for Ocean–Land–Atmosphere
Studies (COLA)/University of Miami with the coupled(atmosphere–ocean) Community Climate System Model,
version 3.0 (CCSM3.0) employing a novel adjustment
technique to nudge the coupled model toward the imposed
SST forcing patterns. The main characteristics of themodels of interest and some of the relevant references arepresented in Table 2.

3. Resultsc. Contributing groups and models
A number of groups have participated in this projectby contributing model runs. While only a few groups
have carried out all of the proposed runs, most havedone at least the baseline set of experiments defined inTable 1. The National Aeronautics and Space Administration’s (NASA’s) Global Modeling and Assimilation
Office (GMAO) contributed runs made with version
one of the NASA Seasonal-to-Interannual Prediction
Project (NSIPP-1) AGCM. The National Oceanic andAtmospheric Administration’s (NOAA’s) Climate Prediction Center, with support from the Climate Test Bed,
contributed runs made with the Global Forecast System(GFS) AGCM, and NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL) contributed runs made with the
Atmosphere Model version 2.1 (AM2.1) AGCM. The
Lamont-Doherty Earth Observatory of Columbia University contributed runs made with the National Center
for Atmospheric Research (NCAR) Community Climate
Model, version 3.0 (CCM3.0) AGCM, and NCAR con-This section provides an overview of the results fromthe five AGCMs. Results from the coupled model(CCSM3.0) will be reported in a separate paper. The main
focus here is on the annual mean response over the UnitedStates to the Pacific SST anomaly pattern. Additional
diagnostics are presented that summarize the results fromall eight combinations of the Pacific and Atlantic forcings
shown in Table 1, as well as provide some assessment of
the seasonality of the responses. The section ends with abrief overview of the responses to the trend pattern.
We begin by examining the ability of the AGCMs toreproduce the observed annual mean precipitation andheight field climatologies based on the available AMIPstyle simulations (runs forced with observed SSTs) fromeach model for the period of 1980–98. We note that theresults from the AMIP runs are quite similar to thosefrom the control runs (PnAn; see Table 1, not shown).
The AMIP runs, however, provide a cleaner, more direct comparison with observations than the control run,and therefore provide a more useful baseline assessmentof model performance.

TABLE 2. Description of the models used in the drought working group simulations.

Model (reference)
AM2.1 (Delworth et al. 2006;
GFDL Global
Atmospheric Model
Development Team 2004)
GFS, version 2 (Campana
and Caplan 2005)
NSIPP-1 (Bacmeister et al.
2000; Schubert et al.
2002)
CCM3.0 (Kiehl et al. 1998;
Seager et al. 2005)
CAM3.5 (online at http://
www.ccsm.ucar.edu/
models/atm-cam/)
CCSM3.0 (Collins et al. 2006)

Resolution

Convection scheme(reference)

Land surface model(reference)

28 3 2.58, L24

Relaxed Arakawa–Schubert(Moorthi and Suarez 1992)

Milly and Shmakin (2002)

T62 (;28 3 28), L64

Simplified Arakawa–Schubert(Grell 1993; Pan and Wu 1995)
Relaxed Arakawa–Schubert(Moorthi and Suarez 1992)

Ek et al. (2003)

38 3 3.758, L34

T42 (;2.88 3 2.88), with 18
hybrid sigma levels
T85, with 27 hybrid sigma levels

Zhang and McFarlane(1995) and Hack (1994)
Oleson et al. (2008)

AGCM: T85, with 26 levels; OGCM:
18 3 18, telescoping to 18 3 1/28 in
the deep tropics with 40 levels

Zhang and McFarlane(1995)

Mosaic (Koster andSuarez 1996)
Bonan (1996)
Community Land Model(Oleson et al. 2008;
Stockli et al. 2008)
Community Land Model

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FIG. 2. Annual mean precipitation (shaded) and 200-mb eddy height field (contoured) averaged over the years of 1980–98. The model
results are from AMIP-style runs from each model (runs forced by observed SSTs). (bottom right) For the observations, the precipitation
is from the Global Precipitation Climatology Project (GPCP; Adler et al. 2003) and the height fields are from the National Centers forEnvironmental Prediction (NCEP)–NCAR reanalyses (Kalnay et al. 1996). The time period was chosen in order to have a common set ofyears for each model. The contour interval for the height field is 20 m (negative values are dashed and the zero line is the first solid
contour). Precipitation is in millimeters per day.

All of the models produce quite reasonable annual
mean stationary wave and precipitation patterns (Fig. 2).
There are, however, differences in the details, including
such features as the strength of the Pacific ITCZ (cf. the

NSIPP-1 and GFS results) and, of particular interest here,
the strength and orientation of the height anomalies overthe Pacific–North American region. In general, themodels tend to overestimate precipitation in the western

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Pacific and Atlantic warm pool regions compared withobservations. Most models also tend to underestimate thestrength and eastward extent of the East Asian trough.
The GFS model is the exception, showing a deeper troughtogether with a stronger ridge over North America compared with the observations and the other models. Aninteresting feature is the wave train that appears to emanate from the central tropical Pacific and extends acrossNorth America into the Atlantic. It appears to be responsible for modifying the structure and amplitude of thewest coast ridge and the trough over eastern North
America. This feature is evident in the observations andall of the models except GFS, where it is at most very
weak. The extent to which such differences in the climatological stationary wave pattern impact the response to
the SST anomalies is unclear. This issue will be revisitedlater in the discussions of the model responses.
Figure 3 provides an overview of the agreement between the models with respect to the annual mean responses to the eight combinations of the Pacific andAtlantic SST patterns. The results are presented as
spatial correlations between the 10 different combinations of the five models (model 1 correlated with model 2,model 1 correlated with model 3, etc.). The scatter of the
10 different combinations gives a sense of the full range ofagreement or disagreement between the various models
for any one forcing pattern. The ordinate is the correlation based on precipitation, and the abscissa is thecorrelation based on the 200-mb height. Figure 3a shows
the results for the global and annual mean distributions.
Here we limit the comparisons to the four basic individual Pacific and Atlantic forcing patterns (no combinations of forcing patterns) to better highlight thedifferences in the level of agreement between responses
to the Pacific and Atlantic forcing patterns. The global
annual mean results show a number of interesting features. First, it is clear that the models are in much stronger
agreement regarding the responses to the Pacific forcing(dark colors), compared with the response to the Atlantic forcing (light colors). It is also clear that in general
there is more agreement in the height response than in
the precipitation response (almost all points lie below
the 458 line). For plots in Figs. 3b–f the results are shown
for all eight forcing patterns. The tropical results (Fig. 3b)show generally very strong agreement in the height response for all forcing combinations. The response to the
Atlantic forcing shows relatively weak precipitation correlations (generally less than 0.6), while the response to
the combined (Pacific and Atlantic) forcing patterns
exhibit correlations that are comparable to the Pacificforcing results, a reflection of the dominance of the Pacific forcing. There are also some interesting seasonal
differences in the global correlations. The results for

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December–February (DJF; Fig. 3c) show a tendency for
greater agreement in the precipitation response than theheight response for the cold Pacific, while the opposite istrue for the warm Pacific cases. This seems to reflect aweaker and less robust height response to a cold Pacificcompared with a warm Pacific forcing. The height response appears to be most robust during March–May(MAM; Fig. 3d), while June–August (JJA) shows overall
the smallest precipitation correlations. The response to
the Atlantic appears to be the most robust (and comparable to the response to the Pacific) during September–
November (SON; see Fig. 3f).
We next turn to a more in-depth comparison of theannual mean global 200-mb height and precipitation responses. A comparison of the spatial distributions of the
response to the warm Pacific (Fig. 4) shows considerable
large-scale similarity among the models (as expected fromthe correlations in Fig. 3). In particular, all models show
the well-known (ENSO type) horseshoe-shaped precipitation response, with a positive precipitation anomaly
in the central tropical Pacific surrounded by negative
anomalies on either side of the equator. All of the models
also show negative anomalies over Central America,
northeastern South America, and the tropical Atlantic.
There is general agreement in the height anomalies withwave trains emanating from the tropical Pacific and extending poleward into both hemispheres. The precipitation anomalies over North America appear strongly
coupled to the detailed structure of the height anomalies.
In particular, the spatial extent of the positive precipitation anomalies over the United States is linked to theorientation and strength of the negative height anomalies
over the continent. The response over the United States
will be discussed further (see reference to Fig. 6).
The global distributions of the precipitation responses
to the cold Pacific (Fig. 5) are largely of an opposite signcompared with the responses to the warm Pacific. In
particular, all of the models again show the familiar
horseshoe-shaped precipitation response with a now
negative precipitation anomaly in the central tropical
Pacific surrounded by positive anomalies on either side ofthe equator. All of the models also show positive anomalies over Central America, northeastern South America,
and the tropical Atlantic. The height anomalies also tend
to be of an opposite sign, but in the extratropics they tend
to be weaker than the response to the warm Pacific, reflecting an asymmetric response to the warm and cold
Pacific forcings. All of the model responses show a tendency for a split ITCZ (also evident in the PwAn response) that is most pronounced in the GFDL and
CAM3.5 models. All of the models also show a ridge over
the North Pacific that extends eastward across the UnitedStates (though less so for the GFS model).

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FIG. 3. Spatial correlations of precipitation anomalies (ordinate) vs spatial correlations of 200-mb height anomalies(abscissa) for the various combinations of models and for the eight combinations of forcing for the Pacific andAtlantic patterns: (a) global correlations of annual means, (b) tropical correlations (6308 latitude) of annual means,(c) global correlations for DJF, (d) global correlations for MAM, (e) global correlations for JJA, and (f) global
correlations for SON. See text for details. The colors refer to the forcing patterns indicated along the bottom of theplots.

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FIG. 4. Annual mean 200-mb height and precipitation responses to the PwAn SST anomaly pattern. The anomalies for each model arecomputed with respect to its annual mean response to a simulation in which the model is forced with climatological SSTs. See text for
details on the SST forcing. Contour interval for height is 10 m (negative values are dashed and the zero line is the first solid contour).
Precipitation is in millimeters per day.

Figure 6 provides a close up of the U.S. surface temperature4 and precipitation responses to the PwAn and4Here and elsewhere in the text the surface temperature overland refers to the skin temperature.

PcAn SST patterns. While all of the models show atendency for wet conditions in response to PwAn forcing
and dry conditions for the PcAn forcing, there are considerable differences among the models. For example,
the NSIPP-1 response tends to be relatively localized

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FIG. 5. Annual mean 200-mb height and precipitation responses to the PcAn SST anomaly pattern. The anomalies for each model arecomputed with respect to its annual mean response to a simulation in which the model is forced with climatological SSTs. See text for
details on the SST forcing. Contour interval for height is 10 m (negative values are dashed and the zero line is the first solid contour).
Precipitation is in millimeters per day.

over the central Great Plains (GP). On the other hand,
the largest GFS response occurs along the southern andwestern tier of the states, while the other models tend toshow more widespread precipitation anomalies. The
magnitude of the precipitation response over the Great

Plains ranges between about 0.2 and 0.8 mm day21. For
comparison, during the Dust Bowl drought the 1932–38
mean observed precipitation deficits over the Great
Plains ranged from 20.2 to 20.4 mm day21 (e.g., Schubert
et al. 2004b). The surface temperature and precipitation

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FIG. 6. Annual mean (top) surface temperature (8C day21) and (bottom) precipitation (mm day21) responses to the (left) PwAn and(right) PcAn SST anomaly patterns. The anomalies for each model are computed with respect to its annual mean response to a simulationin which the model is forced with climatological SSTs. See text for details on the SST forcing.

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FIG. 7. Scatterplots of the Great Plains (see Fig. 9 for definition of region) annual mean precipitation (ordinate) vs surface temperature(abscissa) response to the PwAn (dark dots) and PcAn (light dots) SST anomaly patterns for the Great Plains area average. Each point
represents 1 of 50 (35 yr for the GFS model) yr of each run.

anomalies tend to correspond in the sense that wet anomalies are associated with cold anomalies and dry anomalies
are associated with warm anomalies. The temperature
response of the GFS model is quite different from theother models, showing extensive warming over much of
the United States (cooling is confined to the Southwest)
in response to the PwAn forcing, with only a slight
warming over the southern Great Plains (sGP) in response to the PcAn forcing.
The linkage between the surface temperature andprecipitation responses over the central United States is
explored further in Fig. 7. All of the models show a very
robust precipitation response with a clear separation of
the wet (PwAn) and dry (PcAn) years. There is an
overall tendency for a negative relationship between the
precipitation and surface temperature anomalies. The
exception is again the GFS model, for which the response to the PwAn pattern is generally characterized

by positive temperature anomalies and enhanced precipitation. The negative relationship between the annual
mean temperature and precipitation responses is largelya reflection of the warm season responses (primarily
JJA, but also in some cases MAM and SON; not shown),
and presumably reflects a strong tie between the atmosphere and land surface during the warm season for
all but the GFS model.5 Simply put, wetter conditions
tend to lead to higher evaporative cooling and thus
cooler air temperatures.
We next summarize the annual mean responses overthe continental United States to the eight different
combinations of the Pacific and Atlantic forcing patterns. Figure 8a shows the precipitation responses.

5
The GFS model is in fact known to have a relatively weakland–atmosphere coupling strength (Koster et al. 2006).

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FIG. 8. The annual and continental U.S. mean responses for (a) precipitation (mm day21) and(b) surface temperature (8C) for all eight combinations of the Pacific and Atlantic patterns forthe five AGCMs (see Table 1).

There is agreement among the models that a cold Pacificresults in a reduction of precipitation, whereas a warm
Pacific produces precipitation increases. Also, there is
general agreement that a warm Atlantic leads to reduced
precipitation whereas a cold Atlantic leads to increased
precipitation, though with substantially smaller amplitudes, especially for the response to the cold Atlantic.
Overall, the models agree that the combination of a cold
Pacific and warm Atlantic (PcAw) tends to produces thelargest precipitation deficits, whereas the combinationof a warm Pacific and cold Atlantic (PwAc) tends to
produce the largest precipitation surpluses. There issomewhat less agreement for the surface temperature
responses (Fig. 8b), with generally positive (negative)
temperature anomalies associated with precipitation
deficits (surpluses), but that is not the case for the GFS
model (and to a lesser degree the NSIPP-1 model),which shows strong, warm anomalies associated with

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FIG. 9. The regions of the United States used to form the averages in Figs. 7, 10, and 11. The GP region is further
divided (at 408N latitude) into the nGP and the sGP regions.

enhanced precipitation (consistent with the previous
results).
The scatter in the year-to-year responses to the Pacificforcing shown in Fig. 7 for the Great Plains shows theextent to which internal weather variability obscures thesignal forced by the SST anomalies. This can be formalized in terms of the quantity
R5(x  y),sxywhere x and y represent seasonal values from the experiment and control runs respectively, and the overbar
denotes a 50-yr (35 yr, for the GFS model) mean. Also,s2xy 5 (s2x 1 s2y )/2, where sx2 and sy2 are the variance estimates of x and y, respectively. The mean difference
between the experiment and control (the numerator) is ameasure of the signal, so that R measures the size of thesignal in units of standard deviation and can be viewed as
the signal-to-noise ratio associated with the response.
Figure 9 shows the definitions of the various regions of
the United States. Figure 10 shows R for the precipitation
response to the PwAn and PcAn forcing patterns for eachseason and all models over the different regions. The
results for the U.S. average (top-left panel) show significant (at the 5% level) responses throughout most of
the year for both the PwAn and PcAn forcing patterns.
Somewhat surprisingly the signal-to-noise ratio is smallest(in fact only marginally significant) in DJF and largest
during MAM/April–June (AMJ), when it reaches valuesof 1.5 or greater. We note that a very similar seasonality isfound in the signal itself, so the results do not simply reflect a greater noise during the winter season. We caution
however that, as indicated in section 2, there is no seasonal cycle in the prescribed forcing, whereas observed
SST anomalies are largest in winter.
A comparison of the results for PwAn and PcAn showsthat magnitudes are largely comparable with perhaps
somewhat weaker R values (characterized by less agreement among the models) for the cold Pacific case. The
results for the United States as a whole reflect, to alarge extent, the results in the Great Plains and Southwest, particularly the southern Great Plains. The northern Great Plains (nGP) show a similar seasonality, buthave more modest signal-to-noise ratios. The Northwest
United States has the largest ratios during the northern
spring and summer, while there is little evidence of asignificant response in the Northeast. The Southeast has
the largest signal-to-noise ratios during winter andspring, with minimal (not significant) responses during
the summer season. In general, the models have a very
similar seasonality of the response. The GFS modelshows a somewhat different behavior, especially for thenorthern Great Plains, where the significant response islargely confined to the cold phase during the fall. Also, in
the Southeast the GFS model differs from the others in

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FIG. 10. Seasonality of the signal-to-noise ratios (R; see text) of the 3-month mean precipitation responses for each model over various
regions of the United States to the Pacific warm (red curves) and cold (blue curves) SST anomaly pattern. The numbers along the abscissa
refer to the center month of the 3-month means. Results are based on 50-yr simulations, except for the GFS model (dashed lines), which
was run for 35 yr. See text for the definition of the signal-to-noise ratio. The thin horizontal lines denote the 5% significance levels based ona Student’s t test (for the GFS model the critical t value is 0.49). Values are dimensionless.

that it shows little significant response to the warm phase(PwAn).
The R values for the surface temperature response(Fig. 11) are substantially different from those for precipitation. In addition to having generally smaller amplitudes, there is considerable disagreement among themodels regarding the seasonality and even the sign of R.
For the continental United States as a whole (top-leftpanel) the response to the warm Pacific is significant forthree models during the warm season (May–August),
and large and significant for one model (GFS) during thecold season (March–May). The latter result reflects theunusual (compared to the other models) southward extension of the upper-level high in the GFS response (e.g.,
Fig. 4), and can be traced to the large signal-to-noise ratios
in the northern tier of states (the second, third, and fourth
top panels of Fig. 11). The response to the cold Pacific is
for the most part insignificant or marginally significant,with the largest values occurring during late summer andfall. Excluding the GFS model, the most consistent results

and the largest R values occur for the southern tier ofstates (bottom panels of Fig. 11). For example, thesouthern Great Plains show a consistent warming signal
during the warm season in response to a cold Pacific (withR peaking in late spring), and cold anomalies in response
to a warm Pacific beginning in late winter and extending into late summer. The southwest shows considerable
asymmetry in the cold and warm Pacific responses, with
only marginally significant responses to a cold Pacific,
whereas the response to the warm Pacific shows significant R values for two of the models during late springand early summer. In the Southeast (bottom-right panel ofFig. 11), all of the models agree on having little skill during
the late summer to early winter seasons in response to thecold Pacific. Four models show a tendency for a significant
warming in late spring and early summer in response to
the cold Pacific, while three models show cooling during
the summer in response to the warm Pacific.
The overall level of agreement between the models
can be quantified in terms of another signal-to-noise

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FIG. 11. Seasonality of the signal-to-noise ratios (R; see text) of the 3-month mean surface temperature responses for each model overvarious regions of the United States to the Pacific warm (red curves) and cold (blue curves) SST anomaly pattern. The numbers along theabscissa refer to the center month of the 3-month means. Results are based on 50-yr simulations, except for the GFS model (dashed lines),
which was run for 35 yr. See text for the definition of the signal-to-noise ratio. The thin horizontal lines denote the 5% significance levels
based on a Student’s t test (for the GFS model the critical t value is 0.49).Values are dimensionless.

ratio that, in this case, measures the similarity of the
R values in units of standard deviation. In particular, we
compute the ratio



jmR j
,
S5
sR
where mR is the five-model average R value for a given
seasonal (3 month) mean, sR is the standard deviation of
the five R values, the vertical lines denote an absolutevalue, and the brackets denote an average over the
12 seasonal mean (3-month running mean) values. The
results (presented in Table 3) highlight the substantiallygreater intermodel agreement in the R values (larger S)
for the precipitation responses compared with the surface temperature responses. For example, for the U.S.
continental average response to the warm (cold) Pacific,
the S value for the precipitation response is about 5 (2)times larger than that for the surface temperature response. The results also quantify the asymmetries thatexist in the level of agreement between the responses
to the warm and cold Pacific. For example, over theSoutheast United States the S value for the precipitation(surface temperature) response to the cold Pacific is
more than twice (3 times) that of the response to thewarm Pacific.
Finally, we present a brief overview of the responses ofthe models to the trend pattern. The focus is on the surface temperature response to the positive trend pattern,
and the results are compared to those from the companion set of runs in which the models were forced with a
globally uniform SST warming of 0.168C. The precipitation response to the trend (not shown) is weak for all of
the models, with anomalies that exceed 0.5 mm day21 inamplitude largely confined to a few locations in thetropical Pacific and Indian Oceans. The basic responseof all of the models (left panels of Fig. 12) is a tendency
for warming over most of the world’s land areas, though
there are substantial regional variations that differ between models. The GFS and GFDL models show the1 OCTOBER 2009

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SCHUBERT ET AL.

TABLE 3. The intermodel agreement in the R values (S) for precipitation and surface temperature. Here, S is equal to the absolute valueof the ensemble (five models) mean of R divided by the standard deviation of R. The results are averaged over the annual cycle. The
regions are defined in Fig. 9. Values are dimensionless. See text for details.

Precipitation
Warm PacificCold PacificSurface temperatureWarm PacificCold PacificUnitedStatesGreat
PlainsNorthern
Great Plains

Southern
Great Plains

Northwest

Southwest

Northeast

Southeast

3.71
3.05

2.67
3.38

1.57
1.63

2.44
3.29

1.61
1.61

2.58
1.46

1.36
1.44

1.20
2.48

0.73
1.38

0.86
1.97

1.05
1.34

1.13
2.25

1.21
1.10

0.94
1.72

0.79
1.02

0.49
1.60

strongest warming, with especially large values (exceeding 0.58C) concentrated over North America andparts of Asia and Australia. CAM3.5 and CCM3 show
the weakest warming, while the NSIPP-1 model showsintermediate values, with the largest warming occurring
over North America. Focusing on North America, theGFS model shows strong warming over most of thecontinent (especially the western half), while the GFDL
and NSIPP-1 models show warming that is more confined to the central and eastern United States. All but
the GFS model show a substantial area of coolingspanning much of northern Canada and Alaska.
The impact of any regional variations in the SST trend
forcing pattern can be deduced from comparisons withthe runs forced by the globally uniform warming pattern6 (right panels of Fig. 12). The comparison showsthat many of the features of the response to the trend
pattern are reproduced in the response to uniform
warming. For example, some of the enhanced warming
over North America in the GFS model, the enhanced
warming over Asia in the GFDL model, and, moregenerally, the spatial pattern of the warming over muchof Asia in all of the models is reproduced in the uniform
warming case. The global spatial correlations between
the responses to the trend and uniform warming of theannual mean values over land range from 0.34 for the
NSIPP-1 model to 0.43 for the GFDL model. There is,however, a large seasonal variation in the correlations
for some models. For example, for the GFDL model, thecorrelations range from 0.7 in March to 0.15 in July. A
key difference between the two sets of responses over
North America is that the cooling in northern Canada
and Alaska noted earlier does not show up in the response to the uniform warming, indicating that this feature is primarily the result of regional variations in the
SST trend pattern. Also, the localized warming responses
over the United States in the GFDL and NSIPP-1 models
are not reproduced in the case of uniform warming.
6

This run was not done with CAM3.5.

4. Summary and discussion
The U.S. CLIVAR drought working group recently
initiated a coordinated (multi-institutional and multimodel) effort to produce a set of idealized simulations
designed to address fundamental questions regarding
the physical mechanisms that link SST variations to regional drought, including an assessment of the role ofland–atmosphere feedbacks. The set of experiments
consist of multiyear simulations in which the models wereforced by a number of idealized SST forcing patterns
consisting of the leading rotated EOFs of SST variability
on interannual and longer time scales. The main set of
EOF forcing patterns include a global trend pattern, aPacific ENSO-like pattern, and an Atlantic pattern that
resembles the Atlantic multidecadal oscillation. Additional SST forcing patterns were designed to isolate
ENSO and longer (decadal) time scales, and to isolate theinfluence of the tropical SST. A number of groups also
ran experiments in which the land–atmosphere interactions were disabled by prescribing the soil moisture.
This paper, in addition to providing a general overview of the project, attempts to provide a broad-ranged
assessment of the model results focusing on overall behavior and highlighting where the models tend to agreeand disagree. Results are limited to the responses to thetwo leading Pacific and Atlantic forcing patterns, andthe trend pattern. While showing some aspects of the
global-scale response, much of the focus of this paper ison the responses over the continental United States.
A number of key results emerge from this initial analysis of the experiments. First, all of the models produce
similar (though different in detail) precipitation anomalies over the continental United States in response to the
Pacific forcing pattern, with a tendency for reduced precipitation when forced with a cold Pacific and a tendency
for enhanced precipitation when forced with a warm
Pacific. The response to the Atlantic pattern is not as
robust as the response to the Pacific, though there is atendency for reduced precipitation when forced with awarm Atlantic and a tendency for enhanced precipitation

FIG. 12. (left) The annual mean surface temperature response (8C) to the positive phase of the trend pattern. (right) The annual mean surface temperature response (8C) to a globally
uniform SST warming of 0.168C. The uniform warming run was not performed with CAM3.5.

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when forced with a cold Atlantic. There is generalagreement among the models that the largest precipitation response over the continental United States tends tooccur when the two oceans have anomalies of opposite
signs. That is, a cold Pacific and warm Atlantic tend to
produce the largest precipitation reductions, whereas awarm Pacific and cold Atlantic tend to produce thegreatest precipitation enhancements.
The above results are to a large extent consistent withobservationally based studies of the impact of SST
anomalies on drought over the United States. For example, numerous observational studies have linked Pacific SST variability (on ENSO and/or decadal timescales) to summertime precipitation and drought over
the United States (e.g., Ting and Wang 1997; Nigam
et al. 1999; Barlow et al. 2001). There is evidence thatduring AMO warm events (examples are the 1930s and1950s) much of the United States experiences less-thannormal rainfall (e.g., Enfield et al. 2001). Also, McCabe
et al. (2004), found that a number of U.S. droughts in thelast few decades were associated with a positive AMO
and a negative PDO, consistent with the model results.
The models tend to agree less on the area mean U.S.
surface temperature response to both the Atlantic andPacific forcing, though (with the exception of the GFS
model) there is a general tendency for wet conditions tobe associated with cold surface temperature anomalies
and dry conditions to be associated with warm surface
temperature anomalies. Differences in the land surface
models very likely account for some of these model
differences; however, the apparent sensitivity of the
response over the United States to small (on planetaryscales) shifts in the upper-level wave response to the SST
forcing may also play a role. The fact that there is lesssensitivity for the precipitation response is surprising,though it may be partly that the area mean of the more
localized precipitation anomalies is less sensitive toshifts in the planetary wave forcing. The model differences are highlighted by the GFS response to the warm
Pacific SST forcing, which tends to place the upper-level
ridge of the response over North America considerably
farther south compared to the other models, leading towarm surface temperature anomalies that extend wellinto the southern tier of the states.
Another key area of agreement among the models isin the seasonality of the signal-to-noise ratio (R) of
continental U.S. precipitation associated with the Pacificforcing. All of the models show that the largest R values
occur in spring with surprisingly small (not significant)R values during winter. The above results for the UnitedStates as a whole reflect those in the Great Plains and theSouthwest, particularly the southern Great Plains. In
contrast to these results, the R values of the surface

5269

temperature response to the Pacific forcing are generally
lower and show considerably less agreement among themodels. For the continental United States as a whole the
GFS model stands out as having very high R values
during the cold season (March–May). This again appears to reflect the unusual (compared to the othermodels) southward extension of the upper-level high in
the GFS response and can be traced to the large R values
that occur in the northern tier of states. For the othermodels, the most consistent results and the largest
R values occur for the southern tier of states, with, forexample, the southern Great Plains showing a consistent
warm anomaly during the warm season in response to acold Pacific (with R peaking in late spring), and cold
anomalies in response to a warm Pacific beginning inlate winter and extending into late summer.
The surface temperature response to the positive SST
global trend forcing pattern shows substantial regionalvariations that are in part reproduced in runs forced witha globally uniform SST trend forcing. There is, however,substantial disagreement among the models in theregionality (e.g., the enhanced surface temperature response produced over North America by some of themodels), highlighting the challenge of predicting regional impacts of global warming. The precipitation
response to the trend forcing was found to be weak in allof the models.
The differences in the responses to the Pacific forcing
pattern over the United States between the GFS and theother models are intriguing. Understanding these differences is important in view of the critical role that the
GFS model (as part of the NOAA/Climate Forecast
System) plays in seasonal prediction, and more generally
regarding the increasingly important role that climate
models play in providing information on the regional
impacts of global climate variability and change. While
it is beyond the scope of this paper to fully address thereasons for these differences, it appears likely that thedifferences in the stationary wave patterns play a role.
The GFS model produces a very reasonable stationary
wave pattern and arguably produces the most realistic overall structure of the Pacific trough and North
American ridge (Fig. 2). The North American ridge is,however, stronger, and the trough to the east is considerably weaker compared with the other models and theobservations. A preliminary linear model analysis of the
NSIPP-1 model stationary waves (results not shown)indicates that the eastern North American trough isparticularly sensitive to the heating in the western tropical Pacific, suggesting that the differences we see in the
climatological precipitation in that region may play animportant role. On the other hand, most of the othermodels produce a rather weak Pacific trough—a problem

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that very likely also contributes to deficiencies in the response over North America. Clearly more work is needed
to not only improve our understanding of the sensitivityof stationary waves to the climatological forcing, but also
to determine the extent to which that sensitivity translates into uncertainties in the extratropical response to
SST anomalies.
The results of this initial analysis of the model experiments serve to highlight and quantify the importantrole of SST anomalies (especially those in the Pacific)
in generating drought and pluvial conditions over theUnited States. The differences between the model results provide an assessment of the current uncertainties
in our ability to model the global response to SST
forcing (including the feedbacks associated with land–
atmosphere interactions) and reinforce the need to improve our climate models. We expect that these results, aswell as those reported in the other contributions to thisspecial issue on drought, will serve to stimulate further
analysis of the simulations, as well as suggest new researchon the physical mechanisms contributing to hydroclimatic
variability and change throughout the world.
Acknowledgments. This project was carried out aspart of a U.S. CLIVAR drought working group activity
supported by NASA, NOAA, and NSF to coordinate
and compare climate model simulations forced with acommon set of idealized SST patterns. The authors
thank NASA’s Global Modeling and Assimilation Office(GMAO) for making the NSIPP-1 runs available, theLamont-Doherty Earth Observatory of Columbia University for making their CCM3 runs available, NOAA’s
Climate Prediction Center (CPC)/Climate Test Bed(CTB) for making the GFS runs available, NOAA’s
Geophysical Fluid Dynamics Laboratory (GFDL) formaking the AM2.1 runs available, the National Center
for Atmospheric Research (NCAR) for making the
CAM3.5 runs available, and the Center for Ocean Land
Atmosphere (COLA) and the University of Miami’s
Rosenstiel School of Marine and Atmospheric Science
for making the CCSM3.0 coupled model runs available.
The NASA GMAO contributions to this project were
supported by funding from the NASA Energy and Water
Cycle Study (NEWS) program, and the NASA Modeling,
Analysis and Prediction (MAP) Program.

APPENDIX
Auxiliary Experiments
In addition to forcing the models with the three main
SST patterns presented in the text (the Pacific, Atlantic,
and trend), the participating groups were encouraged to

VOLUME 22

force their models with other patterns consisting of thetropical-only version of the Pacific and Atlantic patterns, and low- and high-frequency versions of the Pacific SST patterns. Details about these patterns are
discussed below.
To differentiate between the impacts of the tropics
and extratropics, another set of SST ‘‘tropical’’ forcing
patterns was produced based on the interannual Pacificand Atlantic patterns. For the Pacific case, this was doneby linearly tapering the values to zero between 158 and218 latitude. The taper is such that the full amplitude
occurs at 158, ½ of the full amplitude remains at 188, andthe anomaly is identically zero from 218 latitude to thepole. Also, the meridional edges are such that thewestern boundary occurs at 1208E. For the Atlantic case,
the Atlantic pattern was modified so that the edgesof the box with the full anomalies were chosen as
888–138W, and 128–188N. The anomalies were tapered
linearly north and south, with latitudes of 98 and 218N
getting ½ the anomaly, and with the anomaly going to 0
at latitudes of 68 and 248N.
In addition to the patterns described above (based on
annual mean SST), two other patterns were produced
for the Pacific that attempt to separate the ENSO andlonger-term patterns of variability. The long time scales
were isolated by applying a filter to the monthly SST
data that retains time scales of about 6 yr and longer(Zhang et al. 1997). The high (residual) frequencies(shorter than 6 yr) were obtained by subtracting the lowfrequency filtered data from the unfiltered monthly data.
The leading REOFs and associated PCs from both the
low-pass (time scales of 6 yr and longer) and residual(time scales shorter than 6 yr) data are shown in Fig. A1.
In the case of the low-pass data we focus on the second
REOF (the first is again the trend pattern shown in Fig. 1).
The second low-frequency REOF shows the well-known,
meridionally extensive pan-Pacific decadal pattern ofvariability (e.g., Barlow et al. 2001), with substantial
middle-latitude amplitude that in the Northern Hemisphere is linked to the Pacific decadal oscillation (Zhang
et al. 1997). In contrast, the leading REOF of the residual(high frequency) SST shows a clear ENSO structure withsignificant amplitudes that are largely confined to thecentral and eastern equatorial Pacific.
A final set of experiments was designed to assess theimpact of soil moisture feedbacks. In this case either astandard multidecadal AMIP-style simulation or thecontrol run was used to derive the climatological seasonal cycles of soil moisture content for each soil layer at
each land point on the globe. Here the weekly resolution
was preferred, but in cases where these were not available monthly data were used. Subsets of the above idealized SST simulations were then repeated in such a way

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5271

FIG. A1. (top) The second REOF and PC of the low-pass-filtered (time scales greater than 6 yr) monthly SST data. (bottom) The first
REOF and PC of the high-pass-filtered (time scales less than 6 yr) monthly SST data. The results are based on the period of 1901–2004. The
values are scaled so that the product of the REOF and PC gives units of 8C.

that at each time step, the simulated soil moisture states
were thrown out and replaced with states that were interpolated from the climatological values established in
the first simulation. Details of the various experiments
and other information and links relevant to the U.S.
CLIVAR drought working group project may be foundonline (available at http://gmao.gsfc.nasa.gov/research/
clivar_drought_wg/index.html).
REFERENCES
Adler, R. F., and Coauthors, 2003: The version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation
Analysis (1979–present). J. Hydrometeor., 4, 1147–1167.
An, S.-I., and J. Choi, 2008: Seasonal locking of the ENSO asymmetry and its influence on the seasonal cycle of the tropical eastern Pacific sea surface temperature. Atmos. Res., 94,
3–9, doi:10.1016/j.atmosres.2008.09.029.
Bacmeister, J., P. J. Pegion, S. D. Schubert, and M. J. Suarez, 2000:
An Atlas of Seasonal Means Simulated by the NSIPP 1 Atmospheric GCM. NASA Tech. Memo. 104606, Vol. 17, Goddard
Space Flight Center, 194 pp.

Barlow, M., S. Nigam, and E. H. Berbery, 2001: ENSO, Pacificdecadal variability, and U.S. summertime precipitation, drought,
and streamflow. J. Climate, 14, 2105–2128.
Bonan, G. B., 1996: The NCAR land surface model (LSM version1.0) coupled to the NCAR community climate model. NCAR
Tech. Rep. NCAR/TN-429 1 STR, 171 pp.
Campana, K., and P. Caplan, Eds., 2005: Technical procedurebulletin for the T382 Global Forecast System. NOAA/NCEP/
EMC. [Available online at http://www.emc.ncep.noaa.gov/
gc_wmb/Documentation/TPBoct05/T382.TPB.FINAL.htm.]
Chen, J., A. D. Del Genio, B. E. Carlson, and M. G. Bosilovich,
2008: The spatiotemporal structure of twentieth-century climate variations in observations and reanalyses. Part I: Longterm trend. J. Climate, 21, 2611–2633.
Collins, W. D., and Coauthors, 2006: The Community Climate
System Model version 3 (CCSM3). J. Climate, 19, 2122–2143.
Delworth, T. L., and Coauthors, 2006: GFDL’s CM2 global coupled climate models. Part I: Formulation and simulation
characteristics. J. Climate, 19, 643–674.
Ek, M., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren,
G. Gayno, and J. D. Tarpley, 2003: Implementation of Noahland surface model advances in the National Centers forEnvironmental Prediction operational mesoscale Eta model.
J. Geophys. Res., 108, 8851, doi:10.1029/2002JD003296.

5272

JOURNAL OF CLIMATE

Enfield, D., A. Mestas-Nuñez, and P. Trimble, 2001: The Atlantic
Multidecadal Oscillation and its relation to rainfall and riverflows in the continental U.S. Geophys. Res. Lett., 28, 2077–2080.
Gates, W. L., and Coauthors, 1999: An overview of the results of
the Atmospheric Model Intercomparison Project (AMIP I).
Bull. Amer. Meteor. Soc., 80, 29–55.
GFDL Global Atmospheric Model Development Team, 2004: The
new GFDL Global Atmosphere and Land Model AM2–LM2:
Evaluation with prescribed SST simulations. J. Climate, 17,4641–4673.
Grell, G. A., 1993: Prognostic evaluation of assumptions used bycumulus parametrizations. Mon. Wea. Rev., 121, 764–787.
Gutzler, D. and S. Schubert, 2007: The U.S. CLIVAR Working
Group on Long-Term Drought. CLIVAR Variations, Vol. 5,
No. 1, International CLIVAR Project Office, Southampton,
United Kingdom, 6–7.
Hack, J. J., 1994: Parameterization of moist convection in the
National Center for Atmospheric Research Community Climate Model (CCM2). J. Geophys. Res., 99, 5551–5568.
Hoerling, M. P., and A. Kumar, 2003: The perfect ocean fordrought. Science, 299, 691–699.
Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–471.
Kiehl, J. T., J. J. Hack, G. Bonan, B. A. Boville, D. Williamson, and
P. Rasch, 1998: The National Center for Atmospheric ResearchCommunity Climate Model: CCM3. J. Climate, 11, 1131–1149.
Koster, R. D., and M. J. Suarez, 1996: Energy and water balancecalculations in the Mosaic LSM. NASA Tech. Memo. 104606,
Vol. 9, 194 pp.
——, ——, R. W. Higgins, and H. Van den Dool, 2003: Observational evidence that soil moisture variations affect precipitation. Geophys. Res. Lett., 30, 1241, doi:10.1029/2002GL016571.
——, and Coauthors, 2006: GLACE: The Global Land–Atmosphere
Coupling Experiment. Part I: Overview. J. Hydrometeor.,
7, 590–610.
McCabe, G. J., M. A. Palecki, and J. L. Betancourt, 2004: Pacificand Atlantic Ocean influences on multidecadal drought frequency in the United States. Proc. Natl. Acad. Sci. USA, 101,
4136–4141.
Milly, P. C. D., and A. B. Shmakin, 2002: Global modeling of land
water and energy balances. Part I: The Land Dynamics (LaD)model. J. Hydrometeor., 3, 283–299.
Mo, K. C., J. N. Paegle, and R. W. Higgins, 1997: Atmosphericprocesses associated with summer floods and droughts in thecentral United States. J. Climate, 10, 3028–3046.
Moorthi, S., and M. J. Suarez, 1992: Relaxed Arakawa–Schubert:
A parameterization of moist convection for general circulation models. Mon. Wea. Rev., 120, 978–1002.
Nigam, S., M. Barlow, and E. H. Berbery, 1999: Pacific decadal SST
variability: Impact on U.S. drought and streamflow. Eos,Trans. Amer. Geophys. Union, 80, 621–625.
Oleson, K. W., and Coauthors, 2008: Improvements to the Community Land Model and their impact on the hydrological cycle. J. Geophys. Res., 113, G01021, doi:10.1029/2007JG000563.
Pan, H.-L., and W.-S. Wu, 1995: Implementing a mass flux convection parametrization for the NMC Office Note 409, 40 pp.

VOLUME 22[Available from NOAA/NCEP/EMC, 5200 Auth Rd., Suitland,
MD 20746.]
Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland,
L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan,
2003: Global analyses of SST, sea ice, and night marine airtemperature since the late nineteenth century. J. Geophys.
Res., 108, 4407, doi:10.1029/2002JD002670.
Richman, M. B., 1986: Rotation of principal components. J. Climatol., 6, 293–335.
Ruiz-Barradas, A., and S. Nigam, 2004: Warm season rainfall
variability over the U.S. Great Plains in observations, NCEP
and ERA-40 reanalyses, and NCAR and NASA atmosphericmodel simulations. J. Climate, 18, 1808–1830.
Schubert, S. D., M. J. Suarez, P. J. Pegion, M. A. Kistler, andA. Kumer, 2002: Predictability of zonal means during boreal
summer. J. Climate, 15, 420–434.
——, ——, ——, R. D. Koster, and J. T. Bacmeister, 2004a: Causesof long-term drought in the U.S. Great Plains. J. Climate, 17,
485–503.
——, ——, ——, ——, and ——, 2004b: On the cause of the
1930s Dust Bowl. Science, 303, 1855–1859, doi:10.1126/
science.1095048.
Seager, R., Y. Kushnir, C. Herweijer, N. Naik, and J. Velez, 2005:
Modeling of tropical forcing of persistent droughts and pluvials over western North America: 1856–2000. J. Climate, 18,
4068–4091.
Stockli, R., and Coauthors, 2008: Use of FLUXNET in the Community Land Model development. J. Geophys. Res., 113,
G01025, doi:10.1029/2007JG000562.
Ting, M., and H. Wang, 1997: Summertime U.S. precipitation
variability and its relation to Pacific sea surface temperature.
J. Climate, 10, 1853–1873.
Trenberth, K. E., and J. W. Hurrell, 1994: Decadal atmosphereocean variations in the Pacific. Climate Dyn., 9, 303–319.
——, and C. J. Guillemot, 1996: Physical processes involved in the1988 drought and 1993 floods in North America. J. Climate, 9,1288–1298.
Vecchi, A. G., A. Clement, and B. J. Soden, 2008: Examining thetropical Pacific’s response to global warming. Eos, Trans.
Amer. Geophys. Union, 89, 81, doi:10.1029/2008EO090002.
Wang, C., D. B. Enfield, S.-K. Lee, and C. W. Landsea, 2006: Influences of the Atlantic warm pool on Western Hemisphere summer rainfall and Atlantic hurricanes. J. Climate, 19, 3011–3028.
——, S.-K. Lee, and D. B. Enfield, 2008: Climate response toanomalously large and small Atlantic warm pools during thesummer. J. Climate, 21, 2437–2450.
Wang, H., S. Schubert, M. Suarez, J. Chen, M. Hoerling, A. Kumar,
and P. Pegion, 2009: Attribution of the seasonality andregionality in climate trends over the United States during
1950–2000. J. Climate, 22, 2571–2590.
Zhang, G. J., and N. A. McFarlane, 1995: Sensitivity of climate
simulations to the parameterization of cumulus convectionin the Canadian Climate Centre general circulation model.
Atmos.–Ocean, 33, 407–446.
Zhang, Y., J. M. Wallace, and D. S. Battisti, 1997: ENSO-like interdecadal variability. J. Climate, 10, 1004–1020.