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JOURNAL OF CLIMATEVOLUME 22

Atmospheric Circulation Trends, 1950–2000: The Relative Roles of Sea Surface
Temperature Forcing and Direct Atmospheric Radiative Forcing
CLARA DESER AND ADAM S. PHILLIPS
Climate and Global Dynamics Division, National Center for Atmospheric Research,* Boulder, Colorado(Manuscript received 30 January 2008, in final form 30 June 2008)
ABSTRACTThe relative roles of direct atmospheric radiative forcing (due to observed changes in well-mixed greenhouse gases, tropospheric and stratospheric ozone, sulfate and volcanic aerosols, and solar output) andobserved sea surface temperature (SST) forcing of global December–February atmospheric circulation
trends during the second half of the twentieth century are investigated by means of experiments with anatmospheric general circulation model, Community Atmospheric Model, version 3 (CAM3). The modelexperiments are conducted by specifying the observed time-varying SSTs and atmospheric radiative quantities individually and in combination. This approach allows the authors to isolate the direct impact of eachtype of forcing agent as well as to evaluate their combined effect and the degree to which their impacts are
additive. CAM3 realistically simulates the global patterns of sea level pressure and 500-hPa geopotential
height trends when both forcings are specified. SST forcing and direct atmospheric radiative forcing drive
distinctive circulation responses that contribute about equally to the global pattern of circulation trends.
These distinctive circulation responses are approximately additive and partially offsetting. Atmosphericradiative changes directly drive the strengthening and poleward shift of the midlatitude westerly winds in
the Southern Hemisphere (and to a lesser extent may contribute to those over the Atlantic–Eurasian sector
in the Northern Hemisphere), whereas SST trends (specifically those in the tropics) are responsible for theintensification of the Aleutian low and weakening of the tropical Walker circulation. Discrepancies between
the atmospheric circulation trends simulated by CAM3 and Community Climate System Model, version 3(CCSM3), a coupled model driven by the same atmospheric radiative forcing as CAM3, are traced todifferences in their tropical SST trends: in particular, a 60% weaker warming of the tropical Indo-Pacific in
the CCSM3 ensemble mean than in nature.

1. Introduction
The atmospheric circulation plays a central role inclimate, influencing the global distributions of precipitation and air temperature. Since the middle of thetwentieth century, the large-scale circulation has exhibited numerous changes, including an intensification of
the midlatitude westerlies over the North Pacific, North
Atlantic, and Southern Oceans, and a weakening of the
trade winds over the tropical Pacific (Trenberth andHurrell 1994; Hurrell 1995; Clarke and Lebedev 1996;* The National Center for Atmospheric Research is sponsoredby the National Science Foundation.

Corresponding author address: Dr. Clara Deser, Climate andGlobal Dynamics Division, NCAR, P.O. Box 3000, Boulder, CO
80307.
E-mail: cdeser@ucar.edu
DOI: 10.1175/2008JCLI2453.1
Ó 2009 American Meteorological Society

Hines et al. 2000; Thompson et al. 2000; Marshall 2003;
Tanaka et al. 2004; Power and Smith 2007). These circulation trends have contributed to coherent patterns
of precipitation and temperature anomalies worldwide(Hurrell 1996; Hurrell and van Loon 1997; Deser et al.
2004; Hegerl and Kenyon 2008).
The causes of observed atmospheric circulation
trends over the last half century are a subject of ongoing investigation. Greenhouse gas concentration increases and stratospheric ozone depletion are considered to be the main factors responsible for the intensification of the westerlies over the Southern Ocean(Stephenson and Held 1993; Fyfe et al. 1999; Kushneret al. 2001; Thompson and Solomon 2002; Arblasterand Meehl 2006; Gillett and Thompson 2003; Thompson and Solomon 2005; Cai and Cowan 2007; Hegerl etal. 2007), while tropical sea surface temperature (SST)changes are implicated in the circulation trends over
the North Pacific (Graham 1994; Trenberth and Hurrell

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DESER AND PHILLIPS

1994; Deser et al. 2004). The latter may also play acontributing role to circulation trends over the North
Atlantic (Hoerling et al. 2004; Hurrell et al. 2004), although intrinsic variability due to nonlinear atmospheric dynamics is also important (Schneider et al.
2003; Hurrell et al. 2004; Bracco et al. 2004). Thus,
there is evidence that both SST changes and atmospheric radiative forcing due to changes in greenhousegas and ozone concentrations have contributed to atmospheric circulation trends in recent decades. Note,however, that the two forcing agents are not necessarily
independent, since SSTs also respond to changes in atmospheric radiative forcing.
Global coupled climate models can be insightful forunderstanding the atmospheric circulation response toincreasing greenhouse gas concentrations, since they
contain both the direct effects of atmospheric radiative
forcing (via differential changes in atmospheric heating) and the indirect effects (via changes in SST). However, for the purpose of attribution in the real climate
system, they also have inherent limitations, since their
SST response (and especially the regional details of
their SST response) may differ from the observed SST
evolution, thereby affecting the fidelity of the simulated
circulation trends (Gillett et al. 2005; Hegerl et al.
2007).
One way to circumvent this limitation is to specify
the observed time-evolving distribution of SSTs and atmospheric radiative forcing in an atmosphere-onlymodel. While this approach does not address the origin(natural or anthropogenic) of the SST changes, it does
ensure that the oceanic forcing is realistic. We note that
there is a long history of atmospheric model integrations driven by observed time-varying SSTs as thelower boundary condition, the so-called Atmospheric
Model Intercomparison Project (AMIP) configuration(Gates et al. 1999). These traditional AMIP experiments do not, however, take into account the direct
effects of atmospheric radiative forcing upon the circulation.
The methodology of specifying both the observed
time-evolving distributions of SST and atmospheric radiative forcing in an atmosphere-only model was first
introduced by Folland et al. (1998). Note that this approach does not ‘‘double count’’ the atmospheric radiative forcing because only the direct effect is specified,
whereas the indirect effect is included in the prescribed
SST forcing. Folland et al. (1998) focused on the vertical structure of the temperature trend response in the
troposphere and lower stratosphere during 1961–94 to
SST versus direct atmospheric radiative forcing. Usinga similar methodology, Compo and Sardeshmukh(2008) found that the recent warming over land has

397

occurred as a response to oceanic warming through enhanced downward longwave radiation due to increased
atmospheric moisture rather than as a direct response
to increasing greenhouse gases.
Following the same approach, Bracco et al. (2004)
investigated the atmospheric circulation response toobserved SST forcing versus direct atmospheric radiative forcing over the second half of the twentieth century using an atmospheric GCM of intermediate complexity and idealized atmospheric radiative forcing. Focusing on the response of the extratropical Northern
Hemisphere circulation during winter, they found that
SST variations contributed to trends over the Pacific–
North American sector with a partially counteracting
effect from greenhouse gas increases, while trends over
the North Atlantic–European region are primarily consistent with intrinsic variability and a weak (and notstatistically significant) contribution from greenhousegas increases.
The same methodology has been used for understanding future projections of atmospheric circulation
change. In particular, Cash et al. (2005) found that thecirculation response of an atmospheric general circulation model (AGCM) to combined forcing from doubled
CO2 and tropical SST changes at the time of CO2 doubling is consistent with the circulation response to
doubled CO2 in a coupled ocean–atmosphere GCM
from which the SST changes were taken. Cash et al.
(2005) did not investigate the individual impacts of CO2
and tropical SST anomalies upon the model’s atmospheric circulation. This issue was addressed in an earlier study by Stephenson and Held (1993), who foundthat the winter stationary wave response over the North
Pacific and Canada as well as the increase in the Southern Hemisphere westerlies in a doubled CO2 scenario
was due to SST forcing rather than direct atmosphericradiative forcing.
In this study, we analyze the relative roles of SST andatmospheric radiative changes in forcing global atmospheric circulation trends during 1950–2000 by meansof ensemble simulations with Community Atmospheric
Model, version 3 (CAM3). The atmospheric radiative
forcing includes changes in greenhouse gases, tropospheric and stratospheric ozone, sulfate and volcanic
aerosols, and solar output, taken from Meehl et al.
(2003). The CAM3 experiments are conducted by
specifying the observed time-varying SSTs and atmospheric radiative quantities individually and in combination. This approach allows us to isolate the direct
impact of each type of forcing agent, as well as to evaluate their combined effect and the degree to which theirimpacts are additive. We also assess the role of tropical
SST variations by means of additional CAM3 integra-

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tions, and compare the simulated atmospheric circulation trends in CAM3 with those in the coupled Community Climate System Model, version 3 (CCSM3, of
which CAM3 is the atmospheric component model)
over the same time period. It is important to note that
this study does not attempt to distinguish anthropogenic from natural forcing of recent atmospheric circulation trends; rather, it assesses the relative roles ofoceanic and direct atmospheric radiative driving. However, we draw some possible inferences regarding therole of anthropogenic forcing from the comparison between the CAM3 and CCSM3 results.
There are shortcomings to the design and physicalinterpretation of all atmosphere-only model experiments because of the lack of coupling with an underlying ocean. In particular, traditional AMIP experiments
have been criticized for specifying SST variations inregions where these are due to, and not a cause of,overlying atmospheric circulation changes (e.g., Kumar
and Hoerling 1998; Bretherton and Battisti 2000). Thisis particularly an issue in the extratropics where thedominant direction of interaction is that of the atmosphere forcing the ocean (e.g., Cayan 1992; Deser andTimlin 1997) and may also be a factor in certain portions of the tropics, for example the Indian Ocean (Kumar and Hoerling 1998; Klein et al. 1999; Deser andPhillips 2006); it is less of a concern in the tropical
Pacific where SST anomalies are known to exert a direct effect on deep atmospheric convection. As we shallshow, the atmospheric circulation trends simulated by
CAM3 in response to SST forcing are largely a result of
SST changes in the tropics, and in particular, rainfallchanges over the equatorial Pacific. Thus, we do notconsider the AMIP experimental design to be unduly
problematic in our case, although it remains a caveat in
the interpretation of our results. By the same token,
atmosphere-only model experiments forced withchanges in greenhouse gas concentrations and other radiatively important chemical constituents contain artificially large downward surface energy fluxes becauseof the lack of an interactive ocean. Nevertheless, atmosphere-only model experiments remain a useful albeit
imperfect tool for probing the physical mechanisms of
the atmospheric circulation response to different typesof forcing.
In this study, we examine the global atmospheric circulation trends during 1950–2000 and address the following questions. How realistic are the trends simulated by CAM3 when forced with the combined observed evolution of SST forcing and atmosphericradiative forcing? Which components of the simulated
trends are due to SST and which to direct atmosphericradiative forcing, and how are they related? To what

VOLUME 22

extent are the responses to the two types of forcing
additive? How do the trends in the CAM3 simulations
compare with those in CCSM3, and what is the origin of
their differences? We focus on the boreal winter season, December–February (DJF), when the simulated
circulation trends are most similar to their observedcounterparts. Model simulations are not available past
the year 2000, precluding any analysis of trends extending into the twenty-first century.
The rest of the paper is organized as follows. The
model simulations, observational datasets, and methodology are described in section 2. The results are presented in section 3. A summary and discussion are provided in section 4.

2. Data and methods
a. Model and observational datasets
The primary model archive for this study is a set ofsimulations conducted with CAM3 at two horizontal
resolutions: T85, equivalent to 1.48 latitude 3 1.48 longitude; and T42, equivalent to 2.88 latitude 3 2.88 longitude. The physical and numerical methods used in
CAM3 are given in Collins et al. (2006) and references
therein. The model’s mean state and interannual variability are described in Hurrell et al. (2006), Hack et al.
(2006), and Deser et al. (2006).
Four sets of ensemble integrations were performed
with CAM3, labeled according to their forcing characteristics (see Table 1). The ‘‘SST1ATM’’ ensemble isforced by the observed evolution of global SSTs and sea
ice concentrations plus atmospheric chemical composition (greenhouse gases, tropospheric and stratosphericozone, and sulfate and volcanic aerosols) and solar output during 1950–2000. The ‘‘SST’’ ensemble is forced by
the observed evolution of global SSTs and sea ice concentrations during 1950–2000, with atmospheric chemical composition and solar output set at 1990 levels. The‘‘Tropical SST’’ ensemble is identical to the SST ensemble except that SSTs poleward of 208N and poleward of 208S are set to their climatological mean seasonal cycles. The ATM ensemble is forced by the observed evolution of atmospheric chemical compositionand solar output during 1950–2000 (identical to that in
SST1ATM), with SSTs and sea ice concentrations set
to their climatological mean seasonal cycles.
The SST and sea ice concentrations are from thedataset of Hurrell et al. (2008), and the atmosphericforcing fields are given in Meehl et al. (2006) and are
identical to those used for the twentieth-century integrations of CCSM3. The SST1ATM, SST and Tropical
SST ensembles each consist of 5 integrations at T85
resolution and 5 integrations at T42 resolution. Given

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DESER AND PHILLIPSTABLE 1. The forcing characteristics, horizontal resolution, and ensemble size of the CAM3 integrations used in this study.

SST1ATM
SST

Tropical SST

ATM

Forcing characteristics

Horizontal resolution

Ensemble size

Observed evolution of global SSTs, sea ice concentrations, atmosphericchemical composition, and solar output during 1950–2000
Observed evolution of global SSTs and sea ice concentrations during
1950–2000; atmospheric chemical composition and solar output fixedat 1990 levels
Observed evolution of tropical (208N–208S) SSTs during 1950–2000;
SSTs elsewhere and sea ice concentrations set to their climatologicalseasonal cycles; atmospheric chemical composition and solar output
fixed at 1990 levels
Observed evolution of atmospheric chemical composition and solar
output during 1950–2000; global SSTs and sea ice concentrations set
to their climatological seasonal cycles

T42
T85
T42
T85

5
5
5
5

T42
T85

5
5

T42

10

the similarity between the ensemble mean atmospheric circulation trends at T42 and T85 (not shown),
we have averaged the trends from the two model resolutions together to form 10-member ensemble means.
The ATM ensemble consists of 10 integrations at T42
resolution.
In addition to the atmosphere-only model integrations, we analyze a 7-member ensemble of twentiethcentury (1870–1999) simulations with CCSM3 (Collins
et al. 2006), a global coupled climate model run without
flux adjustments. CCSM3 is composed of an atmospheric model (CAM3), an ocean model (the Parallel
Ocean Program), a land model (the Community Land
Model), and a sea ice model (the Community Sea Ice
Model version 4). The CCSM3 simulations are conducted at T85 horizontal resolution and forced with thesame evolution of atmospheric chemical compositionand solar output as the SST1ATM and ATM experiments. The evolution of global mean air and ocean temperatures over the twentieth century from the CCSM3
simulations are documented in Meehl et al. (2006).
We make use of several observational datasets,
including: sea level pressure (SLP) from the International Comprehensive Ocean–Atmosphere Dataset(ICOADS; Worley et al. 2005) on a 28 latitude 3 28
longitude grid; SLP from the Hadley Centre’s Mean
Sea Level Pressure Dataset (HadSLP2; Allan andAnsell 2006) on a 58 latitude 3 58 longitude grid; andSLP and 500-hPa geopotential heights (Z500) from the
National Centers for Environmental Prediction–
National Center for Atmospheric Research (NCEP–
NCAR) 40-Year (50-Year) Reanalysis Project (Kalnay
et al. 1996; Kistler et al. 2001) on a 2.58 latitude 3 2.58
longitude grid. These observational archives differ intheir spatial coverage as well as in their degree and sophistication of data processing: ICOADS is an archiveof surface marine observations containing minimal dataprocessing (e.g., standard quality control procedures but

no filling in of missing data); HadSLP2 is an optimallyinterpolated blended archive of land station records andmarine observations from the ICOADS; and the
NCEP–NCAR 40-Year (50-Year) Reanalysis Project isa state-of-the-art model–data assimilation system. For
the data-sparse high-latitude region of the Southern
Hemisphere, we use station-based SLP indices of thesouthern annular mode (SAM) from an updated versionof Marshall (2003).

b. Methods
We use linear trend analysis as a simple way of characterizing changes in atmospheric circulation that haveoccurred over the period of study as a whole (1950–
2000). This approach is validated using epoch difference analysis [(1981–2000) 2 (1950–61)], which yields
practically identical results (not shown). To computethe linear trends, we first formed monthly anomalies bysubtracting the long-term monthly means based on the
period 1950–2000 from each calendar month. The
monthly anomalies were then averaged over the boreal
winter season (DJF), and the DJF trends computed
using least squares linear regression. The statistical significance of the linear trend values was assessed bymeans of a 2-sided Student’s t test taking into accountserial correlation (von Storch and Zwiers 1999). Thus,
the statistical significances of the trend values areevaluated with respect to interannual variability.
For the ICOADS, which contains substantial amountsof missing data, a trend was computed only if at
least 25% of the winters in a given grid box had data.
To improve the readability of the trend maps based on
the ICOADS, linear interpolation (across gaps not exceeding 5 points in longitude and 3 points in latitude)
and weighted binomial smoothing (9 points in longitude
and 3 points in latitude) were applied to the trend values.

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JOURNAL OF CLIMATEVOLUME 22

FIG. 1. Linear trends [color shading; hPa (51 yr)21] of DJF SLP during 1950–2000 from (left) observations and(right) CAM3 model simulations. Results are shown for 3 observational datasets: (a) HadSLP2, (b) ICOADS, and(c) NCEP–NCAR reanalyses; and 3 sets of CAM3 model simulations: (d) SST1ATM, (e) SST, and (f) ATM.
Values south of 408S are omitted in the left-hand panels because of a lack of reliable observations dating back to1950. Dashed contours indicate trends that are significantly different from zero at the 95% level.

3. Resultsa. SST1ATM versus observations
Observed DJF SLP trends from 1950 to 2000 are
shown in Fig. 1 (left) based on three different datasets(HadSLP2, ICOADS, and NCEP–NCAR reanalyses).
Values south of 408S are omitted because of a lack ofreliable observations dating back to 1950 (Hines et al.
2000; Marshall 2003). The three observational datasets
agree on the overall pattern of SLP trends: negative values
over high latitudes of the Northern Hemisphere and theNorth Pacific and positive values over midlatitudes of theNorthern Hemisphere except the Pacific sector (maxi-

mum amplitudes approximately 4–6 hPa (51 yr)21, andweaker trends over the tropics (negative values over theeastern tropical Pacific and positive values elsewhere). The
trends in the NCEP–NCAR reanalyses are larger thanthose in either the ICOADS or HadSLP2. Regionally, thelargest trends occur over the North Pacific, North Atlantic, Eurasia, and northern Africa: all are significantly different from zero at the 95% level. The negative trend over
the North Pacific is indicative of a deepening of the
Aleutian low, while over the Atlantic–Eurasian sector thenegative trend over high latitudes coupled with the positive trend over midlatitudes is indicative of a positive trend
in the North Atlantic Oscillation (NAO; Hurrell 1995).

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The pattern of SLP trends simulated in the
SST1ATM ensemble mean (Fig. 1d) compares wellwith observations: the pattern correlation is 0.73 with
HadSLP2 and 0.65 with NCEP–NCAR reanalyses
based on area-weighted anomalies north of 408S. In
particular, the model simulates the deepening trend of
the Aleutian low, the positive trend in the NAO (although not statistically significant as it is in observations), and the pattern of trends over the tropics (especially when compared with the HadSLP2 dataset). The
magnitudes of the simulated SLP trends are generally
smaller than observed over the Northern Hemisphere
extratropics, while they are comparable to the HadSLP2 and ICOADS observations but weaker than the
NCEP–NCAR reanalyses over the tropics.
In the Southern Hemisphere, the SST1ATM ensemble mean simulates a zonally symmetric pattern ofpositive SLP trends in midlatitudes (358–558S) andnegative trends at high latitudes (south of 558S), indicative of a positive trend in the SAM (Thompson et al.
2000; Marshall 2003). Although the gridded observational datasets are unreliable for computing SLP trends
south of 408S before the 1970s (see Arblaster andMeehl 2006), a dozen station records may be used todefine the SAM back to 1957 (Marshall 2003). Six stations are located within the midlatitude (378–478S) center of action of the SAM and six within the highlatitude center of action (658–718S). The observed SLP
trends for the mid- and high-latitude centers of action
during the period 1957–2000 are 1.6 hPa (44 yr)21 and22.5 hPa (44 yr)21, respectively; statistically significant
at the 95% confidence level. The simulated trends in
the SST1ATM ensemble mean are in good agreementwith the station-based observational estimates, withvalues of 1.4 hPa (44 yr)21 and 23.6 hPa (44 yr)21 for themid- and high-latitude centers of action, respectively,
based on zonally averaged SLP anomalies during the period 1957–2000 (significant at the 95% confidence level).
We defer discussion of the SLP trends simulated in the
SST and ATM ensemble means (Figs. 1d,f) to section 3b.
The observed trends in Z500 from the NCEP–NCAR
reanalyses are shown in Fig. 2a (as for SLP, values southof 408S are omitted because of a lack of reliable data). In
the extratropics, the pattern of Z500 trends is similar tothat of SLP, consistent with the equivalent barotropic
structure of the annular modes and the Aleutian low,which is associated with the Pacific–North American(PNA) pattern in the middle and upper troposphere(Trenberth and Hurrell 1994). In the tropics, the trends
in Z500 [and (Z1000 2 Z500) thickness; not shown] arepositive everywhere, reflecting a general warming of thelower troposphere. The pattern of Z500 trends simu-

401

FIG. 2. Same as in Fig. 1, but for 500-hPa geopotential
height [Z500; m (51 yr)21]. (a) NCEP–NCAR reanalyses, (b)SST1ATM, (c) SST, and (d) ATM.

lated by the SST1ATM ensemble mean (Fig. 2b)compares well with observations: the pattern correlation is 0.76 with the NCEP–NCAR reanalyses based on
area-weighted anomalies north of 408S. Like SLP, theZ500 trend magnitudes are underestimated in the402

JOURNAL OF CLIMATEVOLUME 22(Z500). Analogous histograms of the pattern correlations for each of the 10 SST1ATM ensemble memberswith observations (HadSLP2 for SLP and NCEP–
NCAR reanalysis for Z500) exhibit a larger spread thanthose with the model’s ensemble mean, but the majority(70%–80%) of ensemble members exhibit pattern correlations exceeding 0.5 (Figs. 4c,d). The highest patterncorrelation between a single ensemble member andobservations is 0.76 for SLP and 0.79 for Z500, and thelowest is 20.26 for SLP and 0.20 for Z500.

b. SST forcing versus direct atmospheric radiative
forcing
FIG. 3. Linear trends of DJF SLP during 1950–2000 (color shading) superimposed upon the climatological mean SLP distribution(contours) from the CAM3 SST1ATM ensemble mean. The climatology is contoured every 4 hPa, with dashed contours for values #1008 hPa.

model ensemble mean compared to the NCEP–NCAR
reanalyses.
To provide a context for the circulation changes, thesimulated SLP trend (color shading) is superimposed
upon the climatological mean SLP distribution (contours) from the SST1ATM ensemble mean in Fig. 3.
The negative SLP trend over the North Pacific reflectsa deepening and eastward extension of the Aleutian
low, and a weakening of the subtropical high off thecoast of California. The negative (positive) SLP trend
over the northern (central) North Atlantic extending
eastward over Europe and North Africa reflect an insitu strengthening and a slight northward shift of themean Icelandic low and Azores high. In contrast, in theSouthern Hemisphere, positive SLP trends are locatedalong the southern flanks of the subtropical highs
within the zone of maximum mean meridional SLP gradient. Coupled with negative SLP trends over the meanpolar low, this trend pattern is indicative of a poleward
shift and intensification of the Southern Hemispheremidlatitude westerlies. In the tropics, the SLP trends
are indicative of a weakening of the mean zonal pressure gradient across the Indo-Pacific (e.g., a weakeningof the Walker circulation). Similar relationships between the trend and climatology are found for Z500
from the model simulations and for SLP and Z500 fromobservations (not shown).
To give an idea of the sampling distribution of thesimulated trend patterns in the SST1ATM ensemble,
histograms of the trend pattern correlations between
each ensemble member and the ensemble mean are
shown in Figs. 4a,b for SLP and Z500, respectively. Outof 10 ensemble members, 9 exhibit pattern correlations
with the ensemble mean that exceed 0.6 (0.7) for SLP

Given the overall agreement between the observed
and simulated SST1ATM ensemble mean trend patterns for both SLP and Z500, we proceed to examine
the relative contributions of SST forcing and direct atmospheric radiative forcing in driving the simulated
trends (Figs. 1e,f and 2c,d). It is evident that the different forcing agents produce distinctive spatial patternsof atmospheric circulation response. Direct atmospheric radiative forcing is responsible for the zonallysymmetric circulation response over the Southern
Hemisphere extratropics (e.g., the positive trend in theSAM) and to a lesser extent the positive trend in the
NAO, although the centers of action are northwardshifted with respect to the canonical pattern. Sea surface temperature forcing produces the deepening of the
Aleutian low and the SLP trend pattern over the tropics(negative values in the eastern Pacific and positive values elsewhere). Both forcing agents contribute to thezonally uniform positive Z500 trend in the tropics, withthe contribution from sea surface temperature approximately 3 times larger than that from atmospheric radiative forcing. Sea surface temperature trends also
lead to a ridge response over the Pacific sector of thehigh-latitude Southern Ocean.
The distinctive patterns of atmospheric circulation
response due to sea surface temperature forcing anddirect atmospheric radiative forcing contribute about
equally to the global pattern of circulation trends in the
SST1ATM simulation, and by inference, nature. The
pattern correlations between the SLP (Z500) trends in
the SST1ATM ensemble mean and those in the SST
and ATM ensemble means are 0.54 and 0.52 (0.75 and0.44), respectively. In addition to producing distinctive
circulation trend responses, sea surface temperature
forcing and direct atmospheric radiative forcing also
partially offset one another in certain regions: for example, the central North Pacific, tropical eastern Pacific(SLP only), pan-Atlantic, and Southern Ocean.
Figure 5 summarizes the impacts of sea surface temperature forcing and direct atmospheric radiative forcing

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DESER AND PHILLIPS

403

FIG. 4. Histograms of the pattern correlations of DJF trends during 1950–2000 between eachindividual ensemble member and the ensemble mean from CAM3 SST1ATM experimentsfor (a) SLP and (b) Z500, and between each individual ensemble member from CAM3
SST1ATM and observations north of 408S for (c) SLP and (d) Z500.

upon four regional circulation patterns: the Aleutian low, the NAO, the SAM, and the Southern Oscillation. The simulated trends from observations and themodel ensemble means (labeled according to their forcing characteristics) are denoted by vertical bars, withdark shading indicating trends that are significant at the
95% confidence level; the trends from the individual
ensemble members are denoted by plus signs. The regional circulation patterns are defined as follows. The
Aleutian low is given by the ‘‘North Pacific index’’(NPI; Trenberth and Hurrell 1994), which is the average SLP over the domain (308–658N, 1608E–1408W);
the NAO is the SLP difference between (558–908N,
408W–658E) and (308–558N, 408W–658E); the SAM is
the difference in normalized zonally averaged SLP between the latitude bands 378–478S and 658–718S; andthe Southern Oscillation index (SOI) is the SLP difference between the eastern tropical Pacific (208S–08,
1608–808W) and western tropical Pacific (208S–08,
808E–1808) and is used as a proxy for the strength of theWalker circulation.
The observed NPI trend [22.6 hPa (51 yr)21] is well
reproduced by the SST1ATM ensemble mean [22.2hPa (51 yr)21], and both are statistically significant at
the 95% level (Fig. 5a). Three of the ensemble members exhibit near-zero trends, while the remaining 7
exhibit trends between 22 and 24 hPa (51 yr)21. The

statistically significant NPI trend in the SST ensemble
mean [23.9 hPa (51 yr)21] is partially offset by thetrend in the ATM ensemble mean [0.7 hPa (51 yr)21;
not significant], and there is no overlap between any of
the individual ensemble members of SST and ATM.
The observed SAM trend [5.5 hPa (51 yr)21; notethat this is based on station data during 1957–2000, rescaled to units of (51 yr)21] is also well simulated by the
SST1ATM ensemble mean [3.7 hPa (51 yr)21], andboth are statistically significant at the 95% level. However, there is a sizeable spread among the individual
SST1ATM ensemble members, ranging from 1 to 8hPa (51 yr)21 (Fig. 5c). The statistically significant
SAM trend in the ATM ensemble mean [5.3 hPa (51yr)21] is partially offset by the trend in the SST ensemble mean [20.9 hPa (51 yr)21; not significant], withvery little overlap between any of the individual ensemble members of SST and ATM.
The results for the NAO and SOI are less robust thanthose for the NPI and SAM: only the observed NAO
trend is statistically significant at the 95% level, while
the observed SOI trend and none of the model ensemble mean trends are significant. The observed NAO
and SOI trends [4.3 and 20.65 hPa (51 yr)21, respectively] are underestimated by the SST1ATM ensemble
mean [1.7 and 20.36 hPa (51 yr)21, respectively], andthere is a large spread among the individual ensemble

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FIG. 5. Regional circulation trends [hPa (51 yr)21] based on DJF SLP during 1950–2000 for
the (a) NPI, (b) NAO, (c) SAM, and (d) SOI from (left to right in each panel) observations(OBS), the sum of CAM SST and CAM3 ATM, CAM3 SST 1ATM, CAM3 SST, CAM3
ATM, the sum of CAM3 Tropical SST and CAM3 ATM, and CCSM3. For the model simulations, bars denote ensemble mean trends and the plus signs denote individual ensemble
member trends. Dark (light) shaded bars indicate trends that are (are not) significantly different from zero at the 95% confidence level.

member trends (Figs. 5b,d). The ATM ensemble mean
NAO trend is near zero because of the northward shiftof the response relative to that in the SST1ATM ensemble mean (e.g., Fig. 1d versus Fig. 1f), and there iscomplete overlap between the NAO trends simulated
in the individual ensemble members of SST and ATM.
The SST ensemble mean SOI trend is nearly identicalto that in SST1ATM, while the contribution from theATM ensemble mean is near zero. Some overlap occurs
in the SOI trends between the individual ensemble
members of SST and ATM.

c. Linearity of the response to SST and ATM
forcing
The degree to which the atmospheric circulation
trend responses to sea surface temperature forcing anddirect atmospheric radiative forcing are additive may
be assessed by comparing the SST1ATM ensemble
mean with the sum of the SST and ATM ensemblemeans (Fig. 6). Overall, there is a high degree of linearity as evidenced by the similarity of their spatial
distributions (pattern correlations of 0.87 and 0.92 for
SLP and Z500, respectively) and amplitudes (patternregressions of 1.06 for both SLP and Z500). Regionally,
the amplitudes of the tropical circulation trends arehighly linear (see also the SOI in Fig. 5d), while thecirculation trends over the North Pacific and extratropical Southern Hemisphere are approximately 20%–
30% larger in the sum of the SST and ATM ensemblemeans than in the SST1ATM ensemble mean (reflected also in the NPI and SAM in Figs. 5a,c). The
NAO circulation trends in the SST1ATM ensemble
mean are not well reproduced by adding the trends
from the SST and ATM ensemble means (see also Fig.
5b).
Another view of the degree of linearity of the atmospheric circulation trend responses to sea surface temperature forcing and direct atmospheric radiative15 JANUARY 2009

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405

FIG. 6. Linear trends of DJF SLP [left; hPa (51 yr)21] and Z500 [right; m (51 yr)21] during
1950–2000 from (a), (b) CAM3 SST1ATM; (c), (d) the sum of CAM3 SST and CAM3 ATM;and (e), (f) the sum of CAM3 Tropical SST and CAM3 ATM. Dashed contours indicate trends
that are significantly different from zero at the 95% level.

forcing is given in Fig. 7, which shows the SLP and Z500
trends obtained by subtracting the SST ensemble meanfrom the SST1ATM ensemble mean. Ostensibly attributable to atmospheric radiative forcing, these trends
show similarities and differences with their ATM counterparts (Figs. 1f and 2d). While the SAM and tropicaltrends are similar to those in the ATM ensemble mean,
there are some differences over the extratropicalNorthern Hemisphere: in particular, the negative
trends over the northern center of action of the NAO
are considerably larger in magnitude, and the positive
trends over the North Pacific are stronger and ofgreater spatial extent. The extratropical Northern
Hemisphere circulation trends obtained using the difference between the SST1ATM and SST experiments
exhibit a more zonally symmetric appearance thanthose obtained from the ATM ensemble directly,
especially for Z500. These results are robust for boththe T42 and T85 ensemble means individually (notshown).

d. The role of tropical SSTs
The pattern of atmospheric circulation trends simulated by the SST ensemble is reminiscent of that associated with the warm phase of ENSO (not shown but
see, e.g., Deser et al. 2006). This suggests that the trend
response in the SST ensemble originates from sea surface temperature changes in the tropics. To examine
this possibility, we compare the SLP trends simulatedby the Tropical SST and SST ensemble means in Fig. 8.
There is a high degree of correspondence between the
two trend distributions, with a pattern correlation (regression) coefficient of 0.85 (0.83). Similar results arefound for the Z500 trends (not shown). It is noteworthy
that the extratropical SLP trends, in particular the statistically significant trends over the North Pacific andSouthern Ocean, are very similar between the two setsof experiments, indicating that they originate from thetropical portion of the sea surface temperature forcing.
The main discrepancy between the SLP trend responses

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FIG. 7. Linear trends of DJF (a) SLP [hPa (51 yr)21] and (b) Z500 [m (51 yr)21] during
1950–2000, obtained by subtracting the CAM3 SST ensemble mean from the CAM3
SST1ATM ensemble mean. Dashed contours indicate trends that are significantly different
from zero at the 95% level.

to global versus tropical sea surface temperaturechanges occurs over the North Atlantic, Arctic, andSiberia (e.g., the NAO region) where the responses areopposite in sign, although neither is statistically significant. The tropical control of sea surface temperatureinduced global atmospheric circulation trends has beenpreviously highlighted by Schneider et al. (2003), Schubert et al. (2004), and Huang et al. (2005), among others.
The basic mechanism by which tropical sea surface
temperature variations impact the extratropical atmo-

spheric circulation is through the meridional dispersionof planetary Rossby waves forced by latent heat releasein tropical precipitation, along with attendant feedbacks between the mean flow anomalies and transient
eddy activity in midlatitudes (e.g., Hoskins and Karoly
1981; Trenberth et al. 1998). The tropical precipitation
trends simulated by the SST and Tropical SST ensemble means show nearly identical patterns, with positive values along the equatorial Pacific and negativevalues directly to the north as well as over the northeastern portion of South America (Fig. 8). Except for

FIG. 8. Linear trends of DJF (left) SLP [hPa (51 yr)21] and (right) precipitation [mm day21(51 yr)21] during 1950–2000 from (top) CAM3 SST and (bottom) CAM3 Tropical SST.
Dashed contours indicate trends that are significantly different from zero at the 95% level.

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the reduced magnitudes within the southern Pacificconvergence zone (SPCZ), the simulated precipitation
trends resemble those in nature as inferred from trends
in marine cloudiness and surface wind convergence(Deser and Phillips 2006) and also those that occur inassociation with the warm phase of ENSO (Deser et al.
2006). Although not apparent from Fig. 8 because of
the color scheme used, the SST and Tropical SST ensembles also reproduce the observed pattern of precipitation trends over North and South America (see, e.g.,
Deser et al. 2004), consistent with the results of Huang
et al. (2005).
Given the similarity between the circulation responses in the SST and Tropical SST ensembles as wellas the high degree of linearity of the responses to oceanic and atmospheric radiative forcing, it follows thatthe circulation trend in the SST1ATM ensemble mean
may be reconstructed from the sum of the individual
circulation trend responses in the Tropical SST and
ATM ensemble means, with pattern correlations of 0.89
for SLP and 0.91 for Z500 (Figs. 6e,f). The goodness of
fit of this reconstruction also holds for the regional circulation indices (Fig. 5).

e. Coupled model experiments (CCSM3)
It is of interest to examine the atmospheric circulation trends simulated by CCSM3 and compare themwith those from the CAM3 SST1ATM ensemble,
given that both sets of model integrations are forced
with identical atmospheric radiative changes. We focus
on the CCSM3 ensemble mean to isolate primarily theanthropogenically forced component of the simulated trend (recall that the contributions of solar andvolcanic forcing are small compared to those ofgreenhouse gases, ozone, and sulfate aerosols overthe second half of the twentieth century; Meehl et al.
2003). Thus, any discrepancies in the atmosphericcirculation trends between the CCSM3 ensemble
mean and CAM3 SST1ATM (and by inference,nature) are due to differences in their SST evolutions, either as a result of natural (internal) variability
in the real climate system, deficiencies in CCSM3’s
sea surface temperature response to atmospheric radiative forcing, residual internal variability in the 7-member CCSM3 ensemble mean, or a combination of theabove.
The SLP and Z500 trends from the CCSM3 ensemble
mean exhibit similarities and differences with their
CAM3 SST1ATM ensemble mean counterparts (Fig.
9). Similarities include statistically significant trends
over the Southern Hemisphere extratropics (negativevalues over high latitudes coupled with positive values

407

over midlatitudes, indicative of a positive trend in the
SAM; see also Fig. 5c) and statistically significant Z500
trends in the tropics reflecting a warming of the lower
troposphere as indicated by an increase in 500–1000hPa geopotential thickness (not shown). The most notable discrepancies are the SLP trends over the tropicaleastern Pacific and Atlantic and the SLP and Z500
trends over the North Atlantic–Eurasian sector, which
are of opposite sign in the two models. As a result, the
SOI and NAO trends in CCSM3 are of opposite polarity to those in CAM3 (and nature), although neither isstatistically significant (Fig. 5). In addition, the deepening of the Aleutian low is about a factor of 3 weakerand not statistically significant in CCSM3 compared to
CAM3 (see also Fig. 5a). Overall, the pattern correlation between the ensemble mean CCSM3 and CAM3
SST1ATM trends is 0.47 for SLP and 0.64 for Z500.
The pattern correlation between the CCSM3 ensemble
mean and observations is 0.19 for SLP and 0.37 for
Z500 based on data north of 408S; considerably weaker
than that between the CAM3 SST1ATM ensemble
mean and observations (0.73 for SLP and 0.76 forZ500).
Given that both sets of model integrations (CCSM3
and CAM3 SST1ATM) are driven with identical atmospheric radiative forcings, differences in their SST
trends are likely responsible for the regional discrepancies in their circulation trends. Indeed, the regions
where the two model simulations differ are all areas
where SST forcing, and in particular tropical sea surface temperature forcing, is responsible for the circulation trends in CAM3 SST1ATM and by inference nature, with the exception of the North Atlantic–Eurasian
sector where the trends are not statistically significant
to begin with. Tropical SST trends in CCSM3 are more
spatially uniform and weaker in amplitude than in nature, with an average magnitude of 0.38–0.48C (51 yr)21
in the model compared to ;0.88C (51 yr)21 in the tropical Indian and Atlantic and ;1.28C (51 yr)21 in theeastern Pacific in the observations (Figs. 9e,f). The associated trends in tropical precipitation are considerably smaller in magnitude in CCSM3 compared tothose in CAM3 (Figs. 9g,h), which may account for the
weaker NPI and SOI trends in the coupled model compared to CAM3 and nature. We have verified that the
weak tropical precipitation response in CCSM3 is indeed due to the model’s tropical SST simulation: forcing CAM3 with the time-evolving tropical sea surface
temperatures from the CCSM3 ensemble mean results
in a tropical precipitation trend that is very similar inboth amplitude and spatial distribution to that in
CCSM3 (not shown).

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FIG. 9. Linear trends during 1950–99 of DJF (a), (b) SLP [hPa (51 yr)21]; (c), (d) Z500 [m (51 yr)21]; (e), (f) sea surface temperature [8C(51 yr)21]; and (g), (h) precipitation [mm day21 (51 yr)21] from (left) the CCSM3 ensemble mean and (right) the CAM3 SST1ATM
ensemble mean. Color scales are shown to the right; dashed contours indicate trends that are significantly different from zero at the 95%
level. Note that sea surface temperature trends in regions of sea ice cover in the Arctic are actually surface temperature trends.

4. Summary and discussion
We have investigated the relative roles of observed
sea surface temperature forcing and direct atmosphericradiative forcing (due to observed changes in wellmixed greenhouse gases, tropospheric and stratospheric ozone, sulfate and volcanic aerosols, and solar
output) of global DJF atmospheric circulation trends
over the second half of the twentieth century by meansof ensemble simulations with CAM3, a state-of-the-art

AGCM. The model’s 10-member ensemble mean simulates well the observed patterns of SLP and Z500 trends
over the globe when forced with the observed evolutionof sea surface temperature and atmospheric radiative
forcings combined, lending credence to our approach.
Individually, sea surface temperature forcing and direct
atmospheric radiative forcing produce distinctive circulation responses that contribute about equally to theglobal pattern of circulation trends. These distinctive
circulation responses are approximately additive and15 JANUARY 2009

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409

FIG. 10. Linear trends during 1950–2000 of DJF zonally averaged (left) temperature and(right) geopotential height as a function of latitude and pressure from the CAM3 ATM
ensemble mean.

partially offsetting. In particular, atmospheric radiativechanges directly drive the strengthening and poleward
shift of the midlatitude westerly winds in the Southern
Hemisphere, manifest as a positive trend in the SAM,
and to a lesser extent may contribute to the corresponding feature in the Northern Hemisphere (e.g., a positive
trend in the NAO albeit with a northward shift). On theother hand, sea surface temperature changes (specifically those in the tropics) are responsible for the intensification of the Aleutian low and weakening of thetropical Walker circulation. Our findings are generally
consistent with those of previous studies, and provide
additional insight by clarifying the individual roles ofsea surface temperature forcing and direct atmosphericradiative forcing and by placing the regional circulation
trends into a global context.
The positive trend in the SAM in recent decades has
been attributed to increasing greenhouse gas concentrations and stratospheric ozone depletion in coupled
ocean–atmosphere modeling studies (Arblaster andMeehl 2006; Cai and Cowan 2007). Approximately
50%–70% of the SAM trend in Southern Hemispheresummer (DJF) is estimated to be due to ozone depletion, with increases in greenhouse gases being of lesser
importance (Arblaster and Meehl 2006; Cai and Cowan2007). These and other coupled modeling studies do not
distinguish between the direct impact of atmosphericradiative forcing and the indirect effect due to alteredsea surface temperatures upon the trend in the SAM. In
this study, we have shown explicitly that the direct effectof atmospheric radiative forcing is responsible for the
positive trend in the SAM during the second half of thetwentieth century, with sea surface temperature forcing
counteracting this trend (extratropical sea surface tem-

perature forcing acts as a weak positive feedback, but it isoverwhelmed by the negative feedback due to tropicalsea surface temperature forcing; not shown).
The impact of direct atmospheric radiative forcing on
the SAM can be understood from the structure of thezonally averaged temperature trends in the ATM ensemble mean (Fig. 10a). The troposphere exhibits aweak warming trend [,0.58C (51 yr)21] north of 608S
while the stratosphere exhibits a strong cooling trend at
all latitudes, with amplitudes ; 22.58C (51 yr)21 near
10 hPa and maximum values of 25.58C (51 yr)21 at 100
hPa over the South Pole. Note that the stratosphericcooling is stronger and extends to lower altitudes over
the South Pole compared to the North Pole. The tropospheric warming and stratospheric cooling arelargely a radiative response to greenhouse gas increases, with the enhanced cooling of the lower stratosphere at high latitudes of the Southern Hemisphereradiatively induced by ozone depletion (Randel andWu 1999; Langematz et al. 2003; Shine et al. 2003).
These radiatively induced temperature trends give riseto circulation changes via thermal wind balance, manifest as positive height trends in the troposphere andnegative height trends in the stratosphere: the latterextend to the surface south of 608S (Fig. 10b). A similar
downward extension of negative stratospheric height
trends to the surface over the southern polar cap since
1979 was shown to be a consequence of stratosphericozone depletion by Thompson and Solomon (2002,
2005). The lack of a Northern Hemisphere counterpartof this feature may be due in part to the smaller ozone
depletion over the North Pole compared to the South
Pole (Solomon et al. 2007) coupled with a lack of sunlight in boreal winter.

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FIG. 11. Linear trends during 1950–2000 of DJF precipitation [mm day21 (51 yr)21] from (a)CAM3 SST ensemble mean and (b) CAM3 ATM ensemble mean. Dashed contours indicate
trends that are significantly different from zero at the 95% level.

There is a lack of consensus regarding the origin of
the positive trend in the NAO. Coupled ocean–
atmosphere models (but not CCSM3) collectively indicate a weak upward trend due to atmospheric radiative
forcing (direct and indirect), although this trend is notstatistically significant compared to the models’ internalvariability (Gillett et al. 2005); while atmospheric
GCMs forced with observed sea surface temperatures
yield mixed results, with some indicating a small upward trend due to SST changes in the tropical Indian
Ocean (Hoerling et al. 2004; Hurrell et al. 2004) or
North Atlantic (Rodwell et al. 1999; Mehta et al. 2000),
and others finding no significant oceanic impact but adominant role for intrinsic atmospheric variability(Schneider et al. 2003; Bracco et al. 2004). None of the
NAO trends in the CAM3 ensemble means are statistically significant, and all exhibit large spread among
the individual ensemble members as well as nonlinear
behavior with respect to the contributions of sea surface temperature and direct atmospheric radiative forcing. Direct atmospheric radiative forcing may play arole, but the centers of action of the NAO-like response
are northward shifted with respect to the canonical pattern, precluding a more definitive statement. Thus, theCAM3 results regarding the origin of the positive trend
in the NAO remain inconclusive.
The atmospheric circulation responses to sea surface
temperature and direct atmospheric radiative forcing
tend to be partially offsetting. To explore the origin of
this tendency, we compare the tropical precipitation
trends in the SST and ATM ensemble means (Fig. 11).
Atmospheric radiative forcing produces a general drying trend over the tropical oceans, particularly over thewestern Indian and Atlantic Oceans, South China Sea,and SPCZ. This drying trend is consistent with the stabilizing influence of atmospheric radiative forcing upontropospheric lapse rates, which would inhibit deep con-vection. The general decrease in tropical precipitation
due to atmospheric radiative forcing partially counteracts the increase due to sea surface temperature forcing, although there are differences in the spatial patterns of the two rainfall trends (Fig. 11). The radiatively
induced tropical drying trend may contribute to theweak positive SLP trends over the North Pacific andeastern tropical Pacific in the ATM ensemble (recall
Fig. 1f), providing a mechanism for the partial opposition of sea surface temperature and atmospheric radiative forcing upon the NPI and SOI. Additional experiments using the tropical atmospheric heating response
from CAM3 ATM to force CAM3 are needed to fully
test this idea.
The counteracting impacts of sea surface temperature and atmospheric radiative changes upon the SAM
may also derive from the sea surface temperatureinduced tropical precipitation trends. In particular,
previous work has shown that tropical precipitation
anomalies associated with the warm phase of ENSO
force a negative SAM index via changes in the refractivity and rate of dissipation of equatorwardpropagating transient waves (Chang 1995; Hou and
Molod 1995; Robinson 2002; Seager et al. 2003;
L’Heureux and Thompson 2006). The same mechanism
may apply to the impact of tropical precipitation trends
upon the SAM in the SST ensemble, given that they
resemble those during the warm phase of ENSO (recall
Fig. 8). We interpret the drying (wetting) trend over thesouthern ocean mid- (high) latitudes in the ATM ensemble to be a result of the SAM-related circulation
changes, not a cause.
With our experimental design, we are unable to address the relative contributions of natural and anthropogenic forcing of observed atmospheric circulation
trends during the second half of the twentieth century.
However, comparison of the trends simulated by

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CAM3 and CCSM3, a fully coupled ocean–atmosphere
GCM driven by the identical atmospheric radiative
forcings as CAM3, may shed some light upon this issue.
Recall that the CCSM3 ensemble mean reflects primarily the coupled model’s response to the anthropogenic
component of atmospheric radiative forcing since internally generated variability will be reduced upon averaging across all 7 ensemble members. The amplitude of
CCSM3’s ensemble mean tropical Indo-Pacific sea surface temperature warming trend was shown to be;30% of observed, and the structure is more spatially
uniform than that in nature. As a result, the tropicalprecipitation trend response is considerably weaker in
the CCSM3 ensemble mean compared to the CAM3
SST1ATM ensemble mean, which in turn may explain
the diminished amplitudes of the SOI and NPI trend
responses in CCSM3 compared to CAM3 (and nature).
One possible interpretation of these results is that the
portion of the observed tropical Indo-Pacific SST
warming trend in excess of the simulated warming
trend in the CCSM3 ensemble mean is due to natural(unforced) variability, assuming that the CCSM3 ensemble mean accurately portrays the tropical sea surface temperature response to atmospheric radiative
forcing. Taking this interpretation a step further, one
may infer that the tropical precipitation trends simulated in CAM3 SST1ATM result almost entirely fromthe natural component of the observed tropical sea surface temperature trends since the tropical rainfall response to the forced SST trends in CCSM3 is negligible(recall Fig. 9). This line of reasoning suggests that atmospheric teleconnection patterns forced by tropicalsea surface temperature changes are also due to naturalvariability: in particular, the negative trend in the SOI
indicative of a weakening of the tropical Walker circulation, and the deepening of the Aleutian low (recall
Fig. 8c). Additional support for these conjectures
comes from century-long records of tropical IndoPacific climate indices and the NPI, which show evidence for coherent multidecadal cycles throughout thetwentieth century of which the apparent trend since1950 is a part (Minobe 1997; Zhang et al. 1997; Deser et
al. 2004).
Another valid interpretation is that CCSM3 is deficient in its tropical sea surface temperature and precipitation responses to atmospheric radiative forcing.
However, tropical Indo-Pacific sea surface temperatureand precipitation trends of similar magnitude to thosein the CCSM3 ensemble mean are found in a multimodel ensemble mean derived from 10 different coupled
ocean–atmosphere GCMs (excluding CCSM3) fromthe Third Coupled Model Intercomparison Project(CMIP3) of the World Climate Research Program (andlike CCSM3, the multimodel ensemble mean exhibits
negligible Aleutian low and Southern Oscillation responses; not shown). Note that each coupled model wasforced with the observed history of atmospheric radiative changes, similar to the CCSM3 simulations. Meehl
et al. (2009) also conclude that both natural variability
and anthropogenic forcing have contributed to the observed tropical Pacific sea surface temperature trends
during the second half of the twentieth century basedon a comparison of forced and unforced coupled model
simulations. Although these results are suggestive, additional evidence is needed to clarify the relative rolesof natural and anthropogenic forcing of observed tropical sea surface temperature and precipitation trends
since 1950.
The results of this study highlight the importance ofboth sea surface temperature and atmospheric radiativechanges in forcing global atmospheric circulation trends
during 1950–2000. Accurate simulation of both the atmospheric and oceanic responses to increasing greenhouse gases is thus crucial for accurate projections of
future atmospheric circulation changes. In addition, theongoing role of natural sea surface temperature variability must be taken into account when assessing future climate projections.
Acknowledgments. We thank Drs. Grant Branstator,
Jian Lu, Jerry Meehl, and Ed Schneider for helpful
discussions during the course of this study, and Drs.
Mike Alexander, Jim Hurrell, and the anonymous reviewers for their constructive reviews of the manuscript. We thank Drs. Shang-Ping Xie and Yan Du for
supplying the CMIP3 output. The authors gratefully acknowledge support from the NOAA CLIVAR program.
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