Clim Dyn (2012) 38:527–546
DOI 10.1007/s00382-010-0977-x

Uncertainty in climate change projections: the role of internalvariability
Clara Deser • Adam Phillips • Vincent Bourdette •Haiyan Teng

Received: 30 July 2010 / Accepted: 18 December 2010 / Published online: 31 December 2010
Ó The Author(s) 2010. This article is published with open access at Springerlink.com

Abstract Uncertainty in future climate change presents a
key challenge for adaptation planning. In this study, uncertainty arising from internal climate variability is investigated using a new 40-member ensemble conducted with the
National Center for Atmospheric Research Community
Climate System Model Version 3 (CCSM3) under the SRESA1B greenhouse gas and ozone recovery forcing scenarios
during 2000–2060. The contribution of intrinsic atmospheric variability to the total uncertainty is further examined using a 10,000-year control integration of theatmospheric model component of CCSM3 under fixedboundary conditions. The global climate response is characterized in terms of air temperature, precipitation, and sea
level pressure during winter and summer. The dominantsource of uncertainty in the simulated climate response at
middle and high latitudes is internal atmospheric variability
associated with the annular modes of circulation variability.
Coupled ocean-atmosphere variability plays a dominant rolein the tropics, with attendant effects at higher latitudes via
atmospheric teleconnections. Uncertainties in the forced
response are generally larger for sea level pressure than
precipitation, and smallest for air temperature. Accordingly,
forced changes in air temperature can be detected earlier andwith fewer ensemble members than those in atmosphericcirculation and precipitation. Implications of the results for
detection and attribution of observed climate change and for
multi-model climate assessments are discussed. Internal
C. Deser (&)  A. Phillips  V. Bourdette  H. Teng
Climate and Global Dynamics Division, National Center
for Atmospheric Research, Boulder, CO, USAe-mail: cdeser@ucar.edu
V. BourdetteCentre Européen de Recherche et de Formation Avancée en
Calcul Scientifique (CERFACS), Toulouse, France

variability is estimated to account for at least half of theinter-model spread in projected climate trends during
2005–2060 in the CMIP3 multi-model ensemble.
Keywords Climate change  Uncertainty  Annularmodes  Coupled climate models  Climate detection andattribution

1 Introduction
Characterizing and quantifying uncertainty in climatechange projections is of fundamental importance not only
for purposes of detection and attribution, but also for strategic approaches to adaptation and mitigation. Uncertainty
in future climate change derives from three main sources:
forcing, model response, and internal variability (e.g.,
Hawkins and Sutton 2009; Tebaldi and Knutti 2007).
Forcing uncertainty arises from incomplete knowledge ofexternal factors influencing the climate system, including
future trajectories of anthropogenic emissions of greenhouse gases (GHG), stratospheric ozone concentrations,land use change, etc. Model uncertainty, also termed
response uncertainty, occurs because different models may
yield different responses to the same external forcing as aresult of differences in, for example, physical and numericalformulations. Internal variability is the natural variability of
the climate system that occurs in the absence of external
forcing, and includes processes intrinsic to the atmosphere,
the ocean, and the coupled ocean-atmosphere system.
Internal atmospheric variability, also termed ‘‘climate
noise’’ (e.g., Madden 1976; Schneider and Kinter 1994;
Wunsch 1999; Feldstein 2000), arises from non-linear
dynamical processes intrinsic to the atmosphere. Although
the atmosphere contains little memory beyond a few weeks,

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it exhibits long-time scale variability characteristic of arandom stochastic process. Low frequency variability also
arises from processes internal to the coupled ocean-atmosphere system via dynamic and thermodynamic interactions.
Thermodynamic coupling between the atmosphere andupper ocean mixed layer produces slow climate fluctuations via the ocean’s integration of atmospheric ‘‘white
noise’’ turbulent heat flux forcing (e.g., Frankignoul andHasselmann 1977; Yukimoto et al. 1996; Barsugli andBattisti 1998; Deser et al. 2003; Dommenget and Latif 2008;
Clement et al. 2010). Inclusion of dynamical ocean processes produces additional types of low-frequency coupledvariability including wind-driven ocean-gyre fluctuations
that have been found to play a role in the ‘‘Pacific Decadal
Oscillation’’ (Mantua et al. 1997; Schneider and Miller
2001; Schneider and Cornuelle 2005; Kwon and Deser 2007;
Alexander 2010). Finally, stochastic atmospheric forcing ofinternal oceanic variability may contribute to low-frequency
climate fluctuations: for example variations in the Atlantic
thermohaline circulation may underlie the ‘‘Atlantic Multidecadal Oscillation’’ (e.g., Delworth et al. 1993).
The unprecedented assemblage of climate model projections from the World Climate Research Programme’s(WCRP) Coupled Model Intercomparison Project Phase 3(CMIP3) archive (Meehl et al. 2007) provides a unique
opportunity for estimating uncertainty in climate change.
This archive, consisting of forced twentieth and twenty-first
century integrations from 23 coupled ocean-atmospheremodels, forms the basis for much of the International Panelon Climate Change (IPCC) Fourth Assessment Report(AR4) of Working Group I (Solomon et al. 2007). Uncertainty, as estimated by the spread of the responses across theCMIP3 ensemble relative to the ensemble mean response,
has been assessed for a number of climate variables,
including air temperature, precipitation and the large-scaleatmospheric circulation (see Hegerl et al. 2007; Meehl et al.
2007; and references therein). These uncertainties, as wellas those based on long model control integrations, have also
been used for estimating the contribution of external forcing
to observed climate changes over the twentieth century(Hegerl et al. 2007 and references therein). Recently,
Hawkins and Sutton (2009) used the CMIP3 archive toquantify the relative contributions of each source of
uncertainty for projected decadal-scale changes in global
mean air temperature and precipitation over the twenty-first
century. They found that model (forcing) uncertainty
dominates before (after) *2040, while internal variability
plays a significant role for interannual air temperaturechanges before *2010. A follow-up study by Hawkins andSutton (2010) for regional-scale precipitation found thatinternal variability is the dominant source of uncertainty for
decadal-scale changes in the first few decades, with model
variability becoming dominant thereafter. In both studies,123

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internal variability was defined as the residual from a 4thorder polynomial fit to the regional or global mean timeseries for each model.
An underlying assumption of studies based on the
CMIP3 archive is that the multi-model mean response toexternal forcing yields a more robust estimate of the forced
climate signal than the response of any single model due tothe reduction in uncertainty associated with model andinternal variability (e.g., Tebaldi and Knutti 2007). However, this assumption has been difficult to verify in part due
to the limited number of ensemble members for any given
model and external forcing scenario (most of the CMIP3
models had fewer than 3 integrations for each forcing scenario). Thus, there is merit in performing a large number ofsimulations with a single climate model in order to provide
a robust estimate of that model’s forced response in addition
to its internal variability. One such ensemble is the 62member ‘‘Dutch Challenge Project’’ (Selten et al. 2004)
which employed Community Climate System Model Version 1 (CCSM1; Boville et al. 2001) forced by the ‘‘business-as-usual’’ GHG scenario (similar to the SRES A1
scenario) out to 2080. The individual ensemble members,
which differ only in their atmospheric initial conditions,were found to exhibit large spread in the future state of theextra-tropical northern hemisphere wintertime atmosphericcirculation (Selten et al. 2004; Branstator and Selten 2009).
Here we analyze a new 40-member ensemble for theperiod 2000–2060 performed with one of the CMIP3 models, Community Climate System Model Version 3(CCSM3). Compared to the ‘‘Dutch Challenge Project’’, thisensemble uses an improved and higher resolution state-ofthe-art climate model and also stronger (and arguably more
realistic) forcing consisting primarily of the SRES ‘‘A1B’’
GHG emissions and stratospheric ozone recovery scenarios.
We use this ensemble to characterize the forced climate
response and accompanying uncertainty due to internalvariability. We consider three basic parameters, surface airtemperature (TS), precipitation (Precip) and sea level pressure (SLP), for a broad view of the climate response. We also
examine the responses as a function of season, highlighting
any differences between winter and summer.
The following questions guide our investigation. What is
the geographical distribution, magnitude and seasonal
dependence of the ensemble mean (e.g., forced) response
relative to the internal variability? Does this signal-to-noise
ratio differ among the three climate parameters? What is
the minimum number of ensemble members needed todetect the forced response with 95% statistical confidence?
When can the forced response be detected given an
ensemble of size n where n \ 40? Is there a relationshipbetween the patterns of the forced response and the leading
patterns of internal variability? What are the sources ofinternal variability, and in particular, how large is theC. Deser et al.: Uncertainty in climate change projections

contribution from internal atmospheric variability (thelatter being assessed from a 10,000 year control integrationof the atmospheric component of CCSM3)? What are therelative contributions of internal and model variability touncertainties in climate projections in the multi-model
CMIP3 ensemble? Finally, what are the implications of theresults based on the 40-member CCSM3 ensemble fordetection and attribution studies of observed climatechange and for investigations of future climate projections
based on multi-model ensembles?
The rest of the paper is outlined as follows. The models
and methods are given in Sect. 2. Results are presented in
Sect. 3, structured following the sequence of questions listed
above. A summary and discussion is provided in Sect. 4.

2 Models and methods
2.1 Models
CCSM3, a coupled ocean-atmosphere-land-cryospheregeneral circulation model, has been extensively documented in the J. Climate CCSM3 Special Issue (2006). In
general, CCSM3 realistically simulates the major patternsof internal climate variability, except for ENSO which
exhibits higher regularity and frequency (2–3 year periodicity) than in nature (Deser et al. 2006; Stoner et al. 2009).
The 40-member CCSM3 ensemble uses the T42 version(2.8° latitude by 2.8° longitude resolution for the atmosphere, land, and cryosphere components and nominal 1°
latitude by 1° longitude resolution for the ocean modelcomponent; note that the version of CCSM3 used in the
CMIP3 archive was at T85 resolution). Each ensemble
member undergoes the same external forcing, the main
components of which are the A1B GHG scenario in which
CO2 concentrations increase from approximately 380 ppm
in 2000 to approximately 570 ppm in 2060 and stratospheric ozone recovery by 2060, as well as smaller contributions from sulfate aerosol and black carbon changes (seeMeehl et al. 2006). It is worth noting that for the period ofinterest, 2000–2060, the SRES A1B and A2 scenarios arevery similar, and both are approximately 30% stronger thanthe B1 scenario. The ocean, land, and sea ice initial conditions are identical for each ensemble member, and aretaken from the conditions on January 1, 2000 from a single
20th century CCSM3 integration. The atmospheric initialconditions differ for each ensemble member, and are takenfrom different days during December 1999 and January
2000 from the same twentieth century CCSM3 integration.
Although the use of a single ocean initial condition may
potentially underestimate the true internal variability of thesimulated climate system, a recent predictability study
using the same 40-member ensemble shows that the effect529

of ocean initial conditions is lost within 6–7 years for upper
ocean (0–300 m) heat content, and even more rapidly forsurface temperature (Branstator and Teng 2010). Thus, thefull internal variability is likely to be sampled by perturbing
only the atmospheric initial conditions.
In addition to the 40-member CCSM3 ensemble, we
make use of a 10,000-year control integration of CAM3,
the atmospheric component of CCSM3, at T42 resolution
under present-day GHG concentrations. In this integration,
sea surface temperatures (SSTs) and sea ice are prescribedto vary with a repeating seasonal cycle but no year-to-year
variability. The SST and sea ice conditions are based on
observations during the period 1980–2000 from the data setof Hurrell et al. (2008). As in CCSM3, CAM3 is coupled to
the Community Land Model (CLM; Oleson et al. 2004).
For the purposes of this study, we form our own CMIP3
multi-model ensemble using a single integration from eachof the 21 models forced with the SRES A1B forcing scenario(see Table 10.4 of the IPCC WG1 AR4 Report) excluding
CCSM3 to avoid any overlap with present 40-member
ensemble. Note that the ozone forcing scenario varies among
the CMIP3 models, with nearly half prescribing no changeover the twenty-first century (Son et al. 2008).
2.2 Methods
We used two methods to compute the climate response: (1)epoch differences between the last 10 years (2051–2060)
and the first 10 years (2005–2014); and (2) linear leastsquares trends fit to the period 2005–2060. Note that both
approaches use data beginning in 2005, 6 years after the
integrations start, so as to avoid any artificial reduction in
ensemble spread due to the memory of ocean initial conditions (see Branstator and Teng 2010 and related discussion in the Introduction above). The two methods yieldvirtually identical results.
We evaluated the 95% statistical significance of theensemble mean epoch differences and trends against a null
hypothesis of zero change using a 2-sided Student’s t test
(1-sided for TS since the sign of the response is knowna priori), where the spread is computed using the individual
epoch difference or trend values from the 40 ensemble
members. Each ensemble member’s epoch difference (ortrend) values are assumed to be independent.

3 Results
3.1 Ensemble mean response and minimum ensemble
size requirement
The left-hand panels of Fig. 1a show ensemble-mean
epoch difference maps (2051–2060 minus 2005–2014) for

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Fig. 1 a (Left) CCSM3 40member ensemble mean epoch
differences (2051–2060 minus
2005–2014) in DJF for (top)
SLP, (middle) Precip and(bottom) TS. Stippling indicateswhere the ensemble mean
response is statisticallysignificant at the 95%
confidence level relative to the
spread amongst the ensemble
members. (Right) minimum
number of ensemble members
needed to detect a significant
epoch difference response. Gray
areas indicate locations where
the 40-member ensemble mean
response is not significant at the
95% confidence level. b As is in
a but for JJA

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531

Fig. 2 Minimum number ofensemble members needed todetect a significant epoch
difference response (2028–2037
minus 2005–2014) in (left) DJF
and (right) JJA for (top) SLP,
(middle) Precip and (bottom)
TS. Gray areas indicate
locations where the 40-member
ensemble mean response is not
significant at the 95%
confidence level

SLP, Precip and TS during December–January–February(DJF) for the 40-member CCSM3 ensemble. Stippling
indicates epoch differences that are significantly different
from zero at the 95% confidence level relative to the spreadof the 40 individual epoch differences, computed according
to the formula for the standard error of the mean:p
X=r  ð2= ðN  1Þ
where X is the ensemble mean epoch difference, r is thestandard deviation of the 40 epoch differences, and N is 40.
Thus, approximately, if XC/r [ 1/3 then X is statisticallysignificant at the 95% confidence level. (Note that the factorof ‘2’ in the formula above is replaced by a ‘1’ for TS due tothe use of a 1-sided t test instead of a 2-sided t test.)
The ensemble mean response is statistically significant
over most regions of the globe for all 3 variables. The
large-scale SLP response over the Northern Hemisphere(NH) is characterized by generally negative (positive)
values at high (middle) latitudes, with maximum amplitudes *3 hPa in the Gulf of Alaska and northern Eurasia.
A similar pattern with reversed polarity and somewhat
weaker amplitude (*1 hPa) is found over the Southern
Hemisphere (SH). These patterns project onto the zonallysymmetric Northern and Southern Annular Modes (NAM

and SAM, respectively; e.g., Thompson and Wallace2000). The global distribution of SLP changes is broadly
consistent with that from the set of 22 CMIP3 models
reported in Solomon et al. (2007). We note that thereversed polarity of the response in the SH compared to the
NH is due to stratospheric ozone recovery (e.g., Son et al.
2009).
The tropical Precip response consists of mainly positive
values along the equator flanked by compensating negativevalues, especially to the south, with maximum amplitudes * 2 mm day-1. The subtropics (extra-tropics) generally exhibit reduced (enhanced) Precip, with magnitudes* \ 0.5 mm day-1. Surface temperature increases everywhere, with larger warming over land than ocean andmaximum warming over the ice-covered Arctic Ocean andadjacent continents (maximum values * 6°C), the latter
attributable to Arctic sea ice loss in late autumn (Deseret al. 2010). The Precip and TS responses are similar to
those documented from other models (Solomon et al. 2007)
and the 21 CMIP3 multi-model mean (not shown).
The right-hand panels of Fig. 1a show the minimum
number of ensemble members needed to detect the forced(ensemble mean) response at the 95% significance level at
each grid box, computed by inverting the formula for the

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Fig. 3 Decade when theensemble mean change relative
to the period 2005–2014 first
becomes detectable at the 95%
significance level for an
ensemble size of 40 (left) and 5(right), based on annual
averages subject to a 10-year
running mean for SLP (top),
Precip (middle) and TS(bottom). Year indicateddenotes the mid-point of the 10year period. Gray areas indicate
locations where the ensemble
mean response is not significant
at the 95% confidence levelstandard error of the mean following Sardeshmukh et al.
(2000):
Nmin ¼ 8=ðX=rÞ2(As with the standard error formula, the factor ‘8’ in theformula for Nmin is replaced by a ‘4’ for TS due to the use ofa 1-sided t test.) In general, SLP requires larger values ofNmin than Precip, and TS requires the smallest Nmin. Valuesof Nmin for SLP ranges from \6 in parts of the tropics (thesouthwestern Pacific and Indian Oceans, the northern
Atlantic, and South Africa) to *6–9 for the large-amplitude
response centers in the extra-tropics (for example the west
coast of Canada) and[15–21 over remaining areas, notably
the Mediterranean and high latitudes of both hemispheres.
Precip generally exhibits smaller values of Nmin than SLP,
with values * 3–6 over the Arctic Ocean and northern highlatitude continents, East Antarctica, and many areas of thetropics. Other regions require higher Nmin such as theSouthern Ocean (9–12) and northern middle latitudes([15). TS requires generally fewer than 3 ensemble members, except for isolated regions in the Southern Ocean, theeastern North Atlantic and northwestern Australia.
Ensemble mean epoch difference maps and ensemble
size requirements for the June–July–August (JJA) season

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are shown in Fig. 1b. The SLP response pattern is considerably different in JJA compared to DJF. For example, over the SH the quasi-zonally symmetric pattern in
DJF (e.g., the SAM) is replaced with a regional meridional dipole over the Pacific sector. In addition, thepositive SLP response centered over the Mediterranean
region in DJF is replaced by negative values in JJA. SLP
decreases over the Arctic Ocean in both seasons,although the maximum negative anomalies are centeredover the western Arctic in JJA compared to the eastern
Arctic in DJF. The Precip response pattern in JJA issimilar to that in DJF except that the tropical signals arelargest within the NH following the position of the sun.
The biggest difference between the TS responses in JJA
and DJF is the lack of northern hemisphere polar
amplification in summer, consistent with the muted
influence of Arctic sea ice loss during this season (Deseret al. 2010).
In general, fewer ensemble members are needed for
detecting significant SLP changes in JJA compared to DJF(Fig. 1b, right). For example, Nmin \ 3 over the entire
tropical Pacific, and\12 over the Arctic and portions of theSouthern Ocean. On the other hand, larger (smaller) valuesof Nmin are needed to detect the enhanced Precip over theC. Deser et al.: Uncertainty in climate change projections

533

Fig. 4 Time series of annual
mean (left) TS and (right)
Precip anomalies averaged overthe (top) globe, (middle) landand (bottom) ocean for the 40member ensemble mean (thickblack curve) and the first 10
ensemble members (thincolored curves). The green
shaded curve shows theminimum number of ensemble
members needed to detect a
95% significant change relative
to 2005 as a function of time

Arctic (Southern Ocean) in JJA compared to DJF, in part
related to the weaker (stronger) amplitude of the signal.
The ensemble size requirements for TS in JJA are similar
to those in DJF.
What are the minimum ensemble size requirements for
detecting the forced climate signal near the mid-point of
the integration period? Figure 2 shows the distributions ofNmin in winter and summer for epoch differences based on
the decade 2028–2037 relative to the decade 2005–2014.
Many of the climate changes that become significant in
2051–2060 are not yet significant in 2028–2037 (with an
ensemble of 40 members), and those that are require considerably more ensemble members to detect them. For
example, the forced SLP response over the northernhemisphere in DJF for 2028–2037 is not detectable with a
40 member ensemble except in a few areas of North
America and Siberia where Nmin [ 27. SLP changes in2028–2037 in JJA remain detectable, but Nmin increases by
3–9 compared to that for 2051–2060. Despite that the DJF
SLP response over the NH high latitudes is largely undetectable, the DJF Precip response over the Arctic andadjacent continents is detectable by 2028–2037 albeit witha larger ensemble size (Nmin * 3–9) than for 2051–2060(Nmin \ 3). Precip changes in other regions require an
increase in ensemble size of *3–9 members relative to thatfor 2051–2060, similar to the results for SLP. Finally,values of Nmin needed to detect significant changes in TS in
2028–2037 remain \ 3 over much of the tropics but
increase to 3–9 over portions of North America, Eurasia,
Australia and Antarctica as well as the North Pacific andAtlantic and the Southern Ocean. Coastal regions of Antarctica in austral winter require Nmin [ 21–27.
When does the forced signal first become detectablewith an ensemble of n members (where n is B40)? Herewe consider 10-year running means; less (more) temporalsmoothing would yield later (earlier) detection times.
Figure 3 shows the 10-year period (centered in the year
given) when the forced signal becomes 95% significant
relative to the decade 2005–2014 for an ensemble size of
40 (left panels) and 5 (right panels) based on annual
averages. With a 40-member ensemble, decadal SLPchanges are detectable within approximately 5–10 years(2015–2020) over the tropical western Pacific and tropicalAtlantic Oceans and around 2030 elsewhere; decadal
Precip changes are detectable in the next 5–10 years over
the Arctic, the Southern Ocean, and portions of the tropics, and around 2030 over Europe; and decadal TSchanges are detectable within the next 5 years (e.g., by2015) over most regions (10–15 years over Alaska andthe eastern North Pacific). With a 5-member ensemble,
detection of the forced SLP signal over the tropics isdelayed to 2030–2040, and no detection is possible forextra-tropical SLP. Similarly, detection of the forced
Precip signal with a 5-member ensemble is generally
confined to polar regions, the Southern Ocean and portions of the tropical oceans, with detection times around2030–2040. Although TS continues to be detectable witha 5-member ensemble at nearly all locations, the time ofdetection is delayed to 2020–2030 over much of Eurasia
and North America and parts of the Southern Ocean, and

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Fig. 5 Ten-year running mean DJF time series of the NAM (left) andSAM (right), defined as the zonally-averaged SLP anomaly difference
between high (55°–90°) and middle latitudes (30°–55°) of thenorthern and southern hemisphere, respectively. The thick black

curve denotes the 40-member ensemble mean, and the thin coloredcurves denote the first 10 ensemble members. The green shaded curveshows the minimum number of ensemble members needed to detect a
95% significant change relative to the decade centered on 2010

to 2015–2020 over Africa and portions of South America
as well as the eastern tropical Pacific. A complementary
analysis of the timing of the externally-forced global
warming signal (relative to the period 1910–1959) in theTS field from observations and the CMIP3 twentieth andtwenty-first century model simulations was presented in
Kattsov and Sporyshev (2006).
It is instructive to view Nmin (a measure of the amplitudeof the forced signal relative to the noise) as a function oftime for a given region. Figure 4 shows the ensemble meantime series of annual mean Precip and TS averaged overthe globe, and over land and ocean areas separately, alongwith the associated Nmin time series. Also shown are thePrecip and TS records for each of the first 10 realizations toillustrate the ensemble member spread as a function oftime. A statistically significant increase in TS relative to2005 is detected within a year with an ensemble of 4–6
members, and within approximately 10 years with a single
realization for global, land and oceanic averages. In contrast, detection of a statistically significant increase in
Precip with a 40-member ensemble does not occur until
2012 for global means and approximately 2023 (2020) for
ocean (land) averages. With just a few realizations,detection of a significant Precip change occurs in the mid
2020s for global averages and in the early 2030s for landand ocean averages. The similarity of the detection times
for the marine and terrestrial averages is due to the compensation between the magnitude of the intra-ensemble
spread and the forced signal. That is, the land records
exhibit a larger spread and a larger forced signal than theiroceanic counterparts.
The DJF NAM and SAM indices, defined as the zonally-averaged SLP difference between high (55°–90°) andmiddle latitudes (30°–55°) of their respective hemispheres, are shown in Fig. 5 along with their associatedNmin time series. The indices have been smoothed with a
10-year running mean and are displayed as differences
relative to the period 2005–2014; the calculation of Nminis based on the 10-year running mean records. The
ensemble-mean low-pass filtered NAM record exhibits amonotonic upward trend, but due to the considerable
spread amongst the individual ensemble members, thetime of detection of the forced NAM response does notoccur until 2042 with a 40-member ensemble, and a relatively large number of realizations (*25) is needed todetect the response thereafter. The downward trend in theensemble-mean low-pass filtered SAM record is detectable by 2017 with a 40-member ensemble, and *14–18
realizations are needed to detect the response thereafter.
Thus, the signal-to-noise ratio of the forced trend is larger
for the SAM than the NAM.
A recent study by Xie et al. (2010) emphasized theimportance of spatial gradients in the tropical Sea Surface
Temperature (SST) response to GHG forcing. In particular,
they showed that the pattern of the tropical precipitation
response is positively correlated with spatial deviations of
the SST response from the tropical mean warming. To
explore this aspect, Fig. 6 compares the annual ensemble
mean epoch difference maps (2051–2060 minus
2005–2014) and associated Nmin distributions for Precip(top) and TS* (bottom), defined as the residual SST
response from the tropical mean (30°N–30°S). Consistent
with Xie et al. (2010), the pattern of the tropical Precip
response is similar to that of TS*, with positive values overthe equatorial Pacific, northern Indian Ocean and tropicalAtlantic, and negative values elsewhere. The Nmin distributions associated with these responses are also similar,
with values \ 3–6 in the equatorial and southeastern
Pacific, the tropical Atlantic, and the off-equatorial western
Indian Ocean. Finally, the time of detection of the annual
TS* response is comparable to that of the annual Precip
response, based on 10-year running means relative to2005–2014 (Fig. 7). Detection times are approximately
2015–2020 (2030–2040) based on a 40-member (5-member ensemble) in regions where Nmin \ 3–6 (Fig. 7). Thus,
the spatially-varying component of the forced SST
response in the tropics exhibits a similar spatial pattern andsignal-to-noise ratio (as measured by Nmin and detectiontime) as the total Precip response, corroborating the resultsof Xie et al. (2010).

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Fig. 6 As in Fig. 1a but for tropical Precip (top) and TS* (bottom) based on annual values. TS* is defined as TS minus the tropical mean (30°N–
30°S) TS computed from oceanic grid points only. Values over land are omitted

Fig. 7 As in Fig. 3 but for tropical Precip (top) and TS* (bottom). TS* is defined as TS minus the tropical mean (30°N–30°S) TS computed fromoceanic grid points only. Values over land are omitted

3.2 Characterization and mechanisms for uncertainties
in future climate trends: the role of ‘‘weather
noise’’
To illustrate the range of uncertainty in future SLP changes,
Fig. 8 shows linear trend maps for ensemble members 10–20
individually and for the 40-member ensemble mean (lower
right panel) based on DJF during 2005–2060 (similar results
are obtained for epoch differences; not shown). The individual realizations reveal a wide range of trend responses tothe same external forcing (other ensemble member subsets
show a similar range of patterns; not shown). For example,
members 11 and 13 exhibit similar patterns over the extratropics but generally opposite polarity, while members 13
and 17 exhibit similar patterns and the same (opposite) sign
over the NH (SH). Other members show different spatial
distributions: for example member 19 exhibits a zonal wave
3 response over the southern hemisphere in contrast to themore zonally symmetric responses of members 11 and 14.
The wide variety of SLP responses in individual realizations
underscores the need for a large ensemble (*20–30 members) for accurate estimation of the forced response.

What mechanisms contribute to the spread of the trends
across the ensemble members? First we consider the role ofinternal atmospheric variability using the 10,000-year
CAM3 control integration. In this integration, the specified
repeating seasonal cycles of SST and sea ice are based on
observations from the period 1980–2000. Ideally, theCAM3 control integration should be forced with the SST
and sea ice conditions simulated by the CCSM3 40-member ensemble mean during 2000–2060 to obtain identicalboundary conditions for the two sets of experiments;
however, the differences between atmospheric internalvariability under observed present-day (1980–2000) andsimulated future (2000–2060) SST and sea ice conditions
are likely to be small compared to the magnitude of theinternal variability itself.
The spread of the trends across the ensemble members,
assessed by the standard deviation, are compared for
CCSM3 and CAM3 in Fig. 9a and b for DJF and JJA,
respectively. Trends were computed for the period
2005–2060 (56 years in length) for CCSM3 and for 56year periods from the CAM3 control integration obtainedby dividing the 10,000-year record into 178 consecutive

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Fig. 8 DJF SLP trends forindividual ensemble members
10 through 20, computed overthe period 2005–2060. The
ensemble-mean trend based on
all 40 members is shown in thelower right panel

segments. Standard deviations that differ significantly
between the two sets of experiments, as assessed by anf test at the 95% confidence level, are indicated with stippling on the CAM3 panels. In each season, the spread of
the SLP trends is remarkably similar for the two models inboth pattern and amplitude, with significant differences
only over the tropics and subtropics especially in JJA. In
particular, the large trend standard deviations over the NH
and SH extra-tropics in CCSM3 in both seasons are consistent with the null hypothesis of internal atmosphericvariability. The patterns of the spread in the Precip trends
are also generally similar between CCSM3 and CAM3, but
the magnitudes are significantly greater (by approximately
a factor of 2–3) within the tropics when the ocean isallowed to interact with the atmosphere. The terrestrial
distributions of the spread in TS trends (note that only
terrestrial values are shown for CAM3 due to the lack ofinterannual variability of specified TS values over theoceans) also show similar patterns in CCSM3 and CAM3,
with maximum values of 1–2°C over the high latitude NH
continents in DJF. Most terrestrial regions in the NH in
DJF and in the SH in JJA show no significant differences inamplitude between CCSM3 and CAM3. In summary,internal atmospheric variability contributes substantially to123

the spread of the SLP, Precip and terrestrial TS trends
during 2005–2060 in the 40-member CCSM3 ensemble.
To characterize the dominant patterns of uncertainty infuture climate trends, we have performed EOF analysis on
the set of 40 (178) trend maps from CCSM3 (CAM3) for
each variable (SLP, Precip and TS). A separate EOF
analysis, based on the area-weighted covariance matrix,
has been computed for each hemisphere poleward of 30°,
and also for the tropics (30°N–30°S). Note that because
ensemble mean trends are removed in the EOF procedure,
the results characterize the dominant patterns of the‘‘noise’’ component of the future trends. The leading EOF
of extra-tropical SLP trends for each season (DJF and JJA)
and hemisphere are shown in Fig. 10 for both the 40member CCSM3 ensemble and the ‘‘178-member’’ CAM3
control integration. In both hemispheres and seasons, theleading EOF is characterized by an annular mode structure
consisting of zonally symmetric anomalies of opposite sign
north and south of approximately 55°–60°. These patterns,
referred to as the NAM and SAM, also characterize theleading EOF of the interannual variability (not shown). The
annular modes account for similar percentages of the total
variance in both models, with more variance explained by
the SAM compared to the NAM especially in DJF

C. Deser et al.: Uncertainty in climate change projections

537

Fig. 9 a Trend standard deviations in DJF from the 40-member
CCSM3 ensemble (left) and the ‘‘178-member’’ CAM3 control
integration (right) for SLP (top), Precip (middle) and TS (bottom).
Trends are computed over the period 2005–2060 for CCSM3 and for

56-year non-overlapping segments for CAM3. Stippling in the righthand panels indicates where differences between the two models are
statistically significant at the 95% confidence level. b As in a but for
JJA(60–65% compared to 36–37%). These hemispheric modes
occur independently of one another: e.g., the correlation
between the principal component (PC) records in the NH
and SH is near zero in both CCSM3 and CAM3.
The spatial pattern of the ensemble mean SLP response(Fig. 1) bears some similarity to the leading EOF of thetrends in CCSM3 and the CAM3 control integration in bothseasons. In particular, the SH response in DJF exhibits ahigh spatial correlation (0.88) with CAM3 EOF1 in the SH;
there is also some correspondence between the NH
response in DJF and the NH EOF1 from CAM3 especiallyover the Atlantic-Eurasian sector (spatial correlation of
0.64 for all longitudes, and 0.82 in the longitude band
20°W–140°E). In JJA, the NH ensemble mean response
resembles the NH EOF1 from CAM3 (spatial correlation of0.79), while the SH response bears some similarity to the
SH EOF1 from CAM3 especially over the Pacific sector(spatial correlation of 0.61 over all longitudes and 0.84 in
the longitude band 135°E–45°W). The spatial correspondence between the annular modes of atmospheric circulation variability in twentieth century coupled model
integrations and the forced response to increasing concentrations of greenhouse gases and tropospheric sulfate
aerosols has been documented by Miller et al. (2006) forthe models in the CMIP3 archive.
Given the significant spatial projection of the ensemble
mean response upon the leading EOF of the internal
atmospheric variability, it is relevant to compare the

distributions of the annular mode trends from the individual ensemble members of CCSM3 and CAM3. Figure 11
shows histograms of the annular mode trends in DJF andJJA for both hemispheres, obtained by projecting the trends
from each ensemble member onto EOF1 from CAM3;
similar results are obtained using zonally averaged SLP
differences between middle (30°–55°) and high (55°–90°)
latitudes in place of the projection time series (not shown).
To increase the sample size, individual trends from eachmonth (December, January, February; June, July August)are used, resulting in 3 9 40 (3 9 178) samples for
CCSM3 (CAM3). The spread of the trends in the annularmodes is comparable in both sets of model integrations, forboth hemispheres and seasons. These results indicate thatinternal atmospheric variability accounts for much of the
spread in the future projections of atmospheric circulation
trends associated with the annular modes in the 40-member
CCSM3 ensemble. There is also an overall shift in themean distribution of the annular mode trends in CCSM3
compared to CAM3, reflecting mainly the forced response.
Although small (*0.5–0.75 standard deviations for DJF inboth hemispheres and for JJA in the NH), this shift issignificantly different from the approximately zero meanvalue in the CAM3 control integration.
The leading EOF of SLP trends is associated with Precip
and TS trend anomalies in both seasons and hemispheres as
illustrated in Fig. 12 for CCSM3. In particular, the leading
SLP trend EOF in both seasons is accompanied by out-of-

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Fig. 10 The leading EOF of extra-tropical SLP trends from (left) the
40-member CCSM3 ensemble and (right) the ‘‘178-member’’ CAM3
control integration in (top) DJF and (bottom) JJA. Trends arecomputed over the period 2005–2060 for CCSM3 and for 56-year
non-overlapping segments for CAM3. EOF analysis is performed foreach hemisphere separately but plotted on a single map for

C. Deser et al.: Uncertainty in climate change projections

conciseness. The percent variance explained by each EOF is givenin the upper right corner of each panel, with the first number denoting
the NH and the second number the SH (for example, for CCSM3 in
DJF, EOF1 accounts for 36% of the variance in the NH and 60% of
the variance in the SH)

Fig. 11 Histograms of the SLP
2005–2060 trend projections
onto EOF1 from the CAM3
control integration for the (top)NH and (bottom) SH in (left)
DJF and (right) JJA. The red
open bars show results from the
40-member CCSM3 and thegrey filled bars from the178-member CAM3 control.
The x axis is in units of standard
deviations of the CAM3 control
integration, and the y axis isfrequency (number of ensemble
members divided by the total
number of ensemble members)

phase Precip trend anomalies between high and middle
latitudes of the North Atlantic and Pacific and over theSouthern Ocean, with positive SLP anomalies generally colocated with negative Precip anomalies. Similarly, the123positive phase of the NH annular mode trend EOF is
accompanied by positive air temperature trend anomalies
over Eurasia (central Europe and the United States) andnegative temperature trend anomalies over Canada and theC. Deser et al.: Uncertainty in climate change projections

539

Fig. 12 (Left) Precip and(right) TS trend regressions(shading) associated with theleading EOF of extra-tropical
SLP trends from the 40-member
CCSM3 ensemble in (top) DJF
and (bottom) JJA. Contours
show the SLP trend EOF(contour interval is 0.6 hPa
56 year-1 for DJF, and 0.4 hPa
56 year-1 for JJA; negativevalues are dashed). Trends arecomputed over the period
2005–2060. EOF and regression
analyses are performed for eachhemisphere separately but
plotted on a single map for
concisenessLabrador Sea (Canada) in DJF (JJA). It is also interesting
to note the association between the NH annular mode trend
EOF in JJA with Precip trends over the Sahel and thewestern tropical Pacific. Over the SH, the main TS trend
signal associated with the positive phase of the annularmode trend EOF is that of cooling over Antarctica andAustralia, especially in JJA. The leading EOF of extratropical TS and Precip trends for each season and hemisphere based on the 40-member CCSM3 ensemble (notshown) are very similar to the Precip and TS trend
regression patterns associated with SLP trend EOF1, withpattern correlations ranging from 0.87 to 0.97 (except for
the SH in JJA which exhibits lower pattern correlations of
0.51 for Precip and 0.67 for TS). That is, the dominant
trend EOF within each field is linked by virtue of a common atmospheric circulation-driven mode of variability.

Histograms of regional SLP, Precip and TS trends over
the North Atlantic/Eurasian sector in DJF are shown in
Fig. 13 based on the 40-member CCSM3 ensemble and the‘‘178’’-member CAM3 control integration. The regions
used are those affected by the NAM (recall top panelsof Fig. 10) as follows: (57°–90°N, 20°–120°E) for SLP;
(57°–72°N, 25°W–25°E) for Precip; and (50°–75°N,
0°–125°E) for TS. For each parameter, the spread of thetrends across the 40 members of the coupled model
ensemble is comparable to that across the 178 members of
the atmospheric control integration, indicating that internal
atmospheric variability controls the trend uncertainties.
The forced (ensemble mean) trend in CCSM3 is considerably smaller for SLP and Precip (*0.5 and 0.9 standard
deviations, respectively) than for TS (*3.5 standard
deviations), although all are significantly different from

Fig. 13 Histograms of regionally-averaged trends over the Eurasian–North Atlantic sector in DJF for SLP (left), Precip (middle) and TS(right) in DJF from the 40-member CCSM3 ensemble (open red bars)
and the ‘‘178-member’’ CAM3 control integration (grey filled bars).
Trends are computed over the period 2005–2060 for CCSM3 and for

56-year non-overlapping segments for CAM3. For all panels, thex axis is in units of standard deviation based on CAM3, and the y axis
is in units of the number of ensemble members divided by the total
number of ensemble members

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zero at the 95% level. This result is in keeping with the factthat the amplitude of the dominant extra-tropical pattern ofthe noise component of the trend (Fig. 12) relative to theamplitude of the forced component of the trend (estimatedby the ensemble mean; Fig. 1) is large (*100%) for SLP
and small (*10%) for TS. The histograms also indicatethat the forced component of the NAM circulation trend
makes a negligible contribution to the forced component of
the TS trend over Eurasia in DJF.
We have shown that internal atmospheric variability
accounts for the dominant pattern of the ‘‘noise’’ component of extra-tropical SLP trends, which in turn drives
the dominant pattern of the ‘‘noise’’ component of theextra-tropical Precip and air temperature trends in the
40-member CCSM3 ensemble. However, the extra-tropicalatmospheric circulation is also known to be sensitive to
conditions in the tropics, particularly over the Indo-Pacificsector as occurs during El Nino and La Nina events (e.g.,Trenberth et al. 1998; Alexander et al. 2002). What is therole of internal variability of the tropical coupled oceanatmosphere system in the inter-ensemble spread of future
SLP trends over the extra-tropics? Figure 14 (upper left)
shows the global distribution of SLP trend anomalies
regressed upon the leading PC of tropical SLP trends basedon annual means from the 40-member CCSM3 ensemble.
Although the EOF analysis was restricted to the tropics, thelargest regression coefficient amplitudes occur over middle
and high latitudes in both hemispheres. Within the tropics,
EOF1 is reminiscent of the Southern Oscillation (SO), withnegative anomalies over the eastern Pacific and positive
anomalies over the western Pacific/Indian Ocean. Thispattern must be a result of ocean-atmosphere couplingsince internal atmospheric variability from the CAM3
control integration yields a very different EOF1 pattern,
namely a zonally-symmetric structure with one sign
throughout the tropics and middle latitudes and opposite
sign at high latitudes (Fig. 14, lower left). The leading EOF
of tropical SLP trends in CCSM3 is accompanied byincreased Precip over the western and central equatorial
Pacific and decreased Precip over the eastern Indian Ocean(not shown). These Precip anomalies in turn force global
atmospheric teleconnections including a deepening of the
Aleutian Low and a Rossby-like wave train over the South
Pacific, similar to those which occur in association withinterannual ENSO events (see Deser et al. 2006 for a
description of ENSO teleconnections in CCSM3). Notethat although the extra-tropical teleconnections are maximized in the winter hemisphere (not shown), the use of

Fig. 14 (Left) The leading tropical EOF of annual SLP trends from(top) the 40-member CCSM3 ensemble and (bottom) the ‘‘178member’’ CAM3 control integration. Trends are computed over theperiod 2005–2060 for CCSM3 and for 56-year non-overlapping
segments for CAM3. The domain used for the EOF analysis isconfined to the tropics, but the results are displayed for the entire
globe by regressing the SLP trends at each grid point upon the tropical
PC1 record. The percent variance explained by each EOF is given in
the upper right corner. (Right) The second EOF of extra-tropicalannual SLP trends from (top) the 40-member CCSM3 ensemble and(bottom) the ‘‘178-member’’ CAM3 control integration. EOF analysis
is performed for each hemisphere separately but plotted on a single
map for conciseness. The percent variance explained by each EOF isgiven in the upper right corner of each panel, with the first number
denoting the NH and the second number the SH. For each model,
EOF2 is scaled by the correlation coefficient between its PC andtropical PC1123

C. Deser et al.: Uncertainty in climate change projections

annual mean data in the EOF analysis brings out the connection to both hemispheres simultaneously.
The second EOFs of annual SLP trends over the NH andSH (Fig. 14, upper right) bear a close resemblance to the
SLP regressions associated with the leading EOF of tropical SLP trends in their respective hemispheres, suggestingthat they are due at least in part to coupled ocean-atmosphere variability within the tropics. For example, EOF2 of
the NH exhibits negative SLP trend anomalies over the
North Pacific and over northern Eurasia in the vicinity of
the Arctic coastline, similar albeit not identical to the NH
teleconnection pattern associated with tropical EOF1. The
second EOF of the SH exhibits a NE-SW oriented dipole
over the South Pacific and negative SLP trend anomalies
over the Indian Ocean sector of the Southern Ocean andAntarctica, generally consistent with the SH teleconnection
pattern associated with tropical EOF1. There are somedifferences in the shapes and relative amplitudes of thecenters of action of the extra-tropical EOF2 patterns andthose associated with tropical EOF1, most notably over theSouth Pacific. These differences may indicate that internal
atmospheric variability also contributes to the former.
Indeed, EOF2 in the NH and SH from the CAM3 control
integration (Fig. 14, lower right) exhibits centers of action
over northern Eurasia and in the South Pacific north ofWest Antarctica, respectively.
3.3 Comparison with nature
The annular modes of extra-tropical atmospheric circulation variability play an important role not only in the forced
climate response but also in the noise component of theFig. 15 Power spectra of the daily NAM (left) and SAM (right)
indices, defined as the zonally-averaged SLP anomaly difference
between high (55°–90°) and middle latitudes (30°–55°) of thenorthern and southern hemisphere, respectively, from the NCEP/
NCAR Reanalysis (solid black curve; detrended version depicted by541

response. The variability in the noise component of theannular mode response was in turn shown to be primarily aresult of processes intrinsic to the atmosphere. Thus, anatural question to address is, how realistically does CAM3
depict the temporal behavior of the annular modes?
Figure 15 compares the power spectra of the observed
and simulated annular mode indices, defined as the zonally averaged SLP anomaly difference between middle(30°–55°) and high (55°–90°) latitudes in each hemispherebased on daily data. The CAM3 spectra are based on a
200-year segment of the 10,000-year control integration(solid gray curves), and the observed spectra (solid black
curves for raw data, dashed black curves for detrendeddata) are based on the NCEP/NCAR Reanalyses for
1948–2008 over the NH (similar results are obtained for
1979–2008; not shown), and 1979–2008 over the SH inview of the limited spatial coverage before the incorporation of satellite data in 1979. Due to the broad range of
frequencies spanned, the spectra are displayed in a log
frequency—log power format and thus do not preserve
variance (e.g., the relative amount of variance in eachfrequency band is not a simple integral under the power
spectrum curve). Note also that the spectra have a smootherappearance at low frequencies compared to high frequencies due to the higher spectral resolution at shorter periods.
The overall shape and magnitude of the observed andsimulated spectra are similar, with a rapid increase inpower with decreasing frequency for periods shorter than a
few months, and approximately constant or slightly
increasing power for periods longer than about 1 year.
CAM3 overestimates the power in the NAM for periods
between about 30 days and 10 years. The daily annular

the dashed black curve), a 200-year segment of the CAM3 control
integration (solid gray curve), and a 200-year segment of the CCSM3
control integration (dashed gray curve). The period 1979–2008(1948–2008) was used for the Reanalysis in the SH (NH)

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mode power spectra from a 200-year segment of a CCSM3
pre-industrial control integration, indicated by the dashedgray curves in Fig. 15 (only periods longer than 2 years areplotted for clarity), does not differ significantly fromCAM3 over the range of periods relevant for this study(\60 years), confirming that interannual-to-decadal variability of the simulated annular modes is predominantly
due to processes internal to the atmosphere. Thus, the null
hypothesis of intrinsic atmospheric variability is a usefulbenchmark against which to test for the presence of
externally forced trends in the annular modes in both
coupled models and nature.
How realistic is the simulation of internal climate variability on decadal time scales in CCSM3? Traditionally,
the evaluation of internally-generated climate variability incoupled models has been accomplished using long (several
hundred—1,000 year) control simulations (e.g., Karoly andWu 2005). Here we use the set of 40 CCSM3 integrations
during 2005–2060, a total of 2,280 years, to evaluate
internal decadal variability. To provide a baseline comparison of the amount of variance at periods of a decadeand longer in nature and as simulated by the 40-member
CCSM3 ensemble, we compare maps of the standard
deviation of 8-year low-pass filtered data in DJF and JJA(Fig. 16a, b, respectively). The 8-year low-pass filter wasachieved by smoothing the data with a 5-point binomial
filter (weights 1-3-4-3-1) for each season separately(Trenberth et al. 2007). To reduce the influence of externally-forced signals in the low-pass filtered data (e.g., toisolate the internally-generated component of decadal
variability), we have removed the linear trend from theobserved records, and removed the ensemble mean fromeach ensemble member at each time step from the CCSM3
output. The standard deviations of the resulting low-passfiltered model output were then averaged across the 40
ensemble members. We use 2 m air temperature and SLP
observations from the NCEP/NCAR Reanalyses (Kistler
et al. 2001) and precipitation from the Global Precipitation
Climatology Project (Huffman et al. 2001) for the period
1979–2008. Generally similar results are found for otherdata sets and longer periods of record (not shown).
The spatial distributions of the standard deviations of the
8-year low-pass filtered data from observations (left) andCCSM3 (right) are similar for each variable and season, andthe magnitudes are of the same order. For example, thestandard deviations of SLP are largest at high latitudes of
the winter hemisphere, with values \ 0.4 hPa within thetropics increasing to *2 hPa and greater in polar regions.
The model tends to overestimate low-frequency SLP variability over the extra-tropical NH by approximately 30% in
DJF and 50% in JJA. Like SLP, Precip low-frequency
variability is comparable in the model and observations
except for the double ITCZ-bias over the western two-thirdsof the tropical Pacific in the model that is reflected in theFig. 16 a Standard deviation maps of 8-year low-pass filtered SLP(top), Precip (middle) and TS (bottom) anomalies in DJF fromobservations (left) and the 40-member CCSM3 ensemble (right). For
observations, the linear trend over the period 1979–2008 wasremoved before filtering. For the model, the ensemble mean wasremoved from each ensemble member at each time step, and thestandard deviations averaged across the 40 ensemble members. SLP
and TS observations are from the NCEP/NCAR Reanalysis, andPrecip observations are from the Global Precipitation Climatology
Project. b As in a but for JJA

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pattern of simulated variability especially in JJA. Simulatednear-surface air temperature also exhibits realistic patterns
and magnitudes of low-frequency variability, with larger
values over land and the marginal sea ice zones compared toocean. The highest amplitude variability occurs the NH
continents in winter, with values * 1.2–1.5°C in naturecompared to 1.5–1.8°C in the model. The overestimate of
low-frequency wintertime air temperature variability overEurasia and Alaska in the model may be partly due to thestronger-than-observed atmospheric circulation (e.g., SLP)
variability. In summary, the 40-member CCSM3 ensemble
generally simulates a realistic order-of-magnitude for lowfrequency ([8 years) variability in near-surface air temperature, Precip and SLP.
3.4 Contribution of internal variability to the CMIP3
multi-model ensemble
As mentioned in the Introduction, uncertainties in theforced climate change signals simulated by the multimodel mean in the IPCC WG1 4th Assessment Report(Solomon et al. 2007) contains contributions from model
uncertainty and internal variability. As a first step in separating the two contributions, we have compared theinternal variability of trends during 2005–2060 from the543

40-member CCSM3 ensemble with the model-plus-internalvariability of similarly-computed trends from the 21-model
CMIP3 ensemble forced by the SRES A1B GHG scenario(see Table 8.1 in Solomon et al. 2007 for a list of models).
To help mitigate seasonal biases between different models,
we have used annual mean values in our trend calculations.
While the internal variability estimated from one model
does not necessarily represent the internal variability
averaged across all models, our estimate of the contributionof internal variability to the spread of trends within the
CMIP3 ensemble is intended to serve as a benchmark until
sufficiently large ensembles are completed for the othermodels.
The spatial patterns of the standard deviations of annualmean trends computed over the period 2005–2060 are
similar for both model ensembles, with larger magnitudes
for the CMIP3 ensemble (left-hand panels of Fig. 17)
compared to CCSM3 (not shown but recall the seasonal
trend distributions in Fig. 9) in keeping with the notion thatthe former contains contributions from both inter-modeland internal-climate variability. The ratio of the standard
deviation of annual-mean trends (CCSM3 divided by
CMIP3) is shown in the right-hand panels of Fig. 17, withstippling indicating where the two sets of standard deviations differ significantly at the 95% confidence level. NoteFig. 17 (Left) Trend standard
deviation maps from the
21-member CMIP3 ensembleand (right) the ratio of the trend
standard deviations from the
40-member CCSM3 ensemble
and the 21-member CMIP3
ensemble, based on annuallyaveraged data for (top) SLP,
(middle) Precip, and (bottom)
TS. Trends are computed overthe period 2005–2060 for bothmodel ensembles. Stippling
indicates where the ratios are
significantly different from one
at the 95% confidence level.
Units for the plots in the left
column are hPa 56 year-1(SLP), mm day-1 56 year-1(Precip), and °C 56 year-1 (TS)

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that a ratio greater (smaller) than 0.5 indicates a larger(smaller) contribution from internal variability compared tomodel variability. In general, ratios [ 0.75 correspond toareas where the spread in the CCSM3 trends does not differ
significantly from the spread in the CMIP3 trends. For SLP,
internal variability within the CCSM3 ensemble generally
accounts for 25–50% of the total variability within the
CMIP3 ensemble over much of the tropics, and approximately 50–75% ([75%) over middle and high latitudes ofthe southern (northern) hemisphere. For Precip, ratios are*25–50% in the tropics and *50–100% at higher latitudes. And for TS, ratios are *25–50% over most areas
except Eurasia, Europe and western North America where
they are *50–100%. In summary, internal climate variability is more important than (comparable to) model
variability for uncertainties in forced annual-mean extratropical SLP and Precip trends (TS trends over North
America, Eurasia and Antarctica) during 2005–2060.
Elsewhere, the contribution of internal variability is lessthan that of model variability, but rarely below half.

4 Summary and discussion
We have investigated the forced climate response andassociated uncertainties from a new 40-member ensembleof CCSM3 simulations forced with the SRES A1B GHG
and ozone recovery scenarios during 2000–2060. The large
ensemble size has enabled not only a robust estimate of themodel’s forced response, but also an evaluation of the
spread in the response due to internal (natural) variabilityof the climate system. The contribution of intrinsic atmospheric variability to uncertainty in the forced response wasassessed using a long (10,000-year) control integration of
the atmospheric model component of CCSM3. The
response was characterized for 3 basic climate parameters(surface air temperature, precipitation, and sea level pressure) and two seasons (DJF and JJA). The main results are
summarized below.
Similar to the average response of the 21 models in the
CMIP3 archive (Hegerl et al. 2007), the 40-member CCSM3
ensemble mean response is characterized by: increased
Precip along the equator and at high latitudes, and decreased
Precip within the tropics and subtropics; more warming overland than ocean, and strongest warming over the Arctic andadjacent high latitude continents in winter; and a general
pattern of decreased SLP at high latitudes and increased SLP
in middle latitudes, except for the SH in summer which
exhibits a response of the opposite sign as a result of prescribed recovery of the stratospheric ozone hole.
Due to the relative amplitudes of the forced response
and natural variability, fewer ensemble members are needed to detect a significant response in TS compared to123

C. Deser et al.: Uncertainty in climate change projections

either Precip or SLP. More specifically, only 1 realizationis needed to detect a significant (at the 95% confidence
level) warming in the 2050s decade compared to the 2010s
at nearly all locations, compared to approximately 3–6([15) ensemble members for tropical and high latitude(middle latitude) Precip, and approximately 3–6 (9–30)
members for tropical (extra-tropical) SLP, depending on
location and season. Larger ensemble sizes are needed todetect a significant response in the 2030s, even for TS at
middle and high latitudes where 3–12 members are
required. With a 40-member ensemble, significant decadal
TS changes are detectable within the next few years overmost regions, while decadal SLP and Precip changes aredetectable within approximately 5–10 years over portionsof the tropics (and the Arctic and Southern Ocean for
Precip) and around 2030 elsewhere. With a 5-member
ensemble, detection of the forced signal is delayed to
2025–2040 for tropical SLP and for Precip over the Arctic,
equatorial and Southern Oceans, and 2020–2030 for TS
over Eurasia and North America; no detection is possible
for extra-tropical SLP and middle latitude Precip. Although
the spatially-uniform component of the forced tropical SST
warming emerges within a few years and with a small
number of realizations (\3), the spatially-varying component is subject to a lower signal-to-noise ratio that iscommensurate with the characteristics of the tropical precipitation response. The forced decadal-scale responses of
the NAM and SAM require a relatively large number ofrealizations for detection (*25 and 15, respectively, in
DJF) and a time horizon of detection of 2–3 decades(*2040 and 2030, respectively, in DJF), underscoring thelow signal-to-noise ratios in even the large-scale patterns of
extra-tropical atmospheric circulation response.
The leading pattern of uncertainty in the extra-tropicalresponses of TS, Precip and SLP, as determined from EOF
analysis of the 40 individual responses, is associated withthe annular mode of atmospheric circulation variability inboth seasons and hemispheres. This mode, in turn, is primarily due to intrinsic atmospheric dynamics (e.g.,‘‘weather noise’’: Madden 1976) and, as such, contains no
predictability beyond a few months. The leading mode of
uncertainty in the extra-tropical SLP response bears someresemblance to the forced (ensemble mean) SLP response,
especially in the summer hemisphere. The leading patternof uncertainty in the tropics displays a spatial structure
reminiscent of the ‘‘Inter-decadal Pacific Oscillation’’(Power et al. 1999) or ‘‘Pacific Decadal Oscillation’’ (e.g.,
Zhang et al. 1997). This pattern does not occur in theatmospheric control integration of CAM3, and thus owes
its existence to ocean-atmosphere coupling. We note that
thermodynamic coupling between the global atmosphereocean mixed layer system is sufficient to produce much of
the spatial structure of this pattern (e.g., Yukimoto et al.

C. Deser et al.: Uncertainty in climate change projections

1996; Dommenget and Latif 2008; Clement et al. 2010).
This tropical mode affects the extra-tropical atmosphericcirculation via precipitation-induced teleconnections which
in turn impact TS and Precip over middle and high latitudes. Indeed, the second EOF of the extra-tropical SLP
response is linked in part to the leading EOF of the tropical
SLP response.
The fact that forced changes in TS are more readily
detectable than those in SLP over the extra-tropics by the
middle of the twenty-first century indicates that the thermodynamically-induced signal in the TS response is larger
than the dynamically-induced (via atmospheric circulation
changes) uncertainty in the TS response (similar comments
apply to the winter precipitation response over the northernhigh latitudes). For changes in earlier decades, the effect of
circulation uncertainty on detection of the forced TS
response is more evident (e.g., the minimum number ofensemble members needed to detect a significant TS
response in the 2030s is markedly larger over the NH
continents and Antarctica, regions affected by the annularmodes, than other areas; recall Fig. 3). These results are in
keeping with the studies of Yiou et al. (2007), Boé et al.
(2009) and Vautard and Yiou (2009) focused on western
Europe.
Our results have implications for detection and attribution of the twenty-first century climate response toanthropogenic forcing in nature and in the multi-model
CMIP3 archive used in the AR4 IPCC assessment, and may
also be useful for strategic guidance of the upcoming
CMIP5 protocol of model experiments in support of theAR5. To the extent that the 40-member CCSM3 ensemble
exhibits generally realistic levels of decadal variability as
estimated from observational data sets spanning the past
30–50 years, attribution of observed future decadal changes in TS, Precip and SLP to anthropogenic forcing will be
subject to similar levels of uncertainty reported here, taking
into account any differences in climate sensitivity andforcing amplitude between CCSM3 and nature. The
observed atmospheric circulation response over the extratropical NH may exhibit less uncertainty than indicated by
CCSM3 due to the model’s overestimation of decadal SLP
variability in this region (by approximately 30% in DJF).
We have shown that internal variability as estimated fromthe 40-member CCSM3 ensemble makes an appreciable
contribution to the total (model plus internal) uncertainty inthe future climate response simulated by the 21-model
mean from the CMIP3 archive. In particular, internal variability was shown to be more important than model variability for annual-mean SLP and Precip responses in theextra-tropics, while the two sources of uncertainty are of
the same order for the annual-mean TS responses over
North America, Eurasia and Antarctica. The magnitude of
uncertainty due to internal variability is rarely less than545

half that due to model variability for forced linear climate
trends during 2005–2060. Given our results, the planned setof CMIP5 model projections of twenty-first century climate
in support of the AR5 IPCC Assessment should take into
account the relatively high levels of uncertainty due tointernal climate variability (of which internal atmosphericvariability is an important component) by running enoughensemble members to provide robust assessments of theforced response in each model, perhaps by taking an
adaptive approach based on the time horizon and climateparameter of interest. Similarly, given the inevitablecompetition between ensemble size and model resolution
for a fixed level of computational resources, the former
should not be sacrificed at the expense of the latter.
We have shown that the response to anthropogenic
forcing is more detectable in surface temperature than inprecipitation or atmospheric circulation. Thus, monitoringof observed climate change may be best served by focusing
on thermodynamic components of the climate system suchas air temperature and integrated quantities such as top-ofatmosphere radiation or ocean heat storage, rather than on
dynamical components related to atmospheric and oceanic
circulation changes.
Acknowledgments We thank Dr. Grant Branstator for useful discussions during the course of this work and for providing the CAM3
control integration. Dr. Michael Alexander provided helpful comments on the original manuscript. We also thank the two anonymous
reviewers, Dr. Laurent Terray and Dr. Shang-Ping Xie for constructive suggestions. NCAR is sponsored by the National Science
Foundation. Haiyan Teng is supported by the Department of Energy
under Cooperative Agreement No. DE-FC02-97ER62402. VB’s was
supported by Meteo-France.
Open Access This article is distributed under the terms of theCreative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any
medium, provided the original author(s) and source are credited.

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