Intrinsic versus Forced Variation in Coupled Climate Model Simulations over theArctic during the Twentieth Century*

MUYIN WANG

Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Seattle, Washington

JAMES E. OVERLAND

National Oceanic and Atmospheric Administration/Pacific Marine Environmental Laboratory, Seattle, Washington

VLADIMIR KATTSOV

Voeikov Main Geophysical Observatory, St. Petersburg, Russia

JOHN E. WALSH AND XIANGDONG ZHANG

International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, Alaska

TATYANA PAVLOVA

Voeikov Main Geophysical Observatory, St. Petersburg, Russia

(Manuscript received 4 May 2005, in final form 17 July 2006)

ABSTRACT

There were two major multiyear, Arctic-wide (60°–90°N) warm anomalies (�0.7°C) in land surface airtemperature (LSAT) during the twentieth century, between 1920 and 1950 and again at the end of thecentury after 1979. Reproducing this decadal and longer variability in coupled general circulation models(GCMs) is a critical test for understanding processes in the Arctic climate system and increasing theconfidence in the Intergovernmental Panel on Climate Change (IPCC) model projections. This studyevaluated 63 realizations generated by 20 coupled GCMs made available for the IPCC Fourth Assessmentfor their twentieth-century climate in coupled models (20C3M) and corresponding control runs (PIcntrl).Warm anomalies in the Arctic during the last two decades are reproduced by all ensemble members, withconsiderable variability in amplitude among models. In contrast, only eight models generated warmanomaly amplitude of at least two-thirds of the observed midcentury warm event in at least one realization,but not its timing. The durations of the midcentury warm events in all the models are decadal, while thatof the observed was interdecadal. The variance of the control runs in nine models was comparable with thevariance in the observations. The random timing of midcentury warm anomalies in 20C3M simulations andthe similar variance of the control runs in about half of the models suggest that the observed midcenturywarm period is consistent with intrinsic climate variability. Five models were considered to compare some-what favorably to Arctic observations in both matching the variance of the observed temperature record intheir control runs and representing the decadal mean temperature anomaly amplitude in their 20C3Msimulations. Seven additional models could be given further consideration. Results support selecting asubset of GCMs when making predictions for future climate by using performance criteria based oncomparison with retrospective data.

* Joint Institute for the Study of the Atmosphere and Ocean Contribution Number 1132 and Pacific Marine EnvironmentalLaboratory Contribution Number 2804.

Corresponding author address: Muyin Wang, JISAO/PMEL, 7600 Sand Point Way NE, Seattle, WA 98115.E-mail: muyin.wang@noaa.gov

15 MARCH 2007 W A N G E T A L . 1093

DOI: 10.1175/JCLI4043.1

© 2007 American Meteorological Society

JCLI4043

1. Introduction

Climate changes are being experienced in the Arctic.These changes pose challenges to the resilience of Arc-tic life including humans (Symon et al. 2005; Overlandand Wang 2005). Considering that the Arctic domain isa relatively small fraction of the earth, it is likely thatlarger anomalies of temperature and other variableswould occur in the Arctic-wide mean compared to theglobal mean. On the other hand, large multiyearanomalies are also possible based on internal positivefeedbacks in the Arctic associated with sea ice, ocean,and land processes. Due to the lack of pan-Arctic ob-servations in the past and the complexity of processesinvolved, coupled atmosphere–ocean general circula-tion models (AOGCMs) are tools for studying Arcticclimate and its response to changing external forcing.

To assess whether recent changes in the arctic cli-mate are outside the range of natural variability, it ishelpful to compare observations from recent decadeswith those from earlier in the twentieth century. Sur-face air temperature (SAT) is related to regional en-ergy budgets and is a robust climate parameter, in thesense that it can show large-scale anomaly patterns andis generally well observed (Lambert and Boer 2001).Previous studies show that warming during the last twodecades exhibits the greatest trends in the high latitudesof the Northern Hemisphere (Hansen et al. 1999; Joneset al. 1999), and are attributed to anthropogenic forcingchanges (Houghton et al. 2001; Broccoli et al. 2003).The two 20-yr periods of largest temperature anomaliesin the Arctic for the twentieth century are 1925–44(midcentury), and 1979 to present.

Although the warm anomalies in midcentury are re-cently receiving attention from the polar research com-munity, the mechanisms behind them are still in debate(Polyakov et al. 2002; Overland et al. 2004). Bengtssonet al. (2004) suggest that the existence of the multiyear,midcentury warm anomalies are associated with consid-erable internal variations over several years initiated bythe stochastic variations of the high-latitude atmo-spheric circulation and subsequently enhanced andmaintained by sea ice feedbacks, particularly, over theBarents Sea. Overland et al. (2004) supports naturalvariability as the source of these warm anomalies,based on regional and temporal variability in the ob-served atmospheric circulation. Delworth and Knutson(2000) also propose that this warming was a manifesta-tion of internal variability. By comparing index trendsin observations and model simulations, Karoly et al.(2003) conclude that the observed warming from 1900to 1949 over North America was likely due to naturalclimate variation, whereas the trend from 1950 to 1999

was consistent with simulations that include anthropo-genic forcing from increasing atmospheric greenhousegases and sulfate aerosols.

However, closure has not been reached on the ulti-mate cause of the midcentury warm event. The lengthand spatial coverage of the meteorological records inthe Arctic, particularly upper air, do not allow a fullanalysis of the dynamics of the early/midtwentieth-cen-tury multiyear event, whereas results from AOGCMsprovide useful information in studying the causes ofthese anomalies. In addition to model evaluation, weinvestigate the hypothesis that the midcentury multi-year warm event is based on intrinsic atmospheric vari-ability amplified by internal Arctic feedback processes.Thus, we do not require the models to have a year-to-year correspondence to data, but the models should beable to replicate similar multiyear events.

A suite of scenario simulations were conducted bycoupled AOGCM from worldwide sources for the In-tergovernmental Panel on Climate Change (IPCC)Fourth Assessment Report (AR4). Section 2 summa-rizes these models and their ensemble runs togetherwith the observed datasets used in present study. Sec-tion 3 analyzes the simulation results for the Arctic inrecent decades, and in the early part of the twentiethcentury with emphasis on the midcentury warm anoma-lies based on two scenarios: the twentieth-century cli-mate in coupled models (20C3M) and the correspond-ing control runs (PIcntrl). Spatial distributions of tem-perature anomalies from a subgroup of models arediscussed in section 4, followed by the conclusions.

2. Coupled atmosphere–ocean models andobservation data

Several scenarios are provided by modeling groupsfor their simulation experiments of late-nineteenth–twentieth- and twenty-first–twenty-third-century pro-jection for IPCC AR4. These state-of-the-art modeloutputs are archived by the Program for Climate ModelDiagnosis and Intercomparison (PCMDI) at LawrenceLivermore National Laboratory (LLNL). In this studywe analyze the 20C3M simulation, which includes 63realizations produced by 20 models. The 20C3M en-semble runs are initialized from their correspondingcontrol runs (PIcntrl), which have neither natural noranthropogenic external forcing (solar, volcanic aero-sol), and then continued with prescribed anthropogenicand, in many cases, natural external forcings based onobservations for the twentieth century. Global energy-related and industrial CO2 emissions were relativelylow in the first half of the twentieth century; pro-nounced increases occur after the 1950s (Nakicenovic

1094 J O U R N A L O F C L I M A T E VOLUME 20

and Swart 2000, their Fig. 1–3). The resultant CO2 con-centration was below 310 ppm before 1950 and thengradually increased to 368 ppm by the year 2000 (Wat-son et al. 2001). This set of model runs represents thevarious groups’ best effort to simulate the twentieth-century climate.

Table 1 briefly summarizes the features of the modelsthat were contributed to the 20C3M intercomparisonproject, including their simulation length, number ofrealizations, and length of control runs (PIcntrl). Bycomparison of Table 1 with Table 8.1 from the IPCCThird Assessment Report (TAR: Houghton et al.2001), both horizontal and vertical resolution ofAOGCMs have been improved since TAR. The spec-tral resolution of the atmosphere GCM was R15 to T47(only 1) in TAR. Among the 20 models for AR4, 13 arespectral, and the remaining 7 are grid point. The lowestspectral resolution is T42, and the highest resolution isT106, close to 1° in longitude and latitude {Model forInterdisciplinary Research on Climate 3.2, high-resolution version [MIROC3.2(hires)]}. The gridpointmodels have about the same spatial resolutions as inTAR. The vertical resolution ranges from 13 levels[Goddard Institute for Space Studies Model E-R(GISS-ER)] to 56 [MIROC3.2(hires)] with 14 modelshaving more than 20 vertical levels. For TAR 27 of 31models had less than 20 vertical levels.

Studies of temperature change over land areas basedon the meteorological station network are routinelymade by groups such as the University of East Anglia(UEA), United Kingdom (Jones et al. 1982, 1999),GISS (Hansen and Lebedeff 1987; Hansen et al. 1999),and the National Climatic Data Center (NCDC)(Peterson and Vose 1997; Quayle et al. 1999). Becauseof different methods for handling data issues, such asincomplete spatial and temporal coverage, urban influ-ences, etc., magnitudes of the derived data fields vary insome degree. The Arctic region is especially sensitiveas few stations are available, and the length of therecords varies considerably. The variance-adjusted ver-sion 5° � 5° land SAT anomalies [Climate ResearchUnit temperature dataset version 2 (CRUTEM2v)]are widely used; however, a relatively large portion ofthe high Arctic is data void. Using an “anomaly” ap-proach that attempts to maximize available station datain space and time, the group from the Climate ResearchUnit (CRU) of UEA constructed a dataset with meanmonthly SAT at 0.5° grid horizontal resolution (Newet al. 1999, 2000). The anomaly approach first definesthe fields of monthly climate anomalies relative to astandard normal period (in this case, 1961–90) mean.The anomaly grids were then combined with this high-resolution mean climatology to arrive at grids of

monthly climate over the 100-yr period to estimatemonthly surface climate. This monthly SAT covers theglobal land surface (excluding Antarctica) for 1901–2000 (CRUTS2.0). Our previous comparisons of thisdataset with observed records show encouraging resultsfor the Arctic, particularly after 1930 with increasedstation coverage (Wang and Overland 2004).

Figure 1 shows the comparison of the winter (No-vember–March) averaged land SAT (LSAT) anomalytime series for the Arctic north of 60°N (solid), mid-latitude (45°–60°N) (dashed), and global (dotted) basedon CRUTS2.0. We will refer to “winter” as this5-month average. The positive anomalies in the globalmean SAT in recent decades are consequences of thewarming in both the Arctic and midlatitude; however,polar amplification is more significant during the mid-century from late 1920s to early 1940s (Johannessen etal. 2004) when a 1901–80 base period is used. For com-parison, the winter LSAT anomalies for the Arcticbased on CRUTEM2v are also shown (dash–dottedline) in Fig. 1. Both datasets indicate prolonged Arctic-wide warm anomalies of more than 0.7°C at midcen-tury. They both show a “twin peak” structure in themidcentury warm anomalies, though with a slightly dif-ferent amplitude. After 1950 there is little differencebetween the two datasets. The cold period of the 1960s–70s is distinct, and the warm anomalies since the 1980sshow an upward trend. In the remainder of this studywe focus on the Arctic, and contrast the two periods ofwarm anomalies as simulated by the AR4 climate mod-els.

3. Simulations from climate models

Time series of winter-averaged LSAT for all modelensemble members and observations (thick yellow line)over the Arctic land area (60°–90°N) is shown in Fig. 2.To be consistent among the models, and to avoid theimpact from late-twentieth-century warming, all theanomalies are calculated relative to the 1901–80 periodmean in each realization. It is apparent that almost allmodel realizations are able to reproduce positive tem-perature anomalies in the last two decades. Some of therealizations have relatively large interannual to decadalvariability, while the others are less variable. All threeruns from the Flexible Global Ocean–Atmosphere–Land System Model gridpoint version 1.0 (FGOALS-g1.0; magenta thin lines with open triangles in Fig. 2)started from relative warm states, contrary to simula-tions from other models and observations. The sea icesimulation by this model apparently shows inappropri-ate initialization for simulating the climate of the twen-

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1096 J O U R N A L O F C L I M A T E VOLUME 20

tieth century (Zhang and Walsh 2006). Another expla-nation is that the model is still in a nonequilibrium state(Y. Yu 2005, IPCC workshop, personal communica-tion). Because of this, the results from FGOALS-g1.0are excluded from the statistics and discussions in thenext sections.

a. Late-twentieth-century warm anomalies

Stott et al. (2000) demonstrate that global mean sur-face air temperature changes since 1979 have contribu-tions from both natural and anthropogenic factorsbased on their Third Hadley Centre Coupled Ocean–

Atmosphere General Circulation Model (HadCM3)simulations. Over the Arctic we see that the majority ofthe ensemble members show warm anomalies duringthe last two decades, which are comparable with theobserved (Fig. 2) in their 20C3M simulations. Figure 3(top) displays the averaged LSAT anomalies for the1979–99 period for all ensemble members from 19 mod-els. The period 1979–99 is chosen, because nearly halfof the 20C3M simulations (27 runs out of 63) ended atDecember 1999 (Table 1). Although there are differ-ences among the models and each of their realizations,all ensemble members from all models show positiveanomalies for the last two decades in various degrees.The smallest amplitudes are from ensemble membersof the three models: the Geophysical Fluid DynamicsLaboratory Climate Model version 2.1 (GFDL-CM2.1;bars 22–24), the GISS-EH (bars 27–31), and the GISS-ER (bars 32–40). In addition there are four realiza-tions—the ECHAM5/Max Planck Institute OceanModel (MPI-OM): run 2, the GFDL-CM2.0: run 3, theECHAM5 Hamburg Ocean Primitive Equation(ECHO-G): run 2, and the single run from the MetOffice (UKMO-HadCM3) in which amplitudes ofaveraged anomalies in these two decades are less thantwo-thirds of the observed value. On the other hand,there are 10 realizations in which the warm anomaliesare one-third larger than observed. Among these, three

FIG. 2. Time series of LSAT anomalies over the Arctic (60°–90°N) based on 63 realizations from 20models investigated in their 20C3M simulations. The observed series based on CRUTS2.0 is shown bya thick orange line. The anomalies are relative to the mean of 1901–80. All curves are smoothed with5-yr running mean, in units of °C.

FIG. 1. Winter land surface air temperature (LSAT) anomaliesaveraged over the Arctic (60°–90°N) (thick solid line), midlatitude(45°–60°N) (dashed), and globe (dotted) based on CRUTS2.0 inunits of °C. Also shown is the Arctic averages based onCRUTEM2v dataset. The anomalies are relative to a 1901–80base period. All curves are smoothed with 5-yr running mean.

15 MARCH 2007 W A N G E T A L . 1097

Fig 2 live 4/C

are from the Community Climate System Model ver-sion 3 (CCSM3: bars 3, 5, and 8), two from the Meteo-rological Research Institute Coupled GCM version2.3.2 (MRI-CGCM2.3.2: bars 53 and 54), and one re-alization each from the Commonwealth Scientific andIndustrial Research Organisation Mark version 3.0(CSIRO-Mk3.0: bar 14), and the Parallel ClimateModel (PCM: bar 57). The remaining three are thesingle realizations provided by the Canadian Centre forClimate Modelling and Analysis Coupled GCM version3.1 (CCCma-CGCM3.1-T47: bar 10), the Centre Na-tional de Recherches Météorologiques Coupled GlobalClimate Model version 3 (CNRM-CM3: bar 12), andMIROC3.2(hires) (bar 43).

The bottom panel in Fig. 3 shows the model en-semble means of the anomalies for the last two decades.Confidence limits are estimated as � two standard de-viations of the detrended time series from the corre-sponding control run (PIcntrl) of each model. That allbut two ensemble mean anomalies are different fromzero, with the lower bounds of the confidence limits

being above the zero line, suggests that the warmanomalies in these two decades are beyond the range ofnatural variability. In other words, differences causedby intrinsic variability, which have essentially cancelledeach other out, imply that the late-twentieth-centurywarm anomalies could be a consequence of long-termchange in external forcing. Nine models show anoma-lies at least the same or larger than the amplitudes ofobserved [CCSM3, CGCM3.1-T47, CGCM3.1-T63,CNRM-CM3, CSIRO-MK3.0, the Institute of Numeri-cal Mathematics Coupled Model version 3.0 (INM-CM3.0), MIROC3.2(hires), MRI-CGCM2.3.2, andPCM], and another seven models show the ensemblemeans are within two-thirds of the observed, these areECHAM5/MPI-OM, GFDL-CM2.0, the Goddard In-stitute for Space Studies Atmosphere–Ocean Model(GISS-AOM), the L’Institut Pierre-Simon LaplaceCoupled Model version 4 (IPSL-CM4), the Model forInterdisciplinary Research on Climate 3.2, medium-resolution version [MIROC3.2(medres)], ECHO-G,and UKMO-HadCM3. Ensemble means from the three

FIG. 3. Mean winter arctic LSAT anomalies for the 1979–99 period from observation (first bar in eachpanel, light shaded) and model simulations in the 20C3M scenario. (top) Individual realizations of eachmodel (bars 2–61), and (bottom) the ensemble mean for models when more than one realization isprovided, or the only realization available. The confidence limits are two standard deviations derivedfrom the detrended control run time series. Due to an abrupt change in the GISS-EH control run, theconfidence limit is not shown. The last bar in the bottom panel shows the ensemble mean from all runsof all models.

1098 J O U R N A L O F C L I M A T E VOLUME 20

models (GFDL-CM2.1, GISS-EH, and GISS-ER) thathave small amplitude of warm anomalies in every singlerealization, are less than two-thirds of the observed. Asa group, the multimodel mean of averaged winter Arc-tic LSAT anomalies is 0.62°C (rightmost bar in bottompanel in Fig. 3) for 1979–99, which is close to the ob-served value of 0.64°C (leftmost bar) from CRUTS2.0.This is encouraging. However, between model differ-ences are not small.

b. Midcentury warm anomalies

As discussed in section 2 there were prolonged warmanomalies of more than 0.7°C in the Arctic in the mid-century from the late 1920s to the 1940s. The decadalmean of individual realizations from the IPCC modelsfor 1939–49 display large variability in magnitude andsign (Fig. 4): among 61 realizations, 30 of the decadalmean SAT anomalies are positive, 21 are negative, andanother 9 are near zero. None of the decadal meananomalies from any model is greater than the observedvalue. In contrast to the warm anomalies simulated bymodels for the last two decades (Fig. 3), the large dis-crepancies with observations for the 1940s among themodels and among their ensemble members indicate thepotential for large internal variability within the mod-els. It is interesting to note that at least one realizationhas the opposite sign of decadal mean anomalies fromother ensemble members when multiple realizationsare provided for a single model (except for CSIRO-Mk3.0 which generated negative anomalies for all threerealizations in this decade).

Based on Fig. 4, our hypothesis is that intrinsic natu-ral variability is the main cause behind the large anoma-lies in the early/midpart of the century. Thus, the mod-

els should not necessarily replicate the year-to-yearchanges in the observations, but should produce eventswith the same type of multiyear behavior as the obser-vations. Separate panels in Fig. 5 display the modelsimulated and observed (thick black solid line) winterLSAT over the Arctic from the late nineteenth to twen-tieth century. Each panel shows the ensemble membersfrom one model, and all time series are presented with5-yr running mean. All realizations are different. Forexample, four of eight runs from CCSM3 (top-leftpanel of Fig. 5) have relatively sizable amplitudes ofanomalies during the midcentury (red, blue, yellow,and black line) with run 1 (thin red line) matching theobservations in both amplitude and timing. One real-ization from GFDL-CM2.0 (blue line) matches the tim-ing and amplitude of the observed time series, while theother two (red and green lines) have an amplitude simi-lar to the warm anomaly in the 1950s. The amplitudesof anomalies from GFDL-CM2.1 are slightly weakerthan the observed, but one realization has a long dura-tion (red line). The two Canadian models CGCM3.1-T47 and CGCM3.1-T63 present similar results: bothshow twin peak warm anomalies in the midcentury withamplitude weaker than observations. Two realizationsfrom CSIRO-Mk3 (red and blue line) produce warmanomalies in the 1930s, which last about 10 years.ECHAM5/MPI-OM has one realization (red line) inwhich the warm anomalies are close to those observedaround 1940s, while other realizations show weakeramplitudes at later times. Similar situations are seen inMIROC3.2(medres). The warm anomalies from allPCM and ECHO-G realizations show comparable am-plitude, but are not synchronized with observations.This is also true for the single realization from CNRM-

FIG. 4. Decadal mean winter LSAT anomalies for the 1939–49 period based on individual realizationsfrom each model over the region of 60°–90°N. The first bar on the left is the observed mean(CRUTS2.0). Units are in °C.

15 MARCH 2007 W A N G E T A L . 1099

CM3 (late), INM-CM3.0 (late), and UKMO-HadCM3(early). Two GISS models (GISS-EH and GISS-ER)have little variability through their entire runs, even atthe end of twentieth century. Another two models(GISS-AOM and ISPL-CM4) also have a rather flatcurve for the first 100 years until the end of the twen-tieth century. Large warm anomalies are simulated bythe high-resolution model developed by Japan[MIROC3.2(hires)] at the end of twentieth century, butwarm anomalies in the midcentury are weak. In manycases the warm anomalies simulated by models have

comparable amplitude to the observed midcenturywarm events, but with a shorter duration.

To provide a quantified estimate of model perfor-mance, we assess the models’ ability to reproduce amidcentury-type warm anomaly by applying the follow-ing criterion, labeled 2/3CRU. A 5-yr running windowis applied to the simulated winter LSAT time series. Adecadal mean is calculated around the maximum valuefound in the models during any portion of a 50-yr pe-riod (1911–60). If the decadal averaged anomaly equalsto or exceeds two-thirds (2/3) of the observed decadal

FIG. 5. Winter LSAT anomalies over the Arctic for individualrealizations of each model. Thick solid black line is the observedtime series based on CRUTS2.0. (left), (middle) The models withnatural forcing included, (right) the models without natural forc-ing in their simulations. All the time series are smoothed with 5-yrrunning mean. Units are in °C.

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Fig 5 live 4/C

mean (0.36°C), then it is considered to be a comparablesimulation. Although the 2/3CRU criterion is an arbi-trary selection, it does provide a quantitative measure.Because we are interested in decadal and longer phe-nomenon, a 2/3CRU criterion of continuous positivetemperature anomalies for 10 years is a minimum re-quirement for examining warm events. The decadalmean of the SAT anomalies for each realization isshown in Fig. 6. Compared with Fig. 3 where 21 real-izations are found to be at least the same or larger thanthe observed at the end of twentieth century, only 3realizations (one each from three models: CCSM3,ECHO-G, and PCM) produced warm anomalies largerthan the observed in the midcentury. Another 14 real-izations from 8 models produced warm anomaly ampli-tudes within two-thirds of the observed value: CCSM3,CISRO-Mk3.0, ECHAM5/MPI-OM, GFDL-CM2.0,GFDL-CM2.1, INM-CM3.0, ECHO-G, and PCM.Over 60% of the realizations (37 out of 60) do notproduce midcentury warm anomalies greater than halfof the observed CRUTS2.0 value (0.27°C). One run[run 2 from MIROC3.2(medres), bar 45] missed the2/3CRU cutoff line by a small fraction. A summary ofthe success rate of the twentieth-century simulations(20C3M) under this criterion is provided in Table 2(third column).

A second test is to compare the variance of the con-trol runs with the variance from observations. Whilethere is almost no “error” in estimating the control runvariance because of their length, one can consider esti-mated confidence limits of the standard deviation fromobservations. The standard deviation of CRUTS2.0 onan interannual time scale is computed from the de-trended time series for 1902–59. The decadal and mul-tidecadal scales are represented by the detrended timeseries with a 5-yr and 15-yr running mean. A simple test

is whether the model standard deviations are less thanthe value of observed standard deviation minus the90% confidence interval based on �2 estimates. Theratios of the model/observed standard deviations ontime scales from interannual to multidecadal are shownin Fig. 7. The effective sample size is estimated basedon a formula by Santer et al. (2000). As a result, the90% normalized confidence limits are (0.83, 1.27),(0.58, 4.42), and (0.51, 15.95) for the three time scales.Nine models [ECHAM5/MPI-OM, GFDL-CM2.0,GFDL-CM2.1, GISS-AOM, GISS-ER, IPSL-CM4,MIROC3.2(hires), MIROC3.2(medres), and MRI-CGCM2.3.2] lie outside the range of the observed vari-ability on decadal to interdecadal time scales (Figs. 7band 7c). The GISS-EH model is excluded due to a largeabrupt change in the time series of its control run. Anautocorrelation analysis further revealed that there isno preferred times scale in all of the model control runs.

Model standard deviations from their control runsare listed in Table 2 (last three columns), with thosewith values within the confidence limit range of theobservations shown bold. Five models (CCSM3,CSIRO-Mk3.0, INM-CM3.0, ECHO-G, and PCM)passed both the variance test in control runs and the2/3CRU criterion in 20C3M simulations (highlighted byyellow). Another four models (CGCM3.1-T47,CGCM3.1-T63, CNRM-CM3, and UKMO-HadCM3)also passed the 90% confidence limit in their controlrun, indicating that these models may have enough in-trinsic variability from the interannual to interdecadaltime scale, yet they fail the 2/3CRU criterion (high-lighted by blue) in their single realization of the 20C3Msimulations. It is therefore important to have multipleensemble runs to evaluate a model’s performance.Three models (ECHAM5/MPI-OM, GFDL-CM2.0,and GFDL-CM2.1) show quite reasonable amplitude

FIG. 6. LSAT anomalies averaged over a decade that is centered in the peak value detected during the 1910–60period in the 20C3M simulation. The thin black line indicates a value that is two-thirds of the observed amplitude.The first gray bar is based on CRUTS2.0. Units are in °C.

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and duration of the midcentury warm events but do nothave enough variance in their control runs based onvariance test. The reason behind this is unclear.

The warm event criterion (2/3CRU) was also appliedto the control runs based on 100-yr segments. As thelength of the control runs of each model ranges from100 to 500 years, the number of the truncated timeseries is different among the models. The “yes” in col-umn 4 of Table 2 indicates that at least one of thetruncated control run time series passes the 2/3CRUcriterion. All of the nine models that passed the vari-ance test for decadal and interdecadal time scales alsopassed the 2/3CRU criterion. The 2/3CRU criterion hasgood correspondence between the control runs and the20C3M simulations with exception for the single runsimulations. The MIROC3.2(medres) model fails to re-produce the midcentury warm events in all three20C3M simulations, even though it passed the 2/3CRUcriterion in its control runs. However this model showsonly enough variance on the interannual time scale, butnot on longer time scales. Two more models(ECHAM5/MPI-OM and GFDL-CM2.0) passed the

2/3CRU criterion in the control run without passing thevariance test in any scale.

In summary, seven models do not have enough vari-ance nor do they produce enough magnitude comparableto the midcentury warm event. These are GISS-AOM,GISS-EH, GISS-ER, IPSL-CM4, MIROC3.2(hires),MIROC3.2(medres), and MRI-CGCM2.3.2. TheFGOALS-g1.0 model has an unrealistic initial condi-tion in its 20C3M simulations and also has a largeabrupt change in its control run, and is therefore ex-cluded.

Rather than calculating across-model means whenassessing projections for future climate, one shouldconcentrate on those models that simulate reasonableresults in the past. Based on the present study, we sug-gest a subgroup of 12 models, for further review, thatpassed either the criterion based on their 20C3M simu-lations or the variance test in their control runs. Fivemodels are of special note, passing both criteria:CCSM3, CSIRO-Mk3.0, INM-CM3.0, ECHO-G, andPCM. Seven other models have, at best, limited appli-cability for projections of change relative to natural

TABLE 2. Statistics of model runs that produced two-thirds the amplitude of the observed winter LSAT anomalies over for 60°–90°Nin midcentury and variance analysis of their corresponding control runs. The bold fonts indicate that the standard deviations could notbe excluded compared to the 90% confidence limit of the observed time series based on the �2 test. The yellow highlights the modelspassing both the 2/3CRU test in 20C3M simulations and the variance test in control run, the blue highlights the models passing thevariance test but not the 2/3CRU test, and the pink highlights the model passing the 2/3CRU test in both 20C3M simulation and controlruns, but not the variance test in control runs. The green highlights the model passing the test only in 20C3M.

Model

20C3M runs Control runs

Numberof runs 2/3CRU 2/3CRU

Std dev (°C)

Interannual Decadal Interdecadal

CCSM3 8 3 Yes 0.58 0.37 0.29CGCM3.1 (T47) 1 0 Yes 0.69 0.40 0.27CGCM3.1 (T63) 1 0 Yes 0.68 0.39 0.25CNRM-CM3 1 0 Yes 0.67 0.42 0.31CSIRO-Mk3.0 3 2 Yes 0.61 0.38 0.26ECHAM5/MPI-OM 3 1 Yes 0.5 0.29 0.18FGOALS-g1.0 3* 0*GFDL-CM2.0 3 3 Yes 0.42 0.24 0.17GFDL-CM2.1 3 1 No 0.38 0.20 0.14GISS-AOM 2 0 No 0.36 0.17 0.11GISS-EH 5 0GISS-ER 9 0 No 0.37 0.19 0.13INM-CM3.0 1 1 Yes 0.82 0.46 0.3IPSL-CM4 1 0 No 0.5 0.27 0.18MIROC3.2 (hires) 1 0 No 0.42 0.21 0.09MIROC3.2(medres) 3 0 Yes 0.57 0.34 0.19ECHO-G 5 4 Yes 0.66 0.37 0.24MRI-CGCM2.3.2 5 0 No 0.42 0.19 0.11PCM 4 2 Yes 0.62 0.33 0.22UKMO-HADCM3 1 0 Yes 0.57 0.36 0.24Total ensemble runs 60 17 (28%)Total No. of models 19 8 12 10 9 9

* Indicates that the number is excluded from the statistics.

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Fig Table 2 live 4/C

variability: CGCM3.1-T47, CGCM3.1-T63, CNRM-CM3, UKMO-HadCM3 (passed the standard deviationtest), ECHAM5/MPI-OM, and GFDL-CM2.0 andGFDL-CM2.1 (passed the 2/3CRU test). Figure 8 (toppanel) shows the time series from sixteen 20C3M real-izations from eight models that replicated a reasonablemagnitude compared to the observed midcentury warmevent. Almost all the realizations that replicate the mid-century warm anomaly amplitudes at random timingalso produce reasonable magnitude of warm anomaliesat the end of twentieth century. The bottom panel inFig. 8 shows the truncated 100-yr time series from con-trol runs from the nine models that passed the variance

test. The maximum anomaly of these control runs islined up at year 1937 with the CRUTS2.0 analysis. Fig-ure 8b shows that the midcentury warm anomalies inthe models can be reproduced under no-external forc-ing conditions, whereas the late-twentieth-centurywarming cannot.

Because external forcing (either natural or anthro-pogenic) is not imposed on the control runs, we con-sider variability in Fig. 8b to be representative of in-trinsic climate variability, including internal feedbackprocesses from atmosphere–sea ice–ocean interac-tions. Although more than half of the twentieth-centurysimulations have natural forcing, that is, solar and vol-

FIG. 7. The ratio of standard deviation of model control runs to the observed (CRUTS2.0)on (a) interannual, (b) decadal, and (c) interdecadal time scales. GISS-EH is excluded fromthe figure due to a large abrupt change found in its control run. All standard deviations arecalculated after the time series is detrended and a (b) 5-yr and (c) 15-yr running mean applied,respectively. The dashed line indicates the lower range of the 90% confidence limit on thestandard deviation normalized by CRUTS2.0.

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canic aerosols (as shown in the last column of Table 1),comparison of the magnitude of SAT anomalies inthe Arctic for twentieth-century simulations before1980 with those of the control runs, and the randomtiming of midcentury warm events in the 20C3M simu-lations, support the conclusion that intrinsic variabilityis a first-order effect of arctic climate. The similarity

of midcentury events in the 20C3M simulations com-pared to the control runs and the qualitatively differentbehavior of the time series at the end of twentieth cen-tury are evidence that the midcentury Arctic warmingevent in the observational data was due to differentcauses from those of the late-twentieth-century warm-ing.

FIG. 8. (top) Winter LSAT anomalies averaged over the Arctic based on model ensemble runs that passed theproposed 2/3CRU criterion in their 20C3M simulations. (bottom) The truncated 100-yr-long time series fromcontrol runs of the nine models that pass the variance test. All time series are smoothed with a 5-yr running mean.Models with natural forcing are shown in solid lines, while those without are shown in dashed lines. All modelsshow upward warming trend in the Arctic for the last two decades in 20C3M scenario, while none is shown in thecontrol runs. The range of variation during 1911–60 is about the same in the 20C3M simulations as well as in thecontrol runs.

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Fig 8 live 4/C

4. The spatial distribution of midcentury warmanomalies

Based on observations from 59 Arctic stations, Over-land et al. (2004) found that warm anomalies in themidcentury were regional and episodic, that is, interan-nual to multiyear. An example of the spatial distribu-tion of 5-yr-averaged winter temperature anomaliesfrom CRUTS2.0 (Fig. 9, top left) shows that in themid-1930s the warm anomalies were regional in extent:Eurasia and Greenland were dominated by warmanomalies with largest amplitude in northern Scandina-via, while North America was occupied by cold anoma-lies. Around the 1940s (Fig. 9, bottom left), warmanomalies were found with the largest amplitudes overcentral Alaska and east Siberia. During both periodsthere was an out of phase (seesaw) pattern between theeastern and western Arctic.

The spatial patterns from one realization for each ofthe eight models identified by the 2/3CRU criterion areshown in Fig. 9. All eight display warm anomalies overAlaska, except ECHAM5/MPI-OM (bottom-rightpanel). Five models (CSIRO-Mk3.0, ECHAM5/MPI-OM, ECHO-G, INM-CM3.0, and GFDL-CM2.0) pro-duced the seesaw pattern. CCSM3 and both GFDLmodels produced a pattern similar to the mid-1930s ofCRUTS2.0 (top-left panel in Fig. 9) with warm anoma-lies over the central Eurasian continent and coldanomalies over the central North American continentand along the east coast of Greenland. The ECHAM5/MPI-OM model also produced a similar feature; how-ever, the center is over the continent instead of overScandinavia. The pattern produced by CSIRO-Mk3.0 issimilar to the 1940s observations (bottom-left panel inFig. 9). The ECHO-G model displays the seesaw pat-tern, with the amplitudes of the warm anomalies

FIG. 9. Spatial distribution of LSAT anomalies based on (left) CRUTS2.0 and (right) one realization each from the eight models thatpass the 2/3CRU criterion. All patterns have a 5-yr running mean applied. The years selected are around maxima of the warm anomaliesduring 1911–60 for both the observation and models. Contour interval is 0.5°C.

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Fig 9 live 4/C

weaker than the observed, and the position of the coldanomaly center shifted to the east. The PCM model haswavenumber-1 pattern, but the region of the coldanomalies is smaller than the observed. The contrast ofobserved warm anomalies in 60°–70°N and cold anoma-lies between 50° and 60°N over Eurasia is not seen inthese models. Even though models agree on domain-averaged anomalies, their spatial distributions differ sig-nificantly, which also suggests the importance of re-gional intrinsic variability due to shifts in atmosphericcirculation patterns.

5. Conclusions

The mid-twentieth-century warm event in the Arcticis an interesting phenomenon of relevance to currentclimate change issues. The simulated results byAOGCMs add to the understanding of this event. Weare encouraged that all model ensembles for IPCCAR4 portray upward trends to various degrees for theArctic in the last two decades of the twentieth century.Five models reproduce somewhat reasonable ampli-tudes compared to the midcentury event and have com-parable variance to arctic temperature observations:(CCSM3, CRISO-Mk3.0, INM-CM3.0, ECHO-G, andPCM). Three other models (ECHAM5/MPI-OM,GFDL-CM2.0, and GFDL-CM2.1) also reproducedreasonable magnitudes compared to the midcenturywarm event, even though the intrinsic variability in thecontrol runs is small. However, all of these models donot have the sustained duration of the observed mid-century event. Four additional models cannot be ex-cluded based on the variance test of their control runs(CGCM3.1-T47, CGCM3.1-T63, CNRM-CM3, andUKMO-HadCM3), but they fail to reproduce the re-quired magnitude of midcentury warm anomalies intheir single realization in the 20C3M simulations.We consider that the eight models [GISS-AOM, GISS-EH, GISS-ER, IPSL-CM4, MIROC3.2(hires),MIROC3.2(medres), MRI-CGCM2.3.2, and FGOALS-g1.0] that did not pass both criteria (magnitude in20C3M simulation and control runs variance) do nothave enough intrinsic decadal variability to produce areasonable magnitude for arctic warm anomalies. Pass-ing our criteria is not a complete acceptance of themodels for climate projections in the Arctic, only thatthey should be given priority in assessments of pro-jected change relative to natural variability.

The random timing of the midcentury warm anoma-lies in the model 20C3M simulations together with thesimilarity of midcentury events in the 20C3M simula-tions to the control runs (with neither natural nor an-thropogenic external forcing), and the qualitative dif-ference in the behavior of their time series in the early

and end of the twentieth century, are evidence that themidcentury Arctic warming event in the observationaldata was due to different causes from those of the latetwentieth century. The intrinsic variability of the atmo-sphere together with the feedbacks between the atmo-sphere and other components of the climate system(e.g., sea ice, ocean, and land processes) are likely re-sponsible for the observed warm anomalies in the mid-century, as also noted by Bengtsson et al. (2004).

Finally, in IPCC TAR, the ACIA report, and otherdocuments, the projections from the climate models areoften the averages from all of the models and theirensemble members. We suggest that the projection ofthe future climate should be based on a subgroup ofmodels that perform reasonable simulations of the pastbased on fixed criteria. Here, eight models have seriouslimitations for the near-term Arctic climate predictions(20–50 yr), because of the lack of the potential interplayof anthropogenic contributions and intrinsic variability.On the other hand, five models show promise in thisaspect, and another seven might be further consideredwith reservation. Our results are a step toward con-straining the currently scattered projections of the Arc-tic climate (see, e.g., Symon et al. 2005).

Acknowledgments. We acknowledge the interna-tional modeling groups for providing their results foranalysis, the PCMDI for collecting and archiving themodel data, the JSC/CLIVAR Working Group onCoupled Modelling (WGCM) and their Coupled ModelIntercomparison Project (CMIP) and Climate Simula-tion Panel for organizing the model data analysis activ-ity, and the IPCC WG1 TSU for technical support. TheIPCC data archive at LLNL is supported by the Officeof Science, U.S. Department of Energy. We thank threeanonymous reviewers for their thorough review andsuggestion in the review process, which helped up keepgood focus on the discussion. This research is supportedby the NOAA/CMEP Project of Office of Global Pro-grams and the NOAA/Arctic Research Program.Kattsov and Pavlova were supported by the NSF via theIARC (Subaward UAF05-0074 of OPP-0327664).Zhang was supported by Japan Agency for Marine-Earth Science and Technology. Preparation of thismanuscript was supported by the NOAA/Arctic Re-search Office. This publication is partially completedthrough the Joint Institute for the Study of the Atmo-sphere and Ocean (JISAO) under NOAA CooperativeAgreement NA17RJ1232.

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