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Externally Forced and Internal Variability in Ensemble Climate Simulations of theMaunder Minimum

MASAKAZU YOSHIMORI,* THOMAS F. STOCKER, CHRISTOPH C. RAIBLE, AND MANUEL RENOLD

Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland

(Manuscript received 2 December 2004, in final form 21 April 2005)

ABSTRACT

The response of the climate system to natural, external forcing during the Maunder Minimum (ca. A.D.1645–1715) is investigated using a comprehensive climate model. An ensemble of six transient simulationsis produced in order to examine the relative importance of externally forced and internally generatedvariability. The simulated annual Northern Hemisphere and zonal-mean near-surface air temperature agreewell with proxy-based reconstructions on decadal time scales. A mean cooling signal during the MaunderMinimum is masked by the internal unforced variability in some regions such as Alaska, Greenland, andnorthern Europe. In general, temperature exhibits a better signal-to-noise ratio than precipitation. Meansalinity changes are found in basin averages. The model also shows clear response patterns to volcaniceruptions. In particular, volcanic forcing is projected onto the winter North Atlantic Oscillation indexfollowing the eruptions. It is demonstrated that the significant spread of ensemble members is possible evenon multidecadal time scales, which has an important implication in coordinating comparisons betweenmodel simulations and regional reconstructions.

1. Introduction

There are several reasons for studying climate vari-ability in the last millennium even though only proxy-based reconstructions are available during most of theperiod (Jones et al. 2001; Bradley et al. 2003; Moberg etal. 2005). Climate change in the last decades is de-scribed with a long-term perspective, and its relevanceto anthropogenic forcing is inferred from reconstruc-tions (Mann et al. 2003, and references therein) andtested by climate models. In addition, long-termrecords are necessary to obtain a robust picture of vari-ability on decadal to centennial time scales. Climateduring the preindustrial period is influenced mainly bynatural, external forcing and little “contaminated” byvarious anthropogenic radiative forcing agents. Theknowledge of the response pattern and natural variabil-

ity will lead to a better understanding of the climatesystem and contribute to the attribution of the recentclimate change.

Evolution of climate in the past and the future maybe viewed as one trajectory in phase space whereby itsstructure is determined by physical principles, and itsexact path is subject to stochasticity (Lorenz 1963).From this point of view, analyses using observationalrecords and reconstructions suffer from stochastic un-certainty as they represent only a single realization.This uncertainty is, indeed, taken into account in thedetection of climate change (Mitchell et al. 2001) andthe Bayesian estimate of model and forcing parameters(Forest et al. 2002; Knutti et al. 2002). In the frameworkof climate modeling, the stochastic uncertainty corre-sponds to the variability that is present without time-varying external forcing. On the other hand, Palmer(1998, 1999) formulated the fundamental paradigm thata weak, externally forced signal is captured as changesin probability density functions of the internal noise inthe Lorenz model. In consequence, it may be difficult todistinguish externally forced and internal variabilities.Hence there is no direct way to verify the internal vari-ability generated by models. Nevertheless, some confi-dence in model viability in simulating realistic variabil-ity may be gained by examining the natural variability.

* Current affiliation: Center for Environmental Prediction,Rutgers–The State University of New Jersey, New Brunswick,New Jersey.

Corresponding author address: Masakazu Yoshimori, Centerfor Environmental Prediction, Rutgers–The State University ofNew Jersey, 14 College Farm Road, New Brunswick, NJ 08901-8551.E-mail: [email protected]

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© 2005 American Meteorological Society

JCLI3537

There have been a number of studies that focus onthe comparison of time series of global variables overthe last centuries between model simulations and re-constructions (e.g., Robock 1978a,b; Free and Robock1999; Crowley 2000; Bertrand et al. 2002; Bauer et al.2003; Gerber et al. 2003). Usually, these studies areconducted using relatively simple models owing to therequired length of the integration, but they are useful inaddressing the evolution of global or hemispheric vari-ables. Also, such computationally efficient models areable to perform a large number of ensemble simula-tions and hence to incorporate forcing uncertainties. Toextend these studies further to regional scales (Luter-bacher et al. 2002, 2004), comprehensive climate mod-els are, however, necessary. The possible steps wouldbe to 1) extract the externally forced signal from en-semble simulations, 2) compare with reconstructions,and 3) understand the underlying mechanisms. In thepresent study, we focus primarily on the first step in thecontext of all three being a long-term goal. CoupledGCMs are computationally expensive, and often only asingle simulation is conducted (Zorita et al. 2004). Toacquire a reasonable size of ensemble members, werestrict our time window to the relatively short periodof the Maunder Minimum.

The Maunder Minimum is characterized by a glob-ally distinct and prolonged cold period of a few decadesin the late seventeenth/early eighteenth century (Eddy1976). It is well known that the sunspot number, aproxy of solar activity, was significantly low during thisperiod. In a long-term perspective, it is sometimes de-scribed as a climax of the Little Ice Age from whichtime the earth gradually warms up until the warming isfurther accelerated by anthropogenic greenhouse gases.Concurrent volcanic eruptions during the MaunderMinimum and the presence of the internal climate vari-ability complicate the picture. The relative importanceand combination of these effects are not fully under-stood. We address issues on extracting the externallyforced signal during this period.

The outline of the rest of this paper is as follows. Inthe next section we briefly describe the model. Section3 presents the experimental design, the results of whichare presented in section 4, and the discussion follows insection 5. We conclude with a summary in section 6.

2. Model description

The model used in this study is the Community Cli-mate System Model (CCSM) version 2.0.1 developedby the National Center for Atmospheric Research(NCAR; Kiehl and Gent 2004). The model consists ofatmosphere, ocean, land surface, and sea ice compo-nents, and they are coupled without flux corrections.

We choose the lower-resolution setting from the twodifferent resolutions of the model officially supportedby NCAR, based on the available computational re-sources.

The atmospheric component is a primitive equationmodel in which the spectral transform method, withtriangular truncation at zonal wavenumber 31, is em-ployed to represent horizontal variations. This yields anapproximate resolution of 3.75° in both latitude andlongitude. For vertical discretization, a finite-differenceformulation on a hybrid coordinate system is used.There are 26 unevenly spaced sigma-pressure levels be-tween the surface and 2.9 hPa: about 11 to 16 levelsfrom the surface up to the tropopause. The land surfacecomponent shares the same horizontal resolution withthe associated Gaussian grid of the atmospheric com-ponent. There are 10 soil layers, in which temperatureand moisture (water and ice) are computed, and thick-ness-dependent multiple snow layers with a maximumof 5. A grid box may fractionally contain up to 5 dif-ferent surface types (glacier, lake, wetland, urban, andvegetated) with 4 to 16 different vegetation types. Riverrouting is predetermined based on topography data.

The ocean component is a primitive equation modelin which the finite-difference method is employed forthe discretization. It has a longitudinal resolution of�3.6° and variable latitudinal resolutions, which arerefined in the Tropics up to �0.9° with an average of�1.8°. Numerical poles are located in Greenland andAntarctica. There are 25 unevenly spaced depth levelsbetween the surface and 5000 m in depth: 5 and 13levels from the surface down to 100 and 1000 m, re-spectively. The sea ice component shares the same hori-zontal resolution as the ocean component. It includesmulticategory ice thickness distribution, thermodynam-ics, and dynamics and employs the elastic–viscous–plastic ice rheology.

The coupled model reproduces overall a realisticpresent-day climatology except that the Atlantic me-ridional overturning circulation is weak [�7 Sv (1 Sv �106 m3 s�1)]. This contributes to a large cold bias innorthwestern Europe. More detailed descriptions ofthe model components are available online at theNCAR CCSM Web page (http://www.cgd.ucar.edu/csm/).

3. Experimental design

We conduct four types of model simulations: a pres-ent-day control (hereafter CTRL), a steady Maunder Mini-mum (SMM), a transient Maunder Minimum (TMM),and a solar-forcing-only TMM (STMM) simulation. Inthe CTRL and SMM simulations, perpetual A.D. 1990and 1640 forcings are applied, respectively (Table 1).

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In the CTRL simulation, the model is integrated for270 years starting from the modern climate state basedon observations. After about 100 years of integration,the model reaches a quasi-steady state, and we use thedata of the last 76 yr in our analysis. The 76 yr corre-spond to the length of each TMM simulation (A.D.1640–1715). Independent of this simulation, we also usemodel output from the CTRL simulation with the iden-tical model except that the computation is made on adifferent computer (Linux PC cluster) in which themodel is integrated for as much as 565 years (Renold etal. 2004). The data of the last 465 yr are used in ouranalysis. (For clarity, we note that these data are usedonly in Figs. 2, 4, and 10 in which a longer controlsimulation is preferable.) Note also that there is nosignificant difference in the climatological mean statebetween the two CTRL simulations.

For the SMM simulation, we spin up the model, start-ing from the steady-state CTRL simulation with forcingconditions of A.D. 1640 (Table 1), until the upper �100m of the ocean temperature reach an approximatesteady state. We use the so-called acceleration tech-nique (Bryan 1984) in the early part of this spinupphase to speed up the ocean model convergence to thesteady-state solution. The model is then integratedwithout acceleration during the later part of the spinupphase to ensure the consistency of the solutions. Themodel is further integrated for another 76 years to beused in our analysis and to provide initial conditions forthe TMM simulations.

In the TMM simulations, a time-varying forcing,which includes the effect of solar activity and volcaniceruptions, is applied. The forcing is crudely represented

in the model in terms of changes in the total solar ir-radiance (TSI) without any change in the spectral dis-tributions. The volcanic forcing data are based onCrowley (2000), and solar forcing data are constructedby linearly scaling Crowley (2000) to Lean et al. (1995)over the last 400 years. Note that there are large un-certainties in these reconstructions, which are discussedin section 5 in detail. The annual mean TSI values re-lated to solar variations are linearly interpolated intime, while those related to volcanic eruptions are keptconstant throughout the year, resulting in a squarelikeforcing (Fig. 1). By doing so, we allow the annual ra-diative forcing due to volcanic eruptions to be consis-tent with that used by Crowley (2000) and also intro-duce no bias in forcing to a particular season. Note thata similar implementation was employed by Zorita et al.(2004). The solar forcing gradually decreases from 1640to 1688 by 1.30 W m�2 in TSI, followed by a continuousincrease until 1715 by 0.47 W m�2. There are five majorvolcanic forcing peaks: A.D. 1641, 1667, 1674, 1681, and1695. These volcanic forcing peaks range from �22.0 to�9.6 W m�2 in TSI. In the real world, peaks of volcanicforcing could occur in the following year of volcaniceruptions as it takes on the order of months for strato-spheric sulfate aerosols to spread over from low to highlatitudes. In this paper, however, we simply define peakvolcanic forcing years as volcanic eruption years. Weconduct an ensemble of six transient simulations, inwhich the integrations start from different years in theSMM simulation. Starting years are at least 10 yearsapart from each other to maintain the independence ofeach ensemble member. The greenhouse gas concen-trations (CO2, CH4, N2O, CFC-11, and CFC-12) arefixed at the level in A.D. 1640 based on ice core records(Blunier et al. 1995; Flückiger et al. 2002; Siegenthaleret al. 2005) because radiative forcing changes due tothese gases during the Maunder Minimum are esti-mated to be too small to significantly influence the cli-mate. Additionally, a single STMM simulation is con-ducted in which volcanic forcing is removed from theTMM simulations.

Note that the effect of variations in orbital param-eters is not considered in the present study, that is, A.D.1990 orbital parameters are used in all experiments.Forcings associated with land-use change and tropo-spheric aerosols are also not included.

4. Results

a. Temperature changes

To facilitate the interpretation of the results andcharacterize the model against other coupled GCMs,we estimate the approximate climate sensitivity of the

TABLE 1. Forcing used in the CTRL and SMM simulations. Thedifference in radiative forcing between the two simulations is alsopresented. Radiative forcing due to greenhouse gases is calculatedusing the formula in Ramaswamy et al. (2001), which includes theeffect of fast stratospheric adjustments. For the CO2, only therange of estimates yielded by the three different formulas is pre-sented. TSI denotes the total solar irradiance. A planetary albedoof 0.3 is assumed in converting the TSI to the instantaneous ra-diative forcing.

Radiativeforcingagent

CTRL(A.D. 1990)

SMM(A.D. 1640)

Differencein radiative

forcing(W m�2)

TSI 1367.0 W m�2 1365.1 W m�2 0.33CO2 355 ppmv 276 ppmv 1.34–1.42CH4 1714 ppbv 691 ppbv 0.47N2O 311 ppbv 270 ppbv 0.13CFC-11 0.280 ppbv 0 ppbv 0.07CFC-12 0.503 ppbv 0 ppbv 0.16

Total — — 2.51–2.58


model from the CTRL and SMM simulations. The an-nual global-mean near-surface air temperature in theCTRL and SMM simulations are 13.8° and 12.3°C, re-spectively. After taking into account the radiative im-balance at the top of the atmosphere, it yields the cli-mate sensitivity parameter of about 0.68 K (W m�2)�1

(Table 1). This is equivalent to 2.5°C global warmingbetween equilibrium states under the doubling of at-mospheric CO2 concentration (3.7 W m�2: Ramaswamyet al. 2001). This value is slightly larger than the one inthe higher-resolution setting of the same model (Kiehland Gent 2004), and it belongs to the lower climatesensitivity group in the models used for the projectionsof future climate change in the IntergovernmentalPanel on Climate Change (IPCC) Third AssessmentReport (TAR; Cubasch et al. 2001). Therefore, we as-

sume that the results in this study are conservative anda higher sensitivity would show even larger responses.

The annual global-mean near-surface air tempera-ture changes are shown in Fig. 1. Temperature changesin the SMM and TMM simulations are distinctive, andthe effect of external forcing on global-mean tempera-ture is obvious. The departures in the TMM simulationsfrom the SMM simulation is prominent during the yearsof major volcanic eruptions. In addition to these abruptexcursions, low-frequency variability is noticeable.There is a cooling trend approximately from 1660 to1680 and a warming trend from 1680 to 1715. The iden-tification of the causes of the low-frequency variabilitybetween solar and volcanic forcing is not self-evident.Although the shape of the long-term trend in tempera-ture changes is somewhat similar to that in the solar

FIG. 1. (top) Total solar irradiance used in the TMM simulations. Abrupt drops correspondto volcanic forcing, while gradual changes represent solar forcing. (bottom) Simulated annualglobal-mean near-surface air temperature changes. Gray lines represent each ensemble mem-ber in the TMM simulations, thick solid line represents the TMM ensemble mean, and thinsolid line represents the STMM simulation. The horizontal dashed–dotted lines indicate �1standard deviation of the SMM simulation centered on its mean.


forcing, the repeating volcanic eruptions and the inertiaof the climate system might be able to create a similarshape. Nevertheless, we attribute the long-term trendsmainly to the solar forcing owing to the following rea-sons: the peak-to-peak solar forcing between 1640 and1688 is about 0.23 W m�2 in radiative forcing and mayaccount for as much as 0.16°C temperature change ifthe system is allowed to adjust to a modified forcing foran extended period of time. In addition, the responsetime of the global-mean near-surface air temperature tovolcanic forcing is about 4 yr in this model (discussed inmore detail in section 4d; see also Fig. 17), and there-fore the signals from each volcanic forcing should bewell separated. Although it is only one realization, theSTMM simulation supports this interpretation.

The standard deviation of the annual global-meannear-surface air temperature in SMM and TMM simu-lations are 0.10° and 0.12°C, respectively. The lattervalue is the average of six standard deviations calcu-lated from each TMM simulation. In detection and at-tribution studies of climate change, uncertainty rangesare often estimated using the model-generated internalvariability, and hence the results rely on the assumptionthat models produce the correct magnitude of internalvariability (Mitchell et al. 2001). There is no direct wayto verify the model-generated internal variability be-cause the instrumental records are already subject toincreasing atmospheric greenhouse gases and other an-thropogenic, radiatively active species. To some extent,the confidence may be given if the model is capable ofstimulating the natural variability seen in proxy recon-structions. Some reconstructions of temperature exhibitlarger variability than that generated internally by mod-els (Mitchell et al. 2001). The result shown here sug-gests that the difference could be explained by the natu-ral, external forcing. Since we have only one realizationof the SMM simulation, we also compare the meanstandard deviation of six TMM simulations with stan-dard deviations of a large number of 76-yr data seg-ments from the long CTRL simulation, in order to gaina more robust picture (Fig. 2). The ratio in standarddeviation of the TMM simulations to the CTRL simu-lation varies over a wide range of values. Nevertheless,it generally shows the increase of variability, consistentwith the above result.

A comparison of low-pass filtered Northern Hemi-sphere near-surface air temperature changes betweenmodel simulations and proxy-based reconstructions isgiven in Fig. 3. The low-pass filter is imposed to mini-mize the effect of the internal, interannual variabilityand sampling fluctuations on the comparison. The com-parison is made in terms of departures from the average

FIG. 2. Histogram for ratios in standard deviation of the TMMsimulations to the CTRL simulation. Each ratio is calculated be-tween the average standard deviations of six TMM simulationsand the standard deviation of each segment of the time seriesextracted from the CTRL simulation by applying a 76-yr-longsliding window. A total of 390 time windows are sampled. The binwidth is 0.05. The level where the two standard deviations areequal is indicated by the vertical dashed line.

FIG. 3. Comparison of Northern Hemisphere near-surface airtemperature changes between the ensemble mean of the TMMsimulations and proxy-based reconstructions of Briffa et al.(1998), Jones et al. (1998), Mann et al. (1999), and Esper et al.(2002). For clarity and the emphasis on decadal variations, a low-pass filter is imposed on each time series, which removes fluctua-tions with periods less than 4 yr (Hurrell 1995). Temperatureanomalies are expressed as departures from the mean of eachtime series. The dark and light shadings, respectively, represent�1 and �2 standard deviations of ensemble members centered onthe ensemble mean. The size of �2 � errors averaged over theMaunder Minimum period in Mann et al. (1999) is also indicatedby a vertical line.


temperatures over the Maunder Minimum. The simu-lated Northern Hemisphere temperature changes agreerelatively well with the reconstructions by Briffa et al.(1998), Jones et al. (1998), and Mann et al. (1999) ex-cept near 1690 when the model ensemble members ex-hibit unusually small spread by chance and all recon-structions exceed two standard deviations from the en-semble mean. There is little agreement between themodel simulation and Esper et al. (2002). Mann et al.(1999) shows a smaller cooling in the early 1640s com-pared to other data whereas Jones et al. (1998) exhibitslarger cooling in the late 1690s compared to other data.The correlation coefficients between the ensemblemean and reconstructions by Briffa et al. (1998), Joneset al. (1998), Mann et al. (1999), and Esper et al. (2002)are 0.60, 0.68, 0.68, and 0.17, respectively. Esper et al.(2002) has been criticized in that they use only tree ringrecords, which tend to bias the reconstruction towardwarm growing seasons, rather than annual mean, andalso that they used records from only 14 extratropicalsites (Mann et al. 2002). We test this possibility by usingonly simulated data from those 14 grid points that cor-respond to tree ring sites in Esper et al. (2002) andduring the warm season (April to September). The cor-relation coefficients between the “new” and “original”Northern Hemisphere temperature records calculatedfrom the six TMM simulations range from 0.58 to 0.69with a mean of 0.63 (0.75 to 0.85 with a mean of 0.82 iflow-pass filtered). Also, the new record does not im-prove agreement with model simulations. We cannot,however, completely eliminate the possibility that thereconstruction by Esper et al. (2002) represents a morerealistic temperature history than other reconstruc-tions. The simulated results do not contain informationrelated to the long-term memory in the climate systemprior to 1640 and hence may underestimate the centen-nial-scale variability. Indeed, the most recent recon-struction by Moberg et al. (2005), whose correlationcoefficient with the TMM ensemble mean is 0.50, showsmuch larger centennial variability than previously con-sidered.

The agreement of TMM simulations with some re-constructions does not guarantee that the climate sen-sitivity of the model, defined as the equilibrium tem-perature response to the external forcing, is correcteven if the forcing were accurate. Poor correlations be-tween the STMM simulation and the reconstructionssuggest that the agreement arises mainly from volcanicevents, and the volcanic forcing is too short for theclimate system to fully adjust. On the other hand, itgives some confidence that the model is capable ofsimulating a realistic decadal variability in response to

the natural external forcing, at least on hemisphericscales.

Since all ensemble members show the consistentglobal and hemispheric mean temperature changes(Figs. 1 and 3), the relatively high correlations betweensimulations and proxy reconstructions do not likely oc-cur by chance. To ensure this, we also calculate thecorrelation coefficients in the Northern Hemispheretemperature between reconstructions and a large num-ber of 76-yr data segments from the long CTRL simu-lation (Fig. 4). The correlation coefficients betweeneach TMM ensemble member and Jones et al. (1998),or each TMM ensemble member and Mann et al.(1999), range from 0.40 to 0.81 or 0.41 to 0.71, respec-tively. Correlation coefficients between the two recon-structions and some segments of the CTRL simulationexceeds the lowest thresholds of these ranges. How-ever, the average of six correlation coefficients calcu-lated from each TMM ensemble member and Jones etal. (1998) or each TMM ensemble member and Mann etal. (1999) are both 0.58, and there is no combination ofsix segments of the CTRL simulation that reaches thisvalue. In addition, correlation coefficients between theensemble mean and the two reconstructions are muchhigher than this value. Therefore, we conclude that thehigh correlations between simulations and proxy recon-structions do not occur by chance. This additional evi-dence supports our interpretation that the model doesreproduce a realistic hemispheric-scale temperaturevariability on decadal time scales. Note that these re-sults are insensitive to the use of the low-pass filter andthe window length.

Figures 5a and 5b show the temporal evolution of theannual zonal-mean near-surface air temperatureanomaly in the model ensemble mean and the recon-struction by Mann et al. (1999), respectively. As statedalready, a large cooling in the early 1640s in the modelensemble mean is missing in the reconstruction, and awarming near 1690 in the reconstruction is missing inthe model ensemble mean. Another noticeable differ-ence is that the model ensemble mean shows large vari-abilities in the Northern Hemisphere mid- to high lati-tudes, while the reconstruction only shows moderatevariabilities. Nevertheless, the overall agreement be-tween the two is encouraging. Note that the comparisonin the north of 60°N is not shown as there are few datapoints that are unlikely to represent a zonal mean. Eachmodel ensemble member shows different temporal evo-lution in those latitudes, indicating a dominance of in-ternal variability and reflecting noise rather than signal.To the south of 60°N, however, all ensemble membersexhibit a structure similar to the ensemble mean (notshown), and hence the result is robust. In general,


proxy-based reconstructions necessarily but heavilyrely on tree ring records, which have a bias to representwarm, growing seasons. We tested this possibility byusing only warm season data from model output, butbetter agreement is not obtained.

An important question in investigating the externallyforced variability is whether the response is distinguish-

able from the internal variability. This notion is particu-larly important when one goes toward regional scalesand attempts to compare directly with proxy records.As TSI forcing in the TMM simulations is alwayssmaller than the SMM simulation and the MaunderMinimum is known to be one of the distinct cold peri-ods during the last centuries, we choose the mean tem-perature over the 76-yr Maunder Minimum as a targetvariable and examine if the difference in the mean tem-perature between SMM and TMM simulations is dis-tinguishable.

FIG. 4. Histogram for correlation coefficients between proxy-based reconstructions and 390 time windows along the CTRLsimulation in Northern Hemisphere near-surface air temperature.Each correlation coefficient is calculated between each segmentof the time series extracted from the CTRL simulation by apply-ing a 76-yr-long sliding window and reconstructions from A.D.1640 to 1715: (a) Jones et al. (1998) and (b) Mann et al. (1999).The bin width is 0.05. Vertical dashed lines indicate the mean ofthe correlation coefficients between each ensemble member in theTMM simulations and reconstructions. Vertical dashed–dottedlines indicate the correlation coefficient between the ensemblemean and reconstructions. None of the CTRL windows reachesthe high correlations between the forced ensemble and the proxyreconstruction.

FIG. 5. Hovmöller diagram for zonal-mean near-surface air tem-perature changes during the Maunder Minimum (°C): (a) en-semble mean of the TMM simulations and (b) Mann et al. (1999)reconstruction. All values are subject to the same low-pass filterused in Fig. 3. The zonal-mean field of the model output is ob-tained by first interpolating the data to reconstruction grids, andthen using the values only at grid points where reconstruction dataexist. Contour interval is 0.03°C and zero contours are omitted.Negative values are indicated by shading.


First, we investigate large-scale signal-to-noise rela-tions (Fig. 6). Here signal and noise denote the exter-nally forced response and internal variability, respec-tively. The mean temperature of the SMM simulation isestimated with a 5%–95% confidence interval. Themean temperature of each TMM ensemble member isthen calculated and evaluated in terms of a departurefrom the mean of the SMM simulation. This procedureis repeated for all regions individually. Although thequestion of the suitability of the ensemble size remains,all ensemble members in the TMM simulations are wellseparated from the SMM simulation. The NorthernHemisphere and land averages show larger internalvariability in the SMM simulation and a wider spread ofensemble members than others, presumably because ofthe smaller heat capacity of the continents.

Second, we investigate regional-scale signal-to-noiserelations (Figs. 7 and 8). Although the use of predeter-mined regional masks is somewhat subjective, the in-terpretation of the results is rather straightforward, andit provides useful information in terms of the possibilityof comparison with proxy records. Figure 8 exhibitscooling in the TMM ensemble members in most re-gions. However, in Alaska, Greenland, and northernEurope, the TMM ensemble members are not clearly

distinguishable from the SMM simulation. This impliesthat the comparison with proxy records in these regionsis challenging. Some other regions such as southernSouth America, western North America, and centralNorth America show that one of the ensemble mem-bers overlaps with the uncertainty range of the SMMsimulation, implying that proxy records may not cap-ture the externally forced signal beyond internal vari-ability in these regions. In winter, the signal-to-noiserelation deteriorates in most regions whereas it im-proves in some regions and deteriorates in others insummer (not shown). In addition to the difference inthe mean, we also investigates the signal-to-noise rela-tion on multidecadal time scales, that is, a spatial pat-tern that corresponds to a positive trend from 1680 to1715 in annual global-mean near-surface air tempera-ture (Fig. 1). A linear trend in each TMM ensemblemember is calculated, and the range of trends in allensemble members is analyzed and compared withtrends calculated from the Mann et al. (1999) recon-struction (Fig. 8). The ensemble mean shows positivetrends in most of the regions. Owing to the internalvariability inferred from the spread of ensemble mem-bers, even the signs of the trends cannot be definitelydetermined. This implies that caution has to be exer-cised if the model simulations are compared directlywith proxy records because proxy records representonly one unique realization. In contrast to the modelsimulations, the reconstruction exhibits virtually notrend in most regions. It seems that the model overes-timates the trend in many regions although reconstruc-tions still reside within the range of ensemble membersin about half of the regions.

FIG. 6. Comparison in mean Maunder Minimum near-surfaceair temperature between the SMM and TMM simulations. Themean temperatures in the SMM simulation are indicated byempty circles, but shifted to zero. The 5%–95% confidence inter-vals for the estimates of the mean assuming the t distribution arerepresented by vertical bars. The mean temperatures in each en-semble member of the TMM simulations are indicated by solidcircles. Deviations of mean Maunder Minimum temperatures(1640–1715) from the long-term preindustrial means (1716–1850),calculated from reconstructions, are also shown. These are indi-cated by squares, in the order of Briffa et al. (1998), Mann et al.(1999), Esper et al. (2002), and Jones et al. (1998) from the top.

FIG. 7. Region masks used to analyze data over land. The defi-nition of regions in terms of latitude and longitude follows Giorgiand Francisco (2000).


b. Precipitation changes

The annual global-mean precipitation in the present-day and SMM simulations are 2.79 and 2.71 mm day�1,respectively. Assuming a linearity of hydrological re-sponse to radiative forcing, it corresponds to roughly1.3% (W m�2)�1, or equivalently the hydrological sen-sitivity of 4.8% under the doubling of atmospheric CO2

concentration. This value belongs to the lower hydro-logical sensitivity group in the models considered inCubasch et al. (2001), consistent with the low climatesensitivity.

The annual global-mean precipitation changes areshown in Fig. 9. The structure of the temporal evolutionin precipitation is similar to temperature changes (Fig.1), that is, sudden drops due to volcanic eruptions, and

a slow decreasing trend approximately from 1660 to1680, and an increasing trend thereafter. The differencebetween the SMM and TMM simulations is clear inmost of the period.

The standard deviations of annual global-mean pre-cipitation in the SMM and TMM simulations are 9.4 �10�3 and 10.5 � 10�3 mm day�1, respectively. The lat-ter value is the average of six standard deviations cal-culated from each TMM simulation. As in the analysisfor temperature in the previous section, we also com-pare the mean standard deviations of the six TMMsimulations with standard deviations of a large numberof 76-yr data segments from the long CTRL simulation,in order to gain a more robust picture (Fig. 10). It ex-

FIG. 10. As in Fig. 2, but for precipitation rate and the binwidth of 0.02.

FIG. 8. Near-surface air temperature changes in regions definedin Fig. 7. Model output is first interpolated onto 1° by 1° latitude–longitude grids, and then averaged over each region. Values fromMann et al. (1999) are indicated by squares and are calculatedonly in those regions where the existing data cover more than50% of the area of each region. (top) As in Fig. 6; (bottom) lineartrends from 1680 to 1715. The range of trends for six ensemblemembers of the TMM simulations are represented by vertical barswith its mean indicated by solid circles.

FIG. 9. As in Fig. 1 (bottom), but for precipitation rate.


hibits a consistent 10%–30% increase in the standarddeviation when the model is externally forced.

As in the analysis for temperature (Fig. 6), large-scale signal-to-noise relations for precipitation are in-vestigated (Fig. 11). In contrast to temperature, some ofthe TMM ensemble members overlap with the uncer-tainty range of the SMM simulation in hemispheric andland averages. This implies that precipitation has asmaller signal-to-noise ratio than temperature, makingit more difficult to extract the externally forced signal.The regional-scale analysis shows further deteriorationof the signal-to-noise relation (Fig. 12). The externallyforced signal is distinguishable from the internal vari-ability only in Greenland. If we assume that six en-semble members represent the full range of probabilitydensity functions (PDFs) under the solar and volcanicforcing of the TMM simulations, it can be said that thePDFs shift toward more precipitation in some regions(e.g., regions 4, 5, 6, 10, and 19) and less precipitation inothers (e.g., regions 8 and 21) under the TMM forcingconditions. However, there is no guarantee that proxyrecords capture such signals. In winter, the externallyforced signal is not clearly distinguishable from the in-ternal variability in all regions (not shown). In summer,it is distinctive only in western North America wherethe externally forced signal indicates wetter conditions,consistent with Cook et al. (2004).

c. Changes in the upper ocean

The annual global-mean ocean temperature changesin the upper �100 m layer are shown in Fig. 13. Theyexhibit a similar structure in the temporal evolution to

the near-surface air temperatures and, visually, they aredistinguishable from the SMM simulation. Salinity, onthe other hand, does not show a significant differencefrom the SMM simulation (not shown) although thepresence of a small drift in salinity field of the SMMsimulation makes the interpretation slightly more com-plicated. Note that the analysis in each level of theocean, rather than the average over 100 m in depth,does not make much difference in the results.

The basin-scale signal-to-noise relation is investi-gated for different ocean regions as given in Fig. 14. Forocean temperature, the signal is detected in all basins

FIG. 13. As in Fig. 1 (bottom), but for global-mean oceantemperature in the upper 100-m layer.

FIG. 11. As in Fig. 6, but for precipitation rate. The precipitationrate is normalized by the mean precipitation rate of each region inthe SMM simulation.

FIG. 12. As in Fig. 8, but for precipitation rate. The precipitationrate is normalized by the mean precipitation rate of each region inthe SMM simulation.


except in the Arctic where the sea ice insulates theocean from the atmosphere (Fig. 15a). The sign oflarge-scale cooling in sea surface temperature (SST)during periods of reduced solar activity is indicatedfrom several proxy records in the western tropical Pa-cific based on coral Sr/Ca and U/Ca records (Hendy etal. 2002), in the eastern tropical Pacific based on coral�18O records (Dunbar et al. 1994), and in the SargassoSeas based on �18O in marine sediments (Keigwin1996). SSTs in the corresponding three locations in theTMM simulation also exhibit coolings compared to theSMM simulation (indices 1, 5, and 6 in Fig. 16). On thecontrary, planktonic foraminifera abundance in marinesediments from the Laurentian Fan (off Newfound-land) indicates warm conditions (Keigwin and Pickart1999), while the model does not show such a warming(not shown). This may be due to poor representation ofthe Gulf Stream, which influences the SST variations ofthis region, in the coarse-resolution ocean model. Inaddition, the marine sediment records from the Sar-gasso Sea and Laurentian Fan, respectively, representcooling and warming over the entire Little Ice Age andare not necessarily captured by the forced experimentsduring the Maunder Minimum alone. Clearly, recon-structions of spatial patterns of the SST anomaly duringthe Maunder Minimum would be useful for the model–data comparison.

In contrast to temperature, salinity does not show aclear difference between TMM and SMM simulationson the global average, which could be influenced byatmospheric moisture content and sea ice volume (Fig.15b). Note that both atmospheric moisture content andsea ice volume are linked to “detected” global-mean

temperature. Interestingly, however, in the Atlanticand Pacific, the salinity signal is detectable with oppo-site signs, that is, decreased salinity in the Atlantic andincreased salinity in the Pacific. This is consistent withcoral �18O records from the Great Barrier Reef thatshow higher salinity in the western tropical Pacific dur-ing the Little Ice Age (Hendy et al. 2002). Sea surfacesalinity (SSS) in the corresponding region from theTMM simulations also exhibits higher values comparedto the SMM simulation (index 2 in Fig. 16). This iscaused by the reduced net moisture transport from theAtlantic to the Indo–Pacific basins in low latitudes. Thetropical cooling due to reduced solar activity and vol-canic eruptions does not only decrease specific humid-ity, but also weakens the tropical convective activityresulting in the weakening of the Hadley circulationand the easterlies in the midtroposphere (indices 3 and

FIG. 14. Region masks used to analyze data in the ocean: At-lantic, Pacific, Arctic, Indian, and Southern Oceans. Also shownare regions of indices in Fig. 16.

FIG. 15. As in Fig. 6, but for the upper 100-m layer of theocean: (a) mean temperature and (b) mean salinity.


4 in Fig. 16). The net loss of salinity in the Atlantic incold climate is consistent with the study by Zaucker andBroecker (1992) in which an atmospheric GCM experi-ment with doubling of CO2 exhibits the net gain ofsalinity in the Atlantic due to increased net moisturetransport from the Atlantic to the Indo–Pacific basins.The ensemble members also tend to show larger salin-ity in the Arctic where temperature shows little differ-ence (Fig. 15). Salinity increase in the Arctic is causedby the enhanced flow through Bering Strait rather thanchanges in surface freshwater fluxes. The throughflowincreases by about 5% in the TMM ensemble meanrelative to the SMM simulation. Surface freshwaterfluxes also increases in the Arctic, which reduces salin-ity there. Note that the effect of increased salinity in thePacific on the salt transport from the Pacific to theArctic is negligible compared to the change in thethroughflow strength. These results suggest that non-

thermal proxy records could be useful to constrain thepast history of the multidecadal variability.

d. Response to volcanic forcing

“Direct” response to volcanic forcing is analyzed us-ing a composite (or superposed epoch) analysis: 30composite members are constructed, for �1 to �6 yrrelative to the volcanic eruptions, from the six TMMensemble members in which each ensemble membercontains five major eruptions; then the differences inthe mean of the composite members between �1 yr(prevolcanic) and 0 to �6 (mid- and postvolcanic) yearsare tested using the nonparametric Mann–Whitney test.We include only one prevolcanic year, for each volcaniceruption, which represents “normal” or volcanic forc-ing free states in order to avoid overlap with responsesto previous eruptions. Note that the Student’s t testgives essentially the same results in the following. Notealso that the inclusion of corresponding years from theSTMM simulation in normal years does not change theresults either.

The annual global-mean near-surface air tempera-ture response to volcanic forcing is shown in Fig. 17.The temporal evolution exhibits overall a similar struc-ture as observations and other model simulations forrecent volcanic eruptions (e.g., Robock and Liu 1994;Robock and Mao 1995; Soden et al. 2002; Broccoli et al.2003). Near-surface air temperature is statistically dis-tinguishable from internal variability up to 3 years after

FIG. 16. Comparison between the SMM and TMM simulationsin various variables averaged over the entire Maunder Minimum.The mean value in the SMM simulation is indicated by emptycircles, but shifted to zero. The 5%–95% confidence intervals forthe estimates of the mean are represented by vertical bars. Themean values in each ensemble member of the TMM simulationsare indicated by solid circles. The indices on the horizontal axisdefine the following variables. Index 1: SST (°C) from 20°S–20°N,120°E–170°W; index 2: SSS from 20°S–20°N, 120°E–170°W; index3: specific humidity (g kg�1) at 850 hPa from 5°–15°N, 120°–20°W;index 4: zonal wind (m s�1) at 500 hPa from 5°S–5°N, 120°–20°W(positive eastward); index 5: SST (°C) from 1°S, 92°W; and index6: SST (°C) from 32°N, 62°W. Note that 5°–15°N is chosen forspecific humidity as the annual mean trade wind is strongest nearthe Atlantic–Pacific border, and 5°S–5°N is chosen for zonal windas the annual mean specific humidity is highest in the model.Indices 1 and 2 correspond to the region where proxies from theGreat Barrier Reef are available, index 5 corresponds to Galápa-gos, and index 6 corresponds to Sargasso Sea.

FIG. 17. Composite mean response of near-surface air tempera-ture to 30 volcanic eruptions in the TMM simulations. Year ofpeak volcanic forcing is defined as year zero. Solid and emptysymbols, respectively, represent statistically significant and non-significant values at the 10% level (of incorrectly rejecting the nullhypothesis) using the Mann–Whitney test. See text for details.


the volcanic eruptions, in agreement with Robock andMao (1995) who used observational data. The strengthof the response seems to be associated with the heatcapacity of the system; that is, response over land islarger than that over ocean, and the Northern Hemi-sphere shows a larger response than the SouthernHemisphere. Observational and previous model studiesalso show that the magnitude of the response is largerin the Northern Hemisphere mean than the SouthernHemisphere mean (Robock and Liu 1994; Robock andMao 1995). There is a lack in statistical significance inthe following year of eruptions except for the land av-erage, indicating a large spread of responses amongcomposite members. Although statistically insignifi-cant, we consider the response in this year as physicallyimportant since the response curve is consistent withthe previous and following years. Furthermore, wespeculate that it may suggest that volcanic forcing af-fects not only the mean climate but also the variance ofthe climate by changing its probability distribution. Theprecipitation exhibits a rather unstructured temporalresponse to volcanic forcing except for the global andland averages in which similar shapes of the responsecurve to those of temperature are seen (not shown). Weinterpret this unstructured temporal response in pre-cipitation without a strong statistical significance as ahighly “contaminated” volcanic signal by internal vari-ability. The smaller signal-to-noise ratio in precipitationthan temperature is also simulated by Robock and Liu(1994). In their study, however, Northern Hemispheremean precipitation shows a “structured” volcanic re-sponse, while it is buried in noise in our simulations. Inthe global and land averages, there are reductions ofprecipitation in years of volcanic eruptions and gradualrecoveries to normal values thereafter, and the signal isstatistically distinguishable up to 3 years after the erup-tions.

As demonstrated earlier, on regional scales, the de-tection of mean changes and multidecadal trends inboth temperature and precipitation during the Maun-der Minimum is challenging. This does not, however,decrease the potential of regional analysis if the signalis carefully extracted. For example, Fig. 18 shows thewinter composite mean response to 30 volcanic erup-tions. Although more striking in the second winter,positive North Atlantic Oscillation (NAO)-like pat-terns, that is, enhanced Icelandic low and Azores high,are seen (Figs. 18a–d). In addition, the spatial patternsof temperature response, warming in northern Europeand northwestern North America and cooling in west-ern Greenland, eastern Canada, the Mediterranean,and northern Africa, are consistent with a typical posi-tive NAO phase during the instrumental period (Figs.

18e and 18f; see also Hurrell 1995). The dynamicallyconsistent patterns between pressure and temperaturesupport the reliability of the analysis. However, thewarming in northern Europe is slightly smaller thanobservations of recent volcanic eruptions (Robock andMao 1995), and little of the warming is statistically sig-nificant. The positive NAO-like pattern following thevolcanic eruptions and associated temperature re-sponse patterns have been found also in observations(Robock and Mao 1992, 1995; Stenchikov et al. 2002),other model studies (Graf et al. 1993; Stenchikov et al.2002; Shindell et al. 2001b, 2003, 2004), and reconstruc-tions (Fischer 2003). It is worthy to mention that thestronger NAO response in the second winter than thefirst winter is in good agreement with reconstructedcomposite mean response patterns from the 16 largevolcanic eruptions during the last 500 years (Fischer2003).

It has been suggested that tropical stratosphericwarming after volcanic eruptions is important in pro-ducing a high NAO index because it increases a me-ridional temperature gradient, which induces a dynami-cal feedback (Shindell et al. 2001b). This mechanismdoes not work in our simulations as stratospheric forc-ing is missing in the experimental setup. Stenchikov etal. (2002) found that tropospheric forcing alone canproduce a high NAO index. The geopotential heightdifferences of �55 and �75 m between centers of ac-tion at 500 hPa, respectively, in the first and secondwinters in our simulations are slightly larger than thoseof Stenchikov et al. (2002) in which �50 m is simulatedin both winters (�70 m, only in the first winters withstratospheric forcing). They argue that the reduction ofvertically propagating stationary waves associated withthe reduced meridional temperature gradient in the tro-posphere results in the stronger stratospheric polar vor-tex, which in turn leads to a high NAO index. On thecontrary, in our simulations, the stationary wave activ-ity increases, presumably because of the increasedland–sea thermal contrast in high latitudes. We attrib-ute the high NAO index to the changes in transienteddy activity and its interaction with mean flow. Activ-ity of transient eddies with periods longer than �10days decreases in the northeastern North Atlantic.Eliassen–Palm flux indicates the reduced westerly de-celeration in the upper troposphere although the neteffect of the meridional eddy heat transport on the de-celeration is controversial (Pfeffer 1987; Hartmann andLo 1998). Activity of synoptic-scale eddies with periodsshorter than �10 days increases in the northern NorthAtlantic along the southern flank of sea ice edge ex-tended to the south after the eruptions. The enhancedbaroclinic eddies and westerly acceleration are consis-


tent with the theory of the so-called eddy-driven jet(e.g., Vallis et al. 2004). The westerly jet is, however,counteracted by the reduced meridional temperaturegradient in northern Europe more strongly in the first

winters. In addition, it is possible that we underesti-mated the first winter response, as the model exhibitsreduced stratospheric meridional temperature gradientwithout stratospheric forcing.

FIG. 18. Composite mean patterns of Northern Hemispheric response to 30 volcanic erup-tions in the TMM simulations. Left and right columns represent the first and second winter(Dec–Feb) following the volcanic eruptions, respectively: (a), (b) geopotential height at 500hPa with a contour interval (CI) of 5 gpm; (c), (d) sea level pressure with CI of 0.5 hPa; and(e), (f) near-surface air temperature with CI of 0.5°C. Shadings represent values that arestatistically significant at the 10% level.


Summer composite mean responses to volcanic erup-tions, during the volcanic years and in the first andsecond years after the volcanic eruptions, exhibit glob-ally colder temperatures with enhanced cooling overland (not shown). The cooling signal is statistically sig-nificant at many grid points over land, implying that thesignal is detectable on grid scales and consistent withFig. 17.

5. Discussion

a. Forcing uncertainty

In the above analysis, one of the very important fac-tors of uncertainty, the accuracy of solar and volcanicforcing history, is not addressed. The uncertainty in re-constructions of solar forcing arises from the lack oflong-term observations, in which satellite measure-ments are available only since the late 1970s, and poorunderstanding of physical mechanisms of secular solarvariability. This makes it difficult to convert solar mag-netic activity to solar irradiance. There is a lack of con-sensus on interpretation and calibration methods ofproxy records. Previous studies have yielded wide-ranging estimates for the TSI reduction in the MaunderMinimum relative to the present day, that is, 0.1%–1%(Reid 1991; Lean et al. 1992; Baliunas and Jastrow1993; Hoyt and Schatten 1993; Nesme-Ribes et al. 1993;Zhang et al. 1994; Reid 1997; Solanki and Fligge 1998;Cliver et al. 1998). In addition, it has been suggestedthat the spectral distribution of solar irradiance changesis important. With spectrally discriminated solar irradi-ances and consequent changes in stratospheric ozoneconcentration, Shindell et al. (2001a) simulated en-hanced regional winter cooling associated with a lowindex state of the NAO during the Maunder Minimum.More recently, however, Lean et al. (2002) questionedthe validity of the geomagnetic and cosmogenic proxiesfor solar irradiance and hence reconstructed low solarirradiance during the Maunder Minimum.

The uncertainty in reconstructions of volcanic forcingmainly occurs in extracting signals, from ice corerecords, representing only the stratospheric sulphateaerosol concentrations associated with climatically sig-nificant volcanic eruptions (Robock and Free 1995,1996; Robock 2000). There are at least two volcanicforcing reconstructions that cover the Maunder Mini-mum: Crowley (2000) and Robertson et al. (2001).When converted to radiative forcing (E. Zorita 2004,personal communication), volcanic forcing peaks in1641, 1667, and 1674 are 2–3 times larger in Crowley(2000) than Robertson et al. (2001), and a volcanic forc-ing peak in 1681 in Crowley (2000) is missing in Rob-ertson et al. (2001). Both reconstructions give a similar

magnitude of radiative forcing for the volcanic forcingpeak in 1695. In our implementation of the volcanicforcing, stratospheric forcing processes, that is, absorp-tion of near-infrared and infrared radiation by strato-spheric sulphate aerosols (Stenchikov et al. 1998), aremissing.

Having such a large uncertainty in both solar andvolcanic forcing and the absence of stratospheric forc-ing owing to our experimental setup, it is possible thatthe model underestimates the response with overesti-mated forcing (or vice versa). Nevertheless, the agree-ment with three reconstructions in the Northern Hemi-sphere temperature changes during the Maunder Mini-mum provide some comfort to address the issues inextracting the externally forced signal and comparing itwith proxy records. In any case, the results presentedhere should be taken as a conceptual guideline towarda meaningful comparison between model simulationsand regional reconstructions.

b. Ensemble size

The number of ensemble members used in thepresent study is constrained by the available computa-tional resources. As the number of ensemble membersincreases (�30), it becomes more statistically meaning-ful to estimate a confidence interval for the ensemblemean, and we expect that the discrimination of the ex-ternally forced signal from the internal variability im-proves in some cases. Therefore, it is possible that weunderestimate the signal-to-noise ratio in our analysisdue to the small number of ensemble members. Nev-ertheless, the proxy records represent only a single re-alization, and it would be difficult to extract physicallymeaningful information from the record, if six exter-nally forced model ensemble members are statisticallyindistinguishable from the internal variability.

6. Summary and conclusions

The isolation of the externally forced variability fromthe internal variability is investigated in the context ofthe ensemble climate simulations of the Maunder Mini-mum. Four types of experiments are conducted usingthe comprehensive climate model: CTRL, SMM, TMM,and STMM simulations. In contrast to the CTRL andSMM simulations, time-varying forcing, including theeffect of solar activity and volcanic eruptions, is im-posed, and six ensemble members are generated in theTMM simulations. Additionally, a solar-forcing-onlysimulation, STMM, is also conducted.

The simulated annual Northern Hemisphere andzonal-mean near-surface air temperatures with time-


varying natural forcing agree well with proxy-based re-constructions on decadal time scales within the uncer-tainty of the reconstructions. The comparison betweenmodel simulations and reconstructions generally be-comes more difficult on regional scales, due to both adecreasing externally forced signal level and an increas-ing internally generated noise level. It is demonstratedthat the significant spread of ensemble members is pos-sible even on multidecadal time scales. In our experi-ments, for example, the externally forced temperaturesignal averaged over the Maunder Minimum is notclearly detectable in Alaska, Greenland, and northernEurope. We find that temperature exhibits a better sig-nal-to-noise ratio than precipitation in many regions.These results have important implications in searchingfor suitable proxies and regions in order to reconstructclimatic response to natural forcing in the past. We alsofind that the externally forced salinity signal is distin-guishable on basin scales. This result suggests that thereis a large potential in understanding the oceanic re-sponse mechanism to natural forcing by optimizing thespatial averaging in capturing the regional signal andcharacterizing the response patterns in ensemble simu-lations and reconstructions. In contrast to decadal vari-ability, the volcanically forced signal on shorter timescales is clear on both global and regional scales. Themodel shows that there is a tendency for the NorthernHemisphere atmosphere to be in the high index state ofNAO in winters after volcanic eruptions and that thesecond winter exhibits the strongest response.

There is a limitation in interpreting solely recon-structed records that represent a single trajectory of theclimate system and include both externally forced andinternal variability. With this limitation, there remainsan issue in verifying the model-simulated natural vari-ability, as it is not possible to remove the possibility thatmodels overestimate the internal variability, and there-fore reconstructions fit within the uncertainty rangeand one might wrongly conclude that they are “consis-tent.” More studies are needed in order to achieve ameaningful comparison between model simulations andregional reconstructions. Reducing the forcing uncer-tainty is by far the most important issue. Describing theexternally forced variability in terms of probability den-sity functions with an ensemble approach and the in-termodel comparison would be useful for both under-standing of the climatic response to natural forcing andselection of specific time window and regions in focusedreconstructions.

Acknowledgments. We are indebted to NCAR fortheir continuous effort in developing and improving theCommunity Climate System Model. We greatly appre-

ciate technical help from the NCAR Ocean ModelWorking Group. We are grateful for authors of citedreconstructions in making their data available to thecommunity. We thank Jan Esper for providing tree ringsite information, Stefan Gerber for preparing somedata, and Anthony Broccoli for useful suggestions onthe manuscript. We enjoyed discussions with Erich Fis-cher. We also thank anonymous reviewers for helpfulcomments that improved the manuscript. Developersof freely available softwares, SCRIP, NCL and Ferret,are appreciated. This work is supported by the NationalCentre of Competence in Research (NCCR) Climatefunded by the Swiss National Science Foundation. Asubstantial part of the computations was conducted onIBM Power 4 at the Swiss National SupercomputingCentre (CSCS) in Manno. Urs Beyerle and Stefan Zol-ler maintained the local computing facility.

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