OCTOBER 1997 2497P A L U T I K O F E T A L .

q 1997 American Meteorological Society

The Simulation of Daily Temperature Time Series from GCM Output. Part I:Comparison of Model Data with Observations

J. P. PALUTIKOF

Climatic Research Unit, University of East Anglia, Norwich, United Kingdom

J. A. WINKLER

Department of Geography, Michigan State University, East Lansing, Michigan

C. M. GOODESS

Climatic Research Unit, University of East Anglia, Norwich, United Kingdom

J. A. ANDRESEN

Department of Geography, Michigan State University, East Lansing, Michigan

(Manuscript received 19 April 1996, in final form 24 March 1997)

ABSTRACT

For climate change impact analyses, local scenarios of surface variables at the daily scales are frequentlyrequired. Empirical transfer functions are a widely used technique to generate scenarios from GCM data at thesescales. For successful downscaling, the impact analyst should take into account certain considerations. First, itmust be demonstrated that the GCM simulations of the required variable are unrealistic and therefore thatdownscaling is required. Second, it must be shown that the GCM simulations of the selected predictor variablesare realistic. Where errors occur, attempts must be made to compensate for their effect on the transfer function–generated predictions or, where this is not possible, the effect on the transfer function–generated climate seriesmust be understood. Third, the changes in the predictors between the control and perturbed simulation must beexamined in the light of the implications for the change in the predicted variable. Finally, the effect of decisionsmade during the development of the transfer functions on the final result should be explored. This study, presentedin two parts, addresses these considerations with respect to the development of local scenarios for daily maximum(TMAX) and minimum (TMIN) temperature for two sites, one in North America (Eau Claire, Michigan) andone in Europe (Alcantarilla, Spain).

Part I confirms for a selected GCM that simulations of daily TMAX and TMIN, whether taken from thenearest land grid point, or obtained by interpolation to the site location, are inadequate. Differences betweenthe GCM 1 3 CO2 and observed temperature series arise because of a 08C threshold in the model data. At bothsites, variability is suppressed during periods affected by the threshold. The thresholds persist into the perturbedsimulation, affecting not only GCM-predicted 2 3 CO2 temperatures but also, because the duration and timingof the threshold effect changes in the perturbed simulation, the magnitude and seasonal distribution of the 2 3CO2 – 1 3 CO2 GCM differences.

Comparison of modeled and observed 500-hPa geopotential height (Z500) and sea level pressure (SLP) showsthat, although systematic errors of the type associated with the 08C threshold in the temperature data are absent,significant errors do occur in certain seasons at both sites. For example, SLP is poorly modeled at Alcantarilla,where the control and observed means differ significantly in every season. The worst results at both sites arein summer. These results will affect the performance of the transfer functions when initialized with model data.Whereas little change is found to occur in SLP at either site between the 1 3 CO2 and 2 3 CO2 simulation,there is a noticeable increase in Z500. Other things being equal, therefore, the temperature changes predictedby the transfer functions are likely to be greatest when Z500 contributes the most to the explained variances.

In Part II, a range of transfer functions are developed from the free atmosphere variables and validated, usingobservations. The performance of these transfer functions when initialized with model data is evaluated in the lightof the findings in Part I. The sensitivity of the perturbed climate scenarios to a range of user decisions is explored.

Corresponding author address: Jean P. Palutikof, Climatic Re-search Unit, University of East Anglia, Norwich NR4 7TJ, UnitedKingdom.E-mail: j.palutikof@uea.ac.uk

1. Introduction

To study the potential impact of enhanced greenhousewarming on economic activities such as agriculture, cli-mate change scenarios at the local scale are often re-quired. Most commonly, local scenario construction has

2498 VOLUME 10J O U R N A L O F C L I M A T E

concentrated on the perturbation in the mean monthly,seasonal, or even annual values of meteorological vari-ables such as temperature or precipitation (for reviewssee Cohen 1990; Giorgi and Mearns 1991). However,these scenarios are often of limited value to impact an-alysts. Changes in variability may be at least as im-portant as changes in the mean (Cao et al. 1992; Katzand Brown 1992; Mearns et al. 1995), and shifts in thetiming of annually occurring thresholds such as the datesof first and last frosts also have important implicationsfor agriculture. Such changes can be evaluated onlyfrom daily time series of the relevant climate variable.

Empirical transfer functions are one approach to‘‘downscaling’’ coarse-scale GCM output to the finertemporal and spatial scale requested by policy analysts.These functions, which statistically relate large-scale av-erages or patterns of one or more predictor variables tolocal values of a surface climate variable (typically tem-perature or precipitation), can be broadly classified intothree types. In the first, the transfer functions relatelarge-scale averages of a surface climate variable to theirlocal values, on the assumption that GCM gridpointsimulations are representative of areal averages (Kim etal. 1984; Carbone and Bramante 1995). A modificationof this approach is to include gridpoint values of cir-culation variables (e.g., sea level pressure and 700- and500-hPa geopotential height) as predictors (Wigley etal. 1990), since the deviation of a climate variable at aparticular site from the areal average may depend inpart on the airflow conditions. The second type of em-pirical transfer function uses only free atmosphere vari-ables as predictors. The argument here is that the GCMsimulations of surface variables should not be includedas predictors because they are influenced by boundaryconditions that may be inadequately parameterized inthe model (e.g., Karl et al. 1990). Examples of thissecond type of transfer function range in complexityfrom those that use a single predictor variable, such astropospheric free atmosphere mean temperature (Chenand Robinson 1991) or 500–1000-hPa thickness (Jo-hannesson et al. 1995) to those that simulate a suite ofsurface variables (maximum and minimum temperature,precipitation, and cloud ceiling) from an ensemble offree atmosphere variables (Karl et al. 1990). The thirdtype of transfer function also uses only free atmospherevariables as predictors, but in addition assumes that thepredictive capacity of GCMs is greatest at the multiplerather than the single gridpoint level (von Storch et al.1993). These transfer functions relate large-scale cir-culation modes (e.g., sea level pressure patterns) to sur-face variables (Hewitson and Crane 1992a,b; Hewiston1994).

No matter what downscaling method is used, the re-sulting local, daily scenarios must be a realistic descrip-tion of conditions for a control climate and a plausibledescription of conditions for a perturbed climate. In ad-dition, we argue that, for the useful and meaningful

interpretation of transfer function–generated scenarios,impact analysts should consider the following points.

First, analysts must verify that the GCM simulationsof the required surface variable, from either the nearestgrid point or interpolated to the study site, are an un-realistic scenario for impact analysis, thus demonstrat-ing the need for downscaling. GCM output is rarely, ifever, directly employed for local climate scenarios, asthis is viewed as a mismatch of spatial scale. Obser-vations are point specific, while the models are usuallythought to represent an areal integration covering abroad area, often of diverse topography (Portman et al.1992; Skelly and Henderson-Sellers 1996). Results fromsome studies, however, have shown a surprisingly strongagreement, especially for surface temperature, betweendaily GCM-simulated series and observed values at sin-gle locations (Portman et al. 1992; Robinson et al. 1993)or averaged over only a few (three or fewer) stations(Rind et al. 1989). Thus, we cannot simply assume thatthe agreement between GCM gridpoint values and sta-tion observations is poor, and that downscaling tech-niques produce more useful scenarios. In fact, there areconsiderable advantages to directly using GCM resultsif these can be shown to agree reasonably with obser-vations. The primary one is that GCM simulations areinternally and physically consistent, incorporating thecomplex interactions between all modeled parametersinfluencing the surface climate variable under consid-eration, whereas empirical transfer functions consideronly changes in the selected predictor variables. Ad-ditionally, transfer function methods must assume thatthe statistical relationship between the predictor vari-ables and predictand established for the current climateremains valid for the perturbed climate. This relation-ship could be invalidated if, for example, cloudinessincreases or decreases substantially in a perturbed cli-mate.

Second, as part of the transfer function development,impact analysts must identify deviations of the GCM 13 CO2 simulations of the candidate predictor variablesfrom the observed series used to calibrate the transferfunctions and evaluate the potential impact of these de-viations on the local climate scenarios. A factor sharedin common by all three types of transfer functions isthat they are initially developed and validated on ob-served series of the predictor variables and predictandsand then applied using GCM simulations of the predic-tor variables. An implicit assumption is that the GCMsimulations of the predictors are an accurate represen-tation of the observed series. However, previous studieshave shown substantial deviations between observationsand the GCM 1 3 CO2 simulations of candidate pre-dictor variables, including the large-scale average of thepredictand (Grotch and MacCracken 1991), gridpointtime series of free atmosphere variables such as 500-hPa geopotential height (Robinson et al. 1993), and thesynoptic-scale fields of free atmosphere variables (Hul-

OCTOBER 1997 2499P A L U T I K O F E T A L .

FIG. 1. The locations of Alcantarilla and Eau Claire. The CCC GCM grid around each site isshown.

me et al. 1993; McKendry et al. 1995; Takle et al. 1995;Risbey and Stone 1996).

Third, climate impact analysts must evaluate thechanges in the predictor variables between the GCMcontrol and perturbed simulations. Perturbed minus con-trol differences in the scenarios can be the result onlyof changes in the predictor variables. Analysts mustunderstand how the predictor variables change in orderto interpret the changes in the predictand suggested bythe perturbed climate scenarios, and make a meaningfulcomparison between these and the predictand changesprojected directly by the GCM.

Fourth, analysts must be aware of the sensitivity oftheir transfer function methodology to subjectivechoices made when designing the functions. Thesechoices include the selection of the calibration period,the definition of the seasons for which separate equa-tions are defined, the choice of function form, and thedecision to adjust or not to adjust for error in the pre-dictor variables.

In this study, we address these considerations in re-gard to one empirical transfer function methodology.Our intention is to illustrate some of the limitations oftransfer function techniques so that impact analysts canmore meaningfully interpret and evaluate climate sce-narios generated by these methods. A modified versionof the Climatological Projection by Model Statistics(CPMS) method (Karl et al. 1990), which relates grid-point values of free atmosphere variables to local sur-face climate variables, is employed to generate localscenarios of daily maximum (TMAX) and minimum(TMIN) temperature for two sites (Alcantarilla, Spain,and Eau Claire, Michigan) with diverse climates. Therelationships are first developed on observed series ofthe predictands and predictors, and then applied to 1 3CO2 and 2 3 CO2 simulations of the predictor variablesfrom the Canadian Climate Centre (CCC) second-gen-eration GCM. The GCM experiments are described byBoer et al. (1992) and McFarlane et al. (1992).

The study is presented in two parts. In Part I, the firstthree issues, the agreement between GCM output and

observations of the predictands, the accuracy of theGCM control simulations of the predictor variables, andthe GCM-projected changes in the predictor variables,are addressed. The sensitivity of the transfer functionmethod to several user choices is evaluated in Part II(Winkler et al. 1997).

2. Data and methods

a. Observed maximum and minimum temperature

Alcantarilla and Eau Claire were chosen as study sitesbecause they represent challenging locations for whichto construct scenarios. Surface temperature at both sitesis strongly affected by local geography. Alcantarilla,located at 388N, 1.28W, 75 m above sea level, is at thejunction of two river valleys some 40 km from the Med-iterranean Sea. The main river flows southwest to north-east, and to the southeast there is a mountain range risingto around 500 m between the site and the sea. Eau Claire(428N, 86.28W, altitude 265 m) has a temperature regimemoderated by enhanced cloudiness downwind of nearby(30 km west) Lake Michigan (Fig. 1).

The stations were also chosen because of their longreliable time series of daily TMAX and TMIN. Reliableobserved series are essential in order to 1) rule out sta-tion inhomogeneities as a source of differences betweenthe GCM series and observations, and 2) develop stabletransfer functions. Extensive checks for series homo-geneity and station discontinuities have been carried outon the Eau Claire record (Andresen and Harman 1994).Less extensive checks were performed on the Alcan-tarilla record, but overlapping 10-yr means and standarddeviations were inspected and found to remain stablewith time. The record was also examined for anoma-lously high and low values (outliers).

TMAX and TMIN observations for two decades,1965–74 and 1975–84, were used for comparison withthe GCM control temperature series. These decades are,respectively, the validation and calibration periods forthe transfer functions developed in Part II. A 10-yr ob-

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servation period was chosen to be consistent with thelength of the CCC GCM control series. There is, ofcourse, the possibility that these two sample periods maynot be typical of the long-term observed record (Skaggset al. 1995). However, statistical tests on the means andstandard deviations failed to find any significant differ-ences between the two periods, or between the two pe-riods and the complete available record (1946–84 at EauClaire, 1958–1987 at Alcantarilla).

b. GCM series of maximum and minimumtemperature

As the first step in the analysis, the observed tem-perature series at Alcantarilla and Eau Claire were com-pared to the 1 3 CO2 temperature simulations from theCCC GCM. We emphasize that the purpose of thesecomparisons is to evaluate the feasibility of using theGCM simulations at a point scale. This cannot be con-sidered a validation in the exact sense, since GCM sim-ulations are not designed to provide local scenarios. TheCCC second-generation GCM equilibrium simulationswere chosen over others simply because the output isreadily available from the model developers.

The CCC spectral model improves on earlier versionsin that it has a higher horizontal resolution (i.e., a tri-angular 32-wave spectral truncation), full diurnal andannual cycles are included in the model, ocean trans-ports are specified resulting in a good simulation of 13 CO2 ocean temperature distribution and ice bound-aries, land surface processes and hydrology are moreadequately modeled, and cloud optical property feed-backs are parameterized (Boer et al. 1992; McFarlaneet al. 1992). McFarlane et al. (1992) point to the sim-plicity of the ocean and sea-ice components of the mod-el, the treatment of surface processes, clouds, and therepresentation of vertical mixing processes associatedwith shallow cumulus convection as deficiencies in themodel to be rectified in the future.

Screen temperatures provided by the CCC modelinggroup, rather than air temperatures for the lowest modellayer (approximately 200 m), are used in the compari-sons with observations. Screen temperatures are cal-culated from the modeled temperature at the ground andat the lowest model level [see Eq. (2.7) in McFarlaneet al. 1992] and are a diagnostic rather than a prognosticquantity. The model developers argue that, althoughsomewhat crude, screen temperature is more represen-tative of observed air temperature than either the modelsurface or lowest layer temperature. Ten years of dailyTMAX and TMIN data for the 1 3 CO2 and 2 3 CO2

equilibrium simulations are available for a 3.718 lat 3approximately 3.758 long transform grid.

Alcantarilla is located around 170 km from the nearestGCM grid point (to the northeast of the site in Fig. 1).However, this is a sea point in the GCM, and the nearestland grid point is around 290 km to the northwest. Thesedistances, combined with the fact that Alcantarilla is

surrounded by a mixture of land and sea grid points,mean that there are substantial differences between theraw grid point and the interpolated temperatures. Thecomparisons between the control simulation and obser-vations were therefore performed in two ways. First, theGCM screen temperatures were interpolated to the lo-cation of Alcantarilla using a 16-point Bessel schemethat weights the gridpoint values by the distance fromthe station location and the strength of the north–southand east–west gradients. This interpolation scheme waschosen to match that provided by the National Centerfor Atmospheric Research (NCAR) for use with theirNational Meteorological Center (NMC, now known asthe National Centers for Environmental Prediction) da-tasets (see below) and is used for all interpolationsthroughout this paper. The scheme does not differentiatebetween land and ocean grid points. Second, the ob-servations are compared to the GCM simulations for thenearest land grid point.

At Eau Claire, the 16 GCM grid points used in theinterpolation scheme are all located over land (the GreatLakes do not appear in the model geography), and thenearest point is only around 75-km distance (see Fig.1). As a result, the interpolated temperatures are almostidentical to the GCM series from the nearest point, andgridpoint model data are not shown here.

c. Choice of transfer function methodology

The CPMS methodology was chosen a priori as thetransfer function method for downscaling for severalreasons. First, it is derived from two short-range fore-casting methods, perfect prog and model output statis-tics (Klein 1982; Glahn 1985), that have been success-fully used to ‘‘downscale’’ output from numerical fore-cast models to local sites. Second, Karl et al. (1990)demonstrated that the method was able to recreate thepresent-day climate for several diverse locations in theUnited States. Third, the results from earlier studies sug-gest that other downscaling methods, particularly thosethat use circulation modes as predictors, may not beappropriate for CCC GCM simulations. McKendry etal. (1995) found statistically significant differences be-tween observed and CCC GCM 1 3 CO2 frequenciesof synoptic-scale 500-hPa geopotential height and meansea level pressure patterns over western North America.Takle et al. (1995) also found that the modeled fre-quency of synoptic-scale surface pressure fields differsfrom observed frequencies for North America.

d. Sources of free atmosphere variables

Since the CPMS method uses point values of freeatmosphere variables as predictors, an important initialstep is to evaluate whether the 1 3 CO2 GCM series ofthese variables deviate substantially from the observedseries used for transfer function development. We con-strained our choice of candidate predictor variables to

OCTOBER 1997 2501P A L U T I K O F E T A L .

those free atmosphere variables retained by most mod-eling groups so that, once derived using observations,the transfer functions can be applied to simulations ofthe predictor variables from any GCM. The two freeatmosphere variables most often archived, and for whichcomparable observed series are widely available, are500-hPa geopotential height and sea level pressure (orsurface pressure).

The observed analyses used for the comparisons arethe twice daily (0000 UTC and 1200 UTC) NMC op-erational fields for the Northern Hemisphere. These havebeen used by several authors for comparison with GCMcontrol simulations and to develop transfer functions(Hewitson and Crane 1992a,b; McKendry et al. 1995;Risbey and Stone 1996). The grid consists of equallyspaced points on a polar stereographic projection witha horizontal resolution of approximately 380 km (Jenne1975).

As for all operational analyses, discontinuities existin the NMC dataset, which may influence the interpre-tation of the differences between the observed and GCMfields. Trenberth and Olson (1988) note that over theperiod 1979–87, major changes to the NMC numericalmodels were introduced in 1980, 1982, 1986, and 1987.The two latter years lie outside our period of interest,and the 1982 changes are thought to have had the great-est impact in tropical areas. It is possible that the 1980changes affected the stability of the observations of freeatmosphere variables used here. However, we did notfind statistically significant differences in the free at-mosphere variables between the calibration (1975–84)and validation (1965–84) periods, and time series ofdaily means for 1975–84 are smooth and without dis-continuities (see Fig. 7 and 9).

The CCC 1 3 CO2 and 2 3 CO2 simulations of thefree atmosphere variables are twice-daily (0000 and1200 UTC) 10-yr series for equilibrium climates. Beforeinterpolation to the sites of Alcantarilla and Eau Claire,surface pressure was converted to SLP using the hyp-sometric relationship and the model orography at eachgrid point.

3. Comparison of the observed andGCM 1 3 CO2 series of maximum andminimum temperature

To investigate whether modeled daily TMAX andTMIN could be used directly for impact analysis, com-parisons with the observed daily series focus on sum-mary statistics (i.e., means of daily data calculated atthe annual, seasonal, and daily scale, and standard de-viations of daily data calculated at the annual and sea-sonal scale), the shape of the frequency distributionsand the extreme daily values.

The differences in the annual and seasonal means andstandard deviations 1) between the GCM 1 3 CO2 seriesand the 1975–84 observed series and 2) between theGCM 1 3 CO2 and 2 3 CO2 simulations were tested

for statistical significance. Significance testing is com-plicated by the serial dependence of the daily values.Standard statistical tests assume independent observa-tions, and when applied to serially correlated seriesoverestimate the degrees of freedom. Thus, they are toolikely to reject the null hypothesis that the means andstandard deviations are equal when it is in fact true.Several methods have been proposed for testing the sig-nificance of serially correlated series (e.g., Chervin andSchneider 1976; Katz 1988; Portman et al. 1992; Buis-hand and Beersma 1996) although the relative meritsand performance of these methods has not been eval-uated. We chose for simplicity to use standard tests: thet test assuming unequal variance for evaluating differ-ences in the means and Bartlett’s F test for differencesin the standard deviations. The null hypothesis is re-jected at a significance level of 5% for the means andat 10% for the standard deviations.

To examine the effects of serial correlation, the testswere first performed on the complete time series, andthen applied to a set of subsamples created by system-atically sampling the series at every nth value where nis set in turn to 5, 10, 20, and 40. Inspection of theresults, and of the lagged autocorrelation coefficientsfor the TMAX and TMIN observations (not discussedhere), suggests that the significance tests performed forn set to 10 are a reliable guide to differences and sim-ilarities between the datasets. References to significanceand nonsignificance in the following relate always tothe tests performed for n 5 10. For 10 yr of daily data,the number of values in each calculation of significanceis around 90 at the seasonal scale, and around 360 atthe annual scale.

The seasonal cycle has not been removed from thedata prior to the significance testing. In particular, thiswill have the effect of exaggerating the variance in thedata, so that the sensitivity of the t test for the differencein the means is reduced. However, as shown in the fol-lowing tables, the tests are able to discriminate betweenseasons and locations where the modeled and observeddata are clearly different, and those where they are sim-ilar.

The comparisons presented below indicate that forboth locations the GCM 1 3 CO2 series of screen tem-perature, either interpolated to the station location or,in the case of Alcantarilla, taken from the nearest landgrid point, differ significantly from the observed seriesof TMAX and TMIN. Consequently, the use of CCCGCM series of screen temperature at finer temporal andspatial scales than those for which they were designedis difficult to justify. Data for both observed periods(1965–74 and 1975–84) are presented in the followingtables, but only the comparisons of the GCM obser-vations with the observed values for the 1975–84 decadeare shown in the figures.

a. Eau ClaireBecause the temperature regime at Eau Claire is mod-

ified by Lake Michigan, a feature not included in the

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TABLE 1. Means and standard deviations for Alcantarilla and EauClaire temperatures (8C), calculated from 10 yr of observations andmodel run. Here, * indicates significant differences (control against1975–84 observations and perturbed against control). Model data forAlcantarilla are from the interpolated series.

Alcantarilla

Mean S.D.

Eau Claire

Mean S.D.

AnnualTMAX 1975–84TMAX 1965–74TMAX controlTMAX perturbedTMIN 1975–84TMIN 1965–74TMIN controlTMIN perturbed

24.2823.8520.88*25.34*11.1811.0113.60*17.28*

6.796.917.307.796.016.175.886.81*

14.5214.7711.75*18.08*

4.484.724.59

10.78*

11.9211.6112.5512.3310.43

9.9712.94*10.63*

WinterTMAX 1975–84TMAX 1965–74TMAX controlTMAX perturbedTMIN 1975–84TMIN 1965–74TMIN controlTMIN perturbed

17.2616.6712.52*16.17*

5.084.537.69*9.86*

3.633.431.69*1.523.753.571.28*1.73*

0.060.89

22.53*3.07*

27.6926.8128.9620.05*

6.325.774.76*3.40*6.686.018.40*2.94*

SpringTMAX 1975–84TMAX 1965–74TMAX controlTMAX perturbedTMIN 1975–84TMIN 1965–74TMIN controlTMIN perturbed

22.4322.4618.15*22.89*

9.258.95

10.83*14.47*

4.084.793.724.333.493.803.153.90*

14.2014.10

7.54*15.95*

3.263.11

20.07*8.24*

8.808.319.447.807.256.83

10.15*7.16*

SummerTMAX 1975–84TMAX 1965–74TMAX controlTMAX perturbedTMIN 1975–84TMIN 1965–74TMIN controlTMIN perturbed

32.0731.7129.70*34.91*17.9917.9521.03*25.82*

3.503.593.052.912.722.662.872.56

27.3827.5226.44*32.48*15.7815.5819.14*24.29*

3.583.933.754.733.813.844.52*4.12*

FallTMAX 1975–84TMAX 1965–74TMAX controlTMAX perturbedTMIN 1975–84TMIN 1965–74TMIN controlTMIN perturbed

25.2424.4222.98*27.24*12.3012.5014.72*18.80*

5.305.305.275.324.704.654.394.75

16.1216.2515.3220.55*

6.316.738.00*

10.43*

7.958.097.438.466.636.646.898.31

CCC GCM geography, our initial assumption was thatthe GCM daily simulations of TMAX would be toowarm and the TMIN simulations would be too cold.However, the comparisons with observations did notconfirm this assumption. Compared to the observed se-ries, the CCC GCM series of TMAX is colder, ratherthan warmer. Not only is the annual mean significantlycolder, but the means for all seasons except fall alsodiffer significantly (Table 1, all means computed from

daily observations). For TMIN, the annual mean of theGCM series agrees with the observed value, but theGCM and observed seasonal means are similar only inwinter. During the remainder of the year the GCM val-ues are significantly too cold (spring) or too warm (sum-mer and fall). The observed and GCM 1 3 CO2 seriesalso show some differences in terms of temperature vari-ability. For TMIN, the annual and seasonal standarddeviations are significantly larger than observed valuesfor the year as a whole and for all seasons except fall.On the other hand, the only significant standard devi-ation for TMAX occurs during winter when it is toosmall.

Deviations at a finer temporal scale are evident fromthe daily means (Fig. 2). Observed and GCM dailymeans of TMAX are in good agreement from approx-imately day 175 (midsummer) until day 40 (late winter),but deviate during the remainder of the year (spring andearly summer) when the GCM daily means are muchtoo cold. The GCM daily means of TMIN are persis-tently below observed values between days 25 to 120(late winter to midspring) and warmer than observedvalues during much of the rest of the year.

The frequency distributions of TMAX and TMIN fur-ther point to substantial deviations between the observedand GCM series. These are shown in Fig. 3 for winterand summer at both sites, and for spring and fall in Fig.4 for Eau Claire alone. For winter, the most obviousdifference is the anomalously high percentage of values,more than 40% for TMAX and more than 30% forTMIN, falling within the bin between 218C and 118C.The position of this modal bin is eccentric with respectto the complete distribution, particularly for TMIN. Forboth TMAX and TMIN the lower tail of the wintertimedistribution is well modeled, but the upper tail is trun-cated by about 108C for TMAX and 68C for TMIN. Anunrealistic feature of the springtime distributions is theirbimodal character (clearest for TMAX), with one fre-quency maximum centered on 08C. Also, the upper tailof the TMAX distribution for spring is truncated, where-as the lower tail of the TMIN distribution is overlyelongated. Agreement is better in summer when theshape of the distributions of TMAX and TMIN is similarto the approximately normal distribution of the obser-vations, and the extremes of the frequency distributionsare generally well modeled. The fall distribution ofTMAX is also well modeled, but the TMIN distributionis highly truncated at 08C.

The unrealistic frequency distributions are the resultof a threshold near 08C in the GCM time series, evidentin the plots of the daily highest and lowest values ofTMAX and TMIN (Fig. 5). The threshold has a ‘‘hys-teresis effect’’ in that eventually the barrier is overcomeand temperature extremes drop below the threshold inthe fall and rise above it in the spring. The probablecause of this threshold is the interpolation of GCMscreen temperatures from the prognostically derivedground temperature and lowest model-layer tempera-

OCTOBER 1997 2503P A L U T I K O F E T A L .

FIG. 2. Ten-year mean daily maximum (max) and minimum (min) temperatures (8C) for Al-cantarilla (A) and Eau Claire (EC). Thick solid line, observations (1975–84); dashed line, modelcontrol run; thin solid line, model perturbed run.

ture. The ground temperature will not rise above 08Cin the spring until the soil has thawed and the snow hasmelted, and will not fall beneath 08C in the fall untilthe soil is frozen once more (Zwiers 1996, personalcommunication).

We also consider in the tables and figures the resultsfor the GCM 2 3 CO2 series of TMAX and TMIN inorder to illustrate that the near-freezing threshold has apronounced influence on the perturbed simulation aswell. The most extreme effect is seen in winter, whenthe threshold in the 2 3 CO2 series of TMIN continuesalmost uninterrupted (Fig. 5). TMIN in the perturbedsimulation falls between 218C and 18C on 71% of thedays, compared with only 34% of the days in the control(Fig. 3). Not surprisingly, the mean diurnal range of the2 3 CO2 series (not shown here) is implausibly small(#58C) from approximately day 330 to day 75.

The GCM-projected warming is also affected by thepresence of the threshold, which we can illustrate withrespect to TMIN. The significant increase of almost 98Cin winter (Table 1) between the control and perturbedsimulations is in part dictated by the failure of TMINto drop below the 08C threshold in winter in the per-turbed simulation. The equally large and significant in-crease in spring is, conversely, partly due to the removalof the influence of the temperature threshold in the 23 CO2 series. The small amount of warming evident inFig. 2 from approximately day 260 to about day 350for TMIN corresponds to the continued influence in the2 3 CO2 series of the threshold present in the 1 3 CO2

series.

b. Alcantarilla

We anticipated, because of the mixture of land andocean grid points used in the interpolation scheme, thatthe 1 3 CO2 interpolated values of TMAX would besubstantially too cold and those of TMIN would be toowarm (i.e., the influence of the ocean grid points wouldbe to depress the daily range). The comparisons withobservations bear this out. The differences between ob-served and control values of the annual and seasonalmeans of TMAX and TMIN are significant in everyseason (Table 1). The daily means of the interpolatedseries of TMAX are consistently too cold and the valuesof TMIN are too warm, although the size of the dif-ference varies seasonally with the smallest differencesfound in spring and fall, especially for TMIN (Fig. 2).

Differences beyond those introduced by the inter-polation scheme are also evident. For example, the win-tertime frequency distributions of TMAX and TMIN arenot simply shifted to the left or right of the observeddistributions but rather have considerably differentshapes (Fig. 3). In particular, the kurtosis of the 1 3CO2 distributions is much larger than that of the ob-served distributions, with over 40% of the daily TMAXand TMIN observations falling within the modal bin.Also, the spread of the interpolated wintertime valuesof TMAX and TMIN is much too small. The observedand interpolated GCM distributions for summer are inbetter agreement, although the GCM series has a largerleft skew. Another indication of error at the local scalein the GCM series is that from day 300 to day 100 athreshold, similar to that observed for Eau Claire, is

2504 VOLUME 10J O U R N A L O F C L I M A T E

FIG. 3. Frequency distributions of maximum and minimum temperature (8C) at Alcantarilla and Eau Claire for winter and summer.

OCTOBER 1997 2505P A L U T I K O F E T A L .

FIG. 4. Frequency distributions of maximum and minimum temperatures (8C) at Eau Clairefor spring and fall.

FIG. 5. Extreme high and low daily maximum (max) and minimum (min) temperatures (8C)for Alcantarilla (A) and Eau Claire (EC), from 10 yr of observations, 10 yr of control run, and10 yr of perturbed run (pert). Amax, Amin, ECmax, and ECmin: dotted lines indicate observedvalues (1975–84), solid lines control run values. Apert and ECpert: dotted lines indicate max-imum temperatures, solid lines minimum temperatures.

2506 VOLUME 10J O U R N A L O F C L I M A T E

FIG. 6. Comparison of temperature observations (1975–84) at Alcantarilla with GCM tem-peratures from the nearest land grid point (8C); thin dotted line, observations; solid line, modelcontrol run. Left-hand side: 10-yr mean daily maximum (max) and minimum (min) temperatures.Right-hand side: extreme high and low daily maximum (max) and minimum (min) temperatures.

TABLE 2. Means and standard deviations (8C), calculated from dailydata, for TMAX and TMIN at Alcantarilla for observations, the in-terpolated CCC GCM data, and for the nearest land grid point. Here,* indicates significant differences between GCM values and 1975–84 observations. GP 5 data from nearest land grid point.

TMAX

Obs Interp. GP

TMIN

Obs Interp. GP

MeanAnnualWinterSpringSummerFall

24.317.322.432.125.2

20.9*12.5*18.2*29.7*23.0*

21.7*10.8*18.0*34.2*23.7

11.25.19.3

18.012.3

13.6*7.7*

10.8*21.0*14.7*

9.0*2.7*6.5*

18.18.7*

S.D.AnnualWinterSpringSummerFall

6.83.64.13.55.3

7.31.7*3.73.15.3

10.22.6*5.25.37.7

6.03.83.52.74.7

5.91.3*3.22.94.4

7.3*2.7*4.5*4.96.0

present in the plots of the daily extremes of TMIN, butis not seen for TMAX (Fig. 5). The higher value of thethreshold (58–68C at Alcantarilla compared to 08C atEau Claire) is the result of including sea points in theinterpolation. At the neighboring land grid points, thethreshold is found at 08C. Because of this threshold, thestandard deviation of TMIN is significantly underesti-mated in winter. The standard deviation for controlTMAX is also very low in winter suggesting that, al-though the threshold is not apparent in Fig. 5, it maybe having some effect.

To show the effects of interpolation at Alcantarilla,Fig. 6 compares observed temperatures for Alcantarillawith daily data from the nearest land grid point. Theleft side shows mean daily TMAX and TMIN. It is clearthat the seasonal cycle in the model over land is toolarge, particularly for TMAX. The right side shows thehighest and lowest values on each day, and the presenceof the 08C threshold in the TMIN series throughout thewinter season is obvious. Summary statistics are pre-sented in Table 2. For both TMAX and TMIN, gridpointannual means are too cold compared to the observations,and this pattern is repeated in every season except sum-mer, when for TMAX the gridpoint mean is significantlytoo high, and for TMIN the modeled means are veryclose to the observed. Gridpoint standard deviations aretoo high in spring, summer and fall, although the dif-ference is only significant in spring for TMIN. This isin contrast to the standard deviations of the interpolatedseries, which were close to the observed in these threeseasons. Gridpoint winter standard deviations are toolow, but the error is less than for the interpolated series.

Table 3 shows the 2 3 CO2 – 1 3 CO2 differencesfor the gridpoint and interpolated GCM series at Al-cantarilla. All the changes in mean temperature are sig-nificant. The projected increase in interpolated meanTMIN between the control and perturbed simulation islargest for summer and smallest for winter. These sea-sonal differences may be, at least in part, an artifact ofthe temperature threshold in the temperature series dur-ing winter. Unlike the 1 3 CO2 interpolated series, nothreshold is present in the 2 3 CO2 series (Fig. 5). If

OCTOBER 1997 2507P A L U T I K O F E T A L .

TABLE 3. For the interpolated CCC GCM data, and for the nearestland grid point, 2 3 CO2 2 1 3 CO2 change in TMAX and TMINat Alcantarilla. The difference in the means is expressed in 8C; thedifference in the standard deviations (S.D.) is expressed as a per-centage of the 1 3 CO2 value. Here, * indicates significant differencesbetween 2 3 CO2 and 1 3 CO2 series. GP 5 data from nearest landgrid point.

Change in TMAX

Interpolated Grid point

Change in TMIN

Interpolated Grid point

Mean

AnnualWinterSpringSummerFall

4.46*3.65*4.74*5.21*4.26*

5.08*3.52*5.29*6.95*4.49*

3.68*2.17*3.64*4.79*4.08*

3.87*1.91*3.24*6.30*4.00*

S.D.

AnnualWinterSpringSummerFall

6.7210.1

16.424.6

0.9

11.2215.7*

27.622.5

1.0

15.8*35.2*23.8*

210.88.2

17.2*21.0*16.9*21.210.6

the 1 3 CO2 interpolated TMIN had followed a realisticseasonal cycle, instead of remaining above the thresholdthroughout the winter season, then the 2 3 CO2 – 1 3CO2 warming in winter would probably have been great-er. However, the contribution of the threshold effect can-not be determined. Interpolated TMAX shows the sameseasonal cycle in temperature change (lowest in winterand highest in summer) and yet the threshold effect isnot apparent for this variable in Fig. 5.

Temperature series taken from the nearest land gridpoint show the same seasonal cycle of 2 3 CO2 – 1 3CO2 changes as the interpolated series, with the greatestchange in summer and the least in winter. The seasonalcontrasts are greater than for the interpolated series. Forgridpoint TMAX the changes are greater than for theinterpolated series in every season except winter. Forgridpoint TMIN the change in the annual mean is slight-ly larger than for the interpolated series, but this is be-cause of a very large increase in summer (6.38C). Inthe other seasons the interpolated series show the greaterchange.

The percentage change in the standard deviations isalso shown in Table 3. The sign of the change is alwaysthe same in the interpolated and in the gridpoint series.The change is generally greater in the gridpoint seriesthan in the interpolated series for TMAX (the exceptionbeing summer), whereas for TMIN the seasonal changesare generally smaller in the gridpoint series, with theexception of fall.

It is clear that, even without employing downscalingtechniques, differences in the predictions of temperaturechange due to global warming can arise depending onhow the gridpoint data are treated (Skelly and Hender-son-Sellers 1996). Here, the differences depend onwhether gridpoint or interpolated data are used. Differ-

ent interpolation schemes would doubtless also producedifferent answers. This is a particular problem wherethe site of interest is located midway between gridpoints, and/or where the interpolation involves a mixtureof land and sea points, as at Alcantarilla. At Eau Claire,which is very close to a grid point, the interpolated andgridpoint series are very similar (and not shown here).

4. Comparison of the observed and GCM series ofthe free atmosphere variables

To investigate the suitability of free atmosphere vari-ables for transfer function development, summary sta-tistics and frequency distributions of daily data are ex-amined. Only the comparisons for the 0000 UTC valuesof SLP and Z500 are discussed. Significance testingresults for n 5 10 are again used in the discussion whichfollows. We found, however, that the results of signif-icance testing for the free atmosphere variables are morelikely to remain the same over all values of n than arethe results for TMAX and TMIN. The comparisons be-low indicate that statistically significant differences ex-ist between the observed and GCM 1 3 CO2 series ofthe free atmosphere variables, but that systematic dif-ferences, such as those associated with the presence ofthe 08C threshold in modeled temperatures, do not exist.

a. Sea level pressure

A particularly striking feature of the plot of observedand GCM-simulated daily mean sea level pressure forAlcantarilla (Fig. 7) is the exaggerated annual cycle ofthe GCM series. The 1 3 CO2 seasonal means are sig-nificantly larger than the observed values in all seasonsexcept summer, when the GCM mean is significantlysmaller (Table 4). For the year as a whole, modeledmean SLP is within 2 hPa of the observed annual means,but this difference is still statistically significant becauseof the very small standard deviations in the daily timeseries. Not surprisingly, given the larger range of valuesfor the GCM series, the annual standard deviation issignificantly larger than the observed value. At the sea-sonal scale, the standard deviations do not differ sig-nificantly except in spring and summer, when they areagain higher in the model. Inspection of Fig. 8 pointsto modest error in the shapes of the frequency distri-butions of daily SLP, especially for summer when theGCM 1 3 CO2 distribution is flatter than the observed.No obvious thresholds, such as those seen for screentemperature, are evident.

At Eau Claire, there is better agreement between theobserved and GCM 1 3 CO2 series of SLP. The dailymeans of the two series closely correspond from day325 through day 150 (Fig. 7), but during the rest of theyear the GCM values are too small. These differencesare reflected in the seasonal statistics. Summer and fallare the only seasons for which the differences in themeans are significant. However, the differences in the

2508 VOLUME 10J O U R N A L O F C L I M A T E

FIG. 7. Mean daily sea level pressure in hPa, calculated from data at 0000 UTC for Alcantarilla(A) and Eau Claire (EC). In the left-hand graphs, the thin dotted line indicates observations(1975–84), solid line the model control run. In the right-hand graphs, the solid line indicatesthe model control run, the dotted line the model perturbed run.

TABLE 4. Summary statistics for Alcantarilla and Eau Claire sea levelpressure (hPa), calculated from 10 yr of observations and model run.Here, * indicates significant differences (control against 1975–84observations and perturbed against control).

Alcantarilla

Mean S.D.

Eau Claire

Mean S.D.

Annual1975–841965–74ControlPerturbed

1018.11017.41019.3*1017.6*

6.255.968.56*8.29

1016.41016.31014.6*1014.4

7.407.187.327.04

Winter1975–841965–74ControlPerturbed

1020.51019.21026.8*1025.2

8.087.657.456.25

1018.51018.51016.81016.3

9.248.898.23*8.31

Spring1975–841965–74ControlPerturbed

1016.41016.31019.1*1017.4

5.696.076.87*6.36

1014.81015.11015.11014.2

7.437.378.10*7.51

Summer1975–841965–74ControlPerturbed

1016.51016.11010.9*1009.0*

3.173.334.54*4.30

1014.91014.71011.6*1011.8

4.564.485.074.25*

Fall1975–841965–74ControlPerturbed

1018.91018.01020.6*1019.0*

6.075.476.506.58

1017.41017.11014.9*1015.6

6.866.666.436.58

seasonal standard deviations are significant in winter(lower in the model) and spring (higher in the model).At the annual level, the standard deviations of the GCMand observed series are not significantly different, butthe modeled mean is significantly smaller, reflecting thelower values of SLP during summer and fall. The shapesof the observed and GCM frequency distributions ofSLP are very similar for summer, but in winter the lowertail of the GCM distribution is truncated (Fig. 8).

b. 500-hPa geopotential height

At both sites, the seasonal means of the GCM 1 3CO2 series of Z500 are significantly different from theobserved values in spring and summer, but in the falland winter the agreement is good (Table 5). At EauClaire the modeled means are too low in spring andsummer (see Fig. 9), whereas at Alcantarilla springmeans are too high and summer means are too low. Asa result, only at Eau Claire are the annual modeledmeans significantly different from the observations.

The frequency distributions of the GCM 1 3 CO2

series of Z500 are too peaked and the spread of valuesis too small compared to the observed distributions (Fig.10). This pattern is evident for both locations and allseasons (only winter and summer are displayed), al-though differences are most obvious in winter. Consis-tent with the smaller range of values, the GCM standarddeviations are significantly smaller than the observedvalues for the year as a whole and for all seasons atAlcantarilla (Table 5). At Eau Claire, standard devia-

OCTOBER 1997 2509P A L U T I K O F E T A L .

FIG. 8. Frequency distributions of sea level pressure (hPa) at Alcantarilla and Eau Claire for winter and summer.

tions are significant smaller for the year as a whole,winter and spring.

5. Comparison of the 1 3 CO2 and 2 3 CO2 seriesof the free atmosphere variables

The final goal of Part I is to assess the changes inSLP and Z500 between the 1 3 CO2 and 2 3 CO2 GCMsimulations. This assessment is relevant to the discus-sion of the transfer function-generated temperature sce-narios in Part II, since changes in these scenarios canarise only from changes in the predictor variables de-rived from SLP and Z500. We show below that the majordifferences between the control and perturbed simula-tions of the free atmosphere variables are found forZ500, which increases substantially at both sites in the2 3 CO2 simulation.

a. Sea level pressure

Reference to Fig. 7 and Table 4 shows that changesin mean daily SLP between the 1 3 CO2 and 2 3 CO2

experiments of the CCC GCM are small. At Eau Claire,the daily means are almost identical, and the 2 3 CO2

– 1 3 CO2 differences in the annual and seasonal meansare insignificant. Slightly larger differences are evidentfor Alcantarilla, especially in late summer and early fall(about day 190 to day 300) when the daily means ofthe 2 3 CO2 series are smaller than the values for the

control experiment. This leads to significant differencesbetween the annual, summer, and fall means.

At both locations, frequency distributions for the 23 CO2 series appear slightly more peaked than the 13 CO2 distributions of SLP (Fig. 8). Also, the range ofvalues for the 2 3 CO2 distributions appears slightlysmaller, although this is not clearly reflected by changesin the standard deviations. At Alcantarilla there are nosignificant differences between the 1 3 CO2 and 2 3CO2 standard deviations and at Eau Claire the only sig-nificant difference is in the summer.

b. 500-hPa geopotential height

In contrast to SLP, Z500 increases substantially in the2 3 CO2 experiment, as indicated by the significant 23 CO2 – 1 3 CO2 differences in the annual and seasonalmeans at both sites (Table 5). This increase is fairlyuniform throughout the year as seen in the plots of thedaily means (Fig. 9). On the other hand, changes in thevariability of Z500 are small and never significantlydifferent.

The frequency distributions of Z500 display the ex-pected shift to the right in the perturbed experimentvalues at both sites in winter and summer (Fig. 10),while maintaining the negative skew seen in the controlexperiment. At Alcantarilla, the kurtosis increases in theperturbed experiment in both seasons, but particularlyin summer.

2510 VOLUME 10J O U R N A L O F C L I M A T E

TABLE 5. Summary statistics for Alcantarilla and Eau Claire 500-hPa geopotential height (m) calculated from 10 yr of observationsand model run. Here, * indicates significant differences (controlagainst 1975–84 observations and perturbed against control).

Alcantarilla

Mean S.D.

Eau Claire

Mean S.D.

Annual1975–841965–74ControlPerturbed

5732.15721.95722.45803.7*

127.4133.7

94.2*94.9

5634.15628.55600.6*5693.9*

181.5180.1160.0*158.2

Winter1975–841965–74ControlPerturbed

5652.35627.65649.15722.3*

114.8110.8

70.3*69.2

5449.25450.55431.95531.1*

129.8126.7

93.2*96.3

Spring1975–841965–74ControlPerturbed

5657.15654.25673.0*5759.8*

96.2109.6

71.6*75.4

5595.85583.35546.3*5630.0*

132.9137.4112.3*117.3

Summer1975–841965–74ControlPerturbed

5854.55853.95812.6*5901.8*

65.569.754.4*47.1

5823.75810.45773.7*5861.3*

69.670.562.454.3

Fall1975–841965–74ControlPerturbed

5766.65749.25753.85829.3*

100.0102.9

74.6*67.1

5669.25665.15647.65750.5*

140.3147.6117.4111.5

FIG. 9. Mean daily 500-hPa geopotential height in meters, calculated from data at 0000 UTCfor Alcantarilla (A) and Eau Claire (EC). In the left-hand graphs, the thin dotted line indicatesobservations (1975–84), the solid line the model control run. In the right-hand graphs, the solidline indicates the model control run, the dotted line the model perturbed run.

6. Discussion and conclusions

In this paper we have addressed the preliminary stepsthat are necessary before deriving empirical transferfunctions to downscale GCM output to the local, dailyscale. These steps include 1) demonstrating that theagreement between GCM control output and observedlocal values of the predictands is sufficiently poor thatdownscaling is necessary, 2) identifying any deviationsbetween the GCM control simulations of the predictorvariables and the observed series chosen to calibrate thetransfer functions, and 3) identifying the GCM-pro-jected changes in the predictor variables.

We found for both Alcantarilla and Eau Claire thatthe observed daily series of TMAX and TMIN differsignificantly from the daily GCM control series inter-polated to the site location. The poor agreement iscaused in part by an unrealistic near-freezing thresholdduring spring and fall in the GCM series at Eau Clairein both TMAX and TMIN, and throughout the winterseason at Alcantarilla in TMIN. The presence of thisthreshold affects not only the agreement between ob-served and modeled temperatures, but also the size ofthe perturbation between the 1 3 CO2 and the 2 3 CO2

simulations. At Eau Claire, for example, the very largeincrease in TMIN in winter (almost 98C) is in part anartifact of the failure of temperatures to drop below the08C threshold in the perturbed simulation, whereas theequally large change in spring (TMAX and TMIN) iscaused by the removal of the influence of the thresholdin the 2 3 CO2 series.

OCTOBER 1997 2511P A L U T I K O F E T A L .

FIG. 10. Frequency distributions of 500-hPa geopotential height (m) at Alcantarilla and Eau Claire for winter and summer.

Eau Claire is located close to a grid point, and there-fore the temperature series interpolated from a numberof GCM grid points tend to be very similar to thosetaken from the nearest GCM grid point (which were notshown). Alcantarilla is located midway between gridpoints, and we investigated the effect of the interpolationscheme (a 16-point Bessel interpolation) on the com-parisons of the GCM output with observations. Due tothe maritime location of this site, both land and sea gridpoints are included in the interpolation. The influenceof the sea grid points produces modeled TMAX valueswhich are too low compared to observations, and TMINvalues which are too high. However, the situation is notimproved by comparing Alcantarilla observations withmodel data from the nearest land grid point, althoughthe errors are different. The seasonal cycle of meantemperature is exaggerated in the gridpoint data, andthe standard deviations in spring, summer, and fall aretoo large (for the interpolated series the agreement be-tween the modeled and observed standard deviations inthese three seasons was good).

At Alcantarilla, the projected perturbation due toglobal warming varies depending on whether gridpointor interpolated model data are used. A larger change issuggested for the gridpoint series. Thus, even withoutemploying downscaling techniques, differences in thepredictions of temperature change for a location canarise depending on how the gridpoint data are treated.It is likely that different interpolation schemes (e.g.,differential weighting of land and sea grid points), al-

though not investigated here, would also produce dif-ferent projections of warming.

The poor agreement between the GCM and observedseries of TMAX and TMIN supports the continued con-sideration of downscaling techniques to generate local,daily temperature scenarios. The method explored inPart II is the use of free atmosphere variables in transferfunctions to predict temperature. In Part I, therefore, wehave compared observed and 1 3 CO2 SLP and Z500,and looked at the perturbation in the 2 3 CO2 simu-lation.

Systematic errors of the type associated with thethreshold in the temperature series were not found indaily 1 3 CO2 SLP and Z500. However, the modeledseries of these two free atmosphere variables were foundto differ significantly from observed series, although thelack of agreement varied by season and location. Thecomparisons between observed and 1 3 CO2 series ofthe free atmosphere variables suggest that the perfor-mance of the transfer functions, when initialized withmodel data, will depend partly on the relative impor-tance of SLP and Z500 as predictors. For example, atAlcantarilla SLP is not well simulated, with significantdifferences between the means of the observed and con-trol series in every season, whereas for Z500 significantdifferences are found only in the spring and summer.Poorest results at both sites may be expected in springand summer when the GCM 1 3 CO2 series of SLPand Z500 deviate the most from observations.

The most obvious change in the free atmosphere vari-

2512 VOLUME 10J O U R N A L O F C L I M A T E

ables between the control and perturbed experiments isthe increase in Z500, noticeable at both sites. SLP, incontrast, changes little at Eau Claire and is only slightlylower at Alcantarilla in the perturbed simulation. Littlechange in variability is evident for either variable. Ig-noring such considerations as the explained variancesof the transfer functions, therefore, scenarios for a per-turbed climate should differ from control climate sce-narios primarily in terms of mean temperature ratherthan variability. Also, the projected warming is likelyto be greater for locations and/or seasons where Z500contributes the most to the explained variance comparedto the locations and/or seasons where SLP is the primarypredictor variable.

We have shown in this paper that errors in the GCMsimulation of local temperature can be fully understoodonly by careful analysis of daily time series, and yetthese errors have the potential to affect the GCM pre-diction of the perturbation due to global warming at theseasonal and annual scale. Attempts to overcome suchproblems by replacing GCM temperature series withdownscaled series generated from free atmosphere vari-ables must take into account errors in local values ofthe modeled free atmosphere variables themselves. Thesize of the 2 3 CO2 – 1 3 CO2 perturbation in transferfunction–generated temperatures can, and should, be ex-plained in the context of the relative importance of thepredictor variables in the transfer functions, and of themodeled changes in the free atmosphere variables. InPart II, downscaled scenarios of TMAX and TMIN aredeveloped using free atmosphere variables as predictorsand explored with respect to these considerations. Arange of scenarios are constructed and the results ana-lyzed with respect to the sensitivity of the results to userdecisions made when developing the transfer functions.

Acknowledgments. This work was funded by theCommission of the European Union (Contract EV5V-CT92-0164), the Midwestern Regional Center of theNational Institute for Global Environmental Change,and the NATO Collaborative Research Grants Pro-gramme. We express our appreciation to the CanadianClimate Centre for making their model output readilyavailable to impacts researchers. We thank Sarah Wat-kins for her help in the preparation of the figures, andfor her careful editorial work.

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