Barrow and Semenov 1995. Climate Change Escenarios With High Spatial and Temporal Resolution for Agricultural Applications

7/29/2019 Barrow and Semenov 1995. Climate Change Escenarios With High Spatial and Temporal Resolution for Agricultural

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Climate change scenarios w ith highspatial and temporal resolution foragricultural applicationsE. M. BARROW 1 AND M . A. SEMEN OV 21 Climatic Research Unit, University of East Angha, N orwic h, N R4 7T J, England2 Long Ashton Research Station, IACR, University of Bristol, Long Ashton, Bristol, BS18 9AF, England

SummaryScenarios of climate change with high spatial and temporal resolutions are required for theassessment of the impact of such change on agriculture. A method of producing high resolutionscenarios based on regression downscaling techniques linked with a stochastic weather generatoris described. Regression relationships were initially determined between observed large-scale andsite-specific climate. By assuming that these relationships would be valid in a future climate, theywere subsequently used to downscale general circulation model (GCM) data. The UK Meteoro-logical Office high resolution GCM transient experiment (UKTR) was used to construct the cli-mate change scenarios. Site-specific, UKTR-derived changes in a number of weather statisticswere used to perturb the parameters of the local stochastic weather generator (LARS-WG),which had initially been calibrated using observed daily climate data. LARS-WG was used tosimulate the site-specific daily weather data required by crop growth simulation models. Thismethod permits changes to a wider set of climate parameters in the scenario, including variabil-ity. Simulated wheat yields were shown to be more sensitive to changes in climate variabilitythan to changes in the mean. Results are presented for two European sites.

Introduction , c ,/- change scenarios tor impact assessment (GiorgiTo construct scenarios for the assessment of the and M earn s, 1991; Kenny et al., 1993; Rosen-impact of climate change on crop produ ction, it zweig et al., 1993), but changes in variability ofis necessary to analyse the sensitivity of a crop clim ate can have a significant effect on cropsystem to climate variables. T he changes in grow th and developm ent. For exam ple, changesweather param eters that may result in notice- in the variability of tempe rature can greatlyable changes in yield pot enti al or associated influence dry m atte r prod uctio n since both highagricultural risk should be incorporated in the and low temperat ures decrease the rate of dryclimate change scenarios. To dat e, in most cli- ma tter produ ction and, at th e extreme, canmate change stud ies, mean cha nges in climate cause pro duc tion to cease (Grace, 1988). Severevariables have been applied to historical wa ter deficits imm ediately before flowering canweather or climate d ata to co nstruct climate lead to pollen sterility and a decrease in grain

f, Vol. 68 , No 4, 1995


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35 0 FORESTRYset (Saini and Aspinall, 1982). Furthermore,there is a non-linear relationship between pre-cipitation amount and the water-use efficiencyof plants. Average amounts of precipitation areused relatively more efficiently by plants thanlarger amounts because of the rapid movementof excess water to deep and unavailable layersin the soil (Keulen and Wolf, 1986). In turn, thiscan lead to excess nitrate leaching to groundwater, especially if the rate of nitrogen mineral-ization by soil microbes is stimulated bywarmer temperatures. Extreme weather events,such as drought or hot or cold spells, can havesevere consequences for crops and their fre-quency of occurrence is better correlated withchanges in the variability of climate variables,as opposed to changes in the mean values (Katzand Bro wn, 1992). Cro p simulation modelsreflect the mixture of linear and non-linearresponses and, in the broadest sense, transforma distribution of weather sequences into a dis-tribution of total dry matter and, in the case ofcrop plants, harvestable yield. Assessments ofthe effects of climate on agricultural produc-tion, and the appraisal of associated risks to thefood supply, need to bear the above in mind.General circulation models (GCMs) are thetools that are now most widely used to generatescenarios of climate change for impacts assess-men t (Giorgi and M earns, 1991; Viner andHulme, 1994). There are a number of factors,however, which limit direct use of their resultsin scenario construction. These include: (1) theability of the control experiment to adequatelysimulate the large-scale features of present-dayclimate. This is one of the reasons that the dif-ference between the control and perturbed inte-grations is used, rather than the raw data fromthe integration s themselves; (2) the coarse spa-tial gridoutput is on the scale of hundreds ofkilometres rather than the tens of kilometresneeded for impacts assessment (e.g., 2.9 lati-tudelongitude grid box resolution is approxi-mately equivalent to 300 X 300 km ). T hiscoarse resolution also means that sub-grid scaleprocesses, such as precipitation, are not ade-quately represented and important regionaltopographic features are omitted. Hence,although GCMs may be able to simulate large-scale features of climate well, their simulationof regional climate is considerably poo rer.

Consequently, it is necessary to 'downscale'the coarse-resolution GCM output to the scalesrequired in regional impacts assessment. D own-scaling should be tailored to the impacts appli-cation for which the climate change scenariosare being constructed. For example, in the caseof agriculture, information is usually needed fora suite of climate variables at a high spatial andtemporal resolution. Information about changesin climate variability is also important for thereasons mentioned above.There are currently two main generic meth-ods in use to downscale large-scale climateinformation to the site level. These are: (1)regression methods (e.g., Kim et al., 1984;W igleyc ta/., 1990; Karl et al, 1990) and (2) cir-culation patterns (e.g. Bardossy and Caspary,1991; Matyasovszky et al., 1993). Both methodsmake the basic assumption that the presentempirical relationships between large-scale andlocal climate and the observed relationshipsbetween variables will continue to be valid inany future climate. Each method uses differentsets of GCM-derived variables and hence, tosome extent, makes assumptions about the reli-ability of such variables. For example, regres-sion techniques may use any number ofvariables including mean sea level pressure(MSLP), temperature, precipitation etc., whilethe circulation pattern (CP) method restricts thenumber of variables to MSLP and possibly theheight of another pressure surface (e.g., 500mbar). Both methods use existing instrumentaldatabases to determine the relationshipsbetween large-scale and local climate. Regres-sion techniques develop statistical relationshipsbetween local station data and grid-point scale,area average values of, say, temperature andprecipitation and other meteorological vari-ables, whereas in the CP approach atmosphericcirculation is classified according to type andlinks are then determined between the circula-tion pattern and the relevant climate variable(e.g., precipitation).In this paper, the downscaling process and itsapplication will be described, from the con-struction of a high resolution climate changescenario using regression techniques, to its usein a crop-growth simulation model. In order toproduce climate data at the correct temporalresolution for such a simulation model, the


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CLIM A TE CH A NG E S CEN A RIO S 351downscaling technique was used in conjunctionwith a stochastic weather generator (Racsko etal., 1991; Semenov and Porter, 1994). This is amethodologically consistent approach to incor-porate changes in climate variability into cli-mate change scenarios instead of usingperturbed historical data as input to a crop sim-ulation model (as was done, for example, inSemenov etal., 1993). A stochastic weather gen-erator allows temporal extrapolation ofobserved weather data for agricultural riskassessment as well as providing an expandedspatial source of weather data by interpolationbetween the point-based parameters used todefine the weather generator (Hutchinson,1991). For analysis of the effects of climatechange, a stochastic weather generator can playan important role by providing flexibility in theconstruction of weather scenarios and by link-ing information about possible climate changes,derived from GCMs using downscaling meth-ods, to local weather characteristics (Wilks,1992). The procedure is analogous to the con-ventional practice of applying changes in meansto observed data. The essential difference is thatchanges in the variability of climate parametersare permitted.

D evelopment of high resolution climatechange scenariosGCM scenariosThe climate change scenarios used here havebeen co nstructed using the UK MeteorologicalOffice high resolution (2.5 latitude by 3.75longitude) GCM transient experiment (UKTR;Viner and H ulm e, 1993; M urph y, 1995; Mu rphyand Mitchell, 1995). Mean monthly changes fora number of climate variables (mean tempera-ture, precipitation, MSLP and northsouth andeast-west pressure gradients) were calculatedfor each grid box in the European area for thelast decade (years 6675) of the model experi-ment using the equivalent years of the controland perturbed integrations. The change in tem-perature variability was also determined byanalysing the daily mean temperature varianceof the control and perturbed integrations forthis decade.

It is difficult to relate the control and per-turbed integrations of the UKTR experimentdirectly to calendar years for a number of rea-sons, including the 'cold start' problem (Vinerand H ulm e, 1993; M urph y, 1995). How ever, bycombining the UKTR global-mean warming forthis decade (1.76C) with the results from a sim-ple climate model, e.g., MAGICC (Model forthe Assessment of Greenhouse gas Induced Cli-mate Change; Wigley and Raper, 1992; Wigley,1994; Hulme et al., 1955), it is possible to cal-culate a range of future dates when this warm-ing may occur. If the negative effects of sulphateaerosols on global warming are omitted (theUKTR experiment did not include the negativeforcing of aerosols on climate), then the UKTRglobal warming for decade 66-75 may bereached as early as 2038 if climate sensitivity ishigh, or after 2100 if the climate sensitivity islow. The best estimate data for a global-meanwarming of 1.76C to be reached is 2065.

Downscaling: calibration of the regressionequationsTwo sites were selected in Europe (Rothamsted,UK, and Sevilla, Spain) for which downscalingwas to be undertak en. U nfortunately, insuffi-cient Spanish data were available to constructregional area averages of mean temperature andprecipitation, and hence the regression relation-ships, in time for inclusion in this paper. D ow n-scaling was therefore undertaken only atRothamsted and the direct UKTR changes forthe relevant grid box were used for Sevilla.The first step in this process was the calcula-tion of regression relationships between theobserved large-scale and local climate for eachmonth. These relationships were formulated interms of anomalies from the long-term 1961-90mean for the variable under consideration.D eriving relatio nship s in this form simplifies theprocess of applying the GCM changes to theseequations. The observed large-scale climate wasdetermined by averaging data from a number ofsites located within the appropriate grid box.Figure 1 shows the locations of the sites chosenin relation to the UKTR grid boxes for theRothamsted site. For this site, anomalies weresimply averaged to produce regionally-averagedvalues. N o weighting of sites was necessary


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35 2 FORESTRY62.5

60.0

57.5

i155.0

52.5

50.0-

47.5-11.25 3.75

Figure 1. Location of the sites used to calculate area-average mean temperature and precipitation for the gridboxes relating to IACR-Rothamsted, UK. The shaded area illustrates UKTR land grid boxes. Unshaded cellsrepresent ocean area.

because of their approximate even distributionthroughout the grid and the likely homogeneityof temperature and precipitation anomalies inthis particular area (M. Hulme, personal com-munication). Anomalies for three pressure vari-ables were also calculated for this grid box:MSLP and the north-south and eastwest pres-sure gradients.Once the anomalies had been calculated, thedata were divided into two time periods,1961-83 and 1984-88. (The gridded MSLPrecord initially used was complete up to 1988,although it was extended to 1990 in the latterstages of this work.) The first period was usedto calibrate the regression equations, while thelatter period was used to verify the performanceof the regression models. Regression analyseswere then undertaken for each month using sta-

tion mean temperature and precipitation anom-alies as predictands and the regionally-averaged anomalies of temperature, precipita-tion, MSLP and northsouth and east-westpressure gradients as predictors. Table 1 illus-trates the performance of the regression modelsfor each month for the Rothamsted site. To beconfident that the regression model works well,it must explain a high proportion of the vari-ance in the data and for it to be applicable forany future climate, the scenario changes in tem-perature and precipitation should be within theobserved anomaly range. Table l(a) indicatesthe variance explained by the regression modelfor each month, while Table l(b) shows the ver-ification results. As would be expected, themodel generally performs better for temperaturethan for precipitation, especially in spring and


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CLIM A TE CH A NG E S CEN A RIO S 353Table 1: Performance of the regression models(a) Calibration of regression m odel: 1961-83; Variance explained (%)

T M PPPT

(b) Verification

T M PPPT

Jan99.285.9

Fe b99.190.3

of regressionJa n

0.9990.960

Feb0.9990.961

M ar98.688.2

Apr98.073.9

M ay97.376.4

Jun98.381.9

Jul98.579.7

model: correlations between observed andM ar

0.9940.896

Apr0.9910.989

M ay0.9950.859

Jun0.9880.953

Jul0.9890.918

Aug98.580.9

Sep96.890.3

O ct99.190.1

predicted; 1984-88Aug

0.9800.956

Sep0.9780.977

Oct0.9350.996

N ov97.993.5

N ov0.9930.990

D ec99.093.7

D ec0.9860.988

TMP = temperature; PPT = precipitation.

summer. The correlations shown in Table l(b)may be misleadingly high because of the smallnumber of data points used.Table 2 shows the mean square correlationsbetween predictands (expressed as a percentage)for mean temperature (Table 2(a)) and precipi-tation (Table 2(b)). Table 2(a) illustrates thatthe areal-mean temp erature anomaly is the mostimportant variable in determining Rothamstedmean temperature. While the areal-mean pre-cipitation anomaly is also the most importantvariable for Rothamsted precipitation, MSLPalso has an important role in determining thesite precipitation anomalies (Table 2(b)).Downscaling: application ofGCM changesOnce the regression equations had been deter-mined using observed data, the next step was tocalculate the site changes in mean temperatureand precipitation using the changes derivedfrom UKTR for the predictor variables. TheUKTR grid box values of the predictor vari-ables are assumed to be equivalent to theregionally-averaged values derived from theobservational data. The appropriate UKTRchanges were substituted into the regressionequations to obtain predictions for the Rotham-sted site. Table 3 illustrates the changes in meantemperature (Table 3(a)) and precipitation(Table 3(b)) for the appropriate grid box and

for Rothamsted. It is apparent that downscalingthe grid box changes to the Rothamsted sitemakes little difference to temperature. Theobserved climate within the grid box containingRothamsted is actually quite homogeneous, andso it is to be expected that there is little differ-ence between site and areal climate and hence,little difference is obtained by downscalingGCM data. Larger differences between grid boxand site values may have been obtained by usinga site at an altitud e which was considerably dif-ferent from that of the grid box itself. However,downscaling has a larger effect on precipitation.In some cases, the sign of the precipitationchange is reversed, e.g., in May the grid boxchange indicates an increase of 0.09 mm day" 1,whereas at the site a decrease of 0.07 mm day" 1is predicted.Table 3(c) shows the observed 1961-90anomaly range for the predictor variables. Inthe case of temperature (Table 3(a)), it is appar-ent that the grid box changes are outside theanomaly ranges shown in Table 3(c) on only afew occasions. For precipitation the changes areinside the anomaly ranges on all occasions.As well as changes in mean values beingextracted from UKTR, daily data were alsoanalysed from the last decade of the modelexperiment (years 66-75) in order to derivechanges in temperature variability and length ofwet and dry spells.


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35 4 FORESTRYTable 2: Mean square correlations between predic-tands, expressed as a percentage (1961-83).(a) Roth am sted mean tem perature

T r a P u a MSLP Ap r a ApewJanFe bM arAprM ayJunJulAugSepO ctN ovD ec

99.2099.0098.0196.2495.2697.6198.4197.8196.0498.2197.6198.60

11.7015-521.320.160.523.2826.5237.586.764.496.400.25

0.070.232.192.371.720.0064.4932.040.664.881.235.66

72.5944.4943.301.234.370.7415.2121.2512.110.8516.8135.64

3.539.421.613.5016.4030.690.0516.898.3528.5214.980.0001(b) Rothamsted precipitation

T,n MSLP ApnJanFe bM arAprM ayJunJulAugSepO ctN ovD ec

9.3612.321.323.4212.321.3027.3540.455.063.136.500.46

80.8290.0686.8670.0669.2280.2867.0877.7990.2589.8792.9392.54

23.3346.9260.9944.6230.3634.467.9542.1463.5244.4955.3543.43

27.6718.586.1515.686.450.081.3017.1413.8426.7318.759.18

21.9022.8522.3717.5611.168.180.1423.8125.3015.7626.1134.22

= areal mean temperature; P , r a = areal mean precip-itation; MSLP - mean sea level pressure; Ap r a =northsouth p ressur e gradient; Ap,-. = eastwest p ressuregradient .

LARS-W G: stochastic weather generatorLARS-WG is a development of the stochasticweather generator described in Rascko et al.(1991), to which modifications were made inorder to match the output to the meteorologicalinput data required by crop simulation models.The weather generator is based on distributionsof the length of continuous sequences, or series,of dry or wet days. Long dry series are simu-lated better using this approach compared tothe Markov chain method (e.g., Richardson,1981) of simulating precipitation occurrence. A

long dry series, a drought, affects crop growthand development and can dramatically decreasethe yield. Hence, in order to accurately assessagricultural risk, it is important that such eventsare modelled well. The distribution of otherweather variables, such as temperature andsolar radiation, is based on the current status ofthe wet or dry series. Mixed exponential distri-butions were used to model dry and wet seriesso that the model would be applicable to a widerange of locations in Euro pe. D aily minim umand maximum temperature and radiation wereconsidered as stochastic processes with dailymeans and standard deviations conditioned onthe wet and dry series. The techniques used toanalyse the process are very similar to th ose pre-sented in Richardson (1981). The seasonal cycleof means and standard deviations was removedfrom the observed record and the residualsapproximated by a normal distribution. Theseresiduals were used to analyse a time correla-tion within each variable. Fourier series wereused to interpolate seasonal means and standarddeviations. Where radiation data were unavail-able, sunshine hours were used in the simula-tion. Sunshine hours can be converted toradiation by means of the regression relation-ships between these two variables (Rietveld,1978).Adjustment of LARS-W G parameters for cli-mate change scenariosThe regression downscaling proceduredescribed above generated site specific (forRothamsted) changes in mean monthly temper-ature and monthly relative changes in precipita-tion amount. The parameters of LARS-WGwere adjusted accordingly to allow the genera-tion of daily weather sequences for the climatechange scenarios. However, information aboutchanges in temperature and precipitation meansis not enough to define precisely the changes inall of the LARS-WG parameters, especiallychanges in daily temperature variability and theduration of dry and wet spells. Additional infor-mation is required. It seems unlikely that theregression technique used above for downscal-ing mean values will be applicable to analysesof climate variability, although more researchis necessary in this area. In the current study,


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Table 3: Grid box and site changes derived from UKTR, years 66-75(a) Mean temperature (C) change

(c) 1961-90 anomaly ranges

Rothamsted

JanFebMarAprMayJunJulAugSepOctNovDec

UKTR grid box3.430.982.350.541.611.422.153.553.721.482.042.18

Site3.491.032.530.541.931.652.303.723.831.502.002.33

(b) Precipitation change (mm day"1)Rothamsted

JanFebMarAprMayJunJulAugSepOctNovDec

UKTR grid box0.130.300.03

-0.140.090.04

-0.11-0.33-0.25

0.120.350.12

Site-0.07

0.250.08

-0.12-0.07-0.02-0.10-0.59-0.31

0.060.450.13

MSLP (mbar) Apm (mbar) Apew (mbar) Tarea (C) Parc(mm day"')Jan -13.8,+15.0 -2.2,+4. 4 -2.2, +1.9 -6.8,+3.0 -0.8, +0.8Feb -17.9,+17.4 -4.0 ,+3. 8 -1.8 ,+2 .4 -5.2 ,+4. 0 -0.8,+1.7Mar -12.3,+12.1 -2.0 ,+2. 0 -1.3 ,+1. 5 -3.0 ,+2. 5 -0.9 ,+1.1Apr -11.6,+9.6 -1.5,+1.5 -1.2,+ 1.8 -2.0,+2.2 -0.9, +0.9May -9.3, +7.8 -1.9,+1.6 -0.8, +1.0 -1 .3 ,+ 23 -0.7,+ 1.6Jun -6.6,+5 .0 -1.0,+1.4 -0.8,+ 1.1 -2.4,+3. 0 -0.8 ,+1.3Jul -6.9,+4. 8 -1.7,+1.3 -0.8, +0.7 -2.0,+3.6 -0.8, +1.5Aug -5.3, +5.7 -1.7,+1.7 -0.6,+ 0.6 -1.9,+2.6 -0.7,+ 0.8Sep -7.1,+8 .1 -1.4,+2.1 -0.7, +0.6 -2.4,+1.6 -0.7,+ 1.3Oct -12.8,+8.6 -2.3,+1. 9 -1.4, +0.8 -3.1,+2.2 -0.9,+ 1.6Nov -15.2,+9.6 -2.0,+2. 0 -1.1,+ 1.3 -2.3,+1.8 -0.6,+1. 5Dec -19.4,+14.8 -2.5 ,+2. 1 -1.6,+1.8 -3.9 ,+3. 2 -0. 8,+1.0


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35 6 FORESTRYpossible changes in temperature variability andin the duration of dry and wet spells werederived from the analysis of daily grid-boxUKTR data from the last decade (years 66-75)of the transient experiment.

Sensitivity of crop systems to climatevariabilityThe importance of considering effects of climatevariability on crop growth and developmentarose from climate change studies (Mearns etal., 1992; Semenov et al., 1993, Semenov andPorter, 1994). Mearns et al. (1992) investigatedhow changes in climate variability could affectwheat production and performed sensitivityanalyses using the CERES-Wheat crop simula-tion model and perturbed historical climatedata in order to increase the inter-annual vari-ance of the climate variables. Semenov andPorter (1994) used a stochastic weather genera-tor to simulate and alter characteristics ofweather sequences instead of using historicalweather data alone as input to the AFRC

WHEAT2 model (Porter, 1993). This approachprovides a more consistent way of changing theweather parameters, including their variance orthe distribution itself. A sensitivity analysis ofthe AFRCWHEAT2 model to changes in tem-perature was performed. The effects of changesin climate variability and changes in mean cli-mate on wheat growth and development werecompared. The analysis was undertaken forIACR-Rothamsted, UK, using winter wheat cv .Avalon (Figure 2). Changes in the variability oftemperature had a similar effect on potentialgrain yield as changes in the mean values, but alarger effect on the coefficient of variation (CV)than an increase in the temperature mean value.It was concluded that, potentially, increases intemperature variability can decrease potentialcrop yield and increase agricultural risk morethan changes in mean temperature.In this study, the SIRIUS-Wheat growth sim-ulation m odel (Jam ieson, 1989; Jam ieso n, 1993)was used to illustrate the importance of incor-porating downscaled changes in mean climateand also changes in climate variability in cli-ma te change scenarios at t wo sites: IACR-

-100

Figure 2. Temperature sensitivity analyses for the AFRCWHEAT2 model. Relative changes in average grainyield and coefficient of variation (CV) for cv. Avalon at IACR-Rothamsted compared with the baseline cli-mate for different sensitivity scenarios. T+2 and T+4, increase in mean daily temperature by 2C and 4Crespectively; sd*2, doubling of temperature variability; (T+4)sd*2, a scenario combining an increase in meantemperature with an increase in variability.

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C LIMATE C HANGE SC ENAR IOS 357Rothamsted, UK, and Sevilla, Spain. The sitesrepresent different weather conditions. AtIACR-Rothamsted changes in temperature havethe largest effect on crops, whereas at Sevillawater availability is the most limiting factor andchanges in temperature have little effect. SIR-IUS-Wheat was calibrated for these sites usingexperimental data and then run using 30 yearsof generated data both for sensitivity analysesand the UKTR-derived climate change scenariospreviously described. The direct effects ofchanges in CO2 concentration on the crop werenot considered. The following sensitivity sce-narios were selected in addition to using theobserved climate as a baseline (BS): (1) anincrease in mean temperature of 3C (BS+3);(2) a dou bling of tem perature variability w ith-out changes in mean values (TV); (3) a doublingof the length of dry series (PV). Two climatechange scenarios based on the data derivedfrom the UKTR GCM were used. For Rotham-sted, the downscaled changes in mean climatewere applied, whereas at Sevilla the appropriategrid box changes were used for the reasonsmentioned earlier. In the first scenario (CC) aconventional approach was used where onlychanges in mean temperature and the amount ofprecipitation were applied. In the alternativescenario (CCV), temperature variability and theduration of the dry and wet series were per-turbed in accordance with the analysis of dailyUKTR data. The results are shown in Table 4.For Rothamsted there was very little differ-ence between yields simulated using downscaledclimate change data and those simulated usingthe appropriate UKTR grid box changes (notsho wn ). This is because (1) there is very little

difference between the downscaled and grid boxtemperature changes, and, more importantly,(2) although there is a larger difference betweengrid box and site precipitation changes, precip-itation is not limiting at this site. The differenceis simulated grain yield for the CC and CCVscenarios is also not significant because: (1) theincrease in temperature variability predicted byUKTR occurred during the time when it wouldhave little effect on crop growth and develop-ment (late summer/early autumn), and (2) thereare no significant changes in the duration of dryand wet spells at Rothamsted.There was a large difference in the grain yieldpredicted by the two scenarios CC and CCV atSevilla, even without downscaling the UKTRgrid box data. Under the first climate changescenario, CC, wheat production will benefit.Simulated grain yield is slightly increased witha decrease in the CV of 50 per cent. The secondclimate change scenario, CCV, which incorpo-rates changes in climate variability indicates adecrease in mean grain yield of 37 per cent andan increase in the CV of 124 per cent. Theseresults can be explained by the high sensitivityof grain yield to the duration of the dry spell atSevilla. Analysis of daily UKTR data snowedthat the length of the dry spell will increase dur-ing the growing season at this site under the cli-mate change scenarios considered here. Thecumulative probability functions of grain yieldfor the scenarios CC and CCV are presented inFigure 3. The lowest simulated yield for the CCscenario was 4 t ha"1, whereas in the CCV sce-na rio the g rain yield w as less than 4 t ha" 1 inmore than 50 per cent of the simulations. Thismay result in wheat production becoming

Table 4: Average grain yield and coefficient of variation for winter wheat, as simulated by SIRIUS Wheat atIACR-Rothamsted, UK and Sevilla, Spain, using 30 years of data for sensitivity experiments (BS, BS+3, TVand PV) and UKTR climate change scenarios (CC and CCV)

IACR-RothamstedYield (t ha"1)CVSevillaYield (t ha"1)CV

BS

8.230.105.880.22

BSX3

7.060.155.600.12

TV

8.480.155.770.26

PV

8.120.133.000.58

CC

7.850.075.950.11

CC V

7.980.093.670.48


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358 FORESTRY

2 4 6 8Grain yield, t ha " 1

Figure 3. SIRIUS What simulated grain yield cumu-lative probability distributions for winter wheat, cv.Alcala, at Sevilla, Spain, for UKTR climate changescenarios. CC incorporates only changes in meantemperature and precipitation amount; CCV incor-porates changes in temperature variability and thelength of dry and wet spells.

uneconomical in this region as a result of cli-mate chang e. Thu s, the incorporation of climatevariability into climate change scenarios canchange completely the conclusions concerningthe future suitability of wheat production atSevilla.

analyses and in the climate change scenarioexperiments for two locations in Europe. Theresults demonstrate clearly that changes in cli-mate variability sometimes have a larger effecton grain yield and associated risk than changesin average conditions. Moreover, incorporationof climate variability in climate change scenar-ios can qualitatively change the prediction ofthe effect of climate change on wheat growthand development in a particular region.Changes in dry spells at Sevilla, derived fromthe analysis of UKTR data, resulted in a largedecrease in potential yield and a large increasein risk which may make wheat productionunsuitable in this region.Considerations of variability are important inthe light of estimates of the effect of climatechange on agriculture and world food supply(Adams et al., 1990; Rosenzweig et al., 1993).Such studies have not yet examined the possi-bility that a climate with different variabilitymay have serious effects on food productionand trade. If the variability of climate increases,then the frequency distribution of yields is likelyto widen with sequences of years with lowyields becoming more likely and consequentserious impacts on world food supply.

D iscussionGCMs are the most highly developed and inter-nally-consistent tools currently available tomodel climate and the effects of anthropogenicclimate change. The output variables fromGCMs are at a coarse regional scale, often atarou nd 100 000 km 2 . Agricultural models usu-ally need daily weather data within a scale of 5km 2 . Thus, it is necessary to downscale theinformation from GCMs to a finer spatial reso-lution. Here, regression downscaling was usedin conjun ction wit h a stochastic weath er gener-ator to produce climate change scenarios. Theessential difference to the conventionalapproach is that the changes can be applied tothe parameters of a stochastic weather genera-tor which cover almost all the relevant statisticsof local weather, including means and vari-ances. The relative importance of changes in cli-mate variability compared with changes inmean values has been assessed in the sensitivity

AcknowledgementsWe would like to thank D r P.D . Jamieson for pro-viding the SIRIUS Wheat model for the simulationruns. We are also very grateful to D r Mike Hu lme forproviding many constructive comments on thispaper. The UKTR model data were provided by theClimate Impacts LIN K Project (D epartm ent of theEnvironmen t Contract PECD 7/12/96) on behalf ofthe UK Meteorological Office. This work was fundedby the Commission of the European Communities'Environment Programme (Contract EV5V-CT93-0294).ReferencesAdams, R.M., Rosenzweig, C , Peart, R.M., Ritchie,J.T ., M cCarl, B.A., Glyer, J.D ., Curry, B., Jones,J.W., Boote, K.J. and Allen, L.H. Jr. 1990 Globalclimate change and US agriculture. Nature 345,219-224.Bardossy, A. and Caspary, H.J. 1991 Conceptualmodel for the calculation of the regional hydro-logic effects of climate change. In Hydrology for


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Barrow and Semenov 1995. Climate Change Escenarios With High Spatial and Temporal Resolution for Agricultural Applications