Scenarios of climate change for the European Community

Eur. J. Agron., 1993, 2(4), 247-260

Scenarios of climate change for the European Community Elaine M. Barrow Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK.

Accepted 11 August 1993.

Abstract

Two methods of climate scenario construction were developed, based on the output of general circulation models (GCMs), to provide a suite of possible future climate scenarios to be used by impact modellers. The first involved the construction of a composite pattern of climate change derived from the output of seven GCMs, while the second was based on the individual output of three GCMs run in equilibrium mode. The composite scenarios indicated a consistent pattern, but altered magnitude, of climate change over time. In summer, warming was uniform over most of Europe, whereas in winter, warming was greater in northern Europe. There was much more variability in the pattern of precipitation change because of large differences in the precipitation patterns of the individual GCMs used in the construction of the composite. In summer, a decrease in precipitation may occur in southern Mediterranean countries, either no change or a decrease in central Europe and either no change or an increase in northern Europe. An increase in precipitation is likely in winter in central and northern Europe, no change or an increase in southern Europe and no change or a decrease in southern Spain, Italy and Greece. Individual GCMs showed considerable geographical variation in both the magnitude and pattern of climate change, especially for precipitation. For temperature, the magnitude of the warming was similar for all models. Key-words : climate change, scenarios, GCMs.

INTRODUCTION As part of a collaborative research project to investigate the impacts of greenhouse gas-induced climate change on the agricultural and horticultural potential of the European Community, the Climatic Research Unit (CRU) was required to develop, construct and supply a suite of climate change scenarios* to modelling groups in Europe. These groups were investigating the effects of climate change on the develop-

* A scenario can be defined as "a suite of possible future climates, developed by using sound scientific principles, each being internally consistent, but none having a specific probability of occurrence attached" (Robinson and Finkelstein, 1989). Hence, a scenario is one of a number of possible future climates and not a prediction of a climate which will occur at some time in the future. ISSN I 161-030/1931041$ 4.00/ © Gauthier-Villars - ESAg

ment, yield and distribution of a variety of crops . throughout Europe by using statistical and crop growth simulation models. Research into the effects of climate change on agriculture has concentrated on using either arbitrary changes in temperature and precipitation, or on changes in such variables derived from the output of a limited number of general circulation models (GCMs). These changes have been used in combination with agroclimatic indices, such as the effective temperature sum (e.g., Carter et al., 1990) and crop growth simulation models, such as the CERES crop models (e.g., Adams et al., 1990; Rosenzweig, 1990), to determine impacts on agriculture. In principle, GCMs provide a complete and internally consistent (in the sense that all climate variables are simultaneously derived from the same fundamental physics) view of future climate changes. GCMs are sophisticated computer models that partition the Earth's climate system into grid boxes and then

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attempt to represent the physical processes that control this system by solving a series of fundamental equations describing the conservation of momentum, mass and energy. Feedback effects in the climate system are included in these models, although some of the feedback processes are incompletely specified and poorly quantified. In many climate experiments, GCMs are run with specified boundary conditions (such as sea ice, sea surface temperature etc.) and forcing (e.g. greenhouse gas concentrations). Such experiments are usually run with a control level of C0 2 (usually of the order of 300 ppmv) until a steady-state climate is reached. The C02 concentration is then instantaneously doubled (i.e., to, for example, 600 ppmv) and the model re-run until equilibrium is again achieved. The differences between this perturbed climate and the control are indicative of the possible climate changes resulting from an instantaneous doubling of C0 2 . However, accurate prediction of climate change requires the representation of the feedbacks between the atmosphere, oceans and cryosphere and for boundary conditions to be calculated rather than prescribed. Although a number of coupled ocean-atmosphere GCMs have been developed (e.g., Mikolajewicz et al., 1990; Manabe et al., 1991), many GCMs consider only a relatively crude treatment of the ocean and they also neglect other potentially important elements of the climate system. Simulated climate and climate change is particularly sensitive to the treatment of clouds and also to the parameterization of other sub-grid scale processes (Gates et al., 1990). One of the major problems of GCMs run in equilibrium mode is that they do not provide any timedependent information on the rate of change of climate in response to greenhouse gas forcing. In order to obtain such information, GCMs must be run in transient mode, i.e. with time-dependent forcing. Transient change scenarios can be obtained from fully coupled ocean-atmosphere GCMs (e.g. Washington and Meehl, 1989 ; Stouffer et al., 1989) run with time-dependent forcing, but the computing time required is enormous. Scenarios of climate change are typically constructed using observed climate records perturbed to reflect climate changes derived from GCM results. The use of observed records provides an accurate baseline climate from which relative changes portrayed by the GCMs can be constructed. However, this procedure is incapable of depicting changes in climatic variability, which could have important consequences (Katz and Brown, 1992; Mearns et al., 1984). The simple adjustment of the observed time series in this way means that the nature and degree of variability of the historical record is reflected in any scenario constructed. Two methods of scenario construction were considered in this instance, namely, a composite scenario

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which could be scaled according to the range of global-mean temperature changes associated with all the Intergovernmental Panel on Climate Change (IPCC) 1990 emission scenarios (Houghton et al., 1990), and three individual equilibrium scenarios, which represent possible patterns of climate at some point in the future. The IPCC was set up to produce a comprehensive statement on the state of scientific knowledge concerning climate change and humankind's role therein, a task which involved over 300 scientists world-wide. Included in this report was a suite of future greenhouse gas emission scenarios based on forecasts of population and economic growth, energy use etc., as well as on varying degrees of emission control. Seven composite scenarios were constructed which encompassed adequately the range of global mean temperature changes associated with the IPCC emission scenarios and three of these are detailed here, i.e. scenario A (Business-as-Usual) for the years 2010, 2030 and 2050.

METHODS The crop models for which these climate change scenarios were constructed require information about greenhouse gas-induced changes in vapour pressure, relative humidity and wind speed in addition to those of temperature, precipitation and incident solar radiation. These data were required for the whole of the European area, to allow broad-scale analyses of the impacts of climate change (Kenny et al., 1993), and also for about 280 individual sites in Italy (Bindi et al., 1993), France, Denmark, Germany, Norway, The Netherlands, Spain and the United Kingdom (Wolf, 1993; Semenov et al., 1993; Olesen and Grevsen, 1993 ; Wheeler et al., 1993). The climate change fields were interpolated on to a 0.5° latitude by 1.0° longitude grid using a Gaussian space filtering technique, to enable grid point data to be generated at a resolution suitable to allow the extraction of site data. GCM grid point output is supposed to represent a square, but in cases of complex terrain, how representative a single station is of this area is questionable. Interpolation of coarse-scale GCM data on to a finer resolution grid was the only practical means of producing the data required in the time available, since the number of sites involved precluded any detailed analyses. The large number of sites also meant that the simplest way of calculating the required daily climate changes was to use linear interpolation between the GCM-derived average monthly change values, rather than using sophisticated methods of stochastic weather generation (see Discussion). These daily changes were then applied to observed station records and the resulting perturbed time series used by the associated modelling groups to determine the effects of climate change on crop development, yield and distribution. Eur. J. Agron.

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Model evaluation

Initially, an evaluation of the GCMs used in the construction of composite (only five out of the seven models were available, i.e. Geophysical Fluid Dynamics Laboratory (GFDL), Goddard Institute for Space Studies (GISS), Oregon State University (OSU), UK Meteorological Office low resolution (UKMO-L) and UK Meteorological Office high resolution (UKMO-H) models) and equilibrium scenarios was made. Control run simulations from each GCM were compared with observed climatologies for mean sea level pressure (MSLP), temperature and precipitation in order to evaluate the performance of the models in simulating observed climate at the European scale.

Table 1. Characteristics of the GCMs used for model validation and scenario construction.

Model

I. 2. 3. 4. 5. 6. 7.

GFDL GISS

osu

UKMO-L UKMO-H LLNL MPILSG

Resolution Ocean heat Lat. Long. transport

4.5° 7.8° 4.0° 5.0° 2.5° 4.0° 5.6°

7.5°* 10.0° 5.0° 7.5° 3.75° 5.0° 5.6°

Equilibrium global mean change for a L'lT(oC) L'>P(%)

None Prescribed None Prescribed Prescribed 2-layer ocean 11-layer ocean

4.0 4.2 2.8 5.2 3.5 3.8 1.6+

II 9 8 15 II II 3

*A resolution of 4.5° by 7.5° is equivalent to a grid box size of 500 km by 600 km. + The MPILSG experiment was forced by a time-dependent scenario of greenhouse gas concentrations following IPCC scenario A (Houghton et al .. 1990). References : 1. Geophysical Fluid Dynamics Laboratory, Wetherald and Manabe (1986) ; 2. Goddard Institute for Space Studies, Hansen et al. (1984) ; 3. Oregon State University, Schlesinger and Zhao (1989) ; 4. UK Met. Office low resolution, Wilson and Mitchell (1987); 5. UK Met. Office high resolution, Mitchell et al. (1990); 6. Lawrence Livermore National Laboratory, Gates (pers. comm.); 7. Max Planck Institute (Large-scale Geostrophic ocean), Cubasch et a/. (1991 ). L'>T, change in temperature ; L'>P change in precipitation. ~

It is assumed that those GCMs which simulate the observed climate well in their control runs are more reliable in their simulations of the climate under perturbed conditions. However, a good performance by a particular model in simulating the observed climate is no guarantee that its perturbed run simulation is reliable. Although most GCMs' estimations of averages of climate variables over large areas tend to agree Vol. 2.

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well, as the spatial scale is reduced substantial disagreement becomes apparent. In some recent validation work, large-scale averages have been used as a primary measure of model agreement/disagreement. However, even if these averages agree perfectly, there may be very much larger regional or local differences between models and between models and observed climates (Grotch, 1991). On a regional scale, atmospheric GCMs display a wide variety of errors, some of which are related to parameterizations of subgridscale processes and some to the models' limited resolution. Poor representation of underlying topography in most GCMs means, for example, that important variations in climate induced by orography and landwater contrasts are not captured. The MSLP pattern provides a useful characterization of the atmospheric circulation near the surface and is closely related to many aspects of climate. Accurate simulation of MSLP patterns is required if a model is to simulate well the various climate regimes. If a model has large-scale systematic errors in its simulated MSLP fields, then the simulated surface fields of other important variables (e.g. precipitation) will almost certainly be in error (Santer, 1988). On the European-scale there are two major features of the atmospheric circulation which should be simulated by GCMs for any confidence to be placed in their results. These are the Icelandic low- and the Azores high-pressure systems, which have varying influences on the European circulation depending on the time of year. A comparison of observed (195180 mean) and simulated MSLP fields for four GCMs (GFDL, GISS, OSU and UKMO-L) showed that all the models have problems in simulating these two atmospheric features throughout the year (Holt, 1991 ). OSU is the worst, failing to simulate either pressure system in any season. GISS and UKMO-L managed to provide reasonable simulations especially in winter, but had trouble estimating correctly the magnitude of observed pressure fields. GFDL simulated spurious centres of low pressure over Scandinavia and southern Europe and high pressure in the region where the Icelandic low pressure is located. In the case of temperature, all models considered (GFDL, GISS, OSU, UKMO-L and UKMO-H) simulated the average annual temperature cycle for the European area well, although the cycle magnitude is not always correct. GFDL tended to overestimate in summer months by about 3 oc and to underestimate in winter by as much as 5 °C. The "coldest" models are OSU and UKMO-H which underestimate temperature throughout the year. Examination of the monthly spatial pattern correlation coefficients indicated that agreement was good for all models, with correlations generally greater than 0.8. The GISS model was marginally the most successful in simulating the pattern of the observed data, but differences between the

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models were so small that there is little basis for preferring one over another (Barrow, 1993). For precipitation, there was much larger variation among the observed and simulated climatologies (again GFDL, GISS, OSU, UKMO-L and UKMO-H were examined) than exists for temperature and this was reflected in the wider range of spatial pattern correlation coefficients obtained between model simulations and observed climatologies. This is due to poor model resolution and also to problems with parameterization of subgrid-scale processes. The OSU simulation exhibited the lowest spatial pattern correlation coefficient (0.29) which, in addition to the two factors mentioned earlier, is probably related to the fact that this model is not able to simulate European pressure patterns with any fidelity. The UKMO-H simulation was generally the most accurate with a spatial pattern correlation coefficient of 0.64 (Barrow, 1993). Composite scenario construction

The method of composite scenario construction used was developed by Santer et al. (1990). In the case of temperature and precipitation 1 x C0 2 and 2 x C0 2 fields were available from seven GCMs (Table 1). The model average of the 2 x C0 2 minus I x C0 2 surface air temperature change, 11T, was the simplest temperature change scenario to construct. However, such a scenario is biased by the different equilibrium climate sensitivities (most commonly expressed as the equilibrium global-mean temperature changes for a C0 2 doubling and which ranged from 2.8 oc to 5.2 °C) of the seven models. Also, the Max Planck Institute Large Scale Geostrophic (MPILSG) model perturbed field represented only an 80 per cent increase in C0 2-forcing rather than a doubling and is not in equilibrium with that forcing. Hence, a model arithmetic average will underweigh this model as its global-mean warming (1.6 °C) represents the realized rather than the equilibrium response. In order to remove this bias, monthly temperature change at each grid point in each model was expressed as a fraction of the model's equilibrium global climate sensitivity, 11Teq· The best-guess standardized temperature change 11T* was determined by averaging these fractions at each grid point. This method produces patterns of temperature change unbiased by the different model climate sensitivities and also allows the introduction of the time dimension into the scenarios through the time-dependence of the global-mean warming. By using estimates of the transient global-mean warming derived from a simpler climate model (Wigley and Raper, 1987, 1992) GCM-derived regional patterns could then be scaled up to obtain the transient changes associated with IPCC emission scenario A for 2010, 2030 and 2050 (Table 2). This method of composite scenario construction assumes that the spatial pattern of transient changes is similar to that of

the equilibrium changes, although the magnitude of the changes is obviously different. Comparison of output from GCMs run in both equilibrium and transient modes shows that, although there are important differences in the regional patterns of climate change, these differences are located in high latitude areas of oceanic deep-water formation (e.g. the North Atlantic) and, in most other areas, there are strong similarities between the results of the two types of GCM experiment (Bretherton et al., 1990).

Table 2. Details of the IPCC emissions scenarios used for scenario construction. IPCC Scenario

Year

Global mean temperature change ( 0 C)

A A A A high B

2010 2030 2050 2050 2030 2010 2030

0.49 1.06 1.71 2.50 0.75 0.36 0.63

c

D

Composite precipitation scenarios were constructed in a similar manner as outlined above for temperature. However, instead of using difference fields, the percentage change in monthly precipitation was calculated for each pair of model fields. Percentage changes are used rather than 2 x C0 2 minus 1 x C0 2 differences in order to avoid illogical results which may arise as a result of poor model control run simulations. In the very few instances where precipitation increased from zero to some positive value at a grid point, a change of 100 per cent was allocated. This percentage change was then standardized as for temperature by dividing by the equilibrium climate sensitivity for the respective model, 11Teq· The ability of the models to simulate the observed precipitation patterns varied between GCMs much more substantially than it did for temperature, so rather than give each model the same weight in the resulting standardized precipitation scenario, a weighting was introduced which ranged from 0.12 for the GISS and Lawrence Livermore National Laboratory (LLNL) models to 0.76 for the UKMO-H model. This was based on the square of the global pattern correlation coefficient between observed precipitation and the simulated control run precipitation for each respective model (Hulme, 1991a, b, 1992). A composite pattern of precipitation change was also constructed based on weightings derived from a European rather that a global-scale validation. This was done to identify major differences between the two patterns. On comparison, it was apparent that there were some slight differences but, on the whole, patterns of change were very Eur. J. Agron.

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similar. It could be argued that, since the European area was the focus of this work, it was most important for models to perform well in simulating European climate patterns. Conversely, given the "noisiness" of GCM simulations and their unreliability at the regional scale, it could be argued that regional validation results may not reflect the overall veracity of a model. For the remaining climate variables, namely incident solar radiation, vapour pressure, relative humidity and wind speed, 1 x C0 2 and 2 x C0 2 fields were available from only a small number of GCMs (GFDL, GISS and UKMO-L for incident solar radiation and GFDL, GISS, OSU and UKMO-L for the remaining variables). As the reliability of these variables may be less than that of the primary variables, temperature and precipitation, attempts were made initially to derive statistical relationships between them for a number of selected sites in Europe representative of the different climate zones, e.g. maritime and continental. However, these attempts proved futile and it was decided to simply calculate standardized GCMaveraged percentage changes and to apply them evenly across Europe. Relative humidity (RH), or vapour pressure, can be derived from the mixing ratio or specific humidity data. Although, by definition, mixing ratio and specific humidity are slightly different, they can actually be considered as the same because proportions of water vapour in the air are so small (Mcilveen, 1986). Percentage changes in mixing ratio correspond directly to the percentage changes in vapour pressure. Composite scenarios of this variable were constructed in a similar manner to the above. Examination of the 1 x C0 2 and 2 x C0 2 wind speed fields indicated that there were large intermodel differences and, as a result, it was decided not to include changes to the observed wind speed record. Holt ( 1991) found that there were no significant differences between the 1 x C0 2 and 2 x C0 2 pressure patterns as modelled by the GCMs concerned. This implies that substantial changes in wind speed should not be expected ; any changes that were present were also small compared to the magnitude of the natural variability. Equilibrium GCM scenarios

The 2 x C0 2 minus 1 x C0 2 changes for three GCMs (GFDL, GISS and UKMO-L) were used to construct scenarios of equilibrium climate change, which were internally consistent, as defined earlier, and which could be used to examine the extent of the inter-model variability. These three models were chosen because, as well as performing reasonably in model validation exercises, data for all required climate variables were also available. This was not the case for some other models, e.g. UKMO-H. The percentage changes between the 1 x C0 2 and 2 x C0 2 Vol. 2,

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fields were then calculated for all the required climate variables apart from temperature. In the latter case, the 2 x C0 2 minus 1 x C0 2 difference fields were determined. The monthly change fields for all variables were then linearly interpolated to obtain daily values and applied to the observed time series to produce perturbed data. RESULTS

The results of the scenario construction exercise are now outlined for Europe. It must be stressed that these climate change scenarios describe a number of possible future climates that may occur and are not predictions of climates that will occur at some point in the future. Composite scenarios Temperature

The best-estimate seasonal temperature changes associated with composite scenario A for 2010, 2030 and 2050 show that in 2010, warming is predicted to be between 0.5 and 1.0 °C for most of Europe in all seasons. There are a few exceptions : in spring in south-western Spain, southern Italy and southern Greece, warming may be up to 0.5 °C, as is the case in summer for most of the UK and Denmark. In 2030, the largest warming occurred in winter : increases of 1.5-2.0 oc occurred in central Europe, Russia and southern Scandinavia. Elsewhere increases were of the order of 1.0-1.5 °C, except in northern Scandinavia where increases may be as large as 2.5 °C. The largest warming also occurred in winter in 2050 (Figure 1) when most of Europe may experience temperature increases of 2.0-3.0 oc. In the Mediterranean area increases were not as great (1.02.0 °C) whereas in northern Scandinavia they may be of the order of 3.0-4.0 oc. In summer, warming was generally uniform over Europe between 1.0 and 2.0 °C. Larger increases occurred in autumn and spring : in spring in north-east central Europe warming of between 2.0-3.0 oc may be experienced whereas warming of this magnitude in autumn is confined to central Europe, Scandinavia and Russia. Precipitation

In 2010, in general, northern Europe may experience either no change or an increase in precipitation of up to 4 per cent in all seasons. On the other hand, it is likely that southern Europe may experience either no change or a decrease in precipitation in all seasons, by as much as 4 per cent in summer. In 2030 and 2050 the pattern of change is identical, although the increases and decreases in amount are larger than for 2010. Seasonal percentage precipitation changes for 2050 (Figure 2) show that the composite

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Figure 1. Best estimate temperatu re changes (° C ) associated with composite scenario A, year 2050 : (a) Sp ring; (b) Su mmer; (c) Autumn; (d ) Wint er. Eur. I Agron.

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Scenarios of climate change for the EC

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approach to scenario construction produced a pattern of change per degree of global warming ; hence the pattern does not change with time, whereas the magnitude of the change does.

for western Russia. On the whole, models indicated a warming of between 2 and 6 oc for central Europe. In July, the smallest warming occurred with the GISS model, where the temperature increases for most of Europe were between 2 and 4 °C. The GFDL model illustrated increases of between 4 and 8 oc over most of Europe (Figure 3) ; for the same area the UKMOL model indicated a warming between 3 and 7 °C. The situation was similar for October. Once again, the GISS model exhibited the smallest warming, of between 2 and 4 °C, over central Europe, whereas the GFDL and UKMO-L models indicated increases of 26 oc and 5-7 °C, respectively.

Other climate variables Table 3 shows the percentage increases in monthly incident solar radiation for the equilibrium condition. These values were then scaled by the factor ~T1/~Teq (in this case ~Teq is the average climate sensitivity for the models concerned) to determine the timedependent incident solar radiation associated with the IPCC emissions scenarios. Table 3 also shows the average equilibrium percentage changes in mixing ratio. The largest increases occurred in winter (lO per cent) and the smallest in summer (5 per cent). As above, these percentage increases were scaled by the factor ~T 1/~Teq to obtain the changes associated with each of the IPCC emission scenarios outlined in Table 2. ~Teq is the individual model climate sensitivity. In this case the procedure was carried out for each model because of large inter-model differences in ~Teq· Results for individual models were then averaged to obtain scenarios to be applied evenly across Europe. Once these percentage changes in vapour pressure had been applied to the observed vapour pressure data, the RH for each scenario was calculated from the following : RH( %)

Precipitation

Equilibrium preCipitation scenarios showed large inter-model differences. For January, the models were mostly consistent in indicating an increase in precipitation over central and northern Europe. The exception to this was the GFDL model which exhibited either no change or a decrease in precipitation of up to 25 per cent in central Germany (Figure 4 ). Otherwise increases of up to 60 per cent may be experienced in Europe. For April, patterns of change were slightly different. On the whole, increases in precipitation of up to 80 per cent were generally indicated over most of Europe. However, the GFDL model indicated either no change or a decrease in precipitation of as much as 25 per cent over France, Spain and western areas of central Europe. Patterns of change were very different for July. The GFDL model indicated decreases in precipitation over most of Europe by as much as 75 per cent in some areas. On the other hand, the same model indicated increases in precipitation of between 25 and 50 per cent over Greece and in excess of 75 per cent in southern Turkey. The GISS model also indicated increases in precipitation of as much as 120 per cent in Greece, Yugoslavia and southern Turkey in July. Elsewhere in Europe increases were generally of the order of 40 per cent except in western France, southeast UK and northern Spain, where there may be no change or decreases in precipitation of up to 40 per cent. Decreases in precipitation of about 30 per cent were indicated for Spain by the UKMO-L model; elsewhere in Europe there may be no change or increases of between 30 and 60 per cent. This model also indicated decreases in precipitation in eastern

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Equilibrium scenarios Temperature

For January, the pattern of change was generally similar for all three models ; the largest warming occurred in the north and the smallest in the south. In general, most of Europe experienced a warming of between 4 and 8 °C. There was more variation in the pattern of warming for April. The largest temperature increases were associated with the UKMO-L model where increases of as much as 13 °C were indicated

Table 3. Equilibrium changes in incident solar radiation and humidity mixing ratio (%). Variable

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Incident solar radiation

1.7

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Europe. For October, the UKMO-L model indicated either no change or increases in precipitation of between 30 and 60 per cent over most of Europe. The GFDL model also indicated either no change or an increase in precipitation but only for central Europe, northern UK and some parts of eastern Europe. Either no change or decreases in precipitation of about 25 per cent were apparent for western France, most of Spain and southern UK. On the other hand, the GISS model exhibited either no change or increases in precipitation of up to 40 per cent for most of Europe, but either no change or decreases of up to 40 per cent in south-western France and eastern Spain. It is obvious from these figures that there is much more variation in the GCM-derived precipitation scenarios than in those of temperature. Incident solar radiation

All models indicated changes in incident solar radiation of a similar magnitude. Changes are predicted to occur in the amount of solar radiation incident at the Earth's surface as a result of changes in cloud cover. As well as cloud formation being a sub-grid scale process, it is also parameterized differently in the various GCMs, leading to inter-model differences in magnitude and pattern. For January, the GFDL model indicated that either no change or a decrease of up to 15 per cent in solar radiation may occur over southern Europe and the UK. In central and northern Europe there may be either no change or increases of up to 45 per cent. Early in the year in northern Europe and Scandinavia, radiation receipts are low and so even a small absolute increase will result in a large percentage increase. For both April and July most of Europe may experience increases of between 0 and 15 per cent. For October, this is also the case for northern Europe, but in southern Europe decreases in incident solar radiation of between 0 and 15 per cent may occur. The GISS model indicated an increase in incident solar radiation in western Europe and the Mediterranean of up to 10 per cent in January ; in eastern and central Europe there may be decreases of the same magnitude. A similar picture exists for April. For July, most of Europe may experience increases of up to 10 per cent, whereas in Italy and Greece decreases of up to 10 per cent may occur. For October, western and central Europe may experience increases of up to 10 per cent, whereas in eastern Europe and the southern Mediterranean there may be decreases of up to 10 per cent. The UKMOL model indicated that incident solar radiation may decrease up to 10 per cent over most of Europe in January. Southern Spain, however, may experience increases of 10-20 per cent. For April, in northern Europe, southern Spain and Italy increases in incident solar radiation of between 0 and 10 per cent may occur, whilst in central and southern Europe increases may be 10-20 per cent. For July, increases may be up Vol. 2,

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to 10 per cent in western and southern Europe, whereas central Europe may experience decreases of up to 10 per cent. For October, all of Europe may experience increases in incident solar radiation of 1020 per cent in central Europe and up to 10 per cent in northern Europe and the Mediterranean. Humidity

All of Europe may experience increases in mixing ratio and humidity of between 20 and 60 per cent for January and April. For July, increases were smallest; GFDL indicated an increase of up to 20 per cent, whereas GISS indicated increases of between 10 and 30 per cent. Similar changes occurred for October for the GISS model, but GFDL indicated increases of between 20 and 40 per cent. The UKMO-L model showed increases of between 20 and 60 per cent for January and July over most of Europe. For April, the increases were smaller, between 20 and 40 per cent, but in October they were larger with most of central and northern Europe experiencing an approximate doubling of humidity . .Increases in southern Europe were slightly smaller. Wind speed

The pattern and magnitude of changes in wind speed were very variable. The UKMO-L model indicated either no change or a decrease in wind speed of up to 10 per cent over most of Europe in January, April, July and October. For January, the GISS model indicated decreases of between 0 and 50 per cent over most of Europe. On the other hand, the GFDL model indicated decreases in wind speed of up to 25 per cent in northern Europe, but increases in southern Europe of up to 100 per cent in some areas. For April, increases of up to 25 per cent were indicated by the GISS model for central Europe. Northern and southern Europe were again divided in the GFDL model ; the north may experience increases of up to 50 per cent, whereas decreases of up to 25 per cent may occur in the south. For July, most of Europe may experience decreases of up to 50 per cent in the GISS model, whereas GFDL indicated increases in wind speed, by as much as 100 per cent, in some areas. For October, decreases of up to 50 per cent were indicated in central Europe by the GFDL model ; in the north and south, however, wind speed may double in some areas. The GISS model indicated that increases in wind speed of up to 75 per cent may occur in western Europe, but decreases of similar magnitudes may occur in the east. DISCUSSION There were differences in the patterns of climate change produced by the composite and equilibrium

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scenarios which resulted from the way in which they are constructed. The composite scenario indicated uniform warming over Europe in all seasons, although warming was more pronounced in the north in winter, whereas equilibrium scenarios showed more variation in the pattern of temperature increase. For summer precipitation, the composite pattern illustrated increases in northern Europe and decreases in southern Europe, which were most severe in the Mediterranean area. In winter, increases are likely over most of Europe, with areas of decrease in precipitation being confined to the southern Mediterranean countries. The equilibrium scenarios, however, showed very different patterns and magnitudes of precipitation change. Advantages and disadvantages are associated with both methods of scenario construction. The main advantage of the composite scenario is that it produces a pattern of climate change per degree of global warming, and so allows the introduction of a time-dimension. This pattern of change can be scaled by the transient global mean warming for any point in the future. However, averaging standardized GCM output, as in the composite scenario, has the drawback that the internal consistency of individual GCMs is lost and inter-model variability is masked. The extent of this variability may be ascertained by using the standardized inter-model standard deviation as a measure of the inter-model difference, or by comparing results from individual models. By assuming that the limited sample of seven GCMs used in the construction of the composite is normally distributed, then the standardized inter-model standard deviation can be used together with the standardised temperature change, Ll T*, to calculate the lower and upper 90% confidence limits about L'lT*. Ideally, it would be preferable to have a large number of independent realizations of the L'lT (2 x C0 2 minus 1 x C0 2 temperature change) fields to calculate the standardized inter-model standard deviation. As this was not possible, it was decided to use the output of the three equilibrium GCM experiments to obtain a measure of inter-model difference. The problem of loss of internal consistency, however, may not be as important as initially thought. GCMs are internally consistent in so far as the climate variables are simultaneously determined by the same fundamental physical relationships. However, internal consistency in the real world may be very different from that in a GCM. Hewitson and Crane (1992) have shown a lack of internal consistency between mean sea level pressure and temperature for the GISS model and the internal consistency has also been questioned between mean sea level pressure, temperature and precipitation for both the MPILSG and UKMO-H models in some seasons (Hulme, pers. comm.). In equilibrium GCM scenarios it was assumed, because of model internal consistency, that changes in humidity and solar radiation were consis-

E. M. Barrow

tent with the changes in temperature and precipitation indicated by the models. For composite scenarios, attempts were made to derive statistical relationships between changes in precipitation and temperature and those of humidity and solar radiation. No statistically significant relationships were determined, so it was decided to construct average standardized fields for these two climate variables. Hence, changes in humidity and solar radiation, in this case, may not be consistent with the changes in temperature and precipitation calculated using this method. The major disadvantage of equilibrium scenarios is that they do not provide any information concerning the rate of climate change in response to greenhouse gas forcing. To obtain such information GCMs must be run in transient mode, as mentioned earlier. However, transient change scenarios also suffer from drawbacks as climate scenarios as they correspond to only a single value of climate sensitivity, to a single realization of the lag effect of oceanic thermal inertia and to only one future greenhouse gas emissions scenario. These three factors are primary uncertainties in current climate models and the impracticality of making multiple time-dependent GCM experiments precludes the explicit treatment of model uncertainties for a given greenhouse gas forcing scenario. Also, uncertainties in individual models are difficult to assess and the results of a single experiment would be confounded by the noise of the model's natural variability. In the three equilibrium GCM scenarios used there were large qualitative and quantitative differences, most pronounced for precipitation. This is mainly because of the coarse grid resolution and consequent poor representation of sub-grid scale processes. As a result, topographic features, which have important effects on regional/local climate, are inadequately represented. Consequently, most GCMs tend to agree well in their estimation of averages of observed climate variables over large areas, but as spatial scale is reduced, substantial disagreement becomes apparent. Even if large-scale averages agree perfectly, there may be very much larger regional or pointwise differences between models and also between models and observed climates (Grotch, 1991). Two ways around this problem of coarse resolution are either to embed a higher-resolution limited-area model within a GCM (or to drive such a model "off-line" with GCM output; Giorgi, 1990; Giorgi and Mearns, 1991), or to use statistical interpolation methods to provide enhanced spatial detail (e.g., Kim et al., 1984; Karl et al., 1990; Wigley et al., 1990). Another problem associated with GCMs is that although daily changes (and even smaller time-scale changes) in the climate variables are computed, they are not always archived, resulting in only monthly mean data being available. For some impact studies this time-scale is usually sufficient, but in the case of Eur. J. Agron.

Scenarios of climate change for the EC

agriculture daily changes are necessary to determine the impacts of frost, high temperature events etc. One way of producing daily data is to use stochastic "weather generation" methods (e.g. Richardson, 1981 ; Racsko et al., 1991). These are statistical tools designed to produce time series of daily weather-variable data using stochastic simulation techniques. For instance, daily precipitation time series can be generated using a recurrence model (such as a Markov chain) to obtain wet day/dry day sequences and an amount model to describe the distribution of rainfall amounts on wet days. Temperature and solar radiation data can then be generated based on their relationships with precipitation. By perturbing the various model parameters in different ways, longer time-scale changes consistent with GCM output can be simulated, and the resulting daily data used in impacts models. Illustrations of this method in the climate change context can be found in Wilks (1988, 1989a, b, 1992) and Cole et al. (1991 ). Scenario construction and development is currently a major area of interest in both the climate and impacts communities. It is likely that methods of scenario construction will continually be developed and refined in the future. As GCMs are modified and their resolution is increased the information produced will be more useful for determining the effects of greenhouse gas-induced climate change, including the impacts on agriculture. ACKNOWLEDGEMENTS I would like to express my thanks to Dr. Richard Warrick (formerly of the Climatic Research Unit) for his help and guidance in the early stages of this work. I am also very grateful to Dr. Mike Hulme for his advice and recommendations throughout the lifetime of the project. This work was funded by the Commission of the European Communities' EPOCH Programme (contract number: EPOC-CT900031TSTS). REFERENCES Adams R. M., Rosenzweig C., Peart R. M., Ritchie J. T., McCarl B. A., Glyer J. D., Curry R. B., Jones J. W., Boote K. J. and Allen L. H., Jr. (1990). Global climate change and US agriculture. Nature, 345, 219-224. Barrow E. M. (1993). Future scenarios of climate change for Europe. In: Kenny G. J., Harrison P. A. and Parry M. L. (Eds). The effect of climate change on agricultural and horticultural potential in Europe. Research Report No 2, Environmental Change Unit, University of Oxford, pp. 11-39. Bindi M., Castellani M., Maracchi G. and Miglietta F. (1993). The ontogenesis of wheat under scenarios of increased air temperature: a simulation study. Eur. J. Agron., 2, 261-280. Vol. 2.

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