Developing scenarios of atmosphere, weather and climate for northern regions Timothy R. Carter Agricultural Research Centre ofFinland, Office address: Finnish Meteorological Institute, Box 503, FIN-00101 Helsinki, Finland Future changes in atmospheric composition and consequent global and regional climate change are of increasing concern to policy makers, planners and the public. However, predictions of these changes are uncertain. In the absence of single, firm predictions, the next best approach is to identify sets of plausible future conditions termed scenarios. This paper focuses on the development ofclimate change scenarios for northern high latitude regions. Three methods of scenario development can be identified; use of analogues having conditions similar to those expected in the study region, application of general circulation model results, and composite methods that combine information from different sources. A composite approach has been used to produce scenarios of temperature, precipitation, carbon dioxide and sea-level change for Finland up to 2100, as part of the Finnish Research Programme on Climate Change (SILMU). Tools for applying these scenarios in impact assessment studies, including stochastic weather generators and spatial downscaling techniques, are also examined. The SILMU scenarios attempt to capture uncertainties both in future emissions of greenhouse gases and aerosols into the atmosphere and in the global climate response to these emissions. Two types of scenario were developed: (i) simple “policy-oriented” scenarios and (ii) detailed “scientific” scenar- ios. These are compared with new model estimates of future climate and recent observed changes in climate over certain high latitude regions. Key words', climate change, temperature, precipitation, carbon dioxide, sea-level, uncertainty, base- line, Finland ntroduction One of the major constraints on agriculture in northern high latitude regions is climate. Crop growth and production is limitedby a prolonged and often severe winter and a short growing sea- son. Crops are frequently grown close to their northern limits of potential, where the reliabili- ty of production is closely governed by year-to- year variations in the weather. In historical times, periods of benign climate tended to favour the © Agricultural and Food Science in Finland Manuscript received February 1996 235 Vol. 5 (1996): 235-249. AGRICULTURAL AND FOOD SCIENCE IN FINLAND clearance and colonisation of agricultural land in the high latitude zone, whilst runs of unfa- vourable weather contributed to crop losses, fam- ine, farm abandonment and depopulation (e.g. Utterström 1955,Parry 1978,Bergthörsson et al. 1988). Given the sensitivity ofagriculture to climate in these regions, the prospect of a future global climatic warming due to anthropogenic causes could be of considerable significance. There is an increasing body of evidence to suggest that this warming could exceed any recorded change since the end of the last glacial period 10,000 years ago (IPCC 1996). In high latitude regions the warming may be greater than the global av- erage. However, there are still large uncertain- ties surrounding predictions of future changes. This paper outlines some approaches used to project future climate change in northern high latitude agricultural regions. The geographical scope of the discussion is the circumpolar bore- al zone; broadly the region north of about 60°N in Europe and northern Russia, and extending south of 50°N in parts of North America and eastern Siberia (Hämet-Ahti 1981). Its focus is on scenarios of changes in atmospheric compo- sition and associated changes in regional climate, both of which may have important consequences for agriculture. An example of an approach to develop scenarios for Finland is described in more detail. These scenarios have been prepared for the Finnish Research Programme on Climate Change (SILMU), and have been applied in sev- eral SILMU studies reported in this volume to assess possible impacts of climate change on agriculture. The changing atmosphere and its effect on climate During recent decades, measurements of the Earth’s atmosphere have indicatedrapid increas- es in concentration of two important types of constituent: (i) the so-called “greenhouse” gas- es, including carbon dioxide (C0 2 ), methane (CH4), nitrous oxide (N 2 0) and halocarbons, and (ii) atmospheric aerosols, especially sulphur compounds. Increases in all of these are associ- ated with human activities, in particular fossil fuel combustion, intensive agriculture and de- forestation. Rising concentrations of some of these con- stituents (e.g. C02, tropospheric ozone (0 3 ) and sulphur dioxide (S0 2 )) can have direct effects on the surface biosphere, including agricultural plants (see, for example, Hakala and Mela 1996, Bowes et al. 1996). Changes in all of them can affect the radiation balance of the Earth, and hence the global climate. Greenhouse gases warm the surface and lower atmosphere by im- peding the escape of terrestrial longwave radia- tion through the atmosphere and re-radiating some to the surface. In contrast, aerosols usual- ly have a cooling effect on the climate both di- rectly, by absorbing incoming solar radiation, and indirectly, through their role in the forma- tion of clouds which reflect solar radiation out to space. Estimates of the relative effects of these dif- ferent constituents in perturbing the radiation balance of the global climate system (“radiative forcing”) since pre-industrial times are shown in Figure 1. These estimates are based on a com- prehensive review of available evidence (IPCC 1996). They are compared in the figure with es- timates of the global forcing due to natural changes in solar irradiance since 1850. Volcanic eruptions are another source of negative forc- ing, of a similar magnitude as the positive green- house gas forcing shown in Figure 1, but effec- tive for only a year or two after a large eruption (IPCC 1996). It should also be noted that the regional effects of changes in atmospheric com- position on climate may differ (sometimes in sign) from the global effects. The best tools available for evaluating the response of global climate to the radiative forc- ings shown in Figure 1 are numerical climate models. These are based on physical laws, and attempt to simulate the major processes control- ling the climate in the atmosphere, oceans and on land. There is a hierarchy of climate models 236 Carter, T.R.: Developing scenarios ofatmosphere, weather and climate AGRICULTURAL AND FOOD SCIENCE IN FINLAND Vol. 5 (1996): 235-249. ranging from simple box-models, which have only a few variables, to sophisticated coupled general circulation models (GCMs) of the atmos- phere and oceans. They are described further below. However, none of these models are able to capture the full complexities of the climate system, and there are large uncertainties around estimates of regional climate change from GCMs. The need for scenarios Notwithstanding the low confidence in individ- ual model predictions, in order for actions to be taken to prevent or to slow down changes in the atmosphere, policy-makers need to be informed about the possible changes to be expected.Like- wise, scientists require projections of these changes so they can examine their likely impacts. It is also important to recognise that the un- certainties in projections are not due solely to the shortcomings of climate models. Estimation of regional climate change can be thought of as the final step in a sequence of assumptions and uncertainties relating to: (i) future emissions of greenhouse gases and aerosols into the atmos- phere, depending on factors such as population growth and economic development; (ii) future atmospheric composition, affected by the quan- tity, mixing, reactions and residence time of dif- ferent constituents; (iii) the global climate re- sponse to changing atmospheric composition; and (iv) climate changes at the regional and sea- sonal level. It is at the regional level (where the uncertainty is greatest) that information is most needed in impact assessments. Since accurate predictions of climate change are not available, an alternative approach is to develop scenarios. These are alternative projec- tions which are meteorologically plausible (i.e. physically, temporally and geographically real- istic) and embrace our best available estimates of the uncertainties in projections. The main emphasis in the following sections is on scenar- ios of future climate, but it should be noted throughout that these scenarios need to be con- sistent, in time and space, with projections of other related environmental variables such as atmospheric composition and sea-level. Fig. 1. Estimates of the global an- nual mean radiative forcing (Wm 2 ) from 1850 to 1990 for a number of potential climate change mech- anisms. Column heights represent mid-range estimates of the forcing, error bars largely represent the spread of published values and the confidence levels givenat the base of the diagram are a subjective as- sessment of the confidence that the actual forcing lies within the error bar. Source: IPCC (1996). 237 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Methods of developing climatic scenarios Three main approaches have been used in previ- ous studies to construct scenarios of regional climate change, involving the use of; (i) ana- logues, (ii) general circulation models, and (iii) compositing. These approaches are described briefly below, with examples mainly drawn from high latituderegions. More extensive reviews of these approaches can be found elsewhere (e.g. Giorgi and Mearns 1991, Pittock 1993). Analogue scenarios Analogue scenarios are constructed by identify- ing recorded climatic regimes that may serve as analogues for the future climate in a given re- gion. These records can be obtained either from the past (temporal analogues) or from another region at the present (spatial analogues). Temporal analogues Temporal analogues are oftwo types: those based on past instrumental observations, usually with- in the last century (e.g. Lough et al. 1983), and those based on proxy data, using palaeoclimatic indicators from the more distant past such as plant or animal remains and sedimentary depos- its (e.g. Budyko 1989). Both have been used to identify periods when the global temperature is thought to have been warmer than today. Other features of the climate during these warm peri- ods (e.g. precipitation, air pressure, windspeed), if known, are then combined with the tempera- ture pattern to define the scenario climate. Al- though the spatial pattern of change sometimes bears similarities with model projections of fu- ture climate (see below) a major problem of this technique is that the physical mechanisms giv- ing rise to the warmer climate in the past almost certainly differed from those involved in green- house gas induced warming. Spatial analogues A spatial analogue involves the identification of a region today having a climate analogous to that anticipated for the study region in the future. For example, spatial analogues for five northern case study regions are shown in Figure 2 assuming a mean annual warming of about 4°C. The main drawback of this approach is the frequent lack of correspondence between other non-climatic features of two regions that may affect the local response of agriculture (e.g. daylength, terrain or soils). Given the many weaknesses of analogue sce- narios, their use to represent future climate is not generally recommended (IPCC 1990), though they can contribute useful information for de- veloping composite scenarios (see below). Scenarios from general circulation models While simple numerical models can be used to provide quick estimates of the globally-averaged temperature response to a given forcing mecha- nism and require little computing power, the geographical pattern of theresponse can only be estimated with the aid of general circulation models (GCMs). These have been reviewed thor- oughly by the Intergovernmental Panel on Cli- mate Change (IPCC - Gates et al. 1992, Katten- berg et al. 1996). GCMs represent the three-di- mensional spatial distribution of atmospheric variables such as temperature, pressure, mois- ture and wind at regular intervals over the entire globe. The computational requirements of such models are immense, and simulations with state- of-the-art GCMs are only possible on supercom- puters. Even then, these models are currently incapable of capturing the full complexities of the real climate system. Some of the main weak- nesses of these models are (i) a poor representa- tion of cloud processes, (ii) an inability to re- solve other sub-grid-scale features such as oro- graphic precipitation and frontal activity, and (iii) a simplified representation of land-atmosphere and ocean-atmosphere interactions. In spite of 238 Carter, T.R.: Developing scenarios ofatmosphere, weather and climate AGRICULTURAL AND FOOD SCIENCE IN FINLAND Vol. 5 (1996): 235-249. recent advances in GCM development, includ- ing the coupling of dynamic ocean models to atmospheric models (Gates et al. 1992) and the simultaneous modelling of aerosol and green- house gas effects on climate (Kattenberg et al. 1996), regional climate predictions from GCMs remain highly uncertain. Compositing A further methodof scenario development com- bines elements of the above techniques in a com- positing approach. This method can range from subjective pooling ofregional knowledge on past trends in climate, palaeoclimatic patterns and information from GCMs (e.g. Pittock and Salin- ger 1982, Johannesson et al. 1995) to a more quantitative approach, such as averaging the outputs from different GCMs (e.g. Santer et al. 1990). A quantitative compositing method has also been adopted in developing the scenarios for Finland described in this paper. Future climate change in Finland: The SILMU scenarios This section outlines the climatic scenarios that have been developed for the Finnish Research Programme on Climate Change (SILMU). These scenarios were provided to scientists working in SILMU in the form of a computer program and user’s guide (Carter et al. 1995). Only a short description is given here. More details can be found in Carter et al. (1996a). Model-based estimates The scenarios were developed by combining the results from two sets of models: (i) MAGICC, a framework of simple global models and (ii) three coupled ocean-atmosphere GCMs (Figure 3). Globalprojections from MAGICC The Model for the Assessment of Greenhouse- Fig. 2. Spatial analogues for five high latitude regions under the temperature and precipitation changes simulated in the Goddard Institute for Space Studies equilibrium 2 x CO, model run (Hansen et al. 1983). Modified from Parry and Carter (1988). 239 AGRICULTURAL AND FOOD SCIENCE IN FINLAND gas Impacts and Climate Change (MAGICC) is a set of linked models for estimating changes in atmospheric composition and radiative forcing under different emissions scenarios and their effect on global mean annual temperature and sea-level (Hulme et al. 1995). It includes all the major greenhouse gases (except tropospheric ozone), fossil fuel derived SO, emissions and their effects on climate as aerosols, and the ef- fect of halocarbon-induced stratospheric ozone depletion. MAGICC comprises the following compo- nents: (i) a carbon cycle model for computing C02 concentrations; (ii) simple mass balance models for computing concentrations of meth- ane, N2O and halocarbons; (iii) a sulphate aero- sol model for SO, emissions from fossil sources; (iv) various schemes for converting gas and aerosol concentrations to radiative forcing; (v) an upwelling-diffusion, energy balance model to compute global mean annual temperature and the oceanic thermal expansion component of global mean sea-level rise; and (vi) ice melt models for "small" glaciers and the Greenland and Antarc- tic ice sheets. These component models, although simple, produce results that are similar to those obtained from more complex, state-of-the-art models. Details about individual model compo- nents and full references can be found in the MAGICC Reference Manual (Wigley 1994). The primary inputs to MAGICC are emis- sions scenarios at decadal intervals between 1990 and 2100 for the following: fossil CO,, net land- use-change CO,, CH 4 , N,O, CO, NOx , VOCs, CFCII, CFCI2, HCFC22, HFCI34a and SO, (Wigley 1994). Emissions scenarios can be se- lected from a list of published scenarios or can be user-specified. The models calculate the ra- diative forcing due to emissions over the period 1765-2100, the global mean annual temperature response to a given forcing and the global mean sea-level effect of the temperature change. Model parameter uncertainties are also represented in model outputs. MAGICC was used in this application to rep- resent two major sources of uncertainty in glo- bal estimates of temperature change. The first is therange ofpossible future emissions, which was based on three IPCC (1592) emissions scenarios (IPCC 1992). The second is the climate sensi- tivity, which is a measure of the response of glo- bal mean temperature to a given radiative forc- ing (conventionally a doubling of atmospheric C02 concentration). The IPCC has specified a range of possible climate sensitivities, based on GCM simulations, of 1.5-4.5°C, with a best es- timate of 2.5°C (IPCC 1992). Three combinations of these sources of un- certainty were selected for SILMU, to represent a central, "best guess" projection and the extreme range: - Combination 1: Central - central emissions/ central climate sensitivity (IS92a/2.5°C) Fig. 3. Method of developing scenarios for SILMU (sche- matic). Boxes with double lines are models; boxes with single lines are model inputs and outputs; boxes with bold lines are the programs used for generating scenarios. Ar- rows represent flows of information. 240 Carter, T.R.: Developing scenarios ofatmosphere, weather and climate AGRICULTURAL AND FOOD SCIENCE IN FINLAND Vol. 5 (1996): 235-249. Combination 2: Low - low emissions/low sensitivity (IS92c/l .5°C) Combination 3: High - high emissions/high sensitivity (IS92f/4.5°C). MAGICC was run with these three combina- tions to give a range of C02 concentrations (based on the emissions scenarios), global mean annual temperature change estimates and sea- level rise estimates for the period 1990-2100. The cooling effect of sulphates was also account- ed for in the model runs. The global tempera- ture changes form the basis for the construction of regional climatic scenarios for SILMU (see below). The CO, and sea-level rise estimates can be applied globally and are used directly in the SILMU scenarios. Regional projections from GCMs Outputs from three general circulation models (GCMs) were used to develop regional scenari- os: the Geophysical Fluid Dynamics Laboratory (GFDL) model (Manabe et al. 1991), the United Kingdom Meteorological Office model transient run (UKTR - Murphy 1995) and the Max Planck Institut fiir Meteorologie (MPI) model (also known as ECHAM-1 - Cubasch et al. 1992).All three models have been used to simulate the tran- sient response of climate to a gradual increase in atmospheric greenhouse gas concentrations for varying periods into the future. The models represent the state of knowledge in the early 19905. As such, the regional pattern of climate change simulated with these models was for greenhouse gas forcing only, and did not account for sulphate aerosols. An intercomparison of the performance of these models, along with four others, in simulating the present-day regional climate has been reported by Räisänen (1995). Each GCM produced a different large-scale pattern of climate change for a given forcing, and this varied over time. However, the abso- lute timing of these changes could not be evalu- ated directly from the models because future sim- ulations were only started from the present day situation. Since there is a time lag between green- house gas forcing and the climate response to this forcing (typically of several decades) due to the thermal inertia of the oceans, the simulat- ed response was unrealistically small in the first few decades of the model runs because they failed to account for the historical build-up of greenhouse gases to which the climate should already have been responding - the so-called “cold start” problem (Hasselmann et al. 1993). Combining the model outputs To overcome the cold start problem, rates ofglo- bal warming over 1990-2100were obtainedfrom MAGICC (which does not share the problem) for the scenario combinations described above. Plots of global mean annual temperature change were next constructed for the three GCM simu- lations. The form of the warming trend given by all three GCMs was close to linear, resembling closely the central estimate curve produced by MAGICC. The modelled years in which the cli- mate warming estimated by the GCMs reached the same level as that obtained from MAGICC for 2020, 2050 and 2100 were extracted from the graphs for each model. By returning to the gridded GCM outputs, the regional changes as- sociated with a given global mean temperature change could now be assigned a date in the fu- ture. A period of years of modelled climate around each selected year was used for comput- ing standard climatological statistics. The SILMU scenarios Two sets of scenarios were developed for SIL- MU based on the above approach: policy-orient- ed and scientific scenarios. SILMU policy scenarios The SILMU policy-oriented scenarios attempt to capture a range of uncertainties in estimating future climate over Finland. At the same time, they are designed to be simple for scientists to apply and for policy makers to interpret. They depict seasonal changes and are uniform over the 241 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Carter, T.R.: Developing scenarios ofatmosphere, weatherand climate Table 1. Rates of temperature and precipitation change under the SILMU Policy Scenarios, 1990-2100. Period Temperature change (°C/decade) Precipitation change (%/decade) 1 (Central) 2 (Low) 3 (High) 1 (Central) 2 (Low) 3 (High) Spring (MAM) 0.4 0.1 0.6 0.5 0.125 0.75 Summer (JJA) 0.3 0.075 0.45 1.0 0.25 1.5 Autumn (SON) 0.4 0.1 0.6 1.0 0.25 1.5 Winter (DJF) 0.6 0.125 0.75 2.0 0.42 2.5 Annual 0.4 0.1 0.6 1.0 0.25 1.5 whole country. Three “policy scenarios” have been developed: - SILMU Scenario 1; Central - SILMU Scenario 2: Low - SILMU Scenario 3: High The scenarios were developed using the pro- cedures described above. The climate change estimates are GCM grid box values of tempera- ture and precipitation change averaged over the Finnish region and averaged across the three GCMs. They represent regional climate changes over Finland that are consistent with global mean temperature changes obtained from MAG- ICC for each of the three combinations of glo- bal emissions and climate sensitivity shown above. Percentage precipitation changes for Sce- narios 2 and 3 are scaled down or up from Sce- nario 1 estimates in proportion to the respective temperature changes. In this way, upper, lower and central estimates of the rate of temperature and precipitation change up to 2100 are given for Finland (Table 1). Note that while the estimates of seasonal long-term temperature change are quite similar between individual models, those of precipita- tion change vary considerably (sometimes in sign). These variations are not expressed in the policy scenarios due to the averaging procedure in the compositing and because of the need to restrict the scenarios to a manageable number. However, they are apparent in the SILMU sci- entific scenarios (see below). In view of its importance for examining im- pacts on agricultural plants, carbon dioxide con- centrations computed with MAGICC for 2020, 2050 and 2100 under each SILMU policy sce- nario are shown in Table 2 alongside the corre- sponding mean annual temperature and precipi- tation changes. Also shown are estimates ofglo- bal sea-level rise. Except for the largest esti- mates, however, sea-level rise appears likely to be compensated in Finland by the ongoing iso- static uplift of land areas following the last gla- ciation. While possible changes in the wind re- gime over the Baltic, which also affects sea-lev- el, complicates this prognosis, future changes in sea-level would appear to pose only a minor threat to agriculture. Table 2. Global mean carbon dioxide concentration (abso- lute), mean annual temperature and precipitation change over Finland and global meansea-level rise relative to 1990 for 2020, 2050 and 2100 under the three SILMU policy scenarios. SILMU Policy ScenariosYear and attribute 1 (Central) 2 (Low) 3 (High) 2020 C02 concentration (ppm) 425.6 408.8 433.7 Temperaturechange (°C) 1.2 0.3 1.8 Precipitation change (%) 3.0 0.75 4.5 Sea-level rise (cm) 8.9 2.1 19.2 2050 C02 concentration (ppm) 523.0 456.1 554.8 Temperature change (°C) 2.4 0.6 3.6 Precipitation change (%) 6.0 1.5 9.0 Sea-level rise (cm) 20.8 4.6 43.3 2100 C02 concentration (ppm) 733.3 484.9 848.2 Temperature change (°C) 4.4 1.1 6.6 Precipitation change (%) 11.0 2.75 16.5 Sea-level rise (cm) 45.4 7.4 95.0 242 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Vol. 5 (1996): 235-249. SILMU scientific scenarios A second set of SILMU scenarios refer to sce- narios that are derived directly from GCM out- puts. They provide spatial and temporal varia- tions that the policy scenarios do not. This makes them more technically demanding to apply and to describe, which is why they are labelled “sci- entific” scenarios, to distinguish them from the simpler policy scenarios. Three scientific sce- narios have been developed, based on the three GCMs, and with the same emissions and climate sensitivity assumptions as policy Scenario 1: SILMU Scenario la: GFDL SILMU Scenario lb: UKTR SILMU Scenario 1c: MPI The scenarios reflect the pattern of climate change over the Nordic region simulated by each GCM on a monthly basis. They reveal some of the model-to-model differences that are hidden by the compositing technique in the policy sce- narios, especially in precipitation projections. Special routines were included in the computer program supplied to SILMU researchers that lin- early interpolate to individual dates and to indi- vidual locations in the Nordic region. Alterna- tively, scenarios can be depicted over a finer- scale 1° by 2° latitude-longitude grid covering the Baltic region, or a 10 km grid over Finland. Examples of the regional pattern of mean sum- mer (June-August) temperature change over Fin- land by 2050 for the three scenarios are shown in Figure 4, Comparisons with recent GCM simulations Since the SILMU scenarios were prepared, more realistic climate change simulations have been conducted that account for both greenhouse gas forcing and the negative regional forcing of sul- phate aerosols (Taylor and Fenner 1994, Mitch- ell et al. 1995). The latter of these was with a coupled ocean-atmosphere GCM run beginning Fig. 4. Mean summer (June - August) temperature change over Finland by 2050 relative to 1990 under the three SILMU scientific scenarios: (a) Scenario la (Geophysical Fluid Dynamics Laboratory model), (b) Scenario lb (United Kingdom Meteorological Office transient model run) and (c) Scenario I c (Max Planck Institut model). 243 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Table 3. Rates of temperature change in some northern high latitude regions estimated by the Hadley Centre GCM(Mitchell et al. 1995), computed using the SILMU method, (uncertainty range in parentheses), and absolute changes observed between 1951-1980 and 1981-1990 (Folland et al. 1992). Values are taken from maps and are approximate. Model or W, E. Fenno- N. E. observations Period Alaska Canada Canada Iceland Scandia Russia Russia Hadley Centre - regional aerosols Annual 0.4 0.3 0.3 0.3 0.4 0.3 0.4 (°C/decade ) SILMU Method- Annual - 0.15(0.05-0.25)0.4(0.1-0.6) global aerosols Winter - - 0.15(0.05-0.25)0.5(0.1-0.8) - (°C/decade) Summer - 0.15(0.05-0.25)0.3(0.05-0.45) - Observed: Annual 0.75 °C 0.5 °C 0.25 °C -0.5 °C 0.25°C 0.75 °C 0.5 °C 1981-90 minus Winter >l.O °C >l.O °C 0.25 °C -0.5 °C 0.25°C >l.O °C 0.75 °C 1951-80 Summer 0.25 °C 0.5 °C 0.25 °C -0.5 °C -0.25°C 0.25 °C 0.25 °C late last century, thus avoiding the cold start problem. The results from this model indicate a rate of increase of global mean annual tempera- ture of about O.2°C per decade for the effects of aerosol and greenhouse gas forcing combined, compared with a rate of O.3°C per decade due to greenhouse warming alone. This reduced rate of warming is much more in accord with the rate observed globally during the present century, enabling the Intergovernmental Panel on Climate Change to declare recently that “the balance of evidence suggests that there is a discernible hu- man influence on global climate” (IPCC 1996). Changes in mean annual temperature and pre- cipitation for regions in the circumboreal zone have been extracted from mapped outputs of the Hadley Centre model runs (Mitchell et al. 1995) in Table 3. These have been compared to sce- narios prepared for Iceland and Fennoscandia using the SILMU method. Note that the SILMU approach also accounts for aerosol forcing, us- ing MAGICC, but this is treated at a global rather than a regional scale. The Hadley Centre results indicate mean rates of warming at high latitudes that are above the global mean. Over Fennos- candia these estimates are consistent with the SILMU scenarios, but over the central North Atlantic region (including Iceland), the SILMU scenario is of a reduced rate of warming, which does not show up in the Hadley Centre simula- lion. The SILMU scenario reflects a weakening of the thermohaline circulation found in the vi- cinity of Iceland in all three GCMs used to con- struct the scenario. In fact, the Hadley Centre model, which includes regional aerosol forcing, also shows this effect but its region of influence is shifted to the west of Iceland. Also shown are observed changes in temper- ature over the same region between the periods 1951-1980and 1981-1990 (expressed as abso- lute changes), providing a tentative comparison with the projected changes. Over continental areas there has been a clear increase in tempera- ture, especially during the winter, while in re- gions influenced by the North Atlantic recent changes have been smaller or even negative. Thus, the observed pattern of changes, while covering only a short period, does appear to be consistent with the pattern ofchanges anticipat- ed under greenhouse gas induced climate change. Applying scenarios in impact assessment Several alternative methods exist for applying climate change scenarios in impact studies. Four issues are addressed here: the baseline climate. 244 Carter, T.R.: Developing scenarios ofatmosphere, weatherand climate AGRICULTURAL AND FOOD SCIENCE IN FINLAND adjusting the baseline, downscaling, and the use of a stochastic weather generator. The baseline climate It is important at the outset to define the base- line period against which scenarios are to be compared. Conventionally meteorologists adopt the most recent 30-year climatological “normal” period, currently 1961-1990. This is the period adopted in SILMU. However, in some high lati- tude regions, including Canada, use of this peri- od as a reference has been resisted, since it is thought to contain a signal of climatic warming (R. Street, personal communication, and see Ta- ble 3). Workers in such regions may prefer to adopt an earlier normal period such as 1951- 1980. Adjusting the baseline climate according to a scenario Scenario changes in climate are usually ex- pressed either as differences (temperatures are usually handled this way) or as percentages (commonly applied to precipitation). There are two distinct methods that can be used to apply such changes as adjustments to the baseline cli- mate: the fixed change and transient change ap- proach. The fixed change approach The conventional approach applies a “fixed” scenario change for a given date in the future to all years of the baseline period. The approach is simple and quick to apply. However, it implicit- ly assumes that the future climate, like the base- line climate, is stationary, whereas in reality, the future climate is likely to be undergoing contin- ual change. The transient change approach A method which accounts for the gradual or “transient” change in climate, adjusts the base- line according to a trend. For example, a linear warming scenario for 2050 could be applied to the 1961-1990 baseline as a trend, with warm- ing by 2036 used to adjust temperatures in 1961, warming by 2037 to adjust 1962 temperatures through to warming by 2065, which is used to adjust temperatures in 1990. Note that the thir- ty-year statistical frequency distribution of a sce- nario climate adjusted according to the transient change approach exhibits greater variability than the corresponding scenario based on the fixed change approach. This may be of some impor- tance when assessing impacts. Downscaling One of the main problems with using informa- tion from GCMs is their coarse spatial resolu- tion.Even in the highest resolution GCMs, a sin- gle grid box spans an area of more than 50,000 km 2 . The large scale climate can be greatly mod- ified within an area of this size, by factors such as terrain, vegetation cover or water surfaces. Simple interpolation from grid box scale to lo- cal scale, which was used in the SILMU scenar- ios, neglects these sub-grid-scale features which are not resolved by GCMs. Local variations in climate can, ofcourse, have large effects on ag- ricultural productivity or water supply. Two alternative approaches have been devel- oped for downscaling from GCM to local scale. The first approach involves the establishmentof statistical relationships between large-scale cli- mate and sub-grid-scale climate using past ob- servations (e.g. Wigley et al. 1990, Karl et al. 1990, Bardossy and Plate 1992). The approach assumes that the statistical relationships between these two scales remain unchanged under a fu- ture climate. The second downscaling approach involves the use of limited area high resolution numeri- cal models. These are physically-based models that can be run at sub-continental scale at a res- olution of some 50 x 50 km. They can be linked to GCMs using various nesting techniques, whereby the GCM provides information on large 245 Vol. 5 (1996): 235-249. AGRICULTURAL AND FOOD SCIENCE IN FINLAND Carter, T.R.: Developing scenarios ofatmosphere, weather and climate scale flows to the limited area model, which is then run at higher resolution. Early results from such model runs, including high latituderegions of Europe and North America, are now availa- ble for impact assessment (e.g. Giorgi et al. 1992, Jones et al. 1995). Use of stochastic weather generators Many impact assessments require detailed cli- matological data on a daily time step as input to simulation models. Crop growth models are typ- ical examples in agriculture. Daily data are sel- dom available as outputs from GCMs, and in any case they are not readily applicable in impact studies. An alternative is to use stochastic weath- er generators. These consist of sets of parame- ters describing statistical properties of climatic variables observed historically at individual lo- cations. They can be used to generate time se- ries of unlimited length having similar statisti- cal properties to those observed. The parame- ters of a generator can also be adjusted accord- ing to scenarios of future climate. This offers a very flexible tool for conducting sensitivity test- ing of models, where changes in both the mean and variability of climate can be readily simu- lated (Wilks 1992, Semenov and Porter 1995). A stochastic weather generator for Finland, CLIGEN, has been developed for SILMU (Posch 1994) and provided to researchers in con- junction with the climatic scenarios (Carter et al. 1995). CLIGEN first simulates time series of precipitation, which is the independent variable in the procedure. Daily temperatures and cloud- iness values are then correlated with the occur- rence of wet and dry days, based on the method of Richardson and Wright (1984). Time series can be generated for any location in Finland, by interpolating the parameters of the generator from adjacent weather stations. CLIGEN has been applied over a 10km grid across Finland, to estimate effects of SILMU scenario climates on potato late blight (Carter et al. 1996b). One drawback of the generator re- vealed in that study is a tendency to underesti- mate the frequency and duration of persistent events like drought and warm or cold spells. It is these episodes that oftenresult in the greatest impacts on agriculture. Figure 5 compares the observed and generated frequencies of dry spells (< 0.1 mm) at Jokioinen. CLIGEN significantly underestimates the frequency of dry spells of 10 days or longer. Further work is required on the generator to correct this problem. Fig. 5. Frequency distribution of length of dry spells (precipitation < 0.1 mm) at Jokioinen, southern Finland, observed (1961-1990) and for five 30-year simulationswith the CLIGEN weather generator. 246 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Vol. 5 (1996): 235-249. Conclusions This paper has presented some estimates of pos- sible environmental changes in northern agricul- tural regions. Carbon dioxide concentrations in the atmosphere are expected to continue to rise globally, with probable beneficial effects for agricultural crops. Sea-level rise as a conse- quence of global warming may be of minor sig- nificance for agriculture in most regions, since many high latitude land areas are still recover- ing following deglaciation. Overall, warming at these latitudes (with the possible exception of the North Atlantic region) is anticipated to be larger than the global average. Wintertime pre- cipitation is expected to increase, while the amount and even the sign of precipitation change during the growing season are very uncertain. The warming alone, however, could transform the potential for agricultural production in some areas. As was illustrated in Figure 2, the climate of the late 21st century in a marginal agricultur- al region such as southern Finland might resem- ble that today in Denmark or northern Germany. Inspection of present-day crop production sta- tistics in Denmark reveals levels of yield twice or even three times those found in Finland to- day. While Denmark may not be a perfect ana- logue of a future Finland (for example, there are differences in soils, farm size and structure), a substantial portion of this disparity in produc- tion potential is climatically induced. The large uncertainties attached to scenarios of future regional climate are exemplified by the SILMU scenarios. While there is some scope for improving model predictions, using higher res- olution models which accurately account for the most important processes in the climate system, these advances are likely to be gradual and piece- meal. Moreover, rapid improvements in the pro- jections of future population growth, regional economic activity, greenhouse gas emissions and atmospheric composition seem unlikely. Thus, although opportunities do exist to narrow the range of scenario uncertainty, it still seems prob- able that scenarios will continue to play an im- portant role in policy-making and assessment for some time to come. Acknowledgements. I am grateful to Heikki Tuomenvirta of the Finnish Meteorological Institute, Helsinki, who as- sisted in developing the SILMU scenarios and to Dr. Max- imilianPosch of the National Institute of Public Health and Environmental Protection, Bilthoven, The Netherlands, who developed the stochastic weather generator, CLIGEN. Spe- cial thanks are also due to Dr. David Viner and colleagues in the Climate Impacts LINK Project, University of East Anglia, Norwich, UK for supplying GCM information and to Professor Tom Wigley of the University Corporation for Atmospheric Research, Boulder, CO, USA for providing a version of MAGICC. This work was funded by the Finnish Research Programme on Climate Change (SILMU). References Bardossy, A. & Plate, E.J. 1992. Space-time model for daily rainfall using atmospheric circulation patterns. Wa- ter Resources Research 28: 1247-1259. Bergthörsson, P., Björnsson, H., Dyrmundsson, Ö., Gudmundsson, 8., Helgadöttir, Ä. & Jönmundsson, J.V. 1988, The effects of climatic variations on agricul- ture in Iceland. In: Parry, M L. et al. (eds.).The impact of climatic variations on agriculture. Volume 1. Assessments in cool temperate and cold regions, Kluwer, Dordrecht, The Netherlands, p. 381-509. Budyko, M.l. 1989. Empirical estimates of imminent cli- matic changes. Soviet Meteorology and Hydrology 10: 1-8. Carter,!., Posch, M. & Tuomenvirta, H. 1995. SILMUS- CEN and CLIGEN user’s guide: Guidelines for the con- struction of climatic scenarios and use of a stochastic weather generator in the Finnish Research Programme on Climate Change (SILMU). Publications of the Acade- my of Finland 5/95, Helsinki. 60 p. plus diskette. —, Posch, M. & Tuomenvirta, H. 1996a. The SILMU scenarios: specifying Finland's future climate for use in impact assessment. Geophysica 32: (in press). - , Saarikko, R. A. & Niemi, K. J. 1996b. Assessing the risks and uncertainties of regional crop potential under a changing climate in Finland. Agricultural and Food Sci- ence in Finland 5: 329-350 (this issue). Cubasch, U., Hasselmann, K., Höck, H., Maier-Reimer. E., Mikolajewicz, U., Santer, B.D. & Sausen, R. 1992. Time-dependentgreenhouse warming computations with a coupled ocean-atmosphere model. Climate Dynamics 247 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Carter, T.R.: Developing scenarios ofatmosphere, weatherand climate 7: 55-69. Folland, C.K., Karl, T.R., Nicholls, N., Nyenzi, 8.5., Parker, D.E. & Vinnikov, K. Ya. 1992. Observed climate variability and change. In: Houghton, J.T. et al. (eds.). Climate change 1992: The supplementary report to the IPCCscientific assessment. Cambridge University Press, p. 135-170. Gates, W.L., Mitchell, J.F.8., Boer, G.J., Cubasch, U. & Meleshko, V.P. 1992. Climate modelling, climate pre- diction and model validation. In: Houghton, J.T. et al. (eds.). Climate change 1992: The supplementary report to the IPCC scientific assessment. Cambridge Universi- ty Press, p. 97-134. Giorgi, F. & Mearns, L.O. 1991. Approaches to the sim- ulation of regional climate change: a review. Reviews of Geophysics 29: 191-216. —, Marinucci, M.R. & Visconti, G. 1992. A 2 x C02 cli- mate change scenario over Europe generated using a limited area model nested in a general circulation model. 2. Climate change scenario. Journal of Geophysical Re- search 97(D9): 10011-10028. Hämet-Ahti, L. 1981. The boreal zone and its subdivi- sion. Fennia 159: 69-75. Hansen, J., Russell, G., Rind, D., Stone, P., Lacis, A., Lebedeff, S., Ruedy, R. & Travis, L. 1983. Efficient three-dimensional global models for climate studies: models I and 11. Monthly Weather Review 111: 609-662. Hasselmann, K., Sausen, R., Maier-Reimer, E. & Voss, R. 1993. On the cold start problem in transient simula- tions with coupled atmosphere-ocean models. Climate Dynamics 9: 53-61. IPCC 1990. Climate change:TheIPCC scientific assess- ment. Report ofWorking Group I of the Intergovernmen- tal Panel on Climate Change (Houghton, J.T, et al. eds.). Cambridge University Press, Cambridge, UK. 365 p. 1992, Climate change 1992. The supplementary re- port to the IPCC scientific assessment (Houghton, J.T. et al. eds.). Cambridge University Press, Cambridge, UK. 200 p. 1996. Climate change 1995. The science of climate change. Contribution of Working Group I to the Second Assessment Report of the Intergovernmental Panel on Climate Change (Houghton, J.T. et al. eds.). Cambridge University Press, Cambridge, UK. 572 p. Johannesson, T., Jönsson, T., Kallen, E. & Kaas, E. 1995. Climate change scenarios tor the Nordic countries. Climate Research 5: 181-195. Jones, R.G., Murphy, J.M. & Noguer, M. 1995. Simula- tion of climate change over Europe using a nested re- gional-climate model. I: Assessment of control climate, including sensitivity to location of lateral boundaries. Quarterly Journal of the Royal Meteorological Society 121: 1413-1449. Karl, T.R., Wang, W.-C., Schlesinger, M.E., Knight, R.W. & Portman, D. 1990. A method of relating general circulation model simulated climate to the observed lo- cal climate. Part I: Seasonal statistics. Journal of Climate 3: 1053-1079. Kattenberg, A., Giorgi, F., Grassl, H., Meehl, G.A., Mitchell, J.F.8., Stouffer, R.J.,Tokioka,T., Weaver, AJ. & Wigley, T.M.L. 1996. Climate models - projections of future climate. In: Houghton, J.T. et al. (eds.). Climate change 1995.The science of climate change. Cambridge University Press, Cambridge, UK. p. 285-357. Lough, J.M.,WigIey,T.M.L. & Palutikof, J.P. 1983. Cli- mate and climate impact scenarios for Europe in a warmer world. Journal of Climate and Applied Meteorology 22: 1673-1684. Manabe, S., Stouffer, R.J., Spelman, M.J. & Bryan, K. 1991. Transient responses of a coupled ocean-atmos- phere model to gradual changes of atmospheric C0.,. Part I: Annual mean response. Journal of Climate 4: 785-818 Mitchell, J.F.8., Johns, T.C., Gregory, J.M. & Tett, S.F.B. 1995. Climate response to increasing levels of greenhouse gases and sulphate aerosols. Nature 376: 501-504. Murphy, J.M. 1995. Transient response of the Hadley Centre coupled ocean-atmosphere model to increasing carbon dioxide. Part I. Control climate and flux correc- tion. Journal of Climate 8: 36-56. Parry, M.L. 1978. Climatic change, agriculture and set- tlement. Dawson, Folkestone. 214 p. & Carter,T.R. 1988. The assessment of effects of cli- matic variations on agriculture: aims, methods and sum- mary of results. In: Parry, M.L. et al. (eds.). The impact of climatic variations on agriculture. Volume 1. Assess- ments in cool temperate and cold regions, Kluwer, Dor- drecht, The Netherlands, p. 11-95. Pittock, A.B. 1993. Climate scenario development. In: Jakeman, A.J. et al. (eds.). Modelling change in environ- mental systems. John Wiley, Chichester, p. 481-503. & Salinger, M.J. 1982. Toward regional scenarios for a CCywarmed Earth. Climatic Change 4: 23-40. Posch, M. 1994. Development of a weather generator for Finland 11. In: Kanninen, M. and Heikinheimo, P. (eds.). The Finnish Research Programme on Climate Change Second progress report. Publications of the Academy of Finland 1/94, Helsinki, p. 323-328. Räisänen, J. 1995. A comparison of the results of seven GCM experiments in northern Europe. Geophysica 30: 3-30. Richardson, C.W. & Wright, D.A. 1984. WGEN: A mod- el for generating daily weather variables. U.S. Depart- ment of Agriculture, Agricultural Research Service, ARS- 8. 83 p. Santer, 8.D.,Wigley. T.M.L.. Schlesinger, M.E. & Mitch- ell, J.F.B. 1990. Developing climate scenarios from equi- librium GCM results. Report No. 47, Max-Planck-lnstitut fur Meteorologie, Hamburg. 29 p. Semenov, M.A. & Porter, J.R. 1995. Climatic variability and the modelling of crop yields. Agricultural and Forest Meteorology 73: 265-283. Taylor, K.E. & Penner, J.E. 1994. Response of the cli- mate system to atmospheric aerosols and greenhouse gases. Nature 369: 734-737. Utterström, G. 1955. Climatic fluctuations and popula- tion problems in early modern history. Scandinavian Eco- nomic History Review 3: 3-47. Wigley, T.M.L. 1994. MAGICC user's guide and scientif- ic reference manual. Climatic Research Unit, University of East Anglia, Norwich. 23 p. —, Jones, P.D., Briffa, K.R. & Smith, G. 1990. Obtain- ing sub-grid-scale information from coarse-resolution general circulation model output. Journal of Geophysical Research 95(D2): 1943-1953. Wilks, D.S. 1992. Adapting stochastic weather genera- tion algorithms tor climate change studies. Climatic Change 22: 67-84. 248 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Vol. 5 (1996): 235-249. SELOSTUS Ilmakehä-, sää- ja ilmastoskenaarioiden kehittäminen pohjoisille alueille Timothy R. Carter Maatalouden tutkimuskeskus Tulevat ilmakehän koostumusmuutokset ja niitä seu- raavat maailmanlaajuiset ja alueelliset ilmastonmuu- tokset huolestuttavat enenevästi poliittisia päätöksen- tekijöitä, suunnittelijoita ja yhteiskuntaa, Muutosen- nusteet ovat kuitenkin epävarmoja. Koska luotettavaa ennustetta ei ole, paras lähestymistapa on identifioi- da joukko mahdollisia tulevaisuuden kehitysnäkymiä ts. skenaarioita. Tämä artikkeli käsittelee ilmastoskenaarioiden kehittämistä korkeiden pohjoisten leveysasteiden alueille. Skenaarioiden laatimisessa voidaan erottaa kolme eri menetelmää: etsitään analogioita, jotka muistuttavat tulevia tutkimusalueen oloja, käytetään ilmastomallien tuloksia tai yhdistetään useiden mene- telmien tuloksia. Suomalaisen ilmakehänmuutosten tutkimusohjelmassa SILMUssa käytettiin yhdistelmä- menetelmää laadittaessa vuoteen 2100 ulottuvia ske- naarioita lämpötilan, sademäärän, ilman hiilidioksi- dipitoisuuden ja merenpinnan korkeuden muutoksis- ta Suomen alueella. Artikkelissa käsitellään lisäksi eri keinoja käyttää skenaarioita ilmastonmuutoksen vai- kutuksia selvittävissä tutkimuksissa. Tällaisiin kei- noihin lukeutuvat stokastiset säägeneraattorit ja tilas- tolliset menetelmät, joilla paikalliset olot liitetään il- mastomallien suuren mittakaavan virtauksiin. SILMU-skenaarioilla pyritään kuvaamaan epävar- muutta, joka johtuu sekä tulevista kasvihuonekaasu- jen ja aerosolien päästöistä että maapallon ilmaston vasteesta näihin päästöihin. Skenaarioita laadittiin kahta eri tyyppiä: (i) yksinkertaisia perusskenaarioi- ta ja (ii) yksityiskohtaisia tieteellisiä skenaarioita. Skenaarioita verrataan uusimpiin tulevaisuuden il- mastosta tehtyihin malliarvioihin sekä viimeaikaisiin tietyillä korkeiden leveysasteiden alueilla havaittui- hin ilmastonmuutoksiin. 249 AGRICULTURAL AND FOOD SCIENCE IN FINLAND