View of Developing scenarios of atmosphere, weather and climate for northern regions

Developing scenarios of atmosphere, weather and climate for northern regions

Timothy R. Carter

AgriculturalResearch Centreof^{Finland, Office}address: Finnish MeteorologicalInstitute, Box503,FIN-00101 Helsinki,Finland

Future changes in atmospheric composition and consequent globalandregional climatechangeare ofincreasingconcern^topolicy makers, plannersand thepublic. However, predictions of thesechanges are uncertain. Inthe absence ofsingle, firm predictions, the next best approach is toidentify setsof plausible future conditions termed scenarios.

This paper focusesonthedevelopment of climatechangescenarios for northernhigh latituderegions.

Threemethods of scenariodevelopmentcanbeidentified; useofanalogues havingconditions similar tothoseexpected inthestudy region, applicationofgeneralcirculation modelresults,andcomposite methods that combine information from different sources.A composite approach has been used to producescenarios of temperature,precipitation, carbon dioxide and sea-levelchangefor Finland up to2100,aspart of the Finnish ResearchProgrammeon^{ClimateChange(SILMU).Tools forapplying} these scenarios in impact^assessment studies, including stochastic weather generators and spatial downscaling techniques, arealso examined.

The SILMU scenarios attempt to capture uncertainties both in future emissions ofgreenhousegases and aerosols into theatmosphereandin the globalclimate response to these emissions. Two types of scenarioweredeveloped: (i) simple “policy-oriented” scenarios and(ii)detailed “scientific”scenar- ios. Theseare^{compared withnew}model estimates of future climate and recent observedchanges in climateovercertainhigh latituderegions.

Key ^words',climatechange, temperature,precipitation, carbondioxide, sea-level, uncertainty, base- line,Finland

ntroduction

One of the majorconstraints on agriculture ⁱⁿ northern high latitude regions is climate. Crop growth and production is limited by _aprolonged

and oftenseverewinter andashortgrowing sea- son. Crops are frequently grown close _totheir northern limits of potential, where the reliabili-

tyof production is closely governed by _year-to- year variationsin the weather. In historical^times, periods of benign climate tendedtofavour the

© Agriculturaland Food ScienceinFinland ManuscriptreceivedFebruary 1996

clearance and colonisation of agricultural land in the high latitude zone, whilst runs of unfa- vourable weather contributedtocroplosses,fam- ine, farm abandonment and depopulation (e.g.

Utterström 1955,Parry 1978, Bergthörssonetal.

1988).

Given the sensitivity ofagriculturetoclimate in these regions, theprospectofafuture 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 begreaterthan the globalav- erage. However, thereare still large uncertain- ties surrounding predictions of future changes.

This paper outlinessomeapproaches usedto 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 onscenarios of changes in atmospheric compo- sition and associated changes in regionalclimate, both of which may have important consequences for agriculture. An example of an approachto develop scenarios for Finland is described in moredetail. These scenarios have been prepared for the Finnish Research Programmeon Climate Change^(SILMU),and have been applied insev- eral SILMU studies reported in this volumeto 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 (C0₂^), methane

(CH4^),nitrous oxide(N₂0)andhalocarbons,and (ii) atmospheric aerosols, especially sulphur compounds. Increases in all of theseareassoci- ated with human activities, in particular fossil fuel combustion, intensive agriculture and de- forestation.

Rising concentrations ofsome of thesecon- stituents (e.g. C0₂,troposphericozone(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 etal. 1996). Changes in all of themcan 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 someto the surface. In^contrast,aerosols usual- ly have a cooling effectonthe climate both di- rectly, by absorbing incoming solarradiation, 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 climatesystem(“radiative forcing”) since pre-industrial timesare shown in Figure

1.

1996).They arecompared in the figure withes- timates of the global forcing due to natural changes in solar irradiance since 1850. Volcanic eruptions areanother source of negative forc- ing, ofasimilar magnitudeasthe positive green- house gas forcing shown in Figure 1, but effec- tive for onlyayearortwo afteralarge eruption (IPCC 1996). It should also be noted that the regional effects of changes in atmosphericcom- position _on climate may differ (sometimes in sign) from the global effects.

The best tools available for evaluating the response of global climatetothe radiative forc- ings shown in Figure 1 are numerical climate models. These arebased on physical laws, and attempttosimulate the major processes control- ling the climate in the atmosphere, oceansand onland. There is ahierarchy of climate models

ranging from simple box-models, which have only _afew variables, to sophisticated coupled general circulation models (GCMs) of theatmos- phere and _oceans. They are described further below.However, none of these modelsare able

to capture the full complexities of the climate system,and therearelarge 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 actionstobe takentoprevent ortoslow down changes in the atmosphere, policy-makers needtobe informed about the possible changestobe expected.Like- wise, scientists require projections of these changessotheycanexamine their likely impacts.

It is also important^torecognise that theun- certainties in projections are not due solely to the shortcomings of climate models. Estimation of regional climate change can be thought ofas the final stepin a sequence ofassumptions and

uncertainties relating to:(i) future emissions of greenhouse gases and aerosols into the ^atmos- phere, depending on factors such aspopulation 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 climatere- sponse to changing atmospheric composition;

and(iv)climate changes^atthe regional andsea- sonal level. It isatthe regional level ^(where the uncertainty is greatest) that information is ^most needed in impact assessments.

Sinceaccuratepredictions of climate change are not available, an alternative approach is to develop scenarios. Thesearealternative projec- tions whichare meteorologically plausible ^(i.e.

physically, temporally and geographically real- istic) and embrace _ourbest available estimates ofthe uncertainties in projections. The main emphasis in the following sections ison scenar- ios of future climate, but it should be noted throughout that these scenarios need^tobecon- sistent, in time and space, with projections of other related environmental variables such as atmospheric composition and sea-level.

Fig. 1.Estimates of theglobal an- nualmeanradiative forcing^(Wm2) from 1850to 1990foranumber ofpotentialclimatechangemech-

anisms. Columnheightsrepresent mid-rangeestimates of theforcing, errorbars largely represent the spreadofpublishedvalues and the confidence levelsgivenatthe base of thediagramare asubjectiveas- sessmentof the confidence that the actualforcinglies within theerror bar. Source: IPCC (1996).

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 circulationmodels, and (iii) compositing. These approaches are described brieflybelow,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 scenariosareconstructed by identify- ing recorded climatic regimes that mayserve as analogues for the future climate in a given re- gion. These recordscanbe obtained either from thepast (temporal analogues) _or from another regionatthe present(spatial analogues).

Temporal analogues

Temporal analoguesareoftwotypes:those based on pastinstrumentalobservations, usually with- in the last century(e.g. Lough etal. 1983),and those basedon proxydata,using palaeoclimatic indicators from the more distant past such as plantoranimal remains and sedimentary depos- its (e.g. Budyko ^1989).Both have been usedto identify periods when the globaltemperature is thought^to have beenwarmerthan today. Other features of the climate during thesewarm peri- ods (e.g. precipitation, air pressure, windspeed), ifknown, are then combined with the tempera- turepattern todefine the scenario climate. Al- though the spatial pattern of change sometimes bears similarities with model projections of fu- tureclimate(see below)amajor problem of this technique is that the physical mechanisms giv- ing rise tothewarmerclimate in thepastalmost certainly differed from those involved in green- house gas induced warming.

Spatialanalogues

A spatial analogue involves the identification of aregion today having_aclimate analogoustothat anticipated for the study region in the future. For example, spatial analogues for five northerncase study regionsare 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 oftworegions that may affect the local response ofagriculture (e.g. daylength, terrain or soils).

Given the many weaknesses of analoguesce- narios, their_use to represent future climate is notgenerally 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 temperatureresponseto agiven forcing mecha- nism and require little computing power, the geographical patternof the responsecanonly be estimated with the aid of general circulation models (GCMs). These have been reviewed thor- oughly by the Intergovernmental Panel on Cli- mateChange(IPCC^- Gatesetal. ^1992,Katten- bergetal. 1996). GCMsrepresent the three-di- mensional spatial distribution of atmospheric variables such as temperature, pressure, mois- tureand windatregular intervalsoverthe entire globe. The computational requirements of such modelsare immense,and simulations withstate- of-the-art GCMsareonly possible onsupercom- puters. Even then, these models are currently incapable of capturing the full complexities of the real climate_system.Some of the main weak- nessesof these models_are(i)_apoorrepresenta- 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

recent advances in GCM development, includ- ing the coupling of dynamic ocean models to atmospheric models(Gates etal. 1992)and the simultaneous modelling of aerosol and green- house gas effectson climate (Kattenberg et al.

1996), regional climate predictions from GCMs remain highly uncertain.

Compositing

Afurther method of scenario developmentcom- bines elements of the above techniques ina com- positing approach. This methodcanrange 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 etal.

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

Thissection outlines the climaticscenariosthat

have been developed for the Finnish Research ProgrammeonClimate Change(SILMU).These scenarioswereprovidedtoscientists working in SILMU in the form ofa computerprogram and user’s guide (Carter etal. 1995). Only a short description is given here. More details can be found in Carteretal. (1996a).

Model-based estimates

The scenariosweredeveloped bycombining the results from two setsof models: (i)MAGICC, a framework of simple global models and⁽ⁱⁱ⁾ three coupled ocean-atmosphere GCMs (Figure 3).

Globalprojections

from

The Model for the Assessment of Greenhouse- Fig. 2. Spatial analogues for five highlatituderegions under the temperature andprecipitation ^changessimulatedin the GoddardInstitute forSpaceStudiesequilibrium 2xCO,modelrun(Hansen et al. 1983). Modified fromParryand Carter (1988).

gasImpacts and Climate Change(MAGICC)is a setof linked models for estimating changes in atmospheric composition and radiative forcing under different emissions scenarios and their effecton global mean annual temperature and sea-level (Hulmeetal. 1995).It includes all the major greenhouse gases (except tropospheric ozone), fossil fuel derived SO, emissions and their effects on climateas 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 andhalocarbons; (iii)_asulphate aero-

sol model for SO,emissions from fossil sources;

(iv) various schemes for converting gas and aerosol concentrationstoradiative forcing; ^(v) anupwelling-diffusion, energy balance modelto computeglobalmeanannualtemperatureand the oceanic thermal expansion^component of global meansea-level^rise;and (vi)ice melt models for

"small" glaciers and the Greenland and Antarc- tic ice sheets. Thesecomponent models,although simple, produce results that_are similartothose 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 scenariosatdecadal intervals between 1990 and2100 for the following: fossil CO,,^netland- use-change CO,, ^CH4^, N,O, ^{CO, NO}_{x ,} ^VOCs, CFCII, CFCI2, HCFC22, HFCI34a and SO, (Wigley 1994).Emissions scenarios canbe se- lected from _a list of published scenariosor can be user-specified. The models calculate thera- diative forcing duetoemissionsoverthe period

1765-2100, the globalmean annualtemperature response toagiven forcing and the global _mean sea-level effect of thetemperaturechange. Model parameter uncertainties _are also represented in model _outputs.

MAGICC wasused in this applicationtorep- resent two major sources of uncertainty in glo- bal estimates oftemperaturechange. The first is the range ofpossible future emissions,whichwas basedon three IPCC(1592)emissions scenarios (IPCC 1992).The second is the climate sensi- tivity, which isa measure of the response of glo- bal mean temperature toa given radiative forc- ing (conventionally a doubling of atmospheric C0₂ concentration). The IPCC has specified a range ofpossible climatesensitivities, basedon GCMsimulations, of1.5-4.5°C,withabestes- timate of 2.5°C (IPCC 1992).

Three combinations of these sources ofun- certaintywereselected forSILMU, to represent a central,"bestguess" projection and theextreme range:

- Combination 1: Central^- central emissions/

central climate sensitivity(IS92a/2.5°C) Fig. 3.Method ofdevelopingscenarios for SILMU (sche-

matic). Boxes with double lines ^aremodels; boxes with singlelines^aremodelinputsand outputs; boxes with bold lines arethe programs used forgenerating scenarios.Ar- rowsrepresent flows of information.

Combination 2: Low ^- low emissions/low sensitivity (IS92c/l .5°C)

Combination3: High^- high emissions/high sensitivity (IS92f/4.5°C).

MAGICCwas runwith these three combina- tions to give a range of C0₂ concentrations (basedon the emissionsscenarios), globalmean annual temperature change estimates and sea- level rise estimates for the period 1990-2100.

The cooling effect of sulphateswasalsoaccount- 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). TheCO,and sea-level rise estimatescan be applied globally and are used directly in the SILMU scenarios.

Regionalprojections

from

Outputs from three general circulation models (GCMs) were usedtodevelop regional scenari- os:the Geophysical Fluid Dynamics Laboratory (GFDL) model(Manabe^etal. 1991),the United Kingdom Meteorological Office model transient run (UKTR^-Murphy 1995)and the Max Planck Institut fiir Meteorologie (MPI) model (also known asECHAM-1 ^- Cubaschetal. 1992).All three models have been usedtosimulate thetran- sient response of climate ^toa 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 didnot account for sulphate aerosols. An intercomparison of the performance of thesemodels, along with four others, in simulating the present-day regional climate has been reported by Räisänen (1995).

Each GCM produceda different large-scale pattern of climate change for a given forcing, and this variedover time. However, the abso- lute timing of these changes could^notbe evalu- ated directly from the models because future sim- ulationswere only started from thepresent day situation. Since there is atime lag between green- house gas forcing and the climate response to

this forcing (typically of several decades) due tothe thermal inertia of the^oceans,the simulat- ed response wasunrealistically 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 (Hasselmannetal. 1993).

Combining the model outputs

Toovercomethe cold startproblem,rates of glo- bal warmingover 1990-2100wereobtained from MAGICC (which does not share the problem) for the scenario combinations described above.

Plots of globalmeanannualtemperaturechange were ^nextconstructed for the three GCM simu-

lations. The form of the warming trend given by all three GCMswas close tolinear, resembling closely the central estimate _curveproduced by MAGICC. The modelled years in which the cli- mate warming estimated by the GCMs reached the _same levelasthat obtained from MAGICC for 2020, 2050 and 2100 were extracted from the graphs for each model. By returning ^tothe gridded GCMoutputs, the regional changes as- sociated with a given global mean temperature change couldnow be assigneda date in the fu- ture. A period of years of modelled climate around each selected year_was used forcomput- ing standard climatological statistics.

The SILMU ^scenarios

Two setsof scenarios were developed for SIL- MU basedonthe above approach: policy-orient- ed and scientific scenarios.

SILMU policy scenarios

The SILMU policy-oriented scenariosattempt^to capture a range of uncertainties in estimating future climate_overFinland. At the same time, they are designed tobe simple for scientists to apply and for policy makers ^to interpret. They depict seasonal changes andareuniformoverthe

Table 1.Rates of temperature andprecipitation changeunder the SILMUPolicy 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 scenarioswere developed using the pro- cedures described above. The climate change estimates _areGCM grid box values oftempera- tureand precipitation change averagedoverthe Finnish region and averaged ^across the three GCMs. They represent regional climate changes over Finland that are consistent with global meantemperature 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- narios2 and 3 arescaled downorup from Sce- nario 1 estimates in proportiontothe respective temperature changes. In this way, upper, lower and central estimates of therate oftemperature and precipitation change up to 2100 are given for Finland (Table 1).

Note that while the estimates of seasonal long-term temperaturechange _arequite similar between individual models, those of precipita- tion change vary considerably (sometimes in sign). These variationsarenot expressed in the policy scenarios duetothe averaging procedure in the compositing and because of the need _to restrict the scenarios to a manageable number.

However, theyare apparent in the SILMU sci- entific scenarios(see below).

In view of its importance for examining im- pacts onagricultural plants, carbon dioxidecon- centrations computed with MAGICC for 2020,

2050 and 2100 under each SILMU policy sce- narioare shown in Table 2 alongside thecorre- sponding meanannualtemperature and precipi- tation changes. Also shownareestimates of glo- bal sea-level rise. Except for the largest esti- mates, however, sea-level rise appears likelyto

be compensated in Finland by the ongoing iso- static uplift of land areasfollowing the last gla- ciation. While possible changes in the windre- gimeovertheBaltic, which also affects sea-lev- el,complicates this prognosis, future changes in sea-level would appear to pose only a minor threatto agriculture.

Table2.^{Global mean}carbon dioxide concentration (abso- lute), mean annual temperature andprecipitation change overFinland andglobal^meansea-level rise relative to 1990 for2020, 2050 and 2100under the three SILMUpolicy scenarios.

SILMU Policy Scenarios Year and attribute

1^(Central) 2^(Low) 3 (High) 2020

C0₂concentration (ppm) 425.6 408.8 433.7 Temperaturechange^(°C) 1.2 0.3 1.8 Precipitation change^(%) 3.0 0.75 4.5 Sea-levelrise (cm) 8.9 2.1 19.2 2050

C02concentration(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-levelrise (cm) 20.8 4.6 43.3 2100

C0₂concentration(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-levelrise (cm) 45.4 7.4 95.0

SILMU

scientific

A secondset of SILMU scenarios referto sce- narios that_are derived directly from GCM out- puts. They provide spatial and temporal varia- tions that the policy scenarios donot. This makes themmore technically demandingto apply and todescribe, which is why theyarelabelled “sci- entific”scenarios, todistinguish them from the simpler policy scenarios. Three scientific sce- narios have been developed, based onthe three GCMs,and with thesameemissions and climate sensitivity assumptions _aspolicy Scenario 1:

SILMU Scenario la: GFDL SILMU Scenario lb: UKTR SILMU Scenario 1c:^MPI

The scenarios reflect thepattern of climate changeoverthe Nordic region simulated by each GCM _{on a}monthly basis. They reveal _some of the model-to-model differences thatare hidden by the compositing technique in the policy sce- narios, especially in precipitation projections.

Special routines were included in thecomputer programsupplied toSILMU researchers that lin- early interpolatetoindividual dates andto 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 gridoverFinland.

Examples of the regional pattern ofmean sum- mer(June-August)temperaturechangeoverFin- land by 2050 for the three scenariosare shown in Figure ^4,

Comparisons with recent GCM simulations

Since the SILMU scenarios_wereprepared,_more realistic climate change simulations have been conducted that_accountfor both greenhouse gas forcing and the negative regional forcing of sul- phate aerosols (Taylor and Fenner 1994, Mitch- ell etal. 1995). The latter of thesewas witha coupled ocean-atmosphere GCMrunbeginning

Fig. 4. Mean summer(June^- August)temperature changeoverFinland by 2050 relative to 1990under the three SILMU scientific scenarios: (a) Scenario la (GeophysicalFluidDynamics Laboratorymodel), (b) Scenario lb (UnitedKingdom Meteorological Office transient model run) and (c) Scenario Ic(Max Planck Institut model).

Table3.Rates of temperaturechange insomenorthern highlatituderegions estimatedbytheHadleyCentre GCM (Mitchell etal. 1995),computed usingthe SILMUmethod,(uncertainty range inparentheses), and absolute changes observed between 1951-1980and 1981-1990 (Follandetal. 1992). Valuesaretaken from maps andareapproximate.

Model^{or W,} E. Fenno- N. E.

observations Period Alaska Canada Canada Iceland Scandia Russia Russia

HadleyCentre^-

regional aerosols Annual 0.4 0.3 0.3 0.3 0.4 0.3 0.4

(°C/decade)

SILMUMethod- Annual ^- 0.15(0.05-0.25)0.4(0.1-0.6)

globalaerosols 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

1981-90minus 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 indicatea rate of increase of global meanannualtempera- tureof about O.2°C per decade for the effects of aerosol and greenhouse gas forcing combined, compared with_arate of O.3°C per decade dueto greenhouse warming alone. This reducedrate of warming is much _more in accord with therate observed globally during the present century, enabling the Intergovernmental Panelon Climate Changetodeclare recently that “the balance of evidencesuggests that there isadiscernible hu- maninfluenceonglobal climate”(IPCC 1996).

Changes inmean^annualtemperature ^andpre- cipitation for regions in the circumborealzone

have been extracted from mappedoutputsof the Hadley Centre modelruns (Mitchell ^etal. 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 accountsfor aerosol forcing, us- ing MAGICC,but this is treatedataglobal rather than _aregional scale. The Hadley Centre results indicatemeanratesof warmingathigh latitudes that _are above the global mean. Over Fennos- candia these estimatesare consistent with the SILMU ^scenarios, but over the central North Atlantic region (including^Iceland), the SILMU scenario is ofareducedrate of warming, which does not show up in the Hadley Centre simula-

lion. The SILMU scenario reflects aweakening of the thermohaline circulation found in the vi- cinity of Iceland in all three GCMs usedtocon- struct the scenario. In ^fact, the Hadley Centre model, which includes regional aerosol forcing, also shows this effect but its region of influence is shiftedto the westof Iceland.

Also shownareobserved changes in temper- ature overthe _sameregion between the periods

1951-1980and 1981-1990 (expressed asabso- lute changes), providingatentative comparison with the projected changes. Over continental areas there has beenaclear increase intempera- ture, especially during the winter, while in re- gions influenced by the North Atlantic recent changes have been smalleror even negative.

Thus, the observed pattern of changes, while covering onlyashort period, does appear tobe consistent with thepattern 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 areaddressed here: the baseline climate.

adjusting thebaseline,downscaling, and theuse ofastochastic weathergenerator.

The baseline climate

It is important at the outset to define the base-

line period against which scenariosare tobe compared. Conventionally meteorologists adopt themost recent30-year climatological “normal”

period, currently 1961-1990. This is the period adopted in SILMU. However, insomehigh lati- tude regions, includingCanada, useof this peri- od as a reference has beenresisted, since it is thought tocontainasignal of climatic warming (R. Street,personalcommunication,andseeTa- 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 eitheras differences (temperatures are usually handled this way) or as percentages (commonly applied toprecipitation). There are two distinct methods thatcan be used toapply such changes asadjustments tothe baseline cli- mate: the fixed change and transient change ap- proach.

Thefixedchange approach

The conventional approach applies a “fixed”

scenario change foragiven date in the future^to all years of the baseline period. The approach is simple and quicktoapply.However, it implicit- lyassumesthat the futureclimate, like the base- lineclimate, is stationary, whereas in reality, the future climate is likelytobe undergoing contin- ual change.

The transientchange approach

A method which accounts for the gradual or

“transient” change in climate, adjusts the base-

line according toatrend. For example,a linear warming scenario for 2050 could be applied to the 1961-1990 baseline as a trend, withwarm-

ing by 2036 usedtoadjusttemperaturesin 1961, warming by 2037 to adjust 1962temperatures through to warming by 2065, which is used to adjusttemperatures in 1990. Note that the thir- ty-yearstatistical frequency distribution ofa sce- nario climate adjusted accordingtothe transient change approach exhibitsgreatervariability than the corresponding scenario based on the fixed change approach. This may be of some impor- tancewhen assessing impacts.

Downscaling

One of the mainproblems with using informa- tion from GCMs is theircoarse spatial resolu- tion.Even in the highest resolutionGCMs, asin- gle grid box spansan areaofmore than 50,000 km^{2 .}The large scale climatecanbe greatly mod- ified withinan area of this size,by factors such as terrain, vegetation cover or water surfaces.

Simple interpolation from grid box scale tolo- calscale,which wasused in the SILMUscenar- ios,neglects these sub-grid-scale features which are notresolved by GCMs. Local variations in climatecan, ofcourse, have large effects on ag- ricultural productivity orwater supply.

Two alternative approaches have been devel- oped for downscaling from GCMtolocal scale.

The first approach involves the establishment of statistical relationships between large-scale cli- mate and sub-grid-scale climate usingpast ob- servations (e.g. Wigley etal. 1990, Karl et al.

1990, Bardossy and Plate 1992).The approach assumesthat the statistical relationships between thesetwo scales remain unchanged under a fu- tureclimate.

The second downscaling approach involves the use of limited area high resolution numeri- cal models. Thesearephysically-based models thatcanberun atsub-continental scale_{ata res-} olution ofsome50 x 50 km. They canbe linked to GCMs using various nesting techniques, whereby the GCM provides informationon large

scale flows to the limitedarea model, which is thenrun athigher resolution. Early results from such modelruns,including high latituderegions of Europe and NorthAmerica, are now availa- ble for impact^assessment(e.g. Giorgietal. 1992, Jonesetal. 1995).

Use of stochastic weather generators

Many impact assessments require detailed cli- matological dataon adaily time_{step as}inputto simulation models. Crop growth modelsare typ- ical examples in agriculture. Daily dataaresel- dom availableas outputsfromGCMs,and in any case they are not readily applicable in impact studies. An alternative is ^tousestochastic weath- er generators. These consist ofsets of parame- ters describing statistical properties of climatic variables observed historically atindividual lo- cations. They canbe used togenerate timese- ries of unlimited length having similar statisti- cal properties to those observed. The parame-

ters ofa generator canalso be adjusted accord- ing toscenarios of future climate. This offers a very flexible tool forconducting sensitivity_test- ing ofmodels, where changes in both the ^mean and variability of climatecan be readily simu-

lated(Wilks 1992, Semenov and Porter 1995).

A stochastic weathergenerator forFinland, CLIGEN, has been developed for SILMU (Posch 1994)and provided^toresearchers incon- 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 valuesare then correlated with the occur- renceof^wet and dry days, basedon the method of Richardson and Wright (1984). Time series canbe generated for any location inFinland,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 climateson^potato late blight(Carter et al. 1996^b). One drawback of the generator re- vealed in that study is a tendencyto underesti- mate the frequency and duration of persistent

eventslike drought and warm or cold spells. It

is these episodes that often result in thegreatest 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 correctthis problem.

Fig. 5. Frequency distribution oflength ofdry spells (precipitation ^<0.1 mm) at Jokioinen,southern Finland, observed (1961-1990) andfor five30-yearsimulationswith the CLIGENweathergenerator.

Conclusions

This paper has presentedsomeestimates of pos- sible environmental changes in northern agricul- tural regions. Carbon dioxide concentrations in the atmosphereare expected _tocontinue _torise 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 mostregions, since manyhigh latitude land areas are stillrecover- ing following deglaciation. Overall,warmingat 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

amountandeven 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 wasillustrated in Figure 2, the climate of the late21st centuryina marginal agricultur- al region suchassouthern Finland mightresem- ble that today in Denmarkornorthern Germany.

Inspection of present-day crop production sta-

tistics in Denmark reveals levels of yield twice or even three times those found in Finlandto- day. While Denmark may notbe aperfect ana- logue ofafuture Finland (for example, thereare differences in soils, farm size and structure), a

substantial portion of this disparity in produc- tion potential is climatically induced.

The large uncertainties attached toscenarios of future regional climateareexemplified by the SILMU scenarios. While there issome scope for improving model predictions, using higherres- olution models which accurately^accountfor the mostimportant processes in the climatesystem, these advances^arelikely^tobe 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 seemsprob- able that scenarios will continue ^toplay anim-

portantrole in policy-making and^assessmentfor some timeto come.

Acknowledgements. I am grateful toHeikki Tuomenvirta of the Finnish Meteorological Institute, Helsinki, whoas- sistedindevelopingthe SILMU scenarios and toDr.Max- imilianPosch of the National Institute of Public Health and EnvironmentalProtection, Bilthoven,_TheNetherlands,who developedthe stochastic weather generator, CLIGEN.Spe- cial thanksarealso due toDr. David Viner andcolleagues inthe Climate ImpactsLINKProject, University of East Anglia, Norwich,UK forsupplying^GCMinformation and toProfessor TomWigleyof theUniversity Corporationfor Atmospheric Research, Boulder,CO, USA forprovidinga version of MAGICC. This workwasfundedbythe Finnish ResearchProgramme onClimateChange (SILMU).

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