Supporting InformationCiscar et al. 10.1073/pnas.1011612108SI TextHere we summarize the methodological framework of the in-tegrated assessment. We describe first the main elements of thestudy architecture and other issues, such as the common climatescenarios. We continue with the presentation of the sectoralphysical-impact models and the computable general equilibrium(CGE) economic model used in the integrated assessment. Fi-nally, the climate variables used in each sectoral impact assess-ment are detailed.For specific technical details the reader is directed to the final

report of the Projection of Economic Impacts of Climate Changein Sectors of the European Union Based on Bottom-up Analysisproject (PESETA) (1) and the five technical reports related toeach of the impact categories of the study [agriculture (2), riverflood (3), coastal systems (4), tourism (5), and human health (6).They can be found on the project web site: http://peseta.jrc.ec.europa.eu/results.html.

Overview of Methodological FrameworkFig. S2 indicates the various stages of the research project. Therectangles symbolize models, and the circles indicate input dataor numerical results. The first stage is the modeling of climatefutures. The selected socioeconomic scenarios incorporate as-sumptions on the drivers of climate change, i.e., economic growthand population dynamics. The resulting greenhouse gas (GHG)emissions are the input to the climate models, which yield theclimate variables.The second stage is the physical-impact assessment, using the

climate variables as input. Several impact models have beenused. The agriculture, coastal systems, and river flooding impactmodels are process-based. The tourism and human health modelsare based on statistical relationships. The Decision Support Systemfor Agrotechnology Transfer (DSSAT, http://www.icasa.net/dssat/)crop models have been used to quantify the physical impacts onagriculture in terms of yield changes. Estimates of changes in thefrequency and severity of river floods are based on simulationswith the LISFLOOD model and an extreme value analysis. Im-pacts of sea level rise (SLR) in coastal systems have been quan-tified with the DIVA model. The tourism study has modeled themajor intra-Europe tourism flows by assessing the relationshipbetween hotel bed nights and a climate-related index of humancomfort. The human health study considers the relationship be-tween mortality and temperature using evidence from epidemio-logical studies.The third stage relates to the evaluation of the direct and

indirect economic effects of the physical impacts. A multisectorCGE model for Europe has been run to assess the effects of thevarious impacts on household welfare and GDP. MulticountryCGE models provide an explicit treatment of the interactionsbetween different economic sectors and markets (productionfactors and goods and services markets) while taking into accountthe trade flows between countries. This framework captures notonly the direct effects of a particular climate impact but also theindirect effects on the rest of the economy—the second andhigher-order effects.

Grouping of CountriesThe assessment covers all European Union (EU) countries, withthe exception of Luxemburg, Malta, and Cyprus. To present theresults, EU countries have been grouped into five regions:Southern Europe, Central Europe South, Central Europe North,the British Isles, andNorthern Europe. Given that themain driver

of the projected impacts is climate change and that there are somecoherent spatial patterns of climate change, the main criterion forgrouping countries is geographical position.However, the grouping of countries also has tried to ensure that

each region is of comparable economic size, as defined by theshare in 2000 EU gross domestic product (GDP). With the ex-ception of the Northern Europe region, which accounts for only6% of the EU GDP, the regions have a size in the range of 18–32%. The difference in the economic scale of the regions mustbe considered when interpreting the results. Fig. S1 shows theEU countries by assigned region.

Socioeconomic Scenarios and Climate ModelsTwo global emission scenarios belonging to the A2 and B2 story-lines have been selected from the Special Report on EmissionsScenarios (SRES) of the Intergovernmental Panel on ClimateChange (IPCC) (7). This choice partly covers the range of un-certainty associated with the driving forces of global emissions:demographic change, economic development, and technologicalchange. In the A2 scenario, in which the storyline focus is on na-tional enterprise, global GHG emissions are assumed to increasemore significantly, and CO2 concentration triples by the end of thiscentury. The B2 storyline focuses on local stewardship and resultsin an atmospheric CO2 concentration double the current level.Moreover, for each of the SRES scenarios two combinations of

global circulation models (GCMs) and regional climate models(RCMs)have beenused to reflect the uncertainty in both theGCM(the response of the climate system toGHG concentration) and inthe RCM (the downscaling of the GCM). The four scenarios aredistinguished by the EU temperature increase, with the followingSRES scenario, GCM and RCM: (i) 2.5 °C: B2 SRES scenario,Hadley Centre Coupled Model, version 3 (HadAM3) as GCMand High Resolution Limited Area Model (HIRHAM) as RCM;(ii) 3.9 °C: A2 SRES scenario, HadAM3 as GCM, and HIRHAMas RCM; (iii) 4.1 °C: B2 SRES scenario, the fourth generation ofthe European Centre Hamburg Model (ECHAM4) as GCM, andRossby Centre Regional Atmosphere-Ocean model (RCAO) asRCM; and (iv) 5.4 °C: A2 SRES scenario, ECHAM4 asGCM, andRCAO as RCM. All these climate data come from the Predictionof Regional Scenarios and Uncertainties for Defining EuropeanClimate Change Risks and Effects (PRUDENCE) project (8).Figs. S3 and S4 show the mean annual temperature and pre-cipitation changes for the four scenarios.

Agriculture ModelThe most important determinants of changes in agriculturalproduction are changes in agroclimatic regions, crop productivity,and crop management (deliberate adjustments of the crop cal-endar, use of nitrogen fertilizer, and the amount of irrigationwaterrequired to optimize productivity in each scenario); livestockproduction is not considered, except for the possible influence oncrop productivity.To date, studies assessing potential agricultural responses to

21st century climate conditions in Europe have projected regionaldifferences. However, these studies have lacked a common set ofassumptions that would have allowed an analysis on the sub-regional scale of climate–agriculture relationships across largecontinental areas. The nonlinear response of crop productionto climate variables will affect regions with unstable, low pre-cipitation more seriously.The agriculture model developed European scenarios of agri-

cultural change for the 2080s based on global scenarios of changes

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in environmental and social variables and the understanding of thesensitivity of each agricultural region to these changes. First,changes in agroclimatic regions were identified. Second, statisticalmodels of crop-yield response basedonprocess-based cropmodelswere developed, linking productivity, management, and climatevariables, as detailedbelow; livestockproduction is not considered,except for the possible inference of crop productivity. Third, theexpected change in future cropproductivity inEurope is calculatedby applying the climate scenarios to the derived models.

Estimation of Changes in Agroclimatic Regions. Nine agroclimaticregions are defined based on K-mean cluster analysis of temper-ature and precipitation data from 247 meteorological stations,district crop yield data, and irrigation data. Shifts in agroclimaticzones are considered for the application of the climate-changescenarios, so the crop types simulated in the future are consistentwith the agroclimatic conditions of the future.The future zones arederived in the sameway as the zones in the current climate, but theclimate of the station is modified by the changes in the climatescenarios.

Estimation of Changes in Crop Productivity. Process-based cropmodelswereused toderive informationofcropresponses toclimateand management when experimental data were not available (9–11). Nevertheless, process-based crop models are data intensive,including daily climate data, soil characteristics, and definition ofcrop management; data constraints usually limit the use of modelsto sites where the information necessary for calibration is avail-able. In this study, nine sites were selected to represent the majoragroclimatic regions in Europe derived earlier. At each site, pro-cess-based crop responses to climate and management are simu-lated by using the DSSAT crop models for wheat, maize, andsoybeans (12). The DSSAT models simulate daily phenologicaldevelopment and growth in response to environmental factors(soil and climate) and management (crop variety, planting con-ditions, nitrogen fertilization, and irrigation). The DSSAT modelscan simulate the current understanding of the effect of CO2 oncrops (13). Daily climate data for the 1961–1990 time period wereobtained from the National Oceanic and Atmospheric Adminis-tration (NOAA, http://www.noaa.gov/); soil characteristics andmanagement data were obtained from agricultural research sta-tions. Crop distribution and production data were obtained fromEUROSTAT (http://epp.eurostat.ec.europa.eu/).Farm-level (bottom-up) adaptation, referring to changes in

management decisions over time that take place considering thatfarmers learn from previous crop yield outcomes, has been con-sidered. The key factors whose possible modification has beenconsidered are planting date, use of nitrogen fertilizer, and waterfor irrigation.For each of the nine sites, three crops, and 30 y of daily climate,

a sensitivity analysis was conducted to environmental variables(temperature, precipitation, and CO2 levels) and managementvariables (3,600 simulations per site). The resulting output thenwas used to define statistical models of yield response for eachsite. This approach has proven useful for analyses in China (14),Spain (4, 15, 16), and globally (3, 17–19). Variables explaininga significant proportion of simulated yield variance are cropwater (sum of precipitation and irrigation) and temperature overthe growing season. The functional forms for each region rep-resent the realistic water-limited and potential conditions for themix of crops, management alternatives, and potential endoge-nous adaptation to climate assumed in each area.The methodology expands process-based crop model results

over large areas and therefore overcomes the limitation of datarequirements for process-based crop models, includes conditionsthat are outside the range of historical observations of crop yielddata, and includes simulation of optimal management and there-fore estimates agricultural responses to changes in regional cli-

mate. Because of the nature of the assumptions, the results canbe considered as representing an agricultural policy scenario thatdoes not impose major additional environmental restrictions be-yond those currently implemented or include pollution taxes (e.g.,for nitrogen emissions to mitigate climate change).

Modeling Floods in River BasinsEstimates of changes in the frequency and severity of river floodsare based on simulations with the LISFLOOD model followed byextreme value analysis. The LISFLOOD model is a spatially dis-tributed, conceptually mixed, and physically based hydrologicalmodel developed for flood forecasting and impact assessmentstudies at theEuropean scale (20).Driven bymeteorological input,the model calculates actual evaporation and transpiration ratesbased on vegetation characteristics, leaf area index, and soilproperties. Processes simulated for each grid cell include snow-melt, soil freezing, surface runoff, infiltration into the soil, pref-erential flow, redistribution of soil moisture within the soil profile,drainage of water to the groundwater system, groundwater storage,and groundwater base flow. Runoff produced for every grid cell isrouted through the river network using a kinematic wave approach.The current European-wide model set-up with a 5-km grid

resolution uses spatially variable parameters on soil, vegetation,and land use derived from European datasets. A set of eightparameters that control infiltration, snowmelt, overland and riverflow, and residence times in the soil and subsurface reservoirs wereestimated in 231 catchments by calibrating the model againsthistorical records of river discharge. The calibration period variedbetween the different catchments depending on the availability ofdischargemeasurements, but all spanned at least 4 y between 1995and 2002. The meteorological variables used to force the model inthe calibration exercise were obtained from the MeteorologicalArchiving and Retrieving System (MARS) database (21). Forcatchments where discharge measurements were not available,simple regionalization techniques (regional averages) have beenapplied to obtain the parameters. A more detailed description ofthe different model processes and governing equations, as well asof the European-wide model setup and calibration exercise, canbe found in refs. 14, 22, and 23.After regridding to the 5 × 5 km grid of the hydrological model

using a nearest neighbor approach based on the center points ofthe 50 × 50 km grid cells of the RCMs, LISFLOOD transfers theclimate-forcing data (temperature, precipitation, radiation, windspeed, and humidity) into river-runoff estimates. From the dailydischarge time series for the control and future time window, theannual maxima have been selected, through which a Gumbeldistribution has been fitted using Maximum Likelihood estima-tion. The fitted parameters of the extreme value distributionhave been used to derive flood return discharges for return pe-riods ranging between 2 and 500 y (24, 25).Using a planar approximation approach, the simulated dis-

charges with return periods of 2, 5, 10, 20, 50, 100, 250, and 500 yhave been converted intoflood inundation extents anddepths. Thelatter have been translated into direct monetary damage fromcontact with floodwaters using country-specific flood depth–damage functions (26) and land-use information (27). For eachcountry and for each land-use class (e.g., residential or industrial),the depth–damage functions represent the absolute amount ofdamage as a function of flood inundation depth. The country-specific depth–damage functions have been rescaled further bythe GDP per capita of Nomenclature of Territorial Units forStatistics administrative level 2 (NUTS2) to account for the largeregional differences in exposed assets for a given land-use classthat exist in some countries. Population exposure has been as-sessed by overlaying the flood inundation information with dataon population density (28). By linearly interpolating damages andpopulation exposed between the different return periods, weconstructed damage and population exposure probability func-

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tions under present and future climate scenarios. From the latter,the expected annual damage and expected annual population ex-posed have been calculated.Country-specific protective capacities for floods have been

considered by truncating the damage and population exposureprobability functions at certain return periods. Various flood-protection levels were imposed depending on country GDP percapita (protection to 100-y, 75-y, and 50-y return periods). Toassess the impact of climate change on current exposure levels,adaptation to changes in flood magnitude has not been taken intoaccount by assuming static protective capacities.

Coastal Systems ModelImpacts of sea-level rise (SLR) in coastal systems have beenquantified with the Dynamic Interactive Vulnerability Assess-ment (DIVA) model (29–35), an integrated model of coastalsystems that assesses biophysical and socioeconomic impacts ofSLR and socioeconomic development. DIVA operates at thelevel of individual linear coastal segments, which are consideredindependently. The database contains more than 80 parametersfor each variable-length segment which are used to describe fullythe physical characteristics of the coast line.DIVA is driven by climatic and socioeconomic scenarios. The

climatic scenarios consist of the variables temperature change andSLR. The socioeconomic scenarios consist of the variables land-use class, coastal population growth, and GDP growth. The land-use classes are equivalent to the 19 land-use classes of theIMAGE 2.2 model (http://www.pbl.nl/en/publications/2004/The_Atmosphere-Ocean_System_of_IMAGE_2_2), although the im-portant distinction is between agricultural and nonagriculturalland use. DIVA first downscales to relative sea-level rise (RSLR)by combining the scenarios of SLR resulting from globalwarming with vertical land movement. The latter is a combina-tion of glacial-isostatic adjustment and a uniform 2 mm/y sub-sidence in deltas. Human-induced subsidence (caused by groundfluid abstraction or drainage) is not considered because of thelack of consistent data or scenarios.Based on the RSLR, four types of biophysical impacts are

assessed for each coastline segment: (i) dry land loss caused bycoastal erosion; (ii) flooding caused by surges and the backwatereffect on rivers; (iii) salinity intrusion in deltas and estuaries; and(iv) coastal wetland change and loss.An important innovation introduced by DIVA is the explicit in-

corporation of a range of adaptation options; impacts depend notonly on the selected climatic and socioeconomic scenarios but alsoon the selected adaptation strategy. The two main adaptationoptions that are considered are dike building/raising and beach/shore nourishment. The length of defended coast is determined,and costs are assessed using standard methods. Hence, a series ofimpactsboth inmonetaryandnonmonetarytermscanbedeveloped.These costs include the costs of adaptation, if this option is in-vestigated. A general observation fromusingDIVA is that the costsof protection generally are lower than the cost of the damageavoided.Thisresult showsthe importanceofconsideringadaptationmore fully.

Tourism ModelThe tourism study aims at modeling the spatial and seasonaldistribution of international tourist visitation in Europe. The in-fluence of the climate has been considered explicitly by includingthe tourism climate index (TCI), developed primarily for generaloutdoor activities (excluding winter sports) (36) in the statisticalregression analysis of tourism bed nights. The TCI is based on thenotion of “human comfort” and consists of a weighted index ofmaximum and mean daily temperature, humidity, precipitation,sunshine, and wind. The index was calculated for all grid cellscovered by the climate models, on a monthly basis. The gridvalues subsequently were aggregated to the NUTS2 regional

level, for which the other required statistics were available. Op-erating at a temporal resolution of months and a spatial resolu-tion of NUTS2 regions yielded a meaningful representation ofclimatic suitability for tourism. Climate suitability was one of thevariables in the regression analysis aimed at ascertaining therelative importance of the various determinants of tourist visita-tion. Price levels, income, and fixed seasonal effects are the ad-ditional explanatory variables with the same regional resolution.Tourist visitation was represented by the number of bed nightsoccupied by international visitors. The regression analysis, basedon historical data, yielded a climate elasticity of tourist demand.This climate elasticity was used subsequently to estimate theimpact of climate change on tourist visitation by multiplying theelasticity by the percentage change in TCI scores. The final stepconsisted of putting a monetary value on these changes, bymultiplying the change in the number of bed nights by the averageexpenditure per bed night.The analysis can be considered partial, in that it assumes that

only the climate conditions change. This approach was taken tosingle out the effect of climate change and to avoid having to useprojections for tourism development, for which no commonlyaccepted scenarios are available. Instead, a simple sensitivityanalysis was performed for the strategies chosen by the tourismindustry and the tourists themselves to adapt to climate change,because these strategies are very likely to determine the economicimpacts to a large extent (37, 38). The analysis was built aroundtwo types of possible system responses: a climate-induced changein the overall number of international tourists, and a change inthe seasonal planning of holidays. For each of these responses,two extreme cases were considered: full flexibility and no flexi-bility. Three of the possible four combinations were assessed. Incase one, climate change can result in an unlimited change in theoverall number of international tourists in Europe, and touristshave complete freedom in choosing their preferred month forholiday-making. The results presented in the article relate to thiscase. Case two assumes that the total number of tourists is fixed,whereas the choice of holiday season is free. In case three, thereis no flexibility in either of the two dimensions. Cases one andtwo can be thought of as being consistent with the relaxing ef-fects of aging on tourists’ time constraints, whereas the third caseis consistent with a continued dominant role of school holidays.Particularly for Southern Europe, which faces significant de-terioration of its climate suitability during the summer holidayperiod, the three cases have very different implications.

Human Health AssessmentThe human health assessment uses a detailed bottom-up impactpathway approach. The method combines current health impactassessment and valuation models with daily climate data andempirical climate–health relationships derived from epidemio-logical studies (39, 40). The study uses two functional relation-ships for assessing potential heat- and cold-related temperatureeffects. The first uses a suite of country-specific epidemiologicalstudies, which we report as “country-specific” functions, andwhich consist of relationships based on statistical analysis of daily(or monthly) temperature and mortality. In the absence of a fullrange of country-specific functions, the functions identified inref. 39 were adopted, each with a specific slope and threshold,and the functions derived in one country were transferred toclimatically and socially similar countries. In the absence of age-specific country evidence, all-age mortality functions were used.The second approach involved a statistical analysis of daily

temperatures in each location. Climate-dependent thresholds ineach grid cell were calculated (using the approach in ref. 40), withthresholds taken at the 10th and 95th centiles of daily meantemperature for low- and high-temperature impacts, respectively.For each grid cell, the 10th and 95th centiles of the 30-y dailymean temperature series were identified. These threshold data

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then were used in combination with a single functional form (40),which comprised a fixed single slope gradient, assuming a linearform beyond the threshold point. This method enables the anal-ysis to estimate the additional deaths attributable to heat and coldstress across Europe.The analysisworks ona 50× 50 kmgrid resolution acrossEurope.

The daily responses are aggregated to provide an average annualpercentage change in mortality within each grid cell for eachyear within a 30-y climatological period. Different assumptions foracclimatization are combined with the climate–health functions.Although there is no consensus on the potential extent of accli-matization, we assumed acclimatization to 1 °C warming wouldoccur every 3 decades. There currently are no unit values for thewillingness-to-pay (WTP) derived in the context of avoiding a cli-mate change-induced increase in mortality risk. Most of the avail-able mortality estimates differ in their defining characteristics, butthere is a much closer fit valuation of (avoidance of) air pollution-mortality risks, within which context recent empirical studies havebeen undertaken. For mortality risks, two metrics are used cur-rently: the value of a statistical life (VSL) and the value of a life year(VOLY). Thus, the central VSL (from ref. 41) of €1.11 millionequates to a VOLY of €59,000. The VOLY is applied after ad-justing for the average period of life lost formortality, assumed hereto be 8 y, a value that has been used in previous assessments (33).The quantitative modeling aspects are illustrated below.The annual figures for temperature-related changes are com-

binedwith gridded socioeconomic data onpopulation andmortalityin a database environment to provide the average number of ad-ditional deaths (or hospital admissions or Salmonella cases) in eachgrid cell for each year. The annual estimates are averaged across the30-y climatological period to give the projection of health impactsof climate change coupled with socioeconomic change for thatperiod. These projections then can be compared with the numberof deaths relating to socioeconomic changes alone [i.e., calculatedfor the future period from a combination of present-day (baseline)climate and projected future socioeconomic conditions]. The dif-ference between these two values provides the additional deathsinduced by the climate change alone.

General Equilibrium Modeling of Climate ImpactsThe sectoral effects of climate change have been integrated intoa computable CGE model for Europe, the General EquilibriumModel for Energy-Economy-Environment Interactions (GEM-E3Europe) model (42). The GEM-E3 model is used regularly toassess European Commission policies on climate change (43–45).The CGEmethodology has both solid data and economic theory

foundations (46). The data core of themodel is the Social AccountMatrix (SAM), an input–output table of the economy extended toaccount for the transactions between all the agents of the economy:households, firms, public sector, and external sector. The CGEmodels integrate the optimal behavior of firms (minimizing costs)and households (maximizing welfare), taking explicitly into ac-count the interactions between all the markets (factors and goodsand services) and agents in the economy as well as trade-relatedeffects. Thus, a CGEmodel such asGEM-E3 allows the estimationof the direct and indirect effects of climate change in the overalleconomy. The direct effect on a sector will lead to indirect effectsin the other goods and servicesmarkets through adjustments in thefactor markets (capital and labor markets) and in trade to attainequilibrium between supply and demand in all markets.The economic, energy, and emissions data for GEM-E3 are

based on EUROSTAT databases (input–output tables, national

accounts data, and energy balances). Twenty-four EU economieshave been modeled individually (the whole EU, with the ex-ception of Malta, Cyprus, and Luxemburg), with 18 sectors ineach country with full bilateral trade.As a benchmark, it was assumed that allmarkets are fullyflexible,

i.e., prices in all markets adjust so that demand equals supply. Sucha neoclassical paradigm has been used to represent the new equi-librium in the long termwhenallmarket adjustments haveoccurred.A baseline scenario has been run for 2010 assuming no climate

change. The alternative scenario considered the influence of cli-mate change in the economy. The presented results compare thevalues ofwelfare andGDPof the climate scenariowith those of thebaseline scenario.

Integration of Impacts into the GEM-E3 ModelEach impact category has been modeled differently in the GEM-E3 model, depending on the interpretation of the direct effect.The yield changes computed with the agriculture model havebeen interpreted as a productivity shock to the production side ofthe agriculture sector in the economy.The main economic impacts of river flooding relate to damage to

residential buildings (around 80% of the total impact). It has beenassumed that households will repair buildings and replace lostequipment. This impact is interpreted in the GEM-E3 model asadditional expenditure needed. The damages related to productivesectors are modeled as production and capital losses in the econ-omy, representing only 20% of the damage from flooding and thusonly marginally affecting GDP (Table S2). In the coastal systemassessment, the twomain economic impacts estimated by the DIVAmodel are sea floods and migration costs. It has been assumed thatsea floods lead to capital losses in the model, whereas migrationcosts induce additional expenditure by households. For both riverfloods and coastal systems, this additional expenditure does notprovide any welfare gain: Indeed, it represents a welfare loss, be-cause households are forced to assume it because of climate change.For tourism, it has been assumed that the redistribution of

tourism within Europe leads to changes in exports; some coun-tries have more international tourists that lead to higher ex-penditure within the country in the form of additional exports butlead also to reaction on the supply capacity. The reported resultsin tourism refer to year 2040 to allow the model to adjust to thenew export flows of the sector.GDP and welfare have been selected as the main variables to

synthesize the economic impact.Welfare inCGEmodelsmeasuresthe utility derived from household consumption and leisure time.Its evolution reflects the benefits for households from growth,whereas GDP growth reflects more the growth in domestic eco-nomic activity. In the long-term reference scenario, both indicatorsevolve in parallel, but policies or damage resulting from climatechange might induce some activity growth without generatingwelfare improvements (e.g., repairing houses after floods).

Climate Data Needs of the Sectoral AssessmentsA key criterion for the final selection of scenarios was the need ofthe various physical-impact methods for specific climate data(Table S1). These needs differ from sector to sector, both in thevariables requested and in the preferred temporal and spatialresolution. The river floods model was the most demanding interms of resolution, requiring daily data at 50-km spatial reso-lution and at 12-km resolution for some specific scenarios.

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Fig. S1. Grouping of EU countries in the study.

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Fig. S2. The PESETA Integrated approach.

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Fig. S3. Projected 2080s changes in mean annual temperature.

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Fig. S4. Projected 2080s changes in annual precipitation.

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Table S1. Climate data needs by sector

Sector Variables requested Time resolution Spatial resolution

Agriculture Max/min temperature Monthly 50 × 50 kmPrecipitation Monthly 50 × 50 kmCO2-equivalent concentration Annual 50 × 50 km

River floods Temperature Daily 12 × 12 km and 50 × 50 kmPrecipitation Daily 12 × 12 km and 50 × 50 kmNet (or downward) shortwave (solar) radiation Daily 12 × 12 km and 50 × 50 kmNet (or downward) longwave (thermal) radiation Daily 12 × 12 km and 50 × 50 kmHumidity Daily 12 × 12 km and 50 × 50 kmWind speed Daily 12 × 12 km and 50 × 50 kmFor comparison purposes: evaporation, snow, and runoff Daily 12 × 12 km and 50 × 50 km

Coastal systems Regional surfaces of sea-level rise Annual —

Tourism Maximum/average temperature Monthly 50 × 50 kmHours of sun or cloud cover Monthly 50 × 50 kmWind speed Monthly 50 × 50 kmRelative humidity or vapor pressure Monthly 50 × 50 km

Human health Maximum/min/average temperature Daily 50 × 50 kmRelative humidity or vapor pressure Daily 50 × 50 km

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Table S2. Economic impacts on agriculture, river basins, tourism, and coastal systems for 2080s climate-change scenarios in the currentEuropean economy

Economic impacts

European regions*

SouthernEurope

CentralEuropeSouth

CentralEuropeNorth

BritishIsles

NorthernEurope EU

Economic impacts as estimated by the agriculture modelWelfare change (%)‡

2.5 °C −0.05 0.06 0.01 −0.09 0.58 0.013.9 °C −0.37 0.02 −0.05 −0.11 0.59 −0.104.1 °C −0.15 −0.01 0.04 0.09 0.56 0.025.4 °C −1.00 −0.27 −0.19 0.06 0.72 −0.32

GDP change (%)‡

2.5 °C −0.13 0.11 −0.02 −0.10 0.81 0.023.9 °C −0.52 0.06 −0.06 −0.11 0.85 −0.094.1 °C −0.22 0.00 0.05 0.12 0.76 0.045.4 °C −1.26 −0.28 −0.17 0.16 1.09 −0.29

Economic impacts as estimated by the river flooding modelWelfare change (%)‡

2.5 °C −0.13 −0.16 −0.04 −0.06 0.09 −0.083.9 °C −0.11 −0.25 −0.09 −0.21 0.01 −0.144.1 °C −0.09 −0.15 −0.13 −0.20 0.07 −0.135.4 °C −0.14 −0.31 −0.24 −0.37 0.10 −0.24

GDP change (%)‡

2.5 °C −0.01 −0.01 0.00 0.00 0.00 −0.013.9 °C −0.01 −0.01 −0.01 −0.01 0.00 −0.014.1 °C 0.00 −0.01 −0.01 −0.01 0.00 −0.015.4 °C 0.00 −0.01 −0.02 −0.02 0.00 −0.01

Economic impacts as estimated by the coastal system modelWelfare change (%)‡

2.5 °C −0.07 −0.06 −0.27 −0.17 −0.13 −0.163.9 °C −0.11 −0.08 −0.29 −0.19 −0.14 −0.184.1 °C −0.09 −0.06 −0.28 −0.18 −0.14 −0.175.4 °C −0.10 −0.09 −0.30 −0.20 −0.15 −0.185.4 °C, 88 cm SLR −0.38 −0.19 −0.37 −1.02 −0.35 −0.46

GDP change (%)‡

2.5 °C −0.05 −0.05 −0.38 −0.23 −0.11 −0.193.9 °C −0.05 −0.05 −0.41 −0.24 −0.12 −0.204.1 °C −0.05 −0.05 −0.39 −0.23 −0.11 −0.205.4 °C −0.05 −0.05 −0.42 −0.25 −0.13 −0.215.4 °C, 88 cm SLR −0.04 −0.06 −0.50 −0.26 −0.16 −0.24

Economic impacts as estimated by the tourism modelWelfare change (%)‡

2.5 °C −0.02 0.02 0.01 0.01 0.01 0.003.9 °C −0.03 0.03 0.01 0.01 0.02 0.014.1 °C −0.08 −0.11 0.03 0.05 0.07 −0.025.4 °C −0.12 0.18 0.04 0.06 0.08 0.04

GDP change (%)‡

2.5 °C −0.01 0.00 0.00 0.00 0.00 0.003.9 °C −0.01 0.01 0.00 0.00 0.00 0.004.1 °C −0.03 −0.03 0.01 0.01 0.02 −0.015.4 °C −0.05 0.03 0.02 0.01 0.02 0.01

*European regions: Southern Europe (Portugal, Spain, Italy, Greece, and Bulgaria), Central Europe South (France, Austria, Czech Republic, Slovakia, Hungary,Romania, and Slovenia), Central Europe North (Belgium, The Netherlands, Germany, and Poland), British Isles (Ireland and United Kingdom), and NorthernEurope (Sweden, Finland, Estonia, Latvia, and Lithuania).‡Household welfare and GDP are compared with the 2010 values of the baseline scenario.

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