Abstract
Despite an increasing understanding of potential climate change impacts in Europe, the associated uncertainties remain a key challenge. In many impact studies, the assessment of uncertainties is underemphasised, or is not performed quantitatively. A key source of uncertainty is the variability of climate change projections across different regional climate models (RCMs) forced by different global circulation models (GCMs). This study builds upon an indicator-based NUTS-2 level assessment that quantified potential changes for three climate-related hazards: heat stress, river flood risk, and forest fire risk, based on five GCM/RCM combinations, and non-climatic factors. First, a sensitivity analysis is performed to determine the fractional contribution of each single input factor to the spatial variance of the hazard indicators, followed by an evaluation of uncertainties in terms of spread in hazard indicator values due to inter-model climate variability, with respect to (changes in) impacts for the period 2041–70. The results show that different GCM/RCM combinations lead to substantially varying impact indicators across all three hazards. Furthermore, a strong influence of inter-model variability on the spatial patterns of uncertainties is revealed. For instance, for river flood risk, uncertainties appear to be particularly high in the Mediterranean, whereas model agreement is higher for central Europe. The findings allow for a hazard-specific identification of areas with low vs. high model agreement (and thus confidence of projected impacts) within Europe, which is of key importance for decision makers when prioritising adaptation options.
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References
Ahmad QK, Warrick RA, Downing TE, Nishioka S, Parikh KS, Parmesan C, Schneider SH, Toth F, Yohe G (2001) Methods and tools. In: McCarthy JJ, Canziani OF, Leary NA, Dokken DJ, White KS (eds) Climate change 2001: impacts, adaptation, and vulnerability. Contribution of working group II to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 105–143
Alcamo J, Moreno JM, Nováky B, Bindi M, Corobov R, Devoy RJN, Giannakopoulos C, Martin E, Olesen JE, Shvidenko A (2007) Europe. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (eds) Climate change 2007: impacts, adaptation and vulnerability. Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 541–580
Botev ZI, Grotowski JF, Kroese DP (2010) Kernel density estimation via diffusion. Ann Stat 38:2916–2957
Carvalho AC, Carvalho A, Martins H, Marques C, Rocha A, Borrego C, Viegas DX, Miranda AI (2010) Fire weather risk assessment under climate change using a dynamical downscaling approach. Environ Modell Softw 26:1123–1133
Christensen JH, Carter TR, Giorgi F (2002) PRUDENCE employs new methods to assess European climate change. Eos 83:147
Christensen JH, Kjellström E, Giorgi F, Lenderink G, Rummukainen M (2010) Weight assignment in regional climate models. Climate Res 44:179–194
Dankers R, Feyen L (2009) Flood hazard in Europe in an ensemble of regional climate scenarios. J Geophys Res 114:D16108
Donat MG, Leckebusch GC, Wild S, Ulbrich U (2011) Future changes in European winter storm losses and extreme wind speeds inferred from GCM and RCM multi-model simulations. Nat Hazard Earth Syst Sci 11:1351–1370
Dosio A, Paruolo P (2011) Bias correction of the ENSEMBLES high resolution climate change projections for use by impact models: evaluation on the present climate. J Geophys Res 116:D16106
Dosio A, Paruolo P, Rojas R (2012) Bias correction of the ENSEMBLES high resolution climate change projections for use by impact models: analysis of the climate change signal. J Geophys Res 117:D17110
EEA-JRC-WHO (2008) Impacts of Europe’s changing climate – 2008 indicator-based assessment. Joint EEA-JRC-WHO report, EEA Report No 4/2008, JRC Reference Report No JRC47756. EEA, Copenhagen
ESPON (2011) Climate change and territorial effects on regions and local economies. ESPON climate project, final report, version 31/5/2011. TU Dortmund University, Germany
Feyen L, Dankers R, Bódis K, Salamon P, Barredo JI (2012) Fluvial flood risk in Europe in present and future climates. Clim Change 112:47–62
Füssel HM, Klein RJT (2006) Climate change vulnerability assessments: an evolution of conceptual thinking. Clim Change 75:301–329
Hagemann S, Chen C, Haerter OJ, Heinke J, Gerten D, Piani C (2011) Impact of a statistical bias correction on the projected hydrological changes obtained from three GCMs and two hydrology models. J Hydrometeorol 12:556–578
Hinkel J, Nicholls RJ, Vafeidis AT, Tol RSJ (2010) Assessing risk of an adaptation to sea-level rise in the European Union: an application of DIVA. Mitig Adapt Strateg Glob Change 15:703–719
IPCC (2007) Summary for Policymakers. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 1–18
Jansen MJW (1999) Analysis of variance designs for model output. Comput Phys Commun 117:35–43
Kundzewicz ZW, Lugeri N, Dankers R, Hirabayashi Y, Döll P, Pińskwar I, Dysarz T, Hochrainer S, Matczak P (2010) Assessing river flood risk and adaptation in Europe – review of projections for the future. Mitig Adapt Strateg Glob Change 15:641–656
Lissner TK, Holsten A, Walther C, Kropp JP (2012) Towards sectoral and standardised vulnerability assessments: the example of heatwave impacts on human health. Clim Change 112:687–708
Lopez A, Fung F, New M, Watts G, Weston A, Wilby RL (2009) From climate model ensembles to climate change impacts and adaptation: a case study of water resource management in the southwest of England. Water Resour Res 45:W08419
Lung T, Lavalle C, Hiederer R, Dosio A, Bouwer LM (2013) A multi-hazard regional level impact assessment for Europe combining indicators of climatic and non-climatic change. Global Environ Change 23:522–536
Maslin M, Austin P (2012) Climate models at their limit? Nature 486:183–184
Nakićenović N, Swart R (eds) (2000) Emissions scenarios 2000. Special report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge
OECD (2008) Handbook on constructing composite indicators: methodology and user guide. OECD Publishing
Olsson J, Yang W, Graham LP, Rosberg J, Andreasson J (2011) Using an ensemble of climate projections for simulating recent and near-future hydrological change to lake Vänern in Sweden. Tellus 63A:126–137
Piani C, Haerter JO, Coppola E (2010) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99:187–192
Rannow S, Loibl W, Greiving S, Gruehn D, Meyer BC (2010) Potential impacts of climate change in Germany – identifying regional priorities for adaptation activities in spatial planning. Landscape Urban Plan 98:160–171
Rojas R, Feyen L, Bianchi A, Dosio A (2012) Assessment of future flood hazard in Europe using a large ensemble of bias corrected regional climate simulations. J Geophys Res 117:D17109
Rosenzweig C, Wilbanks TJ (2010) The state of climate change vulnerability, impacts, and adaptation research: strengthening knowledge base and community. Clim Change 100:103–106
Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S (2008) Global sensitivity analysis: the primer. John Wiley & Sons, Chichester
Saltelli A, Annoni P, Azzini I, Campolongo F, Ratto M, Tarantola S (2010) Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput Phys Commun 181:259–270
Sobol’ IM (1993) Sensitivity estimates for nonlinear mathematical models. Math Model Comput Exp 1:407–414
Teutschbein C, Seibert J (2010) Regional climate models for hydrological impact studies at the catchment scale: a review of recent modeling strategies. Geogr Compass 4:834–860
van der Knijff J, Younis J, De Roo A (2010) LISFLOOD: a GIS-based distributed model for river basin scale water balance and flood simulation. Int J Geogr Inf Sci 24:189–212
van der Linden P, Mitchell JFB (2009) ENSEMBLES: climate change and its impacts – summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, Exeter
van Engelen A, Klein Tank A, van der Schrier G, Klok L (2008) European Climate Assessment & Dataset (ECA&D), Report 2008, “Towards an operational system for assessing observed changes in climate extremes”. KNMI, De Bilt, The Netherlands, 68pp
White S, Zwirner O (2007) The use of indicators in the European Commission. Background paper for beyond GDP conference. European Commission, DG Environment, Brussels
Wilby RL, Beven KJ, Reynard NS (2008) Climate change and fluvial risk in the UK: more of the same? Hydrol Process 22:2511–2523
Acknowledgements
This work was funded by the Research Directorate-General of the European Commission through its Seventh Framework Programme project RESPONSES (Grant Agreement number 244092). We thank four anonymous reviewers for their constructive comments.
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The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission.
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Lung, T., Dosio, A., Becker, W. et al. Assessing the influence of climate model uncertainty on EU-wide climate change impact indicators. Climatic Change 120, 211–227 (2013). https://doi.org/10.1007/s10584-013-0825-1
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DOI: https://doi.org/10.1007/s10584-013-0825-1