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Future surface temperature changes for the Iberian Peninsula according to EURO-CORDEX climate projections

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Abstract

Future changes in the mean, maximum and minimum temperature in the Iberian Peninsula were investigated using bias-corrected EURO-CORDEX climate projections. The results show that the future temperatures are projected to substantially increase in all the Iberian Peninsula, particularly towards the end of the century at the south-central region. Mean and maximum temperatures are projected to increase around 2 °C (4 °C) for the 2046–2065 (2081–2100) period, with much higher frequencies of days above 20 (mean temperature) and 30 °C (maximum temperature). However, much higher increases are projected in the south of Spain, Cantabrian and Pyrinees mountain ranges, while lower ones are projected for the Atlantic coastal areas. In the south-central part of the Iberian Peninsula, hot days (mean temperature > 30 °C) are projected to increase 20–35 days/year (40–80 days/year) for the period 2046–2065 (2081–2100), while very hot days (maximum temperature > 40 °C) are projected to increase 10–25 days/year (10–50 days/year) for the period 2046–2065 (2081–2100). These results show a clear tendency, associated with a high confidence, in a significant increase of the surface temperatures and in the frequency of high temperature episodes in the southern part of the Iberian Peninsula, which can have severe impacts on the population, environment and economy. The currently hottest areas located in south-central Iberian Peninsula are also the ones with the highest projected temperature increases, which will significantly exacerbate the temperature stress in these areas.

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References

  • Amengual A, Homar V, Romero R, Alonso S, Ramis C (2012) A statistical adjustment of regional climate model outputs to local scales: application to Platja de Palma, Spain. J Clim 25(3):939–957

    Google Scholar 

  • Amengual A, Homar V, Romero R, Brooks HE, Ramis C, Gordaliza M, Alonso S (2014) Projections of heat waves with high impact on human health in Europe. Glob Planet Change 119:71–84

    Google Scholar 

  • Boulard D et al (2017) Bias correction of dynamically downscaled precipitation to compute soil water deficit for explaining year-to-year variation of tree growth over northeastern France. Agric For Meteorol 232:247–264

    Google Scholar 

  • Bürger G, Sobie SR, Cannon AJ, Werner AT, Murdock TQ (2013) Downscaling extremes: an intercomparison of multiple methods for future climate. J Clim 26:3429–3449

    Google Scholar 

  • Buser CM, Künsch HR, Lüthi D, Wild M, Schär C (2009) Bayesian multi-model projection of climate: bias assumptions and interannual variability. Clim Dyn 33:849–868

    Google Scholar 

  • Cardell MF, Romero R, Amengual A, Homar V, Ramis C (2019) A quantile–quantile adjustment of the EURO-CORDEX projections for temperatures and precipitation. Int J Climatol 39(6):2901–2918

    Google Scholar 

  • Cardoso RM, Soares PMM, Lima DCA, Miranda PMA (2019) Mean and extreme temperatures in a warming climate: EURO CORDEX and WRF regional climate high-resolution projections for Portugal. Clim Dyn 52(1–2):129–157

    Google Scholar 

  • Cornes R, van der Schrier G, van den Besselaar EJM, Jones PD (2018) An ensemble version of the E-OBS temperature and precipitation datasets. J Geophys Res Atmos 123(17):9391–9409

    Google Scholar 

  • Costoya X, Rocha A, Carvalho D (2020) Using bias-correction to improve future projections of offshore wind energy resource: a case study on the Iberian Peninsula. Appl Energy 262:114562

    Google Scholar 

  • Costoya X, deCastro M, Santos F, Sousa MC, Gómez-Gesteira M (2019) Projections of wind energy resources in the Caribbean for the 21st century. Energy 178:356–367

    Google Scholar 

  • Déqué M (2007) Frequency of precipitation and temperature extremes over France in an anthropogenic scenario: model results and statistical correction according to observed values. Glob Planet Change 57:16–26

    Google Scholar 

  • Dosio A (2016) Projections of climate change indices of temperature and precipitation from an ensemble of bias-adjusted high-resolution EURO-CORDEX regional climate models. J Geophys Res Atmos 121(10):5488–5511

    Google Scholar 

  • Ehret U, Zehe E, Wulfmeyer V, Warrach-Sagi K, Liebert J (2012) Hess opinions “should we apply bias correction to global and regional climate model data?”. Hydrol Earth Syst Sci Discuss 9(4):5355–5387

    Google Scholar 

  • Fischer EM, Knutti R (2015) Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat Clim Change 5(6):560–564

    Google Scholar 

  • Fischer EM, Schär C (2010) Consistent geographical patterns of changes in high-impact European heatwaves. Nat Geosci 3(6):398–403

    Google Scholar 

  • Gibbons JD, Chakraborti S (2011) Nonparametric statistical inference, 5th edn. Chapman & Hall/CRC Press, Taylor & Francis Group, Boca Raton

  • Giorgi F, Jones C, Asrar GR (2009) Addressing climate information needs at the regional level: the CORDEX framework. Bull World Meteorol Organ 58:175–183

    Google Scholar 

  • Gobiet A, Suklitsch M, Heinrich G (2015) The effect of empirical-statistical correction of intensity-dependent model errors on the temperature climate change signal. Hydrol Earth Syst Sci 19:4055–4066

    Google Scholar 

  • Gómez-Gesteira M, Gimeno L, deCastro M, Lorenzo MN, Alvarez I, Nieto R, Taboada JJ, Crespo AJC, Ramos AM, Iglesias I, Gomez-Gesteira JL, Santo FE, Barriopedro D, Trigo IF (2001) The state of climate in NW Iberia. Clim Res 48:109–144

    Google Scholar 

  • Gudmundsson L, Bremnes JB, Haugen JE, Engen-Skaugen T (2012) Technical note: downscaling RCM precipitation to the station scale using statistical transformations—a comparison of methods. Hydrol Earth Syst Sci 16:3383–3390

    Google Scholar 

  • Hnilica J, Hanel M, Puš V (2017) Multisite bias correction of precipitation data from regional climate models. Int J Clim 37:2934–2946

    Google Scholar 

  • IPCC AR5 (2013) Climate change: the physical science basis. In: Stocker TF,Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y,Bex V, Midgley PM (eds) Contribution of Working Group I to the FifthAssessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, p 1535

  • IPCC (2014) Climate Change 2014: synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In: Core Writing Team, Pachauri RK, Meyer LA (eds) IPCC, Geneva

  • IPCC (2018) Summary for policymakers. Global Warming of 1.5 °C. An IPCC Special Report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change. World Meteorological Organization, Geneva

  • Jacob D et al (2014) EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg Environ Change 14(2):563–578

    Google Scholar 

  • Jerez S, Montavez JP, Jimenez-Guerrero P, Gomez-Navarro JJ, Lorente-Plazas R, Zorita E (2013) A multi-physics ensemble of present-day climate regional simulations over the Iberian Peninsula. Clim Dyn 40(11–12):3023–3046

    Google Scholar 

  • Katragkou E et al (2015) Regional climate hindcast simulations within EURO-CORDEX: evaluation of a WRF multi-physics ensemble. Geosci Model Dev 8(3):603–618

    Google Scholar 

  • Kjellström E et al (2018) European climate change at global mean temperature increases of 1.5 and 2 °C above pre-industrial conditions as simulated by the EURO-CORDEX regional climate models. Earth Syst Dyn 9:459–478

    Google Scholar 

  • Kotlarski S et al (2014) Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble. Geosci Model Dev 7(4):1297–1333

    Google Scholar 

  • Lafon T, Dadson S, Buys G, Prudhomme C (2013) Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods. Int J Climatol 33:1367–1381

    Google Scholar 

  • Lau NC, Nath MJ (2014) Model simulation and projection of European heat waves in present-day and future climates. J Clim 27(10):3713–3730

    Google Scholar 

  • Li H, Sheffield J, Wood EF (2010) Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. J Geophys Res 115:D10101

    Google Scholar 

  • Li C, Sinha E, Horton DE, Diffenbaugh NS, Michalak AM (2014) Joint bias correction of temperature and precipitation in climate model simulations. J Geophys Res Atmos 119:13153–13162

    Google Scholar 

  • Maraun D (2013) Bias correction, quantile mapping and downscaling: Revisiting the inflation issue. J Clim 26:2137–2143

    Google Scholar 

  • Meehl GA, Tebaldi C (2004) More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305(5686):994–997

    Google Scholar 

  • Meehl GA et al (2007) Global climate projections. In: Solomon S et al (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, New York, pp 749–845

  • Mehrotra R, Sharma A (2016) A multivariate quantile-matching bias correction approach with auto- and cross-dependence across multiple time scales: Implications for downscaling. J Clim 29:3519–3539

    Google Scholar 

  • Miao C, Su L, Sun Q, Duan Q (2016) A nonstationary bias-correction technique to remove bias in GCM simulations. J Geophys Res Atmos 121:5718–5735

    Google Scholar 

  • Milly PC, Betancourt J, Falkenmark M, Hirsch RM, Kundzewicz ZW, Lettenmaier DP, Stouffer RJ (2008) Stationarity is dead: whither water management? Science 319(5863):573–574

    Google Scholar 

  • Moberg A, Jones PD (2004) Regional climate model simulations of daily maximum and minimum near-surface temperatures across Europe compared with observed station data 1961–1990. Clim Dyn 23:695–715

    Google Scholar 

  • Moss RH, Edmonds JA et al (2010) The next generation of scenarios for climate change research and assessment. Nature 463(7282):747–756

    Google Scholar 

  • Ngai ST, Tangang F, Juneng L (2017) Bias correction of global and regional simulated daily precipitation and surface mean temperature over Southeast Asia using quantile mapping method. Glob Planet Change 149:79–90

    Google Scholar 

  • Ouzeau G, Soubeyroux JM, Schneider M, Vautard R, Planton S (2016) Heat waves analysis over France in present and future climate: application of a new method on the EURO-CORDEX ensemble. Clim Serv 4:1–12

    Google Scholar 

  • 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

    Google Scholar 

  • Rajczak J, Kotlarski S, Schär C (2016) Does quantile mapping of simulated precipitation correct for biases in transition probabilities and spell lengths? J Clim 29(1605):1615

    Google Scholar 

  • Ramos AM, Trigo RM, Santo FE (2011) Evolution of extreme temperatures over Portugal: recent changes and future scenarios. Clim Res 48:177–192

    Google Scholar 

  • Riahi K, Rao S, Krey V, Cho C, Chirkov V, Fischer G, Kindermann G, Nakicenovic N, Rafaj P (2011) RCP 8.5—a scenario of comparatively high greenhouse gas emissions. Clim Change 109:33–57

    Google Scholar 

  • Rogelj J, Meinshausen M et al (2012) Global warming under old and new scenarios using IPCC climate sensitivity range estimates. Nat Clim Change 2:248–253

    Google Scholar 

  • Russo S, Sillmann J, Fischer EM (2015) Top ten European heatwaves since 1950 and their occurrence in the coming decades. Environ Res Lett 10(12):124003

    Google Scholar 

  • Santos F, Gómez-Gesteira M, Añel JA, Carvalho D, Costoya X, Dias JM (2018) On the accuracy of CORDEX RCMs to project future winds over the Iberian Peninsula and surrounding ocean. Appl Energy 228:289–300

    Google Scholar 

  • Schär C, Vidale PL, Lüthi D, Frei C, Häberli C, Liniger MA, Appenzeller C (2004) The role of increasing temperature variability in European summer heatwaves. Nature 427(6972):332–336

    Google Scholar 

  • Schoetter R, Cattiaux J, Douville H (2015) Changes of western European heat wave characteristics projected by the CMIP5 ensemble. Clim Dyn 45:1–16

    Google Scholar 

  • Soares PMM, Lima DCA, Cardoso RM, Semedo A (2017) High resolution projections for the Western Iberian Coastal Low level jet in a changing climate. Clim Dyn 49:1547–1566

    Google Scholar 

  • Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93(4):485–498

    Google Scholar 

  • Vautard R et al (2013) The simulation of European heat waves from an ensemble of regional climate models within the EURO-CORDEX project. Clim Dyn 41(9–10):2555–2575

    Google Scholar 

  • Viceto C, Cardoso Pereira S, Rocha A (2019) Climate change projections of extreme temperatures for the Iberian Peninsula. Atmosphere 10(5):229

    Google Scholar 

  • Vogel MM, Zscheischler J, Wartenburger R, Dee D, Seneviratne SI (2019) Concurrent 2018 hot extremes across northern hemisphere due to human-induced climate change. Wiley, Hoboken

    Google Scholar 

  • Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Change 62:189–216

    Google Scholar 

  • Zhao T, Bennett JC, Wang QJ, Schepen A, Wood AW, Robertson DE, Ramos MH (2017) How suitable is quantile mapping for post-processing GCM precipitation forecasts. J Clim 30(9):3185–3196

    Google Scholar 

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Acknowledgements

The authors acknowledge the World Climate Research Programme's Working Group on Regional Climate, the Working Group on Coupled Modelling, the modelling groups listed in Table 1 of this paper for producing and making available their model output, the Earth System Grid Federation infrastructure (an international effort led by the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison), the European Network for Earth System Modelling and other partners in the Global Organisation for Earth System Science Portals (GO-ESSP), the E-OBS dataset from the EU-FP6 project UERRA (https://www.uerra.eu), the Copernicus Climate Change Service, and the data providers in the ECA&D project (https://www.ecad.eu). D. Carvalho acknowledges the Portuguese Foundation for Science and Technology (FCT) for his researcher contract (CEECIND/01726/2017) and the FCT/MCTES for the financial support to CESAM (UIDP/50017/2020+UIDB/50017/2020), through national funds.

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  1. CESAM-Department of Physics, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal

    D. Carvalho, S. Cardoso Pereira & A. Rocha

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  1. D. Carvalho
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Carvalho, D., Cardoso Pereira, S. & Rocha, A. Future surface temperature changes for the Iberian Peninsula according to EURO-CORDEX climate projections. Clim Dyn 56, 123–138 (2021). https://doi.org/10.1007/s00382-020-05472-3

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