Consistency of climate change projections from multiple global and regional model intercomparison projects

  • J. Fernández
  • M. D. Frías
  • W. D. Cabos
  • A. S. Cofiño
  • M. Domínguez
  • L. Fita
  • M. A. Gaertner
  • M. García-Díez
  • J. M. Gutiérrez
  • P. Jiménez-Guerrero
  • G. Liguori
  • J. P. Montávez
  • R. Romera
  • E. Sánchez
Article

Abstract

We present an unprecedented ensemble of 196 future climate projections arising from different global and regional model intercomparison projects (MIPs): CMIP3, CMIP5, ENSEMBLES, ESCENA, EURO- and Med-CORDEX. This multi-MIP ensemble includes all regional climate model (RCM) projections publicly available to date, along with their driving global climate models (GCMs). We illustrate consistent and conflicting messages using continental Spain and the Balearic Islands as target region. The study considers near future (2021–2050) changes and their dependence on several uncertainty sources sampled in the multi-MIP ensemble: GCM, future scenario, internal variability, RCM, and spatial resolution. This initial work focuses on mean seasonal precipitation and temperature changes. The results show that the potential GCM–RCM combinations have been explored very unevenly, with favoured GCMs and large ensembles of a few RCMs that do not respond to any ensemble design. Therefore, the grand-ensemble is weighted towards a few models. The selection of a balanced, credible sub-ensemble is challenged in this study by illustrating several conflicting responses between the RCM and its driving GCM and among different RCMs. Sub-ensembles from different initiatives are dominated by different uncertainty sources, being the driving GCM the main contributor to uncertainty in the grand-ensemble. For this analysis of the near future changes, the emission scenario does not lead to a strong uncertainty. Despite the extra computational effort, for mean seasonal changes, the increase in resolution does not lead to important changes.

Keywords

Regional climate change Near surface temperature precipitation ESCENA ENSEMBLES CORDEX 

Notes

Acknowledgements

Authors are grateful to the modelling groups from the Euro-CORDEX, Med-CORDEX, CMIP3 and CMIP5 initiatives and the ENSEMBLES project. We also thank the ESGF and CERA for data provision and R and CDO developers for providing free libraries and data operators. This work has been funded by the Spanish R+D Program of the Ministry of Environment and Rural and Marine Affairs through ESCENA project (200800050084265) and the Ministry of Economy and Competitiveness, through grants MULTI-SDM (CGL2015-66583-R) and INSIGNIA (CGL2016-79210-R), co-funded by ERDF/FEDER. A.S.C. acknowledges support from the EU-funded FP7 project IS-ENES2 (GA 312979). Universidad de Cantabria simulations have been carried out on the Altamira Supercomputer at the Instituto de Física de Cantabria (IFCA-CSIC), member of the Spanish Supercomputing Network.

Supplementary material

382_2018_4181_MOESM1_ESM.pdf (377 kb)
Precipitation vs mean surface temperature JJA delta changes spatially averaged over Continental Spain and the Balearic Islands for each ensemble member. (a) Deltas for the 196-member ensemble. (b) Deltas by Project. (c) Deltas by Resolution. (d) Deltas by Scenario. (e) Deltas by GCM Family. (f) Deltas by Method. Marginal probability density functions are shown for each variable pooling the whole 196-member ensemble for the different classification, using a Gaussian kernel density estimator. Tickmarks show the ranges from the 5th to 95th percentile (90\% of the sample) and from the 1st to 3rd quartile (50\%). The median is also shown as an inner tickmark (thicker). Raw GCM output deltas are shown as empty circles (PDF 377 KB)
382_2018_4181_MOESM2_ESM.pdf (242 kb)
Precipitation vs mean surface temperature delta changes spatially averaged over Continental Spain and the Balearic Islands for each ensemble member. Values classified by project. (a) DJF. (b) MAM. (c) JJA. (d) SON. Marginal probability density functions are shown for each variable and project pooling the whole 196-member ensemble, using a Gaussian kernel density estimator. Tickmarks show the ranges from the 5th to 95th percentile (90\% of the sample) and from the 1st to 3rd quartile (50\%). The median is also shown as a thicker inner tickmark. (PDF 241 KB)

References

  1. Aalbers E, Lenderink G, van Meijgaard E, van den Hurk B (2017) Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability? Clim Dyn.  https://doi.org/10.1007/s00382-017-3901-9 Google Scholar
  2. Ayar PV, Vrac M, Bastin S, Carreau J, Déqué M, Gallardo C (2016) Intercomparison of statistical and dynamical downscaling models under the EURO- and MED-CORDEX initiative framework: present climate evaluations. Clim Dyn 46(3–4):1301–1329.  https://doi.org/10.1007/s00382-015-2647-5 CrossRefGoogle Scholar
  3. Bavay M, Grünewald T, Lehning M (2013) Response of snow cover and runoff to climate change in high Alpine catchments of Eastern Switzerland. Adv Water Resour 55:4–16.  https://doi.org/10.1016/j.advwatres.2012.12.009 CrossRefGoogle Scholar
  4. Bellprat O, Kotlarski S, Lüthi D, Schör C (2013) Physical constraints for temperature biases in climate models. Geophys Res Lett 40(15):4042–4047.  https://doi.org/10.1002/grl.50737 CrossRefGoogle Scholar
  5. Boberg F, Christensen JH (2012) Overestimation of Mediterranean summer temperature projections due to model deficiencies. Nat Clim Change 2(6):433–436.  https://doi.org/10.1038/nclimate1454 CrossRefGoogle Scholar
  6. Boberg F, Berg P, Thejll P, Gutowski WJ, Christensen JH (2009) Improved confidence in climate change projections of precipitation further evaluated using daily statistics from ENSEMBLES models. Clim Dyn 35(7–8):1509–1520.  https://doi.org/10.1007/s00382-009-0683-8 Google Scholar
  7. Burke M, Dykema J, Lobell DB, Miguel E, Satyanath S (2015) Incorporating climate uncertainty into estimates of climate change impacts. Rev Econ Stat 97(2):461–471.  https://doi.org/10.1162/REST_a_00478 CrossRefGoogle Scholar
  8. Casanueva A, Bedia J, Herrera S, Fernández J, Gutiérrez JM (2018) Direct and component-wise bias correction of multi-variate climate indices: the percentile adjustment function diagnostic tool. Clim Change.  https://doi.org/10.1007/s10584-018-2167-5.Google Scholar
  9. Christensen JH, Christensen OB (2007) A summary of the PRUDENCE model projections of changes in European climate by the end of this century. Clim Chang 81(1):7–30.  https://doi.org/10.1007/s10584-006-9210-7 CrossRefGoogle Scholar
  10. Daron JD, Stainforth DA (2013) On predicting climate under climate change. Environ Res Lett 8(3):034021.  https://doi.org/10.1088/1748-9326/8/3/034021 CrossRefGoogle Scholar
  11. Dell’Aquila A, Mariotti A, Bastin S, Calmanti S, Cavicchia L, Deque M, Djurdjevic V, Dominguez M, Gaertner M, Gualdi S (2016) Evaluation of simulated decadal variations over the Euro-Mediterranean region from ENSEMBLES to Med-CORDEX. Clim Dyn.  https://doi.org/10.1007/s00382-016-3143-2 Google Scholar
  12. Déqué M, Somot S, Sánchez-Gómez E, Goodess CM, Jacob D, Lenderink G, Christensen OB (2012) The spread amongst ENSEMBLES regional scenarios: regional climate models, driving general circulation models and interannual variability. Clim Dyn 38(5–6):951–964.  https://doi.org/10.1007/s00382-011-1053-x CrossRefGoogle Scholar
  13. Dessai S (2003) Heat stress and mortality in Lisbon Part II. An assessment of the potential impacts of climate change. Int J Biometeorol 48(1):37–44.  https://doi.org/10.1007/s00484-003-0180-4 CrossRefGoogle Scholar
  14. Diaconescu EP, Laprise R (2013) Can added value be expected in RCM-simulated large scales? Clim Dyn 41(7):1769–1800.  https://doi.org/10.1007/s00382-012-1649-9 CrossRefGoogle Scholar
  15. Domínguez M, Romera R, Sánchez E, Fita L, Fernández J, Jiménez-Guerrero P, Montávez JP, Cabos WD, Liguori G, Gaertner MA (2013) Present-climate precipitation and temperature extremes over Spain from a set of high resolution RCMs. Clim Res 58(2):149–164.  https://doi.org/10.3354/cr01186 CrossRefGoogle Scholar
  16. Easterling WE, Mearns LO, Hays CJ, Marx D (2001) Comparison of agricultural impacts of climate change calculated from high and low resolution climate change scenarios: Part II. Accounting for adaptation and CO2 direct effects. Clim Chang 51(2):173–197.  https://doi.org/10.1023/A:1012267900745 CrossRefGoogle Scholar
  17. Evans JP, Ji F, Lee C, Smith P, Argüeso D, Fita L (2014) Design of a regional climate modelling projection ensemble experiment—NARCliM. Geosci Model Dev 7(2):621–629.  https://doi.org/10.5194/gmd-7-621-2014 CrossRefGoogle Scholar
  18. Filahi S, Tramblay Y, Mouhir L, Diaconescu EP (2017) Projected changes in temperature and precipitation indices in Morocco from high-resolution regional climate models. Int J Climatol 37:4846–4863.  https://doi.org/10.1002/joc.5127 CrossRefGoogle Scholar
  19. Flaounas E, Drobinski P, Vrac M, Bastin S, Lebeaupin-Brossier C, Stéfanon M, Borga M, Calvet JC (2013) Precipitation and temperature space–time variability and extremes in the Mediterranean region: evaluation of dynamical and statistical downscaling methods. Clim Dyn 40(11–12):2687–2705.  https://doi.org/10.1007/s00382-012-1558-y CrossRefGoogle Scholar
  20. Frei P, Kotlarski S, Liniger MA, Schär C (2018) Future snowfall in the Alps: projections based on the EURO-CORDEX regional climate models. Cryosphere 12(1):1–24.  https://doi.org/10.5194/tc-12-1-2018 CrossRefGoogle Scholar
  21. García-Díez M, Fernández J, Vautard R (2015) An RCM multi-physics ensemble over Europe: multi-variable evaluation to avoid error compensation. Clim Dyn 45(11–12):3141–3156.  https://doi.org/10.1007/s00382-015-2529-x CrossRefGoogle Scholar
  22. Giannini V, Bellucci A, Torresan S (2016) Sharing skills and needs between providers and users of climate information to create climate services: lessons from the Northern Adriatic case study. Earth Perspect.  https://doi.org/10.1186/s40322-016-0033-z Google Scholar
  23. Giorgi F, Gutowski WJ (2015) Regional dynamical downscaling and the CORDEX initiative. Annu Rev Environ Resour 40(1):467–490.  https://doi.org/10.1146/annurev-environ-102014-021217 CrossRefGoogle Scholar
  24. Giorgi F, Diffenbaugh NS, Gao XJ, Coppola E, Dash SK, Frumento O, Rauscher SA, Remedio A, Sanda IS, Steiner A, Sylla B, Zakey AS (2008) The regional climate change hyper-matrix framework. Eos Trans Am Geophys Union 89(45):445–446.  https://doi.org/10.1029/2008EO450001 CrossRefGoogle Scholar
  25. Gobiet A, Kotlarski S, Beniston M, Heinrich G, Rajczak J, Stoffel M (2014) 21st century climate change in the European Alps—a review. Sci Total Environ 493:1138–1151.  https://doi.org/10.1016/j.scitotenv.2013.07.050 CrossRefGoogle Scholar
  26. 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(10):4055–4066.  https://doi.org/10.5194/hess-19-4055-2015 CrossRefGoogle Scholar
  27. Gómez G, Cabos WD, Liguori G, Sein D, Lozano-Galeana S, Fita L, Fernández J, Magariño ME, Jiménez-Guerrero P, Montávez JP, Domínguez M, Romera R, Gaertner MA (2016) Characterization of the wind speed variability and future change in the Iberian Peninsula and the Balearic Islands. Wind Energy 19(7):1223–1237.  https://doi.org/10.1002/we.1893.wE-14-0271.R2 CrossRefGoogle Scholar
  28. Grotch SL, MacCracken MC (1991) The use of general circulation models to predict regional climatic change. J Clim 4(3):286–303.  https://doi.org/10.1175/1520-0442(1991)0040<286:TUOGCM>2.0.CO;2
  29. Gutiérrez JM, San-Martín D, Brands S, Manzanas R, Herrera S (2012) Reassessing statistical downscaling techniques for their robust application under climate change conditions. J Clim 26(1):171–188.  https://doi.org/10.1175/JCLI-D-11-00687.1 CrossRefGoogle Scholar
  30. Hall A (2014) Projecting regional change. Science 346(6216):1461–1462.  https://doi.org/10.1126/science.aaa0629 CrossRefGoogle Scholar
  31. Hall A, Qu X (2006) Using the current seasonal cycle to constrain snow albedo feedback in future climate change. Geophys Res Lett 33(3):L03502.  https://doi.org/10.1029/2005GL025127 CrossRefGoogle Scholar
  32. Hawkins E, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteor Soc 90(8):1095–1107.  https://doi.org/10.1175/2009BAMS2607.1 CrossRefGoogle Scholar
  33. Herrera S, Fita L, Fernández J, Gutiárrez JM (2010) Evaluation of the mean and extreme precipitation regimes from the ensembles regional climate multimodel simulations over Spain. J Geophys Res Atmos.  https://doi.org/10.1029/2010JD013936 Google Scholar
  34. Hewitson BC, Daron J, Crane RG, Zermoglio MF, Jack C (2013) Interrogating empirical-statistical downscaling. Clim Chang 122:539–554.  https://doi.org/10.1007/s10584-013-1021-z CrossRefGoogle Scholar
  35. Hewitson B, Waagsaether K, Wohland J, Kloppers K, Kara T (2017) Climate information websites: an evolving landscape. Wiley Interdiscip Rev Clim Chang.  https://doi.org/10.1002/wcc.470 Google Scholar
  36. Ho CK, Stephenson DB, Collins M, Ferro CAT, Brown SJ (2012) Calibration strategies: a source of additional uncertainty in climate change projections. Bull Am Meteor Soc 93(1):21–26.  https://doi.org/10.1175/2011BAMS3110.1 CrossRefGoogle Scholar
  37. Huynen MMTE, Martens P (2015) Climate change effects on heat- and cold-related mortality in The Netherlands: a scenario-based integrated environmental health impact assessment. Int J Environ Res Public Health 12(10):13295–13320.  https://doi.org/10.3390/ijerph121013295 CrossRefGoogle Scholar
  38. Jacob D, Petersen J, Eggert B, Alias A, Christensen OB, Bouwer LM, Braun A, Colette A, Déqué M, Georgievski G, Georgopoulou E, Gobiet A, Menut L, Nikulin G, Haensler A, Hempelmann N, Jones C, Keuler K, Kovats S, Kröner N, Kotlarski S, Kriegsmann A, Martin E, van Meijgaard E, Moseley C, Pfeifer S, Preuschmann S, Radermacher C, Radtke K, Rechid D, Rounsevell M, Samuelsson P, Somot S, Soussana JF, Teichmann C, Valentini R, Vautard R, Weber B, Yiou P (2014) EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg Environ Change 14(2):563–578.  https://doi.org/10.1007/s10113-013-0499-2 CrossRefGoogle Scholar
  39. Jerez S, Montávez JP, Jiménez-Guerrero P, Gómez-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.  https://doi.org/10.1007/s00382-012-1539-1 CrossRefGoogle Scholar
  40. Jiménez-Guerrero P, Montávez JP, Domínguez M, Romera R, Fita L, Fernández J, Cabos WD, Liguori G, Gaertner MA (2013) Mean fields and interannual variability in RCM simulations over Spain: the ESCENA project. Clim Res 57(3):201–220.  https://doi.org/10.3354/cr01165 CrossRefGoogle Scholar
  41. Kalkstein LS, Greene JS (1997) An evaluation of climate/mortality relationships in large U.S. cities and the possible impacts of a climate change. Environ Health Perspect 105(1):84–93.  https://doi.org/10.2307/3433067 CrossRefGoogle Scholar
  42. Kis A, Pongrácz R, Bartholy J (2017) Multi-model analysis of regional dry and wet conditions for the Carpathian region. Int J Climatol.  https://doi.org/10.1002/joc.5104 Google Scholar
  43. Kjellström E, Boberg F, Castro M, Christensen J, Nikulin G, Sánchez E (2010) Daily and monthly temperature and precipitation statistics as performance indicators for regional climate models. Clim Res 44(2–3):135–150.  https://doi.org/10.3354/cr00932 CrossRefGoogle Scholar
  44. Kjellström E, Nikulin G, Hansson U, Strandberg G, Ullerstig A (2011) 21st century changes in the European climate: uncertainties derived from an ensemble of regional climate model simulations. Tellus A 63(1):24–40.  https://doi.org/10.1111/j.1600-0870.2010.00475.x CrossRefGoogle Scholar
  45. Kjellström E, Nikulin G, Strandberg G, Christensen OB, Jacob D, Keuler K, Lenderink G, van Meijgaard E, Schär C, Somot S, Lund Sørland S, Teichmann C, Vautard R (2017) European climate change at global mean temperature increases of 1.5 and 2 \(^{\circ }\)C above pre-industrial conditions as simulated by the EURO-CORDEX regional climate models. Earth Syst Dyn Discuss 2017:1–32.  https://doi.org/10.5194/esd-2017-104 CrossRefGoogle Scholar
  46. Knist S, Goergen K, Buonomo E, Christensen OB, Colette A, Cardoso RM, Fealy R, Fernández J, García-Díez M, Jacob D, Kartsios S, Katragkou E, Keuler K, Mayer S, van Meijgaard E, Nikulin G, Soares PMM, Sobolowski S, Szepszo G, Teichmann C, Vautard R, Warrach-Sagi K, Wulfmeyer V, Simmer C (2017) Land-atmosphere coupling in euro-cordex evaluation experiments. J Geophys Res Atmos 122(1):79–103.  https://doi.org/10.1002/2016JD025476 CrossRefGoogle Scholar
  47. Knowlton K, Lynn B, Goldberg RA, Rosenzweig C, Hogrefe C, Rosenthal JK, Kinney PL (2007) Projecting heat-related mortality impacts under a changing climate in the New York city region. Am J Public Health 97(11):2028–2034.  https://doi.org/10.2105/AJPH.2006.102947 CrossRefGoogle Scholar
  48. Kotlarski S, Keuler K, Christensen OB, Colette A, Déqué M, Gobiet A, Goergen K, Jacob D, Lüthi D, van Meijgaard E, Nikulin G, Schär C, Teichmann C, Vautard R, Warrach-Sagi K, Wulfmeyer V (2014) Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble. Geosci Model Dev Discussions 7(1):217–293.  https://doi.org/10.5194/gmdd-7-217-2014 CrossRefGoogle Scholar
  49. Koutroulis AG, Grillakis MG, Tsanis IK, Jacob D (2015) Exploring the ability of current climate information to facilitate local climate services for the water sector. Earth Perspect.  https://doi.org/10.1186/s40322-015-0032-5 Google Scholar
  50. Laprise R (2008) Regional climate modelling. J Comput Phys 227(7):3641–3666.  https://doi.org/10.1016/j.jcp.2006.10.024 CrossRefGoogle Scholar
  51. Lautenschlager M, Adamidis P, Kuhn M (2015) Big data research at DKRZ—climate model data production workflow. In: Big data and high performance computing, IOS, pp 133–155Google Scholar
  52. Lemesios G, Giannakopoulos C, Papadaskalopoulou C, Karali A, Varotsos KV, Moustakas K, Malamis D, Zachariou-Dodou M, Petrakis M, Loizidou M (2016) Future heat-related climate change impacts on tourism industry in Cyprus. Reg Environ Chang.  https://doi.org/10.1007/s10113-016-0997-0 Google Scholar
  53. Lémond J, Dandin P, Planton S, Vautard R, Pagé C, Déqué M, Franchistéguy L, Geindre S, Kerdoncuff M, Li L, Moisselin JM, Noël T, Tourre YM (2011) DRIAS: a step toward climate services in France. Adv Sci Res 6(1):179–186.  https://doi.org/10.5194/asr-6-179-2011 CrossRefGoogle Scholar
  54. Liguori G, Di Lorenzo E, Cabos W (2017) A multi-model ensemble view of winter heat flux dynamics and the dipole mode in the mediterranean sea. Clim Dyn 48(3):1089–1108.  https://doi.org/10.1007/s00382-016-3129-0 CrossRefGoogle Scholar
  55. Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ, Widmann M, Brienen S, Rust HW, Sauter T, Themeßl M, Venema VKC, Chun KP, Goodess CM, Jones RG, Onof C, Vrac M, Thiele-Eich I (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48(3):RG3003.  https://doi.org/10.1029/2009RG000314
  56. Maraun D, Widmann M, Gutiérrez JM, Kotlarski S, Chandler RE, Hertig E, Wibig J, Huth R, Wilcke RA (2015) VALUE: a framework to validate downscaling approaches for climate change studies. Earth Future 3:2014EF000259.  https://doi.org/10.1002/2014EF000259
  57. Mearns LO, Sain S, Leung LR, Bukovsky MS, McGinnis S, Biner S, Caya D, Arritt RW, Gutowski W, Takle E, Snyder M, Jones RG, Nunes AMB, Tucker S, Herzmann D, McDaniel L, Sloan L (2013) Climate change projections of the North American Regional Climate Change Assessment Program (NARCCAP). Clim Chang 120(4):965–975.  https://doi.org/10.1007/s10584-013-0831-3 CrossRefGoogle Scholar
  58. Meehl GA, Covey C, Taylor KE, Delworth T, Stouffer RJ, Latif M, McAvaney B, Mitchell JFB (2007) The WCRP CMIP3 multimodel dataset: a new era in climate change research. Bull Am Meteor Soc 88(9):1383–1394.  https://doi.org/10.1175/BAMS-88-9-1383 CrossRefGoogle Scholar
  59. Monerie PA, Sanchez-Gomez E, Boé J (2016) On the range of future Sahel precipitation projections and the selection of a sub-sample of CMIP5 models for impact studies. Clim Dyn.  https://doi.org/10.1007/s00382-016-3236-y Google Scholar
  60. Murphy JM, Sexton DMH, Jenkins GJ, Booth BBB, Brown CC, Clark RT, Collins M, Harris GR, Kendon EJ, Betts RA, Brown SJ, Humphrey KA, McCarthy MP, McDonald RE, Stephens A, Wallace C, Warren R, Wilby R, Wood RA (2009) UK climate projections science report: climate change projections. Meteorological Office Hadley Centre, ExeterGoogle Scholar
  61. 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.  https://doi.org/10.1016/j.cliser.2016.09.002 CrossRefGoogle Scholar
  62. Palmer TN (2000) Predicting uncertainty in forecasts of weather and climate. Rep Prog Phys 63(2):71.  https://doi.org/10.1088/0034-4885/63/2/201 CrossRefGoogle Scholar
  63. Pepin N, Bradley RS, Diaz HF, Baraer M, Caceres EB, Forsythe N, Fowler H, Greenwood G, Hashmi MZ, Liu XD, Miller JR, Ning L, Ohmura A, Palazzi E, Rangwala I, Schöner W, Severskiy I, Shahgedanova M, Wang MB, Williamson SN, Yang DQ (2015) Elevation-dependent warming in mountain regions of the world. Nat Clim Chang 5(5):424–430CrossRefGoogle Scholar
  64. Plieger M, Som de Cerff W, Pagé C, Tatarinova N, Cofiño A, Vega Saldarriaga M, Hutjes R, de Jong F, Bärring L, Sjökvist E (2015) The climate4impact portal: bridging the CMIP5 and CORDEX data infrastructure to impact users. In: EGU general assembly conference abstracts, EGU general assembly conference abstracts, vol 17, p 9721Google Scholar
  65. Quesada B, Vautard R, Yiou P, Hirschi M, Seneviratne SI (2012) Asymmetric European summer heat predictability from wet and dry southern winters and springs. Nat Clim Chang 2(10):736–741.  https://doi.org/10.1038/nclimate1536 CrossRefGoogle Scholar
  66. Räisänen J (2007) How reliable are climate models? Tellus A 59(1):2–29.  https://doi.org/10.1111/j.1600-0870.2006.00211.x CrossRefGoogle Scholar
  67. Räisänen J, Räty O (2013) Projections of daily mean temperature variability in the future: cross-validation tests with ENSEMBLES regional climate simulations. Clim Dyn 41(5–6):1553–1568.  https://doi.org/10.1007/s00382-012-1515-9 CrossRefGoogle Scholar
  68. Rajczak J, Pall P, Schör C (2013) Projections of extreme precipitation events in regional climate simulations for Europe and the Alpine Region. J Geophys Res Atmos 118(9):3610–3626.  https://doi.org/10.1002/jgrd.50297 CrossRefGoogle Scholar
  69. Rios-Entenza A, Soares PMM, Trigo RM, Cardoso RM, Miguez-Macho G (2014) Moisture recycling in the Iberian Peninsula from a regional climate simulation: Spatiotemporal analysis and impact on the precipitation regime. J Geophys Res Atmos 119(10):2013JD021274.  https://doi.org/10.1002/2013JD021274
  70. Rössler O, Fischer AM, Huebener H, Maraun D, Benestad RE, Christodoulides P, Soares PMM, Cardoso RM, Pagé C, Kanamaru H, Kreienkamp F, Vlachogiannis D (2017) Challenges to link climate change data provision and user needs–perspective from the cost-action value. Int J Climatol.  https://doi.org/10.1002/joc.5060 Google Scholar
  71. Rötter RP, Höhn J, Trnka M, Fronzek S, Carter TR, Kahiluoto H (2013) Modelling shifts in agroclimate and crop cultivar response under climate change. Ecol Evol 3(12):4197–4214.  https://doi.org/10.1002/ece3.782 CrossRefGoogle Scholar
  72. Ruelland D, Hublart P, Tramblay Y (2015) Assessing uncertainties in climate change impacts on runoff in Western Mediterranean basins. In: Vaze J, Chiew F, Hughes D, Andreassian V (eds) Hydrologic non-stationarity and extrapolating models to predict the future, vol 371, Copernicus Gesellschaft Mbh, Gottingen, pp 75–81, wOS:000365021100014Google Scholar
  73. Rummukainen M (2010) State-of-the-art with regional climate models. WIREs Clim Chang 1(1):82–96.  https://doi.org/10.1002/wcc.8 CrossRefGoogle Scholar
  74. Rummukainen M (2016) Added value in regional climate modeling. WIREs Clim Chang 7(1):145–159.  https://doi.org/10.1002/wcc.378 CrossRefGoogle Scholar
  75. Ruti PM, Somot S, Giorgi F, Dubois C, Flaounas E, Obermann A, Dell’ Aquila A, Pisacane G, Harzallah A, Lombardi E, Ahrens B, Akhtar N, Alias A, Arsouze T, Aznar R, Bastin S, Bartholy J, Béranger K, Beuvier J, Bouffies-Cloché S, Brauch J, Cabos W, Calmanti S, Calvet JC, Carillo A, Conte D, Coppola E, Djurdjevic V, Drobinski P, Elizalde-Arellano A, Gaertner M, Galàn P, Gallardo C, Gualdi S, Goncalves M, Jorba O, Jordà G, L’Heveder B, Lebeaupin-Brossier C, Li L, Liguori G, Lionello P, Maciàs D, Nabat P, Önol B, Raikovic B, Ramage K, Sevault F, Sannino G, Struglia MV, Sanna A, Torma C, Vervatis V (2016) Med-CORDEX initiative for Mediterranean climate studies. Bull Am Meteor Soc 97(7):1187–1208.  https://doi.org/10.1175/BAMS-D-14-00176.1 CrossRefGoogle Scholar
  76. San-Martín D, Manzanas R, Brands S, Herrera S, Gutiérrez JM (2017) Reassessing model uncertainty for regional projections of precipitation with an ensemble of statistical downscaling methods. J Clim 30(1):203–223.  https://doi.org/10.1175/JCLI-D-16-0366.1 CrossRefGoogle Scholar
  77. Seneviratne SI, Corti T, Davin EL, Hirschi M, Jaeger EB, Lehner I, Orlowsky B, Teuling AJ (2010) Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci Rev 99(3–4):125–161.  https://doi.org/10.1016/j.earscirev.2010.02.004 CrossRefGoogle Scholar
  78. Sima M, Popovici EA, Bălteanu D, Micu DM, Kucsicsa G, Dragotă C, Grigorescu I (2015) A farmer-based analysis of climate change adaptation options of agriculture in the Bărăgan Plain. Earth Perspect, Romania.  https://doi.org/10.1186/s40322-015-0031-6 Google Scholar
  79. Soares PMM, Cardoso RM, Ferreira JJ, Miranda PMA (2014) Climate change and the Portuguese precipitation: ENSEMBLES regional climate models results. Clim Dyn.  https://doi.org/10.1007/s00382-014-2432-x Google Scholar
  80. Stéfanon M, Martin-StPaul NK, Leadley P, Bastin S, Dell’Aquila A, Drobinski P, Gallardo C (2015) Testing climate models using an impact model: what are the advantages? Clim Chang 131(4):649–661.  https://doi.org/10.1007/s10584-015-1412-4 CrossRefGoogle Scholar
  81. Stegehuis AI, Vautard R, Ciais P, Teuling AJ, Jung M, Yiou P (2012) Summer temperatures in Europe and land heat fluxes in observation-based data and regional climate model simulations. Clim Dyn 41(2):455–477.  https://doi.org/10.1007/s00382-012-1559-x CrossRefGoogle Scholar
  82. Stegehuis AI, Teuling AJ, Ciais P, Vautard R, Jung M (2013) Future European temperature change uncertainties reduced by using land heat flux observations. Geophys Res Lett 40(10):2242–2245.  https://doi.org/10.1002/grl.50404 CrossRefGoogle Scholar
  83. Stocker T, Qin D, Plattner G, Tignor M, Allen S, Boschung J, Nauels A, Xia Y, Bex V, Midgley P (eds) (2013) Climate change 2013—the physical science basis: Working Group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  84. Tank A, Beersma J, Bessembinder J, van den Hurk B, Lenderink G (2015) KNMI, 2015: KNMI’14 Climate scenarios the Netherlands. A guide for professionales in climate adaptation. Tech. rep, KNMI, De Bilt, The NetherlandsGoogle Scholar
  85. Taylor KE, Stouffer RJ, Meehl GA (2011) An overview of CMIP5 and the experiment design. Bull Am Meteor Soc 93(4):485–498.  https://doi.org/10.1175/BAMS-D-11-00094.1 CrossRefGoogle Scholar
  86. Terry K (2014) Analysis and prototyping of a climate data manager. Master’s thesis, Science School. Universidad de Cantabria. https://repositorio.unican.es/xmlui/handle/10902/4485. Accessed 23 Mar 2018
  87. Tolika CK, Zanis P, Anagnostopoulou C (2012) Regional climate change scenarios for Greece: future temperature and precipitation projections from ensembles of RCMs. Global NEST J 14(4):407–421Google Scholar
  88. Tramblay Y, Ruelland D, Somot S, Bouaicha R, Servat E (2013) High-resolution Med-CORDEX regional climate model simulations for hydrological impact studies: a first evaluation of the ALADIN-Climate model in Morocco. Hydrol Earth Syst Sci 17(10):3721–3739.  https://doi.org/10.5194/hess-17-3721-2013 CrossRefGoogle Scholar
  89. Turco M, Sanna A, Herrera S, Llasat MC, Gutiérrez JM (2013) Large biases and inconsistent climate change signals in ensembles regional projections. Clim Chang 120(4):859–869.  https://doi.org/10.1007/s10584-013-0844-y CrossRefGoogle Scholar
  90. Turco M, Llasat MC, Herrera S, Gutiérrez JM (2017) Bias correction and downscaling of future RCM precipitation projections using a MOS-Analog technique. J Geophys Res Atmos 122(5):2631–2648.  https://doi.org/10.1002/2016JD025724 CrossRefGoogle Scholar
  91. van der Linden P, Mitchell J (2009) ENSEMBLES: climate change and its impacts: summary of research and results from the ENSEMBLES project. Tech. rep., Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UKGoogle Scholar
  92. van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque JF, Masui T, Meinshausen M, Nakicenovic N, Smith SJ, Rose SK (2011) The representative concentration pathways: an overview. Clim Chang 109(1):5.  https://doi.org/10.1007/s10584-011-0148-z CrossRefGoogle Scholar
  93. Vautard R, Gobiet A, Jacob D, Belda M, Colette A, Déqué M, Fernández J, García-Díez M, Goergen K, Güttler I, Halenka T, Karacostas T, Katragkou E, Keuler K, Kotlarski S, Mayer S, van Meijgaard E, Nikulin G, Patarčić M, Scinocca J, Sobolowski S, Suklitsch M, Teichmann C, Warrach-Sagi K, Wulfmeyer V, Yiou P (2013) The simulation of European heat waves from an ensemble of regional climate models within the EURO-CORDEX project. Clim Dyn 41(9):2555–2575.  https://doi.org/10.1007/s00382-013-1714-z CrossRefGoogle Scholar
  94. von Storch H, Zorita E, Cubasch U (1993) Downscaling of global climate change estimates to regional scales: an application to Iberian rainfall in wintertime. J Clim 6(6):1161–1171.  https://doi.org/10.1175/1520-0442(1993)006<3c1161:DOGCCE>3e2.0.CO;2 CrossRefGoogle Scholar
  95. Wilby RL, Wigley TML (1997) Downscaling general circulation model output: a review of methods and limitations. Progress Phys Geogr 21(4):530–548.  https://doi.org/10.1177/030913339702100403 CrossRefGoogle Scholar
  96. Williams DN, Lautenschlager M, Denvil S, Cinquini L, Ferraro R, Duffy D, Evans B, Trenham C (2017) 6th annual Earth System Grid Federation face-to-face conference report. DOE/SC-0188, U.S. Department of Energy Office of. Science.  https://doi.org/10.2172/1369382. https://esgf.llnl.gov/media/pdf/2017-ESGF_F2F_Conference_Report.pdf. Accessed 23 Mar 2018
  97. Williams DN, Balaji V, Cinquini L, Denvil S, Duffy D, Evans B, Ferraro R, Hansen R, Lautenschlager M, Trenham C (2015) A global repository for planet-sized experiments and observations. Bull Am Meteor Soc 97(5):803–816.  https://doi.org/10.1175/BAMS-D-15-00132.1 CrossRefGoogle Scholar
  98. Xue Y, Janjic Z, Dudhia J, Vasic R, Sales FD (2014) A review on regional dynamical downscaling in intraseasonal to seasonal simulation/prediction and major factors that affect downscaling ability. Atmos Res 147–148:68–85.  https://doi.org/10.1016/j.atmosres.2014.05.001 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • J. Fernández
    • 1
  • M. D. Frías
    • 1
  • W. D. Cabos
    • 2
  • A. S. Cofiño
    • 1
  • M. Domínguez
    • 4
  • L. Fita
    • 5
  • M. A. Gaertner
    • 4
  • M. García-Díez
    • 3
  • J. M. Gutiérrez
    • 6
  • P. Jiménez-Guerrero
    • 7
  • G. Liguori
    • 2
  • J. P. Montávez
    • 7
  • R. Romera
    • 4
  • E. Sánchez
    • 4
  1. 1.Grupo de Meteorología y Computación, Dpto. Matemática Aplicada y Ciencias de la ComputaciónUniversidad de CantabriaSantanderSpain
  2. 2.Dpto. Física y MatemáticaUniversidad de AlcaláMadridSpain
  3. 3.Predictia Intelligent Data SolutionsSantanderSpain
  4. 4.Universidad de Castilla-La ManchaToledoSpain
  5. 5.Centro de Investigaciones del Mar y la Atmósfera (CIMA), CONICET-UBA, CNRS UMI-IFAECIBuenos AiresArgentina
  6. 6.Grupo de MeteorologíaInstituto de Física de Cantabria (IFCA), CSIC-Universidad de CantabriaSantanderSpain
  7. 7.Dpto. Física, Campus de Excelencia Internacional Mare NostrumUniversidad de MurciaMurciaSpain

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