Climate Dynamics

, Volume 41, Issue 9–10, pp 2555–2575 | Cite as

The simulation of European heat waves from an ensemble of regional climate models within the EURO-CORDEX project

  • Robert Vautard
  • Andreas Gobiet
  • Daniela Jacob
  • Michal Belda
  • Augustin Colette
  • Michel Déqué
  • Jesús Fernández
  • Markel García-Díez
  • Klaus Goergen
  • Ivan Güttler
  • Tomáš Halenka
  • Theodore Karacostas
  • Eleni Katragkou
  • Klaus Keuler
  • Sven Kotlarski
  • Stephanie Mayer
  • Erik van Meijgaard
  • Grigory Nikulin
  • Mirta Patarčić
  • John Scinocca
  • Stefan Sobolowski
  • Martin Suklitsch
  • Claas Teichmann
  • Kirsten Warrach-Sagi
  • Volker Wulfmeyer
  • Pascal Yiou
Article

Abstract

The ability of a large ensemble of regional climate models to accurately simulate heat waves at the regional scale of Europe was evaluated. Within the EURO-CORDEX project, several state-of-the art models, including non-hydrostatic meso-scale models, were run for an extended time period (20 years) at high resolution (12 km), over a large domain allowing for the first time the simultaneous representation of atmospheric phenomena over a large range of spatial scales. Eight models were run in this configuration, and thirteen models were run at a classical resolution of 50 km. The models were driven with the same boundary conditions, the ERA-Interim re-analysis, and except for one simulation, no observations were assimilated in the inner domain. Results, which are compared with daily temperature and precipitation observations (ECA&D and E-OBS data sets) show that, even forced by the same re-analysis, the ensemble exhibits a large spread. A preliminary analysis of the sources of spread, using in particular simulations of the same model with different parameterizations, shows that the simulation of hot temperature is primarily sensitive to the convection and the microphysics schemes, which affect incoming energy and the Bowen ratio. Further, most models exhibit an overestimation of summertime temperature extremes in Mediterranean regions and an underestimation over Scandinavia. Even after bias removal, the simulated heat wave events were found to be too persistent, but a higher resolution reduced this deficiency. The amplitude of events as well as the variability beyond the 90th percentile threshold were found to be too strong in almost all simulations and increasing resolution did not generally improve this deficiency. Resolution increase was also shown to induce large-scale 90th percentile warming or cooling for some models, with beneficial or detrimental effects on the overall biases. Even though full causality cannot be established on the basis of this evaluation work, the drivers of such regional differences were shown to be linked to changes in precipitation due to resolution changes, affecting the energy partitioning. Finally, the inter-annual sequence of hot summers over central/southern Europe was found to be fairly well simulated in most experiments despite an overestimation of the number of hot days and of the variability. The accurate simulation of inter-annual variability for a few models is independent of the model bias. This indicates that internal variability of high summer temperatures should not play a major role in controlling inter-annual variability. Despite some improvements, especially along coastlines, the analyses conducted here did not allow us to generally conclude that a higher resolution is clearly beneficial for a correct representation of heat waves by regional climate models. Even though local-scale feedbacks should be better represented at high resolution, combinations of parameterizations have to be improved or adapted accordingly.

Keywords

Regional climate modeling Heat waves Model evaluation Climate projection EURO-CORDEX 

References

  1. Abdella K, McFarlane N (1997) Parameterization of the surface-layer exchange coefficients for atmospheric models. J Atmos Sci 54:1850–1867CrossRefGoogle Scholar
  2. Anderson GB, Bell ML (2009) Heat waves in the United States: mortality risk during heat waves and effect modification by heat wave characteristics in 43 U.S. communities. Environ Health Perspect 119(2):210–218CrossRefGoogle Scholar
  3. Baldauf M, Schulz JP (2004) Prognostic precipitation in the Lokal-Modell (LM) of DWD. COSMO Newslett 4:177–180Google Scholar
  4. Balsamo G, Viterbo P, Beljaars A, van den Hurk BJJM, Hirschi M, Betts A, Scipal K (2009) A revised hydrology for the ECMWF model: verification from field site to terrestrial water storage and impact in the Integrated Forecast System. J Hydrometeorol 10:623–643. doi:10.1175/2008JHM1068.1 CrossRefGoogle Scholar
  5. Barriopedro D, Fischer EM, Luterbacher J, Trigo RM, Garcia-Herrera R (2011) The hot summer of 2010: redrawing the temperature record map of Europe. Science 332:220–224CrossRefGoogle Scholar
  6. Boberg F, Christensen JH (2012) Overestimations of Mediterranean summer temperature projections due to model deficiencies. Nature Clim Chang 2:433–436. doi:10.1038/nclimate1454 CrossRefGoogle Scholar
  7. Bougeault P (1985) A simple parameterization of the large-scale effects of cumulus convection. Mon Weather Rev 113:2108–2121CrossRefGoogle Scholar
  8. Cassou C, Terray L, Phillips AS (2005) Tropical Atlantic influence on European heat waves. J Clim 18:2805–2811CrossRefGoogle Scholar
  9. Champeaux JI, Masson V, Chauvin F (2003) ECOCLIMAP: a global database of land surface parameters at 1 km resolution. Meteorol Appl 12:29–32CrossRefGoogle Scholar
  10. 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 81:7–30CrossRefGoogle Scholar
  11. Christensen JH, Boberg F, Christensen OB, Lucas-Picher P (2008) On the need for bias correction of regional climate change projections of temperature and precipitation. Geophys Res Lett 35:L20709CrossRefGoogle Scholar
  12. Coles S (2001) An introduction to statistical modeling of extreme values. Springer Series in Statistics, Springer, LondonGoogle Scholar
  13. Collins WD, et al (2004) Description of the NCAR community atmosphere model (CAM 3.0). NCAR technical note, NCAR/TN-464+STRGoogle Scholar
  14. Cuxart J, Bougeault P, Redelsperger J-L (2000) A turbulence scheme allowing for mesoscale and large-eddy simulations. Q J R Meteorol Soc 126:1–30CrossRefGoogle Scholar
  15. De Noblet-Ducoudré N, Boissier J-P, Pitman A, Bonan GB, Brovkin V, Cruz F, Delire C, Gayler V, van den Hurk B, Lawrence PJ, van der Mollen MK, Müller C, Reick CH, Strengers BJ, Voldoire A (2012) Determining Robust impacts of land-use-induced land cover changes on surface climate over North America and Eurasia: results from the first set of LUCID experiments. J Clim 25:3261–3281CrossRefGoogle Scholar
  16. Dee DP et al (2011) The ERA-Interim re-analysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597CrossRefGoogle Scholar
  17. Degirmendzic J, Wibig J (2007) Jet stream patterns over Europe in the period 1950–2001—classification and basic statistical properties. Theor Appl Clim 88:149–167CrossRefGoogle Scholar
  18. Déqué M (2010) Regional climate simulation with a mosaic of RCMs. Meteorol Z 19:259–266. doi:10.1127/0941-2948/2010/0455 CrossRefGoogle Scholar
  19. Dickinson RE, Henderson-Sellers A, Kennedy P (1993) Biosphere–atmosphere transfer scheme (BATS) version 1e as coupled to the NCAR community climate model. Technical report, National Center for Atmospheric Research Technical Note NCAR. TN-387+STR, NCAR, Boulder, COGoogle Scholar
  20. Doms G, Förstner J, Heise E, Herzog H-J, Raschendorfer M, Schrodin R, Reinhardt T, Vogel G (2007) A description of the non-hydrostatic regional model LM. Part II: physical parameterization. Available online at http://www.cosmomodel.org/content/model/documentation/core/cosmoPhysParamtr.pdf
  21. Douville H, Planton S, Royer JF, Stephenson DB, Tyteca S, Kergoat L, Lafont S, Betts RA (2000) The importance of vegetation feedbacks in doubled-CO2 time-slice experiments. J Geophys Res 105:14841–14861CrossRefGoogle Scholar
  22. Ek MB, Mitchell KE, Lin Y, Rogers E, Grunmann P, Koren V, Gayno G, Tarpley JD (2003) Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J Geophys Res 108(D22):8851CrossRefGoogle Scholar
  23. Emanuel KA (1991) A scheme for representing cumulus convection in large-scale models. J Atmos Sci 48:2313–2335CrossRefGoogle Scholar
  24. Embrechts P, Klüppelberg C, Mikosch T (1997) Modelling extremal events for insurance and finance, vol 33. Springer, BerlinCrossRefGoogle Scholar
  25. Fischer EM, Schär C (2010) Consistent geographical patterns of changes in high-impact European heatwaves. Nat Geosci 3:398–403CrossRefGoogle Scholar
  26. Fischer EM, Seneviratne SI, Lüthi D, Schär C (2007) Contribution of land-atmosphere coupling to recent European summer heat waves. Geophys Res Lett 34:L06707. doi:10.1029/2006GL029068 CrossRefGoogle Scholar
  27. Fischer EM, Rajczak J, Schär C (2012) Changes in European summer temperature variability revisited. Geophys Res Lett 39:L19702. doi:10.1029/2012GL052730 Google Scholar
  28. Founda D, Giannakopoulos C (2009) The exceptionally hot summer of 2007 in Athens, Greece—a typical summer in the future climate? Global Planet Change 67(3–4):227–236CrossRefGoogle Scholar
  29. Fouquart Y, Bonnel B (1980) Computations of solar heating of the earth’s atmosphere: a new parameterization. Beitr Phys Atmos 53:35–62Google Scholar
  30. García-Díez M, Fernandez J, Casanueva A, Magariño M (2012) Exploring WRF configuration sensitivity over the Euro-Cordex domain. In: Proceedings of CORDEX-WRF Workshop, Tenerife, September 2012Google Scholar
  31. Giorgetta M, Wild M (1995) The water vapor continuum and its representation in ECHAM4. MPI for Meterolo., report no. 162, HamburgGoogle Scholar
  32. Giorgi F, Bates GT (1989) The climatological skill of a regional model over complex terrain. Mon Weather Rev 117:2325–2347CrossRefGoogle Scholar
  33. Giorgi F, Jones C, Asrar GR (2009) Addressing climate information needs at the regional level: the CORDEX framework. WMO Bull 58(3):175–183Google Scholar
  34. Giorgi F, Coppola E, Solmon F, Mariotti L, Sylla MB, Bi X, Elguindi N, Diro GT, Nair V, Giuliani G, Cozzini S, Güttler I, O’Brien TA, Tawfik AB, Shalaby A, Zakey AS, Steiner AL, Stordal F, Sloan LC, Brankovic C (2012) RegCM4: model description and preliminary tests over multiple CORDEX domains. Clim Res 52:7–29CrossRefGoogle Scholar
  35. Gobiet A, Jacob D (2012) A new generation of regional climate simulations for Europe: the EURO-CORDEX initiative. Geophy Res. Abstracts, vol 14, EGU2012-8211, 2012Google Scholar
  36. Grell GA (1993) Prognostic evaluation of assumptions used by cumulus parameterizations. Mon Weather Rev 121:764–787CrossRefGoogle Scholar
  37. Grell GA, Devenyi D (2002) A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys Res Lett 29. doi:10.1029/2002GL015311
  38. Haarsma RJ, Selten F, van den Hurk B, Hazeleger W, Wang X (2009) Drier Mediterranean soils due to greenhouse warming bring easterly winds over summertime central Europe. Geophys Res Lett 36:L04705CrossRefGoogle Scholar
  39. Hagemann S (2002) An improved land surface parameter dataset for global and regional climate models. MPI Rep 336:21Google Scholar
  40. Haylock MR, Hofstra N, Klein Tank AMG, Klok EJ, Jones PD, New M (2008) A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J Geophys Res Atmos 113(D20). doi:10.1029/2008JD010201
  41. Hirschi M et al (2011) Observational evidence for soil-moisture impact on hot extremes in southeastern Europe. Nat Geosci 4:17–21CrossRefGoogle Scholar
  42. Holtslag A, de Bruijn E, Pan HL (1990) A high resolution air mass transformation model for short-range weather forecasting. Mon Weather Rev 118:1561–1575CrossRefGoogle Scholar
  43. Hong S-Y, Lim J-OJ (2006) The WRF single-moment 6-class microphysics scheme (WSM6). J Korean Meteorol Soc 42:129–151Google Scholar
  44. Hong S-Y, Dudhia J, Chen S-H (2004) A revised approach to microphysical processes for the bulk parameterization of cloud and precipitation. Mon Weather Rev 132:103–120CrossRefGoogle Scholar
  45. Hong S-Y, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev 134:2318–2341CrossRefGoogle Scholar
  46. Jacob D, Bärring L, Christensen OB, Christensen JH, De Castro M, Déqué M, Giorgi F, Hagemann S, Hirschi M, Jones R, Kjellström E, Lenderink G, Rockel B, Sánchez E, Schär C, Seneviratne SI, Somot S, Van Ulden A, Van Den Hurk B (2007) An inter-comparison of regional climate models for Europe: model performance in present-day climate. Clim Chang 81:31–52CrossRefGoogle Scholar
  47. Jacob D, Elizalde A, Haensler A, Hagemann S, Kumar P, Podzun R, Rechid D, Remedio AR, Saeed F, Sieck K, Teichmann C, Wilhelm C (2012) Assessing the transferability of the regional climate model REMO to different coordinated regional climate downscaling experiment (CORDEX) regions. Atmosphere 3(1):181–199. doi:10.3390/atmos3010181 CrossRefGoogle Scholar
  48. Jaeger EB, Seneviratne SI (2010) Impact of soil moisture–atmosphere coupling on European climate extremes and trends in a regional climate model. Clim Dyn 36:1919–1939CrossRefGoogle Scholar
  49. Joint Research Centre (2003) Global land cover 2000 database. European Commission, Joint Research Centre. http://bioval.jrc.ec.europa.eu/products/glc2000/glc2000.php
  50. Kain JS (2004) The Kain-Fritsch convective parameterization: an update. J Appl Meteorol 43:170–181CrossRefGoogle Scholar
  51. Kain JS, Fritsch JM (1990) A one-dimensional entraining/detraining plume model and its application in convective parameterization. J Atmos Sci 47:2784–2802CrossRefGoogle Scholar
  52. Kain JS, Fritsch JM (1993) Convective parameterization for mesoscale models: the Kain-Fritsch scheme. The representation of cumulus convection in numerical models. Meteorol Monogr 24:165–170Google Scholar
  53. Kiehl J, Hack J, Bonan G, Boville B, Breigleb B, Williamson D, Rasch P (1996) Description of the NCAR community climate model (CCM3). National Center for Atmospheric Research Tech Note NCAR/TN-420+STR, NCAR, Boulder, COGoogle Scholar
  54. Klein Tank AM et al (2002) Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int J Climatol 22:1441–1453CrossRefGoogle Scholar
  55. Lacono MJ, Delamere JS, Mlawer EJ, Shephard MW, Clough SA, Collins WD (2008) Radiative forcing by long-lived greenhouse gases: calculations with the AER radiative transfer models. J Geophys Res 113:D13103. doi:10.1029/2008JD009944 CrossRefGoogle Scholar
  56. Lenderink G, Holtslag AAM (2004) An updated length-scale formulation for turbulent mixing in clear and cloudy boundary layers. Q J R Meteorol Soc 130:3405–3427. doi:10.1256/qj.03.117 CrossRefGoogle Scholar
  57. Lenderink G, van Ulden A, van den Hurk B, van Meijgaard E (2007) Summertime inter-annual temperature variability in an ensemble of regional model simulations: analysis of the surface energy budget. Clim Chang 81:233–247CrossRefGoogle Scholar
  58. Li J, Barker HW (2005) A radiation algorigthm with correlated-k distribution. Part i: local thermal equilibrium. J Atmos Sci 62:286–309CrossRefGoogle Scholar
  59. Lohmann U, Roeckner E (1996) Design and performance of a new cloud microphysics scheme developed for the ECHAM general circulation model. Clim Dyn 12:557–572CrossRefGoogle Scholar
  60. Louis J-F (1979) A parametric model of vertical eddy fluxes in the atmosphere. Boundary Layer Meteorol 17:187–202CrossRefGoogle Scholar
  61. Lucas-Picher P, Caya D, de Elia R, Laprise R (2008) Investigation of regional climate models’ internal variability with a ten-member ensemble of 10-year simulations over a large domain. Cllim Dyn 31:927–940CrossRefGoogle Scholar
  62. Masson V, Champeaux JL, Chauvin F, M’eriguet C, Lacaze R (2003) A global database of land surface parameters at 1 km resolution for use in meteorological and climate models. J Clim 16:1261–1282CrossRefGoogle Scholar
  63. Mearns LO, Arritt R, Biner S, Bukovsky MS, McGinnis S, Sain S, Caya D, Correia Jr, J, Flory D, Gutowski W, Takle ES, Jones R, Leung R, Moufouma-Okia W, McDaniel L, Nunes AMB, Qian Y, Roads J, Sloan L, Snyder M (2012) The North American regional climate change assessment program: overview of phase I results. doi:10.1175/BAMS-D-11-00223.1
  64. Meehl GA, Tebaldi C (2004) More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305:994–997CrossRefGoogle Scholar
  65. Menut L, Tripathi OP, Colette A, Vautard R, Flaounas E, Bessagnet B (2012) Evaluation of regional climate simulations for air quality modeling purposes. Clim Dyn. doi:10.1007/s00382-012-1345-9
  66. Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102D:16663–16682CrossRefGoogle Scholar
  67. Morcrette J-J, Smith L, Fouquart Y (1986) Pressure and temperature dependence of the absorption in longwave radiation parameterizations. Atmos Phys 59(4):455–469Google Scholar
  68. Morcrette JJ (1990) Impact of changes to the radiation transfer parameterizations plus cloud optical properties in the ECMWF model. Mon Weather Rev 118:847–873CrossRefGoogle Scholar
  69. Morrison H, Thompson G, Tatarskii V (2009) Impact of cloud micrpohysics on the development of trailing stratiform precipitation in a simulated squall line: comparison of one- and two-moment schemes. Mon Weather Rev 137:991–1007CrossRefGoogle Scholar
  70. Neggers RAJ (2009) A dual mass flux framework for boundary layer convection. Part II: clouds. J Atmos Sci 66:1489–1506. doi:10.1175/2008JAS2636.1 CrossRefGoogle Scholar
  71. Neggers RAJ, Koehler M, Beljaars ACM (2009) A dual mass flux framework for boundary layer convection. Part I: transport. J Atmos Sci 66:1465–1487. doi:10.1175/2008JAS2635.1 CrossRefGoogle Scholar
  72. Nikulin G, Kjellström E, Hansson U, Strandberg G, Ullerstig A (2011) Evaluation and future projections of temperature, precipitation and wind extremes over Europe in an ensemble of regional climate simulations. Tellus 63A:41–55Google Scholar
  73. Nikulin G, Jones C, Samuelsson P, Giorgi F, Sylla MB, Asrar G, Büchner M, Cerezo-Mota R, Christensen OB, Déqué M, Fernández J, Hänsler A, van Meijgaard E, Sushama L (2012) Precipitation climatology in an ensemble of CORDEX-Africa regional climate simulations. J Clim 25:6057–6078. doi:10.1175/JCLI-D-11-00375.1 CrossRefGoogle Scholar
  74. Nordeng TE (1994) Extended versions of the convection parametrization scheme at ECMWF and their impact upon the mean climate and transient activity of the model in the tropics. Research Department Technical Memorandum No. 206, ECMWF, Shinfield Park, Reading, Berks, UKGoogle Scholar
  75. Pal JS, Small E, Eltahir E (2000) Simulation of regional-scale water and energy budgets: representation of subgrid cloud and precipitation processes within RegCM. J Geophys Res 105:29579–29594CrossRefGoogle Scholar
  76. Parey S, Malek F, Laurent C, Dacunha-Castelle D (2007) Trends and climate evolution: statistical approach for very high temperatures in France. Clim Chang 81:331–352CrossRefGoogle Scholar
  77. Pfeifer S (2006) Modeling cold cloud processes with the regional climate model REMO. Report Max-Planck Institute for Meteorology, HamburgGoogle Scholar
  78. Quesada B, Vautard R, Yiou P, Hirschi M, Seneviratne S (2012) Asymmetric European summer heat predictability from wet and dry southern winters and springs. Nat Clim Chang 2:736–741. doi:10.1038/NCLIMATE1536 CrossRefGoogle Scholar
  79. Rasch PJ, Kristjánsson JE (1998) A comparison of the CCM3 model climate using diagnosed and predicted condensate parameterizations. J Clim 11:1587–1614CrossRefGoogle Scholar
  80. Rechid D, Hagemann S, Jacob D (2009) Sensitivity of climate models to seasonal variability of snow-free land surface albedo. Theor Appl Climatol 95:197–221CrossRefGoogle Scholar
  81. Ricard JL, Royer JF (1993) A statistical cloud scheme for use in an AGCM. Ann Geophys 11:1095–1115Google Scholar
  82. Ritter B, Geleyn J-F (1992) A comprehensive radiation scheme of numerical weather prediction with potential application to climate simulations. Mon Weather Rev 120:303–325CrossRefGoogle Scholar
  83. Rockel B, Will A, Hense A (eds) (2008) Special issue regional climate modelling with COSMO-CLM (CCLM). Meteorol Z 17Google Scholar
  84. Rotach MW, Ambrosetti P, Ament F, Appenzeller C, Arpagaus M, Bauer H-S, Behrendt A, Bouttier F, Buzzi A, Corazza M, Davolio S, Denhard M, Dorninger M, Fontannaz L, Frick J, Fundel F, Germann U, Gorgas T, Hegg C, Hering A, Keil C, Liniger MA, Marsigli C, McTaggart-Cowan R, Montani A, Mylne K, Ranzi R, Richard E, Rossa A, Santos-Muñoz D, Schär C, Seity Y, Staudinger M, Stoll M, Volkert H, Walser A, Wang Y, Werhahn J, Wulfmeyer V, Zappa M (2009) MAP D-PHASE: real-time demonstration of weather forecast quality in the alpine region. Bull Am Meteorol Soc 90:1321–1336. doi:10.1175/2009BAMS2776.1. Available at http://ams.allenpress.com/archive/1520-0477/90/9/pdf/i1520-0477-90-9-1321.pdf
  85. Samuelsson P, Gollvik S, Ullerstig A (2006) The land-surface scheme of the Rossby Centre regional atmospheric climate model (RCA3). SMHI Rep Met 122:25Google Scholar
  86. Samuelsson P, Jones C, Willen U, Gollvik S, Hansson U et al (2011) The Rossby centre regional climate model RCA3: model description and performance. Tellus 63A:4–23Google Scholar
  87. Sánchez-Gómez E, Somot S, Mariotti A (2009) Future changes in the Mediterranean water budget projected by an ensemble of regional climate models. Geophys Res Lett. doi:10.1029/2009GL040120 Google Scholar
  88. Sass BH, Rontu L, Savijärvi H, Räisänen P (1994) HIRLAM-2 radiation scheme: documentation and tests. SMHI HIRLAM Tech Rep 16Google Scholar
  89. Savijärvi H (1990) A fast radiation scheme for mesoscale model and short-range forecast models. J Appl Meteorol 29:437–447CrossRefGoogle Scholar
  90. Schär C, Vidale PL, Luthi D, Frei C, Haberli C, Liniger MA, Appenzeller C (2004) The role of increasing temperature variability in European summer heatwaves. Nature 427:332–336CrossRefGoogle Scholar
  91. Seneviratne SI, et al (2012) Changes in climate extremes and their impacts on the natural physical environment. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner G-K, Allen SK, Tignor M, Midgley PM (eds) Managing the risks of extreme events and disasters to advance climate change adaptation. IPCC SREX reportGoogle Scholar
  92. 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 99:125–161CrossRefGoogle Scholar
  93. Siebesma AP, Soares PMM, Teixeira J (2007) A combined eddy-diffusivity mass-flux approach for the convective boundary layer. J Atmos Sci 64:1230–1248. doi:10.1175/JAS3888.1 CrossRefGoogle Scholar
  94. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Duda DMBMG, Huang X-Y, Wang W, Powers JG (2008) A description of the advanced research WRF version 3. NCAR TECHNICAL NOTE, 475, NCAR/TN475 + STRGoogle Scholar
  95. Stegehuis A, Vautard R, Ciais P, Teuling R, Jung M, Yiou P (2012) Summer temperatures in Europe and land heat fluxes in observation-based data and regional climate model simulations. Clim Dyn. doi:10.1007/s00382-012-1559-x Google Scholar
  96. Tiedtke M (1989) A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon Weather Rev 117:1779–1799CrossRefGoogle Scholar
  97. Tiedtke M (1993) Representation of clouds in large-scale models. Mon Weather Rev 121:3040–3061CrossRefGoogle Scholar
  98. Tompkins AM, Gierens K, Rädel G (2007) Ice supersaturation in the ECMWF integrated forecast system. QJR Meteorol Soc 133:53–63CrossRefGoogle Scholar
  99. Uppala S, Dee D, Kobayashi S, Berrisford P, Simmons A (2008) Towards a climate data assimilation system: status update of ERA-Interim. ECMWF Newslett 115:12–18. Available at: http://www.ecmwf.int/publications/newsletters/ Google Scholar
  100. Van den Hurk BJJM, Viterbo P, Beljaars ACM, Betts AK (2000) Offline validation of the ERA40 surface scheme. ECMWF Technical report no. 75, ECMWFGoogle Scholar
  101. 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, FitzRoy Road, Exeter EX1 3PB, UKGoogle Scholar
  102. van Meijgaard E, Van Ulft LH, Lenderink G, de Roode SR, Wipfler L, Boers R, Timmermans RMA (2012) Refinement and application of a regional atmospheric model for climate scenario calculations of Western Europe. Climate changes Spatial Planning publication: KvR 054/12, ISBN/EAN 978-90-8815-046-3, pp 44Google Scholar
  103. Verseghy DL (2000) The Canadian land surface scheme (class): its history and future. Atmos Ocean 38:1–13CrossRefGoogle Scholar
  104. Vidale PL, Lüthi D, Wegmann R, Schär C (2007) European summer climate variability in a heterogeneous multi-model ensemble. Clim Chang 81:209–232CrossRefGoogle Scholar
  105. von Salzen K, McFarlane NA (2002) Parameterization of the bulk effects of lateral and cloud-top entrainment in transient shallow cumulus clouds. J Atmos Sci 59:1405–1429CrossRefGoogle Scholar
  106. von Salzen K, Scinocca JF, McFarlane NA, Li J, Cole JNS, Plummer D, Reader MC, Ma X, Lazare M, Solheim L (2013) The Canadian fourth generation atmospheric global climate model (CanAM4). Part I: physical processes. Atmos Ocean 51. doi:10.1080/07055900.2012.755610
  107. Warrach-Sagi K, Görgen K, Vautard R (2012) Experiments with WRF in EURO-CORDEX. In: Proceedings of CORDEX-WRF Workshop, TenerifeGoogle Scholar
  108. Wulfmeyer V, Behrendt A, Kottmeier Ch, Corsmeier U, Barthlott C, Craig GC, Hagen M, Althausen D, Aoshima F, Arpagaus M, Bauer H-S, Bennett L, Blyth A, Brandau C, Champollion C, Crewell S, Dick G, Di Girolamo P, Dorninger M, Dufournet Y, Eigenmann R, Engelmann R, Flamant C, Foken T, Gorgas T, Grzeschik M, Handwerker J, Hauck C, Höller H, Junkermann W, Kalthoff N, Kiemle C, Klink S, König M, Krauss L, Long CN, Madonna F, Mobbs S, Neininger B, Pal S, Peters G, Pigeon G, Richard E, Rotach MW, Russchenberg H, Schwitalla T, Smith V, Steinacker R, Trentmann J, Turner DD, van Baelen J, Vogt S, Volkert H, Weckwerth T, Wernli H, Wieser A, Wirth M (2011) The convective and orographically induced precipitation study (COPS): the scientific strategy, the field phase, and first highlights. Q J R Meteorol Soc 137:3–30. doi:10.1002/qj.752 CrossRefGoogle Scholar
  109. Yiou P, Goubanova K, Li ZX, Nogaj M (2008) Weather regime dependence of extreme value statistics for summer temperature and precipitation. Nonlinear Process Geophys 15:365–378CrossRefGoogle Scholar
  110. Zadra A, Caya D, Côté J, Dugas B, Jones C, Laprise R, Winger K, Caron L-P (2008) The next Canadian regional climate model. Phys Can Spec Issue Fast Comput 64:75–83Google Scholar
  111. Zampieri M, D’Andrea F, Vautard R, Ciais P, de Noblet-Ducoudré N, Yiou P (2009) Hot European Summers and the Role of Soil Moisture in the Propagation of Mediterranean Drought. J Clim 22:4747–4758CrossRefGoogle Scholar
  112. Zhang GJ, McFarlane NA (1995) Sensitivity of climate simulations to the parameterization of cumulus convection in the CCC-GCM. Atmos Ocean 3:407–446CrossRefGoogle Scholar

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Authors and Affiliations

  • Robert Vautard
    • 1
  • Andreas Gobiet
    • 2
  • Daniela Jacob
    • 3
  • Michal Belda
    • 4
  • Augustin Colette
    • 5
  • Michel Déqué
    • 6
  • Jesús Fernández
    • 7
  • Markel García-Díez
    • 8
  • Klaus Goergen
    • 9
  • Ivan Güttler
    • 10
  • Tomáš Halenka
    • 4
  • Theodore Karacostas
    • 11
  • Eleni Katragkou
    • 11
  • Klaus Keuler
    • 12
  • Sven Kotlarski
    • 13
  • Stephanie Mayer
    • 14
  • Erik van Meijgaard
    • 18
  • Grigory Nikulin
    • 15
  • Mirta Patarčić
    • 10
  • John Scinocca
    • 16
  • Stefan Sobolowski
    • 14
  • Martin Suklitsch
    • 2
  • Claas Teichmann
    • 17
  • Kirsten Warrach-Sagi
    • 19
  • Volker Wulfmeyer
    • 19
  • Pascal Yiou
    • 1
  1. 1.Laboratoire des Sciences du Climat et de l’EnvironnementIPSL, CEA/CNRS/UVSQGif sur YvetteFrance
  2. 2.Wegener Center for Climate and Global Change and Institute for Geophysics, Astrophysics, and MeteorologyUniversity of GrazGrazAustria
  3. 3.Climate Service CenterHamburgGermany
  4. 4.Department of Meteorology and Environment ProtectionCharles UniversityPragueCzech Republic
  5. 5.Institut National de l’Environnement industriel et des risques (INERIS)Verneuil en HalatteFrance
  6. 6.Météo-France/CNRM, CNRS/GAMEToulouseFrance
  7. 7.Department Applied Mathematics and Computer ScienceUniversidad de CantabriaSantanderSpain
  8. 8.Instituto de Física de CantabriaCSIC—UCSantanderSpain
  9. 9.Centre de Recherche Public—Gabriel LippmannBelvauxLuxembourg
  10. 10.Croatian Meteorological and Hydrological Service (DHMZ)ZagrebCroatia
  11. 11.Department of Meteorology and Climatology, School of GeologyAristotle University of ThessalonikiThessalonikiGreece
  12. 12.Chair of Environmental MeteorologyBrandenburg University of Technology (BTU) CottbusCottbusGermany
  13. 13.Institute for Atmospheric and Climate ScienceETH ZurichZurichSwitzerland
  14. 14.Uni ResearchBjerknes Center for Climate ResearchBergenNorway
  15. 15.Swedish Meteorological and Hydrological InstituteNorrköpingSweden
  16. 16.Environment CanadaCanadian Centre for Climate Modelling and AnalysisVictoriaBritish Columbia
  17. 17.Max-Planck-Institut für MeteorologieHamburgGermany
  18. 18.Royal Netherlands Meteorological Institute (KNMI)De BiltThe Netherlands
  19. 19.Institute of Physics and MeteorologyUniversity of HohenheimStuttgartGermany

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