Towards a fair comparison of statistical and dynamical downscaling in the framework of the EURO-CORDEX initiative

Abstract

Both statistical and dynamical downscaling methods are well established techniques to bridge the gap between the coarse information produced by global circulation models and the regional-to-local scales required by the climate change Impacts, Adaptation, and Vulnerability (IAV) communities. A number of studies have analyzed the relative merits of each technique by inter-comparing their performance in reproducing the observed climate, as given by a number of climatic indices (e.g. mean values, percentiles, spells). However, in this paper we stress that fair comparisons should be based on indices that are not affected by the calibration towards the observed climate used for some of the methods. We focus on precipitation (over continental Spain) and consider the output of eight Regional Climate Models (RCMs) from the EURO-CORDEX initiative at 0.44 resolution and five Statistical Downscaling Methods (SDMs) —analog resampling, weather typing and generalized linear models— trained using the Spain044 observational gridded dataset on exactly the same RCM grid. The performance of these models is inter-compared in terms of several standard indices —mean precipitation, 90th percentile on wet days, maximum precipitation amount and maximum number of consecutive dry days— taking into account the parameters involved in the SDM training phase. It is shown, that not only the directly affected indices should be carefully analyzed, but also those indirectly influenced (e.g. percentile-based indices for precipitation) which are more difficult to identify. We also analyze how simple transformations (e.g. linear scaling) could be applied to the outputs of the uncalibrated methods in order to put SDMs and RCMs on equal footing, and thus perform a fairer comparison.

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References

  1. Ayar PV, Vrac M, Bastin S, Carreau J, Déqué M, Gallardo C (2015) 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. doi:10.1007/s00382-015-2647-5

    Article  Google Scholar 

  2. Bärring L, Holt T, Linderson M, Radziejewski M, Moriondo M, Palutikof JP (2006) Defining dry/wet spells for point observations, observed area averages, and regional climate model gridboxes in Europe. Clim Res 31(1):35–49. doi:10.3354/cr031035

    Article  Google Scholar 

  3. Benestad RE, Nychka D, Mearns LO (2011) Specification of wet-day daily rainfall quantiles from the mean value. Tellus A 64

  4. Benestad RE, Nychka D, Mearns LO (2012) Spatially and temporally consistent prediction of heavy precipitation from mean values. Nat Clim Chang 2(7):544–547. doi:10.1038/NCLIMATE1497

    Google Scholar 

  5. Brands S, Gutiérrez J, Herrera S, Cofiño A (2012) On the use of reanalysis data for downscaling. J Clim 25:2517–2526

    Article  Google Scholar 

  6. Casanueva A, Kotlarski S, Herrera S, Fernández J, Gutiérrez J, Boberg B, Colette A, Christensen OB, Goergen K, Jacob D, Keuler K, Nikulin G, Teichmann C, Vautard R (2015) Daily precipitation statistics in a EURO-CORDEX RCM ensemble: added value of raw and bias-corrected high-resolution simulations. Climat Dyn:1–19. doi:10.1007/s00382-015-2865-x

  7. Christensen OB, Drews M, Christensen J, Dethloff K, Ketelsen K, Hebestadt I, Rinke A (2007) The HIRHAM Regional Climate Model. Version 5 (beta). Denmark. DanishMeteorological Institute. Technical Report, Danish Climate Centre, Danish Meteorological Institute

  8. Collins M, Booth BBB, Harris GR, Murphy JM, Sexton DMH, Webb MJ (2006) Towards quantifying uncertainty in transient climate change. Clim Dyn 27 (2-3):127–147

    Article  Google Scholar 

  9. Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette J, Park B, Peubey C, De Rosnay P, Tavolato C, Thépaut JN, Vitart F (2011) The ERA-interim reanalysis: configuration and performance of the data assimilation system. Quart J R Meteorol Soc 137:553–597

    Article  Google Scholar 

  10. 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 Chang 57(12):16–26. doi:10.1016/j.gloplacha.2006.11.030

    Article  Google Scholar 

  11. Déqué M, Jones RG, Wild M, Giorgi F, Christensen JH, Hassell DC, Vidale PL, Rockel B, Jacob D, Kjellstróm E, Castro Md, Kucharski F, Hurk BVD (2005) Global high resolution versus limited area model climate change projections over Europe: quantifying confidence level from PRUDENCE results. Clim Dyn 25(6):653–670. doi:10.1007/s00382-005-0052-1

    Article  Google Scholar 

  12. 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 16(9):3391–3404. doi:10.5194/hess-16-3391-2012

    Article  Google Scholar 

  13. Estrada F, Guerrero VM, Gay-García C, Martínez-López B (2013) A cautionary note on automated statistical downscaling methods for climate change. Clim Chang 120(1-2):263–276. doi:10.1007/s10584-013-0791-7

    Article  Google Scholar 

  14. Feser F, Rockel B, von Storch H, Winterfeldt J, Zahn M (2011) Regional climate models add value to global model data: a review and selected examples. Bull Am Meteorol Soc 92(9):1181–1192

    Article  Google Scholar 

  15. Giorgi F (2006) Regional climate modeling: Status and perspectives. J Phys IV Proc 139(1):101–118. doi:10.1051/jp4:2006139008

    Google Scholar 

  16. Goodess C (2005) Statistical and regional dynamical downscaling of extremes for European regions. STARDEX Final Management Report Available at http://www.cru.uea.ac.uk/cru/research/stardex

  17. Gutiérrez J, San-Martín D, Brands S, Manzanas R, Herrera S (2013) Reassessing statistical downscaling techniques for their robust application under climate change conditions. J Clim 26:171–188

    Article  Google Scholar 

  18. Haylock M, Cawley G, Harpham C, Wilby R, Goodess C (2006) Downscaling heavy precipitation over the United Kingdom: a comparison of dynamical and statistical methods and their future scenarios. Int J Climatol 26(10):1397–1415

    Article  Google Scholar 

  19. 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

  20. Herrera S, Gutiérrez J, Ancell R, Pons M, Frías M, Fernández J (2012) Development and analysis of a 50 year high resolution daily gridded precipitation dataset over Spain (Spain02). International Journal of Climatology 10.1002/joc.2256

  21. Herrera S, Fernández J, Gutiérrez J (2015) Update of the Spain02 gridded observational dataset for EURO-CORDEX evaluation: Assessing the effect of the interpolation methodology. Int J Climatol. doi:10.1002/joc.4391

  22. Hertig E, Paxian A, Vogt G, Seubert S, Paeth H, Jacobeit J (2012) Statistical and dynamical downscaling assessments of precipitation extremes in the mediterranean area. Meteorol Zeitsch 21(1):61–77. doi:10.1127/0941-2948/2012/0271

    Article  Google Scholar 

  23. Jacob D, Petersen J, Eggert B, Alias A, Christensen OB, Bouwer L, 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, Króner 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 Chang 14(2):563–578. doi:10.1007/s10113-013-0499-2

    Article  Google Scholar 

  24. Kidson J, Thompson C (1998) A comparison of statistical and model-based downscaling techniques for estimating local climate variations. J Clim 11(4):735–753

    Article  Google Scholar 

  25. Kotlarski S, Keuler K, Christensen O, Colette A, Déqué M, Gobiet A, Goergen K, Jacob D, Lúthi D, van Meijgaard E, Nikulin G, Schár 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 Develop Discuss 7:217–293

    Article  Google Scholar 

  26. Luo Q, Wen L, McGregor JL, Timbal B (2013) A comparison of downscaling techniques in the projection of local climate change and wheat yields. Clim Chang 120 (1-2):249–261. doi:10.1007/s10584-013-0802-8

    Article  Google Scholar 

  27. 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. doi:10.1029/2009RG000314

    Article  Google Scholar 

  28. McCullagh P, Nelder JA (1989) Generalized linear models Monographs on Statistics and Applied Probability. Chapman & Hall, London

    Google Scholar 

  29. Meijgaard EV, Ulft LHV, Lenderink G, Roode SRD, Wipfler EL, Boers R, Timmermans RMA (2012) Refinement and Application of a Regional Atmospheric Model for Climate Scenario Calculations of Western Europe. Programme Office Climate changes Spatial Planning

  30. Murphy J (1999) An evaluation of statistical and dynamical techniques for downscaling local climate. J Clim 12(8):2256–2284

    Article  Google Scholar 

  31. Orlowsky B, Seneviratne SI (2012) Global changes in extreme events: regional and seasonal dimension. Clim Chang 110(3-4):669–696. doi:10.1007/s10584-011-0122-9

    Article  Google Scholar 

  32. Pizzigalli C, Palatella L, Zampieri M, Lionello P, Miglietta M, Paradisi P (2012) Dynamical and statistical downscaling of precipitation and temperature in a Mediterranean area. Ital J Agron 7(1):2. doi:10.4081/ija.2012.e2

    Article  Google Scholar 

  33. Radu R, Déqué M, Somot S (2008) Spectral nudging in a spectral regional climate model. Tellus A 60(5). doi:10.3402/tellusa.v60i5.15501

  34. Rockel B, Will A, Hense A (2008) The Regional Climate Model COSMO-CLM (CCLM). Meteorol Zeitsch 17(4):347–348. doi:10.1127/0941-2948/2008/0309

    Article  Google Scholar 

  35. Samuelsson P, Jones CG, Willén U, Ullerstig A, Gollvik S, Hansson U, Jansson C, Kjellstrm E, Nikulin G, Wyser K (2011) The Rossby Centre Regional Climate model RCA3: model description and performance. Tellus A 63(1). doi:10.3402/tellusa.v63i1.15770

  36. San-Martín D, Manzanas R, Brands S, Herrera S, Gutiérrez J (2016) Reassessing model uncertainty for regional projections of precipitation with an ensemble of statistical downscaling methods. J Clim. submitted

  37. Schmidli J, Goodess C, Frei C, Haylock M, Hundecha Y, Ribalaygua J, Schmith T (2007) Statistical and dynamical downscaling of precipitation: An evaluation and comparison of scenarios for the European Alps. J Geophys Res 112(10.1029)

  38. Sillman J, Roeckner R (2008) Indices for extreme events in projections of anthropogenic climate change. Clim Chang 86:83–104. doi:10.1007/s10584-007-9308-6

    Article  Google Scholar 

  39. Skamarock W, Klemp J, Dudhia J, Gill D, Barker D, Duda M, Wang W, Powers J (2008) A description of the Advanced Research WRF Version 3. Technical Report., NCAR

  40. 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. doi:10.1175/1520-0442(1993)0061161:DOGCCE2.0.CO;2

  41. Tareghian R, Rasmussen PF (2013) Statistical Downscaling of precipitation using quantile regression. J Hydrol 487:122–135. doi:10.1016/j.jhydrol.2013.02.029

    Article  Google Scholar 

  42. Taylor KE (2001) Summarizing multiple aspects of model performace in a single diagram. J Geophys Res 106(D7):7183–7192

    Article  Google Scholar 

  43. Teutschbein C, Seibert J (2012) Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. J Hydrol 456–457:12–29

    Article  Google Scholar 

  44. Teutschbein C, Seibert J (2013) Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions?. Hydrol Earth Syst Sci 17 (12):5061–5077. doi:10.5194/hess-17-5061-2013

    Article  Google Scholar 

  45. Tryhorn L, DeGaetano A (2011) A comparison of techniques for downscaling extreme precipitation over the Northeastern United States. Int J Climatol 31 (13):1975–1989. doi:10.1002/joc.2208

    Article  Google Scholar 

  46. Turco M, Quintana-Segui P, Llasat MC, Herrera S, Gutiérrez JM (2011) Testing MOS precipitation downscaling for ENSEMBLES regional climate models over Spain. J Geophys Res-Atmos 116:14

    Article  Google Scholar 

  47. Wilby RL, Wigley TML (1997) Downscaling general circulation model output: a review of methods and limitations. Progress Phys Geogr 21(4):530–548. doi:10.1177/030913339702100403

    Article  Google Scholar 

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Acknowledgments

We acknowledge the World Climate Research Programme’s Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5. We also thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. We also acknowledge the Earth System Grid Federation infrastructure and AEMET and University of Cantabria for the Spain02 dataset (available at http://www.meteo.unican.es/en/datasets/spain02). All the statistical downscaling experiments have been computed using the MeteoLab software (http://www.meteo.unican.es/software/meteolab), which is an open-source Matlab toolbox for statistical downscaling. This work has been partially supported by CORWES (CGL2010-22158-C02) and EXTREMBLES (CGL2010-21869) projects funded by the Spanish R&D programme. AC thanks the Spanish Ministry of Economy and Competitiveness for the funding provided within the FPI programme (BES-2011-047612 and EEBB-I-13-06354), JMG acknowledges the support from the SPECS project (FP7-ENV-2012-308378) and JF is grateful to the EUPORIAS project (FP7-ENV-2012-308291). We also thank three anonymous referees for their useful comments that helped to improve the original manuscript.

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Casanueva, A., Herrera, S., Fernández, J. et al. Towards a fair comparison of statistical and dynamical downscaling in the framework of the EURO-CORDEX initiative. Climatic Change 137, 411–426 (2016). https://doi.org/10.1007/s10584-016-1683-4

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Keywords

  • Regional Climate Models
  • Statistical downscaling
  • EURO-CORDEX
  • Precipitation indices