Advertisement

Climate Dynamics

, Volume 42, Issue 9–10, pp 2287–2303 | Cite as

Evaluation of delta change and bias correction methods for future daily precipitation: intermodel cross-validation using ENSEMBLES simulations

  • Olle RätyEmail author
  • Jouni Räisänen
  • Jussi S. Ylhäisi
Article

Abstract

Due to inherent limitations in climate models, their output is biased in relation to observed climate and as such does not provide reliable climate projections. In this study, nine methods used to account for biases in daily precipitation are tested. First, cross-validation tests were made using a set of ENSEMBLES regional model simulations to gain insights in the potential performance of the methods in the future climate. The results show that quantile mapping type methods, being able to modify the shape of the precipitation distribution, often outperform other types of methods. Yet, as the performance depends on time of the year, location and part of the distribution considered, it is not possible to distinguish one universally best performing method. In addition, the improvement relative to the projections that would have been obtained assuming unchanged climate is relatively modest, particularly in the early twentyfirst century conditions. Further tests with different method combinations show that the projections could be potentially improved by using several well performing methods in parallel. In the second part of the study, contributions of method and model differences to the overall variation of precipitation projections are assessed. It is shown that although intermodel differences play an important role, uncertainties related to intermethod differences are substantial, particularly in the tails of the distribution. This suggests that method uncertainty should be taken into account when constructing daily precipitation projections, possibly by using several methods in parallel.

Keywords

Climate change Daily variability Climate projection Delta change Bias correction Precipitation ENSEMBLES 

Notes

Acknowledgments

The model simulations used in this work were funded by the EU FP6 Integrated Project ENSEMBLES (Contract Number 505539). This study has been supported by the Academy of Finland RECAST project (Decision 140801) and the Academy of Finland Center of Excellence program (Project No. 272041). The authors thank the two anonymous reviewers for their constructive comments.

References

  1. Aryan VE, Albert KT, Gerard VDS, Lisette K (2008) European Climate Assessment and Dataset (ECA&D): towards an operational system for assessing observed changes in climate extremes. Technical report, KNMI, p 68Google Scholar
  2. Bàrdossy A, Pegram G (2013) Multiscale spatial recorrelation of RCM precipitation to produce unbiased climate change scenarios over large areas and small. Water Resour Res 48:W09502. doi: 10.1029/2011WR011524 Google Scholar
  3. Boberg F, Berg P, Thejll P, Gutowski WJ, Christensen JH (2010) Improved confidence in climate change projections of precipitation further evaluated using daily statistics from ENSEMBLES models. Clim Dyn 35:1509–1520. doi: 10.1007/s00382-009-0683-8 CrossRefGoogle Scholar
  4. Bracegirdle T, Stephenson D (2012) Higher precision estimates of regional polar warming by ensemble regression of climate model projections. Clim Dyn 39:2805–2821. doi: 10.1007/s00382-012-1330-3 CrossRefGoogle Scholar
  5. Buser CM, Künch HR, Lüthi D, Wild M, Schär C (2009) Bayesian multimodel projection of climate: bias assumptions and interannual variability. Clim Dyn 33:849–868. doi: 10.1007/s00382-009-0588-6 CrossRefGoogle Scholar
  6. Chen J, Brissette P, Chaumont D, Braun M (2013) Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America. Water Resour Res 49:4187–4205. doi: 10.1002/wrcr.20331 CrossRefGoogle Scholar
  7. 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:L20709. doi: 10.1029/2008GL035694 CrossRefGoogle Scholar
  8. Déqué M, Somot S, Sanchez-Gomez E, Goodess CM, Jacob D, Lederink G, Christensen OB (2010) The spread amongst ENSEMBLES regional scenarios: regional climate models, driving general circulation models and interannual variability. Clim Dyn 38:951–964. doi: 10.1007/s00382-011-1053-x CrossRefGoogle Scholar
  9. Dosio A, Paruolo P (2011) Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models: evaluation on the present climate. J Geophys Res 116:D16106. doi: 10.1029/2011JD015934 CrossRefGoogle Scholar
  10. Dosio A, Paruolo P, Rojas R (2012) Bias correction of the ENSEMBLES high resolution climate change projections for use by impact modelers: analysis of the climate change signal. J Geophys Res 117:D17110. doi: 10.1029/2012JD017968 Google Scholar
  11. Engen-Skaugen T (2007) Refinement of dynamically downscaled precipitation and temperature scenarios. Clim Change 84:365–382. doi: 10.1007/s10584-007-9251-6 CrossRefGoogle Scholar
  12. Hawkins E, Osborne TM, Ho CK, Challinor AJ (2013) Calibration and bias correction of climate projections for crop modeling: an idealized case study over Europe. Agric For Meteorol 170:19–31CrossRefGoogle Scholar
  13. Johnson F, Sharma A (2012) A nesting model for bias correction of variability at multiple time scales in general circulation model precipitation simulations. Water Resour Res 48:W01504. doi: 10.1029/2011WR010464 Google Scholar
  14. 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. doi: 10.1002/joc.3518 CrossRefGoogle Scholar
  15. Leander RT, Buishand A (2007) Resampling of regional climate model output for the simulation of extreme river flows. J Hydrol 332:487–496CrossRefGoogle Scholar
  16. Maraun D (2012) Nonstationarities of regional climate model biases in European seasonal mean temperature and precipitation sums. Geophys Res Lett 39:L06706. doi: 10.1029/2012GL051210 CrossRefGoogle Scholar
  17. Maraun D (2013a) When will trends in European mean and heavy precipitation emerge? Environ Res Lett 8:1–7. doi: 10.1088/1748-9326/8/1/014004 CrossRefGoogle Scholar
  18. Maraun D (2013b) Bias correction, quantile mapping, and downscaling: revising the inflation issue. J Clim 26:2137–2143. doi: 10.1175/JCLI-D-12-00821.1 CrossRefGoogle Scholar
  19. 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:RG3003. doi: 10.1029/2009RG000314 CrossRefGoogle Scholar
  20. Nakićenović N, Alcamo J, Davis J, de Vries B, Fenhann J, Gaffin S, Gregory K, Gr-übler A, Jung TY, Kram T, Lebre La Rovere E, Michaelis L, Mori S, Morita T, Pepper W, Pitcher H, Price L, Riahi K, Roehrl A, Rogner H-H, Sankovski A, Schlesinger M, Shukla P, Smith S, Swart R, van Rooijen S, Victor N, Dadi Z (2000) Special report on emission scenarios. A special report of Working Group III for the Intergovernmental Panel on Climate Change. Cambridge university press, New YorkGoogle Scholar
  21. Perkins SE, Pitman AJ, Holbrook NJ, McAneney J (2007) Evaluation of the AR4 climate models’ simulated daily maximum temperature, minimum temperature and precipitation over Australia using probability density functions. J Clim 20:4356–4376. doi: 10.1175/JCLI4253.1 CrossRefGoogle Scholar
  22. Piani C, Haerter JO (2012) Two dimensional bias correction of temperature and precipitation copulas in climate models. Geophys Res Lett 39:L20401. doi: 10.1029/2012GL053839 CrossRefGoogle Scholar
  23. Piani C, Haerter JO, Coppola E (2010a) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99:187–192. doi: 10.1007/s00704-009-0134-9 CrossRefGoogle Scholar
  24. Piani C, Weedom GP, Best M, Gomes SM, Viterbo P, Hagemann S, Haerter JO (2010b) Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. J Hydrol 395:199–215. doi: 10.1016/j.jhydrol.2010.10.024 CrossRefGoogle Scholar
  25. Räisänen J, Räty O (2013) Projections of daily mean temperature variability in the future: cross-validation tests with ENSEMBLES regional climate simulations. Clim Dyn 41:1553–1568. doi: 10.1007/s00382-012-1515-9 CrossRefGoogle Scholar
  26. Terink W, Hurkmans RTW, Torfs PJJ, Uijlenhoet R (2010) Evaluation of a bias correction method applied to downscaled precipitation and temperature reanalysis data for the Rhine basin. Hydrol Earth Syst Sci 14:687–703. doi: 10.5194/hess-14-687-2010 CrossRefGoogle Scholar
  27. 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. doi: 10.1016/j.hydrol.2012.05.052 CrossRefGoogle Scholar
  28. Themeβl MJ, Gobiet A, Leuprecht A (2011) Empirical-statistical downscaling and error correction of daily precipitation from regional climate models. Int J Climatol 31:1530–1544. doi: 10.1002/joc.2168 CrossRefGoogle Scholar
  29. van der Linden P, Mitchell JFB (eds) (2009) ENSEMBLES: climate change and its impacts: summary and results from ENSEMBLES project. Met Office Hadley Centre, Fitzroy Road, Exeter EX 1 3 PB, UK, p 160Google Scholar
  30. Watanabe S, Kanae S, Seto S, Yeh PJF, Hirabayashi Y, Oki T (2012) Intercomparison of bias-correction methods for monthly temperature and precipitation simulated by multiple models. J Geophys Res 117:D23114. doi: 10.1029/2012JD018192 CrossRefGoogle Scholar
  31. Wilcke RAI, Mendlik T, Gobiet A (2013) Multi-variable error correction of regional climate models. Clim Change 120:871–887. doi: 10.1007/s10584-013-0845-x CrossRefGoogle Scholar
  32. Wilks DS (2006) Statistical methods in the atmospheric sciences, 2nd edn. Elsevier, Burlington, p 627Google Scholar
  33. Yang W, Andréasson J, Graham LP, Olsson J, Rosberg J, Wetterhall F (2010) Distribution-based scaling to improve the usability of regional climate model projections for hydrological climate change impacts studies. Hydrol Res 41(3–4):211–229CrossRefGoogle Scholar
  34. Yip S, Ferro CAT, Stephenson DB, Hawkins E (2011) A simple, coherent framework for partitioning uncertainty in climate predictions. J Clim 24(17):4636–4643. doi: 10.1175/2011JCLI4085.1 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Olle Räty
    • 1
    Email author
  • Jouni Räisänen
    • 1
  • Jussi S. Ylhäisi
    • 1
  1. 1.Department of PhysicsUniversity of HelsinkiHelsinkiFinland

Personalised recommendations