Abstract
Because of model biases, projections of future climate need to combine model simulations of recent and future climate with information on observed climate. Here, 10 methods for projecting the distribution of daily mean temperatures are compared, using six regional climate change simulations for Europe. Cross validation between the models is used to assess the potential performance of the methods in projecting future climate. Delta change and bias correction type methods show similar cross-validation performance, with methods based on the quantile mapping approach doing best in both groups due to their apparent ability to reduce the errors in the projected time mean temperature change. However, as no single method performs best under all circumstances, the optimal approach might be to use several well-behaving methods in parallel. When applying the various methods to real-world temperature projection for the late 21st century, the largest intermethod differences are found in the tails of the temperature distribution. Although the intermethod variation of the projections is generally smaller than their intermodel variation, it is not negligible. Therefore, it should be preferably included in uncertainty analysis of temperature projections, particularly in applications where the extremes of the distribution are important.
Similar content being viewed by others
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 25:939–957. doi:10.1175/JCLI-D-10-05024.1
Ballester J, Giorgi F, Rodó X (2010) Changes in European temperature extremes can be predicted from changes in PDF central statistics. Clim Change 98:277–284. doi:10.1007/s10584-009-9758-0
Boberg F, Christensen JH (2012) Overestimation of Mediterranean summer temperature projections due to model deficiencies. Nat Clim Change 2:433–436. doi:10.1038/NCLIMATE1454
Bracegirdle TJ, Stephenson DB (2012) Higher precision estimates of regional polar warming by ensemble regression of climate model projections. Clim Dyn. doi:10.1007/s00382-012-1330-3
Buser CM, Künsch HR, Lüthi D, Wild M, Schär C (2009) Bayesian multi-model projection of climate: bias assumptions and interannual variability. Climate Dyn 33:849–868. doi:10.1007/s00382-009-0588-6
Chen C, Haerter JO, Hagemann S, Piani C (2011) On the contribution of statistical bias correction to the uncertainty in the projected hydrological cycle. Geophys Res Lett 38:L20403. doi:10.1029/2011GL049318
Christensen JH, Hewitson B, Busuioc A, Chen A, Gao X, Held I, Jones R, Kolli RK, Kwon W-T, Laprise R, Magaña Rueda V, Mearns L, Menéndez CG, Räisänen J, Rinke A, Sarr A, Whetton P (2007) Regional climate projections. In: Solomon S et al (eds) Climate change 2007: the physical science basis. Cambridge University Press, Cambridge, pp 847–940
Déqué M, Somot S, Sanchez-Gomez 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 38:951–964. doi:10.1007/s00382-011-1053-x
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
Engen-Skaugen T (2007) Refinement of dynamically downscaled precipitation and temperature scenarios. Clim Change 84:365–382. doi:10.1007/s10584-007-9251-6
Haerter JO, Hagemann S, Moseley C, Piani C (2011) Climate model bias correction and the role of timescales. Hydrol Earth Syst Sci 15:1065–1079. doi:10.5194/hess-15-1065-2011
Ho CK, Stephenson DB, Collins M, Ferro CAT, Brown SJ (2012) Calibration strategies. A source of additional uncertainty in climate change projections. Bull Am Meteorolol Soc 93:21–26. doi:10.1175/2011BAMS3110.1
Huth R (2002) Statistical downscaling of daily temperature in Central Europe. J Climate 15:1731–1742. doi:10.1175/1520-0442(2002)015<1731:SDODTI>2.0.CO;2
Kharin VV, Zwiers FW, Zhang X, Hegerl GC (2007) Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations. J Clim 20:1419–1444. doi:10.1175/JCLI4066.1
Kjellström E, Bärring L, Jacob D, Jones R, Lenderink G, Schär C (2007) Modelling daily temperature extremes: recent climate and future changes over Europe. Clim Change 81:249–265. doi:10.1007/s10584-006-9220-5
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
Maraun D, Wetterhall F, Ineson AM, Chandler ER, Kendon EJ, Widmann M, Brienen S, Rust HW, Sauter T, Themeßl MJ, Cenema 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
Meehl GA, Stocker TF, Collins W, Friedlingstein P, Gaye A, Gregory J, Kitoh A, Knutti R, Murphy J, Noda A, Raper S, Watterson I, Weaver A, Zhao Z-C (2007) Global climate projections. In: Solomon S et al (eds) Climate change 2007: the physical science basis. Cambridge University Press, Cambridge, pp 747–845
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 A 63:41–55. doi:10.1111/j.1600-0870.2010.00466.x
Piani C, Weedon GP, Best M, Gomes SM, Viterbo P, Hagemann S, Haerter JO (2010) Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. J Hydrology 395:199–215. doi:10.1016/j.jhydrol.2010.10.024
Räisänen J (2001) CO2-induced climate change in CMIP2 experiments. Quantification of agreement and role of internal variability. J Climate 14:2088–2104. doi:10.1175/1520-0442(2001)014<2088:CICCIC>2.0.CO;2
Räisänen J, Palmer TN (2001) A probability and decision-model analysis of a multi-model ensemble of climate change simulations. J Climate 14:3212–3226. doi:10.1175/1520-0442(2001)014<3212:APADMA>2.0.CO;2
Räisänen J, Ylhäisi JS (2011) How much should climate model output be smoothed in space? J Clim 24:867–880. doi:10.1175/2010JCLI3872.1
Räisänen J, Hansson U, Ullerstig A, Döscher R, Graham LP, Jones C, Meier HEM, Samuelsson P, Willén U (2004) European climate in the late 21st century: regional simulations with two driving global models and two forcing scenarios. Clim Dyn 22:13–31. doi:10.1007/s00382-003-0365-x
Randall DA, Wood RA, Bony S, Colman R, Fichefet T, Fyfe J, Kattsov V, Pitman A, Shukla J, Srinivasan J, Stouffer RJ, Sumi A, Taylor KE (2007) Climate models and their evaluation. In: Solomon S et al (eds) Climate change 2007: the physical science basis. Cambridge University Press, Cambridge, pp 589–662
Taylor KE, Stouffer RJ, Meehl GA (2011) A summary of the CMIP5 experiment design. Available at http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_design.pdf
Themeßl MJ, Gobiet A, Leuprecht A (2011) Empirical-statistical downscaling and error correction of daily precipitation from regional climate models. Int J Climatology 31:1530–1544. doi:10.1002/joc.2168
van der Linden P, Mitchell JFB (eds) (2009) ENSEMBLES: climate change and its impacts: summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3 PB. Available at http://ensembles-eu.metoffice.com/docs/Ensembles_final_report_Nov09.pdf
Yip S, Ferro CAT, Stephenson D (2011) A simple, coherent framework for partitioning uncertainty in climate change predictions. J Climate 24:4634–4643. doi:10.1175/2011JCLI4085.1
Acknowledgments
The model simulations used in this work were funded by the EU FP6 Integrated Project ENSEMBLES (Contract number 505539). This work has been supported by the Academy of Finland RECAST project (decision 140801). The two reviewers are acknowledged for their constructive comments.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Räisänen, J., Räty, O. Projections of daily mean temperature variability in the future: cross-validation tests with ENSEMBLES regional climate simulations. Clim Dyn 41, 1553–1568 (2013). https://doi.org/10.1007/s00382-012-1515-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00382-012-1515-9