Abstract
Spain is one of the European countries with most environmental problems related to water scarcity and droughts. Additionally, several studies suggest trends of increasing temperature and decreasing rainfall, mainly for the Iberian Peninsula, due to climate variability and change. While Regional Climate Models (RCM) are a valuable tool for understanding climate processes, the causes and plausible impacts on variables and meteorological extremes present a wide range of associated uncertainties. The multi-model ensemble approach allows the quantification and reduction of uncertainties in the predictions. The combination of models (RCM in this case), generally increases the reliability of the predictions, although there are different weighting methodologies. In this paper, a strategy is presented for the building of non-stationary PDF (probability density functions) ensembles with the aim of evaluating the spatial pattern of future risk of drought for an area. At the same time, the uncertainty associated with the metric used in the construction of the PDF ensembles is assessed. A comparative study of methodologies based on the application of the Reliability Ensemble Averaging (REA), assessing its factors using two performance measures, on the one hand the Perkins Score Methods, on the other hand the Kolmogorov-Smirnov test, is proposed. The evaluation of the sensitivity of the methodologies used in the construction of ensembles, as proposed in this paper, although without completely eliminating uncertainty, allows a better understanding of the sources and magnitude of the uncertainties involved. Despite the differences between the spatial distribution results from each metric (which can be in the order of 40 % in some areas), both approaches concluded about a plausible significant and widespread increase throughout continental Spain of the mean value of annual maximum dry spell lengths (AMDSL) between the years 1990 and 2050. Finally, the more parsimonious approach in the building of ensembles PDF, based on AMDSL in peninsular Spain, is identified.
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Boberg F, Berg P, Thejll P, Gutowski WJ, Christensen JH (2009) Improved confidence in climate change projections of precipitation evaluated using daily statistics from the PRUDENCE ensemble. Clim Dyn 32:1097–1106
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
Christensen JH, Christensen OB (2007) A summary of the PRUDENCE model projections of changes in European climate by the end of this century. Clim Change 81:7–30
Christensen JH, Rummukainen M, Lenderink G (2009) Formulation of very-high resolution regional climate model ensembles for Europe. In: van der Linden P, Mitchell JFB (eds) ENSEMBLES: climate change and its impacts at seasonal, decadal and centennial timescales: summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, UK, pp 47–58
Dettinger M (2005) From climate-change spaghetti to climate-change distributions for 21st century California. San Francisco Estuary Watershed Sci 3(1), article 4
Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman & Hall, New York
Filliben JJ (1975) The probability plot correlation coefficient test for normality. Technometrics 17(1):111–117
García Galiano SG, Giraldo Osorio JD (2010) Analysis of impacts on hydrometeorological extremes in the Senegal River Basin from REMO RCM. Meteorol Z 19(4):375–384. doi:10.1127/0941-2948/2010/0457
Gibbons JD, Chakraborti S (2003) Nonparametric statistical inference fourth edition, revised and expanded. Marcel Dekker, New York
Giorgi F, Mearns LO (2002) Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulations via the “reliability ensemble averaging” (REA) method. J Clim 15(10):1141–1158
Giorgi F, Mearns LO (2003) Probability of regional climate change based on reliability ensemble averaging (REA) method. Geophys Res Lett 30(12):311–314
Giorgi F, Bi X, Pal J (2004) Mean, interannual variability and trends in a regional climate change experiment over Europe. II: climate change scenarios (2071–2100). Clim Dynam 23:839–858
Giraldo Osorio JD, García Galiano SG (2011) Building hazard maps of extreme daily rainy events from PDF ensemble, via REA method, on Senegal River Basin. Hydrol Earth Syst Sci 15:3605–3615. doi:10.5194/hess-15-3605-2011
Herrera S, Gutiérrez JM, Ancell R, Pons MR, Frías MD, Fernández J (2010) Development and analysis of a 50-year high-resolution daily gridded precipitation dataset over Spain (Spain02). Int J Climatol. doi:10.1002/joc.2256
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 S, 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 Change 81:31–52
Karambiri H, García Galiano SG, Giraldo JD, Yacouba H, Ibrahim B, Barbier B, Polcher J (2011) Assessing the impact of climate variability and climate change on runoff in West Africa: the case of Senegal and Nakambe River basins. At Sci Lett 12(1):109–115
Martín-Vide J, Gómez L (1999) Regionalization of peninsular Spain based on the length of dry spells. Int J Climatol 19:537–555
Murphy JM, Sexton DMH, Barnett DN, Jones GS, Webb MJ, Collins M, Stainforth DA (2004) Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430:768–772
Paeth H, Hall NMJ, Gaertner MA, Domínguez-Alonso M, Moumouni S, Polcher J, Ruti PM, Fink AH, Gosset M, Lebel T, Gaye A, Rowell DP, Moufouma-Okia W, Jacob D, Rockel B, Giorgi F, Rummukainen M (2011) Progress in regional downscaling of West African precipitation. At Sci Lett 12:75–82
Paredes D, Trigo RM, García-Herrera R, Franco Trigo I (2006) Understanding precipitation changes in Iberia in early spring: weather typing and storm-tracking approaches. J Hydrom 7:101–113
Perkins SE, Pitman AJ (2009) Do weak AR4 models bias projections of future climate changes over Australia? Clim Change 93:527–558
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
Räisänen J, Palmer TN (2001) A probability and decision-model analysis of a multimodel ensemble of climate change simulations. J Clim 14:3212–3226
Rigby RA, Stasinopoulos DM (2005) Generalized additive models for location, scale and shape. Appl Stat 54:507–554
Sánchez E, Romera R, Gaertner MA, Gallardo C, Castro M (2009) A weighting proposal for an ensemble of regional climate models over Europe driven by 1961–2000 ERA 40 based on monthly precipitation probability density functions. At Sci Lett 10:241–248
Sánchez E, Domínguez M, Romera R, López de la Franca N, Gaertner MA, Gallardo C, Castro M (2011) Regional modelling of dry spells over the Iberian Peninsula for present climate and climate change conditions: a letter. Clim Change 107:625–634
Sheskin DJ (2000) Handbook of parametric and nonparametric statistical procedures, 2nd edn. Chapman & Hall/CRC, Boca Raton
Smith I, Chandler E (2010) Refining rainfall projections for the Murray Darling Basin of south-east Australia-the effect of sampling model results based on performance. Clim Change 102:377–393
Stasinopoulos DM, Rigby RA (2007) Generalized additive models for location scale and shape (GAMLSS) in R. J Stat Softw 23(7):1–46
Stasinopoulos DM, Rigby RA, Akantziliotou C (2008). Instructions on how to use the gamlss package in R, 2nd edn. http://studweb.north.londonmet.ac.uk/~stasinom/papers/gamlss-manual.pdf. Accessed 28 Nov 2011
Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Phil Trans R Soc A 365:2053–2075
Tebaldi C, Sansó B (2009) Joint projections of temperature and precipitation change from multiple climate models: a hierarchical Bayesian approach. J R Stat Soc A 172(1):83–106
Tebaldi C, Smith RL, Nychka D, Mearns LO (2005) Quantifying Uncertainty in projections of regional climate change: a bayesian approach to the analysis of multimodel ensembles. J Clim 18:1524–1540
van Buuren S, Fredriks M (2001) Worm plot: a simple diagnostic device for modelling growth reference curves. Stat Med 20:1259–1277
Weigel AP, Knutti R, Liniger MA, Appenzeller C (2010) Risks of model weighting in multimodel climate projections. J Clim 23(15):4175–4191
World Health Organization (2009) Protecting health from climate change: connecting science, policy and people. World Health Organization, Geneva
Xu Y, Gao X, Giorgi F (2010) Upgrades to the reliability ensemble averaging method for producing probabilistic climate-change projections. Clim Res 41:61–81
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This work has been developed in the framework of R&D Project CGL2008-02530/BTE, financed by State Secretary of Research of the Spanish Ministry of Science and Innovation (MICINN). The funding received is gratefully acknowledged.
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Giraldo Osorio, J.D., García Galiano, S.G. Assessing uncertainties in the building of ensemble RCMs over Spain based on dry spell lengths probability density functions. Clim Dyn 40, 1271–1290 (2013). https://doi.org/10.1007/s00382-012-1381-5
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DOI: https://doi.org/10.1007/s00382-012-1381-5