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Correcting monthly precipitation in 8 RCMs over Europe. Bla ž Kurnik (European Environment Agency) Andrej Ceglar , Lucka Kajfez – Bogataj (University of Ljubljana). Outline. Regional climate models and observation - observation from E-OBS - RCMs from ENSEMBLES project
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Correcting monthly precipitation in 8 RCMs over Europe Blaž Kurnik (European Environment Agency) Andrej Ceglar, LuckaKajfez – Bogataj (University of Ljubljana)
Outline • Regional climate models and observation • - observation from E-OBS • - RCMs from ENSEMBLES project • Techniques for correcting precipitation prior use in impact models – bias corrections • Validation of the methodology with results
The question Can we use precipitation fields from RCMs directly in impact models?
Climate models Climate model Impact models
Ensembles of Climate models -simplified RCM6 RCM7 RCM5 RCM4 GCM RCM3 RCM2 RCM1
RCMs used in the study * Only 1 scenario - A1B - which is version of A1 SRES scenario
Outputs from RCMs Monthly precipitation PDFs at different locations
Correction of the climate model data – workflow Observations DM1 Bias correction DM2 ETH 25 km x 1 day Europe, between 1961 - 1990 MPI CNR SM1 SM2 KNM
Correction of the climate model data • Adjusting of the distribution function at every grid cell • Long time series (> 40 years) of observation data are needed - correction and validation of the model (20 +20 years) • Corrections are needed for each model separately
Precipitation correction the climate model data – transfer function Piani et al, 2010 Cumulative distribution Probability for dry event cdfobs(y) = cdfsim(x) Fulfilling criteria Modelled precipitation Corrected precipitation
Bias corrected data – ensemble mean of annual/July precipitation Kurnik et al, 2011, submitted to IJC Corrected Simulated Observed Annual 1991 - 2010 Corrected Simulated Observed July 1991 - 2010
RMSE of simulated and corrected simulated corrected
Failed correction – number of models RMSEsim < RMSEcor 1.5 % area all models failed 4.5 % area > 6/8 models failed DM1 90% cases cor(RMSE) < sim(RMSE) ETH 75% cases cor(RMSE) < sim(RMSE)
Brier Score – zero precipitation BS 0: the best probabilistic prediction BS 1: the worst probabilistic prediction simulated corrected
Brier Score – heavy precipitation (RR> 200mm) BS 0: the best probabilistic prediction BS 1: the worst probabilistic prediction simulated corrected
Brier skill score– extremes Kurnik et al, 2011, submitted to IJC BSS=1- BScor/ BSsim BSS < 0: no improvements BSS > 0: corrections improve predictions Dry event RR > 200 mm
Conclusions • Various RCMs have been corrected, using same approach • Bias correction is necessary, prior use of data in impact models – significant improvements • Bias correction needs to be relatively “robust” • Dry months need to be studied carefully • Selection of validation technics isimportant (RMSE, BS, BSS)