Correcting monthly precipitation in 8 RCMs over Europe
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Correcting monthly precipitation in 8 RCMs over Europe
Blaž Kurnik (European Environment Agency)Andrej Ceglar, Lucka Kajfez – Bogataj (University of Ljubljana)
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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
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The question
Can we use precipitation fields from RCMs directly in impact models?
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Climate models
Clim
ate
mod
elIm
pact
mod
els
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Ensembles of Climate models -simplified
RCM1 RCM2RCM3
RCM4
RCM5RCM6RCM7
GCM
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RCMs used in the study RCM
GCM*
SMHIRCA3
MPIREMO
KNMIRACMO
ETHZCLM
DMIHIRLAM
CNRMALADIN
BCMMETNO
ECHAM5MPI
HadCM3QUK - MET
ARPEGECNRM* Only 1 scenario - A1B - which is version of A1 SRES scenario
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Outputs from RCMsMonthly precipitation PDFs at different locations
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Correction of the climate model data – workflow
Observations
SM1
DM2
ETH
MPI
CNR
DM1
SM2
KNM
25 k
m x
1 d
ayEu
rope
, bet
wee
n 19
61 -
1990
Bias correction
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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
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Precipitation correction the climate model data – transfer function
𝑐𝑑𝑓 (𝑥 ,𝛼 , 𝛽 )=∫0
𝑥 𝑒− 𝑥 ′𝛽 𝑥 ′𝛼− 1
Γ (𝛼)𝛽𝛼 𝑑𝑥 ′+cdf (0)
cdfobs(y) = cdfsim(x)
𝑦= 𝑓 (𝑥 )=𝑐𝑑𝑓 𝑜𝑏𝑠− 1 [𝑐𝑑𝑓 𝑠𝑖𝑚(𝑥 )]
Piani et al, 2010
Cumulative distribution
Probability for dry event
Fulfilling criteria
Corrected precipitation Modelled precipitation
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Bias corrected data – ensemble mean of annual/July precipitation
Observed Simulated Corrected
Observed Simulated Corrected
Annual1991 - 2010
July1991 - 2010
Kurnik et al, 2011, submitted to IJC
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RMSE of simulated and corrected
simulated corrected
RMSE cor=1𝑁 √∑i=1
N
(RRcor− RRobs)2
RMSE ¿=1𝑁 √∑i=1
N
(RR¿−RR obs)2
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Failed correction – number of models
RMSEsim < RMSEcor
1.5 % area all models failed4.5 % area > 6/8 models failedDM1 90% cases cor(RMSE) < sim(RMSE)ETH 75% cases cor(RMSE) < sim(RMSE)
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Brier Score – zero precipitation
simulated corrected
BS cor=1𝑁 √∑i=1
N
(PROB (RR=0 )cor−OBS (RR=0))2BS¿=1𝑁 √∑i=1
N
(PROB (RR=0 )¿−OBS (RR=0))2
BS 0: the best probabilistic predictionBS 1: the worst probabilistic prediction
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Brier Score – heavy precipitation (RR> 200mm)
simulated corrected
BS cor=1𝑁 √∑i=1
N
(PROB (R R>200 )cor−OBS (RR>20 0))2BS¿=1𝑁 √∑i=1
N
(PROB (RR>20 0 )¿−OBS (RR>20 0))2
BS 0: the best probabilistic predictionBS 1: the worst probabilistic prediction
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Brier skill score– extremesKurnik et al, 2011, submitted to IJC
Dry event RR > 200 mm
BSS=1- BScor / BSsim
BSS < 0: no improvementsBSS > 0: corrections improve predictions
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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 is important (RMSE,
BS, BSS)