correcting monthly precipitation in 8 rcms over europe
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
Correcting monthly precipitation in 8 RCMs over Europe
Blaž 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
• 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
Clim
ate
mod
elIm
pact
mod
els
Ensembles of Climate models -simplified
RCM1 RCM2RCM3
RCM4
RCM5RCM6RCM7
GCM
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
Outputs from RCMsMonthly precipitation PDFs at different locations
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
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
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
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
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
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)
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
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
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
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)