Correcting monthly precipitation in 8 RCMs over Europe
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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

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Correcting monthly precipitation in 8 rcms over europe

Correcting monthly precipitation in 8 RCMs over Europe

Blaž Kurnik (European Environment Agency)

Andrej Ceglar, LuckaKajfez – Bogataj (University of Ljubljana)


Outline

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

The question

Can we use precipitation fields from RCMs directly

in impact models?


Climate models

Climate models

Climate

model

Impact

models


Ensembles of climate models simplified

Ensembles of Climate models -simplified

RCM6

RCM7

RCM5

RCM4

GCM

RCM3

RCM2

RCM1


Rcms used in the study

RCMs used in the study

* Only 1 scenario - A1B - which is version of A1 SRES scenario


Outputs from rcms

Outputs from RCMs

Monthly precipitation PDFs at different locations


Correction of the climate model data workflow

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

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

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

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

RMSE of simulated and corrected

simulated

corrected


Failed correction number of models

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

Brier Score – zero precipitation

BS  0: the best probabilistic prediction

BS  1: the worst probabilistic prediction

simulated

corrected


Brier score heavy precipitation rr 200mm

Brier Score – heavy precipitation (RR> 200mm)

BS  0: the best probabilistic prediction

BS  1: the worst probabilistic prediction

simulated

corrected


Brier s kill score extremes

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

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)


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