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

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)


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