Estimating future changes in daily precipitation Estimating future changes in daily precipitation distribution from GCM simulationsdistribution from GCM simulations

1111thth International Meeting on Statistical Climatology International Meeting on Statistical Climatology

Edinburgh, 12-16 July 2010Edinburgh, 12-16 July 2010

Jonathan Eden* and Martin WidmannJonathan Eden* and Martin Widmann School of Geography, Earth and Environmental Sciences, University of Birmingham, UK.School of Geography, Earth and Environmental Sciences, University of Birmingham, UK.

Acknowledgements: Acknowledgements:

Xiaoming Cai and Chris KiddXiaoming Cai and Chris Kidd (University of Birmingham) (University of Birmingham)

David GraweDavid Grawe ( (Universität Hamburg, Germany)Universität Hamburg, Germany)

Sebastian RastSebastian Rast ( (Max-Planck-Institut fuer MeteorologieMax-Planck-Institut fuer Meteorologie, Hamburg, Germany), Hamburg, Germany)

Simulating daily precipitationSimulating daily precipitation

Introduction – Method and Setup – Results – Summary

Changes in extremes; 2080-2099 relative to 1980-1999 (IPCC AR4, adapted from Tebaldi et al. (2006).

Performing a nudged simulationPerforming a nudged simulation

Simulated precipitation

Parameterisations

ERA-40 reanalysisLarge-scale circulation reflects temporal variability in observed record.

Simulated precipitation able to capture temporal variability.

Large-scale circulation

ECHAM5 GCM simulation (1958-2001) T63 L31

- Prognostic variables nudged towards corresponding ERA-40 fields.

Krishnamurti et al. (1991); Kaas et al. (2000); Eden et al. (submitted)

Introduction – Method and Setup – Results – Summary

MOS downscaling correctionMOS downscaling correction

Parameterisations

Large-scale circulation

Observed precipitation

Downscaling

Robust MOS downscaling models.

ECHAM5 GCM simulation (1958-2001) T63 L31

Simulated precipitation able to capture temporal variability.Simulated precipitation

Large-scale circulation reflects temporal variability in observed record.

Introduction – Method and Setup – Results – Summary

Skill of simulated precipitation – monthly means

- Correlations of simulated and observed monthly mean precipitation for all months of the year (1979-2001).

- Normal simulation exhibits weak correlation; ~zero

- Nudged simulation able to represent interannual variability; clear to see where model performance is high.

- MOS downscaling correction all show good, though spatially varying, skill and outperform traditional perfect prog approaches.

Eden et al. (submitted, J. Clim)

Introduction – Method and Setup – Results – Summary

Daily precipitation: Comparison with observations

Introduction – Method and Setup – Results – SummaryN

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European RMSE in simulation of daily precipitation at different quantiles (1958-2001).

Long-term extreme daily precipitation (1958-2001)

Introduction – Method and Setup – Results – Summary

DJF

JJA

Downscaling 1: Quantile mapping

Introduction – Method and Setup – Results – Summary

- Leave-one-out cross validation used to estimate observations using independent fitting period.

- Corrections for each year (1958-2001) derived from distributions of observed and simulated precipitation across all other years.

- Each empirical distribution fitted with two-parameter gamma distribution.

Example CDF correction derivation

Downscaling 1: Quantile mapping

Introduction – Method and Setup – Results – Summary

- Correlation between land-only E-OBS and ‘correction’ (using cross-validation); DJF precipitation, 1958-2001.

- Method shows good skill in much of western and southern Europe.

Two approaches to linking a predictand time series (in this case daily precipitation) to a two-dimensional time-dependent predictor field:

- one-dimensional singular value decomposition (SVD) (also known as maximum covariance analysis).

- one-dimensional canonical correlation analysis (CCA) (or equivalently PC multiple linear regression).

- See Widmann (2005) for details on methods.

- Predictor variable is ECHAM5 simulated precipitation.

- Size of spatial domain is constant.

- Only for British Isles at present.

Introduction – Method and Setup – Results – Summary

Downscaling 2: Non-local MOS using SVD and CCA

Downscaling 2: Non-local MOS using SVD and CCA

Introduction – Method and Setup – Results – Summary

SVD CCA (5PCs)

Correlation between observed and corrected daily winter (DJF) precipitation (1958-2001).

Towards a correction of future projections

Introduction – Method and Setup – Results – Summary

Percentage change in 90th percentile DJF precipitation; 2080-2099 relative to 1980-1999

ECHAM5 A1B scenario Downscaled correction

- Downscaled correction based on quantile mapping.

- Correction can be considered skillful where overall model skill is high.

Summary and outlookSummary and outlook

• Quantification of the GCM precipitation skill given a simulated large-scale circulation extends to skill of daily precipitation simulated

• Both local (quantile mapping) and non-local (SVD and CCA) downscaling corrections have been developed.

• Quantile mapping shows good skill, but potential of non-local methods is unclear.

• FUTURE:- Focus on precipitation extremes; potential for estimating

changes in extreme value distribution.- Identical analysis for other GCMs and for other regions

where high-quality observational data is available.

Introduction – Method and Setup – Results – Summary

Thank youThank you

Downscaling 1: Heaviest precipitation events (DJF)

Introduction – Method and Setup – Results – Summary

Difference in corrected and observed 90th percentile of DJF precipitation on wet days.

- Corrected precipitation is generally skillful.

- Largest errors apparent in mountainous regions of central Europe.

Correlation of average precipitation on 5 wettest days (DJF; 1958-2001).

- Average of precipitation of 5 wettest days