Estimating future changes in daily precipitation distribution from GCM simulations

1. Estimating future changes in daily precipitation distribution from GCM simulations 11th International Meeting on Statistical Climatology Edinburgh, 12-16 July 2010 Jonathan Eden* and Martin Widmann School of Geography, Earth and Environmental Sciences, University of Birmingham, UK. Acknowledgements: Xiaoming Cai and Chris Kidd (University of Birmingham) David Grawe (Universität Hamburg, Germany) Sebastian Rast (Max-Planck-Institut fuer Meteorologie, Hamburg, Germany)

2. Simulating 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).

3. Performing a nudged simulation Introduction – Method and Setup – Results – Summary ECHAM5 GCM simulation (1958-2001) T63 L31 Large-scale circulation reflects temporal variability in observed record. Large-scale circulation ERA-40 reanalysis • Prognostic variables nudged towards corresponding ERA-40 fields. Parameterisations Simulated precipitation able to capture temporal variability. Simulated precipitation Krishnamurti et al. (1991); Kaas et al. (2000);Eden et al. (submitted)

4. MOS downscaling correction Introduction – Method and Setup – Results – Summary ECHAM5 GCM simulation (1958-2001) T63 L31 Large-scale circulation reflects temporal variability in observed record. Large-scale circulation Parameterisations Simulated precipitation able to capture temporal variability. Simulated precipitation Downscaling Robust MOS downscaling models. Observed precipitation

5. 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)

6. Introduction – Method and Setup – Results – Summary Daily precipitation: Comparison with observations NORM – E-OBS NUDG – E-OBS European RMSE in simulation of daily precipitation at different quantiles (1958-2001).

7. Introduction – Method and Setup – Results – Summary Long-term extreme daily precipitation (1958-2001) DJF JJA

8. Introduction – Method and Setup – Results – Summary Downscaling 1: Quantile mapping • 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

9. Introduction – Method and Setup – Results – Summary Downscaling 1: Quantile mapping • 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.

10. Introduction – Method and Setup – Results – Summary Downscaling 2: Non-local MOS using SVD and CCA • 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.

11. Introduction – Method and Setup – Results – Summary Downscaling 2: Non-local MOS using SVD and CCA CCA (5PCs) Correlation between observed and corrected daily winter (DJF) precipitation (1958-2001). SVD

12. Introduction – Method and Setup – Results – Summary Towards a correction of future projections 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.

13. Summary and outlook Introduction – Method and Setup – Results – Summary • 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.

14. Thank you

15. Introduction – Method and Setup – Results – Summary Downscaling 1: Heaviest precipitation events (DJF) Correlation of average precipitation on 5 wettest days (DJF; 1958-2001). - Average of precipitation of 5 wettest days • 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.