Development of a Downscaling Prediction System Liqiang Sun

1. Development of a Downscaling Prediction System Liqiang Sun

2. Development of Regional Climate Prediction System • choosing GCM forecasts that have good skill over the region of interest • Identifying a group of regional climate models for downscaling • Determining model resolution and domain size • Customization of regional climate models • Observation • Initialization • Programs to nest regional models within GCMs • Ensemble runs of retrospectiveforecast – forecast skill • Statistical post processing of model output to correct model bias • Forecast product • Forecast verification

3. CLIMATE DYNAMICAL DOWNSCALING FORECAST SYSTEM FOR NORDESTE • HISTORICAL DATA • Extended Simulations • Observations PERSISTED GLOBAL SST ANOMALIES ECHAM4.5 AGCM (T42) NCAR CAMS Persisted SSTA ensembles 1 Mo. lead 10 Post Processing PREDICTED SST ANOMALIES Tropical Pacific Ocean (LDEO Dynamical Model) (NCEP Dynamical Model)(NCEP Statistical CA Model) Tropical Altantic Ocean (CPTEC Statistical CCA Model) Tropical Indian Ocean (IRI Statistical CCA Model) Extratropical Oceans (Damped Persistence) Predicted SSTA ensembles 1-4 Mo. lead 24 RSM97 (60km) RAMS (40km) CPT AGCM INITIAL CONDITIONS UPDATED ENSEMBLES (10+) WITH OBSERVED SSTs IRI FUNCEME Sun et al. (2006)

4. Development of Regional Climate Prediction System • choosing GCM forecasts that have good skill over the region of interest • Identifying a group of regional climate models for downscaling • Determining model resolution and domain size • Customization of regional climate models • Observation • Initialization • Programs to nest regional models within GCMs • Ensemble runs of retrospectiveforecast – forecast skill • Statistical post processing of model output to correct model bias • Forecast product • Forecast verification

7. Development of Regional Climate Prediction System • choosing GCM forecasts that have good skill over the region of interest • Identifying a group of regional climate models for downscaling • Determining model resolution and domain size • Customization of regional climate models • Observation • Initialization • Programs to nest regional models within GCMs • Ensemble runs of retrospectiveforecast – forecast skill • Statistical post processing of model output to correct model bias • Forecast product • Forecast verification

8. Development of Regional Climate Prediction System • choosing GCM forecasts that have good skill over the region of interest • Identifying a group of regional climate models for downscaling • Determining model resolution and domain size • Customization of regional climate models • Observation • Initialization • Programs to nest regional models within GCMs • Ensemble runs of retrospectiveforecast – forecast skill • Statistical post processing of model output to correct model bias • Forecast product • Forecast verification

9. Development of Regional Climate Prediction System • choosing GCM forecasts that have good skill over the region of interest • Identifying a group of regional climate models for downscaling • Determining model resolution and domain size • Customization of regional climate models • Observation • Initialization • Programs to nest regional models within GCMs • Ensemble runs of retrospectiveforecast – forecast skill • Statistical post processing of model output to correct model bias • Forecast product • Forecast verification

10. Development of Regional Climate Prediction System • choosing GCM forecasts that have good skill over the region of interest • Identifying a group of regional climate models for downscaling • Determining model resolution and domain size • Customization of regional climate models • Observation • Initialization • Programs to nest regional models within GCMs • Ensemble runs of retrospectiveforecast – forecast skill • Statistical post processing of model output to correct model bias • Forecast product • Forecast verification

11. Development of Regional Climate Prediction System • choosing GCM forecasts that have good skill over the region of interest • Identifying a group of regional climate models for downscaling • Determining model resolution and domain size • Customization of regional climate models • Observation • Initialization • Programs to nest regional models within GCMs • Ensemble runs of retrospectiveforecast – forecast skill • Statistical post processing of model output to correct model bias • Forecast product • Forecast verification

12. Development of Regional Climate Prediction System • choosing GCM forecasts that have good skill over the region of interest • Identifying a group of regional climate models for downscaling • Determining model resolution and domain size • Customization of regional climate models • Observation • Initialization • Programs to nest regional models within GCMs • Ensemble runs of retrospectiveforecast – forecast skill • Statistical post processing of model output to correct model bias • Forecast product • Forecast verification

13. Development of Regional Climate Prediction System • choosing GCM forecasts that have good skill over the region of interest • Identifying a group of regional climate models for downscaling • Determining model resolution and domain size • Customization of regional climate models • Observation • Initialization • Programs to nest regional models within GCMs • Ensemble runs of retrospectiveforecast – forecast skill • Statistical post processing of model output to correct model bias • Forecast product • Forecast verification

14. Temporal anomaly correlations between the observed and the model ensemble mean rainfall

15. Geographical distributions of RPSS (%) for the hindcasts averaged over the period of 1971-2000

16. Development of Regional Climate Prediction System • choosing GCM forecasts that have good skill over the region of interest • Identifying a group of regional climate models for downscaling • Determining model resolution and domain size • Customization of regional climate models • Observation • Initialization • Programs to nest regional models within GCMs • Ensemble runs of retrospectiveforecast – forecast skill • Statistical post processing of model output to correct model bias • Forecast product • Forecast verification

17. Model Output Statistics (MOS) Jjj

18. Development of Regional Climate Prediction System • choosing GCM forecasts that have good skill over the region of interest • Identifying a group of regional climate models for downscaling • Determining model resolution and domain size • Customization of regional climate models • Observation • Initialization • Programs to nest regional models within GCMs • Ensemble runs of retrospectiveforecast – forecast skill • Statistical post processing of model output to correct model bias • Forecast product • Forecast verification

19. Address relevant scales and quantities –climate variables that are both relevant and predictable Precipitation Temperature Extreme events Onset of rainy season Dry spell & wet spell Tropical cyclones …

20. Development of Regional Climate Prediction System • choosing GCM forecasts that have good skill over the region of interest • Identifying a group of regional climate models for downscaling • Determining model resolution and domain size • Customization of regional climate models • Observation • Initialization • Programs to nest regional models within GCMs • Ensemble runs of retrospectiveforecast – forecast skill • Statistical post processing of model output to correct model bias • Forecast product • Forecast verification

21. Real-Time Forecast Validation

22. Resolution: Probabilities should differ from climatology as much as possible, when appropriate Probabilistic Forecasts Reliability: Forecasts should “mean what they say”. Reliability Diagrams Showing consistency between the a priori stated probabilities of an event and the a posteriori observed relative frequencies of this event.Good reliability is indicated by a 45° diagonal.

23. Development of Regional Climate Prediction System • choosing GCM forecasts that have good skill over the region of interest • Identifying a group of regional climate models for downscaling • Determining model resolution and domain size • Customization of regional climate models • Observation • Initialization • Programs to nest regional models within GCMs • Ensemble runs of retrospectiveforecast – forecast skill • Statistical post processing of model output to correct model bias • Forecast product • Forecast verification

24. Identifying and Correcting Model Errors

25. Optimizing Probabilistic Information Eliminate the ‘bad’ uncertainty -- Reduce systematic errors e.g. MOS correction, calibration Reliably estimate the ‘good’ uncertainty -- Reduce probability sampling errors e.g. Gaussian fitting and Generalized Linear Model (GLM) -- Minimize the random errors e.g. multi-model approach (for both response & forcing) -- Minimize the conditional errors e.g. Conditional Exceedance Probabilities (CEPs)

26. Systematic Spatial Errors Systematic error in locationof mean rainfall, leads tospatial error in interannualrainfall variability, and thusa resulting lack of skilllocally.

27. Systematic Calibration Errors … as well as in the magnitude of its interannual variability. ORIGINAL Statistical recalibration of the model’sclimate and its response characteristicscan improve model reliability. RECALIBRATED ORIGINAL RESCALED Dynamical models may have quantitativeerrors in the mean climate

28. Reducing Systematic ErrorsMOS Correction DJFM rainfall anomaly correlation before and after statistical correction January 25, 2006 UNAM (Tippett et al., 2003, Int. J. Climatol.)

29. Minimizing Random ErrorsMulti-model ensembling Combining models reduces deficiencies of individual models Probabilistic skill scores (RPSS for 2m Temperature (JFM 1950-1995)

30. Correcting Conditional BiasesMETHODOLOGY

31. Conditional Exceedance Probabilities The probability that the observation exceeds the amount forecast depends upon the skill of the model. If the model were perfect, this probability would be constant. If it is imperfect, it will depend on the ensemble member’s value. Identify whether the exceedance probability is conditional upon the value indicated. Generalized linear models with binomial errors can be used, e.g.: Tests can be performed on 1 to identify conditional biases. If 1 = 0 then the system is reliable. 0 can indicate unconditional bias. (Mason et al. 2007, Mon Wea Rev)

32. Idealized CEPs Positive skillSIGNAL too weak PERFECT Reliability β1>0 β1=0 Positive skillSIGNAL too strong Negative skill NO skill β1<0|β1|>|Clim| β1<0 β1= Clim. (from Mason et al. 2007, Mon Wea Rev)

33. Conditional Exceedance Probabilities (CEPs) Standardized anomaly Scale Shift 100% 50% 0% Use CEPs to determinebiased probability ofexceedance. Shift model-predicted PDF towards goal of 50% exceedance probability. Note that scale is a parameter determined in minimizing the model-CEP slope.

34. CEP Recalibrationcan eitherstrengthen orweaken SIGNAL Adjustment decreases signal Adjustment increases signal CEP Recalibrationconsistentlyreduces MSE Adjustment increases MSE Adjustment decreases MSE

35. Effect of Conditional Bias Correction