1. Caio A. S. Coelho Centro de Previsão de Tempo e Estudos Climáticos (CPTEC) Instituto Nacional de Pesquisas Espaciais (INPE) caio.coelho@cptec.inpe.br 2 nd EUROBRISA workshop, Dartmoor, Devon, 21-24 July 2009 PLAN OF TALK 1. Current operational system 2. Investigation on identified issues 3. Advances on use of upper level circulation 4. Applications: river flow and dengue risk transmission prediction 5. Future plans 6. Summary Towards a new EUROBRISA operational system

2. Empirical model Predictors: Atlantic and Pacific SST Predictand: Precipitation Coelho et al. (2006) J. Climate, 19, 3704-3721 Hindcast period: 1987-2001 EUROBRISA integrated forecasting system for South America Integrated forecast U.K.UKMO (GloSea 3) InternationalECMWF System 3 CountryCoupled model  Combined and calibrated coupled + empirical precip. forecasts  Hybrid multi-model probabilistic system

3. Most recent EUROBRISA integrated forecast for ASO 2009 Issued: Jul 2009 Obs. SST anomaly Jun 2009 IntegratedUKMO Empirical Prob. of most likely precip. tercile (%) ECMWF

4. 4 Prior: Likelihood: Posterior: Calibration and combination procedure: Forecast Assimilation Matrices Forecast assimilation uses the first three MCA modes of the matrix Y T X. X: precip. fcsts (coupled + empir.) Y: DJF precipitation Stephenson et al. (2005) Tellus, 57A, 253-264

5. 5 If prior param.: FA becomes: Calibration and combination procedure: Forecast Assimilation Matrices Posterior: Stephenson et al. (2005) Tellus, 57A, 253-264 X: precip. fcsts (coupled + empir.) Y: DJF precipitation

6. Multivariate regression (MCA on Y T X: 3 modes) Principal component regression at each grid point (EOF on X: 1 mode) Why is skill negative for some grid points? Correlation skill: Integrated forecast (precipitation) Issued: Nov Valid: DJF (1987-2001)

7. Predictor: First PC of X Y 1988 1990 1992 1994 1996 1998 2000 Param. estimates sensitive to removal of indiv. data points Grid point with neg. corr. skill How stable are cross-validated predictors and regression parameter estimates? Grid point with pos. corr. skill Stable ENSO First PC of X Y Robust

8. How influential is each data point? Leverage is a function of the predictor alone, and measures the potential for a data point to affect the model parameter estimates H is the hat matrix Leverage: diag(H) n =15 data points p =1 PC 2p/n 1 col. matrix (1 st PC of X)

9. Can precipitation forecasts over the Pacific help improve forecasts over land? Source: Franco Molteni (ECMWF)

10. South America domain (270 o, 300 o, 60 o S, 15 o N) South America+Pacific domain (100 o, 300 o, 60 o S, 15 o N) Correlation skill: Integrated forecast Issued: Nov Valid: DJF (1987-2001)  Use of precip. fcsts over Pac. does help improve fcst. skill in S. America

11. South America domain: ECMWF, UKMO and empirical (limited to common hindcast period) South America + Pacific domain: ECMWF, UKMO, MF, CPTEC and empirical (diff. hind. periods) Can skill be improved by adding more models to the system? 1987-2001 1981-2005 Correlation skill: Integrated forecast (precipitation) Issued: Nov Valid: DJF  Adding more models does help improve skill in S. America

12. Can model predicted circulation variables help improve precip. forecast skill?  Use calibration procedure to explore atmospheric teleconnections

13. Rationale for the use of circulation patterns as predictor for seasonal precip. Precip. is influenced by atmospheric circulation patterns On seasonal timescales the frequency of occurrence of such patterns is influenced by anomalous patterns of sea surface temperatures (particularly in the tropics) The link between tropical SSTs and global circulation patterns involves the generation of quasi-stationary upper level wave trains from tropical diabatic heat sources to remote regions (e.g. ENSO teleconnections to South America) If upper level circulation is well simulated by seasonal climate models, it may then be possible to use upper level circulation predictions to produce precip. predictions for South America (i.e. precip. downscaling from upper level circulation )

14. How well do coupled seasonal forecast models simulate upper level circulation? ECMWFUK Met Office (GloSea 3) Obs NCEP/NCAR Reanalysis Kalnay et al. (1996) BAMS, 77(3), 437-471  Generally good skill in the tropics Pert. stream func. (  ’) Veloc. Poten. (  ) Correlation skill: 1-month lead forecasts for DJF Hindcasts: 1987-2005

15. Downscaling procedure: Forecast Assimilation Matrices Forecast assimilation uses first three leading MCA modes of the matrix Y T X. Y: DJF precipitation X: 1-month lead 200 hPa (  ’,  ) pred. for DJF (ECMWF + UKMO) Stephenson et al. (2005), Tellus A. Vol. 57, 253-264. ECMWF UKMO (  ’,  ) Forecast Assimilation Correlation skill: 1-month lead precipitation forecasts for DJF  Downscaled forecasts obtained with forecast assimilation have improved skill in North and Southeast South America compared to individual model predictions ’’

16. How does this compare with circulation-based and SST-based empirical predictions? Correlation skill: 1-month lead precipitation forecasts for DJF Fcst Assim. Emp: Circ-basedEmp: SST-based Predictor: 1-month lead  ’ pred. for DJF (ECMWF + UKMO) Predictor: Obs  ’ in previous Oct Predictor: Obs SST in previous Oct.

17. Seasonal forecast applications:

18. Flow prediction: Paraná river Issued: Nov Valid: Dec Flow (ONS) 1982-2003: F Oct SST (Reynolds et al. 2002): PC1, PC2 Precip. GPCP (Adler et al. 2003): P Integrated precip. forecasts (EUROBRISA) ECMWF, UKMO, MF, CPTEC: Pr Issued: Nov Valid: Feb Issued: Nov Valid: Jan Corr: 0.38 Corr: 0.43 Corr: 0.02 obsfcts

19. Dengue risk trans. model: Degalier et al. (2005) Environ, Risques & Santé 4 (2), 1-5 Favier et al. (2006) Trop. Med. and Int. Health 11 (3), 332–340 Morse et al. (2005) Tellus, 57A(3), 464-475 Dengue risk transmission index predictions NCEP/NCAR Reanalysis: Kalnay et al. (1996) BAMS, 77(3), 437-471 ECMWF System 3: Anderson et al. (2007) ECMWF Tech. Memo, 503, pp 56 T, RH Sim. risk Fcst. risk Hindcast period: 1981-2005 0 to 5 month lead predictions; 11 ensemble members Bias corr. T RH (climat.) Work by: Caio Coelho Rachel Lowe Nicolas Degallier

20. m: Environmental (climatic) capacity to sustain the development of the vector (optimum disease reproduction rate) n: climatic capacity to ensure transmission of the pathogen (larva/hab. ~ vector density capable of sustaining stable transmission) Both m and n are modelled as function of T and RH if n=m (R=0) if n>m (R>0) favorable conditions for transmission if n<m (R<0) unfavorable conditions for transmission 50<R<100: endemic risk R>100: epidemic risk Dengue risk transmission index (R) Source: Nicolas Degallier (IRD)

21. Skill assessment: Dengue risk transmission index prediction issued in Nov. (Gerrity score: terc. cat.) Valid: Nov Valid: Dec Valid: Jan Valid: Feb Valid: Mar Valid: Apr 0-month lead 1-month lead 3-month lead 2-month lead 4-month lead 5-month lead

22. Example: Dengue risk transmission index prediction issued in Nov 1997, valid for Apr 1998 Brasília Salvador 5-month lead fcst Obs Corr. skill Nov Dec Jan Feb Mar Apr

23. Future plans Investigate alternative methods of dimensionality reduction for the multivariate regression in the FA procedure Implement new version of EUROBRISA forecasting system - able to accommodate models with different hindcast periods - incorporate Meteo-France System 3 and CPTEC forecasts - how to proceed with UK Met Office GloSea 4 Research on seasonal forecast applications (agriculture, hydropower and health) Implement new approaches to visualise forecasts Produce joint EUROBRISA publication

24. Early stage of El Niño: EUROBRISA forecast for ASO 2009 is for below normal precip. in N South America and above normal precip. in SE South America Use of precip. forecasts over Pacific improves robustness of predictors and forecast skill over South America Adding more models to the integrated system helps improve forecast skill Coupled model upper level circulation predictions can be successfully used for producing skilful precip. forecasts for South America Preliminary results on application are encouraging for further developing research using seasonal forecasts New web link http://eurobrisa.cptec.inpe.br Summary