1. Documenting Results of Dynamical Downscaling of Climate Forecasts over the Equatorial East Africa Using Regional Spectral Model Drs. Matayo Indeje, L. Sun, J. Mutemi & L.J. Ogallo 11 th international RSM workshop, August15-19, 2011, National Central University, Jongli, Taiwan

2. STUDY AREA Rainfall Annual Cycle Equatorial Eastern Africa Blue – Eastern half of the region Red – western half of the region

3. GCM OROGRAPHY ON A T42 GRIDRSM OROGRAPHY ON A 55KM GRID Reason for downscaling Displacement of Orography in Global Models

4. Regional Climate Model Challenges Over Eastern Africa Complex Topographic features > Land/sea, Land/Lake contrasts Orographic Forcing > East Africa Highlands, Ethiopian Highlands, Ruwenzoris, Turkana Channeling Effect Diverse Vegetation Types Ethiopian Highlands East African Highlands Lake Victoria Trukana Channel Ruwenzoris Indian Ocean Congo Forest

5. NCEP REGIONAL SPECTRAL MODEL (RSM-CVS) Grid Spacing: 55km (~ x=108, y=69 grid points) Time Step: 200s Simplified Arakawa-Schubert Cumulus Scheme Simulation Time on IBM-RS6000 Computer ~ 24 minutes a day on a single processor Lateral forcing: ECHAM4.5 GCM (Provided by the IRI) Completed RSM climatology of 30 Years (1970-1999) based on 10 Ensemble runs (since 2003) Operational Real Time RSM downscaled Forecasting since 2005

6. 30-YEAR MODEL CLIMATOLOGY GCMRSM OBSERVATION

7. Dynamical downscaling: Nesting a high resolution dynamical model within a global GCM. AGCM (250km res.) Regional Spectral Model (55km res.) OND 1997 OND 1998OND 1999

8. RSM CASE STUDY FOR ANOMALOUS YEARS (El Nino/wet 1997 and La Nina/Dry 1999) Wet 1997 Dry 1999Difference

9. Simulation of Regional Circulation Patterns at 850 and 200-hPa over GHA

10. DAILY RAINFALL REALIZATION: RSM Vs OBSERVATIONS

11. MODEL VALIDATION

12. EOF Analysis: RSM Vs Observation 50 % Variance46 % Variance Corr. Coef = 0.78

13. EXAMPLE OF REAL TIME SEASONAL DYNAMICAL FORECAST: OCTOBER TO DECEMBER 2004 55 KM RESOLUTION AN BN NN

14. Regional Climate Model Products are Tailored for Application in; Crop Modeling (Agriculture and Food Security) Disease Monitoring (Malaria, RVF, etc) Hydrological Applications (hydro-power)

15. CLIMATE RELATED RISK MANAGEMENT. HUNGER - DROUGHT – RAINFALL DEFICIT. DISEASE – RAINFALL AND TEMPERATURE

16. Malaria Epidemic Prediction Model For East Africa. Epidemic malaria in the highlands (Altitude: 1500- 2500 meters above sea level). Malaria cases increased Threfold in the region since 1990

18. Where ER is the epidemic risk Ti is the current mean monthly maximum temperature anomaly Ri is the current mean monthly rainfall above 150 mm threshold for Tm is the maximum intensity index for monthly mean temperature anomaly (Climatology) Rm is the maximum intensity index for monthly mean rainfall anomaly (Climatology) Rainfall above 300 mm per month takes on negative index values as such rainfall causes flashing of larvae thus reducing transmission. Epidemic Risk (ER) above 50% indicates a high risk of an epidemic. The model uses climate data to forecast an epidemic risk

19. Research on Climate Change Downscaled Scenario Physical and Dynamical Mechanisms responsible for the projected trend in rainfall and Temperature