1. Decision Systems Research developing decision tools IRI Underpinning Activity Climate Prediction Applications Workshop Florida State University 9-11 March 2004 N. Ward And C. F. Ropelewski

2. Centrality of exploring and influencing sectoral decisions, based on information that draws on sound biophysical science IRI’s mission is to enhance society's capability to understand, anticipate and manage the impacts of seasonal climate fluctuations, in order to improve human welfare and the environment, especially in developing countries.

3. The Discussion Scope of the Underpinning Activity Illustrations of its presence in Regional Project Settings - NE Brazil and Philippines Water management - Farm level agriculture - Support at regional level for agriculture issue Role of training / capacity building

4. Components of the Work Development of Decision Strategies and Tools (DST) Methodologies to extract relevant environmental information to feed DST Testing of DST based on forecasts/information over past years Experimental implementation Developing decision support information Evaluating and learning through implementation

5. Simulating the Expected Improvements in Reservoir Management Example for Reservoir in Ceara, NE Brazil (collaboration led by Assis de Souza Filho, FUNCEME and Upmanu Lall, IRI additional contributions, especially Sankar Arumugam, IRI)

6. Reliability – Yield Curve

7. Spill (Reliability = 0.9) Simulating the benefits of using climate forecast information in the operation of a reservoir in Ceara over 1950-2000

8. SEASONAL WATER ALLOCATION AND RESERVOIR OPERATION UTILIZING CLIMATE INFORMATION BASED STREAMFLOW FORECASTS A aerial view of the Angat Hydroelectric Plant Courtesy of Mr. Rodolfo German (Angat dam)

9. Hydroclimatology 3-months lag correlation  (Nino3.4,Q JJAS ) = -0.20  (Nino3.4,Q OND ) = -0.51 JJAS – 30% OND – 46%

10. Reservoir Operation Strategy

11. Statistical Downscaling Results for Sri Lanka, 1951-80 Verification Map shows correlation skill (shading) along with contours of elevation Need for caution in regions of complex terrain

12. Mechanism operating in Sri Lanka?

13. a b r = 0.50 r = 0.62 Statistical Downscaling from General Circulation Model (GCM) output to (a) Predict Oct-Dec seasonal rainfall total (b) Predict Crop yield daily weather generator conditioned on GCM predicted wind field – resulting daily weather sequences used to drive a crop model Example for a Site in Kenya

14. Examples of Decisions Represented in Farm-level Models Field-scale crop management decisions: Cultivar selection Planting date Planting density Amount and timing of nitrogen fertilizer application Livestock stocking rates Farm-scale management decisions: Land allocation among crops Feed management (pasture planting and fodder purchase) Borrowing for production inputs (planned) Allocation of household labor among farm vs. non-farm activities (planned)

15. Objective 2: Tailored Products Problem areaUser(s)RequirementApplication 1) Areas of high Rift Valley Fever (RVF) outbreak risk Red Sea Livestock Trade Commission Predict RVF risk areas 3-6 months in advance Identify and treat RVF outbreaks before regional trade barriers are imposed 2) Livestock fodder availability Pastoral communities in northern Kenya and southern Ethiopia Narrow the confidence interval of the current Livestock Early Warning System (LEWS) 90 day fodder outlook using a seasonal forecast Provide improved 90 day early warning to nomadic pastoralists and sedentary agro- pastoralists of expected fodder conditions 3) Livestock fodder availability Organization of African Union/Inter-African Bureau of Animal Resources (OAU/IBAR) Provide 3-6 month early warning of major regional fodder shortages Support IBAR livestock purchase programs 4) Pastoralist livelihood system stress USAID, other donors and international emergency assistance organizations Simulate climate shock impacts on pastoralist livelihood systems and food security Contingency and operational assistance planning Greater Horn of Africa Project

16. Using Global Climate Model Output to Predict Vegetation The index is the satellite estimated Normalized Difference Vegetation Index (NDVI) Index shown is the October-December average for Northeastern Kenya Positive values indicate enhanced greenness in vegetation Correlation between predicted and observed = 0.84

17. Using Global Climate Model Output to Predict NDVI on a 1 degree lat x 1 degree lon grid across Kenya Skill (indicated by shading) is very good in most parts of the domain, less to the NE of Lake Victoria in region of complex orography (to be further investigated) Shading Indicates Correlation between predicted and observed NDVI time-series over 1981-98 Contours are elevation on 1 lat x 1 lon grid

19. Example of page From online course Collaboration with CCNMTL IRI is exploring the potential of online courses http://iri.columbia.edu/outreach

20. Advanced Training Institute on Climatic Variability and Food Security Palisades, New York, USA 8 - 26 July 2002 Dr. James Hansen, Training Institute Director The Advanced Training Institute on Security is designed to equip young developing country professionals with expertise in agriculture and food security to apply advances in climate prediction to their home institutions' ongoing efforts to address climate- sensitive aspects of agricultural production, food insecurity and rural poverty

21. Examples of the underpinning activity within emerging end-to-end regional projects Methodological advances to contribute to faster implementation in other regions

22. 0 0 1000 2000 3000 4000 5000 6000 7000 29 5 5 12 19 26 2 2 9 9 16 23 2 2 9 9 16 23 30 6 6 13 20 27 4 4 11 18 25 Date Cases JanuaryDec. February MarchAprilMay The forecasting principle Early warning for changes in the incidence of the meningococcal meningitis disease (Example for West African Sahel region) not preventable Vaccination starts Early_______________ Peak_________________Late______________ +Dust -Rain______

23. Feb-Apr Precipitation and Near-surface wind Predicted by a Regional Climate Model at 10km Resolution across Taiwan and surrounding ocean For case study years 1983 (generally WET) MINUS 1971 (generally DRY). Rainfall difference in mm per day as predicted by the model for 1983 relative to 1971 Illustrates the Need for caution In regions of Complex terrain