MEA 593 Climate Risk Analysis for Adaptation Instructor – Fredrick Semazzi Lecture-7

1. MEA 593 Climate Risk Analysis for Adaptation Instructor – Fredrick Semazzi Lecture-7 The International Climate Outlook Forum Program & Stakeholders of Climate Information

2. Outline Regional Climate Outlook Forum Program (international climate prediction system) Agriculture sector (example)

3. CLIMATE HAZARDS Floods

4. Regional Climate Prediction Program A Worldwide Regional Prediction Process(http://www.wmo.int/pages/prog/wcp/wcasp/RCOFReview2008.html)

5. Birth of RCOFs Regional Climate Outlook Forums (RCOFs) were initiated at a Workshop on Reducing Climate-Related Vulnerability in Southern Africa in October 1996 at Victoria Falls, Zimbabwe – Chair Fredrick Semazzi The first RCOF was organized for the South African Region (SARCOF) in September 1997

6. WMO Regional Climate Centers (RCCs) Serve over 3 billion people. Regional Climate Centers Serve the Great Demand for Skillful Prediction Climate Prediction • AFRICAN COFS • GHACOF: Greater Horn of Africa COF • SARCOF: Southern Africa COF • PRESAO: Western Africa • COFPRESAC: Central Africa CO • REST OF THE THE WORLD • FOCRAII: Forum on regional Climate monitoring, assessment and prediction for Regional Association II (Asia) • SSACOF: Southeast of South America COF • WCSACOF: Western Coast of South America COF • CCOF: Caribbean COF • FCCA: Foro Regional del Clima de América Central • PICOF: Pacific Islands COF • SEECOF: SouthEastern Europe COF

7. A training workshop on seasonal climate prediction to strengthen the capacity of the national and regional climate scientists; • Regional and international climate experts meet & develop a consensus for the regional climate outlook using national, regional and global information, typically in a probabilistic form; • The Forum proper, involves climate scientists, representatives from the user sectors & the media, for identification of impacts and implications, and the formulation of response & effective communicationsstrategies; WorldwideRCOF Process

8. NEACC BCC TCC ACMAD ICPAC CIIFEN SADC-CSC Designated RCCs Pilot RCCs Pilot RCC Networks Pilot RCCs by 2012 Pilot RCC Networks by 2012 Pilot RCCs in development GFCS RCCs

9. Seasonal Forecast Development A seasonal forecast is developed at national level mainly using empirical statistical Techniques e.g., CPT (Climate Predictability Tool). The scientists participating in the national conference go to the regional COF with the national forecast for updating and fine-tuning.

10. Seasonal Forecast DevelopmentDownscaling • Downscaling of the COF Forecast is done immediately after the scientists return from the COF meetings A statement to the public is developed. It comprises • A review and projection of expected climate systems (ENSO etc) • Seasonal outlook – Rainfall • An advisory (expected impacts) to major user sector A Press Conference involving climate scientists, key stakeholders and the media is convened where the forecast and its implications are discussed detail before being disseminated to the public and other users

11. ICPAC (IGAD Climate Prediction and Applications Centre) and SADC (Southern African Development Community) OND 2008 – Tercile Forecats Tercile-category probability maps Tercile categories have a baseline probability of 33.3% and by definition are expected to occur, on average, once in every three years. On the prediction maps, color shading shows the forecast probability of the three categories: Above Normal Normal Below Normal

12. Products and dissemination Process • Products • Seasonal outlook • Climate forecasts (Dekadal and monthly) • Agro-meteorological bulletins (dekadal and monthly bulletins)

13. The meaning of tercile probabilities in climate forecastsTerciles are used to represent three broad sectors of the probability distribution that are equally likely, climatologically. Recall that for each location and season, the terciles correspond to actual temperature or precipitation ranges, based on the set of historical observations. In using tercile forecasts, users need to know the ranges (i.e. the two main cutoff values that define the terciles) to which the terciles refer for the location/season of concern. These are given on the web, usually on a map other than the one showing the probability forecasts since all this information would be too much to post on one map. Without any forecast clues, the probability that any of the three outcomes will occur is one-third, or 33.3%, which means that if the situation could be "rerun" many times, each outcome would occur one out of three times. However, with forecast clues, such as the presence of an El Nino, a La Nina, or other climate event, the probabilities of the terciles might no longer be equal, so that the probability of one (or two) of them would be greater than 33.3% and the remaining one(s) less than 33.3%. This deviation from the climatological 33.3-33.3-33.3% represents a forecast, because it suggests increases and decreases in the likelihoods of occurrence of terciles relative to the likelihoods reflected in the long-term observations. Forecasts are expressed in terms of the likelihood of terciles because of the typically large amount of uncertainty in the forecasts. This uncertainty makes the forecasting of exact temperatures, or amounts of precipitation, misleading, since large errors are often likely. (Such errors would not be as large, however, as the errors that would result from random guessing, or from always forecasting the climatological average.) The use of tercile probabilities provides both the direction of the forecast relative to climatology, as well as the uncertainty of the forecast. For example, suppose a forecast calls for precipitation probabilities of 20% for the dry tercile, 35% for the middle tercile, and 45% for the wet tercile. Since the wet tercile is above 33.3% and the dry tercile is below 33.3%, this forecast suggests that above normal precipitation is more likely than it usually is, and below normal is less likely than usual. Note, however, that there is much uncertainty implied in the forecast. Even though it is in the direction of above-normal precipitation, the probability for above normal precipitation is still less than 50%. And the probability of below normal precipitation is still 20%, implying that one time out of 5 cases of this climate situation, below normal precipitation would be expected. It is clear that even though this forecast shows a tilt of the odds toward wetness relative to the climatological probabilities, there is much uncertainty in the outlook

14. Additional Guidance for Class Project in the Agriculture Sector(Will not to be examined)

15. Qn1: What do the Agric User Community need from the climate community (ICPAC, National Mets and WMO) • We need accurate forecasts/predictions indicating: • Expected onsets • Expected cessation dates • Rainfall distribution (in time and space) • Temperature information (i.e. frost and heat waves) • Future climatic scenarios and trends • Higher spatial resolution for daily, five-day and 10 day forecasts for all Mets • Mainstream and improve forecast dissemination by using the right channels that reach users • Provide long range forecasts and climate related scenarios culminating from climate variability

16. Cont’d • Sensitise communities on how to interpret weather forecasts for effective utilisation • Establishment of user Multi-Stakeholder Platforms and institutionalizing them at all levels (regional, national & local levels) for effective use of forecast information • Support interpretation of forecasts, and the development & dissemination of downscaled climate information to the user communities • Capacity building for policy makers to understand the forecast process and the importance of climate information

17. Qn2: What can users provide to the climate community • Provide feedback on experiences and impacts of climate from our sectors • Collect and provide voluntary observations (esp. rainfall, temp.) at local level if given relevant equipment • Provide feedback on the performance of the previous forecast (COF products) • Provide information about vulnerability thresholds

18. Qn3: Why have we not been using climate information as was expected • Limited access to info: The current major channels of dissemination of climate information (internet, TV, Newspaper) are not adequate to reach the majority of grass root users • There are very weak agro-meteorological extension services in the region • Because users are not aware of the uncertainties underlying the forecasts, & because sometimes the information turns out not to be accurate, farmers do not have the confidence to continue using climate forecast information • Some policies on data sharing also limit usage

19. Qn4: Suggest how differently the COF Products could be improved to support the Agric & FS Sector • Conduct early COFs so as to allow enough time for the NMHS to provide downscaled information to users on time (e.g. 3 or more weeks time) • This ensures timely panning and implementation • The climate community should be encouraged to have more interaction with the information users including the policy makers • Provide definitions and/or meanings for the technical terms used; also provide additional information to accompany the forecasts (e.g. analog statistics)

20. Recommended policy strategies • Climate community should regularly conduct impact studies; needs assessments; tracking dissemination and use of climate information products; and capture any other related feedback from the users • Greater Horn of Africa COF (GHACOF) to attempt to conduct case studies on long range forecasts and provide relevant future climatic scenarios e.g. future climate changes & the anticipated impacts • Greater Horn of Africa COF (GHACOF) to be done 3 or more weeks before onset of the season

21. Agriculture Relevant climate Information • All NMHS to provide timely information on: • Expected onsets • Expected cessation dates • Rainfall amounts and distribution (in time and space) • Brief on the potential anticipated hazards and associated impacts (both –ve and +ve) • Information on temperature including frost and heat waves • A general advisory on how to use the climate information profitably

22. Agriculture Relevant climate Information • Provide timely early warning messages and pre-disaster assessment scenarios • Report promptly any observed deviation from the predicted or anticipated scenario, e.g. unforeseen dry spells