Climate Scenario and Uncertainties in the Caribbean

1. ???? SIS 06 The Threat of Dengue Fever - Assessment of Impacts and Adaptation to Climate Change in Human Health in the Caribbean Climate Scenario and Uncertainties in the Caribbean Anthony Chen,Cassandra Rhoden,Albert Owino Climate Studies Group Mona,Department of Physics University of the West Indies,Mona, Jamaica

2. Outline • Scenario needs • General Problems with GCM in Scenario Generation, briefly • Downscaling results 1 • Problems with downscaling • Local problems • Downscaling results2 • Discussion • Conclusion

3. What is your Scenario need? •  How many scenarios do you want? Which uncertainties are you going to explore? •  What non-climate information do you need in your scenario(s)? • Do you need local data for case studies/sites, or national/regional coverage? • What spatial resolution do you really need – 300k, 100k, 50k, 10k, 1k? Can you justify this choice? •  Do you need changes in average climate, or in variability? • Do you need changes in daily weather, or just monthly totals? • What climate variables are essential for your study?

4. What are SIS06’s needs? •  How many scenarios do you want? - Statistical (A2 & B2) and Dynamics (Will not be available until 6 mths time) • Which uncertainties are you going to explore- Model uncertainties (Annual & seasonal) •  What non-climate information do you need in your scenario(s) – Use IPCC SRES • Do you need local data for case studies/sites, or national/regional coverage? - Local

5. SIS06 needs (cont • What spatial resolution do you really need – point (SDSM), 50 km (PRECIS) •  Do you need changes in average climate, or in variability? – average climate • Do you need changes in daily weather, or just monthly totals? Daily • What climate variables are essential for your study? – Temperature, Precip, Relative Humidity )

6. Problems with With GCM in creating Climate Scenarios Problem 1. Models are not accurate …. … so we ‘cannot’ use data from climate models directly in environmental or social simulation models

7. Problems with GCM in creating Climate Scenarios • Problem 2. Different climate models give different results … • … so we have difficulty knowing which climate model(s) to use

8. Model vs Observation Pattern Correlation over the Caribbean by Dr. Ben Santer, Lawrence Livermore NL

9. Problems with GCM in creating Climate Scenarios • Problem 3. It is expensive to run many (global/regional) climate model experiments for many future emissions …. • .… so we often have to make choices about which emissions scenarios from which we build our climate scenarios

10. Problems with GCM increating Climate Scenarios – many different Storylines

11. Problems with GCM in creating Climate Scenarios • Problem 4. Climate models give us results at the ‘wrong’ spatial scale … • … so we have to develop and apply one or more downscaling methods.

12. Problems with GCM in creating Climate Scenarios • Problem 4. Historical climate data may not be available… necessary as a baseline and also to explore historical/current variability/vulnerability

13. Downscaling A technique to take GCM atmospheric fields and derive climate information at a spatial / temporal scale finer than that of the GCM.

14. MAGICC/SCENGEN

17. Barbados, St Lucia, Trinidad (Temp)Magicc/Scengen

18. Barbados, St. Lucia, Trinidad (%Precip)Magicc/Scengen

19. Scenarios from Weather Generators (SDSM) • Downloaded from http://www.sdsm.org.uk/ Multiple, low cost, single-site scenarios of daily surface weather variables under current and future climate forcing

20. Main Advantages and Disadvantages of the SDSM. Advantages of SDSM • site or locality specific scenarios, long and multiple daily weather sequences produced • Use of specific Scenarios, depending on how the climate system is changing. (Site or locality specific) • Cheap, computationally undemanding. Disadvantages of SDSM • Requires high quality daily data for model calibration (30 years of historic data ) • based on empirical relationships which may change. • SDSM cannot analyze extreme events of weather thus a regional climate model (RCM) has to be developed

21. GCM Models vs SDSM - Baseline Temp for Trinidad

22. GCM Models vs SDSM - Temp Scenarios for Trinidad

23. GCM Models vs SDSM - Baseline Precip for Trinidad

24. GCM Models vs SDSM - Precip Scenarios for Trinidad

25. GCM Models vs SDSM - Baseline Temp for Barbados

26. GCM Models vs SDSM - Temp Scenarios for Barbados

27. GCM Models vs SDSM - Baseline Precip for Barbados

28. GCM Models vs SDSM - Precip Scenarios for Barbados

29. Seasonal Analysis • Seasonal variations are important for SIS06 • Baseline comparisons are good for annual data but falls down for seasonal data

32. Winter in the summer? Model does not simulate mid-summer drought properly

35. Barbados T(Min) -2080

39. Downscaling UncertainitesAssumption 1 “Local” Climate = f (larger scale atmospheric forcing) R = f (L) R: predictand - (a set of) regional scale variables L: predictors - large scale variables from GCM f: stochastic or quantitative transfer function conditioned by L, or a dynamical regional climate model.

40. Downscaling • Assumptions: • f is valid under altered climatic conditions - stationarity

41. Since downscaling propagates the GCM error,consider another assumption • Assumption 2: • The GCM is skillful (enough) with regard to the predictors used in the downscaling -- Are they “adequately” simulated by the GCM? • “Adequate” requires evaluating the GCM in terms of the predictor variables at the space and time scales of use! • e.g: For RCMs this could mean the full 3-dimensional fields of motion, temperature, and humidity, on a 6-12 hour time interval, over the domain of interest.

42. Problems encountered locally adding to uncertainties • Absence of quality data • Available predictors may not be the major drivers of climate • Lack of Resources to do ensembles • Lack of adequate understanding of regional climate for reliable prognosis • Seasonal biases in SDSM

43. Absence of Quality Data in SIS06 • Jamaica’s daily data prior to 1992 were lost due to a fire in the Met Office. • No daily Relative Humidity available • Quality control not assured

44. Attempt to fill in missing daily temperature data using monthly mean data daily temperature = daily anomalies + long-term monthly temperature average: Algorithm for calculating daily anomaly uses daily data from a station elsewhere in the island Source of algorithm – Dr. Xianfu Lu

45. Graph of WP synthetic daily vs WP observed daily (Temp ◦C)

46. Annual and Seasonal % change in Temperature for SIA with respect to Baseline A2 B2

47. Attempt to fill in missing daily precipitation data using monthly mean data • Similar to temperature method but used proportionalities • Source of algorithm – Dr. Xianfu Lu

48. Graph of WP synthetic daily vs WP observed daily (Precip mm/day)

49. Annual and Seasonal % change in Precip for SIA with respect to Baseline A2 B2

50. The problem with bias