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

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

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?

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

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

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

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

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

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

Problems with GCM increating Climate Scenarios – many different Storylines

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.

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

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

MAGICC/SCENGEN

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

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

Scenarios from Weather Generators (SDSM) Downloaded from Multiple, low cost, single-site scenarios of daily surface weather variables under current and future climate forcing

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

GCM Models vs SDSM - Baseline Temp for Trinidad

GCM Models vs SDSM - Temp Scenarios for Trinidad

GCM Models vs SDSM - Baseline Precip for Trinidad

GCM Models vs SDSM - Precip Scenarios for Trinidad

GCM Models vs SDSM - Baseline Temp for Barbados

GCM Models vs SDSM - Temp Scenarios for Barbados

GCM Models vs SDSM - Baseline Precip for Barbados

GCM Models vs SDSM - Precip Scenarios for Barbados

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

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

Barbados T(Min) -2080

Downscaling Uncertainites Assumption 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.

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

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.

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

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

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

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

Annual and Seasonal % change in Temperature for SIA with respect to Baseline A2B2

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

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

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

The problem with bias

Predictors not most applicable for SIS06 Sea Surface Temperatures (SST’s) are significant predictors for climate in the Caribbean, likely also for all SIS’s Historical gridded SST’s are available GCM SST predictors not readily available for use in SDSM

Lack of Resources to do ensemble in SIS06 MACC project however will do runs using PRECIS regional model Results will not be available until 2005

Inadequate Understanding of Climate in SIS06 Several papers have been published on interannual and seasonal variability However no adequate predictive model for variability has been produced for SIS06 countries due to inadequate understanding

Recall Downscaling Uncertainites Assumptions “Local” Climate = f (larger scale atmospheric forcing) R = f (L) R: predictand - (a set of) regional scale variable L: predictors - large scale variables from GCM f: stochastic or quantitative transfer function conditioned by L, or a dynamical regional climate model. We need to know f more accurately

Problems encountered in running SDSM Overflow in Calculation - This is due to the number of missing data

Conclusion Downscaling can add value to GCM outputs, works better for Temperature than for Precip A few cases where GCM agreed with SDSM (Temp for Trinidad) For site studies, one must use downscaled results (Those cases where SDSM does not agree with GCM) Problems compounded by lack of adequate predictor and observed data There were seasonal biases in SDSM calibration especially for precipitation. SDSM cannot analyze extreme events of weather thus a regional climate model (RCM) has to be developed. Since SDSM and other statistical models assume that climate transfer function is stationary, we need to know more about current climate, to fully evaluate SDSM and decrease the uncertainties. Use of many models, ensembles needed to minimize uncertainty

Thank You for listening