1 Report on Statistical Downscaling Scenario generation task of SIS06: The Threat of Dengue Fever - Assessment of Impacts and Adaptation to Climate Change in Human Health in the Caribbean Student: Lawrence Brown Supervisors: Anthony Chen, Albert Owino

2 The purpose of climate scenarios To provide data for impact/adaptation/assessment studies To act as an awareness-raising device To aid strategic planning and or policy formation To scope the range of plausible futures To structure our knowledge (or ignorance) of the future To explore the implications of decisions To function as learning machines, bridging analyses and encouraging participation

3 DownScaling  Basically, downscaling is any process where large (coarse) scale output of models is reduced or made finer.  The first attempt to DownScale the global models was by the production of regional models. These regional models resolutions of 70 to 50 km square.  However there is still a need to go even further by DownScaling.  This is what is require by impact researchers.

4 What we require from downscalin 300km 50km Point General Circulate Models supply... Impact models require... Regional models supply 1m 10km

5 Objectives of SIS06 Downscaling:  To develop future climate scenarios so that the climate health linkages being developed and the impacts being studied can be used to develop adaptaton strategies.  To contribute to the National Communications under the UNFCCC

6 DownScaling Methods  Stochastic DownScaling - Probabalistic  Dynamical DownScaling - running a higher resolution RCM within a coarser resolution GCM.  Weather Typing approaches - grouping of countries with similar weather  Regression-based DownScaling - empirical relationships between local scale predictand and regional scale predictors.

7 Statistical DownScaling Model (SDSM) developed by Dr. Rob Wilby, Dr. Christian Dawson and Dr. Elaine Barrow  Downscales GCM output from course grid to island or local level  SDSM is a hybrid of the multiple Regression and Stochastic DownScaling models.  The model assumes Stationarity - statistical properties of the variable being downscaled will not change over time.

8 Advantages of SDSM  Cheap - not computational demanding and is readily transportable.  Flexible - can be tailored to target a variety of variable e.g. storm surge.  Generates ensemble of climate scenarios - allows risk (uncertainty) analysis.  Rapid application to multiple GCM’s.  Complementary to regional modelling  Fairly simple to use and thus accessible beyond research community

9 Disadvantages of SDSM  Dependent on the realism of the GCM boundary forcing.  DownScaling in general propagates the GCM error.  Require high quality (daily, in our case) data for calibration of the model.  Relationship between predictor and predictand are often non stationary.  Predictor variables used explain only a portion of the variability.  Low frequency climatic variables are problematic to downscale.

10 Steps in downscaling The process involves basically four steps  Gathering of the predictor (model output) and predictand variables (observed).  Screening: to decide which GCM to use, which variables are most relevant, the best predictor predictand relationships, the various locations in the different countries to downscale, the best transfer scheme to use.  Calibrating the model and Developing results for the different time slices.  Interpretation and Presentation of findings.

11  ‘Quality Control’ and ‘Transform’ data  ‘Screen Variables’ (Selection of predictors)  ‘Calibrate Model’ (Regression equations)  ‘Weather Generator’ (Synthetic daily weather);  ‘Analyse Data & Model Output’ (Statistics)  ‘Compare Results’ (Graphs)  ‘Generate Scenario’ (Synthetic daily weather using model predictors) Overview of the SDSM: Key functions of SDSM - Menu driven

12 Domain of interest - map

13 Domain of interest The coordinates for the area we will be looking at is latitude 10 to 22 degrees north and longitude 55 to 90 degrees west.  This area is made up of 27 countries and also sections of five other countries.  At first we will be looking at a subgroup which include Trinidad & Tobago, Jamaica, St Kitts and Barbados  The land area represent nearly 10% of the area of interest.

14 GCM outputs used and to be used:  Preliminary _ 1 st generation Couple General Circulation Model developed by the Canadians (CGCM1)- readily available, output formatted for use in SDSM  Primary model - HadCM3, simulates Caribbean climate best (Santer, 2001) - predictors being prepared in an SDSM friendly format  Others

15 Emission Scenario being used - IS92a IS92 readily available, later SRES not readily available at start of project. IS92 was proposed in the 1992 Supplement (IPCC,1992) to the IPCC First Assessment Report of Basically the IS92 scenarios ranged from a-f (IS92a-f) and considered various factors that would affect emissions up to the year IS92a - a middle range scenario: population reaches 11.3 billion, convention and renewable energy sources are used. Only emission scenarios internationally agreed on and national policies enacted into law are included, e.g., London Amendments and the Montreal Protocol.

16 SRES emission scenarios to be used SRES emission developed after IS92 was evaluated in 1995: New knowledge relating to: e.g. carbon intensity of energy supply, income gap between developed and developing countries and sulphur emissions. Special Report on Emission Scenarios (SRES) has 4 groups and 6 scenarios A1 (which has 3 sections A1FI, A1T and A1B) and A2, B1, B2. - Only A2 and B2 readily available for use in SDSM, others will be considered. o-Emissions

17 SRES Family

18 A2 SRES scenario: A very heterogeneous world with an underlying theme of self reliance and self preservation of local identities. Fertility across regions converges very slowly and this results in continuous increase in population. The A2 world “consolidates” into a series of roughly continental economic regions, emphasizing local culture and roots, e.g., some social and political structure moving towards stronger welfare systems and reduced income inequity while others move towards lean government. Environmental concerns relatively weak. Economic growth and technological change are more fragmented and slower than other storylines.

19 B2 SRES scenario: A world in which emphasis is placed on local solutions to economic, social and environmental sustainability. Education and welfare are widely pursued, reductions in mortality and to a lesser extent fertility, population reaching about 10 billion people by Income per capita grows at an intermediary rate to reach about US$12,000 by This generally high educational level promotes both development and environmental protection. Technological are pushed less than in A1 and B1 but higher than in the A2 scenario.

20 Work done – training and learning  Training under auspices of Caribbean/Canadian CC initiative  Contact made with various agencies and organisations in an effort to fill the gaps in the CSGM database.  Predictors for the HadCM3 (monthly) and CGCM1 gathered.  The transformation of predictors and predictands to use in CGCM1.  The generation of test/preliminary scenarios for Piarco Airport in TNT, Grantley Adam Airport Barbados, Agronomy St. Kitts and Guantanimo Bay Cuba.

21 Analysis to date –Piarco airport TNT Predictants- Rainfall and Temperature Predictors (and predictor code) used Rainfall – Surface vorticity - ncepp__zna.dat 850 hpa vorticity - ncepp8_zna.dat Specific humidity at 500 hpa - nceps500na.dat Specific humidity at 850 hpa - nceps850na.dat Mean temperature at 2 metres - nceptempna.dat

22 Analysis to date –Piarco airport (TNT) temp Predictors used and their code  Mean seas level pressure ncepmslpna  Surface air flow strength ncepp__fna  Surface zonal velocity ncepp__una  Surface meridonal velocity ncepp__vna  Surface vorticity ncepp__zna  Surface divergence ncepp_zhna  500 hpa air flow strength ncepp5_fna  500 hpa vorticity ncepp5_zna  500 hpa geopotential height ncepp500na  850 hpa zonal velocity ncepp8_una  850 hpa specific humidity nceps850na  Near surface specific humidity spncepsphuna  Mean temperature at 2 metre nceptempna

23 SDSM Piarco rainfall validation

24 Sample result - Rainfall

25 Rainfall assessment No significant difference in the annual mean over 90 years

26 SDSM Piarco temp validation Obspiarco T m in Piarco819 0 T n W G st s Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

27 Sample result - temp Obspiarco8 190 Tmi nSt s.: Me an Piarco T nS G2 sts: Me an Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

28 Assessment of Results to date These exercises are a learning experience No importance is placed on these results More detailed results required to know if the length of dry or wet spells will increase, time of year that increases occur, etc.

29 Future Work  Continue collection of data  Look at other GCM outputs – consider ensembles  Look at SST as possible predictor – need AOGCM SST output  Compare SDSM results with RCM results

30 AREAS OF CONCERN  The SDSM uses daily climate data - Do we have enough, high quality daily data (especially temperature )?  Will we have station data at locations to develop correlations with areas of Dengue risk?  SST plays important role in Caribbean weather but is not on the list of predictors for HadCM3.  Only 10% of area of domain is land - GCM see many area as ocean, may lead to damping of results.

31 Steps being taken to reduce problem  Agencies and met offices contacted for data by both the climate and Epidemiology group data  In the case of Jamaica where there was a fire some years ago, steps taken to access data on tape and interaction with the met office has resulted in significant data being gathered.  We are looking at using monthly time scale in case the daily time scale is not feasible as the seem to be a plethora of monthly data around.  Checks will be made to ascertain the level of dampening that might occur.

32 The End