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Developing a Research Agenda for the Caribbean Food System to respond to Global Climate Changes

Developing a Research Agenda for the Caribbean Food System to respond to Global Climate Changes. 19-20 September, 2002 University of the West Indies, St. Augustine.

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Developing a Research Agenda for the Caribbean Food System to respond to Global Climate Changes

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  1. Developing a Research Agenda for the Caribbean Food System to respond to Global Climate Changes 19-20 September, 2002 University of the West Indies, St. Augustine

  2. The Potential for the Application of Climate Models to the Caribbean SituationPrepared byAdrian R. TrotmanAgrometeorologist, Caribbean Institute for Meteorology and Hydrology

  3. A bit about GCMs • GCM based on the principles of fluid dynamics and take into account the interactions between the different components of the climate system • Most predictions have been done with Atmospheric and Ocean Global Climate Models (AOGCMS) that provide projections on future climate on a global or planetary scale and the sub-continental scale.

  4. Problems associated with use of climate models for impact and vulnerability studies with small island states • GCM grids too large for small islands. Climates of islands are being predicted to change in the same way as the surrounding oceans. The horizontal resolution of AOGCMs is, typically, in the range 100 to 500 km. • GCM have been less consistent in terms of seasonal and shorter-term projections. • Lack of geographical detail

  5. The GCM will however be useful in vulnerability and impact studies on the marine environment (e.g. studies on coral reefs and associated biodiversity) since the global models are better at sea surface temperature estimation.

  6. Possible solution of downscaling: • GCMs have been used to represent the more regional situation by ‘forcing’ these models to run at higher resolutions (of about 100 km grid). For the islands of the Caribbean, these grids are too large for detailed information. • Empirical/Statistical and dynamical/statistical models relate historical observations of local climate parameters to parameters on a larger scale. The relationship is then applied to the large scale parameters from scenarios derived from the GCM to estimate the changes in regional and local -scale parameters at the surface in the future

  7. Possible solution of downscaling: Small grids • Smaller grids – more recently, models using higher resolutions (50 km grids) have been used to provide more useful information on a regional scale. These regional models are nested in the GCM, using its information (at least every 6 hours) to describe the boundary conditions to drive these higher-resolution models. • A 25 km version of the RCM PRECIS has been developed and is being tested and evaluated. A 10 km version is being planned for the near future.

  8. Problems with Downscaling methods • Relationships derived for present and recent past climates may not apply to possible future climates • Much of the information needed to develop relationships maybe a problem in remote areas or the data set may be too small because of the relatively short period of data collection at some stations (this may be a serious concern in the Caribbean region) • The accuracy of the predictions from RCMs depends on the quality of the GCM data that drive them • Very demanding on PC resources

  9. The Climate Studies Group, University of the West Indies, Mona (CSGM) • CSGM and Caribbean Epidemiology Centre is conducting a project funded by AIACC on Dengue • A statistical downscaling project forms part of AIACC SIS06 and will last 3 years. The group will be looking at both long term (50 years) and short term (5 - 10 yrs) projections of climate. • There is a second project involving dynamical downscaling using regional climate models. This project will be done in conjunction with IRI (mainly short term) and with MACC/CPACC (long term). • Just learning techniques.

  10. Conclusion • At this stage most projections and scenarios used in impact and vulnerability studies in this region are derived from GCMs. • As the resolution of RCMs get higher and statistical/empirical and statistical/dynamic models get better, information available from such studies will improve. • Until then, we will have to make the best possible use of information coming out of the larger scale models through the use of statistical methods.

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