Research outcome
One important component of the project is climate forecasts. Statistical downscaling method is using to downscale the historical and future-projected precipitation patterns for the south Florida region. The underlying method, which employs a Self-Organizing Maps, explicitly allows for the examination of how specific climate models influence the distribution in state space of the underlying synoptic weather patterns (Ning et al., 2011a;2011b). We are applying this method to examine historical and projected future hydrological variability in south Florida (as embodied in the CMIP5 historical simulations and 21st projections of the IPCC 5th assessment report), and how projected changes in precipitation depend on projected changes in key climate modes such as the NAO and ENSO. These climate scenarios will be used to drive the optimization models and will be used as a basis for the development of rates of optimization criteria needed to relate soil carbon accumulation in the coastal and freshwater Everglades to hydro-climate conditions.
In order to accomplish the research on downscaling of global models for south Florida, we divided the work into three major components:
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Evaluation of performance of CMIP5 models (historical) precipitation and surface temperature against observation over Florida.
    [results]
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Downscaling of CMIP5 model products using Self-Organizing Maps (SOM) for historical periods and evaluation of the skills of techniques.
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Downscaling of CMIP5 models for historical era.    [results]
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Some diagnostic results.     [results]
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Evaluation of precipitation projections and associated uncertainties.
    [results]
* DAILY PRECIPITATION DATA    (ARCHIVE)