Contact Person: Misra, Vasubandhu
Collaborators: Misra, V.
Institutions: Florida State University
Funding Agency: USGS
Abstract: La Florida, the “Land of Flowers” straddles the latitudes that form the northern hemisphere’s desert belt. Orlando lies one degree of latitude south of Cairo, Egypt. Florida’s uniqueness lies in the fact it is a long narrow peninsula surrounded on three sides by warm water. How will Florida’s biodiversity respond to a changing climate? Which species and habitats will increase and which will decrease? What role does human induced land use – land cover (LULC) change play? Before these questions can be answered, accurate regional climate change scenarios must be developed. We propose to down-scale predictions from a suite of coupled Atmospheric-Ocean General Circulation Models (AOGCMs) to make regional scale predictions for the Florida peninsula. We will run three scenarios of LULC: past (circa 1900), present, and future (2030-2050). Additional model runs will address the contribution of green house gasses to climate variability and change over the Florida peninsula. Model perturbation experiments will be performed to address sources of variability and their contribution to the output regional climate change scenarios. We will develop scenarios that specifically address potential changes in temperature (land and near sea surface) and rainfall fields over the peninsula. These outputs will then be used as inputs to a suite of species / habitat / ecosystem models that are currently being used as part of the Comprehensive Everglades Restoration Plan as a proof of concept that down-scaled climate results can work in ecological forecast models. We will then provide these scenarios and modeling results to resource management groups (NGOs, state and federal) via workshops in which the scenarios will be used to predict responses of additional selected species, habitats and ecosystems. This research addresses several of the fundamental charges of the NCCWSC – downscaling GCMs for regional predictions, regional ecological models and risk assessment.