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|Cocke, S., & LaRow, T. E. (2000). Seasonal Predictions Using a Regional Spectral Model Embedded within a Coupled Ocean-Atmosphere Model. Mon. Wea. Rev., 128(3), 689–708.|
|Cocke, S., Boisserie, M., & Shin, D. - W. (2013). A coupled soil moisture initialization scheme for the FSU/COAPS climate model. Inverse Problems in Science and Engineering, 21(3), 420–437.|
|Cocke, S., Christidis, Z., LaRow, T., & Shin, D. W. (2002). Performance of a Coupled Ocean-Amosphere Model on the IBM SP4. In Proceedings from the Tenth Workshop on the Use of Parallel Computers, ECMWF, in Meteorology, Reading, U.K..|
|Cocke, S., LaRow, T. E., & Shin, D. W. (2007). Seasonal rainfall predictions over the southeast United States using the Florida State University nested regional spectral model. J. Geophys. Res., 112(D4).|
|Cocke, S. D., & LaRow, T. E. (1999). ), Seasonal Predictions of ENSO Impacts using a Nested Regional Spectral Model (H. Ritchie, Ed.). CAS/JSC Working Group on Numerical Experimentation, Research Activities in Atmospheric and Oceanic Modeling.|
|Coleman, F. C., Chanton, J. P., & Chassignet, E. P. (2014). Ecological Connectivity in Northeastern Gulf of Mexico – The Deep-C Initiative. In International Oil Spill Conference Proceedings (Vol. 2014, pp. 1972–1984).|
|Coles, V. J., Stukel, M. R., Brooks, M. T., Burd, A., Crump, B. C., Moran, M. A., et al. (2017). Ocean biogeochemistry modeled with emergent trait-based genomics. Science, 358(6367), 1149–1154.|
Coles, V. J., Stukel, M. R., Brooks, M. T., Burd, A., Crump, B. C., Moran, M. A., et al. (2017). Ocean biogeochemistry modeled with emergent trait-based genomics. Science, 358(6367), 1149–1154.
Abstract: Marine ecosystem models have advanced to incorporate metabolic pathways discovered with genomic sequencing, but direct comparisons between models and “omics” data are lacking. We developed a model that directly simulates metagenomes and metatranscriptomes for comparison with observations. Model microbes were randomly assigned genes for specialized functions, and communities of 68 species were simulated in the Atlantic Ocean. Unfit organisms were replaced, and the model self-organized to develop community genomes and transcriptomes. Emergent communities from simulations that were initialized with different cohorts of randomly generated microbes all produced realistic vertical and horizontal ocean nutrient, genome, and transcriptome gradients. Thus, the library of gene functions available to the community, rather than the distribution of functions among specific organisms, drove community assembly and biogeochemical gradients in the model ocean.
Keywords: Atlantic Ocean; Biochemical Phenomena/genetics; Metabolic Networks and Pathways/*genetics; Metagenome; *Metagenomics; Microbial Consortia/*genetics; Models, Biological; Seawater/*microbiology; Transcriptome
|Collier, C. (2012). Effects of Sea State on Offshore Wind Resourcing in Florida. Master's thesis, Florida State University, Tallahassee, FL.|
Conlon, K. C., Kintziger, K. W., Jagger, M., Stefanova, L., Uejio, C. K., & Konrad, C. (2016). Working with Climate Projections to Estimate Disease Burden: Perspectives from Public Health. Int J Environ Res Public Health, 13(8).
Abstract: There is interest among agencies and public health practitioners in the United States (USA) to estimate the future burden of climate-related health outcomes. Calculating disease burden projections can be especially daunting, given the complexities of climate modeling and the multiple pathways by which climate influences public health. Interdisciplinary coordination between public health practitioners and climate scientists is necessary for scientifically derived estimates. We describe a unique partnership of state and regional climate scientists and public health practitioners assembled by the Florida Building Resilience Against Climate Effects (BRACE) program. We provide a background on climate modeling and projections that has been developed specifically for public health practitioners, describe methodologies for combining climate and health data to project disease burden, and demonstrate three examples of this process used in Florida.
Keywords: *Climate Change/statistics & numerical data; Florida; Forecasting; Humans; Models, Theoretical; Public Health/*trends; United States; adaptation; attributable fraction; climate modeling; project disease burden; public health