|Home||<< 1 2 3 4 5 6 7 8 9 10 >> [11–11]|
|Bastola, S., & Misra, V. (2015). Seasonal hydrological and nutrient loading forecasts for watersheds over the Southeastern United States. Environmental Modelling & Software, 73, 90–102.|
|Bastola, S., Misra, V., & Li, H. (2013). Seasonal Hydrological Forecasts for Watersheds over the Southeastern United States for the Boreal Summer and Fall Seasons. Earth Interact., 17(25), 1–22.|
|Belyaev, K. P., Tanajura, C. A. S., & O'Brien, J. J. (2001). A data assimilation method used with an ocean circulation model and its application to the tropical Atlantic. Applied Mathematical Modelling, 25(8), 655–670.|
|Boisserie, M., Shin, D. W., LaRow, T. E., & Cocke, S. (2006). Evaluation of soil moisture in the Florida State University climate model-National Center for Atmospheric Research community land model (FSU-CLM) using two reanalyses (R2 and ERA40) and in situ observations. J. Geophys. Res., 111(D8).|
|Buijsman, M. C., Arbic, B. K., Richman, J. G., Shriver, J. F., Wallcraft, A. J., & Zamudio, L. (2017). Semidiurnal internal tide incoherence in the equatorial Pacific. J. Geophys. Res. Oceans, 12(7), 5286–5305.|
|Cammarano, D., Stefanova, L., Ortiz, B. V., Ramirez-Rodrigues, M., Asseng, S., Misra, V., et al. (2013). Evaluating the fidelity of downscaled climate data on simulated wheat and maize production in the southeastern US. Reg Environ Change, 13(S1), 101–110.|
|Chakraborty, A., Sharma, R., Kumar, R., & Basu, S. (2014). An OGCM assessment of blended OSCAT winds. J. Geophys. Res. Oceans, 119(1), 173–186.|
|Choi, K. - Y., Vecchi, G. A., & Wittenberg, A. T. (2013). ENSO Transition, Duration, and Amplitude Asymmetries: Role of the Nonlinear Wind Stress Coupling in a Conceptual Model. J. Climate, 26(23), 9462–9476.|
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
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