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|Bentamy, A., Piollé, J. F., Grouazel, A., Danielson, R., Gulev, S., Paul, F., et al. (2017). Review and assessment of latent and sensible heat flux accuracy over the global oceans. Remote Sensing of Environment, 201, 196–218.|
|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.|
|Carstens, J. (2017). North Atlantic and Northeast Pacific Tropical Cyclone Intensity Comparison Using Integrated Kinetic Energy. Bachelor's thesis, Florida State University, Tallahassee, FL.|
|Chassignet, E. P., & Xu, X. (2017). Impact of Horizontal Resolution (1/12° to 1/50°) on Gulf Stream Separation, Penetration, and Variability. J. Phys. Oceanogr., 47(8), 1999–2021.|
|Chassignet, E. P., Jones, J. W., Misra, V., & Obeysekera, J. (2017). Florida's Climate: Changes, Variations, & Impacts.|
|Cintro, R., de Campos Velho, H., & Cocke, S. (2017). Tracking the model: Data assimilation by artificial neural network. In 2016 International Joint Conference on Neural Networks (IJCNN) (pp. 403–410). IEEE.|
|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
|Daneshgar Asl, S., Dukhovskoy, D. S., Bourassa, M., & MacDonald, I. R. (2017). Hindcast modeling of oil slick persistence from natural seeps. Remote Sensing of Environment, 189, 96–107.|
|De Souza-Machado, S., Tangborn, A., Sura, P., Hepplewhite, C., & Strow, L. L. (2017). Non-Gaussian Analysis of Observations from the Atmospheric Infrared Sounder Compared with ERA and MERRA Reanalyses. J. Appl. Meteor. Climatol., 56(5), 1463–1481.|