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Bhowmick, S. A., Agarwal, N., Ali, M. M., Kishtawal, C. M., & Sharma, R. (2019). Role of ocean heat content in boosting post-monsoon tropical storms over Bay of Bengal during La-Nina events. Climate Dynamics, 52(12), 7225–7234.
Abstract: This study aims to analyze the role of ocean heat content in boosting the post-monsoon cyclonic activities over Bay of Bengal during La-Niña events. In strong La-Niña years, accumulated cyclone energy in Bay of Bengal is much more as compared to any other year. It is observed that during late June to October of moderate to strong La-Nina years, western Pacific is warmer. Sea surface temperature anomaly of western Pacific Ocean clearly indicates the presence of relatively warmer water mass in the channel connecting the Indian Ocean and Pacific Ocean, situated above Australia. Ocean currents transport the heat zonally from Pacific to South eastern Indian Ocean. Excess heat of the southern Indian Ocean is eventually transported to eastern equatorial Indian Ocean through strong geostrophic component of ocean current. By September the northward transport of this excess heat from eastern equatorial Indian Ocean to Bay of Bengal takes place during La-Nina years boosting the cyclonic activities thereafter.
Keywords: La-Niña; Bay of Bengal; Tropical cyclones; Ocean heat content
|Bourassa, M. A., Legler, D. M., O'Brien, J. J., & Smith, S. R. (2003). SeaWinds validation with research vessels. J. Geophys. Res., 108(C2).|
Bourassa, M. A., and P.J. Hughes. (2018). Surface Heat Fluxes and Wind Remote Sensing. In and J. Verron J. Tintoré A. Pascual E. P. Chassignet (Ed.), (pp. 245–270). Tallahassee, FL: GODAE OceanView.
Abstract: The exchange of heat and momentum through the air-sea surface are critical aspects of ocean forcing and ocean modeling. Over most of the global oceans, there are few in situ observations that can be used to estimate these fluxes. This chapter provides background on the calculation and application of air-sea fluxes, as well as the use of remote sensing to calculate these fluxes. Wind variability makes a large contribution to variability in surface fluxes, and the remote sensing of winds is relatively mature compared to the air sea differences in temperature and humidity, which are the other key variables. Therefore, the remote sensing of wind is presented in greater detail. These details enable the reader to understand how the improper use of satellite winds can result in regional and seasonal biases in fluxes, and how to calculate fluxes in a manner that removes these biases. Examples are given of high-resolution applications of fluxes, which are used to indicate the strengths and weakness of satellite-based calculations of ocean surface fluxes.
Keywords: HEAT; OCEAN SURFACE; WINDS; SCATTEROMETERS; FLUXE; STRESS; RESPONSES
|Chakraborty, A., Sharma, R., Kumar, R., & Basu, S. (2014). An OGCM assessment of blended OSCAT winds. J. Geophys. Res. Oceans, 119(1), 173–186.|
|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.|
|Chen, X., Zhang, Y., Zhang, M., Feng, Y., Wu, Z., Qiao, F., et al. (2013). Intercomparison between observed and simulated variability in global ocean heat content using empirical mode decomposition, part I: modulated annual cycle. Clim Dyn, 41(11-12), 2797–2815.|
|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
|Danabasoglu, G., Yeager, S. G., Bailey, D., Behrens, E., Bentsen, M., Bi, D., et al. (2014). North Atlantic simulations in Coordinated Ocean-ice Reference Experiments phase II (CORE-II). Part I: Mean states. Ocean Modelling, 73, 76–107.|
|Danabasoglu, G., Yeager, S. G., Kim, W. M., Behrens, E., Bentsen, M., Bi, D., et al. (2016). North Atlantic simulations in Coordinated Ocean-ice Reference Experiments phase II (CORE-II). Part II: Inter-annual to decadal variability. Ocean Modelling, 97, 65–90.|