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|Dukhovskoy, D. S., Ubnoske, J., Blanchard-Wrigglesworth, E., Hiester, H. R., & Proshutinsky, A. (2015). Skill metrics for evaluation and comparison of sea ice models. J. Geophys. Res. Oceans, 120(9), 5910–5931.|
Paget, A. C., Bourassa, M. A., & Anguelova, M. D. (2015). Comparing in situ and satellite-based parameterizations of oceanic whitecaps. J. Geophys. Res. Oceans, 120(4), 2826–2843.
Keywords: whitecap fraction; foam fraction; whitecap coverage; breaking waves; actively breaking waves; air-sea interaction processes; in situ whitecap observations scatterometers; QuikSCAT; WindSat; microwave radiometry; passive remote sensing; satellite oceanography
|Misra, V., Li, H., & Kozar, M. (2014). The precursors in the Intra-Americas Seas to seasonal climate variations over North America. J. Geophys. Res. Oceans, 119(5), 2938–2948.|
|Yu, L., & Jin, X. (2014). Insights on the OAFlux ocean surface vector wind analysis merged from scatterometers and passive microwave radiometers (1987 onward). J. Geophys. Res. Oceans, 119(8), 5244–5269.|
|Hu, A., Meehl, G. A., Han, W., Lu, J., & Strand, W. G. (2013). Energy balance in a warm world without the ocean conveyor belt and sea ice. Geophys. Res. Lett., 40(23), 6242–6246.|
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
Venugopal, T., Ali, M. M., Bourassa, M. A., Zheng, Y., Goni, G. J., Foltz, G. R., et al. (2018). Statistical Evidence for the Role of Southwestern Indian Ocean Heat Content in the Indian Summer Monsoon Rainfall. Sci Rep, 8(1), 12092.
Abstract: This study examines the benefit of using Ocean Mean Temperature (OMT) to aid in the prediction of the sign of Indian Summer Monsoon Rainfall (ISMR) anomalies. This is a statistical examination, rather than a process study. The thermal energy needed for maintaining and intensifying hurricanes and monsoons comes from the upper ocean, not just from the thin layer represented by sea surface temperature (SST) alone. Here, we show that the southwestern Indian OMT down to the depth of the 26 degrees C isotherm during January-March is a better qualitative predictor of the ISMR than SST. The success rate in predicting above- or below-average ISMR is 80% for OMT compared to 60% for SST. Other January-March mean climate indices (e.g., NINO3.4, Indian Ocean Dipole Mode Index, El Nino Southern Oscillation Modoki Index) have less predictability (52%, 48%, and 56%, respectively) than OMT percentage deviation (PD) (80%). Thus, OMT PD in the southwestern Indian Ocean provides a better qualitative prediction of ISMR by the end of March and indicates whether the ISMR will be above or below the climatological mean value.
Keywords: SEA-SURFACE TEMPERATURE; EL-NINO; EQUATORIAL PACIFIC; IMPACT; PREDICTION; ENSO; DIPOLE; REGION; SST