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|Griffies, S. M., Yin, J., Durack, P. J., Goddard, P., Bates, S. C., Behrens, E., et al. (2014). An assessment of global and regional sea level for years 1993-2007 in a suite of interannual CORE-II simulations. Ocean Modelling, 78, 35–89.|
|Hiester, H. R., Morey, S. L., Dukhovskoy, D. S., Chassignet, E. P., Kourafalou, V. H., & Hu, C. (2016). A topological approach for quantitative comparisons of ocean model fields to satellite ocean color data. Methods in Oceanography, 17, 232–250.|
|Hilburn, K. A. (2003). Development of scatterometer-derived surface pressures for the Southern Ocean. J. Geophys. Res., 108(C7).|
Huang, T., Armstrong, E. M., Bourassa, M. A., Cram, T. A., Elya, J., Greguska, F., et al. (2019). An Integrated Data Analytics Platform. Mar. Sci., 6.
Abstract: An Integrated Science Data Analytics Platform is an environment that enables the confluence of resources for scientific investigation. It harmonizes data, tools and computational resources to enable the research community to focus on the investigation rather than spending time on security, data preparation, management, etc. OceanWorks is a NASA technology integration project to establish a cloud-based Integrated Ocean Science Data Analytics Platform for big ocean science at NASA�s Physical Oceanography Distributed Active Archive Center (PO.DAAC) for big ocean science. It focuses on advancement and maturity by bringing together several NASA open-source, big data projects for parallel analytics, anomaly detection, in situ to satellite data matchup, quality-screened data subsetting, search relevancy, and data discovery.
Our communities are relying on data available through distributed data centers to conduct their research. In typical investigations, scientists would (1) search for data, (2) evaluate the relevance of that data, (3) download it, and (4) then apply algorithms to identify trends, anomalies, or other attributes of the data. Such a workflow cannot scale if the research involves a massive amount of data or multi-variate measurements. With the upcoming NASA Surface Water and Ocean Topography (SWOT) mission expected to produce over 20PB of observational data during its 3-year nominal mission, the volume of data will challenge all existing Earth Science data archival, distribution and analysis paradigms. This paper discusses how OceanWorks enhances the analysis of physical ocean data where the computation is done on an elastic cloud platform next to the archive to deliver fast, web-accessible services for working with oceanographic measurements.
Keywords: big data, Cloud computing, Ocean science, data analysis, Matchup, anomaly detection, open source
|Ilicak, M., Drange, H., Wang, Q., Gerdes, R., Aksenov, Y., Bailey, D., et al. (2016). An assessment of the Arctic Ocean in a suite of interannual CORE-II simulations. Part III: Hydrography and fluxes. Ocean Modelling, 100, 141–161.|
|Kara, A. B. (2003). A Fine Resolution Hybrid Coordinate Ocean Model (HYCOM) for the Black Sea with a New Solar Radiation Penetration Scheme. Ph.D. thesis, Florida State University, Tallahassee, FL.|
|Kara, A. B., Rochford, P. A., & Hurlburt, H. E. (2002). Air-Sea Flux Estimates And The 1997-1998 Enso Event. Boundary-Layer Meteorology, 103(3), 439–458.|
Kelly, T. B., Goericke, R., Kahru, M., Song, H., & Stukel, M. R. (2018). CCE II: Spatial and interannual variability in export efficiency and the biological pump in an eastern boundary current upwelling system with substantial lateral advection. Deep Sea Research Part I: Oceanographic Research Papers, 140, 14–25.
Abstract: Estimating interannual variability in carbon export is a key goal of many marine biogeochemical studies. However, due to variations in export mechanisms between regions, generalized models used to estimate global patterns in export often fail when used for intra-regional analysis. We present here a region-specific model of export production for the California Current Ecosystem (CCE) parameterized using intensive Lagrangian process studies conducted during El Niño-Southern Oscillation (ENSO) warm and neutral phases by the CCE Long-Term Ecological Research (LTER) program. We find that, contrary to expectations from prominent global algorithms, export efficiency (e-ratio = export / primary productivity) is positively correlated with temperature and negatively correlated with net primary productivity (NPP). We attribute these results to the substantial horizontal advection found within the region, and verify this assumption by using a Lagrangian particle tracking model to estimate water mass age. We further suggest that sinking particles in the CCE are comprised of a recently-produced, rapidly-sinking component (likely mesozooplankton fecal pellets) and a longer-lived, slowly-sinking component that is likely advected long distances prior to export. We determine a new algorithm for estimating particle export in the CCE from NPP (Export = 0.08 · NPP + 72 mg C m-2 d-1). We apply this algorithm to a two-decade long time series of NPP in the CCE to estimate spatial and interannual variability across multiple ENSO phases. Reduced export during the warm anomaly of 2014-2015 and El Niño 2015-2016 resulted primarily from decreased export in the coastal upwelling region of the CCE; the oligotrophic offshore region exhibited comparatively low seasonal and interannual variability in flux. The model resolves intra-regional patterns of in situ export measurements, and provides a valuable contrast to global export models.
Keywords: CALIFORNIA CURRENT ECOSYSTEM; OCEAN CARBON-CYCLE; COASTAL WATERS; FRONTAL ZONE; TIME-SERIES; FLUX; SINKING; SEA; PACIFIC; ZOOPLANKTON
|Kelly, T. B., Goericke, R., Kahru, M., Song, H., & Stukel, M. R. (2018). CCE II: Spatial and interannual variability in export efficiency and the biological pump in an eastern boundary current upwelling system with substantial lateral advection. Deep Sea Research Part I: Oceanographic Research Papers, 140, 14–25.|
|Krishnamurti, T. N., Jana, S., Krishnamurti, R., Kumar, V., Deepa, R., Papa, F., et al. (2017). Monsoonal intraseasonal oscillations in the ocean heat content over the surface layers of the Bay of Bengal. Journal of Marine Systems, 167, 19–32.|