Abstract: Before complex analysis of oceanographic or any earth science data can occur, it must be placed in the proper domain of computing and software resources. In the past this was nearly always the scientist's personal computer or institutional computer servers. The problem with this approach is that it is necessary to bring the data products directly to these compute resources leading to large data transfers and storage requirements especially for high volume satellite or model datasets. In this presentation we will present a new technological solution under development and implementation at the NASA Jet Propulsion Laboratory for conducting oceanographic and related research based on satellite data and other sources. Fundamentally, our approach for satellite resources is to tile (partition) the data inputs into cloud-optimized and computation friendly databases that allow distributed computing resources to perform on demand and server-side computation and data analytics. This technology, known as NEXUS, has already been implemented in several existing NASA data portals to support oceanographic, sea-level, and gravity data time series analysis with capabilities to output time-average maps, correlation maps, Hovmöller plots, climatological averages and more. A further extension of this technology will integrate ocean in situ observations, event-based data discovery (e.g., natural disasters), data quality screening and additional capabilities. This particular activity is an open source project known as the Apache Science Data Analytics Platform (SDAP) (https://sdap.apache.org), and colloquially as OceanWorks, and is funded by the NASA AIST program. It harmonizes data, tools and computational resources for the researcher allowing them to focus on research results and hypothesis testing, and not be concerned with security, data preparation and management. We will present a few oceanographic and interdisciplinary use cases demonstrating the capabilities for characterizing regional sea-level rise, sea surface temperature anomalies, and ocean hurricane responses.

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.