A statistical-equilibrium, geostrophic-turbulence regime of the stochastically forced, one-layer, reduced-gravity, quasi-geostrophic model is identified in which the numerical solutions are representative of global mean, mid-latitude, open-ocean mesoscale variability. A nominal best fit to observed SSH variance, autocorrelation, eddy, and spectral statistics is obtained for dimensional SSH stochastic-forcing variance production rate 1/4 cm^2/d, an SSH damping rate 1/62 1/wk, and a stochastic forcing autocorrelation timescale near or greater than 1 wk. This ocean mesoscale regime is nonlinear and appears to fall near the stochastic limit, at which wave-mean interaction is just strong enough to begin to reduce the local mesoscale variance production, but is still weak relative to the overall nonlinearity. Comparison of linearly-inverted wavenumber-frequency spectra shows that a strong effect of nonlinearity, the removal of energy from the resonant linear wave field, is resolved by the gridded altimeter SSH data. These inversions further suggest a possible signature in the merged altimeter SSH dataset of signal propagation characteristics from the objective analysis procedure.

To study the role of the Atlantic meridional overturning circulation (AMOC) in climate change, we perform an abrupt CO2–doubling experiment using a coupled atmosphere-ocean- ice model with a simple geometry that separates the ocean into small and large basins. As in observations and high-end climate models, the small basin exhibits a MOC and warms at a faster rate than the large basin. In our set-up, this contrast in heat storage rates is 0.6 ± 0.1 W/m^2, and we argue that this is due to the small basin MOC. However, the MOC weakens significantly, yet this has little impact on the small basin’s heat storage rate. We find this is due to the effects of both compensating warming patterns and interbasin heat transports. Thus, although the presence of a MOC is important for enhanced heat storage, MOC weakening is surprisingly unimportant.

Multi-decadal expansion in the winter maximum sea ice extent (SIE) around Antarctica was interrupted by contraction, beginning in 2016 and continuing into 2019. This unexpected behavior motivates a closer look at factors controlling the position of the outer ice margin.We analyzed sea ice concentration (SIC) estimates derived from passive microwave sensors with differing resolutions (SSM/I, AMSR-E and AMSR2) to identify spatial and temporal statistics of the sea ice edge deVned by 15% SIC. The low-pass Vltered position of the ice edge is similar in different products, with the maximum northward position determined by proximity to the relatively warm waters of the Antarctic Circumpolar Current. Higher resolution SIC products reveal greater spatial detail along the convoluted margin, resulting in a relatively longer sea ice perimeter. Spectral analysis does not identify statistically signiVcant peaks in length scales along the margin; however, visual comparison with geostrophic velocities and sea surface temperature inferred from satellite altimetry suggests that advection of sea ice by mesoscale eddies is an important mechanism for deforming the ice edge in some regions, such as the Bellingshausen Sea. We analyze a high-resolution (dx=5 km), coupled ocean-sea ice model which realistically represents the annual expansion of sea ice to quantify the dynamic and thermodynamic roles of eddies in sea-ice mass balance and SIE. These eddy effects on the sea ice edge are not well represented in coarser-grid ocean reanalysis products such as ECCO-2, motivating an investigation of how to represent eddy/sea-ice interactions in global climate models.

From 2014 to at least 2018, ecosystem health in the eastern boundary upwelling system along the west coast of North America was significantly impacted by a combination of a marine heatwave known as The Blob and an El Niño event, as well as by ongoing climate change. At the northern limit of this upwelling system, in Queen Charlotte Sound on the highly productive central coast of British Columbia, we have demonstrated that changing conditions on the continental shelf and in coastal waters may be skillfully predicted based on observed open-ocean and large-scale atmospheric conditions on seasonal to interannual timescales. In this work, we build on our understanding of this predictability by presenting a statistical model that relates physical and biogeochemical ocean properties in this region to conditions at and beyond the shelf break and large-scale forcing metrics. The model is based on statistical relationships developed using a multi-decadal archive of hydrographic and biogeochemical data in combination with high-temporal-resolution mooring records collected in Queen Charlotte Sound, and is supported by a conceptual understanding of the upwelling and downwelling regimes in this region. We next use the model to examine specifically how the arrival of The Blob and the subsequent El Niño modified ocean conditions on the continental shelf during both upwelling and downwelling, including impacts on nutrient concentrations, dissolved oxygen levels, stratification, and warming. Our results suggest it may be possible to predict changes in this upwelling system caused by future anomalous events and climate change using readily available large-scale data products such as the Argo dataset and NOAA Upwelling Index.

Water quality monitoring is an integral tool in the management of freshwater resources. It identifies trends in water quality and tracks the effects of anthropogenic influences such as shoreline development and eutrophication and the successes of restoration actions. Remote sensing presents a cost efficient complementary approach for a more comprehensive assessment of our freshwater resources. The open access to Earth observation data limits the costs to software maintenance and data processing costs and makes the integration of remote sensing into water quality monitoring programs more attractive for natural resources management agencies. The operational annual satellite retrieval of the water clarity for thousands of lakes across Wisconsin from Landsat 7 ETM+ and Landsat 8 OLI-TIRS data assists the Wisconsin DNR in trophic state assessments for the State of Wisconsin. The results are shared within the Wisconsin DNR for the Wisconsin Water Quality Reports to Congress in compliance with Section 305(b) of the Clean Water Act and with state and local water quality managers, lake organizations, and the public. Summer water clarity maps are shared through the Wisconsin Lakes & Aquatic Invasive Species Mapping Tool and GIS data portal. Current remote sensing activities at the Wisconsin DNR include the transition of image processing efforts from a desktop to cloud environment, the collection of field and satellite match-up data for waterbodies across Wisconsin to support algorithm development and validation efforts by external partners, and the development of strategies for the integration of satellite data products into our water quality monitoring program. This poster provides insights in the different components of the successful integration of satellite data analysis into our water quality monitoring program and can serve as a roadmap for natural resources management agencies.

Since its formation in 2015, the National Centers for Environmental Information (NCEI) has used disparate, legacy systems spread across several IT networks of the National Environmental Satellite, Data, and Information Service (NESDIS) to fulfill its data-stewardship functions. As part of its modernization and consolidation of these functions, NCEI implemented Common Ingest as the functional component that ingests approximately 200 data streams every month into its enterprise archival information system. In parallel, NESDIS completed the Secure Ingest Gateway Project (SIGP), a pilot project to establish standard-enterprise secure methods for NESDIS and the rest of the National Oceanic and Atmospheric Administration (NOAA) to receive data in a cloud environment from their external partners. SIGP is now transitioning to operations as the Operational Secure Ingest Service (OSIS), which will be the on-ramp to NCEI’s “Common Ingest” functionality when it too moves to the cloud. In addition, this ingest function will populate and use a cloud-based metadata catalog, which will be the beating heart of the NESDIS and NCEI information systems in the cloud environment. The vision is to scale their ingest of environmental data to keep pace with its ever increasing volume, veracity, variety, and velocity. In this presentation to the ocean data community, the authors describe NESDIS and NCEI’s challenges and successes with the implementation of the ingest function of their archival information system in a cloud environment.