Hypoxia, defined as dissolved oxygen (DO) < 2 mg/L, in the central basin of Lake Erie has been studied since the mid‐1900s. Even so, spatial patterns of hypoxia, and episodic hypoxia in nearshore areas where drinking water plant intakes are located, are not well characterized owing to limited observations and short‐term dynamics. We evaluated a physically based, DO model with respect to patterns of hypoxia observed in Lake Erie. The DO model used assigned rates of sediment and water column oxygen demand that were temperature dependent but otherwise spatially and temporally uniform. The DO model was linked to National Oceanic and Atmospheric Administration's (NOAA) Lake Erie Operational Forecasting System hydrodynamic model, an application of the Finite Volume Community Ocean Model (FVCOM). Model temperature and DO were compared with observations from ship‐based studies, real‐time sensor networks and an array of moored sensors that we deployed in 2017. In years with dominant southwesterly winds, persistent downwelling occurred along the south shore, which resulted in a thinner thermocline and earlier initiation of hypoxia along the south shore than the north. Occasional northeast winds temporarily reversed this pattern, causing upwelling along the south shore that brought hypoxic water to nearshore locations and water intakes. The DO model reproduced observed spatial and temporal patterns of hypoxia and revealed locations subject to episodes of hypoxia, including nearshore Ohio, north of Pelee Island, and near the Bass Islands. Model skill was limited in some respects, highlighting the importance of accurate simulation of the thermal structure and spatial patterns of oxygen demand rates.

[1] Ocean vector winds measured by SeaWinds can be validated with comparison in situ data that are within a certain time and space range to the satellite overpass. The total amount of random observational error is composed of two primary components, which are quantified in this study: the uncertainty associated with the data sets and the uncertainty associated with the temporal and/or spatial difference between two observations. The variance associated with a temporal difference, which can be translated into a spatial difference using Taylor's hypothesis, between two observations is initially examined in an idealized case that includes only Shipboard Automated Meteorological and Oceanographic System (SAMOS) 1 min data. The results show that the amount of variance in wind speed and direction increases as the time difference increases, while the amount of variance in wind speed increases and direction decreases with larger wind speeds. Collocated SeaWinds and SAMOS observations are used to determine the total amount of variance associated with a temporal (equivalent) difference from 0 to 60 min. For combined differences less than 25 min (equivalent) and the selected wind speed bins, the variance associated with the temporal and spatial difference is dominated by small changes in the wind speed distribution, and the sum of the observational errors is approximately 1.0 m 2 s −2 (12 deg 2 ) and 1.5 m 2 s −2 (10 deg 2 ) for wind speeds between 4 and 7 m s −1 and 7-12 m s −1 . For larger combined differences, the observational error variance is no longer the dominant term; therefore, the total variance is seen to gradually increase with increasing time differences.Citation: May, J. C., and M. A. Bourassa (2011), Quantifying variance due to temporal and spatial difference between ship and satellite winds,

The Naval Research Laboratory (NRL) ocean surface flux (NFLUX) system originally provided operational near-real-time satellite-based surface state parameter and turbulent heat flux fields over the global ocean. This study extends the NFLUX system to include the production of swath-level shortwave and longwave radiative heat fluxes at the ocean surface. A companion paper presents the production of the satellite-based global gridded radiative heat flux analysis fields. The swath-level radiative heat fluxes are produced using the Rapid Radiative Transfer Model for Global Circulation Models (RRTMG), with the primary inputs of satellite-derived atmospheric temperature and moisture profiles and cloud information retrieved from the Microwave Integrated Retrieval System (MIRS). This study uses MIRS data provided for six polar-orbiting satellite platforms. Additional inputs to the RRTMG include sea surface temperature, aerosol optical depths, atmospheric gas concentrations, ocean surface albedo, and ocean surface emissivity. Swath-level shortwave flux estimates are converted into clearness index values, which are used in data assimilation because the clearness index values are less dependent on the solar zenith angle. The NFLUX swath-level shortwave flux, longwave flux, and clearness index estimates are produced for 1 May 2013–30 April 2014 and validated against observations from research vessel and moored buoy platforms. Each of the flux parameters compares well among the various satellites.