Modeling Spatiotemporal Patterns of Ecosystem Metabolism and Organic Carbon Dynamics Affecting Hypoxia on the Louisiana Continental Shelf.

The hypoxic zone on the Louisiana Continental Shelf (LCS) forms each summer due to nutrient enhanced primary production and seasonal stratification associated with freshwater discharges from the Mississippi/Atchafalaya River Basin (MARB). Recent field studies have identified highly productive shallow nearshore waters as an important component of shelf-wide carbon production contributing to hypoxia formation. In this study we present results from a three-dimensional hydrodynamic-biogeochemical model named CGEM (Coastal Generalized Ecosystem Model) applied to quantify the spatial and temporal patterns of hypoxia, carbon production, respiration, and transport between nearshore and middle shelf regions where hypoxia is most prevalent. We first demonstrate that our simulations successfully reproduced spatial and temporal patterns of carbon production, respiration, and bottom-water oxygen gradients compared to field observations. We then used interannual simulations to identify transport of particulate organic carbon (POC) from nearshore areas where riverine organic matter and phytoplankton carbon production are greatest. The spatial disconnect between carbon production and respiration in our simulations was driven by westward and offshore POC flux, a pattern that supported heterotrophic respiration on the middle shelf where hypoxia is frequently observed. These results validate the importance of offshore carbon flux to hypoxia formation, particularly on the west shelf where hypoxic conditions are more variable.

An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI).

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

Comparative analysis of GOCI ocean color products.

The Geostationary Ocean Color Imager (GOCI) is the first geostationary ocean color sensor in orbit that provides bio-optical properties from coastal and open waters around the Korean Peninsula at unprecedented temporal resolution. In this study, we compare the normalized water-leaving radiance (nLw) products generated by the Naval Research Laboratory Automated Processing System (APS) with those produced by the stand-alone software package, the GOCI Data Processing System (GDPS), developed by the Korean Ocean Research & Development Institute (KORDI). Both results are then compared to the nLw measured by the above water radiometer at the Ieodo site. This above-water radiometer is part of the Aerosol Robotic NETwork (AeroNET). The results indicate that the APS and GDPS processed  correlates well within the same image slot where the coefficient of determination (r²) is higher than 0.84 for all the bands from 412 nm to 745 nm. The agreement between APS and the AeroNET data is higher when compared to the GDPS results. The Root-Mean-Squared-Error (RMSE) between AeroNET and APS data ranges from 0.24 [mW/(cm²srµm)] at 555 nm to 0.52 [mW/(cm²srµm)]  at 412 nm while RMSE between AeroNET and GDPS data ranges from 0.47 [mW/(cm²srµm)] at 443 nm to 0.69 [mW/(cm²srµm)]  at 490 nm.

Optical scattering and backscattering by organic and inorganic particulates in U.S. coastal waters.

We present the results of a study of optical scattering and backscattering of particulates for three coastal sites that represent a wide range of optical properties that are found in U.S. near-shore waters. The 6000 scattering and backscattering spectra collected for this study can be well approximated by a power-law function of wavelength. The power-law exponent for particulate scattering changes dramatically from site to site (and within each site) compared with particulate backscattering where all the spectra, except possibly the very clearest waters, cluster around a single wavelength power-law exponent of -0.94. The particulate backscattering-to-scattering ratio (the backscattering ratio) displays a wide range in wavelength dependence. This result is not consistent with scattering models that describe the bulk composition of water as a uniform mix of homogeneous spherical particles with a Junge-like power-law distribution over all particle sizes. Simultaneous particulate organic matter (POM) and particulate inorganic matter (PIM) measurements are available for some of our optical measurements, and site-averaged POM and PIM mass-specific cross sections for scattering and backscattering can be derived. Cross sections for organic and inorganic material differ at each site, and the relative contribution of organic and inorganic material to scattering and backscattering depends differently at each site on the relative amount of material that is present.

Comparisons of optical properties of the coastal ocean derived from satellite ocean color and in situ measurements.

Satellite-derived optical properties are compared to in situ mooring and ship-based measurements at a coastal site. Comparisons include remote sensing reflectance (R(rs)), chlorophyll concentration (Chl) using two different Chl algorithms, and spectral absorption [a(pg)(lambda)] and backscattering coefficients [b(b)(555)] using three different bio-optical algorithms. For mooring/shipboard comparisons, we observed mean relative errors of 70.5%/-3.8% (SeaWiFS OC4v4), -21.4%/-49.3% (SeaWiFS Stumpf), 109.5%/13.4% (MODIS OC3m) and 0.5%/-48.9% (MODIS Stumpf) for Chl. For satellite-derived and mooring comparisons of a(pg)(412), we found mean relative errors of -69.4% (-67.1%), -52.6% (- 48.9%), and -62.7% (-65.4%) for the Arnone, GSM, and QAA algorithms for SeaWiFS (MODIS), respectively. Mean relative errors of 21.3%, 19.9%, and 16.5% were found between SeaWiFS-derived (Arnone, GSM, and QAA algorithms, respectively) and moored b(b)(555) measurements. Discrepancies in Rrs at blue wavelengths are attributed to the satellite atmospheric correction and sea surface variations of the moored radiometers. High spatial and temporal variability of bio-optical properties coupled with differences in measurement techniques (pixel versus point) contribute to inconsistencies between remotely sensed and in situ biooptical properties.

Uniqueness in remote sensing of the inherent optical properties of ocean water.

We examine the problem of uniqueness in the relationship between the remote-sensing reflectance (Rrs) and the inherent optical properties (IOPs) of ocean water. The results point to the fact that diffuse reflectance of plane irradiance from ocean water is inherently ambiguous. Furthermore, in the 400 < lambda < 750 nm region of the spectrum, Rrs(lambda) also suffers from ambiguity caused by the similarity in wavelength dependence of the coefficients of absorption by particulate matter and of absorption by colored dissolved organic matter. The absorption coefficients have overlapping exponential responses, which lead to the fact that more than one combination of IOPs can produce nearly the same Rrs spectrum. This ambiguity in absorption parameters demands that we identify the regions of the Rrs spectrum where we can isolate the effects that are due only to scattering by particulates and to absorption by pure water. The results indicate that the spectral shape of the absorption coefficient of phytoplankton, a(ph)(lambda), cannot be derived from a multiparameter fit to Rrs(lambda). However, the magnitude and the spectral dependence of the absorption coefficient can be estimated from the difference between the measured Rrs(lambda) and the best fit to Rrs(lambda) in terms of IOPs that exclude a(ph)(lambda).