An evaluation of ocean color model estimates of marine primary productivity in coastal and pelagic regions across the globe

Abstract

Nearly half of the earth’s photosynthetically fixed carbon derives from the oceans. To determine global and region specific rates, we rely on models that estimate marine net primary productivity (NPP) thus it is essential that these models are evaluated to determine their accuracy. Here we assessed the skill of 21 ocean color models by comparing their estimates of depth-integrated NPP to 1156 in situ 14C measurements encompassing ten marine regions including the Sargasso Sea, pelagic North Atlantic, coastal Northeast Atlantic, Black Sea, Mediterranean Sea, Arabian Sea, sub-tropical North Pacific, Ross Sea, West Antarctic Peninsula, and the Antarctic Polar Frontal Zone. Average model skill, as determined by root-mean square difference calculations, was lowest in the Black and Mediterranean Seas, highest in the pelagic North Atlantic and the Antarctic Polar Frontal Zone, and intermediate in the other six regions. The maximum fraction of model skill that may be attributable to uncertainties in both the input variables and in situ NPP measurements was nearly 72%. On average, the simplest depth/wavelength integrated models performed no worse than the more complex depth/wavelength resolved models. Ocean color models were not highly challenged in extreme conditions of surface chlorophyll-a and sea surface temperature, nor in high-nitrate low-chlorophyll waters. Water column depth was the primary influence on ocean color model performance such that average skill was significantly higher at depths greater than 250m, suggesting that ocean color models are more challenged in Case-2 waters (coastal) than in Case-1 (pelagic) waters. Given that in situ chlorophyll-a data was used as input data, algorithm improvement is required to eliminate the poor performance of ocean color NPP models in Case-2 waters that are close to coastlines. Finally, ocean color chlorophyll-a algorithms are challenged by optically complex Case-2 waters, thus using satellite-derived chlorophyll-a to estimate NPP in coastal areas would likely further reduce the skill of ocean color models

Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models

“…Previous primary production intercomparison exercises matched in situ carbon uptake rates with NPP estimated mostly from ocean color models (OCMs) [e.g., Balch et al, 1992;Campbell et al, 2002;Saba et al, 2011] and, more recently, from some coupled biogeochemical ocean general circulation models (BOGCMs) [Carr et al, 2006;Friedrichs et al, 2009;Saba et al, 2010] in various regions of the ocean except the Arctic Ocean. Popova et al [2012] and Vancoppenolle et al [2013] reported the results from various BOGCMs and Earth System Models (ESMs) that were compared for the Arctic domain against satellite-derived NPP albeit with known issues.…”

Light Absorption by Phytoplankton in the Upper Mixed Layer of the Black Sea: Seasonality and Parametrization

“…Visible spectral radiometric data are used widely to assess water productivity (Saba et al, 2011) and to study effect of climate change on ocean productivity (Behrenfeld et al, 2006). Optical scanners of Sea-viewing Wide Field-of-view Sensor (SeaWiFS), MEdium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer aboard the Terra and Aqua satellites (MODIS-Aqua/Terra) measure water leaving radiance at several spectral bands (R RS ) (Feldman and McClain, 2013).…”

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“…We therefore focused only on open ocean areas of the Black Sea (see Fig.1 for further details) where the errors in SeaWiFS Chl-a are likely to be small due to low or minimal TSM and aCDOM. We also deployed one of the most accurate PP satellite models based on recent NASA inter-comparisons (Carr et al 2006;Saba et al 2011).…”

“…PPWRM was calculated as follows: (1) This model has proved to be the most accurate satellite PP model for the Atlantic Ocean where modelled values are within 20% of in situ (Tilstone et al, 2009). During recent NASA PP intercomparisons, Carr et al (2006) and Saba et al (2011) found that this model was the most accurate in eight out of ten regions of the global ocean and within ~30% of in situ values.…”