Abstract
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, subtropical 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 250 m, 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.
Highlights
Large-scale estimates of marine net primary productivity (NPP) are an essential component of global carbon budget analyses as nearly half of the earth’s source of photosynthetically fixed carbon derives from the global ocean
It is critical that these models are carefully evaluated by comparing their estimates of NPP to in situ measurements collected from various regions across the globe in order to better understand which types of systems may be most challenging to model and to better constrain the model uncertainties
A multiregional Primary Productivity Algorithm Round Robin (PPARR) analysis that compares output from multiple models to in situ NPP at various regions has not been recently conducted since the study by Campbell et al (2002) and a larger sample size of in situ measurements would strengthen the assessment of model skill and provide insights into the relationship between region type, quality of the input variables, quality of the NPP measurement, and model error
Summary
Large-scale estimates of marine net primary productivity (NPP) are an essential component of global carbon budget analyses as nearly half of the earth’s source of photosynthetically fixed carbon derives from the global ocean. A study comparing NPP estimates of 30 models to in situ data from nearly 1000 stations over 13 years in the tropical Pacific Ocean revealed that ocean color models did not capture a broad scale shift from low biomass-normalized productivity in the 1980s to higher biomass-normalized productivity in the 1990s (Friedrichs et al, 2009). A multiregional PPARR analysis that compares output from multiple models to in situ NPP at various regions has not been recently conducted since the study by Campbell et al (2002) and a larger sample size of in situ measurements would strengthen the assessment of model skill and provide insights into the relationship between region type, quality of the input variables, quality of the NPP measurement, and model error
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