Project: Primary Production Model Intercomparison: Primary Production Algorithm Round Robin 4
The goal of our proposal is to evaluate algorithms which estimate primary production (PP models) from ocean color measured by space-borne sensors. This project aims to continue a project that is nearing completion, originally funded under NASA's Carbon Cycle Science Program, the Primary Production Algorithm Round Robin 3 (PPARR3). We found that greater variability between modeled estimates of primary production occurred at low sea-surface temperature (SST) and low or high chlorophyll concentrations. A comparison to in situ data from the tropical Pacific revealed that the participating models consistently underestimated the variance of depth-integrated primary production (PP). Model performance was independent of traditional groupings of model structure: some of the simplest (depth- and wavelength-integrated) models performed as well as some of the most complex (depth- and wavelength-resolved.) The significant decrease in root-meansquare error of participating models in the equatorial Pacific (by 62% on the equator and 35% off the equator) between PPARR2 and PPARR3 is testimony of the success of the PPARR exercises. We propose to expand the PPARR3 exercise by comparing participating PP models with in situ primary production data acquired during process studies and time series sites associated with the Joint Global Ocean Flux Study (JGOFS), data from the Southern Ocean, including a database from the Antarctic Peninsula, and a compilation of coastal measurements. JGOFS process study and time series data provide high quality measurements of biogeochemically active regions and oligotrophic subtropical gyres respectively. The Southern Ocean presents a daunting challenge for space-based primary production models, as common temperature dependent functions fail at low temperatures in addition to the challenges of determining the chlorophyll concentration at low sun angles. High biomass and the presence of other optically active substances which absorb and reflect light make primary production particularly difficult to model adequately in the coastal ocean. By addressing these focus regions, we will evaluate model performance for different environmental conditions. As in PPARR3 we anticipate that model developers will use these comparisons to refine and reformulate their models.
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