A phytoplankton absorption-based primary productivity model for remote sensing in the Southern Ocean

Recent global environmental changes such as an increase in sea surface temperature (SST) are likely to impact primary productivity of phytoplankton in the Southern Ocean. However, models to estimate net primary production using satellite data use SST and uncertain estimation of chlorophyll a (chl-a) concentration. A primary productivity model for satellite ocean color data from the Southern Ocean, which is based on the light absorption coefficient of phytoplankton to reduce uncertainties of sea surface chl-a estimations and bias in optimal values of chl-a normalized productivity derived from SST, has been developed. The new model was able to estimate net primary productivity in the water column (PP _eu) without dependency on temperature when in the range of −2 to 25°C, and it explained 51% of the observed variability in PP _eu with a root mean square error (RMSE) of 0.15. Application of the model revealed that the SST dependent model has overestimated PP _eu in warmer waters around the Subtropical Front, and underestimated PP _eu in colder waters poleward of the Sub-Antarctic Front. This absorption-based primary productivity model contributes to a study of the relationship among spatio-temporal variations in the physical environment, and biogeochemical cycles in the Southern Ocean.