Global Chlorophyll A Concentrations Of Phytoplankton Functional Types With Detailed Uncertainty Assessment Using Multisensor Ocean Color And Sea Surface Temperature Satellite Products

First, we retune an algorithm based on empirical orthogonal functions (EOFs) for globally retrieving the chlorophyll a concentration (Chl-a) of phytoplankton functional types (PFTs) from multisensor merged ocean color (OC) products. The retuned algorithm, referred to as EOF-SST hybrid algorithm, is improved by: (i) using 23% more matchups between the updated global in situ pigment database and satellite remote sensing reflectance (R-rs) products, and (ii) including sea surface temperature (SST) as an additional input parameter. In addition to the Chl-a of the six PFTs (diatoms, haptophytes, dinoflagellates, green algae, prokaryotes, and Prochlorococcus), the fractions of prokaryote and Prochlorococcus Chl-a to total Chl-a (TChl-a), are also retrieved by the EOF-SST hybrid algorithm. Matchup data are separated for low and high-temperature regimes based on different PFT dependences on SST, to establish SST-separated hybrid algorithms which demonstrate further improvements in performance as compared to the EOF-SST hybrid algorithm. The per-pixel uncertainty of the retrieved TChl-a and PFT products is estimated by taking into account the uncertainties from both input data and model parameters through Monte Carlo simulations and analytical error propagation. The algorithm and its method to determine uncertainties can be transferred to similar OC products until today, enabling long-term continuous satellite observations of global PFT products. Satellite PFT uncertainty is essential to evaluate and improve coupled ecosystem-ocean models which simulate PFTs, and furthermore can be used to directly improve these models via data assimilation.