Global chlorophyll a concentrations of phytoplankton functional types
with detailed uncertainty assessment using multi-sensor ocean color and
sea surface temperature satellite products
Abstract
Firstly, we re-tune an algorithm based on empirical orthogonal functions
(EOF) for globally retrieving the chlorophyll a concentration
(Chl-a) of phytoplankton functional types (PFTs) from multi-sensor
merged ocean color (OC) products. The re-tuned algorithm, namely EOF-SST
hybrid algorithm, is improved by: (i) using 30% more matchups between
the updated global in situ pigment database and satellite remote
sensing reflectance (Rrs) 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
prokaryotes and Prochlorococcus Chl-a to total Chl-a (TChl-a),
are also retrieved by the EOF-SST hybrid algorithm. Matchup data are
further separated for low and high temperature regimes based on
different PFT dependences on SST, to establish the SST-separated hybrid
algorithms which further shows improved 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 uncertainty
assessment provided within this study sets the ground to extend the
long-term continuous satellite observations of global PFT products by
transferring the algorithm and its method to determine uncertainties to
similar OC products until today. Satellite PFT uncertainty is also
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.