Global assimilation of ocean-color data of phytoplankton functional types: Impact of different datasets

Satellite phytoplankton functional type (PFT) data is assimilated into the global coupled ocean-ecosystem model MITgcm-REcoM2 for two years using a local ensemble Kalman filter. The ecosystem model has two PFTs: small phytoplankton (SP) and diatoms. Three different sets of satellite PFT data are assimilated: OC-PFT, PhytoDOAS, and SynSenPFT, which is a synergistic product combining the independent PFT products OC-PFT and PhytoDOAS. The effect of assimilating PFT data is compared with the assimilation of total chlorophyll data (TChla). This constrains both PFTs through multivariate assimilation using ensemble-estimate cross-covariances. While the assimilation of TChla already improves both PFTs individually, the assimilation of PFT data further improves the representation of the phytoplankton community. The effect is particularly large for diatoms where, compared to the assimilation of TChla, the SynSenPFT assimilation results in 57% and 67% reduction of root-mean square error (RMSE) and bias, respectively, while the correlation is increased from 0.45 to 0.54. For SP the assimilation of SynSenPFT data reduces the RMSE and bias by 14% each and increases the correlation by 30%. This shows that satellite data products beyond total chlorophyll are relevant for biogeochemical data assimilation. The separate assimilation of the PFT data products OC-PFT, SynSenPFT, and joint assimilation of OC-PFT and PhytoDOAS data lead to similar results while the assimilation of PhytoDOAS data alone leads to deteriorated SP but improved diatoms. When both OC-PFT and PhytoDOAS data are jointly assimilated, the representation of diatoms is improved compared to the assimilation of only OC-PFT. The results show slightly lower errors than when the synergistic SynSenPFT data is assimilated, which shows that the assimilation successfully combines the separate data sources.