Phenology of Size-Partitioned Phytoplankton Carbon-Biomass from Ocean Color Remote Sensing and CMIP5 Models

We use a novel satellite time series of size-partitioned phytoplankton biomass to construct and analyze classical and novel seasonality metrics. Biomass is computed from SeaWiFS ocean color data using retrievals of the particle size distribution with the KSM09 algorithm and existing allometric relationships to convert volume to carbon. The phenological metrics include the peak blooming date, bloom strength, shape of the seasonal cycle, and reproducibility of the seasonal cycle. We compare the seasonal cycle of total biomass with that of three classical size classes (pico-, nano-, and micro-phytoplankton), which are correlated with phytoplankton functional types (PFTs). The spatial distribution of phenological metrics based on the new biomass and PFT data is qualitatively realistic, and is strongly correlated with bottom-up drivers such as sea surface temperature, mixed layer depth, winds, and photosynthetic available radiation. We find that low-biomass regions and non-blooming seasons are dominated by small phytoplankton sizes while high-biomass regions and blooming seasons are dominated by large phytoplankton. The biomass peak date doesn't change much across PFTs, but the blooming period is more prominent for large PFTs. Small PFTs act as a more constant biomass background, with smoother (less pronounced) seasonal cycles. We find significant differences between seasonality metrics in the SeaWiFS data and the latest generation of IPCC AR5 Earth System models (CMIP5). Models in the CMIP5 archive do not capture the pronounced mid-latitude and frontal PFT patterns found in the satellite data. In models, phytoplankton biomass peaks later at high latitudes and earlier at low latitudes. The models exhibit a higher reproducibility of the biomass seasonal cycle and larger phenological differences between PFTs than observed. Models fail to capture secondary peaks at mid and high latitudes. Continuous improvement of satellite algorithms that retrieve phytoplankton groups is necessary to advance the modelization of phytoplankton in Earth System Models.