Remote Sensing of Phytoplankton Size Distribution: Phenology

We use a new backscattering-based SeaWIFS biomass time series based on the algorithm by Kostadinov et al. (2015) to construct and analyze classical and novel seasonality (phenological) metrics. These metrics include the date of blooming peak, bloom duration and strength, shape of the seasonal cycle, and reproducibility of the seasonal cycle. We compare the seasonal cycle of total biomass with that of three phytoplankton functional types or PFTs (micro, nano, and pico-phytoplankton) and that of Chl. 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 light at surface. We find that low-biomass regions and non-blooming seasons are dominated by pico-phytoplankton while high-biomass regions and blooming seasons are dominated by micro-phytoplankton. The biomass peak date doesn't change much across PFTs, but the blooming period is shorter and more prominent for large PFTs. Small PFTs act as a more constant biomass background, with smoother (less pronounced) seasonal cycles. We find that Chl peaks earlier than total biomass at high latitudes (northward of 50ºN) in the subpolar biome, such that the Chl:C ratio increases during low-light and early-bloom conditions and decreases around the peak in biomass. We find significant differences between seasonality metrics in the SeaWiFS data and across the CMIP5 Earth System models. The models do not capture the pronounced mid-latitude and frontal patterns found in data. In models phytoplankton peak later at high latitudes and earlier at low latitudes. The bloom duration is longer at low latitudes and shorter at high latitudes compared to the data. Finally, the models show a higher reproducibility of the biomass seasonal cycle.