Observations from two Bio-Argo floats deployed in the northern and central South China Sea (SCS) show distinct seasonal patterns of vertical chlorophyll distribution. There is a permanent subsurface chlorophyll maximum (SCM) located between 60 and 80 m throughout the year and a weak seasonality of surface chlorophyll in the central SCS. In the northern SCS, the SCM shoals to the upper mixed layer in winter and surface phytoplankton shows a clear winter bloom pattern. The mechanism driving the spatial and seasonal differences in phytoplankton dynamics in the euphotic zone remains unclear. Here a coupled physical-biological model is developed and applied to the northern and central SCS. With model and satellite data, we show that the contrasting patterns in chlorophyll are induced by the spatial variability in winter mixing dynamics. In the northern SCS, the buoyancy flux-induced mixing plays a dominant role in controlling the seasonal variability of vertical nutrient transport and phytoplankton production, which leads to the peak of surface chlorophyll and the significant shoaling of the SCM in winter. In the central SCS, the intensity of the buoyancy flux is reduced and both buoyancy fluxand wind-induced mixing control the winter mixing dynamics. However, the combination of these two mixing processes is weaker than in the northern SCS and the vertical nutrient transport is limited to the layer above the SCM, resulting in the reduced seasonality of surface chlorophyll and the relatively stable SCM all year round in the central SCS.Plain Language Summary Both satellite and Bio-Argo floats show a significant increase of surface chlorophyll concentration in winter in the northern South China Sea (SCS) but a very weak seasonal change in the central SCS. We used a coupled physical-biological model to systematically study the mechanism driving these spatial and seasonal differences. The model can reasonably simulate the different chlorophyll distribution patterns identified by observations. We found that the buoyancy flux-induced mixing plays a dominant role in controlling the seasonal change of chlorophyll in the northern SCS. In the central SCS, both buoyancy fluxand wind-induced mixing control the winter mixing dynamics. However, the combination of these two mixing dynamics is not as strong as that in the northern SCS and the vertical nutrient transport is only limited to the layer above the SCM, resulting in the reduced seasonality of surface chlorophyll and the relatively stable SCM all year round in the central SCS.

No abstract

Marine biogeochemical models have been widely used to understand ecosystem dynamics and biogeochemical cycles. To resolve more processes, models typically increase in complexity, and require optimization of more parameters. Data assimilation is an essential tool for parameter optimization, which can reduce model uncertainty and improve model predictability. At present, model parameters are often adjusted using sporadic in-situ measurements or satellite-derived total chlorophyll-a concentration at sea surface. However, new ocean datasets and satellite products have become available, providing a unique opportunity to further constrain ecosystem models. Biogeochemical-Argo (BGC-Argo) floats are able to observe the ocean interior continuously and satellite phytoplankton functional type (PFT) data has the potential to optimize biogeochemical models with multiple phytoplankton species. In this study, we assess the value of assimilating BGC-Argo measurements and satellite-derived PFT data in a biogeochemical model in the northern South China Sea (SCS) by using a genetic algorithm. The assimilation of the satellite-derived PFT data was found to improve not only the modeled total chlorophyll-a concentration, but also the individual phytoplankton groups at surface. The improvement of simulated surface diatom provided a better representation of subsurface particulate organic carbon (POC). However, using satellite data alone did not improve vertical distributions of chlorophyll-a and POC. Instead, these distributions were improved by combining the satellite data with BGC-Argo data. As the dominant variability of phytoplankton in the northern SCS is at the seasonal timescale, we find that utilizing monthly-averaged BGC-Argo profiles provides an optimal fit between model outputs and measurements in the region, better than using high-frequency measurements.

No abstract