A Biological‐Parameter‐Optimized Modeling Study of Physical Drivers Controlling Seasonal Chlorophyll Blooms off the Southern Coast of Java Island

A physical‐biogeochemical model consists of many key parameters; theoretically, 10,000 or more numerical experiments are required to obtain a set of optimal biological parameters. The corresponding computing resources are too expensive when running a three‐dimensional and high‐resolution physical‐biogeochemical model. In this study, we improve the performance of a three‐dimensional physical‐biogeochemical model via the conditional nonlinear optimal perturbation method for southern Java Island, where chlorophyll blooms occur seasonally. Based on the improved model results, we calculated the mixed‐layer nutrient budget, thereby characterizing how each physical process transports nutrients into the surface oligotrophic layer and then stimulates the chlorophyll blooms during the southeast monsoon season. Nutrients that upwell along the submarine slope are found in narrow regions along the coast and spread into most of the study area by local wind‐induced offshore transport. The contribution of nonlinearity (advection) is essential to modulating the upslope movement of nutrients off the coast. If local winds are neglected in the model, onshore transport induced by the pressure gradient force in the zonal direction (alongshore) and subsequent downwelling would be dominant, and chlorophyll blooms would diminish in most regions. The role of the South Java Coastal Current varies under the joint influence of remote and local wind forcings. The Indonesian Throughflow from the Lombok Strait also contributes to surface phytoplankton growth along its path off the south of Java Island. Plain Language Summary Many biological parameters must be set empirically when running a physical‐biogeochemical model. It generally takes thousands or more model experiments to manually determine the best set of parameters for the best model performance. Traditional methods become infeasible when running a high‐resolution model that consumes considerable computational resources for each single run. Thus, the conditional nonlinear optimal perturbation method was applied in a realistic physical‐biogeochemical model run, which optimized the parameters in remarkably fewer experiments. The improved model results help to reliably quantify the contribution of key physical drivers controlling seasonal chlorophyll blooms off the southern coast of Java Island. Ekman transport, coastal currents, and ocean circulation modulated by local and remote winds jointly affect nutrient and chlorophyll dynamic