Configuration and Validation of an Oceanic Physical and Biogeochemical Model to Investigate Coastal Eutrophication in the Southern California Bight

The Southern California Bight (SCB), an eastern boundary upwelling system, is impacted by global warming, acidification, and oxygen loss and receives anthropogenic nutrients from a coastal population of 20 million people. We describe the configuration, forcing, and validation of a realistic, submesoscale-resolving ocean model as a tool to investigate coastal eutrophication. This modeling system represents an important achievement because it strikes a balance of capturing the forcing by U.S. Pacific Coast-wide phenomena, while representing the bathymetric features and submesoscale circulation that affect the transport of nutrients from natural and human sources. Moreover, the model allows simulations at time scales that approach the interannual frequencies of ocean variability. The model simulation is evaluated against a broad suite of observational data throughout the SCB, showing realistic depiction of the mean state and its variability with satellite and in situ measurements of state variables and biogeochemical rates. The simulation reproduces the main structure of the seasonal upwelling front, the mean current patterns, the dispersion of wastewater plumes, as well as their seasonal variability. Furthermore, it reproduces the mean distributions of key biogeochemical and ecosystem properties and their variability. Biogeochemical rates reproduced by the model, such as primary production and nitrification, are also consistent with measured rates. This validation exercise demonstrates the utility of using fine-scale resolution modeling and local observations to identify, investigate, and communicate uncertainty to stakeholders to support management decisions on local anthropogenic nutrient discharges to coastal zones.

Automated summary

The study presents a realistic ocean model to investigate coastal eutrophication issues in the Southern California Bight. Through evaluation against a broad suite of observational data, the model simulation effectively captures the state and variability of the region.