Search

In this study, we improve the representation of global river runoff in the Estimating the Circulation and Climate of the Ocean (ECCO) framework, allowing for a more realistic treatment of coastal plume dynamics. We use a suite of experiments to explore the sensitivity of coastal plume regions to runoff forcing, model grid resolution, and grid type. The results show that simulated Sea-Surface Salinity (SSS) is reduced as the model grid resolution increases. Compared to Soil Moisture Active Passive (SMAP) observations, simulated SSS is closest to SMAP when using Daily, Point-source Runoff (DPR) and the intermediate resolution LLC270 grid. The Wilmott skill score, which quantifies agreement between models and observations, yields up to 0.92 for large rivers such as the Amazon. There was no major difference in SSS for tropical and temperate coastal rivers when the model grid type was changed from cube-sphere to latitude-longitude-polar-cap. We also found that using DPR forcing and increasing model resolution from the coarse resolution LLC90 grid to the intermediate resolution LLC270 grid elevates the river plume area, volume, and freshwater transport, along with stabilizing stratification and generally shoaling the mixed layer depth (MLD). Additionally, we find that the impacts of increasing model resolution from intermediate resolution LLC270 grid to high resolution LLC540 grid are regionally dependent. The Mississippi River Plume is more sensitive than others regions, possibly because the wider and shallower Texas-Louisiana shelf drives a stronger baroclinic effect, as well as relatively weak sub-grid vertical mixing and adjustment in this region. The results indicate that due to the complex bathymetry and dynamic behaviour of coastal environments, it may be challenging for spatially-unified resolution models to capture process-rich fidelity and obtain computational efficiency for coastal interfaces on a global scale. Our results offer a benchmark for representing Land-Ocean-Aquatic-Continuum (LOAC) processes in global models and data assimilation products and will help advance predictions of land-ocean-atmospheric feedbacks seamlessly in the next generation of earth system models. [ABSTRACT FROM AUTHOR]