[A-OS18] Physical Oceanography (General)

[AOS18-18] Improving sea surface salinity modeling: high-resolution river runoff implementation in Southeast Asian seas using NEMO

*XINGKUN XU^1,2, Kaushik Sasmal¹, Bijoy Thompson², Pavel Tkalich^1,2, Sumit Dandapat^1,2, Rajesh Kumar³, Kalli Furtado³, Hugh Zhang³, Zhanwei Liu⁴, Ziwei Liu⁴, Yaomin Wang⁴, Xiaogang He ⁴ (1.Technology Centre for Offshore and Marine, Singapore (TCOMS), 2.Tropical Marine Research Institute (TMSI), National University of Singapore (NUS), 3.Centre for Climate Research Singapore (CCRS), 4.Department of Civil and Environmental Engineering, National University of Singapore (NUS))

Keywords:Sea surface salinity, River runoff, Hydro-JULES, NEMO

While sea surface temperature (SST) is currently simulated accurately by modern ocean models, the representation of sea surface salinity (SSS) remains a persistent challenge, particularly near river mouths where freshwater fluxes are often poorly represented. The inability to accurately simulate SSS in these regions arises from the complexity of freshwater input processes, which depend on both the magnitude and spatial distribution of riverine runoff. This limitation hampers our understanding of salinity cycling and dynamics, which are critical for coastal and regional oceanography, owing to SSS influencing stratification, circulation, and biogeochemical processes. To address these issues and to improve SSS simulations, we utilized daily averaged JRA55-do reanalysis data to represent the influence of riverine surface runoff on sea surface salinity in coastal regions. By incorporating these data, we aim to more effectively capture the freshwater input from rivers, particularly in areas where riverine runoff has a strong impact on salinity gradients. However, to further refine the simulation and reduce the uncertainties arising from coarse spatial resolution, we used the river runoff from the Hydro-JULES system and implemented it into the customized regional ocean model NEMO. Hydro-JULES operates at a 4.5 km spatial resolution, providing detailed surface runoff estimates by resolving fine-scale catchment processes and regional hydrological variability. This coupling enables the model to account for localized freshwater fluxes and their interactions with ocean dynamics at a finer scale, improving the accuracy of SSS representation near river mouths. The results of this study demonstrate that the incorporation of river runoff significantly enhances SSS simulations, particularly by capturing the freshwater influence in coastal regions. High-resolution surface runoff data proved to be essential in identifying varying impacts across different catchments. For instance, while improvements in SSS simulation were modest in some regions, the South China Sea exhibited substantial improvement. This disparity highlights the sensitivity of SSS simulations to regional hydrological and oceanographic conditions. We observe that the integration of high-resolution runoff data improves the model’s ability to capture seasonal SSS variability in some regions, as evidenced by improved correlations with observations. This study underscores the importance of integrating high-resolution river runoff simulation into regional ocean models with the goal of refining SSS simulations, improving the representation of freshwater fluxes, as well as enhancing the modeling of salinity dynamics, to advance understanding of hydrological-oceanographic interactions and their responses to environmental changes.