River plumes can strongly influence coastal dynamics but are poorly resolved in global ocean models. Building on a companion study, we perform high-resolution simulations of the Mississippi–Atchafalaya (M-A) plume with the Model for Prediction Across Scales–Ocean (MPAS-O), a global model, and compare with a limited-domain Regional Ocean Modeling System configuration. MPAS-O qualitatively captures the M-A plume seasonality but fails to generate realistic eddies on the Texas–Louisiana shelf.

Physical dynamics of ocean flows at fine spatial scales are challenging for regional and global scale ocean models due to high computational effort associated with smaller grid cell sizes. We compared two ocean models: a regional model (ROMS; Regional Ocean Modeling System) and global model (MPAS-O; Model for Prediction Across Scales – Ocean) across a range of resolutions (10 km to 100 m) in an idealized domain, and found that their similarities may hold promise for future coupling between them.

Our study explores how riverine and coastal flooding during hurricanes is influenced by the interaction of atmosphere, land, river, and ocean conditions. Using an advanced Earth system model, we simulate Hurricane Irene to evaluate how meteorological and hydrological uncertainties affect flood modeling. Our findings reveal the importance of a multi-component modeling system, how hydrological conditions play critical roles in flood modeling, and greater flood risks if multiple factors are present.

Discrete global grid systems, or DGGS, are digital frameworks that help us organize information about our planet. Although scientists have used DGGS in areas like weather and nature, using them in the water cycle has been challenging because some core datasets are missing. We created a way to generate these datasets. We then developed the datasets in the Amazon and Yukon basins, which play important roles in our planet's climate. These datasets may help us improve our water cycle models.

Most modern ocean circulation models only consider the hydrostatic pressure, but for coastal phenomena nonhydrostatic effects become important, creating the need to include the nonhydrostatic pressure. In this work, we present a nonhydrostatic formulation for MPAS-Ocean (MPAS-O NH) and show its correctness on idealized benchmark test cases. MPAS-O NH is the first global nonhydrostatic model at variable resolution and is the first nonhydrostatic ocean model to be fully coupled in a climate model.