This paper provides an overview of the United States (U.S.) Department of Energy’s (DOE’s) Energy Exascale Earth System Model version 2.1 with an Arctic regionally refined mesh (RRM), hereafter referred to as E3SMv2.1-Arctic, for the atmosphere (25 km), land (25 km), and ocean/ice (10 km) components. We evaluate the atmospheric component and its interactions with land, ocean, and cryosphere by comparing the RRM (E3SM2.1-Arctic) historical simulations (1950-2014) with the uniform low-resolution (LR) counterpart, reanalysis products, and observational datasets. The RRM generally reduces biases in the LR model, improving simulations of Arctic large-scale mean fields, such as precipitation, atmospheric circulation, clouds, atmospheric river frequency, and sea ice dynamics. However, the RRM introduces a seasonally dependent surface air temperature bias, reducing the LR cold bias in summer but enhancing the LR warm bias in winter. The RRM also underestimates winter sea ice area and volume, consistent with its strong winter warm bias. Radiative feedback analysis shows similar climate feedback strengths in both RRM and LR, with the RRM exhibiting a more positive surface albedo feedback and contributing to a stronger surface warming than LR. These findings underscore the importance of high-resolution modeling for advancing our understanding of Arctic climate changes and their broader global impacts, although some persistent biases appear to be independent of model resolution at 10-100 km scales.

A growing number of coastal eco-geomorphologic modeling studies have been conducted to understand coastal marsh evolution under sea level rise (SLR). Although these models quantify marsh topographic change as a function of sedimentation and erosion, their representations of vegetation dynamics that control organic sedimentation differ. How vegetation dynamic schemes and parameter values contribute to simulation outcomes is still not quantified. Additionally, the sensitivity of modeling outcomes on parameter selection in the available formulations has not been rigorously tested to date, especially under the influence of an accelerating SLR. This knowledge gap severely limits modeling accuracy and the estimation of the vulnerability of coastal marshes under SLR. In this paper, we used coastal eco-geomorphologic models with different vegetation dynamic schemes to investigate the eco-geomorphologic feedbacks of coastal marshes and parametric sensitivity under SLR scenarios. We found that marsh accretion rate near the seaward boundary can keep pace with moderate and high rates of SLR, while interior marsh regions are vulnerable to a high rate of SLR. The simulations with different vegetation schemes exhibit diversity in elevation and biomass profiles and parametric sensitivity. We also found that the model parametric sensitivity varies with rates of future SLR. Vegetation-related parameters and sediment diffusivity, which are not well measured or discussed in previous studies, are identified as some of the most critical parameters. Our findings provide insights to appropriately choose modeling presentations of key processes and feedbacks for different coastal marsh landscapes under SLR, which has practical implications for coastal ecosystem management and protection.

Coastal saltwater intrusion (SWI) is one key factor affecting the hydrology, nutrient transport, and biogeochemistry of coastal marsh landscapes. Future climate change, especially intensified sea level rise (SLR), is expected to trigger SWI to encroach coastal freshwater aquifers more intensively. Numerous studies have investigated decadal/century scale SWI under SLR by assuming a static coastal landscape topography. However, coastal marshes are highly dynamic systems in response to SLR, and the impact of coastal marsh evolution on SWI has received very little attention. Thus, this study investigated how coastal marsh evolution affects future SWI with a physically-based coastal hydro-eco-geomorphologic model, ATS (Advanced Terrestrial Simulator). Our synthetic modeling experiments showed that it is very likely that the marsh elevation increases with future SLR, and a depression zone is formed due to the different marsh accretion rates between the ocean boundary and the inland. We found that, compared to the cases without marsh evolution, the marsh accretion may significantly reduce the surface saltwater inflow at the ocean boundary, and the evolved topographic depression zone may prolong the residence time of surface ponding saltwater, which causes distinct subsurface salinity distributions. We also found that the marshland may become more sensitive to the upland groundwater table that controls the freshwater flux to the marshes, compared with the cases without marsh evolution. This study demonstrates the importance of marsh evolution to the freshwater-saltwater interaction under sea level rise and can help improve our predictive understanding of the vulnerability of the coastal freshwater system to sea level rise.

Sediment settling velocities are commonly estimated from process-based or parametric data-driven approaches. The process-based approach has theoretical constraints due to the unclear settling physics; the parametric data-driven approach is limited by its mathematical assumptions. To overcome these limitations, this study compiles an aggregated sediment settling experimental database from literature and develops a non-parametric data-driven model to estimate the non-cohesive sediment settling velocity in water. A cross-comparison against five process-based equations and a parametric data-driven equation demonstrates the higher accuracy and better consistency of the new model in estimating sediment settling velocities under various physical regimes. The data-driven model also shows an easy-implemented self-update capability by assimilating theoretical data generated from the process-based equations. The updated model, leveraging experimental and theoretical data of sediment settling process, further improves the accuracy and reduces the uncertainty in estimating sediment settling velocities. This approach illustrates the value of integrating experimental and theoretical knowledge in estimating the complex process in sediment transport, and provides an alternative framework for future sediment transport exploration.