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Abstract Lakes are experiencing ice declines and fundamental changes in winter conditions. For Earth's largest lakes that experience seasonal ice cover, the relationship between ice conditions and evaporation is critical to water balance estimates and global freshwater storage. Here, we analyze robust data sets of net basin supplies, satellite‐derived products, and model estimates of surface turbulent heat flux for the Laurentian Great Lakes during the period 1973–2022. We show that ice cover does not have a strong relationship with lake evaporation in winter months and that often the magnitude of the ice effect on moisture flux reduction is within the range of natural variability and the uncertainty of water budget estimates. This suggests that differences in lake evaporation between cold and warm winters is driven by seasonal overlake atmospheric conditions, more broadly, and that ice cover reduces but does not determine the resultant evaporation. 

Abstract Water temperature dynamics in large inland lakes are interrelated with internal lake physics, ecosystem function, and adjacent land surface meteorology and climatology. Models for simulating and forecasting lake temperatures often rely on remote sensing andin situdata for validation.In situmonitoring platforms have the benefit of providing relatively precise measurements at multiple lake depths, but are often sparser (temporally and spatially) than remote sensing data. Here, we address the challenge of synthesizingin situlake temperature data by creating a standardized database of near-surface and subsurface measurements from 134 sites across 29 large North American lakes, with the primary goal of supporting an ongoing lake model validation study. We utilize data sources ranging from federal agency repositories to local monitoring group samples, with a collective historical record spanning January 1, 2000 through December 31, 2022. Our database has direct utility for validating simulations and forecasts from operational numerical weather prediction systems in large lakes whose extensive surface area may significantly influence nearby weather and climate patterns. 

Abstract Winter ice conditions in the Great Lakes play a crucial role in shaping ecological processes, shoreline dynamics, and regional weather patterns. Although atmospheric factors are widely acknowledged as the primary drivers of ice formation and duration, the influence of subsurface groundwater flow remains largely unexplored. In this study, we evaluate how spatially and temporally variable groundwater flux affects ice formation and thermal structure in Lakes Michigan and Huron, using a coupled hydrodynamic‐ice model. Simulations were conducted for the winters of 2014, 2015, and 2016—a period characterized by distinct atmospheric and ice conditions—and were validated against observed ice concentration maps and temperature profiles. Results show that groundwater enhances ice thickness during colder winters by strengthening water column stability, limiting vertical mixing, and insulating the surface layer, thus promoting thicker, longer‐lasting ice. Sensitivity analyses reveal that moderate increases in groundwater flux intensify stratification and prolong ice concentration, while an extreme, high flux (1000x) disrupts stability and reduces ice thickness. Coastal regions display more pronounced effects due to higher groundwater input, whereas offshore zones exhibit comparatively weaker responses. These findings highlight the significant role of groundwater flux in modulating ice dynamics and stratification in large freshwater systems such as the Great Lakes. This research underscores the importance of incorporating subsurface hydrology into coupled modeling frameworks to improve predictions of ice dynamics and water column stratification. Future work should focus on obtaining high‐resolution observational data on groundwater flux and ice thickness, particularly near shorelines, to further refine coupled hydrodynamic‐ice models. 

Comprehensive assessments of hydrological components are crucial for enhancing operational water supply simulations. However, hydrological models are often evaluated based on their surface flow simulations, while the validation of subsurface and groundwater components tends to be overlooked or not well documented. In this study, we evaluated the outputs of two hydrological models, the Large Basin Runoff Model (LBRM) and the Weather Research and Forecasting – Hydrological modeling extension package (WRF-Hydro), for potential implementation in operational water balance forecasting in the Great Lakes region. We examined the simulated hydrological variables including surface (e.g. snow water equivalent, evapotranspiration, and streamflow), subsurface (e.g. soil moisture at different layers), and groundwater components with observed or reference data from ground-based stations and remotely sensed images. The findings of this study provide valuable insights into the capabilities and limitations of each model. These findings contribute to more informed water management strategies for the Great Lakes region. 

Climate and hydrologic change across the Great Lakes region and other transboundary watersScott Steinschneider, M. Altaf Arain, Paulin Coulibaly, Andrew Gronewold, and Gail Krantzberg, explore climate and hydrologic change across the Great Lakes region in North America and other transboundary waters. Hydroclimate extremes are transforming water landscapes in transboundary regions. These systems are particularly susceptible to hydroclimatic variability due to shared governance structures, interconnected ecosystems, and a wide range of water users. The Great Lakes basin – one of the world’s largest freshwater systems, shared by Canada, the United States, and numerous Indigenous sovereign nations – exemplifies how shifting hydroclimatic conditions are challenging conventional approaches to water management across borders. In this region, the impacts of these changes are evident in increased flooding, shoreline erosion, economic disruption, ecosystem stress, and rising uncertainty surrounding water availability and quality. 

Abstract Lake surface conditions are critical for representing lake‐atmosphere interactions in numerical weather prediction. The Community Land Model's 1‐D lake component (CLM‐lake) is part of NOAA's High‐Resolution Rapid Refresh (HRRR) 3‐km weather/earth‐system model, which assumes that virtually all the two thousand lakes represented in CONUS have distinct (for each lake) but spatially uniform depth. To test the sensitivity of CLM‐lake to bathymetry, we ran CLM‐lake as a stand‐alone model for all of 2019 with two bathymetry data sets for 23 selected lakes: the first had default (uniform within each lake) bathymetry while the second used a new, spatially varying bathymetry. We validated simulated lake surface temperature (LST) with both remote and in situ observations to evaluate the skill of both runs and also intercompared modeled ice cover and evaporation. Though model skill varied considerably from lake to lake, using the new bathymetry resulted in marginal improvement over the default. The more important finding is the influence bathymetry has on modeled LST (i.e., differences between model simulations) where lake‐wide LST deviated as much as 10°C between simulations and individual grid cells experienced even greater departures. This demonstrates the sensitivity of surface conditions in atmospheric models to lake bathymetry. The new bathymetry also improved lake depths over the (often too deep) previous value assumed for unknown‐depth lakes. These results have significant implications for numerical weather prediction, especially in regions near large lakes where lake surface conditions often influence the state of the atmosphere via thermal regulation and lake effect precipitation.