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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 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. 

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. 

Abstract Low‐coverage whole‐genome sequencing (WGS) is increasingly used for the study of evolution and ecology in both model and non‐model organisms; however, effective application of low‐coverage WGS data requires the implementation of probabilistic frameworks to account for the uncertainties in genotype likelihoods.Here, we present a probabilistic framework for using genotype likelihoods for standard population assignment applications. Additionally, we derive the Fisher information for allele frequency from genotype likelihoods and use that to describe a novel metric, theeffective sample size, which figures heavily in assignment accuracy. We make these developments available for application through WGSassign, an open‐source software package that is computationally efficient for working with whole‐genome data.Using simulated and empirical data sets, we demonstrate the behaviour of our assignment method across a range of population structures, sample sizes and read depths. Through these results, we show that WGSassign can provide highly accurate assignment, even for samples with low average read depths (<0.01X) and among weakly differentiated populations.Our simulation results highlight the importance of equalizing the effective sample sizes among source populations in order to achieve accurate population assignment with low‐coverage WGS data. We further provide study design recommendations for population assignment studies and discuss the broad utility of effective sample size for studies using low‐coverage WGS data. 

Abstract The emergence of atomically thin van der Waals magnets provides a new platform for the studies of two-dimensional magnetism and its applications. However, the widely used measurement methods in recent studies cannot provide quantitative information of the magnetization nor achieve nanoscale spatial resolution. These capabilities are essential to explore the rich properties of magnetic domains and spin textures. Here, we employ cryogenic scanning magnetometry using a single-electron spin of a nitrogen-vacancy center in a diamond probe to unambiguously prove the existence of magnetic domains and study their dynamics in atomically thin CrBr 3 . By controlling the magnetic domain evolution as a function of magnetic field, we find that the pinning effect is a dominant coercivity mechanism and determine the magnetization of a CrBr 3 bilayer to be about 26 Bohr magnetons per square nanometer. The high spatial resolution of this technique enables imaging of magnetic domains and allows to locate the sites of defects that pin the domain walls and nucleate the reverse domains. Our work highlights scanning nitrogen-vacancy center magnetometry as a quantitative probe to explore nanoscale features in two-dimensional magnets. 

Moiré superlattices of twisted nonmagnetic two-dimensional (2D) materials are highly controllable platforms for the engineering of exotic correlated and topological states. Here, we report emerging magnetic textures in small-angle twisted 2D magnet chromium triiodide (CrI 3 ). Using single-spin quantum magnetometry, we directly visualized nanoscale magnetic domains and periodic patterns, a signature of moiré magnetism, and measured domain size and magnetization. In twisted bilayer CrI 3 , we observed the coexistence of antiferromagnetic (AFM) and ferromagnetic (FM) domains with disorder-like spatial patterns. In twisted double-trilayer CrI 3 , AFM and FM domains with periodic patterns appear, which is in good agreement with the calculated spatial magnetic structures that arise from the local stacking-dependent interlayer exchange interactions in CrI 3 moiré superlattices. Our results highlight magnetic moiré superlattices as a platform for exploring nanomagnetism.