The seasonal mountain snowpack of the Western US (WUS) is a key water resource to millions of people and an important component of the regional climate system. Impurities at the snow surface can affect snowmelt timing and rate through snow radiative forcing (RF), resulting in earlier streamflow, snow disappearance, and less water availability in dry months. Predicting the locations, timing, and intensity of impurities is challenging, and little is known concerning whether snow RF has changed over recent decades. Here we analyzed the relative magnitude and spatio-temporal variability of snow RF across the WUS at three spatial scales (pixel, watershed, regional) using remotely sensed RF from spatially and temporally complete (STC) MODIS data sets (STC-MODIS Snow Covered Area and Grain Size/MODIS Dust Radiative Forcing on Snow) from 2001 to 2022. To quantify snow RF impacts, we calculated a pixel-integrated metric over each snowmelt season (1st March-30th June) in all 22 years. We tested for long-term trend significance with the Mann-Kendall test and trend magnitude with Theil-Sen's slope. Mean snow RF was highest in the Upper Colorado region, but notable in less-studied regions, including the Great Basin and Pacific Northwest. Watersheds with high snow RF also tended to have high spatial and temporal variability in RF, and these tended to be near arid regions. Snow RF trends were largely absent; only a small percent of mountain ecoregions (0.03%-8%) had significant trends, and these were typically decreasing trends. All mountain ecoregions exhibited a net decline in snow RF. While the spatial extent of significant RF trends was minimal, we found declining trends most frequently in the Sierra Nevada, North Cascades, and Canadian Rockies, and increasing trends in the Idaho Batholith. This study establishes a two-decade chronology of snow impurities in the WUS, helping inform where and when RF impacts on snowmelt may need to be considered in hydrologic models and regional hydroclimate studies.

Ground temperature's sensitivity to climate change has garnered attention. This study aimed to monitor and analyze temporal trends and estimate Active Layer Thickness from a monitoring point at Fildes Peninsula, King George Island, in Antarctica. Quality control and consistency analysis were performed on the data. Methods such as serial autocorrelation, Mann-Kendall, Sen-Slope, Pettitt, and regression analysis tests were applied. Spearman's correlation examined the relationship between air temperature and ground depths. The active layer thickness was estimated using the maximum monthly temperature, and the permafrost lower limit used the minimum monthly temperature. Significant summer seasonal trends were observed with Mann- Kendall tau, positive Sen-Slope, and Pettitt slope at depths of 67.5 and 83.5 cm. The regression analysis was significant and positive for all ground depths and in different seasons. The highest correlation (r=0.82) between air temperature and surface ground depth was found. Freezing prevailed at all depths during 2008-2018. The average Active Layer Thickness (ALT) was 92.61 cm. Temperature is difficult to monitor, and its estimation is still complex. However, it stands out as a fundamental element for studies that refer to the impacts of climate change