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Atmospheric rivers (ARs) play vital roles in the western United States and related regions globally, not only producing heavy precipitation and flooding, but also providing beneficial water supply. This paper introduces a scale for the intensity and impacts of ARs. Its utility may be greatest where ARs are the most impactful storm type and hurricanes, nor’easters, and tornadoes are nearly nonexistent. Two parameters dominate the hydrologic outcomes and impacts of ARs: vertically integrated water vapor transport (IVT) and AR duration [i.e., the duration of at least minimal AR conditions (IVT ≥ 250 kg m−1 s−1)]. The scale uses an observed or predicted time series of IVT at a given geographic location and is based on the maximum IVT and AR duration at that point during an AR event. AR categories 1–5 are defined by thresholds for maximum IVT (3-h average) of 250, 500, 750, 1,000, and 1,250 kg m−1 s−1, and by IVT exceeding 250 kg m−1 s−1 continuously for 24–48 h. If the AR event duration is less than 24 h, it is downgraded by one category. If it is longer than 48 h, it is upgraded one category. The scale recognizes that weak ARs are often mostly beneficial because they can enhance water supply and snowpack, while stronger ARs can become mostly hazardous, for example, if they strike an area with antecedent conditions that enhance vulnerability, such as burn scars or wet conditions. Extended durations can enhance impacts. Short durations can mitigate impacts.

Climate change will impact western USA water supplies by shifting precipitation from snow to rain and driving snowmelt earlier in the season. However, changes at the regional-to-mountain scale is still a major topic of interest. This study addresses the impacts of climate change on mountain snowpack by assessing historical and projected variable-resolution (VR) climate simulations in the community earth system model (VR-CESM) forced by prescribed sea-surface temperatures along with widely used regional downscaling techniques, the coupled model intercomparison projects phase 5 bias corrected and statistically downscaled (CMIP5-BCSD) and the North American regional climate change assessment program (NARCCAP). The multi-model RCP8.5 scenario analysis of winter season SWE for western USA mountains indicates by 2040-2065 mean SWE could decrease −19% (NARCCAP) to −38% (VR-CESM), with an ensemble median change of −27%. Contrary to CMIP5-BCSD and NARCCAP, VR-CESM highlights a more pessimistic outcome for western USA mountain snowpack in latter-parts of the 21st century. This is related to temperature changes altering the snow-albedo feedback, snowpack storage, and precipitation phase, but may indicate that VR-CESM resolves more physically consistent elevational effects lacking in statistically downscaled datasets and teleconnections that are not captured in limited area models. Overall, VR-CESM projects by 2075–2100 that average western USA mountain snowfall decreases by −30%, snow cover by −44%, SWE by −69%, and average surface temperature increase of +5.0 ∘C. This places pressure on western USA states to preemptively invest in climate adaptation measures such as alternative water storage, water use efficiency, and reassess reservoir storage operations.

Hydrologic and land surface models require spatiotemporally complete and accurate hydrometeorological forcings. In mountainous regions, hydrometeorological forcings are often obtained as the output of coupled land‐atmosphere models, like the Weather Research and Forecasting (WRF) model, configured to run at spatial scales that permit orographic convection (e.g., ≤ ${\\le} $4 km). Models like WRF, however, require physical parameterizations, the selection of which significantly influences model predictions of precipitation, temperature, and radiant fluxes used as input to hydrologic and land surface models. Here we investigate the impact of two critical aspects of WRF configurations, namely the selection of the cloud microphysics parameterization and lateral boundary conditions, on modeled hydrometeorological forcings and associated snow conditions in a mountainous region of the western United States. We conducted eight experiments with WRF configured at convection‐permitting scales using two reanalysis data sets as lateral boundary conditions (ERA5 and CFSRv2) and four alternative cloud microphysics schemes. These experiments reveal that the choice of lateral boundary conditions and cloud microphysics schemes imposes substantial variability in simulated surface hydrometeorological conditions, with precipitation and radiation emerging as key factors. When compared to the accumulated precipitation average over the Snow Telemetry (SNOTEL) stations, the relative bias in precipitation across experiments ranges from −18.15% to +15.48%. These biases impact the land surface model, leading to discrepancies in modeled snow. The relative bias in snow water equivalent compared to the SNOTEL average ranges from −39.84% to 10.72%, while for snow depth, it falls between −37.72% and 0.32%. Further comparisons of annual snow fraction and snow disappearance date (SDD) with Moderate Resolution Imaging Spectroradiometer (MODIS) reveal a consistent overestimation at higher elevations, with snow persisting beyond the MODIS SDD. These findings highlight the critical role of model configuration in improving hydrometeorological forcings and enhancing hydrologic predictions in complex terrain.

Conventional climate analyses rely on mean temperature trends to assess climate change impacts, yet this aggregation can obscure asymmetric shifts in the temperature distribution especially in threshold‐sensitive systems like snowpack. Here, we introduce a distributional diagnostic framework that decomposes winter temperature trends into median, cold‐tail (5th percentile), and warm‐tail (95th percentile) components across the Northern Hemisphere. Using 1981–2020 temperature records from the Berkeley Earth data set, we find that mean and median winter trends diverge substantially, with differences ranging from −0.19 to +0.41°C/decade (5th–95th percentile range) across snow‐affected grid cells. Mean trends systematically exceed median trends in 61% of locations, and these divergences are driven by spatially structured and climatology‐dependent tail behavior. We classify these patterns into four types based on directions of cold and warm tail trends relative to median trends and show that each aligns with distinct climatological regimes. Using multivariate regression, we show that median temperature trends consistently outperform means in explaining March snow water equivalent trends, while adding tail metrics further improves explanatory power. Tail contributions vary by climate: in extremely cold zones (≤ ${\\le} $−20°C) warm‐tail trends dominate, in moderately cold zones (−20 to −10°C) median trends and in near‐freezing regions (−10 to 0°C) cold‐tail trends. These results demonstrate that asymmetric distributional change is a key control on spring snowpack trends and highlight the need for percentile‐based diagnostics in climate impact assessments.

Streamflow in high-mountain Asia is influenced by meltwater from snow and glaciers, and determining impacts of climate change on the region’s cryosphere is essential to understand future water supply. Past and future changes in seasonal snow are of particular interest, as specifics at the scale of the full region are largely unknown. Here we combine models with observations to show that regional snowmelt is a more important contributor to streamflow than glacier melt, that snowmelt magnitude and timing changed considerably during 1979–2019 and that future snow meltwater supply may decrease drastically. The expected changes are strongly dependent on the degree of climate change, however, and large variations exist among river basins. The projected response of snowmelt to climate change indicates that to sustain the important seasonal buffering role of the snowpacks in high-mountain Asia, it is imperative to limit future climate change.High-mountain Asia streamflow is strongly impacted by snow and glacier melt. A regional model, combined with observations and climate projections, shows snowmelt decreased during 1979–2019 and was more dominant than glacier melt, and projections suggest declines that vary by river basin.

The temperature rises in Northeast China are anticipated to be among the highest in the world. Investigating the variation in snowpack depth and its characteristics in this region is vital and representative. This study selects the optimal model ensemble using methods such as the Taylor diagram and skill score based on data from CMIP6 models. Statistical methods, including trend and variance analyses, are applied to analyze the spatiotemporal variation of winter snowpack depth in Northeast China from 2025 to 2099 under the SSP1-2.6 (low emission), SSP2-4.5 (moderate emission), and SSP5-8.5 (high emission) scenarios, with possible mechanisms behind these changes. Results indicated that snowpack depth in Northeast China shows no considerable change under SSP1-2.6 and SSP2-4.5, whereas it reduces significantly by 15% under SSP5-8.5. Spatially, the area experiencing a decrease in snowpack depth is roughly 2 to 3 times larger than the area with an increase, compared to the base period. The areas with significant declines in snowpack depth account for 5.6%, 0.9%, and 60.5% under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The most affected area is located southeast of the study area, where a decrease of 6.98 cm is projected by the end of the 21st century. The geopotential height anomaly (low in the west and high in the east) formed at 500 hPa in Northeast Asia, and the future increase in water vapor will contribute to an increase in winter snowfall in this region. However, the rise in temperature will result in a decrease in snowfall. In addition, snowpack depths in the study area under SSP1-2.6 and SSP2-4.5 are nearly identical but differ considerably from those under SSP5-8.5. As emissions rise, snowpack depth reduces, indicating that snowpack depth is negatively related to temperature. Considerable changes in snowpack depth are observed when the temperature rises to a certain threshold.

Western United States wildfire increases have been generally attributed to warming temperatures, either through effects on winter snowpack or summer evaporation. However, near-surface air temperature and evaporative demand are strongly influenced by moisture availability and these interactions and their role in regulating fire activity have never been fully explored. Here we show that previously unnoted declines in summer precipitation from 1979 to 2016 across 31–45% of the forested areas in the western United States are strongly associated with burned area variations. The number of wetting rain days (WRD; days with precipitation ≥2.54 mm) during the fire season partially regulated the temperature and subsequent vapor pressure deficit (VPD) previously implicated as a primary driver of annual wildfire area burned. We use path analysis to decompose the relative influence of declining snowpack, rising temperatures, and declining precipitation on observed fire activity increases. After accounting for interactions, the net effect of WRD anomalies on wildfire area burned was more than 2.5 times greater than the net effect of VPD, and both the WRD and VPD effects were substantially greater than the influence of winter snowpack. These results suggest that precipitation during the fire season exerts the strongest control on burned area either directly through its wetting effects or indirectly through feedbacks to VPD. If these trends persist, decreases in summer precipitation and the associated summertime aridity increases would lead to more burned area across the western United States with farreaching ecological and socioeconomic impacts.

Snowpack stores cold-season precipitation to meet warm-season water demand. Climate change threatens to disturb this balance by altering the fraction of precipitation falling as snow and the timing of snowmelt, which may have profound effects on food production in basins where irrigated agriculture relies heavily on snowmelt runoff. Here, we analyse global patterns of snowmelt and agricultural water uses to identify regions and crops that are most dependent on snowmelt water resources. We find hotspots primarily in high-mountain Asia (the Tibetan Plateau), Central Asia, western Russia, western US and the southern Andes. Using projections of sub-annual runoff under warming scenarios, we identify the basins most at risk from changing snowmelt patterns, where up to 40% of irrigation demand must be met by new alternative water supplies under a 4 °C warming scenario. Our results highlight basins and crops where adaptation of water management and agricultural systems may be especially critical in a changing climate.Snowmelt runoff is an important source of water for irrigating agricultural crops in high-mountain Asia, Central Asia, western Russia, western US and the southern Andes. Climate change places water resources in these basins at risk, indicating the need to adapt water management.

Warming surface temperatures have driven a substantial reduction in the extent and duration of Northern Hemisphere snow cover 1 – 3 . These changes in snow cover affect Earth’s climate system via the surface energy budget, and influence freshwater resources across a large proportion of the Northern Hemisphere 4 – 6 . In contrast to snow extent, reliable quantitative knowledge on seasonal snow mass and its trend is lacking 7 – 9 . Here we use the new GlobSnow 3.0 dataset to show that the 1980–2018 annual maximum snow mass in the Northern Hemisphere was, on average, 3,062 ± 35 billion tonnes (gigatonnes). Our quantification is for March (the month that most closely corresponds to peak snow mass), covers non-alpine regions above 40° N and, crucially, includes a bias correction based on in-field snow observations. We compare our GlobSnow 3.0 estimates with three independent estimates of snow mass, each with and without the bias correction. Across the four datasets, the bias correction decreased the range from 2,433–3,380 gigatonnes (mean 2,867) to 2,846–3,062 gigatonnes (mean 2,938)—a reduction in uncertainty from 33% to 7.4%. On the basis of our bias-corrected GlobSnow 3.0 estimates, we find different continental trends over the 39-year satellite record. For example, snow mass decreased by 46 gigatonnes per decade across North America but had a negligible trend across Eurasia; both continents exhibit high regional variability. Our results enable a better estimation of the role of seasonal snow mass in Earth’s energy, water and carbon budgets. Applying a bias correction to a state-of-the-art dataset covering non-alpine regions of the Northern Hemisphere and to three other datasets yields a more constrained quantification of snow mass in March from 1980 to 2018.