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Throughout the western US snow melted at an alarming rate in April 2021 and by May 1, hydrologic conditions were severely degraded with declining summer water supply forecasts compared to earlier in the winter. The objectives of this study are to (a) quantify the magnitude and climatological context of observed melt rates of snow water equivalent (SWE) and (b) underpin the hydrometeorological drivers during April 2021 based on atmospheric reanalysis and gridded meteorological data. Peak SWE indicated snow drought conditions were widespread (41% of stations between 5th and 20th percentile) but not necessarily extreme (only 9% of stations less than 5th percentile). Here, using observations from the Snow Telemetry (SNOTEL) network we found record 7 day snow melt rates (median of −99 mm; ±one standard deviation of 61 mm) occurred at 24% of SNOTEL sites and in all 11 Western states. Strong upper atmospheric ridging that began initially in the north Pacific with eastward propagation by mid-April to the Pacific Northwest Coast led to near-surface conditions across the western US conducive to rapid snow loss. One heat wave occurred inland across the Rockies the first week of April and then later in April, a second heat wave impacted the Cascades and northern California. We find that ripening of the snowpack by both record high surface solar radiation and air temperatures were factors in driving the rapid snow melt. Equatorial Pacific sea surface temperatures and the La Niña pattern that peaked in winter along with an eastward propagating and intensifying Madden–Julian Oscillation were likely responsible for driving the placement, strength, and progression of the north Pacific Ridge. This study documents the role of two extreme spring ‘sunny heat wave’ events on snowpack, and the cascading drought impacts which are anticipated to become more frequent in a warming world.

Partitioning precipitation into rain and snow with near-surface meteorology is a well-known challenge. However, whether a limit exists to its potential performance remains unknown. Here, we evaluate this possibility by applying a set of benchmark precipitation phase partitioning methods plus three machine learning (ML) models (an artificial neural network, random forest, and XGBoost) to two independent datasets: 38.5 thousand crowdsourced observations and 17.8 million synoptic meteorology reports. The ML methods provide negligible improvements over the best benchmarks, increasing accuracy only by up to 0.6% and reducing rain and snow biases by up to -4.7%. ML methods fail to identify mixed precipitation and sub-freezing rainfall events, while expressing their worst accuracy values from 1.0 °C–2.5 °C. A potential cause of these shortcomings is the air temperature overlap in rain and snow distributions (peaking between 1.0 °C–1.6 °C), which expresses a significant negative relationship ( p  < 0.0005) with partitioning accuracy. Thus, the meteorological characteristics of rain and snow are similar at air temperatures slightly above freezing with increasing overlap associated with decreasing performance. We suggest researchers switch their focus from marginally improving inherently limited precipitation phase partitioning methods using near-surface meteorology to creating new methods that assimilate novel data sources—e.g., crowdsourced precipitation phase observations. This paper shows that the data and methods used to partition precipitation into rain and snow are fundamentally flawed at air temperatures near the freezing point. Machine learning methods cannot overcome the performance dip and biases of the existing traditional techniques, highlighting the need for new approaches.

Snowpack seasonality in the conterminous United States (U.S.) is examined using a recently-released daily, 4 km spatial resolution gridded snow water equivalent and snow depth product developed by assimilating station-based observations and gridded temperature and precipitation estimates from PRISM. Seasonal snowpacks for the period spanning water years 1982–2017 were calculated using two established methods: (1) the classic Sturm approach that requires 60 days of snow cover with a peak depth >50 cm and (2) the snow seasonality metric (SSM) that only requires 60 days of continuous snow cover to define seasonal snow. The latter approach yields continuous values from −1 to +1, where −1 (+1) indicates an ephemeral (seasonal) snowpack. The SSM approach is novel in its ability to identify both seasonal and ephemeral snowpacks. Both approaches identify seasonal snowpacks in western U.S. mountains and the northern central and eastern U.S. The SSM approach identifies greater areas of seasonal snowpacks compared to the Sturm method, particularly in the Upper Midwest, New England, and the Intermountain West. This is a result of the relaxed depth constraint compared to the Sturm approach. Ephemeral snowpacks exist throughout lower elevation regions of the western U.S. and across a broad longitudinal swath centered near 35° N spanning the lee of the Rocky Mountains to the Atlantic coast. Because it lacks a depth constraint, the SSM approach may inform the location of shallow but long-duration snowpacks at risk of transitioning to ephemeral snowpacks with climatic change. A case study in Oregon during an extreme snow drought year (2014/2015) highlights seasonal to ephemeral snowpack transitions. Aggregating seasonal and ephemeral snowpacks to the HUC-8 watershed level in the western U.S. demonstrates the majority of watersheds are at risk of losing seasonal snow.

Reanalysis products support our understanding of how the precipitation phase influences hydrology across scales. However, a lack of validation data hinders the evaluation of a reanalysis‐estimated precipitation phase. In this study, we used a novel dataset from the Mountain Rain or Snow (MRoS) citizen science project to compare 39,680 MRoS observations from January 2020 to July 2023 across the conterminous United States (CONUS) to assess three precipitation phase products. These products included the Global Precipitation Measurement (GPM) mission Integrated Multi‐satellitE Retrievals for GPM (IMERG), the Modern‐Era Retrospective Analysis for Research and Applications (MERRA‐2), and the North American Land Data Assimilation System (NLDAS‐2). The overall critical success indices for detecting rainfall (snowfall) for IMERG, MERRA‐2, and NLDAS‐2 were 0.51 (0.79), 0.49 (0.77), and 0.54 (0.53), respectively. These indices show that IMERG and MERRA‐2 reasonably classify snowfall, whereas NLDAS‐2 overestimates rainfall. All products performed poorly in detecting subfreezing rainfall and snowfall above 2°C. Therefore, crowdsourced data provides a unique validation source to improve the capabilities of reanalysis products. Plain Language Summary Distinguishing between rain and snow is challenging. This study used a unique crowdsourced dataset from the Mountain Rain or Snow (MRoS) project to allow researchers to better assess the accuracy of reanalysis products used to differentiate rain from snow. We compared the citizen science data with results from three reanalysis products. We found that these reanalysis products all performed poorly in detecting rainfall at subfreezing rainfall or snowfall at warmer air temperatures. Crowdsourced data could help enhance methods used to determine precipitation phases and improve real‐time weather forecasts. Key Points The Mountain Rain or Snow citizen science project collected a novel dataset of 39,680 observations of precipitation phases across the US The precipitation reanalysis products performed poorly in detecting subfreezing rainfall and snowfall above 2degree signC The crowdsourced data provides a unique validation source to improve the capabilities of reanalysis products

Atmospheric rivers (ARs) and Santa Ana winds (SAWs) are impactful weather events for California communities. Emergency planning efforts and resource management would benefit from extending lead times of skillful prediction for these and other types of extreme weather patterns. Here we describe a methodology for subseasonal prediction of impactful winter weather in California, including ARs, SAWs and heat extremes. The hybrid approach combines dynamical model and historical information to forecast probabilities of impactful weather outcomes at weeks 1–4 lead. This methodology uses dynamical model information considered most reliable, that is, planetary/synoptic‐scale atmospheric circulation, filters for dynamical model error/uncertainty at longer lead times and increases the sample of likely outcomes by utilizing the full historical record instead of a more limited suite of dynamical forecast model ensemble members. We demonstrate skill above climatology at subseasonal timescales, highlighting potential for use in water, health, land, and fire management decision support. Plain Language Summary California winter weather can alternate between very wet conditions from atmospheric rivers making landfall along the Pacific coast to hot, dry, and windy conditions brought by Santa Ana winds blowing in from the Southwest interior. Atmospheric rivers are important for water resources while also causing flooding, whereas Santa Ana winds are often associated with wildfire, especially following prolonged dry periods. Preparing for these types of weather events is important for managing resources and protecting life and property, yet reliable forecasts beyond about 7–10 days remain a challenge. We have developed a new prediction system that combines information about approaching atmospheric weather patterns from weather forecast models along with historical information relating those patterns to impacts over California to predict the likelihood of impactful weather at 1–4 weeks lead time. By extending the window of opportunity to take management action, this new approach should aid in resource and emergency planning in water, land, and fire sectors as well as protecting residents through improved warning systems. Key Points A hybrid dynamical‐statistical model is described for 1–4‐week forecasts of impactful California winter weather using circulation regimes This hybrid framework reduces the number of forecasts produced, but the ones issued can be interpreted with higher confidence This new methodology provides skillful subseasonal forecasts with potential to improve early warnings for impactful weather events

Increasing wildfire and declining snowpacks in mountain regions threaten water availability. We combine satellite‐based fire detections with snow seasonality classifications to examine fire activity in California's seasonal and ephemeral snow zones. We find a nearly tenfold increase in fire activity during 2020–2021 versus 2001–2019. Accumulation season broadband snow albedo declined 25%–71% at two burned sites (2021 and 2022) according to in‐situ data relative to un‐burned conditions, with greater declines associated with increased burn severity. By enhancing snowpack susceptibility to melt, both decreased snow albedo and canopy drove midwinter melt during a multi‐week dry spell in 2022. Despite similar meteorological conditions in December–February 2013 and 2022–linked to persistent high pressure weather regimes–minimal melt occurred in 2013. Post‐fire snowpack differences are confirmed with satellite measurements. With growing geographical overlap between wildfire and snow, our findings suggest California's snowpack is increasingly vulnerable to the compounding effects of dry spells and wildfire. Plain Language Summary Satellite fire detections indicate substantial increases in wildfire activity in California's snow‐covered landscapes during 2020 and 2021, suggesting wildfire is increasingly altering mountain hydrology. During 2022, a multi‐week mid‐winter drought, or dry spell, occurred. A meteorologically‐similar dry spell occurred in 2013, and the 2022 event provides a test case to examine how post‐fire changes (canopy loss and deposition of burned dark material on snowpack) alter snowmelt patterns. Using field observations, weather station data, and satellite remote sensing of snow, we find large reductions in snow albedo and canopy cover drove rapid melt during the 2022 dry spell in burned areas whereas during 2013, minimal melt occurred. The societal connection between mountains and humans will be strained as mountains face increasing climate‐related stressors. Midwinter drought, snow loss, and increasing wildfire are expectations of a warming world. Addressing these challenges requires innovative water and forest management paradigms. Our findings motivate additional research into assessing and planning for post‐fire hydrologic changes in snow‐dominated landscapes as both wildfire and dry spells will increase in frequency with climate warming. Key Points A 9.8x increase in satellite fire detections in California's snow zones in 2020–2021 versus 2001–2019 implies growing overlap in fire and snow Post‐fire accumulation season broadband snow albedo declined 25%–71%, driving fewer snow‐covered days and lower snow‐cover fraction Compared with the meteorologically similar 2013 dry spell, albedo and canopy declines led to rapid midwinter melt in 2022

On 5–7 April 2018, a landfalling atmospheric river resulted in widespread heavy precipitation in the Sierra Nevada of California and Nevada. Observed snow levels during this event were among the highest snow levels recorded since observations began in 2002 and exceeded 2.75 km for 31 h in the northern Sierra Nevada and 3.75 km for 12 h in the southern Sierra Nevada. The anomalously high snow levels and over 80 mm of precipitation caused flooding, debris flows, and wet snow avalanches in the upper elevations of the Sierra Nevada. The origin of this atmospheric river was super typhoon Jelawat, whose moisture remnants were entrained and maintained by an extratropical cyclone in the northeast Pacific. This event was notable due to its April occurrence, as six other typhoon remnants that caused heavy precipitation with high snow levels (mean = 2.92 km) in the northern Sierra Nevada all occurred during October.

The partitioning of precipitation into frozen and liquid components influences snow-derived water resources and flood hazards in mountain environments. We used a 915-MHz Doppler radar wind profiler upstream of the northern Sierra Nevada to estimate the hourly elevation where snow melts to rain, or the snow level, during winter (December–February) precipitation events spanning water years (WY) 2008–2017. During this ten-year period, a Mann-Kendall test indicated a significant (p < 0.001) positive trend in snow level with a Thiel-Sen slope of 72 m year−1. We estimated total precipitation falling as snow (snow fraction) between WY1951 and 2017 using nine daily mid-elevation (1200–2000 m) climate stations and two hourly stations spanning WY2008–2017. The climate-station-based snow fraction estimates agreed well with snow-level radar values (R2 = 0.95, p < 0.01), indicating that snow fractions represent a reasonable method to estimate changes in frozen precipitation. Snow fraction significantly (p < 0.001) declined during WY2008–2017 at a rate of 0.035 (3.5%) year−1. Single-point correlations between detrended snow fraction and sea-surface temperatures (SST) suggested that positive SST anomalies along the California coast favor liquid phase precipitation during winter. Reanalysis-derived integrated moisture transported upstream of the northern Sierra Nevada was negatively correlated with snow fraction (R2 = 0.90, p < 0.01), with atmospheric rivers representing the likely circulation mechanism producing low-snow-fraction storms.

Engagement strategies are central to the success of community and citizen science (CCS) initiatives; however, relatively little has been written on approaches that support project growth. Here, we assess the four components of the Mountain Rain or Snow engagement strategy (recruitment, training, activation, and retention) as the project transitioned from one region to four to increase participation in documenting precipitation phase. To scale up, we replicated the structure from our single-region effort in new regions while using place-based text messaging with observers across broad geographic areas and a localized approach to building partnerships. We use two sources of data-a participant feedback survey of 443 respondents and participant analytics of 877 new sign-ups and 13,017 observations submitted during the study-to evaluate success relative to project goals established at the outset of the expansion process. The Mountain Rain or Snow engagement strategy met project-wide goals for growing our observer network, for data collection, and for maintaining observer satisfaction with communication tools. We did not meet region-level goals for recruitment and activation in one location. Diverse partnerships and approaches to amplification supported recruitment success for this project. Survey data show that 85% of respondents found our novel approach to training helpful, and 82% found activation text messages helpful to understand when and how to participate. Feedback on communication preferences show that there was unmet demand for text messaging. Our evaluation found that a consistent structure across regions coupled with place-based messaging enhanced engagement while scaling up.

The 1997 New Year's flood event was the most costly in California's history. This compound extreme event was driven by a category 5 atmospheric river that led to widespread snowmelt. Extreme precipitation, snowmelt, and saturated soils produced heavy runoff causing widespread inundation in the Sacramento Valley. This study recreates the 1997 flood using the Regionally Refined Mesh capabilities of the Energy Exascale Earth System Model (RRM‐E3SM) under prescribed ocean conditions. Understanding the processes causing extreme events informs practical efforts to anticipate and prepare for such events in the future, and also provides a rich context to evaluate model skill in representing extremes. Three California‐focused RRM grids, with horizontal resolution refinement of 14 km down to 3.5 km, and six forecast lead times, 28 December 1996 at 00Z through 30 December 1996 at 12Z, are assessed for their ability to recreate the 1997 flood. Planetary to synoptic scale atmospheric circulations and integrated vapor transport are weakly influenced by horizontal resolution refinement over California. Topography and mesoscale circulations, such as the Sierra barrier jet, are better represented at finer horizontal resolutions resulting in better estimates of storm total precipitation and storm duration snowpack changes. Traditional time‐series and causal analysis frameworks are used to examine runoff sensitivities state‐wide and above major reservoirs. These frameworks show that horizontal resolution plays a more prominent role in shaping reservoir inflows, namely the magnitude and time‐series shape, than forecast lead time, 2‐to‐4 days prior to the 1997 flood onset. Plain Language Summary The 1997 California New Year's flood event caused over a billion dollars in damages. This storm became a central part in guiding efforts to reduce flood risks. Earth system models are increasingly asked to recreate extreme weather events. However, the ability of Earth system models to recreate such events requires rigorous testing. Testing ensures that models provide value in anticipating and planning for future flood events. This is particularly important given the changing climate. We evaluated the Department of Energy's flagship Earth system model, the Energy Exascale Earth System Model, in its ability to recreate the weather and flood characteristics of the 1997 flood. The model resolution, important for resolving mountain terrain and storm interactions, and forecast lead time, important for storm progression accuracy, are assessed. The multi‐forecast average from the highest‐resolution model best recreates the observed precipitation, snowpack changes, and flood characteristics. Our findings provide confidence that the highest resolution model could be used to study how a 1997‐like flood event would be altered in a warmer world. Key Points Energy Exascale Earth System Model forecasts at 3.5 km grid spacing skillfully recreate the hydrometeorology of California's 1997 flood Horizontal resolution alters the representation of key flood drivers such as the Sierra barrier jet, precipitation extremes, and snowmelt Forecast lead time 2‐to‐4 days prior to the onset of the 1997 flood minimally influences forecast precipitation and snowmelt skill