Abstract. A physically based snowpack evolution and redistribution model was used to test the effectiveness of assimilating crowd-sourced snow depth measurements collected by citizen scientists. The Community Snow Observations (CSO; https://communitysnowobs.org/, last access: 11 August 2021) project gathers, stores, and distributes measurements of snow depth recorded by recreational users and snow professionals in high mountain environments. These citizen science measurements are valuable since they come from terrain that is relatively undersampled and can offer in situ snow information in locations where snow information is sparse or nonexistent. The present study investigates (1) the improvements to model performance when citizen science measurements are assimilated, and (2) the number of measurements necessary to obtain those improvements. Model performance is assessed by comparing time series of observed (snow pillow) and modeled snow water equivalent values, by comparing spatially distributed maps of observed (remotely sensed) and modeled snow depth, and by comparing fieldwork results from within the study area. The results demonstrate that few citizen science measurements are needed to obtain improvements in model performance, and these improvements are found in 62 % to 78 % of the ensemble simulations, depending on the model year. Model estimations of total water volume from a subregion of the study area also demonstrate improvements in accuracy after CSO measurements have been assimilated. These results suggest that even modest measurement efforts by citizen scientists have the potential to improve efforts to model snowpack processes in high mountain environments, with implications for water resource management and process-based snow modeling.

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In the Northwest United States, warming temperatures threaten mountain snowpacks. Reliable projections of snowfall changes are therefore critical to anticipate the timeline of change. However, producing such projections is challenging, as most state‐of‐the‐art climate models are limited in sufficiently resolving influential topography. Here we leverage atmospheric freezing level to estimate precipitation phase and project twenty‐first‐century snowfall frequency change at Snowpack Telemetry Network stations across the Northwest. Under “moderate” and “business‐as‐usual” emission pathways in Coupled Model Intercomparison Project phase 5 models, snowfall frequency is projected to decline at all stations. Business‐as‐usual declines accelerate after midcentury at most locations, whereas moderate declines decelerate. A “critical year” analysis identifies when decadal‐mean snowfall frequency is projected to fall below 50%, 25%, and 10% of cold‐season wet days. Results highlight regions particularly vulnerable to relatively near‐term change, such as the Cascade Range. Considerable station‐to‐station spatial variability emphasizes the value of this site‐specific approach.

Identifying and characterizing the large‐scale meteorological patterns (LSMPs) associated with local‐scale heavy precipitation improve our understanding of the processes that drive these high‐impact phenomena. Focusing on Portland, Oregon, we identify and characterize the key LSMPs associated with heavy precipitation days, defined using a daily total and hourly intensity threshold. LSMPs are defined at the synoptic scale using sea level pressure, 500 hPa geopotential height (Z500), and 250 hPa wind speed concurrent with precipitation days between 1980 and 2016, to capture synoptic circulation at three diagnostic atmospheric levels. We employ the self‐organizing map (SOM) approach to group the LSMPs into clusters, spanning the full range of synoptic circulation patterns associated with heavy precipitation days across the seasonal cycle. Using an atmospheric river (AR) catalogue of events, we show that ARs are commonly associated with heavy precipitation days, especially in winter and fall; however, heavy precipitation can occur without an AR in all seasons. Spring and summer heavy precipitation days, which are less common than in the fall and winter, tend to be primarily associated with upper level troughs and localized convective precipitation, while in winter they are more commonly associated with a surface cyclone and more widespread, stratiform precipitation. Examination of two case studies, one occurring in summer and one in winter, supports the ability of the SOM approach to realistically capture key observed storm types. Methods developed here may be extensible to other locations and phenomena and results build an observational foundation for assessing impactful LSMPs in climate models.

Climate model projections of atmospheric circulation patterns, their frequency, and associated temperature and precipitation anomalies under a high-end global warming scenario are assessed over the Pacific Northwest of North America for the final three decades of the 21st century. Model simulations are from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and circulation patterns are identified using the self-organizing maps (SOMs) approach, applied to 500 hPa geopotential height (Z500) anomalies. Overall, the range of projected circulation patterns is similar to in the current climate, especially in winter, whereas in summer, the models project a general reduction in the magnitude of Z500 anomalies. Significant changes in pattern frequencies are also projected in summer, with an overall decrease in the frequency of patterns with large Z500 anomalies. In winter, patterns historically associated with anomalously cold weather in northern latitudes are projected to warm the most, while in summer the largest temperature increases are projected over inland areas. Precipitation is found to increase across all seasons and most SOM patterns. However, some summer patterns that are associated with above average precipitation in the current climate are projected to become significantly drier by the end of the century.