SnowClim v1.0: high-resolution snow model and data for the western United States

Abstract

Abstract. Seasonal snowpack dynamics shape the biophysical and societal characteristics of many global regions. However, snowpack accumulation and duration have generally declined in recent decades, largely due to anthropogenic climate change. Mechanistic understanding of snowpack spatiotemporal heterogeneity and climate change impacts will benefit from snow data products that are based on physical principles, simulated at high spatial resolution, and cover large geographic domains. Most existing datasets do not meet these requirements, hindering our ability to understand both contemporary and changing snow regimes and to develop adaptation strategies in regions where snowpack patterns and processes are important components of Earth systems. We developed a computationally efficient process-based snow model, SnowClim, that can be run in the cloud. The model was evaluated and calibrated at Snowpack Telemetry (SNOTEL) sites across the western United States (US), achieving a site-median root-mean-squared error for daily snow water equivalent (SWE) of 64 mm, bias in peak SWE of −2.6 mm, and bias in snow duration of −4.5 d when run hourly. Positive biases were found at sites with mean winter temperature above freezing where the estimation of precipitation phase is prone to errors. The model was applied to the western US (a domain covering 3.1 million square kilometers) using newly developed forcing data created by statistically downscaling pre-industrial, historical, and pseudo-global warming climate data from the Weather Research and Forecasting (WRF) model. The resulting product is the SnowClim dataset, a suite of summary climate and snow metrics, including monthly SWE and snow depth, as well as annual maximum SWE and snow cover duration, for the western US at 210 m spatial resolution (Lute et al., 2021). The physical basis, large extent, and high spatial resolution of this dataset enable novel analyses of changing hydroclimate and its implications for natural and human systems.

Elevated [CO2] and temperature augment gas exchange and shift the fitness landscape in a montane forb

“…In the Rocky Mountains, elevated temperatures have reduced soil moisture availability through declining snowpack and early snowmelt (Rangwala et al ., 2012; Pepin et al ., 2015; Fyfe et al ., 2017). The timing of snowmelt will continue to accelerate under projected climate scenarios (Lute et al ., 2022), exposing B. stricta and other montane species to heightened drought stress. A glasshouse experiment demonstrated that drought stress depresses fecundity in high‐elevation accessions of B. stricta (MacTavish & Anderson, 2020).…”

The consequences of winter climate change for plant performance

“…The black lines show daily mean temperatures across blocks, with dashed horizontal lines at 0°C. I have indicated the observed snowmelt date in each site along with end‐of‐century projections under Representative Concentration Pathway 8.5 (Lute et al, 2022). (B) Models project continued reductions in snowpack under climate change (Lute et al, 2022), which could reduce the duration of winter chilling and delay spring phenological events, such as germination, leaf‐out, and flowering.…”

“…I have indicated the observed snowmelt date in each site along with end‐of‐century projections under Representative Concentration Pathway 8.5 (Lute et al, 2022). (B) Models project continued reductions in snowpack under climate change (Lute et al, 2022), which could reduce the duration of winter chilling and delay spring phenological events, such as germination, leaf‐out, and flowering. Although many species have advanced the timing of spring events in response to climate change, accelerated phenology is not a universal response to early springs (Yu et al, 2010).…”