Characterizing Historical and Future Snowfall Events across the Western US to Inform Water Resources Management

Project ID: 22071
Principal Investigator: Kathleen Holman
Research Topic: Water Supply Forecasting
Funded Fiscal Years: 2022, 2023 and 2024
Keywords: None

Research Question

During this project, we will answer two main questions using the weather typing algorithm.

(1) Under what weather conditions (i.e., weather types) has snowpack accumulation historically occurred across the Reclamation basins of interest, particularly those that result in substantial runoff generation, and how have these patterns varied throughout the historical record?

We hypothesize that the synoptic weather conditions conducive to large snowfall events and associated snowpack accumulation include various forms of low-pressure systems, similar to Perry et al. (2010). We also expect primary snowfall drivers to vary as a function of geographic region. For example, we expect atmospheric rivers to play a larger role in snowfall events in the Klamath River basin region compared to the Sun River basin.

(2) How well does the CESM LE capture these important weather types and how are they projected to change in the future?

We hypothesize that that the CESM LE can simulate synoptic weather types associated with historical large snowfall events across the four Reclamation basins of interest. This hypothesis is supported by previous research demonstrating that global climate models are better able to reproduce large-scale features of the atmosphere-ocean system (e.g., low pressure systems, fronts) than small-scale processes that result in precipitation (McGinnis 1994). Furthermore, Prein et al. (2019) showed that the CESM LE was able to reproduce the weather types associated with historical precipitation across the continental US.

Need and Benefit

Global climate models (GCMs) struggle to simulate fine-scale processes responsible for extreme precipitation and snow. The proposed work speaks to need by 1) characterizing extreme meteorological events (i.e., heavy snowfall events) using large-scale atmospheric predictors (e.g., weather-types) that are well simulated within a GCM and 2) exploring how those large-scale predictors may change under future climate conditions within an ensemble (i.e., probabilistic) framework.

Contributing Partners

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Research Products

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Last Updated: 6/22/20