Climate data for adaptation and vulnerability assessments (SWE) – west

Project: Climate data for adaptation and vulnerability assessments - The ClimAVA (https://climate.usu.edu/climava/) project provides high-resolution (4km), bias-corrected, downscaled future climate projections derived from 17 CMIP6 General Circulation Models. It analyzes climate variables including precipitation (Pr), maximum temperature (Tmax), minimum temperature (Tmin), and snow water equivalent (SWE) — for three Shared Socioeconomic Pathways (SSP245, SSP370, SSP585) on a daily scale. Employing the Spatial Pattern Interactions Downscaling (SPID) method, ClimAVA ensures high-quality downscaling using machine learning models. These models capture the relationship between spatial patterns at Global Circulation Model (GCM) resolution and fine-resolution pixel values. Essentially, a random forest model is trained for each pixel, using the finer reference data as a predictand and nine pixels from the spatially resampled (coarser) version of the reference data as predictors. These models are then utilized to downscale the bias-corrected GCM data. Results from this method have proven to maintain climate realism and greatly represent extreme events.
Summary: The ClimAVA_SWE data set — where ClimAVA stands for Climate Data for Adaptation and Vulnerability Assessments — provides high-resolution (4 km) future climate projections derived from 13 CMIP6 General Circulation Models (GCMs). It focuses on Snow Water Equivalent (SWE), a crucial indicator of water availability, hydrologic extremes, and climate-related vulnerability, and includes projections for three Shared Socioeconomic Pathways (SSP245, SSP370, and SSP585) at a daily temporal scale. The initial release of ClimAVA_SWE covers the entire western United States. ClimAVA_SWE is produced using the newly developed Spatial Interactions Downscaling (SPID) method, which ensures high-quality downscaling through advanced machine learning techniques. SPID captures the relationship between large-scale spatial patterns at GCM resolution and fine-scale pixel values. For each pixel, two Random Forest models (one for the accumulation period and one for the ablation period) were trained using fine-resolution reference data as the predictand, and nine neighboring pixels from a spatially resampled (coarser) version of the reference data as predictors. These trained models are then applied to bias-corrected GCM data to generate the downscaled projections. The resulting dataset maintains strong climate realism and effectively represents extreme events.