A quantitative method to decompose SWE differences between regional climate models and reanalysis datasets.

Area of Science:

• Climate modeling
• Hydrology
• Environmental science

Background:

• Accurate simulation of snow water equivalent (SWE) is crucial for water resource management but remains challenging for regional climate models.
• Complex interactions between precipitation, rain-snow partitioning, and radiative fluxes influence SWE, making precise modeling difficult.

Purpose of the Study:

• To develop a framework for quantitatively decomposing SWE differences between regional climate models and reanalysis datasets.
• To identify specific factors contributing to SWE biases in climate models, guiding future improvements.

Main Methods:

• Applied a novel decomposition framework to four North American Coordinated Regional Downscaling Experiment (NA-CORDEX) models in the Sierra Nevada.
• Analyzed SWE differences attributed to precipitation distribution/magnitude, ablation, temperature, and topography biases across various spatial resolutions (0.44° to 0.11°).

Main Results:

• Models generally underestimated SWE compared to the Landsat-Era Sierra Nevada Snow Reanalysis (SNSR) dataset.
• Higher model resolution (0.11°) significantly improved SWE simulation by 35%, reducing dry and warm biases linked to unresolved topography.
• Other contributing factors to SWE bias included precipitation distribution, cold biases from topographic correction, rain-snow partitioning uncertainties, and high ablation biases.

Conclusions:

• Unresolved topography significantly impacts SWE simulation, with higher resolution models showing improvement.
• Multiple factors, including precipitation patterns and ablation processes, contribute to SWE biases, necessitating a multi-faceted approach for model enhancement.
• This study provides critical insights into climate model performance for SWE simulation and offers guidance for developing more accurate models.