A recent study has introduced advancements in Earth System Models (ESMs) to improve their accuracy in climate projections, especially in mountainous regions.
ESMs traditionally use grids of 50-200 km resolution, which limits their ability to capture small-scale land surface variability.
However, researchers have developed a new subgrid structure to better simulate the effects of topography on climate, particularly focusing on atmospheric variables like precipitation and temperature.
The study, published in the Journal of Advances in Modeling Earth Systems, examined the impact of these improvements on the Energy Exascale Earth System Model (E3SM) Land Model (ELM).
New subgrid structure boosts accuracy in predicting snow water equivalent and runoff in ESMs.By downscaling atmospheric data to match the new subgrid structure, the model now provides more precise predictions of snowfall, snow water equivalent (the water content in snow), and runoff.
These improvements are especially noticeable in regions dominated by mountainous landscapes and areas where precipitation peaks during cooler seasons.
Testing the enhanced model across the contiguous United States (CONUS), the researchers found significant increases in snowfall and snow water equivalent in higher-elevation areas.
Importantly, the model now better reflects real-world observations from Snow Telemetry (SNOTEL) sites in the western U.S., showing an 83% improvement in accuracy.
These advancements have critical implications for water resource management, as more accurate simulations of snowpack and runoff will help predict streamflow in mountain-fed water systems.
The study’s findings highlight the potential for enhanced models to guide more effective water management strategies in response to regional and global climate changes.