New Modeling Improves Precipitation Simulation in Mountains High and Low

Downscaling methods improve representation of terrain effects on precipitation for Earth systems models.

The Science

Earth system models (ESMs) describe interactions between land, freshwater, and the atmosphere. These models cover the Earth’s surface with a mesh to simulate processes, such as clouds, precipitation, water flow, and air movement at each grid point on the mesh. Because computing resources are limited, the mesh is generally too coarse to capture complex terrain characteristics, such as valleys and mountains. However, these features influence precipitation, which has subsequent impacts on runoff, snowpack, vegetation, and other land surface processes. Now, scientists at the U.S. Department of Energy’s (DOE) Pacific Northwest National Laboratory have developed simple methods that use topographic features and airflow characteristics to describe precipitation spatial variation at a resolution smaller than the mesh. These methods could be implemented in ESMs, such as the Energy Exascale Earth System Models (E3SM).

The Impact

About 60% to 90% of the world’s freshwater supply originates from mountains. However, global models of Earth systems poorly represent the water cycle in mountain regions. This study developed and compared simple methods, based in topography and airflow characteristics, to represent precipitation on a smaller scale and account for terrain effects. The minimal computational cost of these methods means they could enhance the ability of ESMs to better simulate freshwater supply without increasing the computational requirements, with downstream implications on water, energy, and food systems.


Terrain influences precipitation through topographic features, such as height and slope, as well as atmospheric processes, such as winds and stability of air mass. This study explored various simple, but physically based downscaling methods of precipitation to represent the effect of complex terrain that is not explicitly resolved in Earth system models. Researchers evaluated and compared the performance of these methods based on their ability to capture the observed spatial pattern of precipitation as depicted in the high-resolution (4-km) Precipitation-elevation Regressions on Independent Slopes Model (PRISM) dataset over the conterminous United States. Two of the four methods tested performed well, as measured by multiple metrics including model errors and sensitivity to model grid sizes. These methods are the Elevation Range with Maximum elevation Method (ERMM), which utilizes elevation measurements of the subgrid landscape within a model grid cell, and the Froude Number Method (FNM), which accounts for mountains blocking airflow. By accounting for blocking of airflow, the FNM method performs slightly better than the ERMM method in mountainous regions consistently across multiple grid sizes. Both the ERMM and FNM methods have minimal computational and input data requirements, making them useful additions to ESMs. They have been implemented in E3SM for downscaling precipitation from the atmosphere model grid to the smaller subgrid units of the land model to improve modeling of land surface processes in mountain regions.

Principal Investigator(s)

L. Ruby Leung
Pacific Northwest National Laboratory

Teklu K. Tesfa
Pacific Northwest National Laboratory


The Office of Biological and Environmental Research, within the U.S. Department of Energy (DOE) Office of Science, supported this research as part of the Earth System Model Development program area through the Climate Model Development and Validation (CMDV) project facilitating model development collaboration between the Next Generation Ecosystem Experiments (NGEE–Tropics and NGEE–Arctic) and the Energy Exascale Earth System Model (E3SM) projects.


Tesfa, T.K., Leung, L.R., Ghan, S.J. “Exploring topography-based methods for downscaling subgrid precipitation for use in Earth system models.” Journal of Geophysical Research: Atmospheres 125(5), e2019JD031456 (2020). [DOI:10.1029/2019JD031456]