UAlbany Atmospheric Scientist Leads $900K DOE Study to Explore Land Surface Effects on Climate
By Mike Nolan
ALBANY, N.Y. (Oct. 26, 2023) — The scientific community is increasingly recognizing the significant role of land surface conditions, such as soil moisture and temperature, snow cover, and vegetation, in weather and climate prediction.
This is particularly true in elevated regions — where anomalies in surface conditions can impact communities thousands of miles away.
In a new study, a team of researchers led by Craig Ferguson at the University at Albany will run a series of simulations to investigate the impact of surface heating anomalies on climate predictability. The simulations will focus on the Tibetan Plateau, the world's highest and largest plateau, and the Rocky Mountains.
The $900,000, three-year study is funded by the U.S. Department of Energy (DOE) and includes researchers from UAlbany, the University of Colorado Boulder, the University of California, Los Angeles, and the Lawrence Livermore National Laboratory (LLNL).
“Until very recently, ocean sea surface temperature anomalies have been considered the primary data source for global weather forecast systems,” said Ferguson, a research faculty member at UAlbany’s Atmospheric Sciences Research Center. “Now, studies are showing land anomalies may instead be most critical to successful sub-seasonal-to-seasonal timescale (seven to 60 days) climate prediction.
“This is an exciting new development that really underscores the need to improve the representation of land surface conditions in our forecast models.”
To evaluate Tibetan Plateau and Rocky Mountain land surface conditions as sources of climate predictability, the researchers will rely on simulations of DOE’s Energy Exascale Earth System Model (E3SM) Version 2, an integration of modules that represent Earth’s complex land, atmosphere, ocean and ice interactions.
The research collaborators will produce a large series of simulations with various temperature patterns to obtain new insight into how abnormal heat conditions over the two regions impact summer precipitation events in the United States.
All simulations will be conducted on the Perlmutter supercomputer at the National Energy Research Scientific Computing Center, a high-performance computing facility operated by Lawrence Berkeley National Laboratory for the DOE Office of Science.
“Atmospheric scientists have observed that above-normal May surface temperatures over Tibet caused by below-average snowpack, for example, is correlated with below-normal June temperatures over the Rockies and precipitation deficits over the eastern U.S.,” Ferguson said.
“This project was motivated by the need to better understand the interactions between elevated land anomalies and sea surface temperature anomalies, and their cumulative impact on precipitation variability in the U.S.”
Advanced Weather Prediction
Ferguson’s research is focused on improving our understanding and modeling of land-atmosphere interactions, especially in the context of extreme wind and precipitation predictability at short-to-seasonal timescales.
His recent research has focused on the Great Plains region, specifically the sensitivity of low-level jet streams, which account for one-third of the central U.S. summer precipitation, to changes in soil moisture and temperature. He has also been a member of the NASA Sounder Discipline Team since 2021.
“This new DOE funding will allow us to further explore these regional land-atmospheric interactions, along with illustrating the potential of future NASA satellite data assimilation to improve seasonal forecasts,” Ferguson said.
Along with the research team, project funding will also support one graduate student at CU-Boulder, one postdoc at UAlbany and one part-time postdoc at LLNL.
The CU-Boulder team will perform detailed investigations of specific past extreme precipitation events, while the UAlbany team will focus on spring-to-summer U.S. forecasting. skill. The LLNL team will assist with the simulations and look to apply lessons learned to further develop the E3SM.