13 New EESM Projects Focus on Understanding Earth System Predictability

The U.S. Department of Energy (DOE) recently announced $8 million in funding for 13 research projects to advance the fundamental understanding of dynamic, physical, and biogeochemical processes required to develop and analyze results from earth system models. 

The awards support the DOE Earth & Environmental Systems Modeling (EESM) program’s Earth System Model Development (ESMD) and Regional & Global Model Analysis (RGMA) program areas. Awarded under DOE funding opportunity announcement DE-FOA-0003228, the awards will support the following: 

• Development of the marine biogeochemical simulations in the Energy Exascale Earth System Model (E3SM);
• Hierarchical modeling, simulations, and analysis using E3SM and other earth system models to enhance understanding of biogeochemical (terrestrial and marine), aerosol, cloud, and/or aerosol-cloud interactions and feedbacks;
• Novel methodologies and techniques for model initialization coupled data assimilation.

With a vision of providing the best possible information about the Earth’s evolving system, EESM research projects strive to develop and demonstrate advanced modeling and simulation capabilities to enhance the predictability of the earth system across multiple temporal and spatial scales. 

“Our new EESM research projects will build upon our understanding of climate processes—knowledge that can be used to inform planning for energy assets and infrastructures,” said Renu Joseph, a DOE program manager who leads the RGMA program area. “With so many excellent proposals, it was difficult to select just 13 projects. We want to thank everyone who took the time and did the hard work to submit proposals. We also want to thank the members of our review panels for their time and thoughtful participation.”

The selected proposals in FY24 were:

	Peyman Abbaszadeh	Portland State University	“Online Coupling of E3SM with Machine Learning-enhanced Data Assimilation for Improved Earth System Predictability”
	Min Chen	University of Wisconsin-Madison	“Understand and Reduce Uncertainty In E3SM’s Land- Atmosphere Feedbacks On Carbon, Water, and Energy in Response to Wildfire Disturbance”
	Anand Gnanadesikan	Johns Hopkins University	“Using Apparent Relationships Derived from Machine Learning Methods to Improve the Simulation of Marine Organisms within the Energy Exascale Earth System Model”
	Xianglei Huang	University of Michigan	“Understanding the Polar Cloud Longwave Feedback and its Confounding Factors through a Spectral Lens”
	Vincent Larson	University of Wisconsin-Milwaukee	“Overfitting and Uncertainty in the Presence of Model Structural Error”
	Nicole Lovenduski	University of Colorado-Boulder	“SOS-Carbon: Southern Ocean Storminess and the Carbon Cycle”
	Daniel McCoy	University of Wyoming	“Linking Aerosol Forcing and Cloud Feedback to Atmospheric Moisture Processing”
	Jefferson Moore	University of California-Irvine	“Improving Representations of Carbon Export and its Climate Sensitivity in E3SM-MARBL”
	Zhaoxia Pu	University of Utah	“Coupled Land-Atmosphere-Ocean Data Assimilation for E3SM with DART for Understanding Subseasonal-to-Seasonal Predictability of Extreme Events”
	Kathleen Schiro	University of Virginia	“Evaluating Mean State Relationships to High Cloud Feedbacks and Climate Sensitivity in CMIP Model Ensembles and E3SM”
	Zheng Shi	University of Oklahoma	“Experimental-Data-Informed, Machine-Learning-Enabled Benchmarking and Development of Land Carbon Cycle in Earth System Models”
	Yang Song	University of Arizona	“An Integrated Artificial Intelligence and E3SM Hierarchal Modeling Framework for Elucidating Environmental Responses of Soil Carbon and Nutrients Dynamics and its Implications for Land Carbon-Climate”
	Ying Sun	Cornell University	“Amazon vs Congo: Understanding the Intercontinental Differences of Tropical Rainforests’ Responses to Climate Variability”

Subject to availability of funds, EESM intends to fund additional proposals in FY25.