Soil plays a crucial role in regulating Earth's carbon cycle. As the largest terrestrial carbon sink, soil has the potential to either mitigate or exacerbate climate change, depending on how it is managed.

The Department of Energy has awarded the University of Arizona $610,166 as part of the department's $8 million initiative to support research to improve climate models. The U of A project, one of 13 selected nationwide, seeks to revolutionize the understanding of how soil microbes influence climate change. This could potentially address a major uncertainty in current global climate projections.

Researchers from the university’s Department of Hydrology and Atmospheric Sciences will combine biological and environmental data with artificial intelligence to enhance the Department of Energy's Energy Exascale Earth System Model. Providing crucial insights into future climate scenarios, E3SM is a state-of-the-art climate model designed to simulate Earth's climate system with high resolution and advanced physics. The U of A project focuses on improving projections of how soil affects climate at a global scale.

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"This project represents a significant step forward in climate science," said project principal investigator Yang Song, assistant professor of hydrology and atmospheric sciences and a faculty member in the university's Ecosystem Genomics Graduate Interdisciplinary Program. "By integrating detailed biological and environmental data and AI into E3SM, we're opening new frontiers in understanding and predicting climate change."

The research addresses the complex role of soil microbes in global carbon cycle and greenhouse gas emissions. Despite their crucial impact on climate, these microscopic communities have long been a challenge to study and incorporate into climate models.

The main driver controlling greenhouse gas emissions from soil is the microbial community, Song said. However, due to their microscopic size and complexity, understanding their role has been challenging until recent biotechnological advancements.

Leveraging cutting-edge genomics data, Song's team aims to track down microbial functions and their responses to changing climate conditions. The researchers seek to understand microbial function diversity and determine how this information can mitigate uncertainties in soil carbon-climate feedback. For example, climate change affects which tiny microorganisms live in the soil and how many there are. These changes in soil microbes then influence both carbon dioxide release into the air and the nutrients available for plants growing above ground, Song said. This influences atmospheric carbon dioxide concentration.

The team has already developed AI models to understand microbial community distribution at the U.S. scale and how environmental changes affect functional diversity of microbes. 

"We're extending our machine learning model to a global scale and integrating it with the Earth system model. This approach will help us better represent soil biogeochemical processes and assess their implication for land carbon-climate feedback at different time scales," Song said. 

The project's findings could potentially influence future reports of the Intergovernmental Panel on Climate Change. The improved models could help scientists better understand and predict how soil carbon will respond to climate change and how this, in turn, will affect atmospheric carbon dioxide concentration and climate.

"Besides providing insights about the role soil microbial communities have on soil biogeochemistry, the findings can help make workable plans to improve our climate models and ultimately our ability to predict and mitigate climate change," Song said.