CAREER: Integrating a Microbial Data System with an Earth System Model for Evaluating Microbial Biogeochemistry

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This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Climate warming has become a key threat to society, and it is projected to become even worse by the end of the twenty-first century. In the U.S., each 1°C of climate warming can lead to roughly 1.2% loss in gross domestic product. Increases in atmospheric concentrations of carbon dioxide (CO2) and methane (CH4) have contributed to more than 90% of climate warming since the Industrial Revolution. Microbes account for more than 65% of global CO2 production, approximately 85% of CH4 production, and 60% of CH4 consumption in soils. However, the ability to predict production and consumption of these greenhouse gases in terrestrial ecosystems is constrained by the poor representation of soil microbes in Earth system computer models, and the lack of comprehensive soil microbial data for model development and validation. To address these limitations, this NSF CAREER project will develop a community dataset of microbial variables and improve a highly-used Earth system model to better represent microbial physiology and community structure in response to changing climate. The unprecedented microbial data system will be integrated with a microbial-explicit Earth system model to better understand how soil microbes respond and feedback to climate change. Next-generation scientists will be trained with state-of-art quantitative skills including meta-analysis, ecological modeling, and machine learning.A team of trainees (high school students, undergraduate students, graduate students, and a postdoc) will be trained to: (A) develop a microbial data system focusing on soil carbon cycling and CH4 processes; (B) improve an extant land surface model (CLM-Microbe) embedded in the Community Earth System Model to explicitly represent microbial physiology and community structure for CO2 and CH4 processes and to further parameterize and validate the model at a site-level; (C) apply the improved model to quantify microbial roles in CO2 and CH4 fluxes at various spatial and temporal scales; and (D) develop a hierarchical mentoring system to recruit students and provide them with training to successfully merge mathematics and ecology through research activities on data-model integration. Four summer tutorial workshops will be organized to broaden participation of undergraduate students from underrepresented groups in research through the San Diego State University Maximizing Access to Research Careers program, promoting recruitment and retention in STEM fields.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.