Public Abstract

DE-SC0025251: Understand and Reduce Uncertainty in E3SM's Land-Atmosphere Feedbacks on Carbon, Water, and Energy in Response to Wildfire Disturbance

Award Status: Active

Institution: Board of Regents of the University of Wisconsin System, operating as University of Wisconsin-Madison, Madison, WI
UEI: LCLSJAGTNZQ7
DUNS: 161202122

Most Recent Award Date: 09/04/2024
Number of Support Periods: 1
PM: Joseph, Renu

Current Budget Period: 09/01/2024 - 02/28/2025
Current Project Period: 09/01/2024 - 08/31/2027
PI: Chen, Min

Supplement Budget Period: N/A

Public Abstract

Understand and Reduce Uncertainty in E3SM’s Land-Atmosphere Feedbacks on Carbon, Water, and Energy in Response to Wildfire Disturbance PI: Min Chen, University of Wisconsin co-PI: Qing Zhu, LBNL, Collaborator: Forrest Hoffman, ORNL Wildfires play a critical role in shaping terrestrial ecosystems and influencing the Earth's climate through intricate feedback mechanisms. However, existing Earth System Models (ESMs), including the advanced Exascale Energy Earth System Model (E3SM), struggle to accurately represent wildfire dynamics and their extensive impacts on land-atmosphere interactions. This project seeks to address these challenges by developing innovative benchmarking metrics for land-atmosphere feedback, which will be integrated into the International Land Model Benchmarking (ILAMB) framework. Moreover, the project will leverage machine learning and surrogate modeling techniques to improve E3SM's wildfire simulations. Comprehensive analyses and factorial experiments will be conducted to reduce uncertainty in the model's predictions of ecosystem post-fire resilience and the consequent atmospheric feedback. By filling these critical gaps in ESMs, the project aims to deepen our understanding of the interactions between wildfires, terrestrial ecosystems, and the global climate, thereby supporting the creation of more effective mitigation and adaptation strategies for climate change.

Understand and Reduce Uncertainty in E3SM’s Land-Atmosphere Feedbacks on Carbon, Water, and Energy in Response to Wildfire Disturbance

PI: Min Chen, University of Wisconsin

co-PI: Qing Zhu, LBNL, Collaborator: Forrest Hoffman, ORNL

Wildfires play a critical role in shaping terrestrial ecosystems and influencing the Earth's climate through intricate feedback mechanisms. However, existing Earth System Models (ESMs), including the advanced Exascale Energy Earth System Model (E3SM), struggle to accurately represent wildfire dynamics and their extensive impacts on land-atmosphere interactions. This project seeks to address these challenges by developing innovative benchmarking metrics for land-atmosphere feedback, which will be integrated into the International Land Model Benchmarking (ILAMB) framework. Moreover, the project will leverage machine learning and surrogate modeling techniques to improve E3SM's wildfire simulations. Comprehensive analyses and factorial experiments will be conducted to reduce uncertainty in the model's predictions of ecosystem post-fire resilience and the consequent atmospheric feedback. By filling these critical gaps in ESMs, the project aims to deepen our understanding of the interactions between wildfires, terrestrial ecosystems, and the global climate, thereby supporting the creation of more effective mitigation and adaptation strategies for climate change.