Primary Convener:
Feng Tao, The Pennsylvania State University

Convener:
Bailey Murphy, Oak Ridge National Laboratory
Lifen Jiang, Cornell University

Chair:
Feng Tao, Tsinghua University
Bailey Murphy, University of Wisconsin-Madison

The advent of big data observations, innovations in experimental techniques, and advances in modeling have revolutionized our understanding of terrestrial ecosystems and their responses to climate change. However, projecting the land carbon cycle dynamics with high accuracy remain a grand challenge, as substantial uncertainties persist in simulating land carbon storage, fluxes, and their responses to climate change across Earth system models. This session seeks to address these challenges by showcasing cutting-edge research that advances land carbon cycle science. We invite contributions that span theoretical, experimental, data-driven, and modeling approaches, including: (1) Insights into the mechanisms regulating land carbon storage, fluxes, and their responses to climate change; (2) Innovations in process-based or data-driven modeling approaches; (3) Development of tools and methodologies for integrating diverse data streams into models; (4) Applications of AI to predict and understand land carbon cycle dynamics; and (5) Experimental studies that inform or validate model development.

Index Terms
0414 Biogeochemical cycles, processes, and modeling
0428 Carbon cycling
0430 Computational methods and data processing
0466 Modeling

Suggested Itineraries:
Climate Change and Global Policy
Biochemistry
Machine Learning and AI
Open Science and Open Data
Global Impacts‚ Solutions‚ & Policies

Neighborhoods:
3. Earth Covering

Cross-Listed:
GC - Global Environmental Change