Predicting land carbon cycle-climate change feedback | 9am PT, Tues July 8, 2025

[Grigory Bronevetsky]

Modeling Talks

Predicting land carbon cycle-climate change feedback Yiqi Luo, Cornell University Tues, July 8, 2025 | 9am PT Meet | Youtube Stream Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at https://sites.google.com/modelingtalks.org/entry/predicting-land-carbon-cycle-climate-change-feedback More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home Abstract: Land ecosystems offer an effective nature-based solution to climate change mitigation by absorbing approximately 30% of anthropogenically emitted carbon. This estimated absorption is primarily based on constraints from atmospheric and oceanic measurements while quantification from direct studies of the land carbon cycle themselves displays great uncertainty. The latter hinders prediction of the future fate of the land carbon sink. This talk will present work done by my lab in the past two decades. Specifically, we have revealed a general dynamic pattern that the land carbon cycle changes in a direction toward a moving attractor in response to global change. This general pattern is fully captured conceptually by dynamic disequilibrium and mathematically by a matrix equation, which unifies land carbon cycle models. We have integrated the matrix equation into neural network to improve accuracy of model prediction and discover mechanisms underlying land carbon cycle dynamics. Meanwhile, we have evaluated various carbon dioxide removal strategies using the knowledge gained from our basic carbon cycle research and, thereby. identified burying woody debris from managed forests probably as the most effective, easily implemented, and highly sustainable strategy.  Knowledge gained from my lab’s research in the past decades is partially presented in the training course, New Advances in Land Carbon Cycle Modeling, and the training course textbook, Land Carbon Cycle Modeling: Matrix Approach, Data Assimilation, Ecological Forecasting, and Machine Learning. The training curse videos and textbook are freely available. Bio: Yiqi Luo is Liberty Hyde Bailey Professor at Cornell University, USA. He obtained his PhD degree from the University of California, Davis in 1991 and did postdoctoral research at UCLA and Stanford University from 1991 to 1994 before he worked at Desert Research Institute as Assistant and Associate Research Professor from 1994 to 1998, the University of Oklahoma as Associate, full, and George Lynn Cross Professor from 1999 to 2017, and Northern Arizona University as full and Regents Professor from 2017 to 2022. His research program has been focused on addressing three key issues: (1) how global change alters structure and functions of terrestrial ecosystems, (2) how terrestrial ecosystems feedback to regulate climate change, and (3) how ecosystem processes can be effectively manipulated to offer nature-based solutions toward carbon neutrality. To address these issues, Dr. Luo’s laboratory has conducted field global change experiments, developed terrestrial ecosystem models, synthesized extensive data sets using meta-analysis methods, integrated data and model using data assimilation techniques and knowledge-guided artificial intelligence (AI)modeling, and carried out theoretical and computational analysis. Professor Luo has published seven books (including translated and edited ones), 37 book chapters, and more than 600 papers in peer-reviewed journals. He was recognized as Highly Cited Researcher by the Web of Science Group, Clarivate Analytics in 2016-2024. He was elected fellow of American Association for the Advancement of Science (AAAS) in 2013, American Geophysical Union (AGU) in 2016, and Ecological Society of America (ESA) in 2018.