This talk covers two related topics. One is on a matrix approach to land carbon cycle modeling. The other is on dryland ecosystem responses to increasing variance in precipitation. The first part of my talk will show one matrix equation to unify land carbon cycle models, help diagnose model performance with new analytics, accelerate computational efficiency for spin-up, and enable data assimilation with complex models. We have converted dozens of land biogeochemical models, including NCAR’s Community Land Model version 5 (CLM5), into matrix equations. We have used one matrix MIP to show model uncertainty can expand or shrink to nearly zero. We have also developed a PROcess-guided machine learning and DAta-driven modeling (PRODA) approach to retrieve mechanisms from big data with the matrix models. The second part of my talk is on an integrated experimental and modeling study of five ecosystems in central New Mexico in response to manipulated precipitation amounts and variations. After one ecosystem model trained with experimental data via data assimilation, we was able to demonstrate that ecosystem net primary production (NPP) increases with precipitation variability when MAP < 300 mm year−1 but decreases when MAP >300 mm year−1. This response pattern is mainly due to changes in precipitation partitioning to transpiration vs. evaporation.
Yiqi Luo is a Regents’ Professor at Northern Arizona 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 and the University of Oklahoma as Associate, full, and George Lynn Cross Professor from 1999 to 2017. His research program (EcoLab) has been focused on addressing two key issues: (1) how global change alters structure and functions of terrestrial ecosystems, and (2) how terrestrial ecosystems regulate climate change. 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 carried out theoretical and computational analysis. Professor Luo has published six books (including translated and edited ones), 36 book chapters, and 500 papers in peer-reviewed journals. He was a Highly Cited Researcher recognized by the Web of Science Group, Clarivate Analytics in 2018-2020. 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.