Our research program is designed to advance predictive understanding of ecosystem ecology and biogeochemistry under the global environmental change via data-model integration. Major issues we are addressing include (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 to mitigate climate change.
We have been using diverse approaches to our research, including global change experiments, observation, data synthesis, process-based modeling, data-model fusion, knowledge-guided artificial intelligence (AI), and theoretical analysis.
Our current research is focused on 1) developing and using knowledge-guided AI tools to discover new mechanisms underlying terrestrial ecosystem dynamics from big data; 2) identifying carbon dioxide removal (CDR) strategies that effectively lengthen carbon residence time (or permanence); 3) developing measurement, monitoring, reporting, and verification (MMRV) methods to evaluate CDR practices; and 4) integrating data from various global change experiments coherently with models according to biogeochemical and ecological principles.
9th Training Course (Virtual) on New Advances in Land Carbon Cycle Modeling, June 1-12, 2026
Science paper "Drought-induced peatland carbon loss exacerbated by elevated CO2 and warming" by Quan et al. was reported by
Cornell News and Georgia Tech
Dr. Luo's Comments in a NewScientist Article on the recent PNAS paper "Asymmetric winter warming reduces microbial carbon use efficiency and growth more than symmetric year-round warming in alpine soils" (Download this article)
