Yang Song joined the Department of Hydrology and Atmospheric Sciences as Assistant Professor in January 2020. Prior to joining UArizona, she was a postdoctoral Research Associate and then an R&D staff scientist in the Environmental Sciences Division at the Climate Change Science Institute, Oak Ridge National Laboratory. She holds a Ph.D. degree in Atmosphere Science from the University of Illinois at Urbana-Champaign, an M.S. degree in Ecology from the Chinese Academy of Sciences, and a B.E. degree in Environmental Engineering from the University of Science and Technology in Beijing.
Song’s multidisciplinary educational background and research experiences led her to explore a cutting-edge research topic: the application of gene science to climate science. She has developed an omics-informed soil biogeochemical model for representing microbial functional diversity and its implication for soil carbon-climate feedbacks. Her lab at UArizona continues to link gene science to climate science with a goal to develop genetically-informed prediction of vegetative and microbial functions in Earth system models. Song’s work also applies research outcomes to assess and improve the climate resilience of dryland ecosystem and agriculture. Overall, her research goal is to advance our understanding and predictive power of the role that vegetation, microbial communities, and humans—the biotic components of the Earth system—play in terrestrial-atmospheric interactions.
As a HAS faculty member and faculty member of the UA BRIDGES program, Song aims to cultivate and inspire the next generation of scholars and citizens through independent thinking, interdisciplinary knowledge, and development of practical abilities to solve climate change-related ecological and societal issues.
She has used this pedagogical approach in both graduate-level courses and non-STEM undergraduate-level courses, including Air Pollution and Introduction of Weather and Climate. Song is developing a new course that will integrate gene science, ecology, data science, and Earth system modeling into climate science-oriented system knowledge.