Rate My Professor Steven Sherwood

Professor Steven Sherwood is a Professor and ARC Laureate Fellow (2016-2020) at the University of New South Wales (UNSW) Sydney, where he is affiliated with the Climate Change Research Centre in the Faculty of Science. He specializes in atmospheric sciences, studying how atmospheric processes establish climate, control climate changes, and interact with oceans and other Earth system components. Sherwood earned a B.S. in Physics from the Massachusetts Institute of Technology in 1987, an M.S. in Engineering Physics/Fluid Mechanics from the University of California San Diego in 1989, and a Ph.D. in Oceanography from the Scripps Institution of Oceanography in 1995. His career includes positions as Professor in the Department of Geology and Geophysics at Yale University from 2001 to 2008, where he advanced from Assistant to Associate Professor, and Research Scientist in the Atmospheric Chemistry and Dynamics branch at NASA Goddard Space Flight Center from 1998 to 2000. He joined UNSW in 2009.

Sherwood leads a research group that applies physics, mathematics, theoretical ideas, observational analyses, statistical techniques including machine learning, and advanced climate models to address clouds, water vapor, and tropospheric convection—key but poorly understood elements crucial for rainfall and global climate. Notable publications include 'Clouds, circulation and climate sensitivity' (Nature Geoscience, 2015), 'Intensification of subhourly heavy rainfall' (Science, 2022), 'Closer limits to human tolerance of global heat' (Proceedings of the National Academy of Sciences, 2023), and the book 'The Copenhagen Diagnosis: updating the world on the latest climate science' (2011). His contributions have advanced climate sensitivity assessments, with a 2020 study recognized among Science magazine's top breakthroughs, and improved model predictions for regional weather and climate. Sherwood was elected a Fellow of the American Geophysical Union in 2024 and supervises PhD students on projects like machine learning for climate modeling.