Ryan L. Sriver is an associate professor of atmospheric sciences at the University of Ilinois at Urbana-Champaign. Prior to joining UIUC in 2012, he worked as a research associate in Penn State's Department of Geosciences and as a NOAA Climate and Global Change postdoctoral fellow in Penn State's Department of Meteorology. Ryan graduated from Purdue University with a PhD in Earth and Atmospheric Sciences.
Ryan's research seeks to develop a deeper understanding about the physical processes influencing variability within Earth's climate system and to quantify relevant uncertainties surrounding future climate projections. His work combines observational products, statistical methods and tools, and numerical models spanning a wide range of complexities and scales to understand how extreme weather and climate events are changing with global warming, what are the physical drivers, and what are the implications for natural and human systems.
His research interests include: Climate Dynamics, Earth System Modeling, Ocean-Atmosphere Interactions, Uncertainty and Risk, Weather and Climate Extremes, Tropical Cyclones, Sea-Level Rise, Seasonal Prediction, and Multi-Sector Dynamics.
- Ph.D. Earth and Atmospheric Science, Purdue University, 2008
- M.S. Physics, Purdue University, 2003
- B.S. Physics, Purdue University, 2001
- ATMS 140: Climate and Global Change
- ATMS 302: Atmospheric Dynamics I
- ATMS 404: Risk Analysis in the Earth Sciences
- ATMS 491: General Circulation of the Atmosphere and Ocean
- ATMS 491: Physical Oceanography
- ATMS 500: Dynamic Meteorology
- ATMS 507: Climate Dynamics
- ATMS 526: Risk Analysis in the Geosciences
- ATMS 571: Professional Development
Additional Campus Affiliations
Associate Professor, Atmospheric Sciences
Associate Professor, National Center for Supercomputing Applications (NCSA)
Harris, T., Li, B., & Sriver, R. (2023). MULTIMODEL ENSEMBLE ANALYSIS WITH NEURAL NETWORK GAUSSIAN PROCESSES. Annals of Applied Statistics, 17(4), 3403-3425. https://doi.org/10.1214/23-AOAS1768
Lafferty, D. C., & Sriver, R. L. (2023). Downscaling and bias-correction contribute considerable uncertainty to local climate projections in CMIP6. npj Climate and Atmospheric Science, 6(1), Article 158. https://doi.org/10.1038/s41612-023-00486-0
Li, S., Sriver, R., & Miller, D. E. (2023). Skillful prediction of UK seasonal energy consumption based on surface climate information. Environmental Research Letters, 18(6), Article 064007. https://doi.org/10.1088/1748-9326/acd072
Srikrishnan, V., Lafferty, D. C., Wong, T. E., Lamontagne, J. R., Quinn, J. D., Sharma, S., Molla, N. J., Herman, J. D., Sriver, R. L., Morris, J. F., & Lee, B. S. (2022). Uncertainty Analysis in Multi-Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems. Earth's Future, 10(8), Article e2021EF002644. https://doi.org/10.1029/2021EF002644
Lafferty, D. C., Sriver, R. L., Haqiqi, I., Hertel, T. W., Keller, K., & Nicholas, R. E. (2021). Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields. Communications Earth and Environment, 2(1), Article 196. https://doi.org/10.1038/s43247-021-00266-9