Research Interests

Paul Ullrich leads several efforts at the interface of human and natural systems, with a focus on water-energy system resilience and resilience to extreme weather. His work emphasizes credibility and ensuring appropriate use of Earth system data. He works closely with practitioners across the United States to understand how Earth system data is being used and where outstanding gaps in this data remain. He joined LLNL in 2023, and holds a joint appointment at the University of California, Davis as a professor of regional and global climate modeling.

 

Projects

Dr. Ullrich is currently principal investigator for the HyperFORMANCE Scientific Focus Area and the Modes of Variability and Evaluation (MOVE) / PCMDI project. He also co-leads the Laboratory Directed Research and Development (LDRD) Hydrologic and Agricultural Resilience via Valley-scale Energy and Soil Technologies (HARVEST) Strategic Initiative.

Ph.D. Atmospheric and Space Science, University of Michigan, Ann Arbor, Michigan

M.Math. Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada

B.Math. Applied Mathematics and Computer Science, University of Waterloo, Waterloo, Ontario, Canada

For a full list, see: Google Scholar | UC Davis Earth System Modeling and Data Group

 

Ullrich, P.A., D.R. Feldman, J. Alder, J. Bewley, M. Bukovsky, K. Dixon, E. Gutmann, K. Jagannathan, A. Jones, R. Kotamarthi, H. Lee, F. Lipschultz, J. Littell, E. Massoud, R. McCrary, R. Moss, S. Rahimi-Esfarjani, A. Schwarz, T.L. Spero, S. Steinschneider, L. Sun and A.M. Wootten (2026) "Advancing a coordinated strategy for decision-relevant climate data products for the United States" Bull. Amer. Meteor. Soc., doi: 10.1175/BAMS-D-24-0303.1. 

Xue, Z., L.-Y. Leung and P.A. Ullrich (2025) "A greening future elevates flash drought risk in northern mid-to-high latitudes" Earth's Future 13 (12), e2025EF006883, doi: 10.1029/2025EF006883.

Ullrich, P.A., E.A. Barnes, W.D. Collins, K. Dagon, S. Duan, J. Elms, J. Lee, L.R. Leung, D. Lu, M.J. Molina and T.A. O'Brien (2025) "Recommendations for comprehensive and independent evaluation of machine learning-based Earth system models" J. Geophys. Res: Mach. Learn. Comp. 2 (1), e2024JH000496, doi: 10.1029/2024JH000496.

Han, Y. and P.A. Ullrich (2025) "The System for Classification of Low-Pressure Systems (SyCLoPS): An all-in-one objective framework for large-scale datasets" J. Geophys. Res. Atm. 130 (1), e2024JD041287, doi: 10.1029/2024JD041287.