Climate Dynamics Group

Understand the Climate System

Advance climate science through interdisciplinary research.

computational-icon

Leverage Computational Tools

Harness advances in computing to model the climate system and produce high-quality public code.

Use Observational Data

Learn from observational data to constrain predictions of Earth’s future.

Climate Dynamics

An Introduction

Caltech’s Climate Dynamics Group comprises physicists, engineers, applied mathematicians, and software engineers, united in advancing climate science and modeling.

Our mission is twofold: to uncover fundamental insights into the drivers of climate change and variability, and to translate these discoveries into advances in modeling and prediction. We specialize in advancing theories related to clouds, convection, and extreme events. Leveraging diverse datasets, including high-resolution simulations and Earth observations, we develop state-of-the-art, physics-based and data-driven models. As a member of the Climate Modeling Alliance (CliMA), we shape the future of climate modeling for the 21st century.

Recent Publications

AGU Editorial Network, 2024: Challenges facing scientific publishing in the field of Earth & space sciences. AGU Advances, 5, e2024AV001334.
[Official version] [Editor’s Highlight]

Schmitt, J.F., Tseng, K.-C., Hughes, M., Johnson, N.C., 2024: Illuminating snow droughts: The future of Western United States snowpack in the SPEAR large ensemble. Journal of Geophysical Research: Atmospheres, 129, e2023JD039754.
[PDF] [Official version]

Wu, J., Levine, M.E., Schneider, T., Stuart, A., 2024: Learning about structural errors in models of complex dynamical systems. Journal of Computational Physics, 513, 113157.
[PDF] [Official version]

Bischoff, T., Deck, K., 2024: Unpaired downscaling of fluid flows with diffusion bridgesArtificial Intelligence for the Earth Systems3, e230039
[PDF] [Official version]

Dunbar, O.R.A., Bieli, M., Garbuno-Inigo, A., Howland, M.F., De Souza, A., Mansfield, L.M., Wagner, G.L., Efrat-Henrici N., 2024: CalibrateEmulateSample.jl: Accelerated parametric uncertainty quantification. Journal of Open Source Software, 9, 6372.
[PDF] [Official version]