Climate Dynamics: An Introduction

Caltech’s Climate Dynamics Group studies atmospheric dynamics, both here on Earth and on other planets, on scales from clouds to the globe.

We aim to elucidate fundamental questions about climate such as, What controls the surface temperatures and winds? What shapes rainfall patterns? Where and when do clouds form in the atmosphere?

To answer such questions, we analyze observational data and perform systematic studies with numerical models, with which we simulate flows ranging from the meter-scale motions in clouds to global circulations. Thanks to the availability of unprecedented observations from space and ever increasing computational power, ours is the age in which the physical laws that govern climate as an aggregate system will likely be discovered. Our goal is to contribute to that discovery.

We strive to translate scientific discoveries into improved models for weather forecasting and climate prediction. The same observations and numerical tools that enable new scientific discoveries have the potential to transform modeling of the climate system. As part of the Climate Modeling Alliance (CliMA), we are contributing to the development of next-generation models and model components that will allow us to predict the climate system more accurately.

Inclusion and Diversity

An inclusive environment built on mutual respect and trust is a priority for us. We welcome and value everyone without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin, disability status, and veteran status.

Recent Publications

  • Souza, A.N., He, J., Bischoff, T., Waruszewski, M., Novak, L., Barra, V., Gibson, T., Sridhar, A., Kandala, S., Byrne, S., Wilcox, L.C., Kozdon, J., Giraldo, F.X., Knoth, O., Marshall, J., Ferrari, R., Schneider, T., 2022: The Flux-Differencing Discontinuous Galerkin Method Applied to an Idealized Fully Compressible Nonhydrostatic Dry Atmosphere. Journal of Advances in Modeling Earth Systems, submitted.
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  • de Jong, E.K., Singer, C.E., Azimi, S., Bartman, P., Derlatka, K., Dula, I., Jaruga, A., Mackay, J.B., Ward, R.X., Arabas, S., 2022: New developments in PySDM and PySDM-examples v2: collisional breakup, immersion freezing, dry aerosol initialization, and adaptive time-stepping. Journal of Open Source Software, submitted.

  • de Jong, E., Bischoff, T., Nadim, A., Schneider, T., 2022: Spanning the gap from bulk to bin: a novel spectral microphysics method. Journal of Advances in Modeling Earth Systems, 14, e2022MS003186.
    [PDF] [Official Version]

  • Bartman, P., Bulenok, O., Górski, K., Jaruga, A., Łazarski, G., Olesik, M.A., Piasecki, B., Singer, C.E., Talar, A., Arabas, S., 2022: PySDM v1: particle-based cloud modeling package for warm-rain microphysics and aqueous chemistry. Journal of Open Source Software, 7, 3219.
    [PDF] [Official Version]

  • Huang, D.Z., Huang, J., Reich, S., Stuart, A.M., 2022: Efficient derivative-free Bayesian inference for large-scale inverse problems. Inverse Problems, submitted.