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

  • Howland, M. F., Dunbar, O. R. A., Schneider, T., 2022: Parameter uncertainty quantification in an idealized GCM with a seasonal cycle, Journal of Advances in Modeling Earth Systems, 14, e2021MS002735.
    [PDF] [Official Version]

  • Zhang, X., Schneider, T., Shen, Z., Pressel, K. G., Eisenman, I., 2022: Seasonal cycle of idealized polar clouds: large eddy simulations driven by a GCM, Journal of Advances in Modeling Earth Systems14, e2021MS002671.
    [PDF] [Official Version]

  • Lopez-Gomez, I., Christopoulos, C., Langeland Ervik, H. L., Dunbar, O. R. A., Cohen, Y., Schneider T., 2022: Training physics-based machine-learning parameterizations with gradient-free ensemble Kalman methods, Journal of Advances in Modeling Earth Systems, submitted.
    Download PDF

  • Shen Z., Sridhar, A., Tan, Z., Jaruga A., Schneider, T., 2022: A library of large-eddy simulations forced by global climate modelsJournal of Advances in Modeling Earth Systems,
    14, e2021MS002631.
    [PDF] [Official Version]

  • Schneider, T., Jeevanjee, N., Socolow, R., 2021: Accelerating progress in climate sciencePhysics Today, 74, 44-51.
    [PDF] [Official Version]