Welcome to our new lab members!



Peer-Reviewed Publications

Submitted/In press (Preprint available)

(Submitted) Grundner, A., T. Beucler et al.: Data-Driven Equation Discovery of a Cloud Cover Parameterization.

(Submitted) Yu, S., W. Hannah, L. Peng, M. Bhouri, R. Gupta, J. Lin, B. Lütjens, J. Will, T. Beucler et al.: ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators.

(Submitted) Iglesias-Suarez, F., P. Gentine, B. Solino-Fernandez, T. Beucler et al.: Causally-informed deep learning to improve climate models and projections.

(Submitted) Zanetta, F., D. Nerini, T. Beucler et al.: Physics-constrained deep learning postprocessing of temperature and humidity.

(Submitted) Mooers, G., M. Pritchard, T. Beucler et al.: Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning.

(Submitted) Beucler, T. et al.: Climate-Invariant Machine Learning.

(Workshop) Mooers, G., T. Beucler et al.: Understanding Extreme Precipitation Changes through Unsupervised Machine Learning. Environmental Data Science. Proceedings of the CCAI Workshop at the 2022 Conference on Neural Information Processing Systems.

(In press) Beucler, T. et al.: Machine Learning for Clouds and Climate (Invited Chapter for the AGU Geophysical Monograph Series: Clouds and Climate).


Ganesh S., S., T. Beucler, F. Tam, M. Gomez et al.: Selecting Robust Features for Machine-Learning Applications using Multidata Causal Discovery. Environmental Data Science, 2: e27. [pdf]


Grundner, A., T. Beucler et al.: Deep Learning Based Cloud Cover Parameterization for ICON. Journal of Advances in Modeling Earth Systems, e2021MS002959. [pdf]

Wu, Z., T. Beucler et al.: Modeling Stratospheric Polar Vortex Variation and Identifying Vortex Extremes Using Explainable Machine Learning. Environmental Data Science 1: e17. [pdf]

Behrens, G., T. Beucler et al.: Non‐Linear Dimensionality Reduction with a Variational Encoder Decoder to Understand Convective Processes in Climate Models. Journal of Advances in Modeling Earth Systems, e2022MS003130. [pdf]


(Workshop) Mangipudi, H., G. Mooers, M. Pritchard, T. Beucler & S. Mandt: Analyzing High-Resolution Clouds and Convection using Multi-Channel VAEs. 2021 Conference on Neural Information Processing Systems.

Gentine, P., V. Eyring & T. BeuclerDeep Learning for the Parametrization of Subgrid Processes in Climate Models. Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences, 307-314.

Griffin, M., M. Pritchard, T. Beucler et al.: Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models with Real-Geography Boundary ConditionsJournal of Advances in Modeling Earth Systems, 13, e2020MS002385. [pdf]

Beucler, T. et al.: Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems. Physical Review Letters, 126.9: 098302. Editors’ Suggestion. [pdf]


Brenowitz, N., T. Beucler, M. Pritchard & C. Bretherton: Interpreting and Stabilizing Machine-Learning Parametrizations of Convection. Journal of the Atmospheric Sciences, 77, 4357-4375.

(Workshop) Beucler, T. et al.: Towards Physically-Consistent, Data-Driven Models of Convection. IEEE International Geoscience and Remote Sensing Symposium 2020. [pdf]

(Workshop) Mooers, G., J. Tuyls, S. Mandt, M. Pritchard & T. BeuclerGenerative Modeling of Atmospheric Convection. Proceedings of the 10th International Conference on Climate Informatics, 98-105. [pdf]

Beucler, T., D. Leutwyler & J. Windmiller: Quantifying Convective Aggregation Using the Tropical Moist Margin’s Length. Journal of Advances in Modeling Earth Systems, 12, e2020MS002092. [pdf]

Abbott, T., T. Cronin & T. BeuclerConvective dynamics and the response of precipitation extremes to warming in radiative-convective equilibrium. Journal of the Atmospheric Sciences, 77, 1637-1660. [pdf]


Beucler, T., T. Abbott, T. Cronin & M. Pritchard: Comparing Convective Self‐Aggregation in Idealized Models to Observed Moist Static Energy Variability Near the Equator. Geophysical Research Letters, 46, 17-18. [pdf]

(Workshop) Beucler, T. et al.: Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling. 2019 International Conference on Machine Learning.

(Thesis) Beucler, T.Interaction between Water Vapor, Radiation and Convection in the Tropics, Ph.D. Thesis in Atmospheric Science.

Beucler, T. & T. Cronin: A Budget for the Size of Convective Self-Aggregation. Quarterly Journal of the Royal Meteorological Society, 145, 947-966.

Beucler, T., T. Cronin & K. Emanuel: A Linear Response Framework for Radiative-Convective Instability. Journal of Advances in Modeling Earth Systems, 10(8), 1924-1951.

Beucler, T. & T. Cronin: Moisture-Radiative Cooling Instability. Journal of Advances in Modeling Earth Systems, 8, 1620–1640.

Beucler, T.A Correlated Stochastic Model for the Large-scale Advection, Condensation and Diffusion of Water Vapour. Quarterly Journal of the Royal Meteorological Society, 142, 1721–1731.

(Thesis) Beucler, T. & K. Emanuel: Self-aggregation phenomenon in cyclogenesisMasters Thesis in Fluid Mechanics.

Selected Conference Presentations


Beucler, T. & M. McGraw: AI for tropical meteorology: Challenges and opportunities. ITU AI for Good” seminar series.

Beucler, T.: Generating Climate Model Hierarchies from Data using ML. Core Science Keynote, 103rd AMS Annual Meeting.


Beucler, T.: Atmospheric Physics-Guided Machine Learning for Climate Modeling and Weather Forecasting. ESiWACE2 2nd Virtual Workshop on Emerging Technologies for Weather and Climate Modelling

Beucler, T.: Climate-Invariant Machine Learning. AGCI Workshop on “Exploring the Frontiers in Earth System Modeling with Machine Learning and Big Data”

Beucler, T.Atmospheric Physics-Guided Machine Learning. IXXI Conference on ML and sampling methods for climate and physics (short version here from AMLD)

Beucler, T.Physics-Guided and Causally-Informed Machine Learning for Climate Modelling. ECMWF Machine Learning Workshop


Beucler, T.Climate-Invariant Nets: Physical Rescalings Help NNs Generalize to Out-of-sample ClimatesSIAM Mathematics of Planet Earth 2020

Beucler, T.Towards Physically-Consistent, Data-Driven and Interpretable Models of ConvectionNOAA STAR Artificial Intelligence Seminar

Beucler, T.Building a Hierarchy of Hybrid, Neural Network Models of Convection. 100th American Meteorological Society Annual Meeting

Beucler, T.Comparing Convective Self-Aggregation in Models to Obs. MSE Variability. 100th American Meteorological Society Annual Meeting


Beucler, T.A Spectral Budget for the Size of Convective Self-Aggregation. 33rd Conference on Hurricanes and Tropical Meteorology

Beucler, T.A Moist Static Energy Perspective on Atmospheric Rivers17th Conference on Mesoscale Processes

Beucler, T.The Vertical Structure of Radiative-Convective Instability21st Conference on Atmospheric and Oceanic Fluid Dynamics

Beucler, T.Instabilities of Radiative Convective Equilibrium with an Interactive Surface32nd Conference on Hurricanes and Tropical Meteorology