Welcome to our new lab members!

Team

Alumni

Publications

Preprints

• (Submitted) Behrens, G., T. Beucler et al.: Improving Atmospheric Processes in Earth System Models with Deep Learning Ensembles and Stochastic Parameterizations.
• (Submitted) Tam, F. I,, T. Beucler & J. Ruppert: Identifying Three-Dimensional Radiative Patterns Associated with Early Tropical Cyclone Intensification.
• (Submitted) Gomez, M., & T. Beucler: Lessons Learned: Reproducibility, Replicability, and When to Stop.
• (Submitted) Lin, J., S. Yu, T. Beucler et al.: Systematic Sampling and Validation of Machine Learning-Parameterizations in Climate Models.
• (Whitepaper) Beucler, T. et al.: Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications. SATW Whitepaper on “AI for Climate Change Mitigation”, 4.2.
• (In press) Mooers, G., T. Beucler et al.: Understanding Precipitation Changes through Unsupervised Machine Learning. Environmental Data Science.
• (In press) Grundner, A., T. Beucler et al.: Data-Driven Equation Discovery of a Cloud Cover ParameterizationJournal of Advances in Modeling Earth Systems.


2024

• Iglesias-Suarez, F., P. Gentine, B. Solino-Fernandez, T. Beucler et al.: Causally-informed deep learning to improve climate models and projections. Journal of Geophysical Research: Atmospheres129, e2023JD039202 [pdf].
Beucler, T. et al.: Climate-Invariant Machine Learning. Science Advances, 10, eadj7250 [pdf].

2023

Beucler, T. et al.: Machine Learning for Clouds and Climate. Clouds and Their Climatic Impact: Radiation, Circulation, and Precipitation, edited by: Sullivan, SC and Hoose, C., Wiley–American Geophysical Union: 327-346. [pdf]
• (Workshop) Lin, J., M. A. Bhouri, T. Beucler et al.: Stress-testing the coupled behavior of hybrid physics-machine learning climate simulations on an unseen, warmer climate. 2023 Conference on Neural Information Processing Systems.
• Mooers, G., M. Pritchard, T. Beucler et al.: Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning. Scientific Reports. [pdf]
• Zanetta, F., D. Nerini, T. Beucler & M. Liniger: Physics-constrained deep learning postprocessing of temperature and humidity. Artificial Intelligence for the Earth Systems, 2, e220089. [pdf]
• (NeurIPS 2023 Conference) Yu, S., W. Hannah, L. Peng, M. Bhouri, R. Gupta, J. Lin, B. Lütjens, J. Will, T. Beucler et al.: ClimSim: A large multi-scale dataset for hybrid physics-machine learning climate emulation. Advances in Neural Information Processing Systems. “Oustanding Datasets and Benchmarks” award. [pdf]
Ganesh S., S., T. Beucler, F. I. Tam, M. Gomez et al.: Selecting Robust Features for Machine-Learning Applications using Multidata Causal DiscoveryEnvironmental Data Science, 2: e27. [pdf]


2022

• 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]


2021

• (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.
• Mooers, G., 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]


2020

• 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]


2016-2019

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 Presentation Recordings

2023

Beucler, T. & M. McGraw: AI for tropical meteorology: Challenges and opportunities. ITUAI for Good” seminar series.
Beucler, T.: Generating Climate Model Hierarchies from Data using MLCore Science Keynote, 103rd AMS Annual Meeting.


2022

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


2020

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


2016-2019

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