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Faculty of Geosciences and Environment

Institute of Earth Surface Dynamics

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Beucler Tom

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2023 | 2022 | 2021 | 2020 | ...

2023

Data-Driven Cloud Cover Parameterizations

Arthur Grundner, Tom Beucler, Pierre Gentine, Marco A. Giorgetta, Fernando Iglesias-Suarez, Veronika Eyring, 2023/05/15..

[DOI][serval:BIB_EEEDA84951CC]

The key role of causal discovery to improve data-driven parameterizations in climate models

Fernando Iglesias-Suarez, Veronika Eyring, Pierre Gentine, Tom Beucler, Michael Pritchard, Jakob Runge, Breixo Solino-Fernandez, 2023/05/15..

[DOI][serval:BIB_C3E55BE5D278]

Extended-range predictability of stratospheric extreme events using explainable neural networks

Zheng Wu, Tom Beucler, Daniela Domeisen, 2023/05/15..

[DOI][serval:BIB_B016D4C134C6]

Causally-informed deep learning to improve climate models and projections

Fernando Iglesias-Suarez, Pierre Gentine, Breixo Solino-Fernandez, Tom Beucler, Michael Pritchard, Jakob Runge, Veronika Eyring, 2023..

[DOI][serval:BIB_64A698C1160F]

preview resource BIB_D0DCB42C4015.P001

Selecting robust features for machine-learning applications using multidata causal discovery

Ganesh S. Saranya, Beucler Tom, Tam Frederick Iat-Hin, Gomez Milton S., Runge Jakob, Gerhardus Andreas, 2023. Environmental Data Science, 2.

[URN][DOI][serval:BIB_D0DCB42C4015]

Systematic Sampling and Validation of Machine Learning-Parameterizations in Climate Models

Jerry Lin, Sungduk Yu, Tom Beucler, Pierre Gentine, David Walling, Mike Pritchard, 2023..

[DOI][serval:BIB_B5276A20F608]

ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators

Sungduk Yu, Walter M. Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius J. M. Busecke et al., 2023..

[DOI][serval:BIB_A51EF14CFD95]

2022

preview resource BIB_768D96868930.P001

Deep Learning Based Cloud Cover Parameterization for ICON

Grundner A., Beucler T., Gentine P., Iglesias-Suarez F., Giorgetta M.A., Eyring V., 2022/12. Journal of advances in modeling earth systems, 14 (12) pp. e2021MS002959. Peer-reviewed.

[URN][DOI][WoS][Pmid][serval:BIB_768D96868930]

preview resource BIB_A387096635C6.P001

Non-Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models.

Behrens G., Beucler T., Gentine P., Iglesias-Suarez F., Pritchard M., Eyring V., 2022/08. Journal of advances in modeling earth systems, 14 (8) pp. e2022MS003130. Peer-reviewed.

[URN][DOI][WoS][Pmid][serval:BIB_A387096635C6]

A Data-Driven Approach to Isolate the Role of Radiative Heating in Tropical Cyclone Intensification

Tam Frederick Iat-Hin, Beucler Tom, Ruppert James, 2022/03/28. dans EGU General Assembly 2022, Vienna, Austria, Copernicus GmbH.

[DOI][serval:BIB_4B1FC77A3734]

An Improved Genesis and Evolution Parameter for Subseasonal Prediction of the North Indian Ocean Tropical Cyclones

Sudheesh Saranya Ganesh, Sahai Atul Kumar, Sukumarapillai Abhilash, Joseph Susmitha, Beucler Tom, 2022/03/27. dans EGU General Assembly 2022, Vienna, Austria, Copernicus GmbH.

[DOI][serval:BIB_BF9285061796]

Climate-Invariant, Causally Consistent Neural Networks as Robust Emulators of Subgrid Processes across Climates

Beucler Tom, Iglesias-Suarez Fernando, Eyring Veronika, Pritchard Michael, Runge Jakob, Gentine Pierre, 2022/03/26. dans EGU General Assembly 2022, Vienna, Austria, Copernicus GmbH.

[DOI][serval:BIB_EE901951DBA9]

Identifying precursors for extreme stratospheric polar vortex events &#160;using an explainable neural network

Wu Zheng, Beucler Tom, de Fondeville Raphaël, Székely Eniko, Obozinski Guillaume, Ball William, Domeisen Daniela, 2022/03/26. dans EGU General Assembly 2022, Vienna, Austria, Copernicus GmbH.

[DOI][serval:BIB_0394979A9B24]

preview resource BIB_0CE5C474601D.P001

Modeling stratospheric polar vortex variation and identifying vortex extremes using explainable machine learning

Wu Zheng, Beucler Tom, Székely Enikő, Ball William T., Domeisen Daniela I.V., 2022. Environmental Data Science, 1 pp. e17. Peer-reviewed.

[URN][DOI][serval:BIB_0CE5C474601D]

2021

Machine Learning for Clouds and Climate (Invited Chapter for the AGU Geophysical Monograph Series "Clouds and Climate")

Beucler Tom, Ebert-Uphoff Imme, Rasp Stephan, Pritchard Michael, Gentine Pierre, 2021/05/03. Essopenarchive.

[DOI][serval:BIB_B7C845AC0449]

Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models With Real‐Geography Boundary Conditions

Mooers Griffin, Pritchard Michael, Beucler Tom, Ott Jordan, Yacalis Galen, Baldi Pierre, Gentine Pierre, 2021/05. Journal of Advances in Modeling Earth Systems, 13 (5). Peer-reviewed.

[DOI][WoS][serval:BIB_A7F04116FCF5]

Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems.

Beucler Tom, Pritchard Michael, Rasp Stephan, Ott Jordan, Baldi Pierre, Gentine Pierre, 2021/03/05. Physical review letters, 126 (9) p. 098302. Peer-reviewed.

[DOI][WoS][Pmid][serval:BIB_A4DC5F3D8416]

2020

Interpreting and Stabilizing Machine-Learning Parametrizations of Convection

Brenowitz Noah D., Beucler Tom, Pritchard Michael, Bretherton Christopher S., 2020/12. Journal of the Atmospheric Sciences, 77 (12) pp. 4357-4375. Peer-reviewed.

[DOI][WoS][serval:BIB_D99C79F5B319]

Quantifying Convective Aggregation Using the Tropical Moist Margin's Length

Beucler Tom, Leutwyler David, Windmiller Julia M., 2020/10. Journal of Advances in Modeling Earth Systems, 12 (10). Peer-reviewed.

[DOI][WoS][serval:BIB_B7E5E07CCE15]

Convective Dynamics and the Response of Precipitation Extremes to Warming in Radiative–Convective Equilibrium

Abbott Tristan H., Cronin Timothy W., Beucler Tom, 2020/05. Journal of the Atmospheric Sciences, 77 (5) pp. 1637-1660. Peer-reviewed.

[DOI][WoS][serval:BIB_E437806CFF27]

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