Search Results for author: Pierre Gentine

Found 10 papers, 7 papers with code

Deep Learning Based Cloud Cover Parameterization for ICON

1 code implementation21 Dec 2021 Arthur Grundner, Tom Beucler, Pierre Gentine, Fernando Iglesias-Suarez, Marco A. Giorgetta, Veronika Eyring

A promising approach to improve cloud parameterizations within climate models and thus climate projections is to use deep learning in combination with training data from storm-resolving model (SRM) simulations.

Feature Importance

Climate-Invariant Machine Learning

1 code implementation14 Dec 2021 Tom Beucler, Michael Pritchard, Janni Yuval, Ankitesh Gupta, Liran Peng, Stephan Rasp, Fiaz Ahmed, Paul A. O'Gorman, J. David Neelin, Nicholas J. Lutsko, Pierre Gentine

Data-driven algorithms, in particular neural networks, can emulate the effects of unresolved processes in coarse-resolution climate models when trained on high-resolution simulation data; however, they often make large generalization errors when evaluated in conditions they were not trained on.

On the Generalization of Agricultural Drought Classification from Climate Data

1 code implementation30 Nov 2021 Julia Gottfriedsen, Max Berrendorf, Pierre Gentine, Markus Reichstein, Katja Weigel, Birgit Hassler, Veronika Eyring

Climate change is expected to increase the likelihood of drought events, with severe implications for food security.

Global Daily CO$_2$ emissions for the year 2020

no code implementations3 Mar 2021 Zhu Liu, Zhu Deng, Philippe Ciais, Jianguang Tan, Biqing Zhu, Steven J. Davis, Robbie Andrew, Olivier Boucher, Simon Ben Arous, Pep Canadel, Xinyu Dou, Pierre Friedlingstein, Pierre Gentine, Rui Guo, Chaopeng Hong, Robert B. Jackson, Daniel M. Kammen, Piyu Ke, Corinne Le Quere, Crippa Monica, Greet Janssens-Maenhout, Glen Peters, Katsumasa Tanaka, Yilong Wang, Bo Zheng, Haiwang Zhong, Taochun Sun, Hans Joachim Schellnhuber

That even substantial world-wide lockdowns of activity led to a one-time decline in global CO$_2$ emissions of only 5. 4% in one year highlights the significant challenges for climate change mitigation that we face in the post-COVID era.

Atmospheric and Oceanic Physics General Economics Economics

Towards Physically-consistent, Data-driven Models of Convection

3 code implementations20 Feb 2020 Tom Beucler, Michael Pritchard, Pierre Gentine, Stephan Rasp

Data-driven algorithms, in particular neural networks, can emulate the effect of sub-grid scale processes in coarse-resolution climate models if trained on high-resolution climate simulations.

Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems

3 code implementations3 Sep 2019 Tom Beucler, Michael Pritchard, Stephan Rasp, Jordan Ott, Pierre Baldi, Pierre Gentine

Neural networks can emulate nonlinear physical systems with high accuracy, yet they may produce physically-inconsistent results when violating fundamental constraints.

Computational Physics Atmospheric and Oceanic Physics

Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling

no code implementations15 Jun 2019 Tom Beucler, Stephan Rasp, Michael Pritchard, Pierre Gentine

Artificial neural-networks have the potential to emulate cloud processes with higher accuracy than the semi-empirical emulators currently used in climate models.

Deep learning to represent sub-grid processes in climate models

3 code implementations12 Jun 2018 Stephan Rasp, Michael S. Pritchard, Pierre Gentine

We train a deep neural network to represent all atmospheric sub-grid processes in a climate model by learning from a multi-scale model in which convection is treated explicitly.

When does vapor pressure deficit drive or reduce evapotranspiration?

1 code implementation14 May 2018 Adam Massmann, Pierre Gentine, Changjie Lin

Here we examine which effect dominates response to increasing VPD: atmospheric demand and increases in ET, or plant physiological response (stomata closure) and decreases in ET.

Atmospheric and Oceanic Physics

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