no code implementations • 4 Mar 2024 • Yongquan Qu, Mohamed Aziz Bhouri, Pierre Gentine
Accurate representations of unknown and sub-grid physical processes through parameterizations (or closure) in numerical simulations with quantified uncertainty are critical for resolving the coarse-grained partial differential equations that govern many problems ranging from weather and climate prediction to turbulence simulations.
1 code implementation • 1 Feb 2024 • Juan Nathaniel, Yongquan Qu, Tung Nguyen, Sungduk Yu, Julius Busecke, Aditya Grover, Pierre Gentine
Thus, we propose ChaosBench, a large-scale, multi-channel, physics-based benchmark for S2S prediction.
no code implementations • 7 Dec 2023 • Kyleen Liao, Jatan Buch, Kara Lamb, Pierre Gentine
The increasing size and severity of wildfires across western North America have generated dangerous levels of PM$_{2. 5}$ pollution in recent years.
1 code implementation • 28 Sep 2023 • Jerry Lin, Sungduk Yu, Tom Beucler, Pierre Gentine, David Walling, Mike Pritchard
The implication is that hundreds of candidate ML models should be evaluated online to detect the effects of parameterization design choices.
no code implementations • 26 Sep 2023 • Francesco Immorlano, Veronika Eyring, Thomas le Monnier de Gouville, Gabriele Accarino, Donatello Elia, Giovanni Aloisio, Pierre Gentine
Accurate and precise climate projections are required for climate adaptation and mitigation, but Earth system models still exhibit great uncertainties.
no code implementations • 19 Sep 2023 • Mohamed Aziz Bhouri, Liran Peng, Michael S. Pritchard, Pierre Gentine
To extrapolate beyond the training data, the MF-RPNs are tested on high-fidelity warming scenarios, $+4K$, data.
1 code implementation • NeurIPS 2023 • Sungduk Yu, Walter Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus Christopher Will, Gunnar Behrens, Julius Busecke, Nora Loose, Charles I Stern, Tom Beucler, Bryce Harrop, Benjamin R Hillman, Andrea Jenney, Savannah Ferretti, Nana Liu, Anima Anandkumar, Noah D Brenowitz, Veronika Eyring, Nicholas Geneva, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Akshay Subramaniam, Carl Vondrick, Rose Yu, Laure Zanna, Tian Zheng, Ryan Abernathey, Fiaz Ahmed, David C Bader, Pierre Baldi, Elizabeth Barnes, Christopher Bretherton, Peter Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David Randall, Sara Shamekh, Mark A Taylor, Nathan Urban, Janni Yuval, Guang Zhang, Michael Pritchard
The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators.
no code implementations • 10 Jan 2023 • Chaopeng Shen, Alison P. Appling, Pierre Gentine, Toshiyuki Bandai, Hoshin Gupta, Alexandre Tartakovsky, Marco Baity-Jesi, Fabrizio Fenicia, Daniel Kifer, Li Li, Xiaofeng Liu, Wei Ren, Yi Zheng, Ciaran J. Harman, Martyn Clark, Matthew Farthing, Dapeng Feng, Praveen Kumar, Doaa Aboelyazeed, Farshid Rahmani, Hylke E. Beck, Tadd Bindas, Dipankar Dwivedi, Kuai Fang, Marvin Höge, Chris Rackauckas, Tirthankar Roy, Chonggang Xu, Binayak Mohanty, Kathryn Lawson
Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier between them and ushering in a paradigm shift.
1 code implementation • 26 Oct 2022 • Mohamed Aziz Bhouri, Pierre Gentine
To address these issues, we develop a new type of parameterization (closure) which is based on a Bayesian formalism for neural networks, to account for uncertainty quantification, and includes memory, to account for the non-instantaneous response of the closure.
no code implementations • 29 Jun 2022 • Jingyi Bu, Guojing Gan, Jiahao Chen, Yanxin Su, Mengjia Yuan, Yanchun Gao, Francisco Domingo, Mirco Migliavacca, Tarek S. El-Madany, Pierre Gentine, Monica Garcia
For the CSIF model, the average R2 for ET estimates also improved when including the effect of soil moisture: from 0. 65 (0. 79) to 0. 71 (0. 84), with RMSE ranging between 0. 023 (0. 22) and 0. 043 (0. 54) mm depending on the site.
1 code implementation • 21 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.
1 code implementation • 14 Dec 2021 • Tom Beucler, Pierre Gentine, Janni Yuval, Ankitesh Gupta, Liran Peng, Jerry Lin, Sungduk Yu, Stephan Rasp, Fiaz Ahmed, Paul A. O'Gorman, J. David Neelin, Nicholas J. Lutsko, Michael Pritchard
Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates.
1 code implementation • 30 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.
no code implementations • 3 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
4 code implementations • 20 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.
4 code implementations • 3 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
no code implementations • 16 Jun 2019 • Soukayna Mouatadid, Pierre Gentine, Wei Yu, Steve Easterbrook
Climate projections suffer from uncertain equilibrium climate sensitivity.
no code implementations • 15 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.
3 code implementations • 12 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.
1 code implementation • 14 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