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