967 citations
- Columbia UniversityUS16 papers
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)DE6 papers
- Goddard Institute for Space StudiesUS5 papers
- University of BremenDE5 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- University of California, Santa CruzUS4 papers
- University of LausanneCH4 papers
- Laboratoire des Sciences du Climat et de l'EnvironnementFR3 papers
- University of California, IrvineUS3 papers
- Yale UniversityUS3 papers
- Boston UniversityUS2 papers
- CEA Paris-SaclayFR2 papers
12 papers · 1 filter
Interpretable multiscale Machine Learning-Based Parameterizations of Convection for ICON
Helge Heuer, Mierk Schwabe, Pierre Gentine +2
Machine learning (ML)-based parameterizations have been developed for Earth System Models (ESMs) with the goal to better represent subgrid-scale processes or to accelerate computat…
Causally-informed deep learning to improve climate models and projections
Fernando Iglesias-Suarez, Pierre Gentine, Breixo Solino-Fernandez +4
Climate models are essential to understand and project climate change, yet long-standing biases and uncertainties in their projections remain. This is largely associated with the r…
Data-Driven Equation Discovery of a Cloud Cover Parameterization
Arthur Grundner, Tom Beucler, Pierre Gentine +1
A promising method for improving the representation of clouds in climate models, and hence climate projections, is to develop machine learning-based parameterizations using output…
Global warming in the pipeline
James E. Hansen, Makiko Sato, Leon Simons +14
Improved knowledge of glacial-to-interglacial global temperature change implies that fast-feedback equilibrium climate sensitivity (ECS) is 1.2 +/- 0.3°C (2) per W/m. Consis…
Machine-learned cloud classes from satellite data for process-oriented climate model evaluation
A. Kaps, A. Lauer, G. Camps-Valls +3
Clouds play a key role in regulating climate change but are difficult to simulate within Earth system models (ESMs). Improving the representation of clouds is one of the key tasks…
Non-Linear Dimensionality Reduction with a Variational Encoder Decoder to Understand Convective Processes in Climate Models
Gunnar Behrens, Tom Beucler, Pierre Gentine +3
Deep learning can accurately represent sub-grid-scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack…