1.1k citations · 3k across the 33 of their papers we have counts for
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Navigating the Noise: Bringing Clarity to ML Parameterization Design with O(100) Ensembles
Jerry Lin, Sungduk Yu, Liran Peng +6
Machine-learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high-re…
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…
Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning
Griffin Mooers, Mike Pritchard, Tom Beucler +5
Global Storm-Resolving Models (GSRMs) have gained widespread interest because of the unprecedented detail with which they resolve the global climate. However, it remains difficult…
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…
Deep Learning Based Cloud Cover Parameterization for ICON
Arthur Grundner, Tom Beucler, Pierre Gentine +3
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-reso…
Zero-Shot Learning of Aerosol Optical Properties with Graph Neural Networks
Kara D. Lamb, Pierre Gentine
Aerosols sourced from combustion such as black carbon (BC) are important short-lived climate forcers whose direct radiative forcing and atmospheric lifetime depend on their morphol…
Global Gridded Daily CO Emissions
Xinyu Dou, Yilong Wang, Philippe Ciais +15
Precise and high-resolution carbon dioxide (CO) emission data is of great importance of achieving the carbon neutrality around the world. Here we present for the first time the…
Causal inference for process understanding in Earth sciences
Adam Massmann, Pierre Gentine, Jakob Runge
There is growing interest in the study of causal methods in the Earth sciences. However, most applications have focused on causal discovery, i.e. inferring the causal relationships…
Global Daily CO emissions for the year 2020
Zhu Liu, Zhu Deng, Philippe Ciais +25
The diurnal cycle CO emissions from fossil fuel combustion and cement production reflect seasonality, weather conditions, working days, and more recently the impact of the COVI…
De-carbonization of global energy use during the COVID-19 pandemic
Zhu Liu, Biqing Zhu, Philippe Ciais +15
The COVID-19 pandemic has disrupted human activities, leading to unprecedented decreases in both global energy demand and GHG emissions. Yet a little known that there is also a low…
Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models with Real-Geography Boundary Conditions
Griffin Mooers, Mike Pritchard, Tom Beucler +4
We explore the potential of feed-forward deep neural networks (DNNs) for emulating cloud superparameterization in realistic geography, using offline fits to data from the Super Par…
Towards Physically-consistent, Data-driven Models of Convection
Tom Beucler, Michael Pritchard, Pierre Gentine +1
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…
Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling
Tom Beucler, Stephan Rasp, Michael Pritchard +1
Artificial neural-networks have the potential to emulate cloud processes with higher accuracy than the semi-empirical emulators currently used in climate models. However, neural-ne…
On the Power-law Scaling of Turbulence Cospectra Part 1: Stably Stratified Atmospheric Boundary Layer
Yu Cheng, Qi Li, Andrey Grachev +3
Turbulent fluxes in the atmospheric surface layer are key input for the prediction of weather, hydrology, and carbon dioxide concentration. In numerical modelling of turbulent flux…
Deep learning to represent sub-grid processes in climate models
Stephan Rasp, Michael S. Pritchard, Pierre Gentine
The representation of nonlinear sub-grid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent…
When does vapor pressure deficit drive or reduce evapotranspiration?
Adam Massmann, Pierre Gentine, Changjie Lin
Increasing vapor pressure deficit (VPD) increases atmospheric demand for water. While increased evapotranspiration (ET) in response to increased atmospheric demand seems intuitive,…