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AI-empowered Next-generation Multiscale Climate Modeling for Mitigation and Adaptation
DATA-DRIVEN SCIENCE: DEVELOPMENTS IN MACHINE LEARNING SUBGRID-SCALE PARAMETERIZATIONS AND IN REANALYSES ACROSS EARTH SYSTEM MODELING I ORAL
nonlinear geophysics | 12 december 2024
Veronika Eyring
Earth System Models (ESMs) are fundamental to understanding and projecting climate change. They have continued to improve, but systematic errors and l...
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Improving the Planetary Boundary Layer Height in ICON with Deep Learning from Vertically Highly-resolved Simulations
DATA-DRIVEN SCIENCE: DEVELOPMENTS IN MACHINE LEARNING SUBGRID-SCALE PARAMETERIZATIONS AND IN REANALYSES ACROSS EARTH SYSTEM MODELING II POSTER
nonlinear geophysics | 12 december 2024
Janis Klamt, Mierk Schwabe, Marco Giorgetta, Veron...
Earth system models (ESMs) are crucial for understanding and predicting climate change. With typical horizontal resolutions of 40-100 km and 20-100 ve...
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ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation
DATA-DRIVEN SCIENCE: DEVELOPMENTS IN MACHINE LEARNING SUBGRID-SCALE PARAMETERIZATIONS AND IN REANALYSES ACROSS EARTH SYSTEM MODELING I ORAL
nonlinear geophysics | 12 december 2024
Akshay Subramaniam, Sungduk Yu, Zeyuan Hu, Walter ...
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing cri...
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Interpretable Machine Learning-based Radiation Emulation for ICON
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR ADVANCING EARTH SYSTEM MODELING, DATA STORAGE, AND PROCESSING III ELIGHTNING
informatics | 10 december 2024
Katharina Hafner, Fernando Iglesias-Suarez, Sara S...
Earth system models (ESMs) are essential to understand and project climate change. ESMs typically run for decades on coarse horizontal scales in the o...
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Interpretable Multiscale Machine Learning‐Based Parameterizations of Convection for ICON
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
22 august 2024
Helge Heuer, Mierk Schwabe, Pierre Gentine, Marco ...
Machine learning (ML)‐based parameterizations have been developed for Earth System Models (ESMs) with the goal to better represent subgrid...
Data‐Driven Equation Discovery of a Cloud Cover Parameterization
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
29 february 2024
Arthur Grundner, Tom Beucler, Pierre Gentine, Vero...
A promising method for improving the representation of clouds in climate models, and hence climate projections, is to develop machine learning̴...
Causally‐Informed Deep Learning to Improve Climate Models and Projections
JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES
19 february 2024
Fernando Iglesias-Suarez, Pierre Gentine, Breixo S...
Climate models are essential to understand and project climate change, yet long‐standing biases and uncertainties in their projections remain...
Machine Learning-Based Parameterizations of Convection for ICON
MACHINE LEARNING SUBGRID-SCALE PARAMETERIZATIONS FOR EARTH SYSTEM MODELING II POSTER
nonlinear geophysics | 14 december 2023
Helge Heuer, Mierk Schwabe, Pierre Gentine, Marco ...
In order to improve climate projections, machine learning (ML)-based parameterizations have been developed in the past for Earth System Models (ESMs) ...
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