climate modeling 1computational efficiency 1interdisciplinary methods 1machine learning emulators 1model reliability 1
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cs.LG2026
How Can Machine Learning Emulators Best Support Climate Science?
Luca Schmidt, Nina Effenberger, Vitus Benson +5
The paper examines how machine‑learning emulators can be designed and deployed to reduce the computational cost of physics‑based climate models, proposing a framework that emphasiz…
cs.LG2024
Atmospheric Transport Modeling of CO with Neural Networks
Vitus Benson, Ana Bastos, Christian Reimers +3
Accurately describing the distribution of CO in the atmosphere with atmospheric tracer transport models is essential for greenhouse gas monitoring and verification support syst…
cs.LG2024
DeepExtremeCubes: Integrating Earth system spatio-temporal data for impact assessment of climate extremes
Chaonan Ji, Tonio Fincke, Vitus Benson +12
With climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show prom…