2 papers
cs.LG2024
The impact of internal variability on benchmarking deep learning climate emulators
Björn Lütjens, Raffaele Ferrari, Duncan Watson-Parris +1
Full-complexity Earth system models (ESMs) are computationally very expensive, limiting their use in exploring the climate outcomes of multiple emission pathways. More efficient em…
physics.ao-ph2024
Machine learning for climate physics and simulations
Ching-Yao Lai, Pedram Hassanzadeh, Aditi Sheshadri +3
We discuss the emerging advances and opportunities at the intersection of machine learning (ML) and climate physics, highlighting the use of ML techniques, including supervised, un…