3 papers
physics.ao-ph2026
Optimal scenario design for climate emulation
Christopher B. Womack, Shahine Bouabid, Andrei Sokolov +4
As deep learning for physical systems continues to grow in popularity, efforts to improve generalizability have primarily focused on designing architectures that embed physical con…
cs.LG2025
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
Origin and Limits of Invariant Warming Patterns in Climate Models
Paolo Giani, Arlene M. Fiore, Glenn Flierl +2
Climate models exhibit an approximately invariant surface warming pattern in typical end-of-century projections. This observation has been used extensively in climate impact assess…