14 citations · 14 across the 2 of their papers we have counts for
2 papers
cs.LG2024★ 14 cited
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-ph2022
Exploring Randomly Wired Neural Networks for Climate Model Emulation
William Yik, Sam J. Silva, Andrew Geiss +1
Exploring the climate impacts of various anthropogenic emissions scenarios is key to making informed decisions for climate change mitigation and adaptation. State-of-the-art Earth…