5 citations · 10 across the 6 of their papers we have counts for
3 papers · 1 filter
Learning Hierarchical Causal Representations of the Effects of Forcings on Temperature in Climate Models
Shan Zhao, Ilija Trajkovic, Julia Kaltenborn +4
Machine learning (ML) emulators provide a fast and cost-effective method to simulate climate change scenarios after being trained on Earth System Models projections. However, the b…
Emulating the Forced Response of Climate Models with Generative Machine Learning
Graham Clyne, Julia Kaltenborn, Peer Nowack +2
Global climate models are essential tools to simulate past and potential future pathways of climate change, as well as associated climate impacts. Shared Socioeconomic Pathways (SS…
Spatiotemporal Detection and Uncertainty Visualization of Atmospheric Blocking Events
Mingzhe Li, Peer Nowack, Bei Wang
Atmospheric blocking events are quasi-stationary high-pressure systems that disrupt the typical paths of polar and subtropical air currents, often producing prolonged extreme weath…