39 citations · 59 across the 5 of their papers we have counts for
6 papers
ACE2-SOM: Coupling an ML atmospheric emulator to a slab ocean and learning the sensitivity of climate to changed CO
Spencer K. Clark, Oliver Watt-Meyer, Anna Kwa +6
While autoregressive machine-learning-based emulators have been trained to produce stable and accurate rollouts in the climate of the present-day and recent past, none so far have…
Improving the predictions of ML-corrected climate models with novelty detection
Clayton Sanford, Anna Kwa, Oliver Watt-Meyer +4
While previous works have shown that machine learning (ML) can improve the prediction accuracy of coarse-grid climate models, these ML-augmented methods are more vulnerable to irre…
Machine-learned climate model corrections from a global storm-resolving model
Anna Kwa, Spencer K. Clark, Brian Henn +6
Due to computational constraints, running global climate models (GCMs) for many years requires a lower spatial grid resolution ( km) than is optimal for accurately res…
Emulating Fast Processes in Climate Models
Noah D. Brenowitz, W. Andre Perkins, Jacqueline M. Nugent +6
Cloud microphysical parameterizations in atmospheric models describe the formation and evolution of clouds and precipitation, a central weather and climate process. Cloud-associate…
Machine Learning Climate Model Dynamics: Offline versus Online Performance
Noah D. Brenowitz, Brian Henn, Jeremy McGibbon +5
Climate models are complicated software systems that approximate atmospheric and oceanic fluid mechanics at a coarse spatial resolution. Typical climate forecasts only explicitly r…
Reconciling the Diversity and Uniformity of Galactic Rotation Curves with Self-Interacting Dark Matter
Tao Ren, Anna Kwa, Manoj Kaplinghat +1
Galactic rotation curves exhibit diverse behavior in the inner regions, while obeying an organizing principle, i.e., they can be approximately described by a radial acceleration re…