Primary Convener:
Björn Lütjens, IBM Research

Convener:
Kalyn Dorheim, St. Olaf College
Paolo Giani, Massachusetts Institute of Technology; Department of Earth, Atmospheric and Planetary Sciences
Pedram Hassanzadeh, Rice University

Early Career Convener:
Björn Lütjens, IBM Research

Chair:
Björn Lütjens, Massachusetts Institute of Technology
Kalyn Dorheim, St. Olaf College
Pedram Hassanzadeh, Rice University
Paolo Giani, Massachusetts Institute of Technology; Department of Earth, Atmospheric and Planetary Sciences

Running Earth system models (ESMs) requires a heavy computational cost. As a result, ESMs are often only run for a few emission scenarios with a limited number of realizations per scenario. Emulators or reduced-complexity models are used to interpolate in between the simulated scenarios or reduce internal variability uncertainty. But, existing emulators, such as linear pattern scaling, can break down for emulating irreversible changes, aerosol forcings, and compound and rarest extreme events. Further, emulating high spatiotemporal outputs that capture the internal variability of the Earth system remains challenging.
With autoregressive machine learning emulators on the rise, we also welcome submissions from the reduced complexity modeling communities to encourage an interdisciplinary exchange including, but not only:-Benchmarking/validation protocols
-Applications of emulators
-Theoretical insights for interpretability, consistency, etc.
-Reduced-complexity models
-Probabilistic emulation techniques, such as Weather generators, diffusion, GPs, EVT
-(Hybrid) deep learning approaches, including operator, autoregressive, foundation models-Atmosphere-ocean coupled emulators

Index Terms
1616 Climate variability
1622 Earth system modeling
1942 Machine learning
4468 Probability distributions, heavy and fat-tailed

Suggested Itineraries:
Disasters‚ Calamities and Extreme Events
Climate Change and Global Policy
Machine Learning and AI
Global Impacts‚ Solutions‚ & Policies

Cross-Listed:
IN - Informatics
NG - Nonlinear Geophysics
A - Atmospheric Sciences
OS - Ocean Sciences

Neighborhoods:
3. Earth Covering