The paper presents a probabilistic deep learning emulator—a ResNet‑inspired Conditional Variational Autoencoder—for the stochastic Holton–Mass model of stratospheric variability, which exhibits rare transitions between strong and weak polar vortex regimes. The emulator accurately reproduces short‑term dynamics, steady‑state distributions, regime persistence, rare transition rates, the committor function, and expected lead times. Analysis of the 32‑dimensional latent space via PCA reveals an unsupervised separation into four physically interpretable clusters that correspond to the two vortex regimes and their stable or transition‑prone states.
By C. Daniel Boscu, Daniel Hernandez, Fabio Alvarez Ventura, Justin Finkel, Ashesh Chattopadhyay, Pedram Hassanzadeh, Dorian S. Abbot
arXiv:2609.37392v1 Announce Type: new
Abstract: Modeling the temporal evolution of macroscopic properties of complex systems is an important scientific task. To predict this evolution without full mi...
By Zhichao Han, Yue Zhao, Qianxiao Li
The paper introduces an explainable contrastive learning model that maps five monthly cloud and radiation fields into a shared representation space to analyze perturbed parameter ensembles (PPEs) in climate simulations. Trained on two 100-member Community Atmosphere Model version 6 PPEs differing only in the warm rain microphysics scheme (KK2000 vs. TAU-ML), the model achieves over 94% linear classification accuracy while preserving seasonal variability and ensemble spread. The learned representations place satellite observations on the same low‑dimensional manifold as the PPEs, with TAU-ML PPE closer to observations, and Integrated Gradients attribution identifies key regional contributions linked to cloud microphysics, boundary layer turbulence, and deep convection.
By Da Fan, David John Gagne II, Gregory S Elsaesser, Brian Medeiros, Addisu G Semie, Qingyuan Yang, Akila Sampath, Subashree Venkatasubramanian
arXiv:2510. 25306v3 Announce Type: replace Abstract: Partial physical knowledge--governing structures known, constitutive relations or their combinations not--pervades spatiotemporal systems.
By Xizhe Wang, Xiaobin Song, Hongbo Zhao, Qingshan Jia, Qianchuan Zhao, Hao Sun, Benben Jiang
SimCast‑S2S is a generative latent‑diffusion model designed for probabilistic subseasonal‑to‑seasonal precipitation forecasting. It tackles three key challenges: it uses a diffusion pipeline to capture uncertainty, operates in a compact latent space to enable efficient large‑ensemble generation, and leverages transfer learning with low‑rank adaptation to train on limited reanalysis data after pretraining on climate simulations. The model outperforms deep‑learning baselines and competes with, or surpasses, operational systems such as the ECMWF‑S2S baseline without requiring extensive post‑processing.
By Hiep V. Dang, Antonios Mamalakis
arXiv:2609.24882v1 Announce Type: new
Abstract: Hybrid AI-physics climate modeling aims to improve coarse (~100km-resolution) Earth system models by learning to parameterize subgrid processes from hi...
By Jurij Sch\"onfeld, Tom Beucler, Julien Savre, Steven Sherwood, Veronika Eyring