arXiv:2606. 07697v1 Announce Type: cross Abstract: As atmospheric environmental prediction continues to improve, interpretable validation of pollution mechanisms and feedback processes has become a main challenge in atmospheric chemistry.
arXiv:2607. 20778v1 Announce Type: new Abstract: Weather forecasting foundation models (FMs) are increasingly fine-tuned to predict air quality, offering fast global pollution forecasts at lower computational cost than conventional chemical transport models.
Tianmu-TC is a physics‑constraints generative AI framework designed for global tropical cyclone forecasting. Trained on Western North Pacific data, it produces controllable outputs with reduced uncertainty, outperforming both deterministic and ensemble meteorological AI models as well as the ECMWF NWP system across global ocean basins. The model also demonstrates strong performance in challenging scenarios such as data sparsity, anomaly tracks, rapid intensification, and weakening, while maintaining significantly lower computational cost.
arXiv:2601. 04268v3 Announce Type: replace Abstract: Weather and climate models rely on parametrisations to represent unresolved sub-grid processes.
arXiv:2607. 23880v1 Announce Type: cross Abstract: Nitrous oxide (N$_2$O) is the dominant ozone-depleting substance emitted in the 21st century, and the third largest contributor to anthropogenic greenhouse gases due to its high potency and long atmospheric lifetime, with more than 70% of N$_2$O emissions occurring as a result of agricultural processes.
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...
arXiv:2607. 07729v1 Announce Type: cross Abstract: As foundation models grow in scale and diversity, coordinating multiple models into cooperative reasoning systems offers a path toward safer, more reliable AI.
arXiv:2511.20109v2 Announce Type: replace Abstract: Climate science demands automated workflows to transform comprehensive questions into data-driven statements across massive, heterogeneous datasets...
arXiv:2606. 13148v1 Announce Type: new Abstract: Climate and environmental decision-making increasingly requires reasoning across heterogeneous inputs, including gridded physical data, satellite imagery, geospatial context, and simulator outputs.
arXiv:2608. 09948v1 Announce Type: cross Abstract: No single AI weather model excels at all variables, pressure levels, and lead times.
arXiv:2606. 17076v1 Announce Type: cross Abstract: The Coupled Model Intercomparison Project Phase 6 (CMIP6) has generated thousands of peer-reviewed publications documenting model configurations, evaluation procedures, emergent constraints, and projection uncertainties.
arXiv:2609.25505v1 Announce Type: cross Abstract: Rapid intensification (RI) remains one of the most consequential and difficult aspects of tropical cyclone (TC) forecasting. Although full-physics nu...
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.