TianJi-Environ: An Autonomous AI Scientist for Atmospheric Environmental Research

Haoluo Zhao; Hongchun Zhang; Nan Li; Jing-Jia Luo; Kaikai Zhang; Mengyang Yu; Nan Chen; Tao Song

physics.ao-phJun 5, 2026

TianJi-Environ: An Autonomous AI Scientist for Atmospheric Environmental Research

Authors: Haoluo Zhao, Hongchun Zhang, Nan Li, Jing-Jia Luo, Kaikai Zhang, Mengyang Yu, Nan Chen, Tao Song, +1 more

Organizations: School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China · College of Environmental Science and Engineering, Nanjing University of Information Science and Technology,2026 Nanjing, China · State Key Laboratory of Climate System Prediction and Risk Management (CPRM), Nanjing University of Information Science and Technology, Nanjing, 210044, China · College of Computer Science and Technology, China University of Petroleum, Qingdao, Shandong, China

Abstract

As atmospheric environmental prediction continues to improve, interpretable validation of pollution mechanisms and feedback processes has become a main challenge in atmospheric chemistry. Yet mechanism validation based on complex numerical models still relies heavily on expert knowledge: mechanistic hypotheses must be operationalized into executable experiments, and model outputs must be organized into traceable evidence. We present TianJi-Environ, an auditable AI Scientist for atmospheric-chemistry mechanism validation. TianJi-Environ establishes the first WRF-Chem-based multi-agent framework that autonomously drives complex atmospheric-chemistry simulations, converting mechanistic hypotheses into executable configurations, testing experiments, and evidence criteria. Using ozone response and particulate-matter feedback as two representative examples, we demonstrate TianJi-Environ's capability for mechanism validation. In a summertime ozone case over the North China Plain, the system detects directionally consistent aerosol-radiation-interaction signals in shortwave radiation and boundary-layer height, but judges the evidence for ozone response to NOx control to be incomplete. In a wintertime PM2.5 case over the Guanzhong Basin, it localizes the unsupported link to insufficient propagation from black-carbon perturbation to particulate response and missing diagnostics of vertical absorptive heating. These results show that TianJi-Environ makes expert-driven mechanism validation explicit, structured, and auditable, offering a reproducible paradigm for multi-agent systems coupled with complex atmospheric-chemistry models.

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Toward Mechanistic Interpretability of an AI Foundation Model Fine-Tuned for Atmospheric Chemistry

Jason Y. Hu, Ivan Higuera-Mendieta, Patrick Obin Sturm, Makoto M. Kelp

Stanford University, Department of Computer Science, Stanford, CA, USA. · Stanford University, Department of Earth System Science, Stanford, CA, USA · University of Southern California, Department of Earth Sciences, Los Angeles, CA, USA · University of Utah, Department of Atmospheric Sciences, Salt Lake City, UT, USA

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. These FMs are typically trained on reanalysis data and generate forecasts through autoregressive rollout. They do not explicitly represent governing physical or chemical processes. Therefore, high forecast skill does not reveal whether a model has learned physical mechanisms or exploits statistical regularities in its training data. Here, we present the first study of what a FM fine-tuned for atmospheric chemistry has learned by examining Microsoft's Aurora model. We impose controlled chemical perturbations on its forecasts and test them against known photochemical relationships. We then examine the internal representations that generate these forecasts. We find that Aurora captures a first-order ozone response to reactive nitrogen but does not enforce the chemical constraints that a process-based model encodes. It generates chemically inconsistent combinations of related species and relaxes localized emission features such as wildfire plumes toward background. Internally, its representations remain largely organized around the meteorology inherited during pretraining, with little structure specific to chemistry. Using sparse autoencoders, we identify internal components that causally control the chemical forecast but do not map cleanly onto individual atmospheric processes. This work provides a framework for testing whether AI forecasting systems learn atmospheric chemistry from reanalysis data. As these models are increasingly positioned to inform environmental policy decisions, we argue that composition forecasts should also be judged by their internal mechanisms rather than by benchmark skill alone.

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Abstract

Explore similar work

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