AI-Powered Project Aims to Untangle How Wildfire Smoke Reshapes Marine Clouds

Marine low clouds are among the most consequential and least understood players in the global climate system. Stretching across vast expanses of the world’s oceans, these cloud decks act almost like a giant planetary reflector, bouncing incoming sunlight back to space and helping to cool the Earth. Now, a team at Washington University in St. Louis has received a $1 million grant from the National Science Foundation to attack one of the most stubborn uncertainties in climate science: how tiny airborne particles known as aerosols alter the brightness and lifetime of these clouds. The project will combine atmospheric field science with cutting-edge artificial intelligence and rigorous statistics to produce predictions that could sharpen forecasts of rainfall, extreme weather, and future warming.

The scientific problem at the heart of the effort is deceptively simple to state and notoriously difficult to solve. Aerosols — microscopic particles released by sources such as wildfire smoke, industrial activity, and sea spray — serve as the seeds around which cloud droplets form. When more aerosols enter a cloud, the available moisture is distributed across a larger number of smaller droplets, which can make the cloud brighter and more reflective. Smaller droplets can also suppress rainfall, allowing the cloud to persist longer before it dissipates. Both effects change how much sunlight the cloud reflects away from the planet, and both remain among the largest sources of uncertainty in projections of Earth’s future climate.

“Small changes in these clouds can have a surprisingly large effect on Earth’s energy balance and climate,” said Jian Wang, a professor of energy, environmental and chemical engineering in the McKelvey School of Engineering and director of the Center for Aerosol Science and Engineering at WashU. Wang, the principal investigator for the project, noted that scientists still do not fully understand how aerosols change marine low clouds. The difficulty lies in separating the genuine fingerprint of aerosols from the noise of natural meteorological variability — winds, temperature, humidity, and large-scale circulation patterns that shape clouds independently of any pollution or smoke influence.

That separation problem is precisely where the new AI-driven approach comes in. The WashU team, which includes co-principal investigators Bo Li and Maxine Yu of the Department of Statistics and Data Science in WashU Arts & Sciences, plans to develop three physics-informed generative AI models trained on real satellite observations and field measurements. The data will capture episodic smoke plumes drifting from North American wildfires over the ocean, providing natural experiments in which a known aerosol source interacts with marine cloud systems. Rather than treating the atmosphere as a snapshot at a single place and time, the models will trace the full history of an air mass as it travels, incorporating the emissions and meteorological conditions it encounters along its path.

The first two models will predict what aerosol concentrations and cloud properties would plausibly look like in the absence of wildfire influence — a statistical counterfactual. The third model will then quantify the probability distributions of aerosol concentration and cloud properties under actual wildfire-smoke influence. Comparing the two worlds allows the researchers to isolate the causal effect of the smoke. “The AI creates the counterfactual: what would the aerosols and clouds likely have looked like if the wildfire had not occurred?” Yu said. “This allows us to separate the aerosol effect from the many meteorological factors that also influence clouds.”

This counterfactual strategy represents a meaningful departure from conventional approaches to aerosol-cloud research. Traditional statistical studies often correlate cloud properties with aerosol loading at a given location and moment, an approach vulnerable to confounding: meteorology influences both where smoke goes and how clouds behave, so simple correlations can misattribute causes. By following air-mass histories and explicitly modeling the world with and without the smoke, the WashU framework borrows ideas from causal inference — a branch of statistics concerned with identifying genuine cause-and-effect relationships rather than mere associations. Li and Yu contribute expertise in AI, statistical learning, environmental and spatial statistics, causal inference, and uncertainty quantification, complementing Wang’s strengths in atmospheric aerosols, clouds, and field observations.

“Bringing these perspectives together within WashU allows us to develop AI methods that are not only technically advanced, but also physically meaningful and statistically rigorous,” Li said. That emphasis on physical meaning matters because purely data-driven models can learn spurious patterns that fail when applied to new conditions. By embedding physical constraints into the generative models, the researchers aim to ensure that the AI’s outputs remain consistent with the known behavior of aerosols, droplets, and radiation, while the statistical machinery quantifies how confident scientists can be in each prediction. The result, the team says, should be a tool that improves understanding of aerosol effects on marine low clouds and strengthens scientists’ capability to predict the environment.

The stakes extend well beyond academic curiosity. According to the NSF, which is funding the work, the research could help American communities prepare for extreme weather, protect public health, and guide sound investments in infrastructure and risk management. “Even for people living far from the ocean, including here in the Midwest, understanding these processes matters because they affect our ability to predict future warming, precipitation and other climate changes,” Wang said. Marine low clouds influence regional and global temperature trends, and better constraints on how aerosols modify them could narrow the range of warming projected for the coming decades — a refinement with direct consequences for policy, planning, and climate adaptation worldwide.

The project also carries a significant workforce dimension. The researchers will train students and researchers in advanced AI methods, supporting the NSF’s priority of building a national AI-ready scientific workforce. “Training is an important part of the project, because the next generation of scientists increasingly needs to understand both the physical sciences and modern AI and statistics,” Li said. The team’s goal is to give students hands-on experience applying sophisticated machine learning to consequential real-world scientific problems, bridging the gap between atmospheric science and data science that often separates the two fields.

Beyond its immediate scientific targets, the framework is designed for reuse. The researchers plan to develop a human-in-the-loop agentic AI framework that can automate parts of the scientific workflow — from preparing and screening data to training models and analyzing results — while keeping scientists in charge of the critical decisions. “Our goal is therefore not just one scientific result, but a reusable tool for tackling similar problems in earth science,” Wang said. The AI and statistical machinery being developed could, in principle, be adapted to disentangle the effects of other environmental drivers from background weather and climate variability, offering earth scientists a general-purpose instrument for one of their field’s most persistent challenges: hearing a clear signal through a noisy atmosphere.

Subject of Research: Aerosol effects on marine low clouds and climate prediction using AI and statistics

Article Title: In the clouds

Article References: In the clouds. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: aerosols, marine low clouds, wildfire smoke, artificial intelligence, climate modeling, causal inference, generative AI, cloud physics, National Science Foundation, uncertainty quantification, climate prediction, Washington University in St. Louis