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Scientists urge use of fine-scale models to predict pollution surges
Over the last two decades, the scientific community has made rapid strides in understanding climate change and air pollution -- but progress on their combined effects remains limited. Traditional models often gloss over the complex web of interactions between land, sea, and sky, especially when
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Weather Gone Wild: Scientists Use Fine-Scale Models to Predict Pollution Surges | Newswise
Newswise -- Over the last two decades, the scientific community has made rapid strides in understanding climate change and air pollution -- but progress on their combined effects remains limited. Traditional models often gloss over the complex web of interactions between land, sea, and sky,
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Researchers from Ocean University of China and Tsinghua University propose using advanced Earth system models with AI integration to better forecast the combined effects of extreme weather and air pollution.
Scientists from the Ocean University of China and Tsinghua University have published a perspective article in Frontiers of Environmental Science & Engineering, highlighting the urgent need for high-resolution Earth system models to better understand and predict the interactions between climate extremes and air pollution
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. The research team, led by Professors Yang Gao and Deliang Chen, emphasizes that traditional models often fail to capture the complex interplay between land, sea, and sky, particularly in densely populated coastal and urban areas where human exposure to environmental hazards is highest2
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Source: Phys.org
The study explores how next-generation Earth system models with kilometer-scale resolution can provide a more detailed and accurate picture of environmental hazards under climate change. These high-resolution models are crucial for simulating compound climate extremes, which involve simultaneous or sequential events that have become increasingly frequent
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. By incorporating fine-scale processes that are often poorly represented in traditional models, such as ozone dry deposition and urban-rural emission differences, the researchers were able to significantly improve pollution forecasts2
.The new simulations demonstrated remarkable improvements in accuracy, reducing ozone overestimates by an average of 62% in heavily polluted regions
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. To address the intensive computing demands of these high-resolution models, the research team proposes integrating artificial intelligence techniques. This integration aims to speed up calculations while maintaining the accuracy of the simulations, reflecting the complex, nonlinear reality of our atmosphere in a changing climate2
.Professor Yang Gao emphasized the importance of understanding how extreme weather and air pollution amplify each other, stating that this knowledge is "essential to protecting lives and ecosystems"
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. The high-resolution Earth system models allow researchers to uncover interactions that were previously invisible, providing decision-makers with crucial information to prepare for future climate risks2
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As climate-related disasters become more frequent, the ability to pinpoint where and when pollution and extreme weather will intersect is increasingly critical. These advanced models have the potential to revolutionize environmental forecasting, offering cities, coastal communities, and health systems the foresight to act swiftly in the face of environmental threats
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. Combined with artificial intelligence, these models could deliver real-time, localized warnings and guide investments in climate adaptation, laying the foundation for more resilient societies in an uncertain future2
.This groundbreaking work was supported by various funding sources, including the National Natural Science Foundation of China, the Science and Technology Innovation Project of Laoshan Laboratory, and the Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City
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. The perspective article was published on May 19, 2025, in Frontiers of Environmental Science & Engineering, a leading forum for peer-reviewed submissions in environmental disciplines2
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