Using AI to Link Heat Waves to Global Warming

Researchers at Stanford and Colorado State University have developed a rapid, low-cost approach for studying how individual extreme weather events have been affected by global warming. Their method, detailed in a Aug. 21 study in Science Advances, uses machine learning to determine how much global warming has contributed to heat waves in the U.S. and elsewhere in recent years. The approach proved highly accurate and could change how scientists study and predict the impact of climate change on a range of extreme weather events. The results can also help to guide climate adaptation strategies and are relevant for lawsuits that seek to collect compensation for damages caused by climate change.

“We’ve seen the impacts that extreme weather events can have on human health, infrastructure, and ecosystems,” said study lead author Jared Trok, a PhD student in Earth system science at the Stanford Doerr School of Sustainability. “To design effective solutions, we need to better understand the extent to which global warming drives changes in these extreme events.”

Trok and his co-authors trained AI models to predict daily maximum temperatures based on the regional weather conditions and the global mean temperature. For training the AI models, they used data from a large database of climate model simulations extending from 1850 to 2100. But once the AI models were trained and verified, the researchers used the actual weather conditions from specific real-world heat waves to predict how hot the heat waves would have been if the exact same weather conditions occurred but at different levels of global warming. They then compared these predictions at different global warming levels to estimate how climate change influenced the frequency and severity of historical weather events.

Read more at: Stanford University

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