Scientists at Stanford University and Colorado State University have created a new, low-cost way to study global warming’s effects on extreme weather. They published their method in Science Advances. This method uses machine learning in event attribution. It helps to see how much human-caused climate change has led to heat waves in the United States and other areas.
AI in Climate and Global Warming
The researchers taught artificial intelligence (AI) models to forecast daily high temperatures. They used regional weather conditions and the global average temperature for this task. They trained the models with data from a climate simulation database. This database covers the years from 1850 to 2100. After validating the AI models, the scientists applied them to real-world heat waves. They predicted the severity of these events if they happened under different global warming levels.
The team compared these predictions. This allowed them to estimate how climate change affects the frequency and intensity of historical extreme weather events. They conducted a case study on the 2023 Texas heat wave. The researchers discovered that global warming made the heat wave 1.18 to 1.42 degrees Celsius (2.12 to 2.56 Fahrenheit) hotter. Without climate change, it wouldn’t have been this intense.
“Machine learning creates a powerful new bridge between the actual meteorological conditions that cause a specific extreme weather event and the climate models that enable us to run more generalized virtual experiments on the Earth system.” ~ senior author Noah Diffenbaugh, the Kara J Foundation Professor and professor of Earth system science in the Stanford Doerr School of Sustainability.
The new AI-based method fixes problems with past methods. It uses real historical weather data. It does not depend only on climate model simulations. This new machine learning way helps to analyze extreme events attribution accurately and cheaply. More regions can now be studied. It is important for making good plans to adapt to climate change. It also allows us to see how much global warming affects specific extreme weather events in real-time.
Conclusion
The research team plans to use their method on more types of extreme weather events. They also want to improve the AI networks to make their predictions more accurate and reliable. This research is an exciting step forward in climate science. It gives scientists a new and powerful tool. This tool helps people understand and lessen the impacts of climate change on extreme weather.
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