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A team of scientists led by CMCC has developed an Artificial Intelligence forecasting system capable of providing useful information four to seven weeks before the summer, which provides valuable time to improve preparation.
Published in Nature Communications Earth & Environment, the study shows how artificial intelligence can improve forecast accuracy by drastically reducing computational costs.
It promises significant benefits for sectors such as agriculture, public health, energy and emergency planning.
The new approach analyses more than 2,000 atmospheric, oceanic and land variables to identify those most strongly associated with heatwave risk. It offers improved forecasts, particularly for northern Europe, where traditional dynamical systems often struggle.
"Machine learning will become a fundamental part of how we study climate variability. This study has demonstrated the usefulness of ML in predicting extreme events, but it is only a first step," said lead researcher McAdam.
The system was trained using paleoclimate simulations spanning the years 0–1850, giving it access to centuries of data.
Despite this unconventional training, it accurately predicted real-world heat waves between 1993 and 2016, including the extreme events of 2003 and 2015./CNA
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