To estimate Boston's future risk of extreme weather such as flooding, policymakers can combine a coarse model's large-scale predictions with a finer-resolution model tuned to estimate how often Boston is likely to experience damaging floods as the climate warms. But this risk analysis is only as accurate as the predictions from that first, coarser climate model. "If you get those wrong for large-scale environments, then you miss everything in terms of what extreme events will look like at smaller scales, such as over individual cities," says Themistoklis Sapsis, the William I. Koch Professor and director of the Center for Ocean Engineering in MIT's Department of Mechanical Engineering. Sapsis and his colleagues have now developed a method to "correct" the predictions from coarse climate models. By combining machine learning with dynamical systems theory, the team's approach "nudges" a climate model's simulations into more realistic patterns over large scales. When paired with smaller-scale models to predict specific weather events such as tropical cyclones or floods, the team's approach produced more accurate predictions for how often specific locations will experience those events over the next few decades, compared to predictions made without the correction scheme. Sapsis says the new correction scheme is general in form and can be applied to any global climate model. Once corrected, the models can help to determine where and how often extreme weather will strike as global temperatures rise over the coming years. "Climate change will have an effect on every aspect of human life and every type of life on the planet, from biodiversity to food security to the economy," Sapsis says. "If we have the capabilities to know accurately how extreme weather will change, especially over specific locations, it can make a lot of difference in terms of preparation and doing the right engineering to come up with solutions. This is the method... |