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Machine learning speeds up climate model simulations at finer resolutions, making them usable on local levels

Climate models are a key technology in predicting the impacts of climate change. By running simulations of the Earth s climate, scientists and policymakers can estimate conditions like sea level rise, flooding, and rising temperatures, and make decisions about how to appropriately respond. But current climate models struggle to provide this information quickly or affordably enough to be useful on smaller scales, such as the size of a city. ....

United States , Massachusetts Institute Of Technology , Puerto Rico , Anamitra Saha , Department Of Earth , Planetary Sciences , Massachusetts Institute , Modeling Earth Systems , Sai Ravela , Grand Challenges , Physical Adversarial Learning From Data , Downscaling Rainfall Extremes ,

Making climate models relevant for local decision-makers

A climate modeling downscaling method developed by MIT scientists leverages a machine-learning technique called adversarial learning to produce simulations faster and at finer resolutions, making them relevant to use on local levels for assessing risks of extreme weather. ....

United States , Puerto Rico , Anamitra Saha , Department Of Earth , Planetary Sciences , Modeling Earth Systems , Climate Grand Challenges ,


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James Hansen , Modeling Earth Systems ,


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James Hansen , Modeling Earth Systems ,