# Earth System Model

An MIT team has developed a training-data optimization method that lets a climate emulator trained only on carefully selected synthetic scenarios predict outcomes of scenarios it never saw, while the traditional emulators evaluated failed to adapt to new situations. The method reduces the number of full earth-system model runs needed to generate training data from six to eight down to one, shortening production time for these fast surrogate models. Emulators trained this way also captured a local pollutant-control case in which one pollutant falls while other emissions keep rising.

An MIT team has developed a training-data optimization method that lets a climate emulator trained only on carefully selected synthetic scenarios predict outcomes of scenarios it never saw, while the traditional emulators evaluated failed to adapt to new situations. The method reduces the number of full earth-system model runs needed to generate training data from six to eight down to one, shortening production time for these fast surrogate models. Emulators trained this way also captured a local pollutant-control case in which one pollutant falls while other emissions keep rising.