Reproducing Internal Variability with Few Ensemble Runs

While large climate model ensembles are invaluable tools for physically consistent climate prediction, they also present a large burden in terms of computational resources and storage requirements. A complementary approach to large initial-condition ensembles is to train a stochastic generator on fewer runs. While simulations from a statistical model cannot capture the complexity of climate model runs, they can address some specific scientific questions of interest, such as sampling the variability of regional trends. We demonstrate this potential by comparing simulations from a large ensemble and a stochastic generator trained with only four runs, and show that the variability of regional temperature trends is almost indistinguishable. Training stochastic generators on fewer runs might prove especially useful in the context of large climate model intercomparison projects where creating large ensembles for each model is not possible.