Climate projections continue to be marred by large uncertainties, which originate in processes that need to be parameterized in models, such as clouds, turbulence, and ecosystems. But breakthroughs in the accuracy of climate projections are finally within reach. New tools from data assimilation and machine learning make it possible to integrate global observations and local high-resolution simulations in an Earth system model (ESM) that systematically learns from both. Scientific, computational, and mathematical challenges need to be confronted to realize such an ESM, for example, developing parameterizations suitable for automated learning, and learning algorithms suitable for ESMs. While these challenges are substantial, building an ESM that learns automatically from diverse data sources is achievable now. Such an ESM offers the key opportunity for dramatic improvements in the accuracy of climate projections.

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