Scale‐Aware Parameterization of Cloud Fraction and Condensate for a Global Atmospheric Model Machine‐Learned From Coarse‐Grained Kilometer‐Scale Simulations

Abstract Kilometer grid‐length simulations over a variety of different locations worldwide are used as training data for a deep‐learning model designed to predict clouds in a global climate model. The inputs to the neural network are profiles of temperature, humidity and pressure from the high‐resolution model, averaged to the scale of the climate model. The outputs are profiles of cloud fraction and in‐cloud liquid and ice water contents. The high‐resolution data is coarse‐grained to a range of sizes, allowing the model to learn how the cloud formation depends on the size of the area being considered. The machine‐learned cloud fraction and cloud condensate scheme is coupled to a global climate model and used to run multi‐year simulations where the clouds predicted by the neural‐network are fully interacting with the rest of the model.