Toward Data‐Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning

It is shown that it is possible to emulate the dynamics of a simple general circulation model with a deep neural network. After being trained on the model, the network can predict the complete model state several time steps ahead—which conceptually is making weather forecasts in the model world. Additionally, after being initialized with an arbitrary model state, the network can through repeatedly feeding back its predictions into its inputs create a climate run, which has similar climate statistics to the climate of the general circulation model. This network climate run shows no long‐term drift, even though no conservation properties were explicitly designed into the network. Plain Language Summary Numerical weather prediction and climate models are complex computer programs that represent the physics of the atmosphere. They are essential tools for predicting the weather and for studying the Earth's climate. Recently, a lot of progress has been made in machine learning methods. These are data‐driven algorithms that learn from existing data. We show that it is possible that such an algorithm learns the dynamics of a simple climate model. After being presented with enough data from the climate model, the network can successfully predict the time evolution of the model's state, thus replacing the dynamics of the model. This finding is an important step toward purely data‐driven weather forecasting—thus weather forecasting without the use of traditional numerical models and also opens up new possibilities for climate modeling. Key Points A neural network can emulate the dynamics of a simple general circulation model The trained network can successfully forecast the model weather The network can produce a realistic representation of the model climate