Implicit Learning of Convective Organization Explains Precipitation Stochasticity

Accurate prediction of precipitation intensity is of crucial importance for both human and natural systems, especially in a warming climate more prone to extreme precipitation. Yet, climate models fail to accurately predict precipitation intensity, particularly extremes. One missing piece of information in traditional climate model parameterizations is sub-grid scale cloud structure and organization, which affects precipitation intensity and stochasticity at coarse resolution. Here using global storm-resolving simulations and machine learning we show that, by implicitly learning sub-grid organization, we can accurately predict precipitation variability and stochasticity with a low dimensional set of latent variables.
Using a neural network to parameterize coarse-grained precipitation, we find that the overall behaviour of precipitation is reasonably predictable using large-scale quantities only; however, the neural network cannot predict the variability of precipitation (R² ~ 0.5) and underestimates precipitation extremes. The performance is significantly improved when the network is informed by our novel organization metric, correctly predicting precipitation extremes and spatial variability (R²~0.9). The organization metric is implicitly learned by training the algorithm on high-resolution precipitable water field, encoding the degree of subgrid organization. The organization metric shows large hysteresis, emphasizing the role of memory created by sub-grid scale structures. We demonstrate this organization metric can be predicted as a simple memory process from information available at the previous time steps. These findings stress the role of organization and memory in accurate prediction of precipitation intensity and extremes and the necessity of parameterizing sub-grid scale convective organization in climate models to better project future changes in the water cycle and extremes.