Understanding and Modelling Convection with Machine Learning

Machine Learning (ML) has demonstrated its potential to improve the performance of an Earth System Model (ESM), yet many challenges remain. ESMs are essential tools to project and understand climate change but have known biases. Convective processes are in general unresolved as their typical length scale is smaller than the grid size of ESMs. The effect of such subgrid processes is estimated with parameterizations, that are often attributed to be sources of biases in ESMs. A way to reduce these limitations of ESMs is to take advantage of ML or deep learning (DL) algorithms that learn actively on output from convection permitting high-resolution simulations which explicitly resolve convective processes. The resulting ML parameterizations are then coupled with an ESM and replace existing traditional subgrid parameterizations in hybrid (physics + ML) ESMs. This thesis presents novel approaches to transform DL algorithms from data science concepts towards an operational use in ESM simulations. First, a DL algorithm is developed that enables to better understand subgrid convective processes and interactions with the large-scale environment. Specifically, the latent space of a Variational Encoder Decoder (VED) reveals the meridional temperature differences between the tropics and poles together with the characteristics of subtropical and subpolar air masses along the mid-latitude storm tracks are key drivers of convective processes. Moreover, the VED separates key characteristics of shallow convective, cumulus, cirrus-like and deep convection regimes. Second, a novel DL algorithm ensemble approach is developed, that provides an improved representation of convective processes. Third, it is demonstrated that the more realistic uncertainty quantification of the ensembles capturing the chaotic nature of subgrid processes stabilizes hybrid simulations and reduces longstanding biases in a hybrid model run of an ESM. This thesis thus advances the modelling of convective processes with DL in Earth system sciences via enhanced representation and understanding of convective processes in ESMs. It provides ways to reduce limitations of state-of-the-art ML models and paves a way forward to the operational use of DL and ML in the next generation of ESMs.