Understanding and modeling atmospheric subgrid processes with deep learning

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In the face of global warming accurate climate predictions are urgently needed. Current climate models, however, still suffer from large uncertainties, which are mainly caused by the approximate representation, also called parameterization, of clouds smaller than the model grid scale. Cloud processes and their interactions with turbulence and radiation are highly chaotic, making traditional parameterization development, based on physical intuition and manual tuning, slow and cumbersome. Recently, it has become possible to run global high-resolution simulations which explicitly resolve complex cloud processes. These simulations are computationally expensive, however, which limits their prediction horizon to a few months at most. Nevertheless, such short-term datasets could be exploited to develop better parameterizations for climate models. In this project, deep learning will be used to systematically leverage short-term high-resolution simulations for climate simulations. Deep learning, a branch of artificial intelligence, is based on multi-layered artificial neural networks, which can learn complex nonlinear relations. In 2018, first studies, including the applicant's, have demonstrated the general feasibility of building a deep learning subgrid parameterization for climate models using high-resolution data. One key objective of the proposed work is to use cutting-edge machine learning techniques to improve the numerical stability and physical consistency of these early studies in order to run realistic climate simulations. Another key goal is to use deep learning parameterizations to learn about the subgrid processes themselves. Deep learning has proven to be an excellent fit for modeling processes with spatial and temporal structure, which are also important in the atmosphere but are not included in most current parameterizations. The proposed deep learning approach allows probing the rich high-resolution dataset for the importance of spatial and temporal structures, as well as process interactions. Building better climate models requires a more efficient use of data. Deep learning provides one way of doing so. The proposed work plan aims to develop essential methodology in order to achieve real climate simulations with a machine learning approach but also to use this novel technology to extract insight from the most detailed simulations ever available. Further, this project will help to bridge the gap between cutting-edge machine learning research and climate modeling.