Opening the neural networks’ black box for climate science

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Opening the neural networks’ black box for climate science

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  • The global climate adaptation and mitigation efforts require reliable information about the future of climate variability and extremes, particularly at regional scales. Integrating observations, theory, and physics-based computer models has resulted in significant advances in climate science in the past century. However, there are still major challenges in obtaining reliable, actionable information due to the large uncertainties in climate projections. Thus, there is a critical need for drastic improvements in our fundamental understanding of the Earth system and modeling capabilities. Here, I will argue that integrating scientific machine learning (ML) with the conventional approach could potentially open new avenues to substantially accelerate climate research, e.g., via developing better and faster weather/climate models, extracting more information from observational data, and even (potentially) improving our fundamental knowledge. However, as scientific ML is in its infancy, there are major challenges for climate applications that need to be addressed first. These challenges include interpretability, stability, extrapolation, and learning in the small-data regime. The talk will highlight some of the recent work on the promises, challenges, and future possibilities of applying scientific ML to accelerate climate research. In particular, a new method will be discussed, based on integrating the Fourier analyses of neural networks and climate data, which enables to explain and connect the learned physics and inner workings of the network. This method is a step toward developing a much-needed general framework to rigorously analyze and understand neural networks for climate applications and making them reliable and effective tools. 


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