Comptes Rendus. Géoscience (Nov 2020)
« Science des données » versus science physique : la technologie des données nous conduit-elle vers une nouvelle synthèse ?
Abstract
We live, it is said, in the age of “data science”. Machine learning (ML) from data astonishes us with its advances, such as autonomous vehicles and translation tools, and also worries us with its ability to monitor and interpret human faces, gestures and behaviors. In science, we are witnessing a new explosion of literature around machine learning, capable of interpreting massive amounts of data, otherwise known as “big data”. Some predict that numerical computation will soon be overtaken by ML as a tool for understanding and predicting dynamic systems.No field of science is as closely related to HPC as meteorology and climate science. Their history dates back to the dawn of numerical computation, the technology that von Neumann and his colleagues pioneered in the post-war era. In this article, we will use the numerical simulation of the Earth system as an example to highlight some of the fundamental questions posed by machine learning. We will return to the history of meteorology to understand the dialectic between knowledge—our understanding of the atmosphere—and forecasting, for example the knowledge of the weather of the next day. This question is raised again today by learning, because it is not necessarily possible to interpret physically because it comes directly from the data. On the other hand, the central role of Earth system simulation to help us decipher the future of the planet and climate change, requires us to get out of the actuality of the data and make comparisons with fictitious Earths (without industrial emissions for example) and several leads to the future, what we call “scenarios”. Here observations do have a role, but it is often data from simulations that are analyzed. Finally, these climate data have a societal weight, and the democratization of access to them has grown strongly in recent years. We will show here some aspects of the evolution of simulation and data technologies and its important stakes for Earth system sciences.
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