Brownian motion theory and its application in the study of complex climate systems

This paper will recall the work of Klaus Hasselmann, a German scientist who won the 2021 Nobel Prize in physics, from the perspective of the development of nonequilibrium statistical physics. Based on Brownian motion theory, he established a stochastic climate model to describe the long-term evolution of climate, as influenced by meteorological weather conditions. He also proposed an optimal fingerprint method to identify the influence of human activity and local natural variability on climate, a complex system. Hasselman’ s work was essentially a successful application of theoretical physics to complex systems. The physical method he used, Brownian motion theory, was well developed by Ming-Chen Wang, who was an outstanding Chinese female physicist, and George Eugene Uhlenbeck in the 1940s based on the work of Albert Einstein^[1，2]. This paper will briefly describe the development of Brownian motion theory and the related contemporary progress of non-equilibrium statistical physics. It will be shown how Hasselman applied the relevant theories to the practical application of long-term climate prediction: (1) He established the theory that the fluctuation of the rapidly changing local weather variables affects the slowly changing global climate variables through the fluctuation-dissipation relationship; (2) He found the key factors of local“noise”and external driving forces that are crucial in affecting climate evolution through the optimal fingerprint method.