布朗运动理论及其在复杂气候系统研究中的应用

文章将从非平衡态统计物理发展和应用的角度，介绍德国科学家哈塞尔曼荣获2021年诺贝尔物理学奖的研究工作——基于布朗运动理论，建立了描述气象(天气)影响气候长期演化的随机气候学模型，并建立了寻求影响气候主因的最优指纹方法，从而能够分辨出人类活动和自然界局部改变对气候这一复杂系统的影响。哈塞尔曼的工作本质上是理论物理在实际复杂系统领域的成功应用，他采用的基础物理方法——布朗运动理论是我国杰出女物理学家王明贞和其导师乌伦贝克在20世纪40年代基于爱因斯坦的工作发展起来的^[1，2]。文章将介绍布朗运动理论的发展及其相关的非平衡统计物理思想的当代发展，以展示哈塞尔曼如何把相关的物理理论巧妙地用于气候长期预测的实际应用研究：(1)建立了快变的局部“气象”变量涨落通过耗散涨落关系影响缓变的整体气候变量的基础理论；(2)通过最优指纹方法，寻找局部“噪音”和外驱动力影响气候演化的关键要素。

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.