We apply two independent data analysis methodologies to locate stable climate states in an intermediate complexity climate model and analyse their interplay. First, drawing from the theory of quasi-potentials, and viewing the state space as an energy landscape with valleys and mountain ridges, we infer the relative likelihood of the identified multistable climate states and investigate the most likely transition trajectories as well as the expected transition times between them. Second, harnessing techniques from data science, and specifically manifold learning, we characterize the data landscape of the simulation output to find climate states and basin boundaries within a fully agnostic and unsupervised framework. Both approaches show remarkable agreement, and reveal, apart from the well known warm and snowball earth states, a third intermediate stable state in one of the two versions of PLASIM, the climate model used in this study. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production via the hydrological cycle drastically change the topography of the dynamical landscape of Earth’s climate.

我们应用两种独立的数据分析方法来定位中等复杂气候模型中的稳定气候状态并分析它们的相互作用。首先，从准势理论出发，将状态空间视为具有山谷和山脊的能量景观，我们推断了已识别的多稳态气候状态的相对可能性，并研究了最可能的转变轨迹以及预期的转变他们之间的时间。其次，利用来自数据科学的技术，特别是流形学习，我们描述了模拟输出的数据景观，以在完全不可知和无监督的框架内找到气候状态和流域边界。两种方法都显示出惊人的一致性，并揭示了除了众所周知的温暖和雪球地球状态之外，本研究中使用的气候模型 PLASIM 的两个版本之一中的第三个中间稳定状态。我们的方法相结合，可以确定海洋热传输的负反馈和通过水文循环产生的熵如何极大地改变地球气候动态景观的地形。