Investigation of past and present climate dynamics and its stability by means of a spatio-temporal analysis of climate data using complex networks

研究气候系统的稳定性及其所经历的变化的影响需要数据分析和建模方法。气候系统中特别敏感的子系统(触发因素)的最新发现引发了人们的担忧，即常见的气候模型仍然低估了气候变化。该项目侧重于数据分析部分，旨在利用复杂网络理论的思想对气候数据进行时空、多变量分析，调查倾倒因素在全球和区域气候和环境变化方面的作用和连通性。这一方法将得到严格的审查和测试。为此，将开发和实施推断间接耦合、极端事件、空间优势方向和边界效应的新方法，并将其应用于南美洲和亚洲的季风区。这包括一种新的各向异性测量方法，它能够量化极端事件局部同步的方向。将研究这些特定倾点要素之间和内部的时空相互作用，以了解所考虑的季风区由于环境变化或临界点的交叉而产生的稳定性或多重稳定性。因此，该项目将极大地促进气候变化研究(更好地了解关键因素)和方法发展(时空分析的新途径)，最终也可用于改进气候模型。

The study of the stability of the climate system and the impacts of the changes it undergoes requires both data analysis and modelling approaches. Recent findings of particularly sensitive sub-systems in the climate system (tipping elements), have risen concerns that common climate models still underestimate climate change. Focusing on the data analysis part, this project aims to investigate the role and connectivity of tipping elements with respect to global and regional climate and environmental change, using ideas from complex network theory for a spatio-temporal, multi-variate analysis of climate data. This approach will be critically reviewed and tested. For this purpose, new methods for inferring indirect couplings, extreme events, spatial predominant directions, and boundary effects will be developed and implemented as well as applied to the monsoon regions in South America and Asia. This includes a novel anisotropy measure which is able to quantify directions along which extreme events synchronize locally. Spatio-temporal interactions between and within these specific tipping elements will investigated in order to understand the stability or multi-stability of the considered monsoon regions due to environmental changes or crossing of tipping points. The project will, therefore, significantly contribute to the climate change studies (better understanding tipping elements) and the methodological development (new approaches for spatio-temporal analysis), which could eventually also be used to improve climate models.