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。最近对气候系统中特别敏感的子系统（倾翻因素）的研究结果引起了人们的关注，即普通气候模型仍然低估了气候变化。本项目以数据分析部分为重点，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，研究全球和区域气候和环境变化的临界要素的作用和连通性。这一办法将受到严格审查和检验。为此目的，推断间接耦合，极端事件，空间主导方向和边界效应的新方法将开发和实施，以及适用于在南美洲和亚洲的季风区。这包括一种新的各向异性措施，它能够量化的方向沿着的极端事件同步本地。这些特定的倾翻要素之间和内部的时空相互作用将进行调查，以了解稳定性或多稳定性的考虑季风地区，由于环境变化或跨越的倾翻点。因此，该项目将大大有助于气候变化研究（更好地了解临界要素）和方法学发展（时空分析的新方法），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.