Investigation of past and present climate dynamics and impact of climate tipping elements 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，将开发和实施推断耦合方向和间接耦合的新方法。将通过时空相互作用来研究倾倒要素，这将有助于理解由于倾倒要素的变化和临界点的交叉而导致的全球气候网络的稳定性或多重稳定性。对这项研究的补充是将现代时空气候相互作用与过去全新世的气候变化进行比较，包括发展一种适当的方法来比较稀疏分布的数据(古数据)和复杂的网络数据(最近的气候相互作用网络)。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 coupling directions and indirect couplings will be developed and implemented. Tipping elements will be studied by their spatio-temporal interactions; this will help to understand the stability or multi-stability of the global climate network due to changes of tipping elements and the crossing of tipping points. This study is complemented by comparing modern spatio-temporal climate interactions with past changes of climate during the Holocene, including the development of an appropriate approach to compare sparsely distributed data (palaeo-data) with complex network data (recent climate interaction network). 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.