Analysis of the dynamics of palaeo and modern climate data under consideration of dating erros and with the focus on climate transitions and interrelations between teleconnections and regional climate

在考虑约会误差的情况下分析古气候和现代气候数据的动态，重点关注气候转变以及遥相关与区域气候之间的相互关系

• 批准号: 160384667
• 负责人: Professor Dr. Jürgen Kurths
• 金额: --
• 依托单位: Potsdam-Institut für Klimafolgenforschung (PIK)
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2010
• 资助国家: 德国
• 起止时间: 2009-12-31 至 2014-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/160384667?language=en
• 关键词: Analysis; dynamics; palaeo; modern; climate

We propose to study multi-proxy data series of climate in order to detect complex interrelations and causal connections by using modern techniques of nonlinear data analysis. Moreover, the model simulation results will be verified with the multi proxy data generated in the research group. In a close collaboration between the expertise from complex systems and nonlinear data analysis (Transdisciplinary Concepts and Methods, PIK) and palaeoclimate archives (Institute of Geosciences at UP; GFZ Potsdam) and modelling (Meteorological Institute of Free University Berlin), we will1. develop a technique for considering dating uncertainties as uncertainties of states and for quantifying the reliability of the analysis over the time scale (e.g., by providing a test for significance);2. analyse the dynamics and similarities of palaeo and modern climate data (IP1, IP3, IP4, IP5, IP6) in order to detect climate transitions and interrelations between teleconnections and regional climate on different time scales;3. evaluate the simulation results (IP8) with the observed situation (pollen, isotope records; input from IP1, IP3, IP6) by analysis of characteristic time scales, transitions and recurrent episodes, and4. adapt and further develop proposed techniques (especially recurrence quantification) for the application to highly multivariate data sets, including the development of a significance test for the recurrence quantification and inclusion of the uncertainties due to the dating errors.

本文提出利用现代非线性数据分析技术，研究气候的多代理数据序列，以发现复杂的相互关系和因果关系。并利用课题组生成的多代理数据对模型仿真结果进行验证。在复杂系统和非线性数据分析（跨学科概念和方法，PIK）、古气候档案（UP地球科学研究所；GFZ波茨坦）和建模（柏林自由大学气象研究所）的专业知识之间的密切合作中，我们将1。1 .开发一种技术，将定年不确定性视为状态的不确定性，并在时间尺度上量化分析的可靠性（例如，通过提供显著性检验）；2 .分析古今气候资料（IP1、IP3、IP4、IP5、IP6）的动态和相似性，以探测不同时间尺度上的气候变化及其遥相关与区域气候的相互关系；通过分析特征时间尺度、转换和反复发作，将模拟结果（IP8）与观测情况（花粉、同位素记录；IP1、IP3、IP6输入）进行评估；适应并进一步发展所提出的技术（特别是递归量化），以应用于高度多元数据集，包括开发递归量化的显著性检验和包含由于测年误差造成的不确定性。