Transient coupling relationships of the Holocene Australian monsoon

Abstract

a b s t r a c tThe northwest Australian summer monsoon owes a notable degree of its interannual variability to interactions with other regional monsoon systems. Therefore, changes in the nature of these relationships may contribute to variability in monsoon strength over longer time scales. Previous attempts to evaluate how proxy records from the IndonesianeAustralian monsoon region correspond to other records from the Indian and East Asian monsoon regions, as well as to El Niño-related proxy records, have been qualitative, relying on 'curve-fitting' methods. Here, we seek a quantitative approach for identifying coupling relationships between paleoclimate proxy records, employing statistical techniques to compute the interdependence of two paleoclimate time series. We verify the use of complex networks to identify coupling relationships between modern climate indices. This method is then extended to a set of paleoclimate proxy records from the Asian, Australasian and South American regions spanning the past 9000 years. The resulting networks demonstrate the existence of coupling relationships between regional monsoon systems on millennial time scales, but also highlight the transient nature of teleconnections during this period. In the context of the northwest Australian summer monsoon, we recognise a shift in coupling relationships from strong interhemispheric links with East Asian and ITCZrelated proxy records in the mid-Holocene to significantly weaker coupling in the later Holocene. Although the identified links cannot explain the underlying physical processes leading to coupling between regional monsoon systems, this method provides a step towards understanding the role that changes in teleconnections play in millennial-to orbital-scale climate variability.

Reconstructing Late Holocene North Atlantic atmospheric circulation changes using functional paleoclimate networks

“…As mentioned in the introduction, functional climate network analysis has recently become an established tool for studies on climate dynamics. Following upon the success of this approach, a few initial studies have transferred the corresponding idea to the analysis of spatial co-variability patterns among paleoclimate archives in a defined region (Rehfeld et al, 2013;McRobie et al, 2015;Oster and Kelley, 2016). Following upon these previous works, the general workflow of functional paleoclimate network analysis is visualized in Fig.…”

“…Beyond the viewpoint of time-independent or average spatial co-variability patterns, evolving functional networks are constructed from the available data covering different time windows and thus allow for studying the evolution of such spatial patterns in time. While evolving functional climate networks have become a widespread tool to analyze modern climate data (Donner et al, 2017;Radebach et al, 2013), applications to paleoclimate data sets have been much less common so far (Rehfeld et al, 2013;McRobie et al, 2015;Oster and Kelley, 2016).…”

Reconstructing Late Holocene North Atlantic atmospheric circulation changes using functional paleoclimate networks

“…Following upon the success of this approach, a few initial studies have transferred the corresponding idea to the analysis of spatial co-variability patterns among paleoclimate archives in a defined region (Rehfeld et al, 2013;McRobie et al, 2015;Oster and Kelley, 2016). Beyond the original framework, we aim here at studying the statistical interdependence structure between subsets of archives from different appropriately defined regions and relate the information inferred from this analysis to a macroscopic index tracing the dominating mode of interannual North Atlantic climate variability at multi-decadal timescales.…”

“…While evolving functional climate networks have become a widespread tool to analyse modern climate data Radebach et al, 2013), applications to paleoclimate data sets have been much less common so far (Rehfeld et al, 2013;McRobie et al, 2015;Oster and Kelley, 2016). Although such network representations rely on the (potentially questionable) assumption that the underlying spatio-temporally continuous climate system has been coarse-grained and represented in some meaningful way by the considered data sets, they take only the existing information into account and do not make any explicit statements on regions not covered by these data.…”

Counting forbidden patterns in irregularly sampled time series. II. Reliability in the presence of highly irregular sampling

“…This may be due to device failure, weather conditions, human error, the nature of the system (e.g., financial transactions data) or the measurement method (e.g., geological data), and other causes. For this study, we are motivated by geoscientific 1 and paleoclimate time series, [2][3][4] which are characterised by missing entries and large chronological gaps. Although there exist several types of irregular sampling, which can vary from rather mildly to highly unevenly spaced data, the majority of established techniques in time series analysis assume regular sampling.…”