ausblenden:
Schlagwörter:
Network analysis; Climatology; Time series analysis; Data mining
Zusammenfassung:
The identification of regions of similar climatological behavior can be utilized for the discovery of spatial relationships over long-range scales, including teleconnections. Additionally, it provides insights for the improvement of corresponding interaction processes in general circulation models. In this regard, the global picture of the interdependence patterns of extreme-rainfall events (EREs) still needs to be further explored. To this end, we propose a top-down complex-network-based clustering workflow, with the combination of consensus clustering and mutual correspondences. Consensus clustering provides a reliable community structure under each dataset, while mutual correspondences build a matching relationship between different community structures obtained from different datasets. This approach ensures the robustness of the identified structures when multiple datasets are available. By applying it simultaneously to two satellite-derived precipitation datasets, we identify consistent synchronized structures of EREs around the globe, during boreal summer. Two of them show independent spatiotemporal characteristics, uncovering the primary compositions of different monsoon systems. They explicitly manifest the primary intraseasonal variability in the context of the global monsoon, in particular, the “monsoon jump” over both East Asia and West Africa and the mid-summer drought over Central America and southern Mexico. Through a case study related to the Asian summer monsoon, we verify that the intraseasonal changes of upper-level atmospheric conditions are preserved by significant connections within the global synchronization structure. Our work advances network-based clustering methodology for (i) decoding the spatiotemporal configuration of interdependence patterns of natural variability and for (ii) the intercomparison of these patterns, especially regarding their spatial distributions over different datasets.
Precipitation variability of monsoons affects over two-thirds of the world’s population, and regional monsoons have their own characteristics due to specific land–ocean and topographic conditions.1 In spite of being distributed in different continental regions, they are essentially driven and synchronized by the annual cycle of solar radiation. The connections between them are via the global divergent circulation characterized by global-scale persistent overturning of the atmosphere varying with time.2,3 An integration of these monsoons forms the concept of the global monsoon in terms of similar dynamics and behaviors. The synchronization in the context of the global monsoon is not limited to the tropical regions, but also extends to the subtropics, with the East Asian monsoon being a typical example. Therefore, identifying the synchronization structure on a global scale helps to understand the interaction with mid-latitude regions. A clustering workflow with higher robustness, by combining consensus clustering and mutual correspondences, is proposed for this purpose.
