Abstract
We analyze large-scale interdependencies between sea surface temperature (SST) and rainfall variability. We propose a novel climate network construction scheme which we call teleconnection climate networks (TCN). On account of this analysis, gridded SST and rainfall data sets are coarse grained by merging grid points that are dynamically similar to each other. The resulting clusters of time series are taken as the nodes of the TCN. The SST and rainfall systems are investigated as two separate climate networks, and teleconnections within the individual climate networks are studied with special focus on dipolar patterns. Our analysis reveals a pronounced rainfall dipole between Southeast Asia and the Afghanistan-Pakistan region, and we discuss the influences of Pacific SST anomalies on this dipole.
Keywords
- Clustering
- Precipitation dipole
- Teleconnections
- Complex networks
- Time series analysis
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Notes
- 1.
Due to the short length of time series we obtain the twin surrogates without embedding.
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Acknowledgements
Funded by DFG, project Investigation of past and present climate dynamics and its stability by means of a spatio-temporal analysis of climate data using complex networks (MA 4759/4-1). Further support by DFG/FAPESP IRTG 1740/TRP 2011/50151-0.
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Rheinwalt, A. et al. (2015). Teleconnections in Climate Networks: A Network-of-Networks Approach to Investigate the Influence of Sea Surface Temperature Variability on Monsoon Systems. In: Lakshmanan, V., Gilleland, E., McGovern, A., Tingley, M. (eds) Machine Learning and Data Mining Approaches to Climate Science. Springer, Cham. https://doi.org/10.1007/978-3-319-17220-0_3
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DOI: https://doi.org/10.1007/978-3-319-17220-0_3
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