Abstract
Sea surface temperature (SST) patterns can – as surface climate forcing – affect weather and climate at large distances. One example is El Niño-Southern Oscillation (ENSO) that causes climate anomalies around the globe via teleconnections. Although several studies identified and characterized these teleconnections, our understanding of climate processes remains incomplete, since interactions and feedbacks are typically exhibited at unique or multiple temporal and spatial scales. This study characterizes the interactions between the cells of a global SST data set at different temporal and spatial scales using climate networks. These networks are constructed using wavelet multi-scale correlation that investigate the correlation between the SST time series at a range of scales allowing instantaneously deeper insights into the correlation patterns compared to traditional methods like empirical orthogonal functions or classical correlation analysis. This allows us to identify and visualise regions of – at a certain timescale – similarly evolving SSTs and distinguish them from those with long-range teleconnections to other ocean regions. Our findings re-confirm accepted knowledge about known highly linked SST patterns like ENSO and the Pacific Decadal Oscillation, but also suggest new insights into the characteristics and origins of long-range teleconnections like the connection between ENSO and Indian Ocean Dipole.
Highlights
The ocean covers more than two-thirds of the Earth’s surface
Among the most well-known teleconnections are a number of defined patterns that are marked by a strong correlation of the sea surface temperature (SST) at different places like the Pacific Decadal Oscillation (PDO) and the North Atlantic Oscillation (NAO), and the link between El Niño Southern Oscillation (ENSO) and the Asian monsoon system[7,9,10,11,12]
We present the results in three parts, first discussing the scale-specific spatial patterns obtained over different timescales, second investigating the occurrence of short- and long-range linkages between the identified scale-specific spatial patterns over different timescales, and providing a 3-D global visualization of the link distributions
Summary
The ocean covers more than two-thirds of the Earth’s surface. Given the higher heat capacity of water as compared to air (by approximately a factor of 4) and greater mass in the ocean than in the atmosphere, the ocean can store about 1000 times as much heat as the atmosphere[1]. Large-scale atmospheric circulation patterns (e.g., quasi-stationary Rossby waves3) form atmospheric linkages between different oceanic regions around the globe and control the spatial extent of the interactions among oceans[4] These long-distance interactions, referred to as teleconnections, are marked by a significant correlation of the climatological variables. It is important to quantify the spatial distance of teleconnection variability between remote ocean regions at different timescales which could provide a quantitative understanding of short-range and long-range coupling between different oceanic regions To uncover such spatial and temporal variable interactions we use the complex network approach[15], which recently has emerged as a powerful framework in extracting information from large high-dimensional datasets[16,17]. Several network measures (such as degree, clustering, betweenness, community structures) have been used on the resultant climate network to capture local and global dependent climatic patterns within and among climate variables[23,27,28,29], like global patterns of extreme rainfall teleconnections[14] and spatial diversity of Indian rainfall teleconnections[30]
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