Climate Dynamics: A Network-Based Approach for the Analysis of Global Precipitation

Abstract

Precipitation is one of the most important meteorological variables for defining the climate dynamics, but the spatial patterns of precipitation have not been fully investigated yet. The complex network theory, which provides a robust tool to investigate the statistical interdependence of many interacting elements, is used here to analyze the spatial dynamics of annual precipitation over seventy years (1941–2010). The precipitation network is built associating a node to a geographical region, which has a temporal distribution of precipitation, and identifying possible links among nodes through the correlation function.The precipitation network reveals significant spatial variability with barely connected regions, as Eastern China and Japan, and highly connected regions, such as the African Sahel, Eastern Australia and, to a lesser extent, Northern Europe. Sahel and Eastern Australia are remarkably dry regions, where low amounts of rainfall are uniformly distributed on continental scales and small-scale extreme events are rare. As a consequence, the precipitation gradient is low, making these regions well connected on a large spatial scale. On the contrary, the Asiatic South-East is often reached by extreme events such as monsoons, tropical cyclones and heat waves, which can all contribute to reduce the correlation to the short-range scale only.Some patterns emerging between mid-latitude and tropical regions suggest a possible impact of the propagation of planetary waves on precipitation at a global scale. Other links can be qualitatively associated to the atmospheric and oceanic circulation. To analyze the sensitivity of the network to the physical closeness of the nodes, short-term connections are broken. The African Sahel, Eastern Australia and Northern Europe regions again appear as the supernodes of the network, confirming furthermore their long-range connection structure. Almost all North-American and Asian nodes vanish, revealing that extreme events can enhance high precipitation gradients, leading to a systematic absence of long-range patterns.

Complex networks for streamflow dynamics

“…Since then, the science of networks has found applications in many different fields, including natural and physical sciences, social sciences, medical sciences, economics, and engineering and technology (e.g., Albert et al, 1999;Bouchaud and Mézard, 2000;Newman, 2001;Liljeros et al, 2001;Tsonis and Roebber, 2004;Davis et al, 2013). In hydrology, applications of networks are just starting to emerge, and so far include river networks, virtual water trade, precipitation, and agricultural pollution due to international trade, among others (Rinaldo et al, 2006;Suweis et al, 2011;Dalin et al, 2012;Boers et al, 2013;Scarsoglio et al, 2013). In a very recent study, Sivakumar (2014) has argued that networks can be useful for studying all types of connections in hydrology and, hence, can provide a generic theory for hydrology.…”

Advancing climate science with knowledge-discovery through data mining

“…The edges or links between the nodes are bidirectional (undirected), commonly do not carry information about the weight of the links (binary), and are inferred using simultaneous linear or non-linear similarity measures such as Pearson correlation, mutual information, or phase synchronization. 27,29,39,40 Often, two nodes that are not linked according to the chosen criterion have their correlations deleted or pruned. In the case of climate fields, however, cell-level pruning can cause loss of robustness in the network inference, and methods that adopt pruning should not be used for intercomparison studies.…”

Temporal dynamics of streamflow: application of complex networks

“…Watts and Strogatz 1998;Jeong et al 2000;Newman 2001;Newman et al 2001;Tsonis and Roebber 2004;Suweis et al 2011;Scarsoglio et al 2013;Woldemeskel 2014, 2015;Halverson and Fleming 2015), suggesting that such networks are not classical random networks, but may be small-world networks or scale-free networks or some other types.…”

“…Applications of the concepts of complex networks in hydrology have been gaining momentum in the last few years. Thus far, they have included studies of river networks (Rinaldo et al 2006;Zaliapin et al 2010;Foufoula-Georgiou 2014, 2015;Rinaldo et al 2014), rainfall monitoring networks (Malik et al 2012;Boers et al 2013;Scarsoglio et al 2013;Sivakumar and Woldemeskel 2015;Jha et al 2015;Jha and Sivakumar 2017;Naufan et al 2017), and streamflow monitoring networks (Tang et al 2010;Sivakumar and Woldemeskel 2014;Halverson and Fleming 2015;Braga et al 2016;Serinaldi and Kilsby 2016;Fang et al 2017). Such studies have employed different methods, including degree centrality, clustering coefficient, degree distribution, closeness centrality, shortest path length, and community structure.…”