Global wildfire synchronicity patterns as revealed by complex network analysis

In light of the ongoing global climate change, a better understanding of global wildfire activity is the key to anticipate future impacts and minimize, as much as possible, their negative consequences on natural ecosystems and human economy. In this work we present a global-scale analysis of the inter-annual synchronicity of wildfires using two types of probabilistic networks, able to capture the underlying structures in the burned area data from two different approaches: correlation-based and bayesian networks. By studying their properties through centrality measures and community detection for the complex network, and inference from the Bayesian network, we seek to gain a better understanding of the interrelationships between fire activity in different regions of the planet, highlighting the most important teleconnection patterns. We expect this exploratory analysis to aid in the development and testing of plausible hypotheses about the underlying mechanisms supporting these relationships.