The LlNC Project

Improving our understanding of the Earth´s complex climate phenomena, such as El Niño-Southern Oscillation (ENSO), has a huge economic and social impact for present and future generations, and can underpin advances in areas as diverse as energy, environment, agricultural and marine sciences. Given the complexity of the inter-relations between the subsystems that constitute our climate, it is important to approach the problem from an interdisciplinary perspective. However,there is a great shortage of qualified workforce to perform this task and a major challenge is the education and training of young qualified researchers that can approach climate phenomena from a complex systems point of view. 

This requires knowledge from several fields such as physics, dynamical systems theory and computer science, and also requires a detailed understanding of Earth sciences such as meteorology and oceanography. There is also a clear need for improving the coordination and cooperation of the research teams working on these issues.

Marie-Curie Initial Training Network (ITN) 

LINC Coordinator: Prof. Cristina Masoller, Universitat Politècnica de Catalunya, 08222, Barcelona, Spain

This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 289447   


WP1: Network Construction and Analysis

We intend to improve and develop techniques for the reconstruction of complex networks from multidimensional climatological data. For this there are important methodological problems to solve because observations from the Earth system are typically rather short, noisy, and far from being stationary.

WP2: Interacting Networks

We propose to develop a framework for studying interacting networks. It will consist of various well- defined models of network interactions, new tools for characterisation and classification of coupled networks, simulation and numerical methods, as well as analytical tools based on percolation theory and generating functions for studying the robustness of interacting networks.

WP3: Natural Climate Variability

We aim to establish a theory that connects changes in network properties to specific physical processes of climate variability on interannual-to-interdecadal time scales. An important issue in this part of LINC will therefore be to connect network characteristics and measures (e.g. closeness centrality, betweenness centrality) to specific physical processes, which provides coupling between WP3 and WP1). 

WP4: Future Climate Change

In LINC we will use the huge CMIP5 data base together with stand-alone atmospheric GCMs to study mechanisms of atmospheric teleconnection and climate variability on inter-annual to inter-decadal time scales. 

WP5: Tipping Points in the Climate System

An important topic in which the innovative introduction of network thinking in climatology can have important rewards is in the characterization of regime transitions in the climate systems. Methodologies to detect regime shifts in past time series have been developed, both in the climatic context and beyond.