Distinguishing the effects of internal and forced atmospheric variability in climate networks

• Published in: Nonlinear Processes in Geophysics
• Main Authors: Deza, Juan Ignacio, Masoller Alonso, Cristina, Barreiro, Marcelo
• Other Authors: Universitat Politècnica de Catalunya. Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya. DONLL - Dinàmica no Lineal, Òptica no Lineal i Làsers
• Format: Article in Journal/Newspaper
• Language: English
• Published: University of Thechnology and Agriculture Nicholas Copernicus University 2014
• Subjects: Àrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia; Climatic changes; Annual variation; Atmospheric dynamics; Climate forcing; Climate prediction; Climate variation; El Nino-Southern Oscillation; Ensemble forecasting; Network analysis; North Atlantic Oscillation; Sea surface temperature; Seasonal variation; Teleconnection; Timescale; Canvis climàtics; Climatologia; Analisi de xarxes (Planificació); North Atlantic; North Atlantic oscillation
• Online Access: http://hdl.handle.net/2117/26768; https://doi.org/10.5194/npg-21-617-2014

The fact that the climate on the earth is a highly complex dynamical system is well-known. In the last few decades great deal of effort has been focused on understanding how climate phenomena in one geographical region affects the climate of other regions. Complex networks are a powerful framework for identifying climate interdependencies. To further exploit the knowledge of the links uncovered via the network analysis (for, e.g., improvements in prediction), a good understanding of the physical mechanisms underlying these links is required. Here we focus on understanding the role of atmospheric variability, and construct climate networks representing internal and forced variability using the output of an ensemble of AGCM runs. A main strength of our work is that we construct the networks using MIOP (mutual information computed from ordinal patterns), which allows the separation of intraseasonal, intra-annual and interannual timescales. This gives further insight to the analysis of climatological data. The connectivity of these networks allows us to assess the influence of two main indices, NINO3.4-one of the indices used to describe ENSO (El Niño-Southern oscillation)-and of the North Atlantic Oscillation (NAO), by calculating the networks from time series where these indices were linearly removed. A main result of our analysis is that the connectivity of the forced variability network is heavily affected by Peer Reviewed Postprint (published version)