| Summary: | Tese de mestrado, Ciências Geofísicas (Meteorologia e Oceanografia), Universidade de Lisboa, Faculdade de Ciências, 2021 The climate system is a dynamic system characterized by its high complexity and dimensionality, governed by non-linear interactions. Over the last few decades, the redefinition of the system’s dimensionality and the extraction of co-related patterns from measurements of atmospheric variables, present challenging tasks. An example is the monthly variability of the pressure field at sea level (SLP) which allows for evidence of certain weather time patterns, characterized by some recurrence (or repetition) and temporal persistence. In this regard, the positive and negative phases of the North Atlantic Oscillation, shows the multimodality of the field’s joint probability distribution. This multivariate variability, whether forced or internally induced, can be separated, using certain multivariate statistical techniques into scalar random components, with as much statistical independence as possible, called independent components. In case of fields having multivariate Gaussian distributions, Independent Component Analysis (ICA) corresponds to Principal Component Analysis (PCA), which is then performed sub-optimally in the non-Gaussian case as it occurs with the geopotential field at the sea level.In this dissertation we intend to apply the SLP field research carried out by NCAR (National Center for Atmospheric Research) reanalysis, wich was provided in a regular grid with a resolution of 2,5º x 2,5º (latitude, longitude) covering the period between January 1948 to December 2017 (70 years). In this dissertation one aims to apply the ICA to the SLP field, obtained from the NCAR reanalysis, thus contributing to the search for independent components within the atmospheric and oceanic phenomena, so that it is possible to deepen the understanding of the fundamental processes behind its variability.As such, a geographic analysis covering the North Atlantic (Atl) and Euro-Atlantic (Eur-Atl)areas was ...
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