Earth's climate is a compelling example of complex systems, with increasing interest in deciphering its underlying dynamics to improve weather forecasts and address climate change. This thesis represents a foundational step towards demystifying the intricate dynamics of Earth's climate system. It addresses the inverse problem of reconstructing a functional representation of this system through a rigorous and consistent Bayesian approach. The study analyzes network ensembles of daily temperature anomalies from 2664 global locations since 1970, uncovering significant changes in the network's structure, particularly after the early 2000s. These changes include a reduction in network connections and a rise in nodes with higher connectivity, notably in climatically important regions like the Antarctic and the Amazon Rainforest. This methodology not only illuminates the dynamic nature of the climate network but also deepens our understanding of its interconnectedness. The research introduces a novel and robust approach, proposing a method to increase our comprehension of the climate system's complexity.
Il clima terrestre, sistema complesso per eccellenza, è sempre più oggetto di studi per migliorare le previsioni meteorologiche e per comprendere e contrastare il cambiamento climatico. Questa tesi costituisce un passo essenziale verso la decifrazione delle complesse dinamiche del sistema climatico della Terra. Affronta il problema inverso di ricostruire una rappresentazione funzionale di questo sistema attraverso un metodo bayesiano rigoroso e sistematico. La ricerca analizza ensamble di reti basati su anomalie di temperature giornaliere registrate in 2664 località in tutto il mondo a partire dal 1970, evidenziando cambiamenti sostanziali nella struttura della rete, soprattutto dopo i primi anni 2000. Questi cambiamenti si manifestano con una diminuzione delle connessioni di rete e un incremento di nodi ad alta connettività, in particolare in aree climaticamente critiche come l'Antartide e la Foresta Amazzonica. Questo metodo non solo mette in luce la natura dinamica della rete climatica, ma arricchisce anche la nostra comprensione della sua interdipendenza. Il lavoro propone un approccio innovativo e solido, aprendo la strada a una maggiore comprensione della complessità del sistema climatico.
Ricostruzione delle dinamiche temporali in networks climatici: un approccio di “ensemble”
GAMBA, LORENZO
2022/2023
Abstract
Earth's climate is a compelling example of complex systems, with increasing interest in deciphering its underlying dynamics to improve weather forecasts and address climate change. This thesis represents a foundational step towards demystifying the intricate dynamics of Earth's climate system. It addresses the inverse problem of reconstructing a functional representation of this system through a rigorous and consistent Bayesian approach. The study analyzes network ensembles of daily temperature anomalies from 2664 global locations since 1970, uncovering significant changes in the network's structure, particularly after the early 2000s. These changes include a reduction in network connections and a rise in nodes with higher connectivity, notably in climatically important regions like the Antarctic and the Amazon Rainforest. This methodology not only illuminates the dynamic nature of the climate network but also deepens our understanding of its interconnectedness. The research introduces a novel and robust approach, proposing a method to increase our comprehension of the climate system's complexity.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12608/60993