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Advisor(s)
Abstract(s)
Global warming and the associated climate changes are being the subject of intensive
research due to their major impact on social, economic and health aspects of the human
life. Surface temperature time-series characterise Earth as a slow dynamics spatiotemporal
system, evidencing long memory behaviour, typical of fractional order systems. Such phenomena
are difficult to model and analyse, demanding for alternative approaches. This
paper studies the complex correlations between global temperature time-series using
the Multidimensional scaling (MDS) approach. MDS provides a graphical representation
of the pattern of climatic similarities between regions around the globe. The similarities
are quantified through two mathematical indices that correlate the monthly average temperatures
observed in meteorological stations, over a given period of time. Furthermore,
time dynamics is analysed by performing the MDS analysis over slices sampling the time
series. MDS generates maps describing the stations’ locus in the perspective that, if they
are perceived to be similar to each other, then they are placed on the map forming clusters.
We show that MDS provides an intuitive and useful visual representation of the complex
relationships that are present among temperature time-series, which are not perceived
on traditional geographic maps. Moreover, MDS avoids sensitivity to the irregular distribution
density of the meteorological stations.
Description
Keywords
Complex systems Temperature time-series Multidimensional scaling Global warming
Citation
Publisher
Elsevier