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Spatio-temporal evolution of global surface temperature distributions

Authors:

Federico Amato,

Fabian Guignard,

Vincent Humphrey,

Mikhail KanevskiAuthors Info & Claims

CI2020: Proceedings of the 10th International Conference on Climate Informatics

Pages 37 - 43

https://doi.org/10.1145/3429309.3429315

Published: 11 January 2021 Publication History

Abstract

Climate is known for being characterised by strong non-linearity and chaotic behaviour. Nevertheless, few studies in climate science adopt statistical methods specifically designed for non-stationary or non-linear systems. Here we show how the use of statistical methods from Information Theory can describe the non-stationary behaviour of climate fields, unveiling spatial and temporal patterns that may otherwise be difficult to recognize. We study the maximum temperature at two meters above ground using the NCEP CDAS1 daily reanalysis data, with a spatial resolution of 2.5° by 2.5° and covering the time period from 1 January 1948 to 30 November 2018. The spatial and temporal evolution of the temperature time series are retrieved using the Fisher Information Measure, which quantifies the information in a signal, and the Shannon Entropy Power, which is a measure of its uncertainty — or unpredictability. The results describe the temporal behaviour of the analysed variable. Our findings suggest that tropical and temperate zones are now characterized by higher levels of entropy. Finally, Fisher-Shannon Complexity is introduced and applied to study the evolution of the daily maximum surface temperature distributions.

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Cited By

Del Giudice FManganelli BDe Paola PTajani FAmato F(2024)An Analysis of the Airbnb Market: A Detailed Look at Four Italian CitiesComputational Science and Its Applications – ICCSA 2024 Workshops10.1007/978-3-031-65318-6_4(49-65)Online publication date: 26-Jul-2024
https://doi.org/10.1007/978-3-031-65318-6_4
Amato FGuignard FWalch AMohajeri NScartezzini JKanevski M(2022)Spatio-temporal estimation of wind speed and wind power using extreme learning machines: predictions, uncertainty and technical potentialStochastic Environmental Research and Risk Assessment10.1007/s00477-022-02219-w36:8(2049-2069)Online publication date: 12-Jul-2022
https://doi.org/10.1007/s00477-022-02219-w
Guignard FGuignard F(2022)Fisher-Shannon AnalysisOn Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory10.1007/978-3-030-95231-0_4(55-79)Online publication date: 13-Mar-2022
https://doi.org/10.1007/978-3-030-95231-0_4
Show More Cited By

Spatio-temporal evolution of global surface temperature distributions
1. Applied computing
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2. Computing methodologies

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CI2020: Proceedings of the 10th International Conference on Climate Informatics

September 2020

138 pages

ISBN:9781450388481

DOI:10.1145/3429309

Copyright © 2020 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 11 January 2021

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Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

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CI2020

CI2020: 10th International Conference on Climate Informatics

September 22 - 25, 2020

virtual, United Kingdom

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Cited By

Del Giudice FManganelli BDe Paola PTajani FAmato F(2024)An Analysis of the Airbnb Market: A Detailed Look at Four Italian CitiesComputational Science and Its Applications – ICCSA 2024 Workshops10.1007/978-3-031-65318-6_4(49-65)Online publication date: 26-Jul-2024
https://doi.org/10.1007/978-3-031-65318-6_4
Amato FGuignard FWalch AMohajeri NScartezzini JKanevski M(2022)Spatio-temporal estimation of wind speed and wind power using extreme learning machines: predictions, uncertainty and technical potentialStochastic Environmental Research and Risk Assessment10.1007/s00477-022-02219-w36:8(2049-2069)Online publication date: 12-Jul-2022
https://doi.org/10.1007/s00477-022-02219-w
Guignard FGuignard F(2022)Fisher-Shannon AnalysisOn Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory10.1007/978-3-030-95231-0_4(55-79)Online publication date: 13-Mar-2022
https://doi.org/10.1007/978-3-030-95231-0_4
Guignard FGuignard F(2022)IntroductionOn Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory10.1007/978-3-030-95231-0_1(1-15)Online publication date: 13-Mar-2022
https://doi.org/10.1007/978-3-030-95231-0_1

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