Articles | Volume 17, issue 3
https://doi.org/10.5194/esd-17-533-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-17-533-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 May 2026
Research article |
| 12 May 2026
| 12 May 2026
Detecting transitions and quantifying differences in two SST datasets using spatial permutation entropy
Departament de Física, Universitat Politècnica de Catalunya, Rambla Sant Nebridi 22, Terrassa 08222, Barcelona, Spain
Giulio Tirabassi
Departament de Informàtica, Matemàtica Aplicada i Estadística, Universitat de Girona, Carrer de la Universitat de Girona 6, Girona 17003, Spain
Cristina Masoller
Departament de Física, Universitat Politècnica de Catalunya, Rambla Sant Nebridi 22, Terrassa 08222, Barcelona, Spain
Marcelo Barreiro
Departamento de Ciencias de la atmósfera y Física de los Océanos, Facultad de Ciencias, Universidad de la República, Montevideo, 11400, Uruguay
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Short summary
In this work, we apply a novel quantifier, the spatial permutation entropy, to sea surface temperatures obtained from two commonly used products: ERA5 and NOAA OI v2 (NOAA Optimal Interpolation version 2). We report small scale differences between these products, as well as persistent trends at the large scale, which could be a consequence of global warming. We also report sudden changes that were not uncovered before, which correlate with different changes in the methodology or data sources of the products.
In this work, we apply a novel quantifier, the spatial permutation entropy, to sea surface...
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