Detecting transitions and quantifying differences in two SST datasets using spatial permutation entropy

Gancio, Juan; Tirabassi, Giulio; Masoller, Cristina; Barreiro, Marcelo

doi:10.5194/esd-17-533-2026

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.

https://doi.org/10.5194/esd-17-533-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 3

Research article

|

12 May 2026

Research article |

| 12 May 2026

Detecting transitions and quantifying differences in two SST datasets using spatial permutation entropy

Juan Gancio, Giulio Tirabassi, Cristina Masoller, and Marcelo Barreiro

Data sets

ERA5 monthly averaged data on single levels from 1940 to present H. Hersbach et al. https://doi.org/10.24381/cds.f17050d7

Model code and software

juangancio/climate-spatial-analysis: Supporting code for EDS submission: "Detecting transitions and quantifying differences in two SST datasets using spatial permutation entropy" (v2.0.0) Juan Gancio https://doi.org/10.5281/zenodo.17250157

Analysis Code Juan Gancio https://github.com/juangancio/climate-spatial-analysis

Video supplement

Supplemental videos for ESD article "Detecting transitions and quantifying differences in two SST datasets using spatial permutation entropy" Juan Gancio https://doi.org/10.5281/zenodo.19051869

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.