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

doi:10.5194/esd-17-533-2026

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

2026-05-12

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DOI：10.5194/esd-17-533-2026

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Abstract. Weather prediction systems rely on the vast amounts of data continuously generated by Earth modeling and monitoring systems; and efficient data analysis techniques are needed to track changes and compare datasets. Here we show that a nonlinear quantifier; the spatial permutation entropy (SPE); is useful to characterize spatio-temporal complex data; allowing detailed analysis at different scales. Specifically; we use SPE to analyze ERA5 and NOAA OI v2 sea surface temperature (SST) anomalies in two key regions; Niño 3.4 and Gulf Stream. We perform a quantitative comparison of these two SST products and find that SPE detects differences at short spatial scales (<1°). We also identify several transitions; including a transition that occurs in 2007 when ERA5 changed its sea–surface boundary condition to OSTIA; in 2013 when OSTIA updated the background error covariances; and in 2021 when NOAA SST changed satellite; from MeteOp-A to MeteOp-C. The robustness and statistical significance of the detected transitions are tested using surrogate data. We demonstrate that; using standard distance and cross-correlation analyses; the transitions are not detected with the same level of statistical significance and robustness as when using ordinal analysis.

Keywords:

spatial permutation entropy

sea surface temperature

data analysis

spatio-temporal complexity

transition detection

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

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