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An in-depth investigation of global sea surface temperature behavior utilizing chaotic modeling

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Abstract

Sea surface temperature (SST), with its complex and dynamic behavior, is a major driver of ocean–atmosphere interactions. The purpose of this study is to investigate the behavior of SST and its prediction using a chaotic approach. Average mutual information (AMI) and Cao methods were used to reconstruct the phase space. The Lyapunov exponent and correlation dimension were used to investigate chaos. The Lyapunov exponent index was used to predict SST with a 5-year average prediction horizon using the local prediction method between 2023 and 2027. The results showed a 3-month delay time for the Pacific and Antarctic Oceans, and a 2-month delay time for the Atlantic, Indian, and Arctic Oceans. The optimal embedding dimension for all oceans is between 6 and 7. Our analysis reveals that the dynamics of SST in all oceans exhibit varying degrees of chaos, as indicated by the correlation dimension. The local prediction method achieves relatively accurate short-term SST predictions due to the clustering of SST points around specific attractors in the phase space. However, in the long term, the accuracy of this method decreases as the points in the phase space of SST can spread randomly. The model performance ranking with a Percent Mean Relative Absolute Error shows that the Indian Ocean has the best performance compared to other oceans, while the Atlantic, Pacific, and Antarctic and Arctic Oceans are in the next ranks. This study contributes to understanding the dynamics of SST and has practical value for use in the development of climate models.

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Data Availability

The PFV53 dataset is freely available for download from the NOAA National Oceanographic Data Center (NCEI) website: https://www.ncei.noaa.gov/access/search/dataset-search?text=SSOD. The codes used to process and analyze the PFV53 data for this study are available upon request from the authors. Please contact the corresponding author via email to request the codes.

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Acknowledgements

The authors acknowledge the NOAA (National Oceanic and Atmospheric Administration) science team for graciously providing the data sets utilized in this study. Additionally, we would like to express our sincere gratitude to Ferdowsi University of Mashhad for their generous financial support.

Funding

Ferdowsi University of Mashhad, Grant Number: FUM- 25143.

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Authors and Affiliations

  1. Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran

    Masoud Minaei & Muhammad Kamangar

  2. Geographic Information Science/System and Remote Sensing Laboratory (GISSRS: Lab), Ferdowsi University of Mashhad, Mashhad, Iran

    Masoud Minaei & Muhammad Kamangar

  3. Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA

    Philip K. Hopke

  4. Institute for a Sustainable Environment, Clarkson University, Potsdam, NY, 13699, USA

    Philip K. Hopke

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  1. Masoud Minaei
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  3. Muhammad Kamangar
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Contributions

Masoud Minaei: conceptualized, curated, and analyzed the data, formal analysis, and writing original draft. Philip K. Hopke: developed the methodology, project administration, validated the results, edited the manuscript, and supervision. Muhammad Kamangar: Data duration, investigation, acquired resources and software, visualized the data, and writing and editing the manuscript.

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Correspondence to Masoud Minaei.

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Minaei, M., Hopke, P.K. & Kamangar, M. An in-depth investigation of global sea surface temperature behavior utilizing chaotic modeling. Environ Sci Pollut Res 31, 39823–39838 (2024). https://doi.org/10.1007/s11356-024-33790-0

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