Preprints
https://doi.org/10.5194/nhess-2023-218
https://doi.org/10.5194/nhess-2023-218
29 Jan 2024
 | 29 Jan 2024
Status: a revised version of this preprint was accepted for the journal NHESS and is expected to appear here in due course.

Revisiting regression methods for estimating long-term trends in sea surface temperature

Ming-Huei Chang, Yen-Chen Huang, Yu-Hsin Cheng, Chuen-Teyr Terng, Jinyi Chen, and Jyh Cherng Jan

Abstract. Global warming has enduring consequences in the ocean, leading to increased sea surface temperatures (SSTs) and subsequent environmental impacts, including coral bleaching and intensified tropical storms. It is imperative to monitor these trends to enable informed decision-making and adaptation. In this study, we comprehensively examine commonly used methods for extracting long-term temperature trends, including the seasonal-trend decomposition procedure based on loess (STL) and the linear regression family, which comprises ordinary least square regression (OLSR), orthogonal regression (OR), and geometric mean regression (GMR). The applicability and limitations of these methods are assessed based on experimental and simulated data. STL stands out as the most accurate method for extracting long-term trends. However, it is associated with a notably sizeable computational time. In contrast, linear regression methods are far more efficient. Among these methods, GMR is not suitable due to its inherent assumption of a random temporal component. OLSR and OR are preferable for general tasks but require correction to accurately account for seasonal signal-induced bias resulting from the phase-distance imbalance. We observe that this bias can be effectively addressed by trimming the SST data to ensure that the time series becomes an even function before applying linear regression. We introduced and evaluated an implementation using both simulated and realistic data.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Ming-Huei Chang, Yen-Chen Huang, Yu-Hsin Cheng, Chuen-Teyr Terng, Jinyi Chen, and Jyh Cherng Jan

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2023-218', Anonymous Referee #1, 30 Jan 2024
    • AC1: 'Reply on RC1', Ming-Huei Chang, 08 Apr 2024
  • RC2: 'Comment on nhess-2023-218', Anonymous Referee #2, 10 Mar 2024
    • AC2: 'Reply on RC2', Ming-Huei Chang, 08 Apr 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2023-218', Anonymous Referee #1, 30 Jan 2024
    • AC1: 'Reply on RC1', Ming-Huei Chang, 08 Apr 2024
  • RC2: 'Comment on nhess-2023-218', Anonymous Referee #2, 10 Mar 2024
    • AC2: 'Reply on RC2', Ming-Huei Chang, 08 Apr 2024
Ming-Huei Chang, Yen-Chen Huang, Yu-Hsin Cheng, Chuen-Teyr Terng, Jinyi Chen, and Jyh Cherng Jan
Ming-Huei Chang, Yen-Chen Huang, Yu-Hsin Cheng, Chuen-Teyr Terng, Jinyi Chen, and Jyh Cherng Jan

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Short summary
Monitoring the long-term trends in sea surface warming is crucial for informed decision-making and adaptation. This study offers a comprehensive examination of prevalent trend extraction methods. We identify ordinary least squares as suitable for general tasks yet highlight the need to address seasonal signal-induced bias, specifically the phase-distance imbalance. Our implementation, evaluated with simulated and realist data, provides a potential solution.
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