the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Sea level reconstruction reveals improved separations of regional climate and trend patterns over the last seven decades
Abstract. Rapidly rising sea level is one of the major adverse consequences of anthropogenic climate change. Sea level rise poses an existential threat to coastal populations, particularly for urban settlements with accelerating growth rates. Contemporary empirical sea level reconstructions have been used to conflate short-term (~3 decades) satellite altimetry geocentric sea level data and long-term (50 years or longer) tide gauge records to better estimate reliable sea level rise towards multi-decadal to centennial time scales. However, adequate separations and quantifications of low-frequency climate patterns and sea level trends globally at regional scales remain elusive. Here, we propose a new sea level reconstruction framework that incorporates Empirical Orthogonal Function (EOF) into the contemporary Cyclostationary EOF with Reduced Space Optimal Interpolation (CSEOF-OI) algorithm to better reconstruct sea level fields. Using 225 selected long-term gap-filled tide gauge records with vertical land motion adjusted and satellite altimetry, our global reconstructed monthly sea level time series, January 1950– January 2022, exhibit distinct delineations between modeled climate patterns and sea level trends at regional scales. The separated sea level patterns include trends, modulated annual cycles, the El Niño Southern Oscillation (ENSO), and the Pacific Decadal Oscillation (PDO). The third principal component of the reconstructed sea level exhibits a Pearson correlation coefficient of 0.87 with the Niño 3.4 ENSO index, and the fourth principal component correlates at 0.75 with the PDO index, indicating excellent agreement. The global mean sea level trend, accounting for the predominant climate periodicities, is 1.9 ± 0.2 mm yr⁻¹ (95 % confidence), and the estimate during the satellite altimetry era (January 1993–December 2021) is 3.2 ± 0.3 mm yr⁻¹ (95 % confidence). Compared with previous studies, we conclude that our 72-year sea-level reconstruction allows us to better separate the ENSO and PDO climate patterns, as well as the sea level they induced. Finally, we show that the short-term (5-year) rates of ENSO and PDO patterns significantly affect sea level both on a global and regional scale, altering global mean sea level trends by up to 1.1 ± 0.5 mm yr¹ (January 2011–January 2016). Over the past seven decades, the climate pattens exerted a minor impact on sea level trends, but substantially modulated apparent regional sea level accelerations, particularly in the western Pacific (e.g., 0.09 ± 0.05 mm yr⁻² at the Kuroshio Current), and in the east and central equatorial Pacific Ocean (e.g., −0.04 ± 0.03 mm yr⁻² near Costa Rica). The reconstructed sea level and analysis results datasets are available at https://doi.org/10.5281/zenodo.15288817 (Wang, 2025).
- Preprint
(33773 KB) - Metadata XML
-
Supplement
(31767 KB) - BibTeX
- EndNote
Status: open (until 11 Jul 2025)
-
RC1: 'Comment on essd-2025-251', Anonymous Referee #1, 24 Jun 2025
reply
The manuscript presents a global sea level reconstruction spanning 1950–2022 at a 1° × 1° resolution, using a combined dataset of tide gauge records (corrected for vertical land motion) and satellite altimetry, implemented through an enhanced CSEOF-OI framework incorporating EOF decomposition. The authors emphasize improved separation of climate modes (e.g., ENSO, PDO) and long-term trends.
While the study contributes to an important topic and provides a potentially valuable dataset to the sea level and climate research community, several key aspects of the methodology and validation require further clarification. In particular, the validation appears limited to coarser 5° × 5° spatial scales, and insufficient attention is given to the accuracy of reconstructed data in the pre-altimetry era (especially before 1993) and in open-ocean regions lacking tide gauge constraints. These issues, along with others detailed below, should be addressed to ensure the robustness and usability of the dataset.
- The manuscript reports strong correlations (r = 0.87 and r = 0.75) between reconstructed principal components and ENSO/PDO indices. However, the interpretation as “excellent agreement” should be moderated, and the authors should clarify whether statistical significance tests were performed and how preprocessing (e.g., detrending) was handled.
- In Fig. 1(a), how many tide gauge stations are included in total? Since the data record lengths vary across stations, the authors should also clarify the proportion of stations within each time span category as shown in the figure. In Fig. 1(b), the authors present the percentage distribution of tide gauge records covering the 1950–2022 period. However, it would be helpful to specify the actual number of stations corresponding to each percentage category.
- The manuscript refers to the data time span as both “January 1950 to December 2021” and “January 1950 to January 2022” in different sections. The authors should ensure consistency and clarify the exact temporal coverage of the reconstructed dataset.
- Lines 141–142 refer to Figs. S1–S3, and line 165 mentions Fig. S4. However, these figures are not provided in the manuscript. The authors should ensure that all referenced supplementary figures are included at the end of the paper.
- The manuscript states that missing data in tide gauge records were filled using AR, PPCA, and EM methods, with EM identified as the optimal approach based on validation experiments. However, it is unclear how the authors handled records with substantial data gaps at the beginning or end of the time series. What was the quality of the gap-filling in such cases? As Fig. 1(b) indicates, some stations have more than 50% missing data — were these records also gap-filled and subsequently used in the sea level reconstruction? Further clarification is needed regarding the treatment and reliability of heavily gapped records.
- The sentence “GIA models have been extensively used to harmonize measurements between altimetry and tide gauge sea level” is misleading. GIA corrections are primarily used to account for long-term vertical land motion, not to harmonize the two types of measurements. The authors should revise this statement to more accurately reflect the role of GIA models.
- The manuscript discusses the limitations of GIA models and emphasizes that GIA is not the sole contributor to vertical land motion at tide gauge locations. However, it remains unclear how GIA corrections were specifically implemented in the reconstruction. The authors should explicitly state which GIA model was used, how the corrections were applied, and whether additional vertical land motion sources (e.g., tectonics, anthropogenic subsidence) were considered or corrected for in the analysis.
- The discussion on vertical land motion (LVM) from lines 209 to 226 reads more like a general literature review and would be more appropriately placed in the Introduction section. The Methods section should focus on describing the specific data sources and procedures used in this study.
- The sentence in lines 227–228 (“An issue that might be regarded as a mere matter of language is examined here; however, imprecise terminology can give rise to subtle yet conceptual misunderstandings”) is vague in both meaning and context. It is unclear what specific issue the authors are referring to in this section. The authors should clarify the intended point or consider removing or relocating the sentence for better coherence.
- What is the spatial resolution of the AVISO Level 4 monthly gridded sea level product used in this study? How are the sea level changes at the tide gauge locations derived from the gridded data? Specifically, what spatial matching or interpolation methods are applied to relate the gridded data to the tide gauge positions?
- The comparison between SA–TG-derived and GNSS-derived VLM estimates shows a median difference of 0.88 mm/yr (r = 0.86) across 253 sites. Even in the subset with the smallest GNSS uncertainties, the median difference remains 0.64 mm/yr. These differences are non-negligible and may influence long-term sea level trend estimates. The authors should discuss the implications of such discrepancies on their reconstruction results. Additionally, the manuscript does not report the average (mean) VLM rates estimated separately by SA–TG and GNSS methods over the 253 sites; this information should be provided to better understand the characteristics of both estimates.
- Lines 283–285: The procedure for transforming the tide gauge time series onto the reference frame of the nearby altimetry point is not clearly explained. Could the authors provide more detail on how this transformation is performed?
- To improve clarity and reproducibility, I suggest the authors include a schematic flowchart of the data processing workflow. This would provide a more intuitive and comprehensive overview than text descriptions alone.
- The manuscript describes the reconstruction of sea level trends from 1950 to 1993 by combining tide gauge and altimetry-derived rates. However, it is unclear how the fusion between tide gauge records and satellite altimetry data is achieved, given that the AVISO dataset begins in 1993. Specifically, how is the 1°×1° gridded product generated for the pre-altimetry period, and how are data gaps filled in offshore or deep-ocean regions where tide gauges are absent or sparse?
- The manuscript refers to the application of a Reduced Space Optimal Interpolation method based on CSEOFs (CSEOF-OI), but the underlying algorithm is not clearly described. For reproducibility and clarity, I suggest the authors provide a concise explanation of how the CSEOF decomposition and the subsequent optimal interpolation are implemented, and how this approach improves upon traditional EOF-OI methods. A schematic or reference to a methodological appendix would be helpful.
- In Figure 6, panels (a) and (b) appear to show very similar results, despite panel (b) involving additional processing steps such as EOF or CSEOF decomposition. Since both panels are based on the same altimetry data, this comparison mainly reflects differences introduced by the processing itself rather than demonstrating any improvement in the quality of the reconstructed data. I suggest that the authors clarify the purpose of this comparison and provide more rigorous evidence to support claims of improved data quality.
- Line 265 mentions that GNSS-derived and SA–TG-derived VLM time series are not consistent. However, the manuscript does not clarify the temporal coverage of GNSS observations used for validation. What is the time period of the GNSS-derived VLM estimates across the 253 tide gauge sites? Are these periods consistent across stations? Moreover, temporal inconsistencies between GNSS and SA–TG estimates may lead to biased trend comparisons, especially if GNSS observations cover shorter or non-overlapping periods with significant non-linear land motion. This potential impact should be addressed and quantified. We note that comparisons of vertical land motion should be conducted over consistent time periods; otherwise, the comparison becomes fundamentally invalid and cannot reliably assess the agreement between the two methods.
- The reconstructed dataset is provided at a 1° × 1° resolution; however, the validation analyses are primarily conducted at 5° × 5° or coarser spatial scales. It is important to assess the accuracy and stability of the reconstruction at the native grid resolution, especially for users interested in regional-scale applications. Furthermore, the quality assessment largely focuses on trend estimates and correlations with known climate indices. While useful, these do not sufficiently address the reliability of the reconstructed sea level fields in the pre-altimetry era (before 1993), particularly in the open ocean where tide gauge constraints are absent. Greater emphasis should be placed on evaluating the uncertainty and credibility of the reconstructed fields in such data-sparse regions.
Citation: https://doi.org/10.5194/essd-2025-251-RC1
Data sets
Modified Sea Level Reconstruction Reveals Improved Separation of Climate and Trend Patterns Shengdao Wang https://doi.org/10.5281/zenodo.15288817
Model code and software
Modified Sea Level Reconstruction Reveals Improved Separation of Climate and Trend Patterns Shengdao Wang https://doi.org/10.5281/zenodo.15288817
Viewed
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
211 | 30 | 11 | 252 | 15 | 8 | 14 |
- HTML: 211
- PDF: 30
- XML: 11
- Total: 252
- Supplement: 15
- BibTeX: 8
- EndNote: 14
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1