the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Feb 2026
PolyU2025 SLA: A global 0.25°×0.25° monthly sea level anomaly dataset (1993–2024) determined from satellite altimetry for sea-level and climate change research
Abstract. Long-term and spatially consistent sea-level anomaly (SLA) products derived from satellite altimetry are fundamental for sea-level and climate change studies. In this study, we develop the PolyU2025 SLA (The Hong Kong Polytechnic University 2025 sea level anomaly), a new global monthly gridded SLA product generated using a fully independent data-processing framework. This product is provided on a regular 0.25° × 0.25° grid and spans the period from January 1993 to December 2024 and is intended to be updated regularly. The PolyU2025 SLA is evaluated through systematic intercomparisons with the Copernicus Climate Change Service (C3S) gridded SLA product as well as with independent tide-gauge observations. The results demonstrate a high level of consistency between PolyU2025 and C3S at global and basin scales, characterized by near-zero differences in global-mean SLA and statistically indistinguishable estimates of global mean sea level trends and accelerations, confirming that both products are suitable for climate-scale applications. At regional and short time scales, differences between the two products become more pronounced, particularly in dynamically active regions, and are mainly associated with differences in the representation of short-term and mesoscale variability. These differences reflect methodological trade-offs in data processing and spatial mapping rather than systematic biases. Overall, the PolyU2025 SLA provides a stable and consistent characterization of sea-level change from regional to global scales and serves as a complementary dataset to existing gridded SLA products, especially for long-term and climate-oriented sea-level studies and multi-product assessments of regional sea-level variability and uncertainty. The PolyU2025 SLA product is openly available at https://doi.org/10.5281/zenodo.17810525 (Yuan et al., 2025).
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Status: final response (author comments only)
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RC1: 'Comment on essd-2026-13', Anonymous Referee #1, 30 Mar 2026
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AC1: 'Reply on RC1', Jiajia Yuan, 22 Apr 2026
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2026-13/essd-2026-13-AC1-supplement.pdf
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AC1: 'Reply on RC1', Jiajia Yuan, 22 Apr 2026
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RC2: 'Comment on essd-2026-13', Anonymous Referee #2, 07 May 2026
Review of “PolyU2025 SLA: A global 0.25°×0.25° monthly sea level anomaly dataset (1993–2024) determined from satellite altimetry for sea-level and climate change research” by Jiajia Yuan, Jianli Chen &, Dongju Peng, submitted to Earth System Science Data
The authors present a new global gridded sea level anomaly (SLA) dataset, named PolyU2025 SLA, designed for climate research applications. This dataset is derived from satellite altimetry observations and is evaluated against the operational climate sea level product distributed by the Copernicus Climate Change Service (C3S) and in situ dataset.
This work represents a useful contribution by providing a complementary gridded SLA dataset based on an independent processing and mapping framework. The dataset has a spatial resolution of 0.25° × 0.25° and covers the period from January 1993 to December 2024, which makes it relevant for long-term climate studies.
The manuscript is well structured and detailed. The description of the altimetry and tide gauge (TG) datasets is clear. The methodology is well presented, including a clear description of the processing workflow (corrections, cross-calibration, filtering, subsampling, and mapping).
An intercomparison between the PolyU 2025 and C3S datasets is provided, along with a quantitative validation against in situ TG observations. The results indicate that the PolyU 2025 and C3S datasets are broadly consistent in terms of large-scale climate signals, which is further supported by the in situ validation. The manuscript also evaluates performance in key regimes, including climate signal retrieval, coastal regions, and the open ocean.
The dataset is publicly accessible via Zenodo (https://doi.org/10.5281/zenodo.17810525), covering the period 1993–2024, and appears usable in its current format.
Major comments
- Line 351 and following:
The statement "positive variance differences, where PolyU exhibits higher SLA variance than C3S…" appears inconsistent with the corresponding figure. Visually, PolyU seems to exhibit overall lower SLA variance than C3S. The regions of positive variance difference are not clearly identifiable.
At the monthly timescale, the interpretation is also unclear. Could you please revisit this paragraph and clarify the description to ensure consistency with the figure. - Discussions: Although the differences between the PolyU and C3S datasets are small, could you discuss potential solutions to improve PolyU reconstruction in coastal regions at short timescales?
Minor comments
- Section 3.4:
The covariance function is defined as purely spatial. Do you think it would be worth considering the inclusion of temporal decorrelation in future reprocessing efforts. This aspect could be briefly discussed in the discussion section. - Figure 4a:
Is there an explanation for the apparent increase in maximum SLA differences after 2017? Have you investigated the geographical locations of these maxima? It would be useful to clarify whether this behavior is linked to anomalies in the C3S product, the PolyU dataset, or both. - Figure 4b:
Please correct the y-axis label to: Mean SLA variance
Final assessment
The dataset and manuscript are of good overall quality and provide a valuable contribution to the community. With minor clarifications and corrections, particularly regarding the interpretation of variance differences, the work would be suitable for publication.
Citation: https://doi.org/10.5194/essd-2026-13-RC2 - Line 351 and following:
Data sets
PolyU2025 SLA: A global 0.25°×0.25° monthly sea level anomaly dataset (1993–2024) determined from satellite altimetry for sea-level and climate change research Jiajia Yuan, Jianli Chen, and Dongju Peng https://doi.org/10.5281/zenodo.17810525
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The paper describes a new sea-level dataset using observations from satellite altimetry, overall it is well written, and the quality meets the standards of ESSD. The sea-level community would applaud the release of this dataset, especially if it is updated routinely. Therefore, I would recommend it for publications.
Major comments:
Gridded sea level products, especially with global coverage, are essential for investigating sea level rise. The C3S product is a prominent dataset that is widely used by the community; the PolyU SLA data perform comparably to the C3S data, demonstrating its reliability, and potential applications. In particular, I believe that the PolyU data would contribute considerably to understanding the sea level rise along coast where the altimetry observations suffer from larger uncertainties; now we have more for further evaluations against tide gauges or other products.
Major suggestions:
Data processing: The mapping approach relies on the LSC method. Authors mentioned that the spatial distributions are important (lines 272-274). This raises a question from me, what if the distributions are uneven, how does it affect the spatial interpolation? More importantly, I concerned with the choice of parameters used in LSC, the relevant discussions on the effect of parameters on the final SLA are missing. Plus, authors claimed that the LSC is unbiased, what if the covariance matrix are not stable, do you consider regularizations? If so, I doubt the solutions are ‘unbiased’. Finally, I am also curious that how do authors implement the LSC, i.e., you estimate the covariance matrix globally, or regions by regions, the latter would require less computation resources but more time, although I am not sure how this affects the final solutions. So please provide more details on the mapping approach.
Comparison with C3S: Authors stated that the major (methodological) differences include the along-track data processing and crossover adjustments, and mapping approach. For instance, we can observe apparent differences from Figures 5 and 7, prominent regions include Kuroshio Current, Gulf Stream, and other dynamically active regions. But how much would two products differ over these regions, adding relevant time-series for comparison would help to understand.
Comparison with TG: authors compared their data to TG observations, instead of selecting the nearest grid point, they considered the grid point that shows the strongest correlation. This is a good choice. But how do authors define the search radius, it is ambiguous (line 445, is it 1 degree or a specific distance, e.g., 50 km). It is also ambiguous how authors compare SLA to TG in Figure 8. Do you remove the linear trends? If not, I would suggest authors compare them after removal of the trends, or better also remove seasonal changes. Please show us examples for both the highest variance reduction and the lowest (i.e., add time series for SLA and TG to figure 8).
Minor suggestions:
Line 22, what do you mean ‘a stable’ characterization?
Line 36, ‘sea level’ missing a hyphen, please use the consistent typing style;
Line 46, global or near-global coverage?
Line 55, what do you mean climate-scale? This term is cited multiple times, but I think it is ambiguous.
Line 90, with plans for continuous updates, you mean you are committed to updating?
Line 145, do authors consider removal of the TG associated with large trends? Because these TG stations may also reflect local changes that cannot be captured by SA. That’s why I recommend authors to remove the trends before comparison.
Line 158, what do you mean ‘SLA variable’
Line 164, why resampling reduces the measurement noise? In my experiences, use more data (especially their average) would reduce the noise.
Line 330, is there any evidence supporting the millimeter level uncertainty? How do you mean exactly, do the lone-term trends have a millimeter uncertainty?
Line 366, in studies ‘focusing’
Line 375, several studies have suggested that sea level trend estimates should consider color noise (e.g., Mu et al., 2025; https://doi.org/10.1029/2025GL117434). Mu et al. (2025) did consider a AR(1) model, but they used yearly data. The monthly data should consider AR(3) or AR(5). I don’t think this is a series issue, but authors should add more wordings, so readers can learn more.
Line 460, in figure 8, I would suggest authors to add several examples of time series for comparison.
Line 480, what is this ‘nested moving-average approach’, any terminology for this method.
Line 520, in figure 10, I would suggest authors to add histogram.
Line 580, in figure 12, it would be nice to show the average for those stations, may be along with their standard deviations.