the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Sep 2025
New Global Mean Dynamic Topography CNES-CLS-22 Combining Drifters, Hydrography Profiles and High Frequency Radar Data
Abstract. The mean dynamic topography (MDT) is a key reference surface for altimetry. It is needed for the calculation of the ocean absolute dynamic topography, and under the geostrophic approximation, the estimation of surface currents. CNES-CLS MDT solutions are calculated by merging information from altimeter data, GRACE, and GOCE gravity field and oceanographic in situ measurements (drifting buoy velocities, High Frequency radar surface velocities, hydrological profiles). The objective of this paper is to present the newly updated CNES-CLS22 MDT. The main improvement of this new CNES-CLS22 MDT over the previous CNES-CLS18 MDT is in the Arctic, with better coverage and a more physical solution (with the disappearance of artifacts from the previous version). This is due to the use of a new first guess estimated with the CNES-CLS22 MSS and the GOCO06s geoid to which optimal filtering has been applied, as well as Lagrangian filtering at the coast to reduce the intensity of currents normal to the coast. Improvements also include updating the drifting buoy and T/S profile databases, and processing to obtain synthetic mean geostrophic velocities and synthetic mean heights. In addition, a new data type, HF radar data, was processed to extract physical content consistent with MDT in the Mid Atlantic Bight of the northeast U.S. coastal region. The study of this region in particular has shown the improvements of the CNES-CLS22 MDT, though there is still work to be done to obtain a more physical solution over the continental shelf. The CNES-CLS22 MDT has been evaluated against independent height and velocity data in comparison with the previous version, the CNES-CLS18. The new solution presents slightly better results, although not identical in all regions of the globe.
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Status: final response (author comments only)
- RC1: 'Comment on essd-2025-429', Chao Liu, 02 Oct 2025
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RC2: 'Comment on essd-2025-429', Marie-Helene Rio, 23 Oct 2025
Review of “New Global Mean Dynamic Topography CNES-CLS-22 Combining Drifters, Hydrography Profiles and High Frequency Radar Data” (essd-2025-429)
General Assessment
The paper presents the latest CNES-CLS Mean Dynamic Topography, an essential altimeter reference surface needed to reconstruct Absolute Dynamic Topography, and, under the geostrophic assumption, ocean surface currents, from the altimeter Sea Level Anomalies. The new calculation is essentially based on the same methodology than previous versions, but includes more recent Mean Sea Surface and geoid solutions together with updated in-situ data and a new, regional dataset of HF radar velocities for the Mid Atlantic Bight.
The continuous improvement of the Mean Dynamic Topography is key for the optimal exploitation of altimeter measurements, all the more in the new era of wide-swath altimetry. The main challenges in this context are to resolve shorter scales than before, improve the quality of the solution in coastal areas where altimeter data quality is lower and in-situ measurements are lacking, as well as at high latitudes.
The present CNES-CLS22 MDT, despite tackling these different challenges, does not depart significantly from the previous CNES-CLS18 version, apart from the Arctic area where clear improvements are obtained.
The paper is well structured and well written but some methodological steps are only superficially described and the obtained results should be discussed more critically.
I recommend publication after minor revisions.
Major comments
- The method used to calculate the new MDT is essentially the same as described in previous papers. One improvement is mentioned in the abstract, then briefly at the beginning of section 3.1 and finally in the conclusion, i.e. the use of a lagrangian filtering at the coast to reduce the intensity of currents normal to the coast when calculating the first guess, but the method is not further described, and the impact of the approach is only qualitatively and superficially discussed in the case of the Mid-Atlantic Bight area. Details about the filtering method shall be provided, together with a more thorough discussion of the impact of the approach for improving the accuracy of the obtained field along the coast.
- The MDT calculation is based on the use of a multivariate objective analysis, which “takes as input the a priori knowledge of the MDT variance and zonal and meridional correlation scales ». An important input information, the observation errors, is not discussed at all. The authors shall provide clear information on how the observation errors are calculated.
- This is particularly important for the Mid-Atlantic Bight Area where different datasets are used simultaneously: T/S profiles, drifter measurements and HF radar. I expect the relative errors associated to these different observations to have a strong impact on the final solution and this shall be discussed. In addition, as the inclusion of the HF radar is another novelty of the paper compared to previous work, an analysis of the added-value of including this information of the final result is needed. For instance, what is the impact of using the HF radar information together with the T/S profiles and the drifters, compared to using only the T/S profiles and the drifters as done in the past?
- Regarding Figure 5, I am surprised to see the difference in energy at scales larger than 125km between the CNES-CLS22 MDT and the first guess. This means that the synthetic observations also bring information at large scale (from the plot, at least until 600km wavelength -> 300km resolution). This shall be discussed.
- The validation results shown in section 4 are not showing significant improvement of the new solution compared to the previous one, except for the Arctic Ocean. The results shall be discussed more critically, also pointing out the limitations of the current approach and the ways of improvement in the future.
- In particular, results of section 4.2.2.1 are surprising: It is not clear why the CNES_CLS MDT22 would be less accurate in such areas? You use more data than previously, but still average them on 1/8, similar to what was done for CNES-CLS18. You use the same covariance (?)...Maybe you use different error levels? Line 381-382 it is mentioned that the “CNES-CLS MDT22 offers improvements in representing the large-scale circulation more realistically”. Not clear which result support this statement? Also because from Figure 10a, 10e and 10f, the difference between the 2 MDTs is rather large scale and it is clearly this large scale gradient difference which causes the large discrepancy in transport. As the CNES-CLS22 MDT large scales come from the first guess, it would be interesting to look at the first guesses, and calculate the mean transport as seen in the first guesses.
Minor comments
Attach you can find an annotated pdf files with typos and comments.
Citation: https://doi.org/10.5194/essd-2025-429-RC2 -
RC3: 'Comment on essd-2025-429', Marie-Helene Rio, 23 Oct 2025
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2025-429/essd-2025-429-RC3-supplement.pdf
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AC1: 'Comment on essd-2025-429', Solene Jousset, 21 Nov 2025
Dear Editors and Reviewers,
We thank both reviewers for their thorough evaluation and constructive comments, which have helped us improve the quality and clarity of our manuscript. Below we provide a detailed response to each reviewer.
Response to RC1
We appreciate the insightful comments regarding the magnitude of improvement, small-scale noise, continental shelf limitations, validation coverage, and regional features.
- Magnitude of Improvement: We have revised the conclusion to explicitly acknowledge the limited global improvement (0.2–0.5%) compared to CLS18, while emphasizing the regional advances, particularly in the Arctic and Nordic Seas.
- Small-Scale Noise: Figure 5 has been updated with a spectral analysis over a larger region to reduce the influence of local structures. We also discuss why synthetic observations can affect large-scale energy (up to 1000 km), noting that synthtics fields not only add small-scale variability but also reinforce large-scale geostrophic currents. The CNES-CLS18 and 22 solutions may be contaminated by noise at scales below 40 km (where the energy decay slope becomes weaker). This noise likely originates from in situ observations, from residual ageostrophic signals or poorly corrected temporal variability.
- Continental Shelf Limitations: We have expanded the discussion to explain why MDT remains problematic over continental shelves, which represents a significant challenge for future developments.
- Validation Coverage: We have highlighted the sparse in situ data coverage.
- Regional Features: The discussion of Arctic improvements has been strengthened, including the Beaufort Gyre representation and its unrealistic extension into the Canadian Archipelago. We also emphasized the improved resolution of the NwAFC along the Mohn Ridge in the Nordic Seas.
Response to RC2
We thank the reviewer for pointing out the need for more methodological details and a critical discussion of the results.
- Lagrangian Filtering: We have added a description of the Lagrangian filtering method applied near the coast, l’objectif de ce filtrage est de diminuer les vitesses normales à la côte dans la bande côtière où l’on sait que l’erreur de la MSS est plus forte ainsi que celle du géoide. Dans la zone du Mid Atlantic Bigth, ces vitesses normales moyennes le plus proche des côtes sont réduite de 6cm/s à 3cm/s.
- Observation Errors: Additional details have been added in the Methods section regarding the errors associated with the synthetic fields. In particular, we included a more detailed description of the error assigned to HF radar velocities in the Mid-Atlantic Bight. In this region, two velocity datasets were used: one derived from drifter observations and another from HF radar measurements. We also reformulated the passage that might have given the impression that only HF radar velocity data were used.
- Spectral Analysis: Figure 5 has been updated with a spectral analysis over a larger region to reduce the influence of local structures. We also discuss why synthetic observations can affect large-scale energy (up to 1000 km), noting that synthtics fields not only add small-scale variability but also reinforce large-scale geostrophic currents. The CNES-CLS18 and 22 solutions may be contaminated by noise at scales below 40 km (where the energy decay slope becomes weaker). This noise likely originates from in situ observations, from residual ageostrophic signals or poorly corrected temporal variability.
- Critical Discussion of Results: We agree that the validation results presented in Section 4 do not show significant improvement of the new solution compared to the previous one, except for the Arctic Ocean. Both Section 4 and the conclusion have been revised to discuss these results more critically. In particular, we agree that the results in Section 4.2.2.1 show that the large-scale component of CNES-CLS22 MDT is less accurate than that of CNES-CLS18 MDT, with the main differences between the two solutions stemming from their first-guess fields. It is this large-scale gradient that causes the significant discrepancy in transport. Section 4.2.2.1 has been revised accordingly, as well as the conclusion.
- Annotated PDF: Most of the minor comments and typos noted in the attached PDF have been addressed.
We believe these revisions significantly improve the manuscript and address all concerns raised by the reviewers. We thank you again for your valuable feedback.
Sincerely,
On behalf of all co-authorsCitation: https://doi.org/10.5194/essd-2025-429-AC1
Data sets
Combined mean dynamic topography - MDT CNES-CLS22 Solène Jousset https://doi.org/10.24400/527896/a01-2023.003
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Thanks for the opportunity to review the manuscript. My comments are attached.