Composite Detection of Continental Shelf Fronts from Sea Surface Temperature and Altimetry: Assessing the Added Value of SWOT KaRIn

Juhl, Marie-Christin; Passaro, Marcello; Dettmering, Denise; Hart-Davis, Michael G.; Saraceno, Martin

doi:10.5194/egusphere-2026-4751

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https://doi.org/10.5194/egusphere-2026-4751

Preprints

13 Aug 2026

| 13 Aug 2026

Status: this preprint is open for discussion and under review for Ocean Science (OS).

Composite Detection of Continental Shelf Fronts from Sea Surface Temperature and Altimetry: Assessing the Added Value of SWOT KaRIn

Marie-Christin Juhl, Marcello Passaro, Denise Dettmering, Michael G. Hart-Davis, and Martin Saraceno

Abstract. We present a composite front detection approach that uses horizontal gradients in Absolute Dynamic Topography (ADT) and Sea Surface Temperature (SST) to identify oceanic frontal structures on the Southwestern Continental Atlantic Shelf, a region characterized by strong mesoscale variability and multiple front types. SST fields are derived from the OSTIA satellite product, while four ADT datasets are compared: SWOT MIOST, CMEMS, OK-STv2, and GLORYS12v1. A joint front probability metric is introduced to quantify the co-occurrence of SST- and ADT-derived frontal signatures and to evaluate consistency and differences across products. The method is applied to assess the spatial and seasonal variability of major regional fronts, including the Shelf-Break Front, the San Matías Front, and the Magellan Plume Front. The ADT datasets showed significant differences in their ability to co-detect continental shelf fronts. The Shelf-Break Front was consistently represented across all datasets, with maximum joint front probabilities occurring in austral summer (DJF) and exceeding 96 % between 35 and 45° S. In contrast, the seasonal and coastal San Matías Front exhibited stronger dataset dependence, with the highest joint probabilities obtained using SWOT MIOST ADT (73.63 %), followed by CMEMS (54.95 %), OK-STv2 (41.86 %), and GLORYS12v1 (35.16 %). In the mid-shelf region (38–41° S), elevated joint frontal probabilities indicate that ADT and SST products, particularly altimetry-based datasets, capture a persistent Mid-Shelf Front. The Magellan Plume Front showed strong joint signatures, most pronounced in SWOT MIOST (97.85 %), followed by CMEMS (80.65 %), OK-STv2 (72.83 %), and GLORYS12v1 (51.09 %), with a distinct coastal plume structure during austral winter (JJA). The approach could be further evaluated using ADT from SWOT KaRIn L3 data during SWOT's Cal/val phase, potentially enabling improved detectability through the sharper ADT gradients provided. Overall, the results show that combining SST- and ADT-based gradient detection enhances the characterization of frontal dynamics. The intercomparison further demonstrates that SWOT KaRIn–enhanced gridded altimetry (SWOT MIOST) substantially improves the detection of coastal and seasonal fronts compared with conventional ADT products.

Received: 06 Aug 2026 – Discussion started: 13 Aug 2026

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Marie-Christin Juhl, Marcello Passaro, Denise Dettmering, Michael G. Hart-Davis, and Martin Saraceno

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'Comment on egusphere-2026-4751', Anonymous Referee #1, 07 Sep 2026 reply
Review of “Composite Detection of Continental Shelf Fronts from Sea Surface Temperature and Altimetry: Assessing the Added Value of SWOT KaRIn” by Marie-Christin Juhl et al.
The authors present a composite front-detection approach based on horizontal gradients of sea surface temperature (SST) and absolute dynamic topography (ADT). The methodology is applied to altimetry-based and assimilated model products distributed by the Copernicus Marine Service, as well as to Level-3 altimetry observations from the SWOT mission and an experimental Level-4 product incorporating SWOT observations distributed by AVISO+. The study aims to characterize the surface dynamics of oceanic fronts over the southwestern Atlantic continental shelf (SWACS) region, with particular emphasis on assessing the potential added value of SWOT KaRIn observations for the detection and characterization of frontal structures. The results indicate that the integration of SWOT KaRIn observations into Level-4 gridded sea-level products may contribute to improved detection of coastal and seasonal fronts, likely benefiting from their enhanced spatial resolution and the resulting sharper ADT gradients. The SWOT KaRIn Level-3 product also appears to provide a more detailed representation of sharper and more spatially confined frontal structures compared with conventional altimetry products, particularly in complex coastal regions. Overall, the study provides interesting evidence that combining SST and ADT gradients, together with the complementary information provided by SWOT observations, can contribute to a more detailed characterization of oceanic fronts over the southwestern Atlantic continental shelf.
In my view, the manuscript is well structured. The objectives are clearly stated, and the datasets and methodology are clearly described. The study addresses relevant scientific questions that are well aligned with the scope of Ocean Science. The results provide a solid basis for the interpretations and conclusions presented by the authors. Overall, I consider the manuscript to be a valuable contribution to the field and potentially suitable for publication in Ocean Science, provided that the authors address the minor comments and points for clarification outlined below.
Minor comments:
The mean front probabilities obtained from ADT-only and SST-only approaches are currently presented in the Appendix. It might be helpful to consider moving these figures to the main text, as they would provide useful context for the reader and help illustrate the motivation for, and added value of, the composite approach.

Maybe add DOI to the datasets description in section 2.1

In the methodology section, the computation of horizontal gradients is described for gridded products. It would be useful to provide some additional details on how the horizontal gradients are computed for the SWOT KaRIn observations. The authors might consider referring to Section 4.6 in the methodology to clarify how the approach is adapted to the specific characteristics of the KaRIn dataset.

The legend of Figure 4 could perhaps be made slightly more explicit regarding the temporal period represented (e.g., “in different seasons” or “DJF season”); If the figure specifically corresponds to the DJF season ?

Line 220: The latitude value appears to be missing before °S. Could the authors please confirm whether the intended value is 40°S?

Based on the results presented, the discussion could perhaps be further developed to address several aspects that would help contextualize the results and their broader implications. In particular, it could be interesting to further discuss some of the potential limitations of the proposed composite front-detection methodology, with a view to providing insight into its robustness and general applicability. Some relevant aspects are already mentioned in the manuscript and could potentially be developed further. For instance, gridded altimetry products, including SWOT-based MIOST, may have an effective spatial resolution that is substantially coarser than their nominal grid resolution. This may limit their ability to resolve some small-scale features, such as submesoscale structures, coastal fronts, etc... It could therefore be useful to discuss further the extent to which current operational Level-4 gridded products can capture these features, as well as whether dedicated gridding approaches could potentially make better use of the high-resolution spatial information provided by SWOT KaRIn observations. As a complementary perspective, the authors may also wish to mention recent or ongoing developments in altimetry–SST mapping approaches aimed at improving the reconstruction of upper-ocean dynamics. For example, approaches such as NEUROST (Martin et al., 2024), VarDyn (Le Guillou et al., 2025), and Ciani et al. (2025) could provide relevant examples in this context. Discussing the application of the proposed front-detection methodology to such experimental mapping approaches could also be an interesting avenue for future work. Finally, the validation of the Level-3 SWOT observations appears to rely on a single pass over a relatively limited time period. This may constrain the robustness of the assessment and the extent to which the reported SWOT impact can be generalized across different dynamical regimes. The authors could perhaps discuss whether similar results might be expected in regions characterized by ocean dynamics different from those of the SWACS region, or when considering multiple KaRIn swaths acquired during the Science Phase. It could also be valuable to discuss the potential contribution of additional in situ observations to further validate and strengthen the findings, particularly with respect to the representation of small-scale frontal structures.

References:
Ciani, D., Fanelli, C., and Buongiorno Nardelli, B.: Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms, Ocean Sci., 21, 199–216, https://doi.org/10.5194/os-21-199-2025, 2025.
Le Guillou, F., Chapron, B., & Rio, M.-H. (2025). VarDyn: Dynamical joint-reconstructions of sea surface height and temperature from multi-sensor satellite observations. Journal of Advances in Modeling Earth System, 17, e2024MS004689. https://doi.org/10.1029/2024MS004689
Martin, S. A., Manucharyan, G. E., & Klein, P. (2024). Deep learning improves global satellite observations of ocean eddy dynamics. Geophysical Research Letters, 51, e2024GL110059. https://doi.org/10.1029/2024GL110059

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Citation: https://doi.org/10.5194/egusphere-2026-4751-RC1

Marie-Christin Juhl, Marcello Passaro, Denise Dettmering, Michael G. Hart-Davis, and Martin Saraceno

Data sets

Daily gridded Sea Level Anomalies from satellite altimetry using Spatio Temporal Window Kriging Marie-Christin Juhl, Marcello Passaro, Denise Dettmering https://doi.org/10.17882/103947

Interactive computing environment

Composite Front Detection (Jupyter Notebook) Marie-Christin Juhl et al. https://github.com/mariejuhl/Composite_Front_Detection

Marie-Christin Juhl, Marcello Passaro, Denise Dettmering, Michael G. Hart-Davis, and Martin Saraceno

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Short summary

Ocean fronts are boundaries where water masses meet, influencing how heat, nutrients, and marine life move across continental shelves. Satellites have long tracked these fronts using sea temperature and sea level, but in coastal waters, they remain difficult to resolve. We combine both signals to map fronts along the Patagonian continental shelf, showing that a new high-resolution satellite mission improves detection of coastal and seasonal fronts compared with conventional satellite data.


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