Preprints
https://doi.org/10.5194/egusphere-2026-4751
https://doi.org/10.5194/egusphere-2026-4751
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 Dettermering, 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.

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

Status: open (until 08 Oct 2026)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Marie-Christin Juhl, Marcello Passaro, Denise Dettermering, 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 Dettermering, Michael G. Hart-Davis, and Martin Saraceno
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Latest update: 13 Aug 2026
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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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