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. 2023 Oct 21;10(1):730.
doi: 10.1038/s41597-023-02615-z.

An ocean front dataset for the Mediterranean sea and southwest Indian ocean

Floriane Sudre  1 , Ismael Hernández-Carrasco  2 , Camille Mazoyer  3 , Joel Sudre  4 , Boris Dewitte  5   6   7   8 , Véronique Garçon  9 , Vincent Rossi  10
Affiliations

An ocean front dataset for the Mediterranean sea and southwest Indian ocean

Floriane Sudre et al. Sci Data. .

Abstract

Fronts are ubiquitous discrete features of the global ocean often associated with enhanced vertical velocities, in turn boosting primary production. Fronts thus form dynamical and ephemeral ecosystems where numerous species meet across all trophic levels. Fronts are also targeted by fisheries. Capturing ocean fronts and studying their long-term variability in relation with climate change is thus key for marine resource management and spatial planning. The Mediterranean Sea and the Southwest Indian Ocean are natural laboratories to study front-marine life interactions due to their energetic flow at sub-to-mesoscales, high biodiversity (including endemic and endangered species) and numerous conservation initiatives. Based on remotely-sensed Sea Surface Temperature and Height, we compute thermal fronts (2003-2020) and attracting Lagrangian coherent structures (1994-2020), in both regions over several decades. We advocate for the combined use of both thermal fronts and attracting Lagrangian coherent structures to study front-marine life interactions. The resulting front dataset differs from other alternatives by its high spatio-temporal resolution, long time coverage, and relevant thresholds defined for ecological provinces.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Fig. 1
Fig. 1
Illustration showing how NOMAD users can go from thermal gradients (resp. backward FSLEs) to thermal (resp. dynamical) fronts via region-specific thresholding, with example of fields before and after thresholding. All fields were taken on July 1st, 2020. The colormap is from cmocean and with the same range as in Fig. 2.
Fig. 2
Fig. 2
Example snapshots of thermal gradient (a,c) and FSLE (b,d) fields in the MedSea (a,b) and SWIO (c,d), for “suitable” pixels only, on July 1st, 2020. The colormap is from cmocean.
Fig. 3
Fig. 3
Spalding’s ecoregions (a,b) and Longhurst’s provinces, (c,d) in the SWIO (a,c) and in the MedSea (b,d).
Fig. 4
Fig. 4
Example of thermal gradient magnitude from the BOA (a) and corresponding MUR analysed SST value (b) for a section at 26°S in the SWIO, for July 1st, 2020. The colormap of Fig. 4a is from cmocean and with the same range in °C/km as in Fig. 2.
Fig. 5
Fig. 5
(a) thermal gradient output from MUR SST and (b) thermal gradient output from SST CCI analysis, in the Mozambique Channel for July 1st, 2020. The colormap is from cmocean.
Fig. 6
Fig. 6
(a) Snapshot of FSLEs computed from geostrophy-only currents from the Copernicus Climate Change Service and (b) its difference from FSLEs computed from total currents (shown in Fig. 2d), in the SWIO for July 1st, 2020. The colormaps are from cmocean.
Fig. 7
Fig. 7
Example of the collocation between randomly-generated points (in red) and FSLE fields for (a) NOMAD backward FSLEs on a 1.5 km grid and for (b) AVISO backward near-real-time FSLEs on a 4 km grid, for Jan 15th, 2020. The colormap is from cmocean and with the same range as in Fig. 2.
Fig. 8
Fig. 8
Distributions of the differences in mean FSLE values (a, b), collocation percentage (c, d) and distance to nearest front (e, f) between NOMAD and AVISO FSLE front fields for 1000 series of 100–200 randomly-generated points. We chose 2 Longhurst, subregions to illustrate the region-specific sensitivity to spatial resolution: EAFR (b), ISSG (c). The vertical dotted lines mark the mean difference for all 1000 trajectories, weighted on the number of points per trajectory.
Fig. 9
Fig. 9
Frequency of suitable days for thermal gradients (a, c) and FSLEs (b, d) in the MedSea (a, b) and the SWIO (c, d). The background white contour shows the 200 m isobath. The colormap is from cmocean.
Fig. 10
Fig. 10
Time average for thermal gradients (a, c) and FSLEs (b, d) in the MedSea (a, b) and the SWIO (c, d). The background white contour shows the 200 m isobath. The colormap is from cmocean.
Fig. 11
Fig. 11
Front frequency for thermal gradients (a, c) and FSLEs (b, d) in the MedSea (a, b) and the SWIO (c, d). The background white contour shows the 200 m isobath. The colormap is from cmocean.
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