Résumé: A new methodology to derive an SST-based upwelling index was based on a rigorous spatial analysis of satellite SST fields and their variability, by referring to previous works, from Wooster et al. (1976) to Santos et al. (2011). The data was precautiously processed by considering data quality aspects (including cloud cover) and the best way to derive accurate coastal SST and its offshore reference. The relevance of the developed index was evaluated by comparing its spatial and seasonal consistency against two wind-based indices as well as with the previous SST-based indices, largely superseding these later ones in term of overall quality and spatio-temporal dynamic. Our index adequately describes the spatio-temporal variability of the coastal upwelling intensity in the Canary Current upwelling system and has the advantage of describing complementary aspects of the coastal dynamics of the region that were not covered by Ekman-based indices. The proposed methodology is generic and can be easily applicable to various coastal upwelling systems, especially the four major eastern boundary upwelling ecosystems.

Résumé: In order to optimize frontal detection in sea surface temperature fields at 4 km resolution, a combined statistical and expert-based approach is applied to test different spatial smoothing of the data prior to the detection process. Fronts are usually detected at 1 km resolution using the histogram-based, single image edge detection (SIED) algorithm developed by Cayula and Cornillon in 1992, with a standard preliminary smoothing using a median filter and a 3 × 3 pixel kernel. Here, detections are performed in three study regions (off Morocco, the Mozambique Channel, and north-western Australia) and across the Indian Ocean basin using the combination of multiple windows (CMW) method developed by Nieto, Demarcq and McClatchie in 2012 which improves on the original Cayula and Cornillon algorithm. Detections at 4 km and 1 km of resolution are compared. Fronts are divided in two intensity classes (“weak” and “strong”) according to their thermal gradient. A preliminary smoothing is applied prior to the detection using different convolutions: three type of filters (median, average and Gaussian) combined with four kernel sizes (3 × 3, 5 × 5, 7 × 7, and 9 × 9 pixels) and three detection window sizes (16 × 16, 24 × 24 and 32 × 32 pixels) to test the effect of these smoothing combinations on reducing the background noise of the data and therefore on improving the frontal detection. The performance of the combinations on 4 km data are evaluated using two criteria: detection efficiency and front length. We find that the optimal combination of preliminary smoothing parameters in enhancing detection efficiency and preserving front length includes a median filter, a 16 × 16 pixel window size, and a 5 × 5 pixel kernel for strong fronts and a 7 × 7 pixel kernel for weak fronts. Results show an improvement in detection performance (from largest to smallest window size) of 71% for strong fronts and 120% for weak fronts. Despite the small window used (16 × 16 pixels), the length of the fronts has been preserved relative to that found with 1 km data. This optimal preliminary smoothing and the CMW detection algorithm on 4 km sea surface temperature data are then used to describe the spatial distribution of the monthly frequencies of occurrence for both strong and weak fronts across the Indian Ocean basin. In general strong fronts are observed in coastal areas whereas weak fronts, with some seasonal exceptions, are mainly located in the open ocean. This study shows that adequate noise reduction done by a preliminary smoothing of the data considerably improves the frontal detection efficiency as well as the global quality of the results. Consequently, the use of 4 km data enables frontal detections similar to 1 km data (using a standard median 3 × 3 convolution) in terms of detectability, length and location. This method, using 4 km data is easily applicable to large regions or at the global scale with far less constraints of data manipulation and processing time relative to 1 km data.

Résumé: This work is part of the MADRidge Project special issue which aims to describe pelagic ecosystems in the vicinity of three prominent shallow seamounts in the South West Indian Ocean: one here named MAD-Ridge (240 m below the surface) plus Walters Shoal (18 m) on the Madagascar Ridge, and La Perouse (60 m) on the abyssal plain east of Madagascar. The three span latitudes 20 degrees S and 33 degrees S, some 1500 km. The study provides the background oceanography for the once-off, multidisciplinary snapshot cruise studies around the seamounts. As life on seamounts is determined by factors such as summit depth, proximity to the light layers of the ocean, and the ambient circulation, a first description of regional spatial-field climatologies (16-22 years) and monthly along-ridge gradients of surface wind (driving force), water column properties of sea surface temperature, mixed layer depth, chlorophyll-a and eddy kinetic energy, plus ocean currents is provided. Being relevant to many applications in the study domain, these properties in particular reveal contrasting environments along the Madagascar Ridge and between the three seamounts that should drive biological differences. Relative to the other two seamounts, MAD-Ridge is in the more extreme situation, being at the end of the East Madagascar Current, where it experiences sturdy, albeit variable, currents and the frequent passing of mesoscale eddies.