My Work#

SWOT on HEALPix#

As part of a CNES project, I am making SWOT altimetry data available on a HEALPix grid. I will test some analysis workflow and compare their performances.

HEALPix is a discrete global grid that has a number of advantages. It is made of pixels of equal area arranged in constant-latitude rings, which facilitates some computations. Resolution is refined by subdividing pixels into 4, simplifying up- and down-scaling. The tools of its ecosystem are able to deal seamlessly with ellipsoids. For more details and a list of tools, see the Grid4Earth project.

Submesoscale fronts and phytoplankton#

Currents at small scales (between 1km and 100km) have a strong local influence on phytoplankton. However, precisely quantifying their impact on the ecosystem as a whole — that is on regions typically spanning thousands of kilometers and long time periods — is challenging.

Satellites images of temperature and chorophyll of the Gulf-Stream meanders

Gulf Stream meanders from space: Chlorophyll-a (top) and SST (bottom). MODIS-Aqua L2 data, 2007-04-22.

To tackle this problem, we computed an Heterogeneity Index (HI) from satellite sea surface temperature to detect small scale fronts and quantified the chlorophyll (also from satellite data) inside and outside these fronts (Haëck C., Lévy M., Mangolte I., Bopp L. “Satellite Data Reveal Earlier and Stronger Phytoplankton Blooms over Fronts in the Gulf Stream Region”. Biogeosciences 20.9 (may 2023). doi:10.5194/bg-20-1741-2023Haëck et al. 2023, Liu X., Levine N. M. “Enhancement of Phytoplankton Chlorophyll by Submesoscale Frontal Dynamics in the North Pacific Subtropical Gyre”. Geophys. Res. Lett. 43.4 (2016). doi:10.1002/2015gl066996Liu & Levine 2016). The HI is relatively easy to compute, gives good results on moderately high-resolution grids (~4km) and provides a direct measure of the front strength. It tends to capture an area around the front, allowing to catch the biological response.

Our results show the importance of taking into account the impacted surface to quantify the regional impact of fronts. We also observed that the impact of fronts depends on their intensity, and measured an earlier bloom onset within fronts by one to two weeks.

Local Chl-a excess over fronts (left) and regional surplus that takes the front surface into account (right) Haëck et al. 2023, figure 9.

Local Chl-a excess over fronts (left) and regional surplus that takes the front surface into account (right) Haëck et al. 2023, figure 9.

We applied the same method in the Mediterranean sea as a scientific case study for the 4DMED-Sea project. We also applied it to data of phytoplankton composition generated by Roy El Hourany (El Hourany R., Abboud‐Abi Saab M., Faour G., Aumont O., Crépon M., Thiria S. “Estimation of Secondary Phytoplankton Pigments from Satellite Observations Using Self-Organizing Maps (SOMs)”. J. Geophys. Res. Oceans 124.2 (feb. 2019), p. 1357-1378. doi:10.1029/2018jc014450El Hourany et al. 2019, Lévy M., Haëck C., Mangolte I., Cassianides A., El Hourany R. “Shift in phytoplankton community composition over fronts” Commun. Earth Environ. 6.1 (jul. 2025), p. 591. doi:10.1038/s43247-025-02553-1Lévy et al. 2025), showing a shift towards more diatoms in fronts. We used the HI to colocate fronts with in-situ data in the California Current region (Mangolte I., Lévy M., Haëck C., Ohman M.D. “Sub-frontal niches of plankton communities driven by transport and trophic interactions at ocean fronts”. Biogeosciences 20.15, (aug. 2023). doi:10.5194/bg-20-3273-2023Mangolte et al. 2023).

Bar plot of phytoplankton community composition over different biomes and front conditions.

Phytoplankton community composition for each biome over non-front, weak fronts and strong front conditions. Computed over 2002−2020. Lévy et al. 2025, figure 5.

The Python implementation of the Heterogeneity Index is available along other front-detection methods in a single package: fronts-toolbox. The code to conduct some of those studies is available at gitlab.in2p3.fr/clementhaeck/submeso-color and the computation of the index at a global scale is in development at gitlab.in2p3.fr/biofronts/submeso-color.

References#

  • El Hourany R., Abboud‐Abi Saab M., Faour G., Aumont O., Crépon M., Thiria S. “Estimation of Secondary Phytoplankton Pigments from Satellite Observations Using Self-Organizing Maps (SOMs)”. J. Geophys. Res. Oceans 124.2 (feb. 2019), p. 1357-1378. doi:10.1029/2018jc014450
  • Haëck C., Lévy M., Mangolte I., Bopp L. “Satellite Data Reveal Earlier and Stronger Phytoplankton Blooms over Fronts in the Gulf Stream Region”. Biogeosciences 20.9 (may 2023). doi:10.5194/bg-20-1741-2023
  • Lévy M., Haëck C., Mangolte I., Cassianides A., El Hourany R. “Shift in phytoplankton community composition over fronts” Commun. Earth Environ. 6.1 (jul. 2025), p. 591. doi:10.1038/s43247-025-02553-1
  • Liu X., Levine N. M. “Enhancement of Phytoplankton Chlorophyll by Submesoscale Frontal Dynamics in the North Pacific Subtropical Gyre”. Geophys. Res. Lett. 43.4 (2016). doi:10.1002/2015gl066996
  • Mangolte I., Lévy M., Haëck C., Ohman M.D. “Sub-frontal niches of plankton communities driven by transport and trophic interactions at ocean fronts”. Biogeosciences 20.15, (aug. 2023). doi:10.5194/bg-20-3273-2023
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