On the Role of Western Boundary Currents to Fertilize Adjacent Continental Shelves at Interannual Scales: a Case Study in the Southwestern Atlantic

The scripts reproduce the analyses presented in the paper, including the processing of satellite-derived chlorophyll-a data, Empirical Orthogonal Function (EOF) decomposition, and Lagrangian particle-tracking simulations performed with OceanParcels.

Authors and affiliations

Carola Ferronato – Instituto Argentino de Oceanografía (IADO-UNS-CONICET), Bahía Blanca, Argentina
Martín Saraceno – Centro de Investigaciones del Mar y de la Atmósfera (CIMA), Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (UBA), Instituto Franco-Argentino de Estudio sobre el Clima y sus Impactos (IFAECI), Departamento de Ciencias del Océano y de la Atmósfera (DCAO), Buenos Aires, Argentina.
Valeria Guinder – Instituto Argentino de Oceanografía (IADO-UNS-CONICET), Bahía Blanca, Argentina

For questions or collaborations: [email protected]

Repository Structure

• 01_download_data.py
  Downloads sea level anomalies and surface geostrophic current data (sla, ugos, vgos) from the Copernicus Marine Service (CMEMS) via API.

• 02_eof_analysis.py
  Processes satellite-derived chlorophyll-a data to compute monthly anomalies, applies temporal filtering, and performs Empirical Orthogonal Function (EOF) decomposition to identify dominant spatial–temporal modes of variability.

• 03_particles_simulation.py
  Runs backward-in-time Lagrangian simulations using the OceanParcels framework. The script analyzes particle trajectories, compares interannual patterns, and classifies differences associated with distinct Malvinas Current jets.

• 04_sla_analysis.py
  Analyzes December sea level anomaly (SLA) composites for years with positive and negative chlorophyll anomalies, highlighting circulation features linked to slope intrusions on the Northern Patagonian Shelf.

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Requirements

• Python ≥ 3.11
• Main libraries:
  xarray, numpy, matplotlib, seaborn, parcels, scipy, pandas, netCDF4, cartopy, tqdm
• Create the environment from environment.yml.

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Data Requirements

1. Chlorophyll-a (MODIS-Aqua)

• Monthly Level-3 composites (4 km resolution) from 2003–2024.
• Downloaded from NASA OceanColor.
• Note: These files are not downloaded automatically. Place them in the folder data/chl/ before running 02_eof_analysis.py.

2. Surface Geostrophic Currents

• Daily sea-level anomalies (SLA) and geostrophic surface velocities were obtained from the
  Copernicus Marine Service product SEALEVEL_GLO_PHY_L4_MY_008_047.
• The fields are derived from multi-mission satellite altimetry and provided on a 1/8° global grid.
• These data are automatically downloaded by the script 01_download_data.py using Copernicus API credentials.

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Output

• EOF results: dominant spatial patterns and principal component time series of chlorophyll-a anomalies.
• Particle simulations: trajectory files (.zarr) and summary plots for each December (2003–2023).
• Figures: stored in the /figures folder, corresponding to those presented in the manuscript.
• Intermediate data: stored in /data/processed.

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Bathymetry data

The bathymetric field (batimetria_SHN.nc) used in the figures is derived from high-resolution hydrographic data provided by the Servicio de Hidrografía Naval (SHN, Argentina).
Due to data license restrictions, this file is not publicly shared in the repository.

If you wish to reproduce the figures including bathymetry contours, please contact the author to request access or replace it with an open-source global product (e.g., GEBCO or ETOPO1).

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