BOCA: Beaufort-Chukchi Altimetry for Dynamic Topography and Geostrophic Currents

When using this dataset, please cite the BOCA Zenodo record.

1. Summary

BOCA (Beaufort–Chukchi Altimetry for Dynamic Topography and Geostrophic Currents) is a high-resolution multi-mission satellite radar altimetry dataset for the Beaufort and Chukchi Seas (65–88°N and 170°E–240°E) covering 2002–2024. It provides sea level anomaly (SLA), absolute dynamic topography (ADT), and eastward and northward surface geostrophic current components on a level-10 icosahedral grid with approximately 8 km spatial spacing. Each daily mapped field is based on observations within a ±5-day window around its reference date. Gaps due to insufficient data are retained.

Version 1.0.

2. Data Processing

The processing methodology used for BOCA builds on the Arctic satellite-altimetry processing described in Pisareva et al. (2025) and extends it to a cross-calibrated multi-mission record covering 2002–2024.

BOCA combines observations from Envisat (European Space Agency, ESA), CryoSat-2 (ESA), and SARAL/AltiKa (Centre national d’ études spatiales, CNES, and the Indian Space Research Organisation, ISRO).

In ice-covered regions, sea-surface-height (SSH) observations were retrieved from radar echoes classified as leads or open water, while sea-ice returns were excluded using an unsupervised sea ice/lead/open-water classification (Müller et al., 2017). Altimeter ranges for Envisat and SARAL/AltiKa were processed with ALES+ (Passaro et al., 2018), enabling a bias-free transition between lead, sea-ice, and open-ocean surface conditions. Atmospheric and geophysical corrections were applied using data from DGFI-TUM’s Open Altimeter Database (OpenADB, Schwatke et al., 2024).

The three satellite missions were cross-calibrated following the method of Bosch et al. (2014), adapted to the Arctic as in Passaro et al. (2020) and referenced to CryoSat-2. A spatially varying offset correction was additionally applied to Envisat SLA observations retrieved from leads.

To remove erroneous SSH from the dataset, an outlier-rejection strategy similar to that of Passaro et al. (2020) was performed. Afterward, ADT was obtained following the methodology proposed in Rio