03 Jan 2022
03 Jan 2022
Status: this preprint is currently under review for the journal BG.

The sensitivity of pCO2 reconstructions in the Southern Ocean to sampling scales: a semi-idealized model sampling and reconstruction approach

Laique Merlin Djeutchouang1,2, Nicolette Chang1, Luke Gregor3, Marcello Vichi2, and Pedro Manuel Scheel Monteiro1 Laique Merlin Djeutchouang et al.
  • 1SOCCO, CSIR, Rosebank, Cape Town, 7700, South Africa
  • 2MARIS, Department of Oceanography, University of Cape Town, Cape Town, 7701, South Africa
  • 3Environmental Physics, Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, Zürich, 8092, Switzerland

Abstract. The Southern Ocean is a complex system yet is sparsely sampled in both space and time. These factors raise questions about the confidence in present sampling strategies and associated machine learning (ML) reconstructions. Previous studies have not yielded a clear understanding of the origin of uncertainties and biases for the reconstructions of the partial pressure of carbon dioxide (pCO2) at the surface ocean (pCO2ocean). Here, we examine these questions by investigating the sensitivity of pCO2ocean reconstruction uncertainties and biases to a series of semi-idealized observing system simulation experiments (OSSEs) that simulate spatio-temporal sampling scales of surface ocean pCO2 in ways that are comparable to ocean CO2 observing platforms (Ship, Waveglider, Carbon-float, Saildrone). These experiments sampled a high spatial resolution (±10 km) coupled physical and biogeochemical model (NEMO-PISCES) within a sub-domain representative of the Sub-Antarctic and Polar Frontal Zones in the Southern Ocean. The reconstructions were done using a two-member ensemble approach that consisted of two machine learning (ML) methods, (1) the feed-forward neural network and (2) the gradient boosting machines. With the baseline observations being from the simulated ships mimicking observations from the Surface Ocean CO2 Atlas (SOCAT), we applied to each of the scale-sampling simulation scenarios the two-member ensemble method ML2, to reconstruct the full sub-domain pCO2ocean and assess the reconstruction skill through a statistical comparison of reconstructed pCO2ocean and model domain mean. The analysis shows that uncertainties and biases for pCO2ocean reconstructions are very sensitive to both the spatial and temporal scales of pCO2 sampling in the model domain. The four key findings from our investigation are the following: (1) improving ML-based pCO2 reconstructions in the Southern Ocean requires simultaneous high resolution observations of the meridional and the seasonal cycle (< 3 days) of pCO2ocean; (2) Saildrones stand out as the optimal platforms to simultaneously address these requirements; (3) Wavegliders with hourly/daily resolution in pseudo-mooring mode improve on Carbon-floats (10-day period), which suggests that sampling aliases from the low temporal frequency have a greater negative impact on their uncertainties, biases and reconstruction means; and (4) the present summer seasonal sampling biases in SOCAT data in the Southern Ocean may be behind a significant winter bias in the reconstructed seasonal cycle of pCO2ocean.

Laique Merlin Djeutchouang et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2021-344', Anonymous Referee #1, 05 Feb 2022
  • RC2: 'Comment on bg-2021-344', Anonymous Referee #2, 07 Apr 2022

Laique Merlin Djeutchouang et al.

Laique Merlin Djeutchouang et al.


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Short summary
Based on observing system simulation experiments using a mesoscale resolving model, we found that to significantly improve uncertainties and biases in carbon dioxide (CO2) mapping in the Southern Ocean, it is essential to resolve the seasonal cycle (SC) of the meridional gradient of CO2 through high frequency (at least daily) observations that also span the meridional axis of the region. We also showed that estimated SC anomaly and mean annual CO2 are highly sensitive to seasonal sampling biases.