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

• Main Authors: Djeutchouang, Laique Merlin, Chang, Nicolette, Gregor, Luke, Vichi, Marcello, Monteiro, Pedro Manuel Scheel
• Format: Text
• Language: English
• Published: 2022
• Subjects: Antarctic; Southern Ocean; Antarc*
• Online Access: https://doi.org/10.5194/bg-2021-344; https://bg.copernicus.org/preprints/bg-2021-344/

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 ( p CO 2 ) at the surface ocean ( p CO 2 ocean ). Here, we examine these questions by investigating the sensitivity of p CO 2 ocean reconstruction uncertainties and biases to a series of semi-idealized observing system simulation experiments (OSSEs) that simulate spatio-temporal sampling scales of surface ocean p CO 2 in ways that are comparable to ocean CO 2 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 CO 2 Atlas (SOCAT), we applied to each of the scale-sampling simulation scenarios the two-member ensemble method ML2, to reconstruct the full sub-domain p CO 2 ocean and assess the reconstruction skill through a statistical comparison of reconstructed p CO 2 ocean and model domain mean. The analysis shows that uncertainties and biases for p CO 2 ocean reconstructions are very sensitive to both the spatial and temporal scales of p CO 2 sampling in the model domain. The four key findings from our investigation are the following: (1) improving ML-based p CO 2 reconstructions in the Southern Ocean requires simultaneous high resolution observations of the meridional and the seasonal cycle (< 3 days) of p CO 2 ocean (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 p CO 2 ocean .