The sensitivity of pCO(2) reconstructions to sampling scales across a Southern Ocean sub-domain: a semi-idealized ocean sampling simulation approach

• Main Authors: Djeutchouang, Laique M., Chang, Nicolette, Gregor, Luke, Vichi, Marcello, Monteiro, Pedro M.S.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus 2022
• Subjects: Antarctic; Southern Ocean; Antarc*
• Online Access: https://hdl.handle.net/20.500.11850/570249; https://doi.org/10.3929/ethz-b-000570249

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 (pCO(2)) at the surface ocean (pCO(2)(ocean)). We examine these questions through a series of semi-idealized observing system simulation experiments (OSSEs) using a high-resolution (+/- 10 km) coupled physical and biogeochemical model (NEMO-PISCES, Nucleus for European Modelling of the Ocean, Pelagic Interactions Scheme for Carbon and Ecosystem Studies). Here we choose 1 year of the model sub-domain of 10 degrees of latitude (40-50 degrees S) by 20 degrees of longitude (10 degrees W-10 degrees E). This domain is crossed by the sub-Antarctic front and thus includes both the sub-Antarctic zone and the polar frontal zone in the south-east Atlantic Ocean, which are the two most sampled sub-regions of the Southern Ocean. We show that while this sub-domain is small relative to the Southern Ocean scales, it is representative of the scales of variability we aim to examine. The OSSEs simulated the observational scales of pCO(2)(ocean) in ways that are comparable to existing ocean CO2 observing platforms (ships, Wave Gliders, carbon floats, Saildrones) in terms of their temporal sampling scales and not necessarily their spatial ones. The pCO(2) reconstructions were carried out 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. The baseline data were from the ship-based simulations mimicking ship-based observations from the Surface Ocean CO2 Atlas (SOCAT). For each of the sampling-scale scenarios, we applied the two-member ensemble method to reconstruct the full sub-domain pCO(2)(ocean). The reconstruction skill was then assessed ...