The sensitivity of pCO2 reconstructions to sampling scales across a Southern Ocean sub-domain: a semi-idealized ocean sampling simulation approach
Authors: Djeutchouang Laique M. , Chang Nicolette , Gregor Luke , Vichi Marcello , Monteiro Pedro M. S. 
Published: September 2022

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). 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∘ of latitude (40–50∘ S) by 20∘ of longitude (10∘ W–10∘ 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
pCO2ocean 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 pCO2 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
pCO2ocean. The reconstruction skill was then
assessed through a statistical comparison of reconstructed
pCO2ocean and the model domain mean. The analysis
shows that uncertainties and biases for
pCO2ocean reconstructions are very sensitive to
both the spatial and the temporal scales of pCO2 sampling in the
model domain. The four key findings from our investigation are as follows: (1) improving ML-based pCO2 reconstructions in the Southern
Ocean requires simultaneous high-resolution observations (<3 d)
of the seasonal cycle of the meridional gradients of
pCO2ocean; (2) Saildrones stand out as the
optimal platforms to simultaneously address these requirements; (3) Wave Gliders with hourly/daily resolution in pseudo-mooring mode improve on
carbon floats (10 d period), which suggests that sampling aliases from the
10 d sampling period might have a greater negative impact on their
uncertainties, biases, and reconstruction means; and (4) the present
seasonal sampling biases (towards summer) in SOCAT data in the Southern
Ocean may be behind a significant winter bias in the reconstructed seasonal
cycle of pCO2ocean.