The sensitivity of 𝒑𝐂𝐎 𝟐 reconstructions to sampling scales across a in the Southern Ocean to sampling scalessub-domain: a semi-idealized oceanmodel sampling and reconstrucsimulation approach

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 10 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 ! ) at the surface ocean ( 𝑝CO ! "#$%& ). Here, wWe examine these questions by investigating the sensitivity of 𝑝CO ! "#$%& reconstruction uncertainties and biases to through a series of semi-idealized observing system simulation experiments (OSSEs) using a high-resolution (±10km) coupled physical and biogeochemical model (NEMO-PISCES). Here we choose one year of the model sub-domain of 10 degrees of latitude (40ºS – 50ºS) by 20 degrees of longitude 15 (10ºW – 10ºE). This domain is crossed by the Sub-Antarctic Front and thus includes both the Sub-Antarctic Zone and 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 that simulated the observational spatio-temporal sampling scales of 𝑝CO ! "#$%& surface ocean 𝑝CO ! in ways that are comparable to existing ocean CO 2 observing platforms (Ships, Wavegliders, Carbon-floats, Saildrones) in terms 20 of their temporal sampling scales, and not necessarily their spatial ones. These experiments sampled a high spatial resolution (±10km) coupled physical and biogeochemical model (NEMO-PISCES) within a sub-domain representative of the SubAntarctic and Polar Frontal Zones in the Southern Ocean. The 𝑝CO ! reconstructions were done using a two-member ensemble approach that consisted of two machine learning (ML) methods, (1) the feed-forward neural network and (2) the gradient boosting machines. With tThe baseline observations data being were from the simulated ship-based simulations mimicking 25 ship-based observations from the Surface Ocean CO 2 Atlas (SOCAT). , For each of the scale-sampling scenarios, we applied to each of the scale-sampling simulation scenarios the two-member ensemble method ML2, to reconstruct the full sub-domain 𝑝CO ! "#$%& . and assess tThe reconstruction skill was then assessed through a statistical comparison of reconstructed 𝑝CO ! "#$%& and model domain mean. The analysis shows that uncertainties and biases for 𝑝CO ! "#$%& reconstructions are very sensitive to both the spatial and temporal scales of 𝑝CO ! sampling in the model domain. The four key findings from our investigation are 30 the following: (1) improving ML-based 𝑝CO ! reconstructions in the Southern Ocean requires simultaneous high- resolution observations (< 3 days) of the seasonal cycle of the meridional gradients and the seasonal cycle (< 3 days) of 𝑝CO ! "#$%& ; (2) Saildrones stand out as the optimal platforms to simultaneously address these requirements; (3) Wavegliders with hourly/daily resolution in pseudo-mooring mode improve on Carbon-floats (10-day period), which suggests that sampling aliases from the 10-day sampling periodlow temporal frequency might have a greater negative impact on their uncertainties, biases, and 35 reconstruction means; and (4) the present summer seasonal sampling biases (towards summer) in SOCAT data in the Southern Ocean may be behind a significant winter bias in the reconstructed seasonal cycle of 𝑝CO ! "#$%& . high frequency (at least daily) 915 observations spanning the meridional axis of the Southern Ocean. We showed that the reconstructed seasonal cycle anomaly and mean annual 𝑝CO ! are highly sensitive to the seasonal sampling biases. The seasonal sampling bias comprises both the temporal and meridional spatial scales of variability. This may explain the significant winter-positive bias in the reconstruction of the seasonal cycle of 𝑝CO ! in the surface Southern Oceandomain, which likely may also be contributing to an exaggeration of the apparent winter-maximum outgassing or weakening of the ingassing of CO 2 fluxesobserved in recent Southern Ocean data products. This points to an urgent need to address the existing seasonal bias (towards summer) bias in the Southern Ocean SOCAT dataset and through improvinge the sampling strategy of the present autonomous platforms deployments, so they are better aligned to the integrated spatial and temporal sampling scale needs. These the baseline data involved in machine learning algorithms behind the main observation-based products of reference. these ship-based observations are seasonally biased (towards summer) to under-sampling during stormy autumn and winter seasons, are likely through observations-based products, their summer seasonal sampling bias may be at the root of persistently elevated uncertainties and a winter-positivepositive winter seasonal bias in the reconstructions. This bias should be this study proposes that a meridional may be an efficient way using observing systems. case, we that ship-based observations of autonomous high-resolution observations cycle of meridional gradients of surface ocean 𝑝CO a follow-up study effectiveness uncertainties biases mean annual 2