The Southern Ocean plays a pre-eminent role in the global carbon-climate system. Model studies show that since the start of the preindustrial era, the region has absorbed about 75% of excess heat and 50% of the oceanic uptake and storage (42±5 PgC) of anthropogenic CO2 emissions. However, due to the spatial and seasonal sparseness of the Southern Ocean CO2 observations (biased toward summer), this role is poorly understood. The seasonal sampling biases have hampered observation-based reconstructions of partial pressure of CO2 at the surface ocean (pCO2) using machine learning (ML) and contributed to the convergence of the root mean squared errors (RMSEs) of ML methods to a common limit known in the literature as the “wall”. The hypothesis here is that addressing the critical missing sampling scale will get the community reconstructions of pCO2 “over the wall”. In this study, I explore the sensitivity of pCO2 reconstructions to these observational scale gaps. Using a scale-sensitive sampling strategy means adopting a sampling strategy which addresses these observational limitations including intra-seasonal as well as seasonal sampling aliases in high eddy kinetic energy and mesoscale-intensive regions. In increasing CO2 sampling efforts in the Southern Ocean using autonomous sampling platforms such as floats, Wave Gliders and Saildrones, the community has tried to answer this problem, but the effectiveness of these efforts has not yet been tested. This study aims to do this evaluation and advance our understanding of the sampling scale sensitivities of surface ocean pCO2 reconstructions from machine-learning techniques and contribute – through a scale-sensitive sampling strategy of observing platforms in the Southern Ocean – to breaking through the proverbial “wall”. This aim was achieved through a series of observing system simulation experiments (OSSEs) applied to a forced mesoscale-resolving (±10km) ocean NEMO-PISCES physics-biogeochemistry model with daily output. In addition to underway ships, the sampling scales of the autonomous sampling platforms such as Floats, WaveGliders and Saildrones, on pCO2 reconstructions were investigated in this series of OSSEs. The primary results showed that two sampling scales, which Saildrones are able to address, are required to improve the RMSE scores of machine-learning techniques and then reduce uncertainties and biases in pCO2 reconstructions. The two sampling scales include (1) the seasonal cycle of the meridional gradients and (2) the intra-seasonal variability. Based on the impacts of these two sampling scales on the RMSE scores and biases, it wasfound that resolving the seasonal cycle of the meridional gradient is the first-order requirement while resolving the intra-seasonal variability is the second. Applying the second-order requirement in the whole Southern Ocean to explore the sensitivity of the clustering choice to the two-step pCO2 reconstruction (clustering- regression). It was found that using an ensemble of clustering methods in this two-step reconstruction performs far much better than using a clustering method. Using these findings, I proposed an observational strategy that is viable and strengthens the limitations in existing underway SOCAT ship- and SOCCOM float-based reconstructions of surface ocean pCO2. More specifically, I proposed a hybrid scale-sensitive sampling strategy for the whole Southern Ocean by integrating underway ships with Saildrones on winter lines. The analysis of these multiple OSSEs indicates that improving the pCO2 reconstructions requires scalesensitive data to supplement the underway ship-based observations gridded in the SOCAT product. It was also found that scale-sensitive data consisting of high-resolution observations ( 1 day) extending over the seasonal cycle and capturing the pCO2 meridional gradients results in breaking through the proverbial “wall”. These findings will contribute to an accurate mean annual global carbon budget which is critical for the trend of the ocean sink feedback on global warming as well as ocean acidification.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uct/oai:localhost:11427/38470 |
| Date | 08 September 2023 |
| Creators | Djeutchouang, Laique Merlin |
| Contributors | Vichi, Marcello, Monteiro Pedro |
| Publisher | Faculty of Science, Department of Oceanography |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Doctoral Thesis, Doctoral, PhD |
| Format | application/pdf |
Page generated in 0.0028 seconds