Browsing by Author "Monteiro Pedro"

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    The meridional and seasonal variability of the carbonate system in the Southeast Atlantic sector of the Southern Ocean: SCALE 2019 Experiment
    (2023) Binase, Zanele; Vichi, Marcello; Monteiro Pedro
    Unlike well-characterized regions that have emerging pH and carbonate data, distant areas like polar oceans remain seasonally undersampled. Consequently, there may be a significant gap in our understanding of biological implications of future ocean acidification (OA). This study seeks to examine the seawater state conditions of the carbonate system that are currently experienced by marine organisms that depend on pH and Aragonite saturation (ΩAr) for calcification. The main focus was on how the physical and carbonate characteristics are sensitive to seasonal variability in the S.E Atlantic sector of the Southern Ocean. We analyzed the temperature, salinity, Dissolved Inorganic Carbon (CT), alkalinity data (AT), pH and Omega (ΩAr) at the surface and in the upper 1000m. We then compared the seasonal meridional gradients in the surface layer and in the upper 1000m water column. The CT and AT were measured using a VINDTA instrument while pH and ΩAr were derived using the CO2SYS program. The initial hypothesis was that the southernmost part of the Southern Ocean would acidify more in winter than in spring. This was based on the idea that colder waters hold more CO2. However, we found remarkable results showing that the surface CT was consistently higher in spring than in winter, with mean seasonal differences ranging from 5.86-21.90µmol/kg although SST was consistently higher in spring than winter, with mean seasonal differences ranging from 0.67-2.12˚C within the ocean front boundaries. It was then hypothesized that the temperature, biological production and CO2 flux seasonal cycles may have been out of phase. Consistent with the CT gradient, the surface pH and ΩAr were ±0.05 units lower in the warmer waters of spring and comparatively higher in winter. The seasonal lag was seen even within the interior layers of the column. There were three main findings in this study: Firstly, since the biogeochemically controlled seasonal CO2 seasonal transition variability lagged the heat flux influence on SST, the expected winter and spring seasonal conditions for carbonate were not reflected in the timing of the winter and spring cruises. Secondly, we observed an uncoupling of pH and ΩAr surface meridional gradients. While pH had no significant meridional gradient trend apart from frontal variability, ΩAr followed the meridional trend that was driven by CT. This was due to the impact of the meridional temperature gradient on K2 that compensated for the impact of the CT gradient on the pH. Lastly, we found that the impact of the seasonal cycle of carbonate stretched down to 1000 m and it was attributed to physical processes. Our findings led us to infer that from a carbonate perspective, the winter cruise was in fact the tail end of autumn while the spring cruise was the tail end of winter.
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    Sampling scale sensitivities in surface ocean pCO2 reconstructions in the Southern Ocean
    (2023) Djeutchouang, Laique Merlin; Vichi, Marcello; Monteiro Pedro
    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.