The global ocean currently absorbs over a third of anthropogenic carbon dioxide (CO₂) emissions, slowing down the growth of atmospheric CO₂, and thus moderating climate change. However, there is significant variability in the strength of the ocean carbon sink on interannual to decadal timescales. There are also uncertainties in the ocean carbon sink, a source of which lies in the coastal ocean. Coastal carbon fluxes are globally relevant and highly variable, but due to the paucity of observations, the coastal ocean remains largely unconstrained. Quantifying and understanding the variability of the ocean carbon sink, and constraining its uncertainties, is essential for supporting climate policy and predicting how the ocean will continue to moderate climate change in the future. This is challenging due to the complex physical and biogeochemical processes in the ocean, as well as the limited observations of ocean carbon. The goal of this thesis is to contribute to the understanding of the ocean carbon cycle and its variability with observations of CO₂ fluxes in the coastal ocean (Chapter 2), a multi-model study of surface carbon interannual variability (Chapter 3), and a mechanistic investigation of decadal variability of air-sea CO₂ fluxes in the global ocean (Chapter 4).
(Chapter 2) Jamaica Bay is a hypereutrophic coastal urban estuary within the land-ocean aquatic continuum. Anthropogenic perturbations to the carbon cycle of the continuum are often excluded from global carbon budgets. Studies have shown that not accounting for the lateral transport of anthropogenic carbon through the continuum can lead to an overestimation of land carbon sinks and an underestimation of ocean carbon sinks. In this study, we used the direct covariance method to make direct estimates of CO₂ fluxes in Jamaica Bay. Over a 587-day observational study, Jamaica Bay emitted CO₂ to the atmosphere at an average rate of 130 gC/m2/yr. However, we find that the waters within the estuary are a strong CO₂ sink (-170 gC/m2/yr). Thus, on average, air-water CO₂ fluxes damp estuary emissions. We find that the water CO₂ sink is strongest in the summer due to the growth of intense algal blooms which likely drawdown CO₂ via photosynthesis. Although the direction of air-water CO₂ flux is ultimately a function of surface carbon concentrations, we find that in the summer, sea-breeze is a near-daily forcing agent for air-water CO₂ fluxes, contributing up to 43% of the mean summer water CO₂ sink rate.
(Chapter 3) The El Nino-Southern Oscillation (ENSO) in the equatorial Pacific is the dominant mode of global air-sea CO₂ flux interannual variability (IAV). Air-sea CO2 fluxes are driven by the difference between atmospheric and surface ocean pCO₂, with variability of the latter driving flux variability. Previous studies found that models in Coupled Model Intercomparison Project Phase 5 (CMIP5) failed to reproduce the observed ENSO-related pattern of CO₂ fluxes and had weak pCO₂ IAV, which were explained by both weak upwelling IAV and weak mean vertical DIC gradients. We assess whether the latest generation of CMIP6 models can reproduce equatorial Pacific pCO₂ IAV by validating models against observations-based data products. We decompose pCO₂ IAV into thermally and non-thermally driven anomalies to examine the balance between these competing anomalies, which explain the total pCO₂ IAV. The majority of CMIP6 models underestimate pCO₂ IAV, while they overestimate SST IAV. Insufficient compensation of non-thermal pCO₂ to thermal pCO₂ IAV in models results in weak total pCO₂ IAV. We compare the relative strengths of the vertical transport of temperature and DIC and evaluate their contributions to thermal and non-thermal pCO₂ anomalies. Model-to-observations-based product comparisons reveal that modeled mean vertical DIC gradients are biased weak relative to their mean vertical temperature gradients, but upwelling acting on these gradients is insufficient to explain the relative magnitudes of thermal and non-thermal pCO₂ anomalies.
(Chapter 4) The ocean carbon sink has absorbed about 25% of anthropogenic emissions, thus mitigating the effects of climate change. Over time, the ocean carbon sink has grown almost proportionally with the growth of atmospheric CO₂ concentrations. However, natural variability in the ocean carbon sink combined with large uncertainties, makes it hard to distinguish changes in the ocean sink due to natural variability versus the forced-trend. Thus, there is a need to understand and quantify the variability in the ocean carbon sink. Using the LDEO-Hybrid Physics Data product (1959-2020), we assess the decadal variability of global air-sea CO₂ fluxes. Here, we compare regional contributions to the decadal variability of the global ocean carbon sink and evaluate global patterns of decadal changes to elucidate the mechanisms that drive the dominant mode of global air-sea CO₂ flux decadal variability.
We find that the dominant mode of decadal air-sea CO₂ flux variability exhibits strong synchronous signals over the tropical Pacific and Southern Ocean. We suggest that the synchronicity between the tropical Pacific and the Southern Ocean is modulated by the Pacific Decadal Oscillation (PDO) index, which is connected to the Multivariate ENSO Index (MEI). The composite patterns over the tropical Pacific can be explained by ENSO-like mechanisms operating on the decadal timescale, while the composite patterns over the Southern Ocean show a different regime where the westerly winds weakened over the composite period, the mixed layer shoaled, and the Southern Ocean sink weakened. Using a box model, we show that this reduction in mixed layer entrainment drives an accumulation of DIC in the mixed layer, which, when amplified by the high Revelle factor in the Southern Ocean, results in a 14-fold amplification in the surface pCO₂, reducing the ocean's capacity to uptake CO₂.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/8ph5-4929 |
| Date | January 2023 |
| Creators | Wong, Suki Cheuk-Kiu |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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