Measurements and modelling of shelf sea productivity using oxygen-argon ratios and oxygen triple isotopes

Pallottino, Francesco (2022) Measurements and modelling of shelf sea productivity using oxygen-argon ratios and oxygen triple isotopes. Doctoral thesis, University of East Anglia.

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This thesis provides net (N) and gross (G) oxygen production estimates for North Sea surface waters during late summer 2019. Net production rates (N(O2/Ar)) were based on the O2/Ar saturation anomaly Δ(O2/Ar), while triple oxygen isotopes were used as tracer for gross production (G(17O)). The research revealed that on average, surface waters were in metabolic balance (N(O2/Ar) = (–3±40) mmol m–2 d–1). Stations located closest to UK coasts were net heterotrophic (N(O2/Ar) = (–29±21) mmol m–2 d–1 < 0) despite high G(17O) values of (500±90) mmol m–2 d–1. This result suggests strong remineralisation occurring in the area.
Using a modelling approach, this thesis also investigated the robustness of the steadystate assumption for net and gross production estimates in a shelf sea environment. Station L4 (Western English Channel), was used as case study. Results showed that the steady-state approach can be improved by using a repeat discrete sampling strategy to estimate as non-steady-state contributions to G(17O) and N(O2/Ar) and reduce the prediction error by 53 % and 75 %, respectively. We also used model simulations to investigate the impact of phytoplankton species-specific isotope effects in photosynthesis on diagnosed G(17O). We found that neglecting these isotope effects can cause a small systematic overestimate of G(17O), rising to up to +50 % during the spring bloom at Station L4. Taken together, these results can be used to design observational studies aimed at determining G(17O) and N(O2/Ar) in dynamic shelf-sea environments like Station L4.

(Super- and subscript is not shown in this abstract due to textbox limitations for complex formatting).

Item Type: Thesis (Doctoral)
Faculty \ School: Faculty of Science > School of Environmental Sciences
Depositing User: Kitty Laine
Date Deposited: 27 Jun 2023 12:39
Last Modified: 27 Jun 2023 12:39

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