Wilson, Jamie
2015.
Constraining marine carbon fluxes
in the ocean interior.
PhD Thesis,
Cardiff University.
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Abstract
The uptake of dissolved CO2 by phytoplankton in the surface ocean and its delivery to the deep ocean via the remineralisation of sinking particles, the biological pump, is an important control on the exchange of CO2 between the ocean and the atmosphere. Ocean biogeochemical models suggest that atmospheric CO2 is sensitive to changes in the depth at which the majority of particles have been remineralised in the ocean interior. However, the key mechanisms involved are not well understood. The function of the biological pump in the past and future is a large uncertainty for the carbon cycle. This thesis uses observations and modelling to further constrain our mechanistic understanding of the biological pump. Geographically Weighted Regression is applied to an updated sediment trap dataset to explore the spatial variability in statistical relationships between organic matter and CaCO3 that are the basis for the ballast hypothesis. No uniform strong relationship at smaller spatial scales and patterns consistent with surface biogeochemistry suggests ecosystem processes may be important. In response to the limited sampling of particulate uxes analysis explored whether annual average uxes could be estimated from a PO4 climatology using modelled ocean transport rates in the form of a transport matrix. The Earth System Model GENIE was used to create a synthetic dataset to test this approach, finding significant sources of uncertainty from errors in the observations, the use of modelled transport rates and the assumption that remineralisation is from particles only. The transport matrix formed a basis for a steady-state phosphorus-only model used to find optimal solutions of spatially varying remineralisation using a 600 member Latin Hypercube ensemble and observed [PO4]. Modelled [PO4] was predominantly sensitive to global mean remineralisation depths although some spatial variability could be constrained. This has implications for using nutrient distributions to validate mechanistic parameterisations in models.
Item Type: | Thesis (PhD) |
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Status: | Unpublished |
Schools: | Earth and Environmental Sciences |
Subjects: | Q Science > Q Science (General) Q Science > QD Chemistry |
Date of First Compliant Deposit: | 30 March 2016 |
Last Modified: | 19 Mar 2016 23:58 |
URI: | https://orca.cardiff.ac.uk/id/eprint/74714 |
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