Investigating the Northeast Pacific Ocean Carbon Sink using a Machine Learning Approach

Simple item page
dc.contributor.authorDuke, Patrick James
dc.contributor.supervisorHamme, Roberta Claire
dc.contributor.supervisorIanson, Debby
dc.date.accessioned2024-05-29T20:28:17Z
dc.date.available2024-05-29T20:28:17Z
dc.date.issued2024
dc.degree.departmentSchool of Earth and Ocean Sciences
dc.degree.levelDoctor of Philosophy PhD
dc.description.abstractImproving our understanding of how the ocean absorbs carbon dioxide (CO2) is critical to climate change mitigation efforts. The global ocean takes up nearly a quarter of anthropogenic CO2 emissions annually, but the variability of this uptake at regional scales remains poorly understood. In this dissertation I compiled an extensive collection of reported surface ocean air-sea CO2 exchange values within each of Canada’s three adjacent ocean basins. I go on to summarize current research and identify steps forward to improve our understanding of the marine carbon sink in Canadian national and offshore waters. I then developed advanced techniques for quantifying air-sea CO2 fluxes in the Northeast Pacific Ocean to improve our understanding of processes driving seasonal, interannual, decadal, and long-term variability, aiding in monitoring, reporting, and verification of future marine carbon dioxide removal, and helping inform carbon and climate policies. Utilizing a neural network approach to interpolate sparse observations, I created monthly gridded seawater partial pressure of CO2 (pCO2) data products from January 1998 to December 2019, at 1/12x1/12 spatial resolution, in the Northeast Pacific Ocean. The two data products, encompassing the open ocean and the coastal ocean, were created using non-linear relationships between pCO2 observations and a range of predictor variables representing processes affecting pCO2, at a spatial resolution four times greater than leading global products. Using an ensemble approach, I was able to produce robust pCO2 estimates, evaluated against independently withheld data, that represent regional variability with better overall performance compared to global products. I conducted a novel sensitivity analysis which identified that the parameters responsible for the neural network’s ability to capture regional pCO2 variability agrees with mechanistic processes. The regional open ocean and coastal products also reproduced pCO2 estimates well within the overlapping domain, with differences influenced by scarcity of observations. Using wind speed and atmospheric CO2, I calculated air-sea CO2 fluxes. In the open ocean, on sub-decadal to decadal timescales, I found that the upwelling strength of the subpolar Alaskan Gyre, driven by large-scale atmospheric forcing, acts as the primary control on air-sea CO2 flux variability. In the coastal ocean, I report an anticorrelation between annual air-sea CO2 flux and its seasonal amplitude with the relationship driven by regional processes. I estimate long-term surface ocean pCO2 increase at a rate below the atmospheric trend. The slowest rate of increase occurs where there is strong interaction with subsurface waters in the Alaskan Gyre and the West Coast upwelling zone. Basin-wide, my results suggest that the region is a net sink for atmospheric CO2 with trends indicating increasing oceanic uptake.
dc.description.embargo2025-05-11
dc.description.scholarlevelGraduate
dc.identifier.urihttps://hdl.handle.net/1828/16570
dc.languageEnglisheng
dc.language.isoen
dc.rightsAvailable to the World Wide Web
dc.subjectclimate change
dc.subjectcarbon cycle
dc.subjectocean biogeochemistry
dc.subjectair-sea gas exchange
dc.subjectmachine learning
dc.titleInvestigating the Northeast Pacific Ocean Carbon Sink using a Machine Learning Approach
dc.typeThesis

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Duke_Patrick_PhD_2024.pdf
Size:
11.42 MB
Format:
Adobe Portable Document Format
Description:
A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY in the School of Earth and Ocean Science
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.62 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Electronic Theses and Dissertations (ETD)
Theses (Earth and Ocean Sciences)
 
University of Victoria Libraries

We acknowledge and respect the Lək̓ʷəŋən (Songhees and Esquimalt) Peoples on whose territory the university stands, and the Lək̓ʷəŋən and W̱SÁNEĆ Peoples whose historical relationships with the land continue to this day.