The natural variability and climate change response in phytoplankton phenology
Large areas of the world’s oceans experience a significant seasonal cycle in phytoplankton biomass. Variability in the phenology of these phytoplankton blooms affect ecosystem dynamics with implications for carbon export production and food availability at higher trophic levels. Climate change is ex...
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| Format: | Thesis |
| Language: | English |
| Published: |
2014
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| Online Access: | https://eprints.soton.ac.uk/362006/ https://eprints.soton.ac.uk/362006/1/HCole_PhDThesis%255B1%255D.pdf |
| Summary: | Large areas of the world’s oceans experience a significant seasonal cycle in phytoplankton biomass. Variability in the phenology of these phytoplankton blooms affect ecosystem dynamics with implications for carbon export production and food availability at higher trophic levels. Climate change is expected to alter phytoplankton seasonality through changes to the underlying physical drivers controlling bloom timing. This thesis focusses on the drivers of contemporary variability and climate change-driven trends in phytoplankton phenology. Satellite-derived chlorophyll data (GlobColour) are used to examine phenological characteristics on a global scale. This dataset is complimented by remotely sensed photosynthetically active radiation (PAR; MODIS), net heat flux (remotely sensed and reanalysis products) and Argo float-derived mixed layer depth datasets in addition to global biogeochemical model output. Four bloom timing metrics are developed to quantify the timing of bloom initiation and termination in a consistent manner. The advantages and limitations of each metric are discussed in the context of the required criteria for a suitable metric definition. The choice of metric definition is based on the performance of the metrics against these criteria. The impact of missing data in the time series on the accuracy of the bloom timing metrics is investigated using the global biogeochemical model NOBM. It is found that missing data cause errors of approximately 30, 15 and 50 days in the date of bloom initiation, peak and termination respectively. The exact cause and implications for phenological studies of these errors is discussed. The physical drivers of interannual variability are examined using global datasets of mixed layer depth, net heat flux and mean mixed layer PAR. The date the net heat flux becomes positive is seen to be a strong predictor for the onset of the subpolar spring bloom, especially in the North Atlantic. This finding is the first to support the critical turbulence hypothesis over Sverdrup’s ... |
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