Periodic Reporting for period 1 - WhiteShift (A calcifying phytoplankter’s response to climate change and its role in sinking carbon in the Subarctic Ocean using spaceborne and in situ observations and ecological modelling)
Reporting period: 2017-05-01 to 2019-04-30
To examine if the enormous amounts of calcite produced by E. huxleyi blooms ballast organic carbon I used optical measurements of particle backscatter, chlorophyll-a fluorescence and beam attenuation on Biogeochemical-Argo profiling floats. My results show the feasibility of identifying E. huxleyi blooms and quantifying associated calcite concentration from optical measurements on floats, consistent with results from an optical model for E. huxleyi that I set up. Results obtained from a Biogeochemical-Argo float that sampled sinking carbon particles associated with three distinct phytoplankton blooms of calcifying and non-calcifying phytoplankton suggests that E. huxleyi blooms promote deeper carbon fluxes compared to carbon fluxes associated with blooms from non-calcifying phytoplankton. These observations are thus the first high-resolution observations of the calcite ballast effect in the ocean.
Biological carbon fluxes are chronically under-sampled leading to large uncertainties in estimates of biological carbon export and sequestration. In the same way as Argo floats have revolutionized our understanding of global ocean circulation, Biogeochemical-Argo floats allow a quantum leap in understanding the ocean’s carbon cycle. WhiteShift contributes to this effort through (1) the development of optical approaches to identify calcifying phytoplankton blooms and quantify associated calcite concentration which are directly transportable to the global fleet of Biogeochemical-Argo floats thanks to uniform float-wide optical technology, (2) examining the widely debated calcite ballast hypothesis; an old hypothesis which has now been tested with new in situ technology for the first time. The resulting parameterizations of calcite ballasting are highly relevant to the carbon cycle modelling community and may facilitate improved predictions of climate models at space and time scales useful to decision-making.