Lomas, Michael: Bigelow Laboratory for Ocean Sciences (Project Lead)
Project Funding:
2016 - 2020
NRA: 2015 NASA: Ocean Biology and Biogeochemistry
Funded by NASA
Abstract:
The Earth's ocean ecosystems are comprised of a myriad of physical, chemical, and biological processes that create adaptive and resilient ecological communities. These ecosystems are an integral part of the planet’s biogeochemical cycles (e.g., carbon, nitrogen, phosphorus, silica, iron, etc.), which, in turn, are coupled to and influenced by the planet’s climate; the ocean’s biological carbon pump is one such cycle (Volk and Hoffert, 1985). The strength and efficiency of the biological pump are controlled by particulate organic carbon (POC) production near the surface and its trophic remineralization with depth (Buesseler et al., 2007; Neuer et al., 2002),
which is extensively regulated by the distribution and abundance of phytoplankton functional types (PFTs).
We propose a bio-optical laboratory study to develop a more extensive phytoplankton spectral library that will be coupled with satellite radiance products and existing time-series datasets of PFTs to improve our ability to observe and predict changes in PFTs in response to climate change and the consequent impacts on the biological carbon pump. Specifically we will (Figure1):
1) Compare phytoplankton spectral shape PFT algorithms (PHYDOTax, Palacios et al.,
2015) and HPLC-based pigment distributions of PFTs with direct observations of PFT
distributions at Ocean time-series sites.
2) Conduct controlled laboratory experiments for each major PFT quantifying changes in
optical fingerprints, pigment content and Chl-a and POC normalized pigment ratios in
response to climate change variables in multi-stressor experiments.
3) Refine existing PFT algorithms by incorporating new measurements of changes in optical properties and pigment content of cultures grown under conditions representative of future climate change.
4) Re-compare the algorithms derived in task 2 and 3 above to the time-series observations to assess 1) if there is improvement in agreement between in situ observations and satellite products and 2) the ability of these new algorithms to detect observed ecosystem changes.
We will leverage access to the facilities of the National Center for Marine Algae and Microbiota (NCMA) at Bigelow Laboratory and grow representatives of each PFT in semi-continuous cultures in conditions representative of present and future ocean conditions. Mannino's group will collect a spectral library of these taxa that will consist of hyperspectral UV-Vis absorbance and multi-spectral and -angular scattering properties and apply radiative transfer numerical modeling to derive hyperspectral UV-Vis reflectance. This experimental spectral library will be used to refine and evaluate the PHYDOTax approach (Palacios et al., 2015) for our open ocean study regions and other spectral shape-based algorithms for retrieval of PFTs from the PACE and
other hyperspectral and multi-spectral ocean color sensors.
We will validate these PFT approaches using data from the Bermuda Atlantic Time-series Study (BATS) site, which has a >20-year record of monthly optical scattering, absorption and remote sensing reflectance measurements (Bermuda BioOptics Program), HPLC pigments (Lomas et al., 2013), and a ~20yr record of PFTs, for pico- and nanophytoplankton (Wallhead et al., 2013), and has been shown to experience strong variability in seasonal forcing as well as longer term climate oscillations (e.g., North Atlantic Oscillation). We will also use the Atlantic Zone Monitoring Program phytoplankton time-series from the Scotian Shelf to provide a contrast in PFT abundance and physical regime. These time-series will allow us to investigate both temporal and spatial patterns in the North Atlantic Ocean.
This Proposal responds to Section A.3 Ocean Biology and Biogeochemistry, subsection 2.1 Research in Ocean Ecology. Specifically it fits the objectives of the PACE science mission and will provide much needed information on improved phytoplankton functional type algorithms.
More details may be found in the following project profile(s):
