ITALIAN NATIONAL AGENCY FOR NEW TECHNOLOGIES, ENERGY AND SUSTAINABLE ECONOMIC DEVELOPMENT (ENEA) (IT)
Living Planet Fellowship research project carried out by Marco Bellacicco.
Phytoplankton is considered to be responsible for approximately 50% of the planetary primary production and is at the basis of the trophic chain. Large scale factors such as climate, ocean circulation, and mostly anthropogenic activities, affect phytoplankton biomass and distribution. For all of these reasons, in the ocean, phytoplankton is defined as a sort of sentinel of changes in the ecosystem, because they rapidly respond to environment perturbations. Light, nutrients and temperature are the most important environmental variables that influence phytoplankton production. Phytoplankton cells respond to changes in light and nutrients with physiological strategies that enhance the efficiency of light capturing, photosynthetic capacity, growth and persistence. There are two different kinds of phytoplankton responses to light: photoadaptation and photoacclimation. The photoadaptation describes changes that might happen at genotype level, and are expected to occur at a long evolutionary time-scale. The photoacclimation is a cellular process that allows phytoplankton to change the intracellular chlorophyll-a concentration (Chl) in relation to environmental factors and it includes, among the others, regulation of the pigment amount and other components of the photosynthetic machinery. The temperature is the other main environmental agent that affects phytoplankton. It has been proved that ocean warming, mostly due to anthropogenic activities, causes an expansion of the low-Chl and low-productivity areas impacting strongly on marine ecosystem.
The most important and easily observable mechanism due to photoacclimation is variation of the photosynthetic pigment concentration (i.e. Chl) at the cellular scale which is thus can be observed and quantified using space-borne observations. Photoacclimation can be described in terms of variation of the ratio between chlorophyll-a and carbon (Chl:C ratio). Unfortunately, this process is currently overlooked by standard operational ocean colour algorithms used to retrieve information about both the phytoplankton standing stock and production. PhysioGlob wants to study the inter-annual physiological response of phytoplankton to global warming using long-term satellite observations (i.e. entire ESA OC-CCI time-series) through the Chl:C ratio. Phytoplankton carbon could be estimated from the particle backscattering (bbp, λ). One of the most used and applied algorithm for bbp (λ) is the Quasi Analytical Algorithm (QAA). We want to re-evaluate retrieval of bbp (λ) over the global ocean with the QAA, using field data of remote-sensing reflectance (Rrs) and inherent optical properties (IOP), and then compare phytoplankton carbon with Chl to estimate the physiological signal. In order to study the trend and oscillation of this process we: i) study the single time series in separate M-SSA analyses to evaluate similarities among the inter-annual variabilities of the Chl:Cphyto ratio, SST, and phytoplankton indices also highlighting possible differences; ii) proceed with a joint M-SSA analysis of the time series to better understand the spatio-temporal structure associated with inter-annual variability in the Chl:Cphyto ratio or phytoplankton indices and global ocean temperature field. This coupled analysis will also help in addressing the question to which extent the inter-annual oscillatory modes found in the Chl:Cphyto ratio or phytoplankton indices can be attributed to its response to inter-annual variability in SST field.
Global maps of Forel–Ule index, hue angle and Secchi disk depth derived from 21 years of monthly ESA Ocean Colour Climate Change Initiative data
Earth Syst. Sci. Data (2021)
Linking Marine Biological Activity to Aerosol Chemical Composition and Cloud‐Relevant Properties Over the North Atlantic Ocean
JGR Atmospheres (2020)
Retrieval of Particulate Backscattering Using Field and Satellite
Remote Sensing (2020)
Improving the Retrieval of Carbon-Based Phytoplankton Biomass from Satellite Ocean Colour Observations
Remote Sensing (2020)
Global variability of optical backscattering by non‐algal particles from a Biogeochemical‐Argo data set
Geophysical Research Letters (2019)
Quantifying the Impactof Linear Regression Model in Deriving Bio-Optical Relationships:The Implications on Ocean Carbon Estimations. Sensors