University of Tasmania, Australia

UTAS Home | UTAS Staff | UTAS Contacts

CSIRO-UTAS PhD Program in Quantitative Marine Science

UTAS Home > IMAS Home > NEW QMS PhD Projects  >  *NEW* Climate-driven variability in tropical Pacific productivity

*NEW* Climate-driven variability in tropical Pacific productivity

Supervision Team

UTAS

Assoc Prof Peter Strutton

Position and School: Associate Professor, IMAS

Area of expertise: Ocean biogeochemistry and physics

*Email: peter.strutton@utas.edu.au

CSIRO

Dr Richard Matear

Position and School: CSIRO Marine and Atmospheric Research

Area of expertise: Ocean biogeochemistry, modelling

Summary

The tropical Pacific spans a quarter of Earth’s circumference and is the origin of the most globally influential mode of climate variability: El Nino. Under normal conditions, upwelling leads to moderate productivity but also a large flux of CO2 to the atmosphere. During El Nino, upwelling, productivity and CO2 flux can be weakened or shut down entirely.
It is important that we understand not only the total variability in tropical Pacific primary productivity, but also the changes in phytoplankton community composition. These changes have consequences for the food web, and for export of carbon to depth.
This project would suit someone with an interest in large scale ocean variability at time scales spanning seasons to decades. The student will use a combination of the following data sets to understand changes in the tropical Pacific ecosystem:

  • archival in situ measurements of nutrients and phytoplankton pigments
  • profiling bio-optical observations of chlorophyll fluorescence, particulate backscatter and attenuation
  • satellite estimates of total chlorophyll, particulate carbon and phytoplankton community composition
  • output from global models that seek to simulate phytoplankton productivity and nutrient dynamics

There is also scope to develop regionally-tuned satellite algorithms.
The goal of the project is to develop a more detailed understanding of the relationship between large scale climate modes and ecosystem function, including future changes.

Essential skills/experience: An undergraduate degree that includes mathematics, physics and chemistry. Experience with programming for data analysis, preferably Matlab. Strong written and oral communication skills.

Desirable skills/experience: Familiarity with ocean biogeochemistry and bio-optics.