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Applications are now open for PhD studentships starting in October 2018. 

Please read the recruitment introduction for more information about eligibility, how to apply, and possibilities for further funding.

The deadline for applications is 8 January 2018.

Project search

HEYWOOD_UENV18EE

HEYWOOD_UENV18EE - Variability in Antarctic waters: searching for fingerprints of winter-time processes in the Southern Ocean

Project description

Figure: Winter Water temperature [A] and salinity [B].

Selected other project supervisors:
Professor David Stevens (UEA)
Dr Helene Hewitt (Met Office)
Dr Patrick Hyder (Met Office)

Scientific background
Climate models are important to help us plan for future change.  These models are still being improved; this applies particularly to ocean processes that are often poorly represented.  Here we focus on a little-studied water mass, Winter Water, the remnant of the previous winter’s mixed layer. In the Southern Ocean, Winter Water is easily identifiable as a temperature-minimum layer at a few hundred metres depth, capped in summer by less dense water warmed by solar heating and freshened by sea ice melt. Winter Water is important because it contains a “fingerprint” of previous winters’ interactions between ocean and atmosphere. This joint project with the Met Office will assess Winter Water and its variability in climate models and observations.

Research methodology
You will analyse the Met Office’s family of models (known as NEMO) at different resolutions and with different physics. Winter Water layer depth, thickness, temperature and salinity, as well as mixed layer depth defined by different density thresholds, will be calculated from model output and from newly-available year-round observations of temperature and salinity. You will assess any correlation between summertime and wintertime Winter Water properties, and use the models to explore physical mechanisms behind any links.

The project will test these hypotheses:
H1.    Winter Water layer depth is a better metric to assess model skill than mixed layer depth;
H2.    Winter Water properties inform about the previous winter’s ocean-atmosphere exchange;
H3.    Spatial variations in Winter Water properties inform about regional ocean dynamics and sea ice variability.

Training
You will be trained in physical oceanography, climate modelling, science communication, data analysis and programming in Matlab and/or Python. You will learn oceanographic observational techniques through participation in a research cruise, and joining the UEA glider science group, using profiling ocean gliders to observe Winter Water close to Antarctica. You will work closely with the Met Office.

Person specification
You will have a physical science degree (e.g. physics, natural sciences, environmental sciences, oceanography, meteorology, geophysics, mathematics). Experience of a programming language such as Matlab or Python is helpful.

References

  1. Schmidtko, S., K.J. Heywood, A.F. Thompson, and S. Aoki (2014) Multidecadal warming of Antarctic waters, Science, 346 (6214), 1227-1231.
  2. Heuzé, C., K.J. Heywood, D.P. Stevens and J.K. Ridley (2013): Southern Ocean bottom water characteristics in CMIP5 models, Geophysical Research Letters, 40, 1407-1414, doi:10.1002/grl.50287.
  3. Heywood, K.J., S. Schmidtko, C. Heuze, J. Kaiser, T.D. Jickells, B.Y. Queste, D.P. Stevens, M. Wadley, A.F. Thompson, S. Fielding, D. Guihen, E. Creed, J.K. Ridley and W. Smith (2014): Ocean processes at the Antarctic continental slope, Philosophical Transactions of the Royal Society A, 372, 20130047, doi:10.1098/rsta.2013.0047.
  4. Heuzé, C., K.J. Heywood, D.P. Stevens and J.K. Ridley (2015): Changes in global ocean bottom properties and volume transports in CMIP5 models under climate change scenarios, Journal of Climate, 28, 2917-2944, doi:10.1175/JCLI-D-14-00381.1.
  5. Shaffrey, L.C., D. Hodson, J. Robson, D.P. Stevens, E. Hawkins, I. Polo, I. Stevens, R.T. Sutton, G. Lister, A. Iwi, D. Smith and A. Stephens (2017): Decadal Predictions with the HiGEM High Resolution Global Coupled Climate Model: Description and Basic Evaluation, Climate Dynamics, 48, 297-311, doi:10.1007/s00382-016-3075-x.