Research Highlights

Southern ocean clouds and sea

Successful airborne measurements of sea ice; preparation of data to inform the next IPCC report; and publication of a novel method that uses machine learning to classify satellite cloud data are some of the recent science highlights from the Deep South Challenge.

Antarctic Fieldwork – Airborne Measurements of Sea Ice

Deep South Challenge sea ice researchers from the Universities of Otago and Canterbury and ocean researchers from NIWA have successfully completed the deployment of an aircraft-towed electromagnetic induction instrument to measure sea ice thickness.  Working from Scott Base in November 2016, the team was able to both deploy the airborne instrumentation and complete a three week validation programme of snow and sea ice thickness and structure measurements, in parallel with ocean structure measurements in McMurdo Sound. This research will be linked to a US-supported icebreaker cruise in 2017 and repeat measurements over McMurdo Sound and the Ross Sea in November 2017.  The results will then be integrated within the Deep South Challenge into the New Zealand Earth System Model.

Project page:
  • Dr. Mike Williams (Director, Deep South Challenge)
  • Prof. Pat Langhorne (Principle Investigator on DSC research)
  • Dr. Wolfang Rack (Principle Investigator on DSC research)
people watch a plane fly over the ice field
Researchers photograph the flight of the electromagnetic instrcument (EM-Bird) being towed under an airplane on an Antarctic ice plain

Contributions to the next IPCC report

As members of the Deep South National Science Challenge’s Earth System Modelling and Prediction programme (led by Dr Olaf Morgenstern) work on the full installation of the New Zealand Earth System Model (NZESM), test simulations using precursor configurations have been under way on NIWA’s supercomputer, Fitzroy for more than 10 months. Meanwhile, the team is supporting the UK Met Office in their preparations for the 6th Coupled Model Intercomparison Project (CMIP6), by producing model input data for them. CMIP6 will lay the foundation for the planned 6th Assessment Report of the International Panel on Climate Change (IPCC), due to be released in 2020/2021. This is an example of how the Deep South National Science Challenge is enabling this international collaboration in climate modelling.

Project page:
  • Dr Olaf Morgenstern (DSC Programme Leader – Earth System Modelling and Prediction)
  • Dr Johnny Williams (DSC funded modeller)
  • Dr Vidya Varma (DSC funded modeller)
  • Dr Erik Behrens (DSC funded modeller)
high waves breaking near rocks
The New Zealand Earth System Model programme is boosting New Zealand’s contribution to international climate modelling efforts

Classifying cloud data – journal article

In October 2016, Associate Professor Adrian McDonald and co-authors published an article in the Journal of Geophysical Research: Atmospheres, which is a high impact international journal.  The article describes a study which explores the application of machine learning methods to classify satellite cloud data, and is the first to apply this technique. This work effectively demonstrates that this technique can identify physically meaningful cloud regimes automatically, by analysing model data and the satellite cloud classes. Efforts are already underway to compare the results of this satellite classification with New Zealand Earth System Model output in order to test the quality of the representation of clouds in this new model. 

Project pages:

This work was funded as part of two Deep South Challenge funded projects, which are both ongoing:

  • Associate Professor Adrian McDonald (DSC Programme Leader – Processes & Observations)
  • Dr. Simon Parsons (DSC funded scientist)
  • McDonald, A. J., J. J. Cassano, B. Jolly, S. Parsons, and A. Schuddeboom (2016), An automated satellite cloud classification scheme using self-organizing maps: Alternative ISCCP weather states, J. Geophys. Res. Atmos., 121, 13,009–13,030, doi:10.1002/2016JD025199.
Clouds against vivid blue sky
Sound representation of cloud data is important for accurate Earth System Modelling output.