What drove the Antarctic sea ice during the last ice age? An Earth System Modelling approach.

Overview

Antarctic sea ice is a critical component of the Earth’s climate system, characterised by physical, biological and chemical processes with global impacts. Seasonal fluctuations in sea ice impact planetary albedo, biological productivity, and the transfer of climatically active gases including CO2 between the oceans and atmosphere. Antarctic ecosystems are closely associated with sea ice, as it provides important biological refugia and feeding opportunities. Sea ice also affects the formation of sub-surface water masses which feed the global ocean circulation system, and create the world’s largest sink of heat and carbon. The properties of the Antarctic sea ice pack are complex, varying across a range of temporal and spatial scales, so that despite its climatic significance, Antarctic sea ice is challenging to observe and to model, leading to low confidence in future projections for a warming climate.

The geological record provides opportunities to set recent trends in sea ice into a longer-term context. At the last glacial maximum (LGM), Antarctic sea-ice extent was likely double that of today. Knowledge of the climatic impacts of these changes is hindered by a sparse empirical data set which has focussed on simply delineating the sea ice margins: uncertainties in the representation of sea ice are reflected in LGM model disagreements (Figure 1; Sime et 2013; Sime et al 2016). The properties of the LGM sea-ice pack are poorly understood, although there is evidence that open waters (polynyas) opened within the sea ice, which would have impacted the ability of the ocean to drawdown atmospheric CO2 during glaciations. Both the availability of the empirical datasets and uncertainties in climate simulations thus limit our use of the geological record to understand the causes and impacts of evolving Antarctic sea ice.

The student will test new UK earth systems models, to assess whether colder ocean waters at the LGM encouraged more extensive summer sea-ice cover and limited formation of open-ocean polynyas (which rely on sea-ice melt), and/or enhanced coastal polynya formation due to enhanced wind speeds (which encourage sea-ice break-up). They will undertake data-model comparison. This work is crucial to evaluate the plausibility of different hypotheses which have been proposed to explain polynyas and glacial CO2 carbon changes (e.g. Morales Maqueda and Rahmstorf, 2002; Ferrari et al., 2014).

Methodology

The student will assess LGM sea-ice extent, seasonality and presence of polynyas through three approaches:
• Using the UKESM1 and UKESM2 earth system models. UKESM1(2) is the UK contribution to the international Climate Model Intercomparison Project CMIP6(7). UKESM1 include atmosphere-ocean coupling (85 vertical levels in the atmosphere, 75 in the ocean), alongside an ice sheet and ice shelf model BISICLES; and the new sea ice model SI3.
• Using model outputs from CMIP5 and CMIP6 to evaluate whether the sea-ice distributions and properties are specific to UKESM1 and UKESM2, and to assess the main controls and impacts of the observed patterns in sea ice across the available models.
• Data synthesis of published and unpublished results from marine sediments, ice cores, and novel archives (snow petrel stomach oil deposits) being generated by the current research team of Supervisor McClymont. Data will include microfossil assemblages and geochemical indicators from sediment/ice (e.g. marine aerosol flux to ice cores, stable isotopes in microfossils), as well as evidence for changing sea ice ecosystems from the stomach oil deposits (e.g. trace metal evidence for krill in the diet).
Supervisor Sime’s team is currently running UKESM1 and the proto-UKESM2 model, and analysing Antarctic sea-ice processes for the CMIP6 Preindustrial and Last Interglacial experiments. Supervisor McClymont’s team is generating new data primarily from snow petrel stomach oil deposits.

Project Timeline

Year 1

• Analyse the outputs of ongoing LGM-UKESM1 simulations. Compare LGM and Pre-industrial (CMIP6) simulations, to identify changes to seasonal sea-ice margins in the Weddell and Lazarev Seas, and to identify polynyas and their distribution (e.g. using 15% sea-ice concentration as a boundary).
• Review of data on LGM sea-ice extent and properties, spanning marine sediments, ice cores, and new data emerging from ongoing projects with Supervisor McClymont.
• Literature review and project design.
• Attend national conferences e.g. UK Sea Ice Group.

Year 2

• Access model outputs from CMIP5 and CMIP6 to evaluate whether the sea-ice distributions and properties are specific to UKESM1, and to assess the main controls and impacts of the observed patterns in sea ice across the available models. Begin work on setting up new UKESM2 LGM simulations.
• Synthesis review of data on LGM sea-ice extent and properties.
• Continue with literature review.
• Attend and present poster at an international conference e.g. INQUA.

Year 3

• Run and analyse new UKESM2 simulations for the LGM. Compare UKESM2 LGM sea-ice extent, seasonality and presence of polynyas to results from UKESM1 and equivalent CMIP6 output. Determine the significance of the new SI3 and BISICLES sea-ice and ice-sheet sub-models for the simulation of LGM sea ice.
• Model-data evaluation work.
• Attend and present talk at an international conference e.g. AGU.

Year 3.5

• International conferences and write-up.

Training
& Skills

We are seeking a strongly numerical candidate with degree in Oceanography, Meteorology, Applied Physics, Applied Maths or similar. The IAPETUS DTP programme will provide comprehensive personal and professional development training alongside extensive opportunities through interactions with a network of academic, research and industrial/policy partners. Additional specific training will be provided through courses including the NCAS Climate Model Support Workshop (Reading, UK) and Unified Model (MetOffice) training course (Exeter, UK). The student may also have the possible opportunity to participate in polar fieldwork and visits to other EU research institutes through EU-TIPES.
References & further reading

Chadwick, Matthew , Allen, Claire S. , Sime, Louise C. , Crosta, Xavier, Hillenbrand, Claus-Dieter . (2021) Reconstructing Antarctic winter sea-ice extent during Marine Isotope Stage 5e [in review]. Climate of the Past: Discussions. 10.5194/cp-2021-102

Ferrari, R., M. F. Jansen, J. F. Adkins, A. Burke, A. L. Stewart, and A. F. Thompson (2014), Antarctic sea ice control on ocean circulation in present and glacial climates, Proceedings of the National Academy of Sciences, 111(24), 8753-8758.

McClymont, Erin L., Bentley, Michael J., Hodgson, Dominic A. , Spencer-Jones, Charlotte L., Wardley, Thomas, West, Martin D., Croudace, Ian W., Berg, Sonja, Gröcke, Darren R., Kuhn, Gerhard, Jamieson, Stewart S. R., Sime, Louise , Phillips, Richard A.. (2021) Summer sea-ice variability on the Antarctic margin during the last glacial period reconstructed from snow petrel (Pagodroma nivea) stomach-oil deposits [in review]. Climate of the Past: Discussions. 10.5194/cp-2021-134

Morales Maqueda, M. A., and S. Rahmstorf (2002), Did Antarctic sea-ice expansion cause glacial CO2 decline?, Geophysical Research Letters, 29(1), 11-11-11-13.

Roche, D. M., X. Crosta, and H. Renssen (2012), Evaluating Southern Ocean sea-ice for the Last Glacial Maximum and pre-industrial climates: PMIP-2 models and data evidence, Quaternary Science Reviews, 56, 99-106.

Sime, L. C., K. E. Kohfeld, C. Le Quéré, E. W. Wolff, A. M. de Boer, R. M. Graham, and L. Bopp (2013), Southern Hemisphere westerly wind changes during the Last Glacial Maximum: model-data comparison, Quaternary Science Reviews, 64(0), 104-120.

Sime, Louise S. , Hodgson, Dominic , Bracegirdle, Thomas J. , Allen, Claire , Perren, Bianca , Roberts, Stephen , de Boer, Agatha M.. (2016) Sea ice led to poleward-shifted winds at the Last Glacial Maximum: the influence of state dependency on CMIP5 and PMIP3 models. Climate of the Past, 12. 2241-2253. doi:10.5194/cp-12-2241-2016

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