QUoRUM: QUantifying and Reducing Uncertainty in Multi-Decadal Projection of Ice Sheet-Sea Level Contribution

The West Antarctic Ice Sheet contains enough ice to cause 3.3 meters of sea level rise. The ice streams of its Amundsen Sea sector, which alone could contribute up to 1.2 meters of sea level rise, are thinning faster than in any other region on earth, and have the potential for rapid collapse due to inland-deepening bedrock. Using a combination of novel inverse modelling, a comprehensive ice-sheet model, and remote sensing we will:

1) Estimate the present state of the critical Amundsen sector
2) Predict its future behaviour
3) Quantify the uncertainty of these estimates and predictions

The physics of ice-sheet retreat is qualitatively understood, but the detailed behaviour is dependent upon a very large number of parameters that cannot be measured directly (e.g, spatially-varying basal traction and ice stiffness). However, numerical ice sheet models have now evolved to the point where a number of relevant physical processes, such as grounding line movement and ice-sheet response to ocean forcing, can be represented accurately. Moreover, the satellite-observational record continues to grow, creating opportunities for assimilation of this new data into models. Such a model-data synthesis can allow key underlying and hidden physical parameters to be determined, facilitating data-driven prediction of future ice-sheet contribution to sea levels.

However, techniques for the assimilation of data using ice sheet models remain at an early stage. A considerable amount of data remains unused and fundamental questions, such as the specific information required for reliable predictions, remain unanswered. Moreover, model simulations of future behaviour of ice sheets generally do not account for the uncertainty inherent in estimates of hidden parameters, which can potentially grow with forecast horizons. Accounting for these uncertainties is vital so that informed risk and cost-benefit analyses of sea-level rise protection and adaptation can be carried out.

In the proposed project we will develop a model-based framework which will efficiently assimilate the data record for the Amundsen sector (Fig. 1), providing estimates of key physical quantities, and predictions of future behaviour. Crucially, measures of uncertainty will be provided for the estimate and predictions. We will further study the impact that different observations have on our model predictions and uncertainty therein, providing information that will be of value to future observational campaigns. While the Amundsen region is chosen as a focus in the interest of critical relevance and timeliness, the methodology can be applied more generally in other regions of Antarctica, or Greenland.

Who might benefit from this research?

a) Government departments with remits of mitigation of adaptation to climate change risks (BEIS and DEFRA). Subgroups concerned with adaptation to sea level rise should be kept aware of current activities and research outcomes in the area of projected sea level rise to inform planning and risk analyses, and those concerned with mitigation will be concerned with "tipping points" and threshold behaviour involving ice-sheet loss in response to climate change.

b) Non-departmental agencies with statutory duties to monitor and plan for flooding risk (e.g. UK Environment Agency, Scottish Environmental Protection Agency (SEPA)). As the impacts of ice loss in West Antarctica on sea levels are felt strongly in the Northern Hemisphere, the outcomes from the proposed research (quantified uncertainties of Amundsen sea level contribution under different warming scenarios) could be used to predict UK coastal flooding.

c) Information sources for government and policy-makers (e.g. SPICE - Scottish parliamentary information resource to provide briefing notes to MSPs, and CAMERAS -- Coordinated Agenda for Marine, Environment and Rural Affairs Science)

d) International space agencies such as ESA (European Space Agency), which seek to optimize the scientific and societal impact generated from the data their satellites gather.

e) The general public: Sea level rise impacts all those living along or close to a coast. The public has a vested interest in the potential for future sea level rise -- and also, in how certain the projections of sea level actually are.

f) NERC, NSF, and other funding agencies engaged in environmental and sea-level research. The decision-makers in these agencies should be kept informed on where and how observational campaigns can have the largest impact on reducing uncertainty in future sea-level rise.

How might they benefit from this research?

a) Engagement with Scottish and UK policy makers. We will engage with stakeholders through ClimateXChange (CXC), Scotland's centre of expertise on climate change, by responding directly through 'call-down mode' requests from agencies and information providers and by working directly with the CXC Policy team to produce reports. Additionally, the PI will visit the Met Office and liaise with the Met Office-Hadley Centre Climate Program, which feeds directly to DEFRA and BEIS.

b) Economic benefit: The quantified uncertainties in our projections comprise risk probabilities that will be of use to agencies involved with planning and protection of coastal environments and infrastructure across the world. Quantified risk will allow these planners to make optimal economic decisions.

c) Allocation of funding of Antarctic fieldwork: it is vital that our results be visible to the relevant decision makers within NERC, such as the Climate System and Earth System Science theme leaders and the Living with Environmental Change programme. We will draft a white paper detailing our results and their significance for policymakers and their relevance in directing future Antarctic research.

d) Public engagement: While the computational tools used in the proposed study are highly mathematical, the current public interest in "big data" and the "data science revolution" provides an entryway for the team to discuss with the public how these mathematical techniques can be used to benefit the public, and not just large corporations. The proposed workshop for adjoint models used in the climate sciences will be organised with the central aim of generating materials for public consumption to educate the public on data science-oriented work in the earth sciences. The intended target is secondary school students transitioning to University, who will be reached through a Massive Open Online Course (MOOC) in geo-data science methods and applications, for which funding is already secured.