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Constraining projections of ice sheet instabilities and future sea level rise

Lead Research Organisation: University of Leeds
Department Name: School of Earth and Environment

Abstract

The largest threat of future rapid sea level rise is from the collapse of ice sheets due to instability and runaway ice loss. It could lead to more than 1 m of sea level rise by 2100, submerging land currently home to 100 million people and causing further destruction in higher-elevation coastal regions through enhanced storm and flood risk. Predicting the future possibility of such instabilities and the resulting plausible 'worst case' sea level change is critical for adequately planning coastal defences and long term infrastructures (e.g. 150 years planning horizon), especially those for which a rare event could have devastating consequences (e.g. nuclear power plants, the Thames barrier, transport networks). Yet, this is extremely challenging, because ice sheet instabilities have not occurred since we started observing ice sheets (the record is too short and ice sheets have been stable in the recent past) and they depend on poorly understood mechanisms (e.g. sliding of ice) that occur in inaccessible areas, such as under kilometres of ice.

There is a solution: ice sheet instabilities have occurred in the geological past, for example in North America, 14,500 years ago (the time of mammoths and modern humans), producing ~7 m sea level rise in 340 years. Ancient ice sheets have left fingerprints of their activity and retreat on the landscape, which have been reconstructed in great detail in places such as the UK, Northern Europe and North America. These records of past ice sheet evolution provide an untapped goldmine of data that could be used to test and improve numerical models, informing future projections. This concept was demonstrated by DeConto and Pollard (2016), who projected Antarctic melting resulting in 15 m of sea level rise by 2500 based on constraints from 3 million years ago (the last time levels of atmospheric carbon dioxide were as high as today). However, there is an important missing piece to this work. In order to reliably translate knowledge from the past into confident future projections, the most important and complex source of uncertainty in modelling past ice sheets needs to be accounted for: the climate. This requires new statistical methods and a person with a unique combination of expertise in statistics, climate and ice sheet instabilities to lead their development. The ambition for this fellowship, is to make that person me .

I will lead an interdisciplinary team of researchers to develop and apply new statistical and physically-based tools to accurately quantify uncertainties in past, present and future climate and ice sheet evolution, thus unlocking the key potential of geological records to constrain future ice sheet instability. This will produce the first robust projection of future ice sheet instability and the resulting sea level change. It will unite and grow the three leading strands of my research: mechanisms of ice sheet instability, climate change, and uncertainty quantification, establishing me as a world leader in using geological data to constrain ice sheet behaviour and future sea level change.

Planned Impact

The main societal benefit from my fellowship is the delivery of reliable projections of extreme sea level change, enabling more effective UK and global decision making on adaptation to and mitigation (avoidance) of dangerous future sea level rise.

1. Planning coastal flood protection
Businesses and governments planning adaptation measures rely on international and national assessments of sea level change (e.g. Intergovernmental Panel on Climate Change report, IPCC AR5; UK climate projections, UKCP). Lack of knowledge on near-future ice sheet collapse meant that AR5 did not assess this worst-case scenario for sea level rise. However, this is crucial information for extreme event contingency planning (e.g. nuclear power plant flooding or Thames Barrier failure in London). For this purpose, the most recent UKCP assessment provided a 'plausible extreme' scenario from expert opinion and extrapolations, but a newly discovered mechanism of ice sheet instability could lead to even greater sea level rise.

This fellowship will support me to revolutionise projections of 'plausible extreme' sea level rise, using robust quantification from complex ice sheet models that incorporate our best understanding of ice dynamics and comprehensive past/present observations. I will deliver projections up to 2300 AD, that are needed by public and private sector planners of long term infrastructure (100-200 years) such as transport, coastal defence and nuclear power plants; enabling more reliable and effective planning of these vital services. The energy industry will strongly benefit because there are 9 nuclear power stations relying on coastal flood defences. It is imperative for UK population safety and energy security that those defences are not breached. My results will enable the Office for Nuclear Regulation to more reliably assess safety and energy companies to more effectively plan flood defences.The Environment Agency; the Department for Environment, Food and Rural Affairs; major UK cities (e.g. London, Bristol, Liverpool) and local councils (e.g. in Yorkshire near the Humber and coastal Lincolnshire) will benefit in the long term from improved flood risk management ensuring better investment and potentially saving billions of pounds by avoiding unnecessary or ineffective infrastructure and preventing flood damage.

My work will benefit the Met Office by providing (i) an improved version of the BISICLES ice sheet model, part of the flagship UK Earth System Model used for IPCC climate projections, and (ii) data on the probability of future ice sheet instabilities that feed UK climate projections.

Internationally, this work will help large coastal cities (Ho Chi Minh City, Mumbai, Bangkok, Shanghai, New Orleans...) and Small Island States (Papua New Guinea, Fiji, the Maldives, Tuvalu...) plan for sea level rise. Governments worldwide rely on projections from the IPCC, which my fellowship will directly inform.

2. Avoiding dangerous sea level change
I will determine the thresholds in sea level response to warming when ice sheet instabilities are triggered. This knowledge gap is possibly the biggest hindrance to determining dangerous climate change associated with ice sheet collapse. Filling it is critical for reaching meaningful, international agreements on mitigating climate change. Sea level rise is a global problem, and unlike the complexity of atmospheric changes, it presents a threat that is easier for a person without related expertise to understand, with the potential to provide clear red lines for 'this is safe, this is not'. As such, it provides strong motivation to establish detailed, actionable measures to meet internationally agreed targets, such as the Paris Agreement to keep global temperature rise below 2 degrees Celsius.

These benefits will be realised in the short to medium term (5-12 years after the fellowship starts) when our results are included in UK and international assessments of sea level change.

Publications

10 25 50
 
Description This project has developed new methods for sampling and quantifying uncertainty in climate and ice sheet evolution over long timescales. This included a statistical method using the Bayes Linear theorem to reconstruct multi-dimensional (e.g. time, latitude, longitude) climate fields using output from different models and sparse observations. Using this method, we produced a new reconstruction of monthly mean sea surface temperatures at the Last Glacial Maximum and ensembles of plausible monthly maps that can be used as inputs for atmosphere models.
We have also demonstrated that the Last Glacial Maximum can be used as a stringent test of coupled climate-ice sheet models. We have revealed that the FAMOUS-ice model had been overtuned to modern-day observations of the Greenland ice sheet. By developing efficient uncertainty quantification techniques, we successfully de-tuned the model to produce plausible simulations of the Northern Hemisphere ice sheets and climate at the Last Glacial Maximum, 21,000 years ago and the Penultimate Glacial Maximum, 140,000 years ago. We have also found that tuning to a stable cold period such as the LGM can also lead to over-tuning resulting in an overly stable ice sheet. Very few parameter combinations produce good results for the Last Glacial maximum and present day, but it is possible to find combinations of parameters that produce a realistic rate of deglaciation.
Exploitation Route The dataset generated by our statistical methods and climate-ice sheet models are useful to researchers investigating environmental changes during the Quaternary (e.g. climate, peatland, ocean circulation, landscape evolution, sea level, vegetation). In particular, the data can be used as input for other climate, sea level, vegetation, or ecosystem models. It may also be used to reconstruct the past evolution of Northern Hemisphere ice sheets to gain information on sea level and solid earth processes. Our uncertainty quantification work will inform future development of old and new versions of the UK Climate model (Unified Model and UK Earth System Model) by the Met Office and NCAS-climate modelling. The statistical techniques we developed are applicable to a wide range of domains involving complex models with large uncertainties. We are currently using the techniques in a number of spin-off project.
Sectors Digital/Communication/Information Technologies (including Software)

Environment

 
Description Contribution to Masters Climate Futures teaching
Geographic Reach Multiple continents/international 
Policy Influence Type Influenced training of practitioners or researchers
Impact Yearly cohorts of 20-30 international students improve their knowledge of polar science and uncertainty quantification as part of a Masters degree. Alumni of this programme go onto careers in policy -making, charities, reinsurance industry and environmental consultancies.
 
Description NSFGEO-NERC:The Collapse of the Cordilleran Ice Sheet: Using Glacial Dipsticks to Constrain Ice Sheet Modeling
Amount £244,501 (GBP)
Funding ID NE/Z000416/1 
Organisation Natural Environment Research Council 
Sector Public
Country United Kingdom
Start 08/2024 
End 06/2027
 
Description Re-evalution of rapid ice sheet expansion during glacial period with a comprehensive ice sheet climate coupled model
Amount ï¿¥12,556,000 (JPY)
Organisation Japan Society for the Promotion of Science (JSPS) 
Sector Public
Country Japan
Start 02/2023 
End 07/2023
 
Description SMB-Gen2: Renewal of "Constraining projections of ice sheet instabilities and future sea level rise"
Amount £601,547 (GBP)
Funding ID MR/Y034228/1 
Organisation United Kingdom Research and Innovation 
Sector Public
Country United Kingdom
Start 02/2025 
End 10/2028
 
Description UK SWAIS 2C
Amount £382,375 (GBP)
Funding ID NE/X009351/1 
Organisation Natural Environment Research Council 
Sector Public
Country United Kingdom
Start 01/2024 
End 12/2026
 
Description Understanding rising seas and ice by linking coupled models and past climates
Amount £634,311 (GBP)
Funding ID NE/T007443/1 
Organisation Natural Environment Research Council 
Sector Public
Country United Kingdom
Start 12/2020 
End 05/2023
 
Title Bayes Linear statistical method for reconstructing multidimensional climate fields from climate model output and sparse observational data. 
Description Any experiment with climate models relies on a potentially large set of spatiotemporal boundary conditions. These can represent both the initial state of the system and/or forcings driving the model output throughout the experiment. These boundary conditions are typically fixed using available reconstructions in climate modelling studies; however, in reality, they are highly uncertain, that uncertainty is unquantified, and the effect on the output of the experiment can be considerable. We develop efficient quantification of these uncertainties that combines relevant data from multiple models and observations. Starting from the coexchangeability model, we develop a coexchangeable process model to capture multiple correlated spatiotemporal fields of variables. We demonstrate that further exchangeability judgements over the parameters within this representation lead to a Bayes linear analogy of a hierarchical model. We use the framework to provide a joint reconstruction of sea-surface temperature and sea-ice concentration boundary conditions at the last glacial maximum (23-19 kya) and use it to force an ensemble of ice-sheet simulations using the FAMOUS-Ice coupled atmosphere and ice-sheet model. We demonstrate that existing boundary conditions typically used in these experiments are implausible given our uncertainties and demonstrate the impact of using more plausible boundary conditions on ice-sheet simulation. 
Type Of Material Improvements to research infrastructure 
Year Produced 2024 
Provided To Others? Yes  
Impact This method enabled our team to produce a reconstruction of monthly mean sea surface temperatures at the Last Glacial Maximum with quantified uncertainties. We were then able to produce ensembles of plausible monthly sea surface temperatures and used them as input to a coupled atmosphere-ice sheet model to evaluate the effect of this uncertainty on northern hemisphere ice sheets. 
 
Title Coexchangeable Process Modeling for Uncertainty Quantification in Joint Climate Reconstruction 
Description Any experiment with climate models relies on a potentially large set of spatio-temporal boundary conditions. These can represent both the initial state of the system and/or forcings driving the model output throughout the experiment. These boundary conditions are typically fixed using available reconstructions in climate modeling studies; however, in reality they are highly uncertain, that uncertainty is unquantified, and the effect on the output of the experiment can be considerable. We develop efficient quantification of these uncertainties that combines relevant data from multiple models and observations. Starting from the coexchangeability model, we develop a coexchangeable process model to capture multiple correlated spatio-temporal fields of variables. We demonstrate that further exchangeability judgments over the parameters within this representation lead to a Bayes linear analogy of a hierarchical model. We use the framework to provide a joint reconstruction of sea-surface temperature and sea-ice concentration boundary conditions at the last glacial maximum (23-19 kya) and use it to force an ensemble of ice-sheet simulations using the FAMOUS-Ice coupled atmosphere and ice-sheet model. We demonstrate that existing boundary conditions typically used in these experiments are implausible given our uncertainties and demonstrate the impact of using more plausible boundary conditions on ice-sheet simulation. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. 
Type Of Material Database/Collection of data 
Year Produced 2024 
Provided To Others? Yes  
Impact Code provided ensure the reproducibility of the work and the potential use of the code for future applications. 
URL https://tandf.figshare.com/articles/dataset/Coexchangeable_process_modelling_for_uncertainty_quantif...
 
Title FAMOUS-Glimmer simulations with interactive North American and Greenland ice sheets (21ka and 140ka) 
Description This dataset contains model inputs and outputs from ensembles of simulations and sensitivity tests performed by FAMOUS-Glimmer of the Last Glacial Maximum (LGM; 21 kilo annum before present (ka BP)) and the Penultimate Glacial Maximum (PGM; 140 ka BP), as used in the publication Patterson et al., 2024 (https://doi.org/10.5194/cp-2024-10). These data were used to help understand the difference between the North American Ice Sheet at the last two glacial maxima, explore the sensitivity of the ice sheet to uncertain model parameters and understand the role of orbit, greenhouse gases and initial conditions on the final ice sheet configurations. The output of 62 ensemble members varying climate and ice sheet model parameters for each of the LGM and PGM are included as well as the results of 8 sensitivity tests using one set of parameters but varying the initial ice sheets and climates. These simulations were created using the atmospheric general circulation model FAMOUS coupled to the Glimmer ice sheet model under LGM and PGM climate boundary conditions, including greenhouse gas concentrations and orbital parameters outlined in the PMIP4 protocols (Kageyama et al., 2017 and Menviel et al., 2019). The model inputs include ancillary files of the prescribed sea surface temperature and sea ice fields and climate model boundary conditions, netCDF files of the ice sheet model initial condition as well as the model configuration file and updated model modifications. The outputs consist of netCDF files of monthly climate model variables from 'Not Ruled Out Yet' ensemble simulations, ice sheet model output from the final timestep of all ensemble simulations (including ice sheet thickness, topography, surface mass balance and velocity) and final ice sheet thickness from the sensitivity tests. Also included are excel spreadsheets of the time series of total ice volumes for all ensemble members and the list of parameter combinations used for each. The climate model data has global coverage on a 7.5x5 degree lat/lon grid. The ice sheet model data covers North America and Greenland on a Lambert Azimuthal Equal Area projection at 40x40km resolution. Each simulation was run for ~1000 climate model years, with the ice sheet model running at 10x acceleration, giving ~10,000 years of ice sheet model output. 
Type Of Material Database/Collection of data 
Year Produced 2024 
Provided To Others? Yes  
Impact This dataset led to further work on modelling ice sheets at the Last Glacial Maximum and understanding biases in the coupled climate ice sheet model used in this study. 
URL https://catalogue.ceda.ac.uk/uuid/5e48b31e413b480792e4156191b654f4
 
Title LGM North America FAMOUS-ice simulation 
Description Archived files for manuscript "De-tuning a coupled Climate Ice Sheet Model to simulate the North American Ice Sheet at the Last Glacial Maximum" by Gandy et al., in prep. Contents; >Netcdf time series for key variables from FAMOUS >Netcdf time series from the Glimmer ice sheet model All experiments are not archived online to reduce the total file size, this is one simulation (from Wave 2) of 380. To receive all the output files please contact Niall Gandy (n.gandy@shu.ac.uk). 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
Impact We are currently using this dataset to constrain and improve the FAMOUS-ice coupled climate-ice sheet model and to demonstrate what past ice sheet changes can tell us about future projections of the Greenland ice sheet and sea level rise. 
URL https://data.mendeley.com/datasets/8kswwpnjyz/1
 
Description NASA GISS Sea LEvel Rise Seminar 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Postgraduate students
Results and Impact NASA Goddard Institute for Space Science, Sea Level Rise Seminar, 2022-05-03
Title: Modelling past ice sheets to improve climate and sea level projections; tackling the climate uncertainty
Online seminar with ~ 40 participants from NASA and universties worldwide. Recording posted on Youtube with 138 views (viewed March 2025) from the general public, undergraduate and postgraduate students learning about the topic.
Year(s) Of Engagement Activity 2022
URL https://www.youtube.com/watch?v=MaKOzw_2aXw&ab_channel=NASAGoddardInstituteforSpaceStudies
 
Description Talk to SEN students 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach Regional
Primary Audience Schools
Results and Impact The postgraduate researcher on the project gave a talk to Special Education Needs students who visited the School of Earth and Environments in 2023. The topic was climate and ice sheets of the past. It sparked questions and discussions afterwards increasing the interest of the students in environmental sciences.
Year(s) Of Engagement Activity 2023