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Previous studies have interpreted Last Interglacial (LIG;∼129–116 ka) sea‐level estimates in multiple different ways to calibrate projections of future Antarctic ice‐sheet (AIS) mass loss and associated sea‐level rise. This study systematically explores the extent to which LIG constraints could inform future Antarctic contributions to sea‐level rise. We develop a Gaussian process emulator of an ice‐sheet model to produce continuous probabilistic projections of Antarctic sea‐level contributions over the LIG and a future high‐emissions scenario. We use a Bayesian approach conditioning emulator projections on a set of LIG constraints to find associated likelihoods of model parameterizations. LIG estimates inform both the probability of past and future ice‐sheet instabilities and projections of future sea‐level rise through 2150. Although best‐available LIG estimates do not meaningfully constrain Antarctic mass loss projections or physical processes until 2060, they become increasingly informative over the next 130 years. Uncertainties of up to 50 cm remain in future projections even if LIG Antarctic mass loss is precisely known (±5 cm), indicating that there is a limit to how informative the LIG could be for ice‐sheet model future projections. The efficacy of LIG constraints on Antarctic mass loss also depends on assumptions about the Greenland ice sheet and LIG sea‐level chronology. However, improved field measurements and understanding of LIG sea levels still have potential to improve future sea‐level projections, highlighting the importance of continued observational efforts.
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Statistical Emulation of Ice-Sheet Model Simulations to Estimate Uncertainty in Future Antarctic Sea-Level Contributions
Observational estimates of Antarctic ice loss have accelerated in recent decades, and worst-case scenarios of modeling studies have suggested potentially catastrophic sea level rise (~2 meters) by the end of the century. However, modeled contributions to global mean sea level from the Antarctic ice-sheet (AIS) in the 21st century are highly uncertain, in part because ice-sheet model parameters are poorly constrained. Individual ice-sheet model runs are also deterministic and not computationally efficient enough to generate the continuous probability distributions required for incorporation into a holistic framework of probabilistic sea-level projections. To address these shortfalls, we statistically emulate an ice-sheet model using Gaussian Process (GP) regression. GP modeling is a non-parametric machine-learning technique which maps inputs (e.g. forcing or model parameters) to target outputs (e.g. sea-level contributions from the Antarctic ice-sheet) and has the inherent and important advantage that emulator uncertainty is explicitly quantified. We construct emulators for the last interglacial period and an RCP8.5 scenario, and separately for the western, eastern, and total AIS. Separate emulation of western and eastern AIS is important because their evolutions and physical responses to climate forcing are distinct. The emulators are trained on 196 ensemble members for each scenario, composed by varying the parameters of maximum rate of ice-cliff wastage and the coefficient of hydrofracturing. We condition the emulators on last interglacial proxy sea-level records and modern GRACE measurements and exclude poor-fitting ensemble members. The resulting emulators are sampled to produce probability distributions that fill intermediate gaps between discrete ice-sheet model outcomes. We invert emulated high and low probability sea-level contributions in 2100 to explore 21st century evolution pathways; results highlight the deep uncertainty of ice-sheet model physics and the importance of using observations to narrow the range of parameters. Our approach is designed to be flexible such that other ice-sheet models or parameter spaces may be substituted and explored with the emulator.
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- Award ID(s):
- 1664013
- PAR ID:
- 10113504
- Date Published:
- Journal Name:
- AGU Fall Meeting
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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