Abstract
The potential contribution of ice sheets remains the largest source of uncertainty in predicting sea-level due to the limited predictive skill of numerical ice sheet models, yet effective planning necessitates that these predictions are credible and accompanied by a defensible assessment of uncertainty. While the use of large ensembles of simulations allows probabilistic assessments, there is no guarantee that these simulations are aligned with observations. Here, we present a probabilistic prediction of 21st century mass loss from the Greenland Ice Sheet calibrated with observations of surface speeds and mass change using a novel two-stage surrogate-based approach. Our results suggest a sea-level contribution ranging from 4 to 30 cm at the year 2100, proviso the assumption that our chosen ice sheet model’s physics represent reality.
| Original language | English |
|---|---|
| Article number | e2022GL099058 |
| Journal | Geophysical Research Letters |
| Volume | 49 |
| Issue number | 19 |
| DOIs | |
| State | Published - Oct 16 2022 |
Funding
This work was supported NSF Grant PLR‐1603799 and NASA Grant 80NSSC21K0748. The authors thank Martin Truffer, Mark Fahnestock, and Tim Bartholomaus for enlightening discussion, and two anonymous reviewers whose insightful comments significantly improved the quality of this manuscript.
| Funders | Funder number |
|---|---|
| PLR‐1603799 | |
| National Aeronautics and Space Administration | 80NSSC21K0748 |
Keywords
- Bayesian calibration
- Greenland
- data assimilation
- ice sheet modeling
- sea level rise
- uncertainty quantification