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Probabilistic Sea-Level Projections from Ice Sheet and Earth System Models

Funding Program Area(s)
Project Type
Scientific Discovery through Advanced Computing (SciDAC)
Project Term
Project Team

Principal Investigator

Project Participant

Polar ice sheets present the largest potential for future sea-level change (~65 m of sea-level equivalent). Confidence in sea-level projections requires accurate simulations of ice sheet evolution using next-generation ice sheet models (ISMs) coupled to Earth System Models (ESMs). Limitations preventing accurate sea level projections include: (1) missing ISM and ESM physics; (2) inadequate ISM initialization methods; (3) incomplete coupling of ISMs and ESMs and incomplete understanding of climate processes relevant to ice sheet evolution; (4) inefficient frameworks for accurately quantifying and forward propagating ISM uncertainties to provide probabilistic sea level distributions.

This project aims to remedy these defficiencies. Our efforts build on previous SciDAC investments in BISICLES and MPAS-FELIX – two numerically and computationally mature, next-generation ISMs – by leveraging the unique capabilities of each model, including multi-model simulations estimating model structural uncertainty. We will implement physically-based representations of missing or oversimplified processes critical for accurate simulations of ice sheet evolution, including new representations of evolutionary subglacial hydrology, damage mechanics, fracture, and iceberg calving, and solid-earth dynamics beneath and proximal to ice sheets. ISM evolution is a strong function of climate, which is coupled through mass and heat fluxes at the ice sheet’s subaerial and submarine boundaries. This coupling impacts the ice sheet through non-linear, globally coupled climate processes, which are themselves impacted by feedbacks with ice sheet evolution. To capture these processes, we will complete, test, and validate new ice sheet and ocean model physics and coupling needed to include ISMs as fully coupled components of DOE's Energy Exascale Earth System Model (E3SM). This will allow for an accurate accounting of climate change impacts on ice sheet evolution (and vice versa). To support this coupling, we will develop improved ISM optimization methods that minimize non-physical transients when coupling to ESMs and that allow for the quantification of model input parameter uncertainties. In turn, this will allow for new “end-to-end” uncertainty quantification frameworks, so that model uncertainties can be propagated forward to model outputs of interest.

The new model physics, coupling, and optimization and uncertainty quantification workflows will be computationally expensive. Thus, e cient ISMs will be critical to the success of our efforts. To counter costs and ensure that our ISMs are affordable and robust as coupled components of E3SM, we will implement and test new methods for stable, accurate, and effcient time stepping and we will conduct performance enhancements necessary for ensuring that our ISMs are portable to, and run effciently on, next-generation HPC architectures.

The synthesis of our efforts will come about through a suite of ISM and ESM experiments that will provide critical waypoints towards the ultimate goal of probabalistic, policy-actionable, E3SM-based sea-level projections. We will focus initially on simplified and idealized experiments, based on and including standard, community-endorsed experiments. These will provide inexpensive baselines for assessing the fidelity and cost of new model physics and well understood experimental protocols for use in assessing the feasibility of new optimization and uncertainty quantification workflows. We will then conduct similar assessments but using large-scale, standardized experiments for Greenland and Antarctica, which will provide critical insight for applying our new models and methods in realistic settings. Finally, we will apply our models in a set of E3SM-based, offine-forced and coupled, future sea-level projection experiments. These will include hindcasting-based model validation over the 1960-2010 time frame and probability distributions on model outputs from 2010-2100.

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