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The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0)

Verjans, Vincent and Robel, Alexander A. and Seroussi, Helene and Ultee, Lizz and Thompson, Andrew F. (2022) The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0). Geoscientific Model Development, 15 (22). pp. 8269-8293. ISSN 1991-9603. doi:10.5194/gmd-15-8269-2022.

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We introduce the first version of the Stochastic Ice-sheet and Sea-level System Model (StISSM v1.0), which adds stochastic parameterizations within a state-of-the-art large-scale ice sheet model. In StISSM v1.0, stochastic parameterizations target climatic fields with internal variability, as well as glaciological processes exhibiting variability that cannot be resolved at the spatiotemporal resolution of ice sheet models: calving and subglacial hydrology. Because both climate and unresolved glaciological processes include internal variability, stochastic parameterizations allow StISSM v1.0 to account for the impacts of their high-frequency variability on ice dynamics and on the long-term evolution of modeled glaciers and ice sheets. StISSM v1.0 additionally includes statistical models to represent surface mass balance and oceanic forcing as autoregressive processes. Such models, once appropriately calibrated, allow users to sample irreducible uncertainty in climate prediction without the need for computationally expensive ensembles from climate models. When combined together, these novel features of StISSM v1.0 enable quantification of irreducible uncertainty in ice sheet model simulations and of ice sheet sensitivity to noisy forcings. We detail the implementation strategy of StISSM v1.0, evaluate its capabilities in idealized model experiments, demonstrate its applicability at the scale of a Greenland ice sheet simulation, and highlight priorities for future developments. Results from our test experiments demonstrate the complexity of ice sheet response to variability, such as asymmetric and/or non-zero mean responses to symmetric, zero-mean imposed variability. They also show differing levels of projection uncertainty for stochastic variability in different processes. These features are in line with results from stochastic experiments in climate and ocean models, as well as with the theoretical expected behavior of noise-forced non-linear systems.

Item Type:Article
Related URLs:
URLURL TypeDescription
Verjans, Vincent0000-0002-3928-9556
Robel, Alexander A.0000-0003-4520-0105
Seroussi, Helene0000-0001-9201-1644
Ultee, Lizz0000-0002-8780-3089
Thompson, Andrew F.0000-0003-0322-4811
Additional Information:Helene Seroussi was also funded by the NSF Navigating the New Arctic program. Computing resources were provided by the Partnership for an Advanced Computing Environment (PACE) at the Georgia Institute of Technology, Atlanta. We acknowledge HPC assistance from Fang (Cherry) Liu. We thank Mathieu Morlighem and Justin Quinn for providing helpful advice about ISSM. We thank Kevin Bulthuis for the implementation of the random number generator. We thank all developers of ISSM for their continuing work on model development. We acknowledge the two anonymous reviewers for their constructive comments that helped improve the quality of the manuscript. Vincent Verjans thanks John Christian for his interest in the study and for insightful discussions about ice sheet sensitivity to variability. This research has been supported by the Heising-Simons Foundation (grant no. 2020-1965).
Group:Division of Geological and Planetary Sciences
Funding AgencyGrant Number
Heising-Simons Foundation2020-1965
Issue or Number:22
Record Number:CaltechAUTHORS:20221201-39196300.2
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:118192
Deposited By: Research Services Depository
Deposited On:23 Dec 2022 18:48
Last Modified:23 Dec 2022 18:52

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