Preprints
https://doi.org/10.5194/egusphere-2022-699
https://doi.org/10.5194/egusphere-2022-699
 
08 Aug 2022
08 Aug 2022

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

Vincent Verjans1, Alexander Robel1, Helene Seroussi2, Lizz Ultee3, and Andrew Thompson4 Vincent Verjans et al.
  • 1School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA
  • 2Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
  • 3Department of Earth and Climate Sciences, Middlebury College, Middlebury, VT, USA
  • 4Environmental Science and Engineering, California Institute of Technology, Pasadena, CA 91125, USA

Abstract. 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 of 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.

Vincent Verjans et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on egusphere-2022-699', Juan Antonio Añel, 24 Aug 2022
    • AC1: 'Reply on CEC1', Vincent Verjans, 26 Aug 2022
    • AC2: 'Reply on CEC1', Vincent Verjans, 26 Aug 2022
  • RC1: 'Comment on egusphere-2022-699', Anonymous Referee #1, 27 Sep 2022
  • RC2: 'Comment on egusphere-2022-699', Anonymous Referee #2, 03 Oct 2022

Vincent Verjans et al.

Vincent Verjans et al.

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Short summary
We describe the development of the first large-scale ice sheet model that accounts for stochasticity in a range of processes. Stochasticity allows to represent the impacts of inherently uncertain processes on ice sheets. This includes climatic uncertainty, as the climate is inherently chaotic. Furthermore, stochastic capabilities also encompass poorly constrained glaciological processes that display strong variability at fine spatiotemporal scales. We present the model and test experiments.