Preprints
https://doi.org/10.5194/egusphere-2022-982
https://doi.org/10.5194/egusphere-2022-982
 
25 Oct 2022
25 Oct 2022
Status: this preprint is open for discussion.

The effects of assimilating a sub-grid scale sea ice thickness distribution in a new Arctic sea ice data assimilation system

Nicholas Williams1, Nicholas Byrne2, Daniel Feltham1, Peter Jan Van Leeuwen2,3, Ross Bannister2, David Schroeder1, Andrew Ridout4, and Lars Nerger5 Nicholas Williams et al.
  • 1Centre for Polar Observation and Modelling, Department of Meteorology, University of Reading, Earley Gate, PO Box 243, Reading, RG6 6BB, UK
  • 2Department of Meteorology, University of Reading, Earley Gate, PO Box 243, Reading, RG6 6BB, UK
  • 3Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, United States
  • 4Centre for Polar Observation and Modelling, Department of Earth Sciences, University College London, London, UK
  • 5Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, Bremerhaven, Germany

Abstract. In the past decade groundbreaking new satellite observations of the Arctic sea ice cover have been made, allowing researchers to understand the state of the Arctic sea ice system in greater detail than before. The derived estimates of sea ice thickness are useful but limited in time and space. In this study the first results of a new sea ice data assimilation system are presented. Observations assimilated (in various combinations) are monthly mean sea ice thickness and monthly mean sea ice thickness distribution from Cryosat-2, and NASA daily Bootstrap sea ice concentration. This system couples the Centre for Polar Observation and Modelling's (CPOM) version of the Los Alamos Sea Ice Model (CICE) to the Localised Ensemble Transform Kalman Filter (LETKF) from the Parallel Data Assimilation Framework (PDAF) library. The impact of assimilating a sub-grid scale sea ice thickness distribution is of particular novelty. The sub-grid scale sea ice thickness distribution is a fundamental component of sea ice models, playing a vital role in the dynamical and thermodynamical processes, yet very little is known of its true state in the Arctic.

This study finds that assimilating Cryosat-2 products for the mean thickness and the sub-grid scale thickness distribution can have significant consequences on the modelled distribution of the ice thickness across the Arctic and particularly in regions of thick multi-year ice. The assimilation of sea ice concentration, mean sea ice thickness and sub-grid scale sea ice thickness distribution together performed best when compared to a subset of Cryosat-2 observations held back for validation. Regional model biases are reduced: the thickness of the thickest ice in the Canadian Archipelago is decreased, but the thickness of the ice in the Central Arctic is increased. When comparing the assimilation of mean thickness with the assimilation of sub-grid scale thickness distribution, it is found that the latter leads to a significant change in the volume of ice in each category. Estimates of the thickest ice improve significantly with the assimilation of sub-grid scale thickness distribution alongside mean thickness.

Nicholas Williams et al.

Status: open (until 20 Dec 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-982', Anonymous Referee #1, 14 Nov 2022 reply
  • RC2: 'Comment on egusphere-2022-982', Anonymous Referee #2, 15 Nov 2022 reply

Nicholas Williams et al.

Model code and software

PDAF v2.0 Lars Nerger https://pdaf.awi.de/trac/wiki

CICE v5.1.2 Elizabeth Hunke, David Bailey, Adrian Turner, William Lipscomb, Alice DuVivier, Nicole Jeffery, Daniela Flocco https://github.com/CICE-Consortium/CICE-svn-trunk/tree/cice-5.1.2

Nicholas Williams et al.

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Short summary
Observations show that the Arctic sea ice cover has reduced over the last 40 years. This study uses ensemble-based data assimilation in a stand-alone sea ice model to investigate the impacts of assimilating three different kinds of sea ice observation, including the novel assimilation of sea ice thickness distribution. We show that assimilating ice thickness distribution has a positive impact on thickness and volume estimates within the ice pack, especially for very thick ice.