16 Feb 2022
16 Feb 2022
Status: this preprint is currently under review for the journal TC.

Predictability of Arctic Sea Ice Drift in Coupled Climate Models

Simon F. Reifenberg1,a and Helge F. Goessling1 Simon F. Reifenberg and Helge F. Goessling
  • 1Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany
  • anow at: MARUM – Center for Marine Environmental Science & Institute of Environmental Physics, University of Bremen, Bremen, Germany

Abstract. Skillful sea ice drift forecasts are crucial for scientific mission planning and marine safety. Wind is the dominant driver of ice motion variability, but more slowly varying components of the climate system, in particular ice thickness and ocean currents, bear the potential to render ice drift more predictable than the wind. In this study, we provide the first assessment of Arctic sea ice drift predictability in four coupled general circulation models (GCMs), using a suite of "perfect-model" ensemble simulations. We find the position vector from Lagrangian trajectories of virtual buoys to remain predictable for at least 90 (45) days lead time for initializations in January (July), reaching about 80 % of the position uncertainty of a climatological reference forecast. In contrast, the uncertainty of Eulerian drift vector predictions reaches the level of the climatological uncertainty within less than four weeks. Spatial patterns of uncertainty, varying with season and across models, develop in all investigated GCMs. For two models providing near-surface wind data (AWI-CM1 and HadGEM1.2), we find spatial patterns and large fractions of the variance to be explained by wind vector uncertainty. The latter implies that sea ice drift is only marginally more predictable than wind. Nevertheless, particularly one of the four models (GFDL-CM3) shows a significant correlation of up to −0.85 between initial ice thickness and target position uncertainty in large parts of the Arctic. Our results provide a first assessment of the inherent predictability of ice motion in coupled climate models, they can be used to put current real-world forecast skill into perspective, and highlight model diversity of sea ice drift predictability.

Simon F. Reifenberg and Helge F. Goessling

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-41', Anonymous Referee #1, 08 Apr 2022
  • RC2: 'Comment on tc-2022-41', Anonymous Referee #2, 06 May 2022

Simon F. Reifenberg and Helge F. Goessling

Simon F. Reifenberg and Helge F. Goessling


Total article views: 437 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
330 95 12 437 8 6
  • HTML: 330
  • PDF: 95
  • XML: 12
  • Total: 437
  • BibTeX: 8
  • EndNote: 6
Views and downloads (calculated since 16 Feb 2022)
Cumulative views and downloads (calculated since 16 Feb 2022)

Viewed (geographical distribution)

Total article views: 408 (including HTML, PDF, and XML) Thereof 408 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 24 May 2022
Short summary
Using model simulations, we analyze the impact of chaotic error growth on Arctic sea ice drift predictions. Regarding forecast uncertainty, our results suggest that it matters in which season ice drift forecasts are initialized, it matters where they are initialized, and that both also varies with the model in use. We find ice velocities to be slightly more predictable than near-surface wind, a main driver of ice drift. This is relevant for future developments of ice drift forecasting systems.