Catalogue | Flanders Marine Institute

Flanders Marine Institute

Platform for marine research


This search interface gives access to the reference database of VLIZ, an extensive collection of (inter)national marine scientific literature references.

You can limit your search to the Belgian marine literature only or to the VLIZ Library catalogue only by checking the 'VLIZ Library' box.

New search
[ report an error in this record ]basket (0): add | show Print this page

Multi-model seasonal forecast of Arctic sea-ice: forecast uncertainty at pan-Arctic and regional scales
Blanchard-Wrigglesworth, E.; Barthélemy, A.; Chevallier, M.; Cullather, R.; Fuckar, N.; Massonnet, F.; Posey, P.; Wang, W.; Zhang, J.; Ardilouze, C.; Bitz, C.M.; Vernieres, G.; Wallcraft, A.; Wang, M. (2017). Multi-model seasonal forecast of Arctic sea-ice: forecast uncertainty at pan-Arctic and regional scales. Clim. Dyn. 49(4): 1399-1410.
In: Climate Dynamics. Springer: Berlin; Heidelberg. ISSN 0930-7575, more
Peer reviewed article  

Available in  Authors 

Author keywords
    Sea ice; Seasonal forecast; Arctic; Forecast uncertainty

Authors  Top 
  • Blanchard-Wrigglesworth, E.
  • Barthélemy, A., more
  • Chevallier, M.
  • Cullather, R.
  • Fuckar, N.
  • Massonnet, F., more
  • Posey, P.
  • Wang, W.
  • Zhang, J.
  • Ardilouze, C.
  • Bitz, C.M.
  • Vernieres, G.
  • Wallcraft, A.
  • Wang, M.

    Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or forecast post-processing (bias correction) techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.

All data in the Integrated Marine Information System (IMIS) is subject to the VLIZ privacy policy Top | Authors