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The Role of Sea Ice Thickness Distribution in the Arctic Sea Ice Potential Predictability: A Diagnostic Approach with a Coupled GCM.
- Source :
- Journal of Climate; Apr2012, Vol. 25 Issue 8, p3025-3038, 14p, 1 Diagram, 8 Graphs, 1 Map
- Publication Year :
- 2012
-
Abstract
- The intrinsic seasonal predictability of Arctic sea ice is investigated in a 400-yr-long preindustrial simulation performed with the Centre National de Recherches Météorologiques Coupled Global Climate Model, version 3.3 (CNRM-CM3.3). The skill of several predictors of the pan-Arctic sea ice area was quantified: the sea ice area itself, the pan-Arctic sea ice volume, and some areal predictors built from the subgrid ice thickness distribution (ITD). Sea ice area provides a potential predictability of about 3 months, which is consistent with previous studies using model and observation data. Sea ice volume predictive skill for winter sea ice area prediction is weak. Nevertheless, there is a higher potential to predict the September ice area with the June volume anomaly than with the June area anomaly. Using ITD-based predictors, two 'regimes' of predictability were highlighted. The first one, a 'persistence regime,' applies to winter/early spring sea ice seasonal predictability. The winter sea ice cover can be predicted in late fall/early winter from the amount of young ice formed since the freeze-up onset in the margins. However, sea ice area itself is potentially the best predictor of winter sea ice area at seasonal time scales. The second regime is a 'memory regime.' It applies to the predictability of summer sea ice area. An ice area anomaly in September is potentially predictable up to 6 months in advance, using the area covered by ice thicker than a critical thickness lying between 0.9 and 1.5 m. Results of this study are preliminary; however, they provide information for the design of future prediction systems and highlight the need for observations and a state-of-the-art sea ice model. [ABSTRACT FROM AUTHOR]
- Subjects :
- SEA ice
THICKNESS measurement
CLIMATE change
LONG-range weather forecasting
Subjects
Details
- Language :
- English
- ISSN :
- 08948755
- Volume :
- 25
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- Journal of Climate
- Publication Type :
- Academic Journal
- Accession number :
- 74131578
- Full Text :
- https://doi.org/10.1175/JCLI-D-11-00209.1