Assessment of Arctic and Antarctic sea ice predictability in CMIP5 decadal hindcasts

• Published in: The Cryosphere
• Main Authors: C.-Y. Yang, J. Liu, Y. Hu, R. M. Horton, L. Chen, X. Cheng
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2016
• Subjects: geo; envir; Arctic; Antarctic; The Antarctic; Pacific; Antarc*; North Atlantic; Sea ice; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-10-2429-2016; https://www.the-cryosphere.net/10/2429/2016/tc-10-2429-2016.pdf; https://doaj.org/article/1a87f61348924851aaee042284001d24

This paper examines the ability of coupled global climate models to predict decadal variability of Arctic and Antarctic sea ice. We analyze decadal hindcasts/predictions of 11 Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Decadal hindcasts exhibit a large multi-model spread in the simulated sea ice extent, with some models deviating significantly from the observations as the predicted ice extent quickly drifts away from the initial constraint. The anomaly correlation analysis between the decadal hindcast and observed sea ice suggests that in the Arctic, for most models, the areas showing significant predictive skill become broader associated with increasing lead times. This area expansion is largely because nearly all the models are capable of predicting the observed decreasing Arctic sea ice cover. Sea ice extent in the North Pacific has better predictive skill than that in the North Atlantic (particularly at a lead time of 3–7 years), but there is a re-emerging predictive skill in the North Atlantic at a lead time of 6–8 years. In contrast to the Arctic, Antarctic sea ice decadal hindcasts do not show broad predictive skill at any timescales, and there is no obvious improvement linking the areal extent of significant predictive skill to lead time increase. This might be because nearly all the models predict a retreating Antarctic sea ice cover, opposite to the observations. For the Arctic, the predictive skill of the multi-model ensemble mean outperforms most models and the persistence prediction at longer timescales, which is not the case for the Antarctic. Overall, for the Arctic, initialized decadal hindcasts show improved predictive skill compared to uninitialized simulations, although this improvement is not present in the Antarctic.