Predicting September Arctic Sea Ice: A Multimodel Seasonal Skill Comparison

作者：Mitchell Bushuk, Sahara Ali, David A. Bailey, Qing Bao, Lauriane Batté, Uma S. Bhatt, Edward Blanchard‐Wrigglesworth, Ed Blockley, Gavin C. Cawley, Junhwa Chi, François Counillon, Philippe Goulet Coulombe, Richard Cullather, Francis X. Diebold, Arlan Dirkson, Eleftheria Exarchou, Maximilian Göbel, William K. Gregory, Virginie Guémas, Lawrence C. Hamilton, Bian He, Sean Horvath, Monica Ioniță, Jennifer E. Kay, Eliot Kim, Noriaki Kimura, Dmitri Kondrashov, Zachary M. Labe, Woosung Lee, Younjoo Lee, Cuihua Li, Xuewei Li, Yongcheng Lin, Yanyun Liu, Wieslaw Maslowski, François Massonnet, Walter N. Meier, William J. Merryfield, H H Myint, Juan C. Acosta Navarro, Alek Petty, Fangli Qiao, David Schröder, Axel Schweiger, Qi Shu, Michael Sigmond, Michael Steele, Julienne Strœve, Nico Sun, Steffen Tietsche, Michel Tsamados, Keguang Wang, Jianwu Wang, Wanqiu Wang, Yiguo Wang, Yun Wang, J. G. Williams, Qinghua Yang, Xiaojun Yuan, Jinlun Zhang, Yongfei Zhang · 发表于：Bulletin of the American Meteorological Society · 年份：2024 · DOI：10.1175/bams-d-23-0163.1 · 被引用次数：23 · 研究领域：Arctic and Antarctic ice dynamics、Climate change and permafrost、Climate variability and models

Abstract This study quantifies the state of the art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multimodel dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–20 for predictions of pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on 1 June, 1 July, 1 August, and 1 September. This diverse set of statistical and dynamical models can individually predict linearly detrended pan-Arctic SIE anomalies with skill, and a multimodel median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and central Arctic sectors. The skill of dynamical and statistical models is generally comparable for pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the...