Prospects for seasonal prediction of summertime trans-Arctic sea ice path

• Published in: Journal of Climate
• Other Authors: Winton, Michael (author), Bushuk, Mitchell (author), Zhang, Yongfei (author), Hurlin, Bill (author), Jia, Liwei (author), Johnson, Nathaniel C. (author), Lu, Feiyu (author)
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2022
• Subjects: Arctic; Arctic Ocean; Greenland; Northwest Passage; Pacific; Northwest passage; Sea ice
• Online Access: https://doi.org/10.1175/JCLI-D-21-0634.1

The continuing decline of the summertime sea ice cover has reduced the sea ice path that must be traversed to Arctic destinations and through the Arctic between the Atlantic and Pacific Oceans, stimulating interest in trans-Arctic Ocean routes. Seasonal prediction of the sea ice cover along these routes could support the increasing summertime ship traffic taking advantage of recent low ice conditions. We introduce the minimum Arctic sea ice path (MIP) between Atlantic and Pacific Oceans as a shipping-relevant metric that is amenable to multidecadal hindcast evaluation. We show, using 1992-2017 retrospective predictions, that bias correction is necessary for the GFDL Seamless System for Prediction and Earth System Research (SPEAR) forecast system to improve upon damped persistence seasonal forecasts of summertime daily MIP between the Atlantic and Pacific Oceans both east and west of Greenland, corresponding roughly to the Northeast and Northwest Passages. Without bias correction, only the Northwest Passage MIP forecasts have lower error than a damped persistence forecast. Using the forecast ensemble spread to estimate a lower bound on forecast error, we find large opportunities for forecast error reduction, especially at lead times of less than 2 months. Most of the potential improvement remains after linear removal of climatological and trend biases, suggesting that significant error reduction might come from improved initialization and simulation of subannual variability. Using a different passive microwave sea ice dataset for calculating error than was used for data assimilation increases the raw forecast errors but not the trend anomaly forecast errors.