Broadening the sea-ice forecaster toolbox with community observations: a case study from the northern Bering Sea

• Published in: Arctic Science
• Main Authors: Gregory J. Deemer, Uma S. Bhatt, Hajo Eicken, Pamela G. Posey, Jennifer K. Hutchings, James Nelson, Rebecca Heim, Richard A. Allard, Helen Wiggins, Kristina Creek
• Format: Article in Journal/Newspaper
• Language: English; French
• Published: Canadian Science Publishing 2018
• Subjects: sea ice; forecasting; bering sea; indigenous knowledge; community observations; operations; Environmental sciences; GE1-350; Environmental engineering; TA170-171; Arctic; Bering Sea; Sea ice
• Online Access: https://doi.org/10.1139/as-2016-0054; https://doaj.org/article/0433d2147dbc4ddbb144c4731bdb0a29

Impacts of a warming climate are amplified in the Arctic. One notorious impact is recent and record-breaking summertime sea-ice loss. Expanding areas of open water and a prolonged ice-free season create opportunity for some industries but challenge indigenous peoples relying on sea ice for transportation and access to food. The observed and projected increase of Arctic maritime activity requires accurate sea-ice forecasts to protect life, environment, and property. Motivated by emerging prediction needs on the operational timescale (≤10 days), this study explores where local indigenous knowledge (LIK) fits into the forecaster toolbox and how it can be woven into useful sea-ice information products. The 2011 spring ice retreat season in the Bering Sea is presented as a forecasting case study. LIK, housed in a database of community-based ice and weather logs, and an ice-ocean forecast model developed by the US Navy are analyzed for their ability to provide information relevant to stakeholder needs. Additionally, metrics for verifying numerical sea-ice forecasts on multiple scales are derived. The model exhibits skill relative to persistence and climatology on the regional scale. At the community scale, we discuss how LIK and new model guidance can enhance public sea-ice information resources.