Statistical characteristics of Arctic forecast busts and their relationship to Arctic weather patterns in summer

• Published in: Atmospheric Science Letters
• Main Authors: Yamagami, Akio, Matsueda, Mio
• Other Authors: Japan Society for the Promotion of Science
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2021
• Subjects: Arctic; Greenland; Sea ice
• Online Access: http://dx.doi.org/10.1002/asl.1038; https://onlinelibrary.wiley.com/doi/pdf/10.1002/asl.1038; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/asl.1038; https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/asl.1038

Abstract Recently, human activity in the Arctic region, such as trans‐Arctic shipping, has increased due to the reduction in Arctic sea ice. Accurate weather forecasts will become increasingly important as the level of human activity in the Arctic continues to increase. Operational numerical weather predictions (NWPs) have been improved considerably over recent decades; however, they still occasionally generate large forecast errors referred to as “forecast busts.” This study investigates forecast busts over the Arctic between 2008 and 2019 using operational forecasts from five leading NWP centers. Forecasts with an anomaly correlation coefficient below its climatological 10th percentile, and a root‐mean‐square error above its 90th percentile at a lead time of 144 hr, are regarded as “busts.” The occurrence frequency of forecast busts decreased from 2008 (13–7%) to 2012 and was between 2 and 6% for the period 2012–2019. Arctic forecast busts were most frequent in the May and July–September periods (~6 to 7%), but less frequent between December and March (~4%). The summertime forecast bust occurred more frequently when the initial pattern was the Greenland Blocking (GB) or Arctic Cyclone (AC) pattern rather than one of the other patterns. Some busts occurred without the weather pattern transition (~22 to 40%), but the others occurred with the pattern transition. These results help users to be careful when they use the forecasts initialized on GB and AC patterns.