Going with the floe: tracking CESM Large Ensemble sea ice in the Arctic provides context for ship-based observations

• Published in: The Cryosphere
• Main Authors: A. K. DuVivier, P. DeRepentigny, M. M. Holland, M. Webster, J. E. Kay, D. Perovich
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2020
• Subjects: Environmental sciences; GE1-350; Geology; QE1-996.5; Arctic; Sea ice; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-14-1259-2020; https://doaj.org/article/fb87d4865668489e9d24fa8d5acf5a79

In recent decades, Arctic sea ice has shifted toward a younger, thinner, seasonal ice regime. Studying and understanding this “new” Arctic will be the focus of a year-long ship campaign beginning in autumn 2019. Lagrangian tracking of sea ice floes in the Community Earth System Model Large Ensemble (CESM-LE) during representative “perennial” and “seasonal” time periods allows for understanding of the conditions that a floe could experience throughout the calendar year. These model tracks, put into context a single year of observations, provide guidance on how observations can optimally shape model development, and how climate models could be used in future campaign planning. The modeled floe tracks show a range of possible trajectories, though a Transpolar Drift trajectory is most likely. There is also a small but emerging possibility of high-risk tracks, including possible melt of the floe before the end of a calendar year. We find that a Lagrangian approach is essential in order to correctly compare the seasonal cycle of sea ice conditions between point-based observations and a model. Because of high variability in the melt season sea ice conditions, we recommend in situ sampling over a large range of ice conditions for a more complete understanding of how ice type and surface conditions affect the observed processes. We find that sea ice predictability emerges rapidly during the autumn freeze-up and anticipate that process-based observations during this period may help elucidate the processes leading to this change in predictability.