Going with the floe: tracking CESM Large Ensemble sea ice in the Arctic provides context for ship-based observations
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...
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Veröffentlicht in: | The cryosphere 2020-04, Vol.14 (4), p.1259-1271 |
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Zusammenfassung: | 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. |
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ISSN: | 1994-0424 1994-0416 1994-0424 1994-0416 |
DOI: | 10.5194/tc-14-1259-2020 |