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| Titel |
Dynamic preconditioning of the September sea-ice extent minimum |
| VerfasserIn |
James Williams, Bruno Tremblay, Robert Newton, Richard Allard |
| Konferenz |
EGU General Assembly 2016
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250121521
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| Publikation (Nr.) |
 EGU/EGU2016-280.pdf |
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| Zusammenfassung |
| There has been an increased interest in seasonal forecasting of the sea-ice extent in recent
years, in particular the minimum sea-ice extent. We propose a dynamical mechanism,
based on winter preconditioning through first year ice formation, that explains a
significant fraction of the variance in the anomaly of the September sea-ice extent
from the long-term linear trend. To this end, we use a Lagrangian trajectory model
to backtrack the September sea-ice edge to any time during the previous winter
and quantify the amount of sea-ice divergence along the Eurasian and Alaskan
coastlines as well as the Fram Strait sea-ice export. We find that coastal divergence that
occurs later in the winter (March, April and May) is highly correlated with the
following September sea-ice extent minimum (r = −0.73). This is because the newly
formed first year ice will melt earlier allowing for other feedbacks (e.g. ice albedo
feedback) to start amplifying the signal early in the melt season when the solar input
is large. We find that the winter mean Fram Strait sea-ice export anomaly is also
correlated with the minimum sea-ice extent the following summer. Next we backtrack a
synthetic ice edge initialized at the beginning of the melt season (June 1st) in order to
develop hindcast models of the September sea-ice extent that do not rely on a-priori
knowledge of the minimum sea-ice extent. We find that using a multi-variate regression
model of the September sea-ice extent anomaly based on coastal divergence and
Fram Strait ice export as predictors reduces the error by 41%. A hindcast model
based on the mean DJFMA Arctic Oscillation index alone reduces the error by 24%. |
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