Predictability of Arctic sea ice on weather time scales
| Autor: | Helge Goessling, Martin Losch, Nils Hutter, Thomas Jung, Mahdi Mohammadi-Aragh |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
geography
Multidisciplinary geography.geographical_feature_category 010504 meteorology & atmospheric sciences lcsh:R LKFS lcsh:Medicine Vorticity 010502 geochemistry & geophysics 01 natural sciences Arctic ice pack Article Physics::Geophysics Atmosphere Arctic Climatology Sea ice Environmental science lcsh:Q Astrophysics::Earth and Planetary Astrophysics Predictability lcsh:Science Sea ice concentration Physics::Atmospheric and Oceanic Physics 0105 earth and related environmental sciences |
| Zdroj: | Scientific Reports Mohammadi-Aragh, M.; Goessling, H.; Losch, M.; Hutter, N.; Jung, T. : Predictability of Arctic sea ice on weather time scales. In: Scientific Reports. Vol. 8 (2018) 6514. (DOI: /10.1038/s41598-018-24660-0) Scientific Reports, Vol 8, Iss 1, Pp 1-7 (2018) |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-018-24660-0 |
| Popis: | The field of Arctic sea ice prediction on “weather time scales” is still in its infancy with little existingunderstanding of the limits of predictability. This is especially true for sea ice deformation alongso-called Linear Kinematic Features (LKFs) including leads that are relevant for marine operations.Here the potential predictability of the sea ice pack in the wintertime Arctic up to ten days aheadis determined, exploiting the fact that sea ice-ocean models start to show skill at representing seaice deformation at high spatial resolutions. Results are based on ensemble simulations with a high-resolutionsea ice-ocean model driven by atmospheric ensemble forecasts. The predictability of LKFsas measured by different metrics drops quickly, with predictability being almost completely lost after4–8 days. In contrast, quantities such as sea ice concentration or the location of the ice edge retainhigh levels of predictability throughout the full 10-day forecast period. It is argued that the rapiderror growth for LKFs is mainly due to the chaotic behaviour of the atmosphere associated with thelow predictability of near surface wind divergence and vorticity; initial condition uncertainty for icethickness is found to be of minor importance as long as LKFs are initialized at the right locations. |
| Databáze: | OpenAIRE |
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