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| Titel |
Seasonal re-emergence of North Atlantic subsurface ocean temperature anomalies and Northern hemisphere climate |
| VerfasserIn |
Bablu Sinha, Adam Blaker, Aurelie Duchez, Jeremy Grist, Helene Hewitt, Joel Hirschi, Patrick Hyder, Simon Josey, Craig MacLachlan, Adrian New |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250145714
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| Publikation (Nr.) |
 EGU/EGU2017-9681.pdf |
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| Zusammenfassung |
| A high-resolution coupled ocean atmosphere model is used to study the effects of seasonal re-emergence of North Atlantic subsurface ocean temperature anomalies on northern hemisphere winter climate. A 50-member control simulation is integrated from September 1 to 28 February and compared with a similar ensemble with perturbed ocean initial conditions. The perturbation consists of a density-compensated subsurface (deeper than 180m) temperature anomaly corresponding to the observed subsurface temperature anomaly for September 2010, which is known to have re-emerged at the ocean surface in subsequent months. The perturbation is confined to the North Atlantic Ocean between the Equator and 65 degrees North.
The model has 1/4 degree horizontal resolution in the ocean and the experiment is repeated for two atmosphere horizontal resolutions (~60km and ~25km) in order to determine whether the sensitivity of the atmosphere to re-emerging temperature anomalies is dependent on resolution.
The ensembles display a wide range of reemergence behaviour, in some cases re-emergence occurs by November, in others it is delayed or does not occur at all. A wide range of amplitudes of the re-emergent temperature anomalies is observed. In cases where re-emergence occurs, there is a marked effect on both the regional (North Atlantic and Europe) and hemispheric surface pressure and temperature patterns.
The results highlight a potentially important process whereby ocean memory of conditions up to a year earlier can significantly enhance seasonal forecast skill. |
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