Seasonal Forecasts of the Pan-Arctic Sea Ice Extent Using a GCM-Based Seasonal Prediction SystemSource: Journal of Climate:;2013:;volume( 026 ):;issue: 016::page 6092Author:Chevallier, Matthieu
,
Salas y Mélia, David
,
Voldoire, Aurore
,
Déqué, Michel
,
Garric, Gilles
DOI: 10.1175/JCLI-D-12-00612.1Publisher: American Meteorological Society
Abstract: n ocean?sea ice model reconstruction spanning the period 1990?2009 is used to initialize ensemble seasonal forecasts with the Centre National de Recherches Météorologiques Coupled Global Climate Model version 5.1 (CNRM-CM5.1) coupled atmosphere?ocean general circulation model. The aim of this study is to assess the skill of fully initialized September and March pan-Arctic sea ice forecasts in terms of climatology and interannual anomalies. The predictions are initialized using ?full field initialization? of each component of the system. In spite of a drift due to radiative biases in the coupled model during the melt season, the full initialization of the sea ice cover on 1 May leads to skillful forecasts of the September sea ice extent (SIE) anomalies. The skill of the prediction is also significantly high when considering anomalies of the SIE relative to the long-term linear trend. It confirms that the anomaly of spring sea ice cover in itself plays a role in preconditioning a September SIE anomaly. The skill of predictions for March SIE initialized on 1 November is also encouraging, and it can be partly attributed to persistent features of the fall sea ice cover. The present study gives insight into the current ability of state-of-the-art coupled climate systems to perform operational seasonal forecasts of the Arctic sea ice cover up to 5 months in advance.
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contributor author | Chevallier, Matthieu | |
contributor author | Salas y Mélia, David | |
contributor author | Voldoire, Aurore | |
contributor author | Déqué, Michel | |
contributor author | Garric, Gilles | |
date accessioned | 2017-06-09T17:07:31Z | |
date available | 2017-06-09T17:07:31Z | |
date copyright | 2013/08/01 | |
date issued | 2013 | |
identifier issn | 0894-8755 | |
identifier other | ams-79759.pdf | |
identifier uri | http://onlinelibrary.yabesh.ir/handle/yetl/4222574 | |
description abstract | n ocean?sea ice model reconstruction spanning the period 1990?2009 is used to initialize ensemble seasonal forecasts with the Centre National de Recherches Météorologiques Coupled Global Climate Model version 5.1 (CNRM-CM5.1) coupled atmosphere?ocean general circulation model. The aim of this study is to assess the skill of fully initialized September and March pan-Arctic sea ice forecasts in terms of climatology and interannual anomalies. The predictions are initialized using ?full field initialization? of each component of the system. In spite of a drift due to radiative biases in the coupled model during the melt season, the full initialization of the sea ice cover on 1 May leads to skillful forecasts of the September sea ice extent (SIE) anomalies. The skill of the prediction is also significantly high when considering anomalies of the SIE relative to the long-term linear trend. It confirms that the anomaly of spring sea ice cover in itself plays a role in preconditioning a September SIE anomaly. The skill of predictions for March SIE initialized on 1 November is also encouraging, and it can be partly attributed to persistent features of the fall sea ice cover. The present study gives insight into the current ability of state-of-the-art coupled climate systems to perform operational seasonal forecasts of the Arctic sea ice cover up to 5 months in advance. | |
publisher | American Meteorological Society | |
title | Seasonal Forecasts of the Pan-Arctic Sea Ice Extent Using a GCM-Based Seasonal Prediction System | |
type | Journal Paper | |
journal volume | 26 | |
journal issue | 16 | |
journal title | Journal of Climate | |
identifier doi | 10.1175/JCLI-D-12-00612.1 | |
journal fristpage | 6092 | |
journal lastpage | 6104 | |
tree | Journal of Climate:;2013:;volume( 026 ):;issue: 016 | |
contenttype | Fulltext |