An anatomy of Arctic sea ice forecast biases in the seasonal prediction system with EC-Earth
Abstract The quality of initial conditions (ICs) in climate predictions controls the level of skill. Both the use of the latest high-quality observations and of the most efficient assimilation method are of paramount importance. Technical challenges make it frequent to assimilate observational infor...
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2021
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Online Access: | http://dx.doi.org/10.1007/s00382-020-05560-4 http://link.springer.com/content/pdf/10.1007/s00382-020-05560-4.pdf http://link.springer.com/article/10.1007/s00382-020-05560-4/fulltext.html |
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crspringernat:10.1007/s00382-020-05560-4 2023-05-15T15:00:05+02:00 An anatomy of Arctic sea ice forecast biases in the seasonal prediction system with EC-Earth Cruz-García, Rubén Ortega, Pablo Guemas, Virginie Acosta Navarro, Juan C. Massonnet, François Doblas-Reyes, Francisco J. APPLICATE FPU - Ministerio de Ciencia, Innovación y Universidades - Gobierno de España Ministerio de Ciencia, Innovación y Universidades 2021 http://dx.doi.org/10.1007/s00382-020-05560-4 http://link.springer.com/content/pdf/10.1007/s00382-020-05560-4.pdf http://link.springer.com/article/10.1007/s00382-020-05560-4/fulltext.html en eng Springer Science and Business Media LLC https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 CC-BY Climate Dynamics volume 56, issue 5-6, page 1799-1813 ISSN 0930-7575 1432-0894 Atmospheric Science journal-article 2021 crspringernat https://doi.org/10.1007/s00382-020-05560-4 2022-01-04T14:48:59Z Abstract The quality of initial conditions (ICs) in climate predictions controls the level of skill. Both the use of the latest high-quality observations and of the most efficient assimilation method are of paramount importance. Technical challenges make it frequent to assimilate observational information independently in the various model components. Inconsistencies between the ICs obtained for the different model components can cause initialization shocks. In this study, we identify and quantify the contribution of the ICs inconsistency relative to the model inherent bias (in which the Arctic is generally too warm) to the development of sea ice concentration forecast biases in a seasonal prediction system with the EC-Earth general circulation model. We estimate that the ICs inconsistency dominates the development of forecast biases for as long as the first 24 (19) days of the forecasts initialized in May (November), while the development of model inherent bias dominates afterwards. The effect of ICs inconsistency is stronger in the Greenland Sea, in particular in November, and mostly associated to a mismatch between the sea ice and ocean ICs. In both May and November, the ICs inconsistency between the ocean and sea ice leads to sea ice melting, but it happens in November (May) in a context of sea ice expansion (shrinking). The ICs inconsistency tend to postpone (accelerate) the November (May) sea ice freezing (melting). Our findings suggest that the ICs inconsistency might have a larger impact than previously suspected. Detecting and filtering out this signal requires the use of high frequency data. Article in Journal/Newspaper Arctic Greenland Greenland Sea Sea ice Springer Nature (via Crossref) Arctic Greenland Climate Dynamics 56 5-6 1799 1813 |
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Open Polar |
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Springer Nature (via Crossref) |
op_collection_id |
crspringernat |
language |
English |
topic |
Atmospheric Science |
spellingShingle |
Atmospheric Science Cruz-García, Rubén Ortega, Pablo Guemas, Virginie Acosta Navarro, Juan C. Massonnet, François Doblas-Reyes, Francisco J. An anatomy of Arctic sea ice forecast biases in the seasonal prediction system with EC-Earth |
topic_facet |
Atmospheric Science |
description |
Abstract The quality of initial conditions (ICs) in climate predictions controls the level of skill. Both the use of the latest high-quality observations and of the most efficient assimilation method are of paramount importance. Technical challenges make it frequent to assimilate observational information independently in the various model components. Inconsistencies between the ICs obtained for the different model components can cause initialization shocks. In this study, we identify and quantify the contribution of the ICs inconsistency relative to the model inherent bias (in which the Arctic is generally too warm) to the development of sea ice concentration forecast biases in a seasonal prediction system with the EC-Earth general circulation model. We estimate that the ICs inconsistency dominates the development of forecast biases for as long as the first 24 (19) days of the forecasts initialized in May (November), while the development of model inherent bias dominates afterwards. The effect of ICs inconsistency is stronger in the Greenland Sea, in particular in November, and mostly associated to a mismatch between the sea ice and ocean ICs. In both May and November, the ICs inconsistency between the ocean and sea ice leads to sea ice melting, but it happens in November (May) in a context of sea ice expansion (shrinking). The ICs inconsistency tend to postpone (accelerate) the November (May) sea ice freezing (melting). Our findings suggest that the ICs inconsistency might have a larger impact than previously suspected. Detecting and filtering out this signal requires the use of high frequency data. |
author2 |
APPLICATE FPU - Ministerio de Ciencia, Innovación y Universidades - Gobierno de España Ministerio de Ciencia, Innovación y Universidades |
format |
Article in Journal/Newspaper |
author |
Cruz-García, Rubén Ortega, Pablo Guemas, Virginie Acosta Navarro, Juan C. Massonnet, François Doblas-Reyes, Francisco J. |
author_facet |
Cruz-García, Rubén Ortega, Pablo Guemas, Virginie Acosta Navarro, Juan C. Massonnet, François Doblas-Reyes, Francisco J. |
author_sort |
Cruz-García, Rubén |
title |
An anatomy of Arctic sea ice forecast biases in the seasonal prediction system with EC-Earth |
title_short |
An anatomy of Arctic sea ice forecast biases in the seasonal prediction system with EC-Earth |
title_full |
An anatomy of Arctic sea ice forecast biases in the seasonal prediction system with EC-Earth |
title_fullStr |
An anatomy of Arctic sea ice forecast biases in the seasonal prediction system with EC-Earth |
title_full_unstemmed |
An anatomy of Arctic sea ice forecast biases in the seasonal prediction system with EC-Earth |
title_sort |
anatomy of arctic sea ice forecast biases in the seasonal prediction system with ec-earth |
publisher |
Springer Science and Business Media LLC |
publishDate |
2021 |
url |
http://dx.doi.org/10.1007/s00382-020-05560-4 http://link.springer.com/content/pdf/10.1007/s00382-020-05560-4.pdf http://link.springer.com/article/10.1007/s00382-020-05560-4/fulltext.html |
geographic |
Arctic Greenland |
geographic_facet |
Arctic Greenland |
genre |
Arctic Greenland Greenland Sea Sea ice |
genre_facet |
Arctic Greenland Greenland Sea Sea ice |
op_source |
Climate Dynamics volume 56, issue 5-6, page 1799-1813 ISSN 0930-7575 1432-0894 |
op_rights |
https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1007/s00382-020-05560-4 |
container_title |
Climate Dynamics |
container_volume |
56 |
container_issue |
5-6 |
container_start_page |
1799 |
op_container_end_page |
1813 |
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1766332192405848064 |