Predictors and prediction skill for marine cold air outbreaks over the Barents Sea ...
Marine cold air outbreaks (MCAOs) create conditions for hazardous maritime mesocyclones (polar lows) posing risks to marine infrastructure. For marine management, skilful predictions of MCAOs would be highly beneficial. For this reason, we investigate (i) the ability of a seasonal prediction system...
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Online Access: | https://dx.doi.org/10.3929/ethz-b-000478568 http://hdl.handle.net/20.500.11850/478568 |
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ftdatacite:10.3929/ethz-b-000478568 2024-04-28T08:10:27+00:00 Predictors and prediction skill for marine cold air outbreaks over the Barents Sea ... Polkova, Iuliia Afargan-Gerstman, Hilla Domeisen, Daniela King, Martin P. Ruggieri, Paolo Athanasiadis, Panos Dobrynin, Mikhail Aarnes, Øivin Kretschmer, Marlene Baehr, Johanna 2021 application/pdf https://dx.doi.org/10.3929/ethz-b-000478568 http://hdl.handle.net/20.500.11850/478568 en eng ETH Zurich info:eu-repo/semantics/openAccess Creative Commons Attribution Non Commercial No Derivatives 4.0 International https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode cc-by-nc-nd-4.0 Arctic climate Marine cold air outbreak Polar low Seasonal prediction Causal drivers article-journal Text ScholarlyArticle Journal Article 2021 ftdatacite https://doi.org/10.3929/ethz-b-000478568 2024-04-02T12:34:54Z Marine cold air outbreaks (MCAOs) create conditions for hazardous maritime mesocyclones (polar lows) posing risks to marine infrastructure. For marine management, skilful predictions of MCAOs would be highly beneficial. For this reason, we investigate (i) the ability of a seasonal prediction system to predict MCAOs and (ii) the possibilities to improve predictions through large‐scale causal drivers. Our results show that the seasonal ensemble predictions have high prediction skill for MCAOs over the Nordic Seas for about 20 days starting from November initial conditions. To study causal drivers of MCAOs, we utilize a causal effect network approach applied to the atmospheric reanalysis ERA‐Interim and identify local sea surface temperature and atmospheric circulation patterns over Scandinavia as valuable predictors. Prediction skill for MCAOs is further improved up to 40 days including MCAO predictors in the analysis. ... : Quarterly Journal of the Royal Meteorological Society, 147 (738) ... Article in Journal/Newspaper Arctic Barents Sea Nordic Seas DataCite Metadata Store (German National Library of Science and Technology) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
Arctic climate Marine cold air outbreak Polar low Seasonal prediction Causal drivers |
spellingShingle |
Arctic climate Marine cold air outbreak Polar low Seasonal prediction Causal drivers Polkova, Iuliia Afargan-Gerstman, Hilla Domeisen, Daniela King, Martin P. Ruggieri, Paolo Athanasiadis, Panos Dobrynin, Mikhail Aarnes, Øivin Kretschmer, Marlene Baehr, Johanna Predictors and prediction skill for marine cold air outbreaks over the Barents Sea ... |
topic_facet |
Arctic climate Marine cold air outbreak Polar low Seasonal prediction Causal drivers |
description |
Marine cold air outbreaks (MCAOs) create conditions for hazardous maritime mesocyclones (polar lows) posing risks to marine infrastructure. For marine management, skilful predictions of MCAOs would be highly beneficial. For this reason, we investigate (i) the ability of a seasonal prediction system to predict MCAOs and (ii) the possibilities to improve predictions through large‐scale causal drivers. Our results show that the seasonal ensemble predictions have high prediction skill for MCAOs over the Nordic Seas for about 20 days starting from November initial conditions. To study causal drivers of MCAOs, we utilize a causal effect network approach applied to the atmospheric reanalysis ERA‐Interim and identify local sea surface temperature and atmospheric circulation patterns over Scandinavia as valuable predictors. Prediction skill for MCAOs is further improved up to 40 days including MCAO predictors in the analysis. ... : Quarterly Journal of the Royal Meteorological Society, 147 (738) ... |
format |
Article in Journal/Newspaper |
author |
Polkova, Iuliia Afargan-Gerstman, Hilla Domeisen, Daniela King, Martin P. Ruggieri, Paolo Athanasiadis, Panos Dobrynin, Mikhail Aarnes, Øivin Kretschmer, Marlene Baehr, Johanna |
author_facet |
Polkova, Iuliia Afargan-Gerstman, Hilla Domeisen, Daniela King, Martin P. Ruggieri, Paolo Athanasiadis, Panos Dobrynin, Mikhail Aarnes, Øivin Kretschmer, Marlene Baehr, Johanna |
author_sort |
Polkova, Iuliia |
title |
Predictors and prediction skill for marine cold air outbreaks over the Barents Sea ... |
title_short |
Predictors and prediction skill for marine cold air outbreaks over the Barents Sea ... |
title_full |
Predictors and prediction skill for marine cold air outbreaks over the Barents Sea ... |
title_fullStr |
Predictors and prediction skill for marine cold air outbreaks over the Barents Sea ... |
title_full_unstemmed |
Predictors and prediction skill for marine cold air outbreaks over the Barents Sea ... |
title_sort |
predictors and prediction skill for marine cold air outbreaks over the barents sea ... |
publisher |
ETH Zurich |
publishDate |
2021 |
url |
https://dx.doi.org/10.3929/ethz-b-000478568 http://hdl.handle.net/20.500.11850/478568 |
genre |
Arctic Barents Sea Nordic Seas |
genre_facet |
Arctic Barents Sea Nordic Seas |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Attribution Non Commercial No Derivatives 4.0 International https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode cc-by-nc-nd-4.0 |
op_doi |
https://doi.org/10.3929/ethz-b-000478568 |
_version_ |
1797578333543727104 |