Subseasonal predictability of the North Atlantic Oscillation
Abstract Skillfully predicting the North Atlantic Oscillation (NAO), and the closely related northern annular mode (NAM), on ‘subseasonal’ (weeks to less than a season) timescales is a high priority for operational forecasting centers, because of the NAO’s association with high-impact weather events...
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crioppubl:10.1088/1748-9326/abe781 2024-09-15T18:22:43+00:00 Subseasonal predictability of the North Atlantic Oscillation Albers, John R Newman, Matthew Climate Program Office Division of Atmospheric and Geospace Sciences 2021 http://dx.doi.org/10.1088/1748-9326/abe781 https://iopscience.iop.org/article/10.1088/1748-9326/abe781 https://iopscience.iop.org/article/10.1088/1748-9326/abe781/pdf unknown IOP Publishing http://creativecommons.org/licenses/by/4.0 https://iopscience.iop.org/info/page/text-and-data-mining Environmental Research Letters volume 16, issue 4, page 044024 ISSN 1748-9326 journal-article 2021 crioppubl https://doi.org/10.1088/1748-9326/abe781 2024-08-05T04:19:14Z Abstract Skillfully predicting the North Atlantic Oscillation (NAO), and the closely related northern annular mode (NAM), on ‘subseasonal’ (weeks to less than a season) timescales is a high priority for operational forecasting centers, because of the NAO’s association with high-impact weather events, particularly during winter. Unfortunately, the relatively fast, weather-related processes dominating total NAO variability are unpredictable beyond about two weeks. On longer timescales, the tropical troposphere and the stratosphere provide some predictability, but they contribute relatively little to total NAO variance. Moreover, subseasonal forecasts are only sporadically skillful, suggesting the practical need to identify the fewer potentially predictable events at the time of forecast. Here we construct an observationally based linear inverse model (LIM) that predicts when, and diagnoses why, subseasonal NAO forecasts will be most skillful. We use the LIM to identify those dynamical modes that, despite capturing only a fraction of overall NAO variability, are largely responsible for extended-range NAO skill. Predictable NAO events stem from the linear superposition of these modes, which represent joint tropical sea-surface temperature-lower stratosphere variability plus a single mode capturing downward propagation from the upper stratosphere. Our method has broad applicability because both the LIM and the state-of-the-art European Centre for Medium-Range Weather Forecasts Integrated Forecast System (IFS) have higher (and comparable) skill for the same set of predicted high skill forecast events, suggesting that the low-dimensional predictable subspace identified by the LIM is relevant to real-world subseasonal NAO predictions. Article in Journal/Newspaper North Atlantic North Atlantic oscillation IOP Publishing Environmental Research Letters 16 4 044024 |
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Abstract Skillfully predicting the North Atlantic Oscillation (NAO), and the closely related northern annular mode (NAM), on ‘subseasonal’ (weeks to less than a season) timescales is a high priority for operational forecasting centers, because of the NAO’s association with high-impact weather events, particularly during winter. Unfortunately, the relatively fast, weather-related processes dominating total NAO variability are unpredictable beyond about two weeks. On longer timescales, the tropical troposphere and the stratosphere provide some predictability, but they contribute relatively little to total NAO variance. Moreover, subseasonal forecasts are only sporadically skillful, suggesting the practical need to identify the fewer potentially predictable events at the time of forecast. Here we construct an observationally based linear inverse model (LIM) that predicts when, and diagnoses why, subseasonal NAO forecasts will be most skillful. We use the LIM to identify those dynamical modes that, despite capturing only a fraction of overall NAO variability, are largely responsible for extended-range NAO skill. Predictable NAO events stem from the linear superposition of these modes, which represent joint tropical sea-surface temperature-lower stratosphere variability plus a single mode capturing downward propagation from the upper stratosphere. Our method has broad applicability because both the LIM and the state-of-the-art European Centre for Medium-Range Weather Forecasts Integrated Forecast System (IFS) have higher (and comparable) skill for the same set of predicted high skill forecast events, suggesting that the low-dimensional predictable subspace identified by the LIM is relevant to real-world subseasonal NAO predictions. |
author2 |
Climate Program Office Division of Atmospheric and Geospace Sciences |
format |
Article in Journal/Newspaper |
author |
Albers, John R Newman, Matthew |
spellingShingle |
Albers, John R Newman, Matthew Subseasonal predictability of the North Atlantic Oscillation |
author_facet |
Albers, John R Newman, Matthew |
author_sort |
Albers, John R |
title |
Subseasonal predictability of the North Atlantic Oscillation |
title_short |
Subseasonal predictability of the North Atlantic Oscillation |
title_full |
Subseasonal predictability of the North Atlantic Oscillation |
title_fullStr |
Subseasonal predictability of the North Atlantic Oscillation |
title_full_unstemmed |
Subseasonal predictability of the North Atlantic Oscillation |
title_sort |
subseasonal predictability of the north atlantic oscillation |
publisher |
IOP Publishing |
publishDate |
2021 |
url |
http://dx.doi.org/10.1088/1748-9326/abe781 https://iopscience.iop.org/article/10.1088/1748-9326/abe781 https://iopscience.iop.org/article/10.1088/1748-9326/abe781/pdf |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Environmental Research Letters volume 16, issue 4, page 044024 ISSN 1748-9326 |
op_rights |
http://creativecommons.org/licenses/by/4.0 https://iopscience.iop.org/info/page/text-and-data-mining |
op_doi |
https://doi.org/10.1088/1748-9326/abe781 |
container_title |
Environmental Research Letters |
container_volume |
16 |
container_issue |
4 |
container_start_page |
044024 |
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1810462675717586944 |