The skill of multi-model seasonal forecasts of the wintertime North Atlantic Oscillation
The skill assessment of a set of wintertime North Atlantic Oscillation (NAO) seasonal predictions in a multi-model ensemble framework has been carried out. The multi-model approach consists in merging the ensemble hindcasts of four atmospheric general circulation models forced with observed sea surf...
Gespeichert in:
Veröffentlicht in: | Climate dynamics 2003-11, Vol.21 (5-6), p.501-514 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 514 |
---|---|
container_issue | 5-6 |
container_start_page | 501 |
container_title | Climate dynamics |
container_volume | 21 |
creator | DOBLAS-REYES, F. J PAVAN, V STEPHENSON, D. B |
description | The skill assessment of a set of wintertime North Atlantic Oscillation (NAO) seasonal predictions in a multi-model ensemble framework has been carried out. The multi-model approach consists in merging the ensemble hindcasts of four atmospheric general circulation models forced with observed sea surface temperatures to create a multi-model ensemble. Deterministic (ensemble-mean based) and probabilistic (categorical) NAO hindcasts have been considered. Two different sets of NAO indices have been used to create the hindcasts. A first set is defined as the projection of model anomalies onto the NAO spatial pattern obtained from atmospheric analyses. The second set obtains the NAO indices by standardizing the leading principal component of each single-model ensemble. Positive skill is found with both sets of indices, especially in the case of the multi-model ensemble. In addition, the NAO definition based upon the single-model leading principal component shows a higher skill than the hindcasts obtained using the projection method. Using the former definition, the multi-model ensemble shows statistically significant (at 5% level) positive skill in a variety of probabilistic scoring measures. This is interpreted as a consequence of the projection method being less suitable because of the presence of errors in the spatial NAO patterns of the models. The positive skill of the seasonal NAO found here seems to be due not to the persistence of the long-term (decadal) variability specified in the initial conditions, but rather to a good simulation of the year-to-year variability. Nevertheless, most of the NAO seasonal predictability seems to be due to the correct prediction of particular cases such as the winter of 1989. The higher skill of the multi-model has been explained on the basis of a more reliable description of large-scale tropospheric wave features by the multi-model ensemble, illustrating the potential of multi-model experiments to better identify mechanisms that explain seasonal variability in the atmosphere.[PUBLICATION ABSTRACT] |
doi_str_mv | 10.1007/s00382-003-0350-4 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_754567648</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>18050242</sourcerecordid><originalsourceid>FETCH-LOGICAL-c429t-72b5fcf0a7ab508bce8654c899a2848b0d9f66b9c2fa5e17f6966d8d0259960e3</originalsourceid><addsrcrecordid>eNp9kU9LHTEUxYO04NP2A7gbCtrV6E0mf5ciWgXRjUJ3IZOXYHRmYnPzKP325vEEwUU398Lldw6Xcwg5onBKAdQZAgya9W32MAjo-R5ZUT60izb8C1mBGaBXQol9coD4DEC5VGxFfj88hQ5f0jR1OXbzZqqpn_M6TB0Gh3lxUxdzCd5hxS1RG_43LTWUmubQ3eVSn7rzOrmlJt_do29Orqa8fCNfo5swfH_fh-Tx6vLh4rq_vf91c3F-23vOTO0VG0X0EZxyowA9-qCl4F4b45jmeoS1iVKOxrPoRKAqSiPlWq-BCWMkhOGQ_Nz5vpb8ZxOw2jmhD-2LJeQNWiW4kEpy3ciT_5JUgwDGWQN_fAKf86a0KNBKOrR01SAbRHeQLxmxhGhfS5pd-Wcp2G0ldleJbdNuK7G8aY7fjR16N8XiFp_wQyiYFlzx4Q28Zosy</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>613350736</pqid></control><display><type>article</type><title>The skill of multi-model seasonal forecasts of the wintertime North Atlantic Oscillation</title><source>SpringerLink (Online service)</source><creator>DOBLAS-REYES, F. J ; PAVAN, V ; STEPHENSON, D. B</creator><creatorcontrib>DOBLAS-REYES, F. J ; PAVAN, V ; STEPHENSON, D. B</creatorcontrib><description>The skill assessment of a set of wintertime North Atlantic Oscillation (NAO) seasonal predictions in a multi-model ensemble framework has been carried out. The multi-model approach consists in merging the ensemble hindcasts of four atmospheric general circulation models forced with observed sea surface temperatures to create a multi-model ensemble. Deterministic (ensemble-mean based) and probabilistic (categorical) NAO hindcasts have been considered. Two different sets of NAO indices have been used to create the hindcasts. A first set is defined as the projection of model anomalies onto the NAO spatial pattern obtained from atmospheric analyses. The second set obtains the NAO indices by standardizing the leading principal component of each single-model ensemble. Positive skill is found with both sets of indices, especially in the case of the multi-model ensemble. In addition, the NAO definition based upon the single-model leading principal component shows a higher skill than the hindcasts obtained using the projection method. Using the former definition, the multi-model ensemble shows statistically significant (at 5% level) positive skill in a variety of probabilistic scoring measures. This is interpreted as a consequence of the projection method being less suitable because of the presence of errors in the spatial NAO patterns of the models. The positive skill of the seasonal NAO found here seems to be due not to the persistence of the long-term (decadal) variability specified in the initial conditions, but rather to a good simulation of the year-to-year variability. Nevertheless, most of the NAO seasonal predictability seems to be due to the correct prediction of particular cases such as the winter of 1989. The higher skill of the multi-model has been explained on the basis of a more reliable description of large-scale tropospheric wave features by the multi-model ensemble, illustrating the potential of multi-model experiments to better identify mechanisms that explain seasonal variability in the atmosphere.[PUBLICATION ABSTRACT]</description><identifier>ISSN: 0930-7575</identifier><identifier>EISSN: 1432-0894</identifier><identifier>DOI: 10.1007/s00382-003-0350-4</identifier><identifier>CODEN: CLDYEM</identifier><language>eng</language><publisher>Heidelberg: Springer</publisher><subject>Atmospheric circulation ; Earth, ocean, space ; Exact sciences and technology ; External geophysics ; General circulation models ; Marine ; Meteorology ; Sea surface temperature ; Seasonal variations ; Weather analysis and prediction ; Winter</subject><ispartof>Climate dynamics, 2003-11, Vol.21 (5-6), p.501-514</ispartof><rights>2004 INIST-CNRS</rights><rights>Springer-Verlag 2003</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c429t-72b5fcf0a7ab508bce8654c899a2848b0d9f66b9c2fa5e17f6966d8d0259960e3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=15285474$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>DOBLAS-REYES, F. J</creatorcontrib><creatorcontrib>PAVAN, V</creatorcontrib><creatorcontrib>STEPHENSON, D. B</creatorcontrib><title>The skill of multi-model seasonal forecasts of the wintertime North Atlantic Oscillation</title><title>Climate dynamics</title><description>The skill assessment of a set of wintertime North Atlantic Oscillation (NAO) seasonal predictions in a multi-model ensemble framework has been carried out. The multi-model approach consists in merging the ensemble hindcasts of four atmospheric general circulation models forced with observed sea surface temperatures to create a multi-model ensemble. Deterministic (ensemble-mean based) and probabilistic (categorical) NAO hindcasts have been considered. Two different sets of NAO indices have been used to create the hindcasts. A first set is defined as the projection of model anomalies onto the NAO spatial pattern obtained from atmospheric analyses. The second set obtains the NAO indices by standardizing the leading principal component of each single-model ensemble. Positive skill is found with both sets of indices, especially in the case of the multi-model ensemble. In addition, the NAO definition based upon the single-model leading principal component shows a higher skill than the hindcasts obtained using the projection method. Using the former definition, the multi-model ensemble shows statistically significant (at 5% level) positive skill in a variety of probabilistic scoring measures. This is interpreted as a consequence of the projection method being less suitable because of the presence of errors in the spatial NAO patterns of the models. The positive skill of the seasonal NAO found here seems to be due not to the persistence of the long-term (decadal) variability specified in the initial conditions, but rather to a good simulation of the year-to-year variability. Nevertheless, most of the NAO seasonal predictability seems to be due to the correct prediction of particular cases such as the winter of 1989. The higher skill of the multi-model has been explained on the basis of a more reliable description of large-scale tropospheric wave features by the multi-model ensemble, illustrating the potential of multi-model experiments to better identify mechanisms that explain seasonal variability in the atmosphere.[PUBLICATION ABSTRACT]</description><subject>Atmospheric circulation</subject><subject>Earth, ocean, space</subject><subject>Exact sciences and technology</subject><subject>External geophysics</subject><subject>General circulation models</subject><subject>Marine</subject><subject>Meteorology</subject><subject>Sea surface temperature</subject><subject>Seasonal variations</subject><subject>Weather analysis and prediction</subject><subject>Winter</subject><issn>0930-7575</issn><issn>1432-0894</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kU9LHTEUxYO04NP2A7gbCtrV6E0mf5ciWgXRjUJ3IZOXYHRmYnPzKP325vEEwUU398Lldw6Xcwg5onBKAdQZAgya9W32MAjo-R5ZUT60izb8C1mBGaBXQol9coD4DEC5VGxFfj88hQ5f0jR1OXbzZqqpn_M6TB0Gh3lxUxdzCd5hxS1RG_43LTWUmubQ3eVSn7rzOrmlJt_do29Orqa8fCNfo5swfH_fh-Tx6vLh4rq_vf91c3F-23vOTO0VG0X0EZxyowA9-qCl4F4b45jmeoS1iVKOxrPoRKAqSiPlWq-BCWMkhOGQ_Nz5vpb8ZxOw2jmhD-2LJeQNWiW4kEpy3ciT_5JUgwDGWQN_fAKf86a0KNBKOrR01SAbRHeQLxmxhGhfS5pd-Wcp2G0ldleJbdNuK7G8aY7fjR16N8XiFp_wQyiYFlzx4Q28Zosy</recordid><startdate>20031101</startdate><enddate>20031101</enddate><creator>DOBLAS-REYES, F. J</creator><creator>PAVAN, V</creator><creator>STEPHENSON, D. B</creator><general>Springer</general><general>Springer Nature B.V</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TG</scope><scope>7TN</scope><scope>7UA</scope><scope>7XB</scope><scope>88F</scope><scope>88I</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>M1Q</scope><scope>M2P</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope></search><sort><creationdate>20031101</creationdate><title>The skill of multi-model seasonal forecasts of the wintertime North Atlantic Oscillation</title><author>DOBLAS-REYES, F. J ; PAVAN, V ; STEPHENSON, D. B</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c429t-72b5fcf0a7ab508bce8654c899a2848b0d9f66b9c2fa5e17f6966d8d0259960e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Atmospheric circulation</topic><topic>Earth, ocean, space</topic><topic>Exact sciences and technology</topic><topic>External geophysics</topic><topic>General circulation models</topic><topic>Marine</topic><topic>Meteorology</topic><topic>Sea surface temperature</topic><topic>Seasonal variations</topic><topic>Weather analysis and prediction</topic><topic>Winter</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>DOBLAS-REYES, F. J</creatorcontrib><creatorcontrib>PAVAN, V</creatorcontrib><creatorcontrib>STEPHENSON, D. B</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Military Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Military Collection</collection><collection>ProQuest Science Journals</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Climate dynamics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>DOBLAS-REYES, F. J</au><au>PAVAN, V</au><au>STEPHENSON, D. B</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The skill of multi-model seasonal forecasts of the wintertime North Atlantic Oscillation</atitle><jtitle>Climate dynamics</jtitle><date>2003-11-01</date><risdate>2003</risdate><volume>21</volume><issue>5-6</issue><spage>501</spage><epage>514</epage><pages>501-514</pages><issn>0930-7575</issn><eissn>1432-0894</eissn><coden>CLDYEM</coden><abstract>The skill assessment of a set of wintertime North Atlantic Oscillation (NAO) seasonal predictions in a multi-model ensemble framework has been carried out. The multi-model approach consists in merging the ensemble hindcasts of four atmospheric general circulation models forced with observed sea surface temperatures to create a multi-model ensemble. Deterministic (ensemble-mean based) and probabilistic (categorical) NAO hindcasts have been considered. Two different sets of NAO indices have been used to create the hindcasts. A first set is defined as the projection of model anomalies onto the NAO spatial pattern obtained from atmospheric analyses. The second set obtains the NAO indices by standardizing the leading principal component of each single-model ensemble. Positive skill is found with both sets of indices, especially in the case of the multi-model ensemble. In addition, the NAO definition based upon the single-model leading principal component shows a higher skill than the hindcasts obtained using the projection method. Using the former definition, the multi-model ensemble shows statistically significant (at 5% level) positive skill in a variety of probabilistic scoring measures. This is interpreted as a consequence of the projection method being less suitable because of the presence of errors in the spatial NAO patterns of the models. The positive skill of the seasonal NAO found here seems to be due not to the persistence of the long-term (decadal) variability specified in the initial conditions, but rather to a good simulation of the year-to-year variability. Nevertheless, most of the NAO seasonal predictability seems to be due to the correct prediction of particular cases such as the winter of 1989. The higher skill of the multi-model has been explained on the basis of a more reliable description of large-scale tropospheric wave features by the multi-model ensemble, illustrating the potential of multi-model experiments to better identify mechanisms that explain seasonal variability in the atmosphere.[PUBLICATION ABSTRACT]</abstract><cop>Heidelberg</cop><cop>Berlin</cop><pub>Springer</pub><doi>10.1007/s00382-003-0350-4</doi><tpages>14</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0930-7575 |
ispartof | Climate dynamics, 2003-11, Vol.21 (5-6), p.501-514 |
issn | 0930-7575 1432-0894 |
language | eng |
recordid | cdi_proquest_miscellaneous_754567648 |
source | SpringerLink (Online service) |
subjects | Atmospheric circulation Earth, ocean, space Exact sciences and technology External geophysics General circulation models Marine Meteorology Sea surface temperature Seasonal variations Weather analysis and prediction Winter |
title | The skill of multi-model seasonal forecasts of the wintertime North Atlantic Oscillation |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T09%3A56%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20skill%20of%20multi-model%20seasonal%20forecasts%20of%20the%20wintertime%20North%20Atlantic%20Oscillation&rft.jtitle=Climate%20dynamics&rft.au=DOBLAS-REYES,%20F.%20J&rft.date=2003-11-01&rft.volume=21&rft.issue=5-6&rft.spage=501&rft.epage=514&rft.pages=501-514&rft.issn=0930-7575&rft.eissn=1432-0894&rft.coden=CLDYEM&rft_id=info:doi/10.1007/s00382-003-0350-4&rft_dat=%3Cproquest_cross%3E18050242%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=613350736&rft_id=info:pmid/&rfr_iscdi=true |