A Predictable AMO-Like Pattern in the GFDL Fully Coupled Ensemble Initialization and Decadal Forecasting System

The decadal predictability of sea surface temperature (SST) and 2-m air temperature (T2m) in the Geophysical Fluid Dynamics Laboratory (GFDL) decadal hindcasts, which are part of the Fifth Coupled Model Intercomparison Project experiments, has been investigated using an average predictability time (...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of climate 2013-01, Vol.26 (2), p.650-661
Hauptverfasser: Yang, Xiaosong, Rosati, Anthony, Zhang, Shaoqing, Delworth, Thomas L., Gudgel, Rich G., Zhang, Rong, Vecchi, Gabriel, Anderson, Whit, Chang, You-Soon, DelSole, Timothy, Dixon, Keith, Msadek, Rym, Stern, William F., Wittenberg, Andrew, Zeng, Fanrong
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 661
container_issue 2
container_start_page 650
container_title Journal of climate
container_volume 26
creator Yang, Xiaosong
Rosati, Anthony
Zhang, Shaoqing
Delworth, Thomas L.
Gudgel, Rich G.
Zhang, Rong
Vecchi, Gabriel
Anderson, Whit
Chang, You-Soon
DelSole, Timothy
Dixon, Keith
Msadek, Rym
Stern, William F.
Wittenberg, Andrew
Zeng, Fanrong
description The decadal predictability of sea surface temperature (SST) and 2-m air temperature (T2m) in the Geophysical Fluid Dynamics Laboratory (GFDL) decadal hindcasts, which are part of the Fifth Coupled Model Intercomparison Project experiments, has been investigated using an average predictability time (APT) analysis. Comparison of retrospective forecasts initialized using the GFDL Ensemble Coupled Data Assimilation system with uninitialized historical forcing simulations using the same model allows identification of the internal multidecadal pattern (IMP) for SST and T2m. The IMP of SST is characterized by an interhemisphere dipole, with warm anomalies centered in the North Atlantic subpolar gyre region and North Pacific subpolar gyre region, and cold anomalies centered in the Antarctic Circumpolar Current region. The IMP of T2m is characterized by a general bipolar seesaw, with warm anomalies centered in Greenland and cold anomalies centered in Antarctica. The retrospective prediction skill of the initialized system, verified against independent observational datasets, indicates that the IMP of SST may be predictable up to 4 (10) yr lead time at 95% (90%) significance level, and the IMP of T2m may be predictable up to 2 (10) yr at the 95% (90%) significance level. The initialization of multidecadal variations of northward oceanic heat transport in the North Atlantic significantly improves the predictive skill of the IMP. The dominant roles of oceanic internal dynamics in decadal prediction are further elucidated by fixed-forcing experiments in which radiative forcing is returned abruptly to 1961 values. These results point toward the possibility of meaningful decadal climate outlooks using dynamical coupled models if they are appropriately initialized from a sustained climate observing system.
doi_str_mv 10.1175/jcli-d-12-00231.1
format Article
fullrecord <record><control><sourceid>jstor_proqu</sourceid><recordid>TN_cdi_proquest_miscellaneous_1285093711</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><jstor_id>26192171</jstor_id><sourcerecordid>26192171</sourcerecordid><originalsourceid>FETCH-LOGICAL-c495t-ae7a0b58a7d1c259adeacacfd5ddd3848228689006e8ef58063f79d8ba1b387f3</originalsourceid><addsrcrecordid>eNp9kUGLFDEQhRtRcFz9AR6EgAheek0lnU5yHGZ21pGWXVDPoSZJa8ZM95ikD-Ovt8dZFDx4qoL63nsFr6peAr0GkOLd3sZQuxpYTSnjcA2PqgUIRmvaNOxxtaBKN7WSQjytnuW8pxRYS-miGpfkPnkXbMFd9GT58a7uwndP7rEUnwYSBlK-eXK7WXdkM8V4IqtxOkbvyM2Q_eGs2Q6hBIzhJ5YwDgQHR9beosNINmOat1zC8JV8OuXiD8-rJz3G7F88zKvqy-bm8-p93d3dblfLrraNFqVGL5HuhELpwDKh0Xm0aHsnnHNcNYox1SpNaeuV74WiLe-ldmqHsONK9vyqenvxPabxx-RzMYeQrY8RBz9O2QBTgmouAWb09T_ofpzSMH9nmAKuZdM0_H8UcJBca8rpTMGFsmnMOfneHFM4YDoZoOZclPmw6rZmPeeb30WZc_6bB2fMFmOfcLAh_xEyCVrztpm5Vxdun8uY_t5b0Awk8F-ejZvR</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1317399030</pqid></control><display><type>article</type><title>A Predictable AMO-Like Pattern in the GFDL Fully Coupled Ensemble Initialization and Decadal Forecasting System</title><source>American Meteorological Society</source><source>JSTOR</source><source>EZB Electronic Journals Library</source><creator>Yang, Xiaosong ; Rosati, Anthony ; Zhang, Shaoqing ; Delworth, Thomas L. ; Gudgel, Rich G. ; Zhang, Rong ; Vecchi, Gabriel ; Anderson, Whit ; Chang, You-Soon ; DelSole, Timothy ; Dixon, Keith ; Msadek, Rym ; Stern, William F. ; Wittenberg, Andrew ; Zeng, Fanrong</creator><creatorcontrib>Yang, Xiaosong ; Rosati, Anthony ; Zhang, Shaoqing ; Delworth, Thomas L. ; Gudgel, Rich G. ; Zhang, Rong ; Vecchi, Gabriel ; Anderson, Whit ; Chang, You-Soon ; DelSole, Timothy ; Dixon, Keith ; Msadek, Rym ; Stern, William F. ; Wittenberg, Andrew ; Zeng, Fanrong</creatorcontrib><description>The decadal predictability of sea surface temperature (SST) and 2-m air temperature (T2m) in the Geophysical Fluid Dynamics Laboratory (GFDL) decadal hindcasts, which are part of the Fifth Coupled Model Intercomparison Project experiments, has been investigated using an average predictability time (APT) analysis. Comparison of retrospective forecasts initialized using the GFDL Ensemble Coupled Data Assimilation system with uninitialized historical forcing simulations using the same model allows identification of the internal multidecadal pattern (IMP) for SST and T2m. The IMP of SST is characterized by an interhemisphere dipole, with warm anomalies centered in the North Atlantic subpolar gyre region and North Pacific subpolar gyre region, and cold anomalies centered in the Antarctic Circumpolar Current region. The IMP of T2m is characterized by a general bipolar seesaw, with warm anomalies centered in Greenland and cold anomalies centered in Antarctica. The retrospective prediction skill of the initialized system, verified against independent observational datasets, indicates that the IMP of SST may be predictable up to 4 (10) yr lead time at 95% (90%) significance level, and the IMP of T2m may be predictable up to 2 (10) yr at the 95% (90%) significance level. The initialization of multidecadal variations of northward oceanic heat transport in the North Atlantic significantly improves the predictive skill of the IMP. The dominant roles of oceanic internal dynamics in decadal prediction are further elucidated by fixed-forcing experiments in which radiative forcing is returned abruptly to 1961 values. These results point toward the possibility of meaningful decadal climate outlooks using dynamical coupled models if they are appropriately initialized from a sustained climate observing system.</description><identifier>ISSN: 0894-8755</identifier><identifier>EISSN: 1520-0442</identifier><identifier>DOI: 10.1175/jcli-d-12-00231.1</identifier><language>eng</language><publisher>Boston, MA: American Meteorological Society</publisher><subject>20th century ; Air temperature ; Anomalies ; Antarctic Circumpolar Current ; Antarctica ; Automatic picture transmission ; Climate ; Climate change ; Climate system ; Climatology. Bioclimatology. Climate change ; Data assimilation ; Data collection ; Datasets ; Dipoles ; Dynamical systems ; Dynamics ; Earth, ocean, space ; Ensemble forecasting ; Exact sciences and technology ; Experiments ; External geophysics ; Fluid dynamics ; Forecasting models ; Geophysical fluids ; Geophysics. Techniques, methods, instrumentation and models ; Gyres ; Heat transport ; Hydrodynamics ; IMP ; Intercomparison ; Lead time ; Marine ; Mathematical models ; Meteorology ; Methods ; Modeling ; Predictability ; Radiative forcing ; Sea surface ; Sea surface temperature ; Sea transportation ; Significance level ; Simulation ; Skills ; Statistical forecasts ; Studies ; Surface temperature ; Time series ; Time series forecasting</subject><ispartof>Journal of climate, 2013-01, Vol.26 (2), p.650-661</ispartof><rights>2013 American Meteorological Society</rights><rights>2015 INIST-CNRS</rights><rights>Copyright American Meteorological Society Jan 15, 2013</rights><rights>Copyright American Meteorological Society 2013</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c495t-ae7a0b58a7d1c259adeacacfd5ddd3848228689006e8ef58063f79d8ba1b387f3</citedby><cites>FETCH-LOGICAL-c495t-ae7a0b58a7d1c259adeacacfd5ddd3848228689006e8ef58063f79d8ba1b387f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26192171$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26192171$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>315,782,786,805,3685,27933,27934,58026,58259</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&amp;idt=27199364$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Yang, Xiaosong</creatorcontrib><creatorcontrib>Rosati, Anthony</creatorcontrib><creatorcontrib>Zhang, Shaoqing</creatorcontrib><creatorcontrib>Delworth, Thomas L.</creatorcontrib><creatorcontrib>Gudgel, Rich G.</creatorcontrib><creatorcontrib>Zhang, Rong</creatorcontrib><creatorcontrib>Vecchi, Gabriel</creatorcontrib><creatorcontrib>Anderson, Whit</creatorcontrib><creatorcontrib>Chang, You-Soon</creatorcontrib><creatorcontrib>DelSole, Timothy</creatorcontrib><creatorcontrib>Dixon, Keith</creatorcontrib><creatorcontrib>Msadek, Rym</creatorcontrib><creatorcontrib>Stern, William F.</creatorcontrib><creatorcontrib>Wittenberg, Andrew</creatorcontrib><creatorcontrib>Zeng, Fanrong</creatorcontrib><title>A Predictable AMO-Like Pattern in the GFDL Fully Coupled Ensemble Initialization and Decadal Forecasting System</title><title>Journal of climate</title><description>The decadal predictability of sea surface temperature (SST) and 2-m air temperature (T2m) in the Geophysical Fluid Dynamics Laboratory (GFDL) decadal hindcasts, which are part of the Fifth Coupled Model Intercomparison Project experiments, has been investigated using an average predictability time (APT) analysis. Comparison of retrospective forecasts initialized using the GFDL Ensemble Coupled Data Assimilation system with uninitialized historical forcing simulations using the same model allows identification of the internal multidecadal pattern (IMP) for SST and T2m. The IMP of SST is characterized by an interhemisphere dipole, with warm anomalies centered in the North Atlantic subpolar gyre region and North Pacific subpolar gyre region, and cold anomalies centered in the Antarctic Circumpolar Current region. The IMP of T2m is characterized by a general bipolar seesaw, with warm anomalies centered in Greenland and cold anomalies centered in Antarctica. The retrospective prediction skill of the initialized system, verified against independent observational datasets, indicates that the IMP of SST may be predictable up to 4 (10) yr lead time at 95% (90%) significance level, and the IMP of T2m may be predictable up to 2 (10) yr at the 95% (90%) significance level. The initialization of multidecadal variations of northward oceanic heat transport in the North Atlantic significantly improves the predictive skill of the IMP. The dominant roles of oceanic internal dynamics in decadal prediction are further elucidated by fixed-forcing experiments in which radiative forcing is returned abruptly to 1961 values. These results point toward the possibility of meaningful decadal climate outlooks using dynamical coupled models if they are appropriately initialized from a sustained climate observing system.</description><subject>20th century</subject><subject>Air temperature</subject><subject>Anomalies</subject><subject>Antarctic Circumpolar Current</subject><subject>Antarctica</subject><subject>Automatic picture transmission</subject><subject>Climate</subject><subject>Climate change</subject><subject>Climate system</subject><subject>Climatology. Bioclimatology. Climate change</subject><subject>Data assimilation</subject><subject>Data collection</subject><subject>Datasets</subject><subject>Dipoles</subject><subject>Dynamical systems</subject><subject>Dynamics</subject><subject>Earth, ocean, space</subject><subject>Ensemble forecasting</subject><subject>Exact sciences and technology</subject><subject>Experiments</subject><subject>External geophysics</subject><subject>Fluid dynamics</subject><subject>Forecasting models</subject><subject>Geophysical fluids</subject><subject>Geophysics. Techniques, methods, instrumentation and models</subject><subject>Gyres</subject><subject>Heat transport</subject><subject>Hydrodynamics</subject><subject>IMP</subject><subject>Intercomparison</subject><subject>Lead time</subject><subject>Marine</subject><subject>Mathematical models</subject><subject>Meteorology</subject><subject>Methods</subject><subject>Modeling</subject><subject>Predictability</subject><subject>Radiative forcing</subject><subject>Sea surface</subject><subject>Sea surface temperature</subject><subject>Sea transportation</subject><subject>Significance level</subject><subject>Simulation</subject><subject>Skills</subject><subject>Statistical forecasts</subject><subject>Studies</subject><subject>Surface temperature</subject><subject>Time series</subject><subject>Time series forecasting</subject><issn>0894-8755</issn><issn>1520-0442</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kUGLFDEQhRtRcFz9AR6EgAheek0lnU5yHGZ21pGWXVDPoSZJa8ZM95ikD-Ovt8dZFDx4qoL63nsFr6peAr0GkOLd3sZQuxpYTSnjcA2PqgUIRmvaNOxxtaBKN7WSQjytnuW8pxRYS-miGpfkPnkXbMFd9GT58a7uwndP7rEUnwYSBlK-eXK7WXdkM8V4IqtxOkbvyM2Q_eGs2Q6hBIzhJ5YwDgQHR9beosNINmOat1zC8JV8OuXiD8-rJz3G7F88zKvqy-bm8-p93d3dblfLrraNFqVGL5HuhELpwDKh0Xm0aHsnnHNcNYox1SpNaeuV74WiLe-ldmqHsONK9vyqenvxPabxx-RzMYeQrY8RBz9O2QBTgmouAWb09T_ofpzSMH9nmAKuZdM0_H8UcJBca8rpTMGFsmnMOfneHFM4YDoZoOZclPmw6rZmPeeb30WZc_6bB2fMFmOfcLAh_xEyCVrztpm5Vxdun8uY_t5b0Awk8F-ejZvR</recordid><startdate>20130115</startdate><enddate>20130115</enddate><creator>Yang, Xiaosong</creator><creator>Rosati, Anthony</creator><creator>Zhang, Shaoqing</creator><creator>Delworth, Thomas L.</creator><creator>Gudgel, Rich G.</creator><creator>Zhang, Rong</creator><creator>Vecchi, Gabriel</creator><creator>Anderson, Whit</creator><creator>Chang, You-Soon</creator><creator>DelSole, Timothy</creator><creator>Dixon, Keith</creator><creator>Msadek, Rym</creator><creator>Stern, William F.</creator><creator>Wittenberg, Andrew</creator><creator>Zeng, Fanrong</creator><general>American Meteorological Society</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QH</scope><scope>7TG</scope><scope>7UA</scope><scope>7X2</scope><scope>7XB</scope><scope>88F</scope><scope>88I</scope><scope>8AF</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>M0K</scope><scope>M1Q</scope><scope>M2O</scope><scope>M2P</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0X</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20130115</creationdate><title>A Predictable AMO-Like Pattern in the GFDL Fully Coupled Ensemble Initialization and Decadal Forecasting System</title><author>Yang, Xiaosong ; Rosati, Anthony ; Zhang, Shaoqing ; Delworth, Thomas L. ; Gudgel, Rich G. ; Zhang, Rong ; Vecchi, Gabriel ; Anderson, Whit ; Chang, You-Soon ; DelSole, Timothy ; Dixon, Keith ; Msadek, Rym ; Stern, William F. ; Wittenberg, Andrew ; Zeng, Fanrong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c495t-ae7a0b58a7d1c259adeacacfd5ddd3848228689006e8ef58063f79d8ba1b387f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>20th century</topic><topic>Air temperature</topic><topic>Anomalies</topic><topic>Antarctic Circumpolar Current</topic><topic>Antarctica</topic><topic>Automatic picture transmission</topic><topic>Climate</topic><topic>Climate change</topic><topic>Climate system</topic><topic>Climatology. Bioclimatology. Climate change</topic><topic>Data assimilation</topic><topic>Data collection</topic><topic>Datasets</topic><topic>Dipoles</topic><topic>Dynamical systems</topic><topic>Dynamics</topic><topic>Earth, ocean, space</topic><topic>Ensemble forecasting</topic><topic>Exact sciences and technology</topic><topic>Experiments</topic><topic>External geophysics</topic><topic>Fluid dynamics</topic><topic>Forecasting models</topic><topic>Geophysical fluids</topic><topic>Geophysics. Techniques, methods, instrumentation and models</topic><topic>Gyres</topic><topic>Heat transport</topic><topic>Hydrodynamics</topic><topic>IMP</topic><topic>Intercomparison</topic><topic>Lead time</topic><topic>Marine</topic><topic>Mathematical models</topic><topic>Meteorology</topic><topic>Methods</topic><topic>Modeling</topic><topic>Predictability</topic><topic>Radiative forcing</topic><topic>Sea surface</topic><topic>Sea surface temperature</topic><topic>Sea transportation</topic><topic>Significance level</topic><topic>Simulation</topic><topic>Skills</topic><topic>Statistical forecasts</topic><topic>Studies</topic><topic>Surface temperature</topic><topic>Time series</topic><topic>Time series forecasting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Xiaosong</creatorcontrib><creatorcontrib>Rosati, Anthony</creatorcontrib><creatorcontrib>Zhang, Shaoqing</creatorcontrib><creatorcontrib>Delworth, Thomas L.</creatorcontrib><creatorcontrib>Gudgel, Rich G.</creatorcontrib><creatorcontrib>Zhang, Rong</creatorcontrib><creatorcontrib>Vecchi, Gabriel</creatorcontrib><creatorcontrib>Anderson, Whit</creatorcontrib><creatorcontrib>Chang, You-Soon</creatorcontrib><creatorcontrib>DelSole, Timothy</creatorcontrib><creatorcontrib>Dixon, Keith</creatorcontrib><creatorcontrib>Msadek, Rym</creatorcontrib><creatorcontrib>Stern, William F.</creatorcontrib><creatorcontrib>Wittenberg, Andrew</creatorcontrib><creatorcontrib>Zeng, Fanrong</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Aqualine</collection><collection>Meteorological &amp; Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Agricultural Science Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Military Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>STEM Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; Aerospace Database‎ (1962 - current)</collection><collection>Agricultural &amp; Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>eLibrary</collection><collection>ProQuest Academic</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric &amp; 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>Research Library Prep</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy &amp; Non-Living Resources</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>Meteorological &amp; Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) Professional</collection><collection>Agriculture Science Database</collection><collection>Military Database (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest_Research Library</collection><collection>ProQuest Science Journals</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric &amp; 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><collection>SIRS Editorial</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Journal of climate</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Xiaosong</au><au>Rosati, Anthony</au><au>Zhang, Shaoqing</au><au>Delworth, Thomas L.</au><au>Gudgel, Rich G.</au><au>Zhang, Rong</au><au>Vecchi, Gabriel</au><au>Anderson, Whit</au><au>Chang, You-Soon</au><au>DelSole, Timothy</au><au>Dixon, Keith</au><au>Msadek, Rym</au><au>Stern, William F.</au><au>Wittenberg, Andrew</au><au>Zeng, Fanrong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Predictable AMO-Like Pattern in the GFDL Fully Coupled Ensemble Initialization and Decadal Forecasting System</atitle><jtitle>Journal of climate</jtitle><date>2013-01-15</date><risdate>2013</risdate><volume>26</volume><issue>2</issue><spage>650</spage><epage>661</epage><pages>650-661</pages><issn>0894-8755</issn><eissn>1520-0442</eissn><abstract>The decadal predictability of sea surface temperature (SST) and 2-m air temperature (T2m) in the Geophysical Fluid Dynamics Laboratory (GFDL) decadal hindcasts, which are part of the Fifth Coupled Model Intercomparison Project experiments, has been investigated using an average predictability time (APT) analysis. Comparison of retrospective forecasts initialized using the GFDL Ensemble Coupled Data Assimilation system with uninitialized historical forcing simulations using the same model allows identification of the internal multidecadal pattern (IMP) for SST and T2m. The IMP of SST is characterized by an interhemisphere dipole, with warm anomalies centered in the North Atlantic subpolar gyre region and North Pacific subpolar gyre region, and cold anomalies centered in the Antarctic Circumpolar Current region. The IMP of T2m is characterized by a general bipolar seesaw, with warm anomalies centered in Greenland and cold anomalies centered in Antarctica. The retrospective prediction skill of the initialized system, verified against independent observational datasets, indicates that the IMP of SST may be predictable up to 4 (10) yr lead time at 95% (90%) significance level, and the IMP of T2m may be predictable up to 2 (10) yr at the 95% (90%) significance level. The initialization of multidecadal variations of northward oceanic heat transport in the North Atlantic significantly improves the predictive skill of the IMP. The dominant roles of oceanic internal dynamics in decadal prediction are further elucidated by fixed-forcing experiments in which radiative forcing is returned abruptly to 1961 values. These results point toward the possibility of meaningful decadal climate outlooks using dynamical coupled models if they are appropriately initialized from a sustained climate observing system.</abstract><cop>Boston, MA</cop><pub>American Meteorological Society</pub><doi>10.1175/jcli-d-12-00231.1</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0894-8755
ispartof Journal of climate, 2013-01, Vol.26 (2), p.650-661
issn 0894-8755
1520-0442
language eng
recordid cdi_proquest_miscellaneous_1285093711
source American Meteorological Society; JSTOR; EZB Electronic Journals Library
subjects 20th century
Air temperature
Anomalies
Antarctic Circumpolar Current
Antarctica
Automatic picture transmission
Climate
Climate change
Climate system
Climatology. Bioclimatology. Climate change
Data assimilation
Data collection
Datasets
Dipoles
Dynamical systems
Dynamics
Earth, ocean, space
Ensemble forecasting
Exact sciences and technology
Experiments
External geophysics
Fluid dynamics
Forecasting models
Geophysical fluids
Geophysics. Techniques, methods, instrumentation and models
Gyres
Heat transport
Hydrodynamics
IMP
Intercomparison
Lead time
Marine
Mathematical models
Meteorology
Methods
Modeling
Predictability
Radiative forcing
Sea surface
Sea surface temperature
Sea transportation
Significance level
Simulation
Skills
Statistical forecasts
Studies
Surface temperature
Time series
Time series forecasting
title A Predictable AMO-Like Pattern in the GFDL Fully Coupled Ensemble Initialization and Decadal Forecasting System
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-11-29T14%3A30%3A01IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstor_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Predictable%20AMO-Like%20Pattern%20in%20the%20GFDL%20Fully%20Coupled%20Ensemble%20Initialization%20and%20Decadal%20Forecasting%20System&rft.jtitle=Journal%20of%20climate&rft.au=Yang,%20Xiaosong&rft.date=2013-01-15&rft.volume=26&rft.issue=2&rft.spage=650&rft.epage=661&rft.pages=650-661&rft.issn=0894-8755&rft.eissn=1520-0442&rft_id=info:doi/10.1175/jcli-d-12-00231.1&rft_dat=%3Cjstor_proqu%3E26192171%3C/jstor_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1317399030&rft_id=info:pmid/&rft_jstor_id=26192171&rfr_iscdi=true