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 (...
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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 |
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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&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 & 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 & Aerospace Database (1962 - 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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> |
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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 |
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