An alternative multi-model ensemble mean approach for near-term projection
An 'alternative multi-model ensemble mean' (AMME) method was developed for the near-term projection of regional climate change by taking into account the capacity of currently available climate models in simulating specific timescale components. These components included a climatological m...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yajie Qi [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2017 |
|---|
| Schlagwörter: |
|---|
| Systematik: |
|
|---|
| Übergeordnetes Werk: |
Enthalten in: International journal of climatology - Chichester [u.a.] : Wiley, 1989, 37(2017), 1, Seite 109 |
|---|---|
| Übergeordnetes Werk: |
volume:37 ; year:2017 ; number:1 ; pages:109 |
| Links: |
|---|
| DOI / URN: |
10.1002/joc.4690 |
|---|
| Katalog-ID: |
OLC1988421799 |
|---|
| LEADER | 01000caa a2200265 4500 | ||
|---|---|---|---|
| 001 | OLC1988421799 | ||
| 003 | DE-627 | ||
| 005 | 20230715021937.0 | ||
| 007 | tu | ||
| 008 | 170207s2017 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1002/joc.4690 |2 doi | |
| 028 | 5 | 2 | |a PQ20170206 |
| 035 | |a (DE-627)OLC1988421799 | ||
| 035 | |a (DE-599)GBVOLC1988421799 | ||
| 035 | |a (PRQ)p816-2f317195673c20007156da935fd23456293f4abc78975a1cfe075b33ae79f5fd3 | ||
| 035 | |a (KEY)0104704320170000037000100109alternativemultimodelensemblemeanapproachfornearte | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 550 |q DNB |
| 084 | |a RA 1000 |q AVZ |2 rvk | ||
| 100 | 0 | |a Yajie Qi |e verfasserin |4 aut | |
| 245 | 1 | 3 | |a An alternative multi-model ensemble mean approach for near-term projection |
| 264 | 1 | |c 2017 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 520 | |a An 'alternative multi-model ensemble mean' (AMME) method was developed for the near-term projection of regional climate change by taking into account the capacity of currently available climate models in simulating specific timescale components. These components included a climatological mean (Mean), an amplitude-frequency modulated annual cycle (MAC), multi-decadal variability (MDV), a secular trend (ST), and short-term variability (SV). The latter four components were extracted adaptively by the ensemble empirical mode decomposition filter from the climate series. For each component, a reconstructed simulation was determined from ensemble of a limited number of model simulations that could reproduce the component in the observation relatively well. An AMME simulation was obtained by combining the five components. The new method was illustrated to construct an AMME simulation of the monthly near-surface temperature series for the training period 1902-1990 in eastern China and was applied to the validation period 1991-2004. For the eastern China average, the best performance arose from MPI-ESM-MR for Mean, IPSL-CM5A-LR for MAC, ACCESS1.3 for MDV, GFDL-ESM2M for ST, and GISS-E2-H-CC for SV. Serving as a novel tool for producing reasonable near-term future climate change scenarios by utilizing currently available model simulations, the AMME exhibited a better performance in reproducing both past and near-term 'future' climate than conventional multi-model ensemble means and weighted average schemes. | ||
| 650 | 4 | |a Simulation | |
| 650 | 4 | |a Climate change | |
| 700 | 0 | |a Cheng Qian |4 oth | |
| 700 | 0 | |a Zhongwei Yan |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |t International journal of climatology |d Chichester [u.a.] : Wiley, 1989 |g 37(2017), 1, Seite 109 |w (DE-627)130763128 |w (DE-600)1000947-4 |w (DE-576)023035773 |x 0899-8418 |7 nnns |
| 773 | 1 | 8 | |g volume:37 |g year:2017 |g number:1 |g pages:109 |
| 856 | 4 | 1 | |u http://dx.doi.org/10.1002/joc.4690 |3 Volltext |
| 856 | 4 | 2 | |u http://search.proquest.com/docview/1854669413 |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-GEO | ||
| 912 | |a SSG-OPC-GGO | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_4311 | ||
| 936 | r | v | |a RA 1000 |
| 951 | |a AR | ||
| 952 | |d 37 |j 2017 |e 1 |h 109 | ||
| author_variant |
y q yq |
|---|---|
| matchkey_str |
article:08998418:2017----::nlentvmlioeesmlmaapocfre |
| hierarchy_sort_str |
2017 |
| publishDate |
2017 |
| allfields |
10.1002/joc.4690 doi PQ20170206 (DE-627)OLC1988421799 (DE-599)GBVOLC1988421799 (PRQ)p816-2f317195673c20007156da935fd23456293f4abc78975a1cfe075b33ae79f5fd3 (KEY)0104704320170000037000100109alternativemultimodelensemblemeanapproachfornearte DE-627 ger DE-627 rakwb eng 550 DNB RA 1000 AVZ rvk Yajie Qi verfasserin aut An alternative multi-model ensemble mean approach for near-term projection 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier An 'alternative multi-model ensemble mean' (AMME) method was developed for the near-term projection of regional climate change by taking into account the capacity of currently available climate models in simulating specific timescale components. These components included a climatological mean (Mean), an amplitude-frequency modulated annual cycle (MAC), multi-decadal variability (MDV), a secular trend (ST), and short-term variability (SV). The latter four components were extracted adaptively by the ensemble empirical mode decomposition filter from the climate series. For each component, a reconstructed simulation was determined from ensemble of a limited number of model simulations that could reproduce the component in the observation relatively well. An AMME simulation was obtained by combining the five components. The new method was illustrated to construct an AMME simulation of the monthly near-surface temperature series for the training period 1902-1990 in eastern China and was applied to the validation period 1991-2004. For the eastern China average, the best performance arose from MPI-ESM-MR for Mean, IPSL-CM5A-LR for MAC, ACCESS1.3 for MDV, GFDL-ESM2M for ST, and GISS-E2-H-CC for SV. Serving as a novel tool for producing reasonable near-term future climate change scenarios by utilizing currently available model simulations, the AMME exhibited a better performance in reproducing both past and near-term 'future' climate than conventional multi-model ensemble means and weighted average schemes. Simulation Climate change Cheng Qian oth Zhongwei Yan oth Enthalten in International journal of climatology Chichester [u.a.] : Wiley, 1989 37(2017), 1, Seite 109 (DE-627)130763128 (DE-600)1000947-4 (DE-576)023035773 0899-8418 nnns volume:37 year:2017 number:1 pages:109 http://dx.doi.org/10.1002/joc.4690 Volltext http://search.proquest.com/docview/1854669413 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_4311 RA 1000 AR 37 2017 1 109 |
| spelling |
10.1002/joc.4690 doi PQ20170206 (DE-627)OLC1988421799 (DE-599)GBVOLC1988421799 (PRQ)p816-2f317195673c20007156da935fd23456293f4abc78975a1cfe075b33ae79f5fd3 (KEY)0104704320170000037000100109alternativemultimodelensemblemeanapproachfornearte DE-627 ger DE-627 rakwb eng 550 DNB RA 1000 AVZ rvk Yajie Qi verfasserin aut An alternative multi-model ensemble mean approach for near-term projection 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier An 'alternative multi-model ensemble mean' (AMME) method was developed for the near-term projection of regional climate change by taking into account the capacity of currently available climate models in simulating specific timescale components. These components included a climatological mean (Mean), an amplitude-frequency modulated annual cycle (MAC), multi-decadal variability (MDV), a secular trend (ST), and short-term variability (SV). The latter four components were extracted adaptively by the ensemble empirical mode decomposition filter from the climate series. For each component, a reconstructed simulation was determined from ensemble of a limited number of model simulations that could reproduce the component in the observation relatively well. An AMME simulation was obtained by combining the five components. The new method was illustrated to construct an AMME simulation of the monthly near-surface temperature series for the training period 1902-1990 in eastern China and was applied to the validation period 1991-2004. For the eastern China average, the best performance arose from MPI-ESM-MR for Mean, IPSL-CM5A-LR for MAC, ACCESS1.3 for MDV, GFDL-ESM2M for ST, and GISS-E2-H-CC for SV. Serving as a novel tool for producing reasonable near-term future climate change scenarios by utilizing currently available model simulations, the AMME exhibited a better performance in reproducing both past and near-term 'future' climate than conventional multi-model ensemble means and weighted average schemes. Simulation Climate change Cheng Qian oth Zhongwei Yan oth Enthalten in International journal of climatology Chichester [u.a.] : Wiley, 1989 37(2017), 1, Seite 109 (DE-627)130763128 (DE-600)1000947-4 (DE-576)023035773 0899-8418 nnns volume:37 year:2017 number:1 pages:109 http://dx.doi.org/10.1002/joc.4690 Volltext http://search.proquest.com/docview/1854669413 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_4311 RA 1000 AR 37 2017 1 109 |
| allfields_unstemmed |
10.1002/joc.4690 doi PQ20170206 (DE-627)OLC1988421799 (DE-599)GBVOLC1988421799 (PRQ)p816-2f317195673c20007156da935fd23456293f4abc78975a1cfe075b33ae79f5fd3 (KEY)0104704320170000037000100109alternativemultimodelensemblemeanapproachfornearte DE-627 ger DE-627 rakwb eng 550 DNB RA 1000 AVZ rvk Yajie Qi verfasserin aut An alternative multi-model ensemble mean approach for near-term projection 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier An 'alternative multi-model ensemble mean' (AMME) method was developed for the near-term projection of regional climate change by taking into account the capacity of currently available climate models in simulating specific timescale components. These components included a climatological mean (Mean), an amplitude-frequency modulated annual cycle (MAC), multi-decadal variability (MDV), a secular trend (ST), and short-term variability (SV). The latter four components were extracted adaptively by the ensemble empirical mode decomposition filter from the climate series. For each component, a reconstructed simulation was determined from ensemble of a limited number of model simulations that could reproduce the component in the observation relatively well. An AMME simulation was obtained by combining the five components. The new method was illustrated to construct an AMME simulation of the monthly near-surface temperature series for the training period 1902-1990 in eastern China and was applied to the validation period 1991-2004. For the eastern China average, the best performance arose from MPI-ESM-MR for Mean, IPSL-CM5A-LR for MAC, ACCESS1.3 for MDV, GFDL-ESM2M for ST, and GISS-E2-H-CC for SV. Serving as a novel tool for producing reasonable near-term future climate change scenarios by utilizing currently available model simulations, the AMME exhibited a better performance in reproducing both past and near-term 'future' climate than conventional multi-model ensemble means and weighted average schemes. Simulation Climate change Cheng Qian oth Zhongwei Yan oth Enthalten in International journal of climatology Chichester [u.a.] : Wiley, 1989 37(2017), 1, Seite 109 (DE-627)130763128 (DE-600)1000947-4 (DE-576)023035773 0899-8418 nnns volume:37 year:2017 number:1 pages:109 http://dx.doi.org/10.1002/joc.4690 Volltext http://search.proquest.com/docview/1854669413 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_4311 RA 1000 AR 37 2017 1 109 |
| allfieldsGer |
10.1002/joc.4690 doi PQ20170206 (DE-627)OLC1988421799 (DE-599)GBVOLC1988421799 (PRQ)p816-2f317195673c20007156da935fd23456293f4abc78975a1cfe075b33ae79f5fd3 (KEY)0104704320170000037000100109alternativemultimodelensemblemeanapproachfornearte DE-627 ger DE-627 rakwb eng 550 DNB RA 1000 AVZ rvk Yajie Qi verfasserin aut An alternative multi-model ensemble mean approach for near-term projection 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier An 'alternative multi-model ensemble mean' (AMME) method was developed for the near-term projection of regional climate change by taking into account the capacity of currently available climate models in simulating specific timescale components. These components included a climatological mean (Mean), an amplitude-frequency modulated annual cycle (MAC), multi-decadal variability (MDV), a secular trend (ST), and short-term variability (SV). The latter four components were extracted adaptively by the ensemble empirical mode decomposition filter from the climate series. For each component, a reconstructed simulation was determined from ensemble of a limited number of model simulations that could reproduce the component in the observation relatively well. An AMME simulation was obtained by combining the five components. The new method was illustrated to construct an AMME simulation of the monthly near-surface temperature series for the training period 1902-1990 in eastern China and was applied to the validation period 1991-2004. For the eastern China average, the best performance arose from MPI-ESM-MR for Mean, IPSL-CM5A-LR for MAC, ACCESS1.3 for MDV, GFDL-ESM2M for ST, and GISS-E2-H-CC for SV. Serving as a novel tool for producing reasonable near-term future climate change scenarios by utilizing currently available model simulations, the AMME exhibited a better performance in reproducing both past and near-term 'future' climate than conventional multi-model ensemble means and weighted average schemes. Simulation Climate change Cheng Qian oth Zhongwei Yan oth Enthalten in International journal of climatology Chichester [u.a.] : Wiley, 1989 37(2017), 1, Seite 109 (DE-627)130763128 (DE-600)1000947-4 (DE-576)023035773 0899-8418 nnns volume:37 year:2017 number:1 pages:109 http://dx.doi.org/10.1002/joc.4690 Volltext http://search.proquest.com/docview/1854669413 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_4311 RA 1000 AR 37 2017 1 109 |
| allfieldsSound |
10.1002/joc.4690 doi PQ20170206 (DE-627)OLC1988421799 (DE-599)GBVOLC1988421799 (PRQ)p816-2f317195673c20007156da935fd23456293f4abc78975a1cfe075b33ae79f5fd3 (KEY)0104704320170000037000100109alternativemultimodelensemblemeanapproachfornearte DE-627 ger DE-627 rakwb eng 550 DNB RA 1000 AVZ rvk Yajie Qi verfasserin aut An alternative multi-model ensemble mean approach for near-term projection 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier An 'alternative multi-model ensemble mean' (AMME) method was developed for the near-term projection of regional climate change by taking into account the capacity of currently available climate models in simulating specific timescale components. These components included a climatological mean (Mean), an amplitude-frequency modulated annual cycle (MAC), multi-decadal variability (MDV), a secular trend (ST), and short-term variability (SV). The latter four components were extracted adaptively by the ensemble empirical mode decomposition filter from the climate series. For each component, a reconstructed simulation was determined from ensemble of a limited number of model simulations that could reproduce the component in the observation relatively well. An AMME simulation was obtained by combining the five components. The new method was illustrated to construct an AMME simulation of the monthly near-surface temperature series for the training period 1902-1990 in eastern China and was applied to the validation period 1991-2004. For the eastern China average, the best performance arose from MPI-ESM-MR for Mean, IPSL-CM5A-LR for MAC, ACCESS1.3 for MDV, GFDL-ESM2M for ST, and GISS-E2-H-CC for SV. Serving as a novel tool for producing reasonable near-term future climate change scenarios by utilizing currently available model simulations, the AMME exhibited a better performance in reproducing both past and near-term 'future' climate than conventional multi-model ensemble means and weighted average schemes. Simulation Climate change Cheng Qian oth Zhongwei Yan oth Enthalten in International journal of climatology Chichester [u.a.] : Wiley, 1989 37(2017), 1, Seite 109 (DE-627)130763128 (DE-600)1000947-4 (DE-576)023035773 0899-8418 nnns volume:37 year:2017 number:1 pages:109 http://dx.doi.org/10.1002/joc.4690 Volltext http://search.proquest.com/docview/1854669413 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_4311 RA 1000 AR 37 2017 1 109 |
| language |
English |
| source |
Enthalten in International journal of climatology 37(2017), 1, Seite 109 volume:37 year:2017 number:1 pages:109 |
| sourceStr |
Enthalten in International journal of climatology 37(2017), 1, Seite 109 volume:37 year:2017 number:1 pages:109 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Simulation Climate change |
| dewey-raw |
550 |
| isfreeaccess_bool |
false |
| container_title |
International journal of climatology |
| authorswithroles_txt_mv |
Yajie Qi @@aut@@ Cheng Qian @@oth@@ Zhongwei Yan @@oth@@ |
| publishDateDaySort_date |
2017-01-01T00:00:00Z |
| hierarchy_top_id |
130763128 |
| dewey-sort |
3550 |
| id |
OLC1988421799 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1988421799</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230715021937.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">170207s2017 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1002/joc.4690</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20170206</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1988421799</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1988421799</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)p816-2f317195673c20007156da935fd23456293f4abc78975a1cfe075b33ae79f5fd3</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0104704320170000037000100109alternativemultimodelensemblemeanapproachfornearte</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">RA 1000</subfield><subfield code="q">AVZ</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Yajie Qi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="3"><subfield code="a">An alternative multi-model ensemble mean approach for near-term projection</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">An 'alternative multi-model ensemble mean' (AMME) method was developed for the near-term projection of regional climate change by taking into account the capacity of currently available climate models in simulating specific timescale components. These components included a climatological mean (Mean), an amplitude-frequency modulated annual cycle (MAC), multi-decadal variability (MDV), a secular trend (ST), and short-term variability (SV). The latter four components were extracted adaptively by the ensemble empirical mode decomposition filter from the climate series. For each component, a reconstructed simulation was determined from ensemble of a limited number of model simulations that could reproduce the component in the observation relatively well. An AMME simulation was obtained by combining the five components. The new method was illustrated to construct an AMME simulation of the monthly near-surface temperature series for the training period 1902-1990 in eastern China and was applied to the validation period 1991-2004. For the eastern China average, the best performance arose from MPI-ESM-MR for Mean, IPSL-CM5A-LR for MAC, ACCESS1.3 for MDV, GFDL-ESM2M for ST, and GISS-E2-H-CC for SV. Serving as a novel tool for producing reasonable near-term future climate change scenarios by utilizing currently available model simulations, the AMME exhibited a better performance in reproducing both past and near-term 'future' climate than conventional multi-model ensemble means and weighted average schemes.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Simulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Climate change</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Cheng Qian</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhongwei Yan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of climatology</subfield><subfield code="d">Chichester [u.a.] : Wiley, 1989</subfield><subfield code="g">37(2017), 1, Seite 109</subfield><subfield code="w">(DE-627)130763128</subfield><subfield code="w">(DE-600)1000947-4</subfield><subfield code="w">(DE-576)023035773</subfield><subfield code="x">0899-8418</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:37</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:109</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1002/joc.4690</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://search.proquest.com/docview/1854669413</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-GEO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GGO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4311</subfield></datafield><datafield tag="936" ind1="r" ind2="v"><subfield code="a">RA 1000</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">37</subfield><subfield code="j">2017</subfield><subfield code="e">1</subfield><subfield code="h">109</subfield></datafield></record></collection>
|
| author |
Yajie Qi |
| spellingShingle |
Yajie Qi ddc 550 rvk RA 1000 misc Simulation misc Climate change An alternative multi-model ensemble mean approach for near-term projection |
| authorStr |
Yajie Qi |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)130763128 |
| format |
Article |
| dewey-ones |
550 - Earth sciences |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0899-8418 |
| topic_title |
550 DNB RA 1000 AVZ rvk An alternative multi-model ensemble mean approach for near-term projection Simulation Climate change |
| topic |
ddc 550 rvk RA 1000 misc Simulation misc Climate change |
| topic_unstemmed |
ddc 550 rvk RA 1000 misc Simulation misc Climate change |
| topic_browse |
ddc 550 rvk RA 1000 misc Simulation misc Climate change |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| author2_variant |
c q cq z y zy |
| hierarchy_parent_title |
International journal of climatology |
| hierarchy_parent_id |
130763128 |
| dewey-tens |
550 - Earth sciences & geology |
| hierarchy_top_title |
International journal of climatology |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)130763128 (DE-600)1000947-4 (DE-576)023035773 |
| title |
An alternative multi-model ensemble mean approach for near-term projection |
| ctrlnum |
(DE-627)OLC1988421799 (DE-599)GBVOLC1988421799 (PRQ)p816-2f317195673c20007156da935fd23456293f4abc78975a1cfe075b33ae79f5fd3 (KEY)0104704320170000037000100109alternativemultimodelensemblemeanapproachfornearte |
| title_full |
An alternative multi-model ensemble mean approach for near-term projection |
| author_sort |
Yajie Qi |
| journal |
International journal of climatology |
| journalStr |
International journal of climatology |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science |
| recordtype |
marc |
| publishDateSort |
2017 |
| contenttype_str_mv |
txt |
| container_start_page |
109 |
| author_browse |
Yajie Qi |
| container_volume |
37 |
| class |
550 DNB RA 1000 AVZ rvk |
| format_se |
Aufsätze |
| author-letter |
Yajie Qi |
| doi_str_mv |
10.1002/joc.4690 |
| dewey-full |
550 |
| title_sort |
alternative multi-model ensemble mean approach for near-term projection |
| title_auth |
An alternative multi-model ensemble mean approach for near-term projection |
| abstract |
An 'alternative multi-model ensemble mean' (AMME) method was developed for the near-term projection of regional climate change by taking into account the capacity of currently available climate models in simulating specific timescale components. These components included a climatological mean (Mean), an amplitude-frequency modulated annual cycle (MAC), multi-decadal variability (MDV), a secular trend (ST), and short-term variability (SV). The latter four components were extracted adaptively by the ensemble empirical mode decomposition filter from the climate series. For each component, a reconstructed simulation was determined from ensemble of a limited number of model simulations that could reproduce the component in the observation relatively well. An AMME simulation was obtained by combining the five components. The new method was illustrated to construct an AMME simulation of the monthly near-surface temperature series for the training period 1902-1990 in eastern China and was applied to the validation period 1991-2004. For the eastern China average, the best performance arose from MPI-ESM-MR for Mean, IPSL-CM5A-LR for MAC, ACCESS1.3 for MDV, GFDL-ESM2M for ST, and GISS-E2-H-CC for SV. Serving as a novel tool for producing reasonable near-term future climate change scenarios by utilizing currently available model simulations, the AMME exhibited a better performance in reproducing both past and near-term 'future' climate than conventional multi-model ensemble means and weighted average schemes. |
| abstractGer |
An 'alternative multi-model ensemble mean' (AMME) method was developed for the near-term projection of regional climate change by taking into account the capacity of currently available climate models in simulating specific timescale components. These components included a climatological mean (Mean), an amplitude-frequency modulated annual cycle (MAC), multi-decadal variability (MDV), a secular trend (ST), and short-term variability (SV). The latter four components were extracted adaptively by the ensemble empirical mode decomposition filter from the climate series. For each component, a reconstructed simulation was determined from ensemble of a limited number of model simulations that could reproduce the component in the observation relatively well. An AMME simulation was obtained by combining the five components. The new method was illustrated to construct an AMME simulation of the monthly near-surface temperature series for the training period 1902-1990 in eastern China and was applied to the validation period 1991-2004. For the eastern China average, the best performance arose from MPI-ESM-MR for Mean, IPSL-CM5A-LR for MAC, ACCESS1.3 for MDV, GFDL-ESM2M for ST, and GISS-E2-H-CC for SV. Serving as a novel tool for producing reasonable near-term future climate change scenarios by utilizing currently available model simulations, the AMME exhibited a better performance in reproducing both past and near-term 'future' climate than conventional multi-model ensemble means and weighted average schemes. |
| abstract_unstemmed |
An 'alternative multi-model ensemble mean' (AMME) method was developed for the near-term projection of regional climate change by taking into account the capacity of currently available climate models in simulating specific timescale components. These components included a climatological mean (Mean), an amplitude-frequency modulated annual cycle (MAC), multi-decadal variability (MDV), a secular trend (ST), and short-term variability (SV). The latter four components were extracted adaptively by the ensemble empirical mode decomposition filter from the climate series. For each component, a reconstructed simulation was determined from ensemble of a limited number of model simulations that could reproduce the component in the observation relatively well. An AMME simulation was obtained by combining the five components. The new method was illustrated to construct an AMME simulation of the monthly near-surface temperature series for the training period 1902-1990 in eastern China and was applied to the validation period 1991-2004. For the eastern China average, the best performance arose from MPI-ESM-MR for Mean, IPSL-CM5A-LR for MAC, ACCESS1.3 for MDV, GFDL-ESM2M for ST, and GISS-E2-H-CC for SV. Serving as a novel tool for producing reasonable near-term future climate change scenarios by utilizing currently available model simulations, the AMME exhibited a better performance in reproducing both past and near-term 'future' climate than conventional multi-model ensemble means and weighted average schemes. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_4311 |
| container_issue |
1 |
| title_short |
An alternative multi-model ensemble mean approach for near-term projection |
| url |
http://dx.doi.org/10.1002/joc.4690 http://search.proquest.com/docview/1854669413 |
| remote_bool |
false |
| author2 |
Cheng Qian Zhongwei Yan |
| author2Str |
Cheng Qian Zhongwei Yan |
| ppnlink |
130763128 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth |
| doi_str |
10.1002/joc.4690 |
| up_date |
2024-07-03T17:49:28.906Z |
| _version_ |
1803581096102723584 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1988421799</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230715021937.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">170207s2017 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1002/joc.4690</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20170206</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1988421799</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1988421799</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)p816-2f317195673c20007156da935fd23456293f4abc78975a1cfe075b33ae79f5fd3</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0104704320170000037000100109alternativemultimodelensemblemeanapproachfornearte</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">RA 1000</subfield><subfield code="q">AVZ</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Yajie Qi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="3"><subfield code="a">An alternative multi-model ensemble mean approach for near-term projection</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">An 'alternative multi-model ensemble mean' (AMME) method was developed for the near-term projection of regional climate change by taking into account the capacity of currently available climate models in simulating specific timescale components. These components included a climatological mean (Mean), an amplitude-frequency modulated annual cycle (MAC), multi-decadal variability (MDV), a secular trend (ST), and short-term variability (SV). The latter four components were extracted adaptively by the ensemble empirical mode decomposition filter from the climate series. For each component, a reconstructed simulation was determined from ensemble of a limited number of model simulations that could reproduce the component in the observation relatively well. An AMME simulation was obtained by combining the five components. The new method was illustrated to construct an AMME simulation of the monthly near-surface temperature series for the training period 1902-1990 in eastern China and was applied to the validation period 1991-2004. For the eastern China average, the best performance arose from MPI-ESM-MR for Mean, IPSL-CM5A-LR for MAC, ACCESS1.3 for MDV, GFDL-ESM2M for ST, and GISS-E2-H-CC for SV. Serving as a novel tool for producing reasonable near-term future climate change scenarios by utilizing currently available model simulations, the AMME exhibited a better performance in reproducing both past and near-term 'future' climate than conventional multi-model ensemble means and weighted average schemes.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Simulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Climate change</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Cheng Qian</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhongwei Yan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of climatology</subfield><subfield code="d">Chichester [u.a.] : Wiley, 1989</subfield><subfield code="g">37(2017), 1, Seite 109</subfield><subfield code="w">(DE-627)130763128</subfield><subfield code="w">(DE-600)1000947-4</subfield><subfield code="w">(DE-576)023035773</subfield><subfield code="x">0899-8418</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:37</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:109</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1002/joc.4690</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://search.proquest.com/docview/1854669413</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-GEO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GGO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4311</subfield></datafield><datafield tag="936" ind1="r" ind2="v"><subfield code="a">RA 1000</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">37</subfield><subfield code="j">2017</subfield><subfield code="e">1</subfield><subfield code="h">109</subfield></datafield></record></collection>
|
| score |
7.4007235 |