Assessment of seasonal prediction of South Pacific Convergence Zone using APCC multi-model ensembles
Abstract We have quantified and examined the South Pacific convergence zone (SPCZ) characteristics for the purpose of its seasonal prediction, by defining two orientation indices, strength and area. The multi-model ensemble (MME) tends to simulate the ENSO-associated shift of SPCZ orientation, espec...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kim, Ok-Yeon [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Climate dynamics - Berlin : Springer, 1986, 50(2017), 9-10 vom: 13. Juli, Seite 3237-3250 |
|---|---|
| Übergeordnetes Werk: |
volume:50 ; year:2017 ; number:9-10 ; day:13 ; month:07 ; pages:3237-3250 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00382-017-3802-y |
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| Katalog-ID: |
SPR004673492 |
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| 520 | |a Abstract We have quantified and examined the South Pacific convergence zone (SPCZ) characteristics for the purpose of its seasonal prediction, by defining two orientation indices, strength and area. The multi-model ensemble (MME) tends to simulate the ENSO-associated shift of SPCZ orientation, especially for the 1-month forecast lead. The migration of the SPCZ orientation indices associated with ENSO phases is clear in the observation and the MME. The variation of the SPCZ strength and area associated with ENSO phases is not as clear as in the SPCZ orientation. In spite of marginal changes in the SPCZ strength and area related to ENSO phases, the SPCZ strength becomes a bit stronger during El Niño and weaker during La Niña, which is represented in individual models and MME. The performance of the MME in simulating the variability of the SPCZ orientation, strength and area is also examined. We found that the MME reasonably predicts the observed interannual variability of the western portion of the SPCZ, with systematic and marginal shift southward. Compared to the western part of the SPCZ, the MME seems to have a limitation in predicting the variability of the eastern part. In comparison to the SPCZ orientation, the MME is not capable of predicting the strength and area of the SPCZ. The interannual variability of the SPCZ strength in the MME is systematically weaker compared to that in the analysis. By comparison with SPCZ orientation and strength, the SPCZ area is not resolved in the MME. The SPCZ is a main source of precipitation in the South Pacific, and the SPCZ predictability also influences high impact weather prediction such as tropical cyclones. Therefore, skillful predictions of seasonal variability of the SPCZ could benefit users who utilize the seasonal forecasting information for their decision making in many applicable sectors. | ||
| 650 | 4 | |a South Pacific convergence zone |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a SPCZ orientation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a SPCZ strength |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a SPCZ area |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Seasonal forecasting |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Multi-model ensembles |7 (dpeaa)DE-He213 | |
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10.1007/s00382-017-3802-y doi (DE-627)SPR004673492 (SPR)s00382-017-3802-y-e DE-627 ger DE-627 rakwb eng 550 ASE 38.80 bkl Kim, Ok-Yeon verfasserin aut Assessment of seasonal prediction of South Pacific Convergence Zone using APCC multi-model ensembles 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We have quantified and examined the South Pacific convergence zone (SPCZ) characteristics for the purpose of its seasonal prediction, by defining two orientation indices, strength and area. The multi-model ensemble (MME) tends to simulate the ENSO-associated shift of SPCZ orientation, especially for the 1-month forecast lead. The migration of the SPCZ orientation indices associated with ENSO phases is clear in the observation and the MME. The variation of the SPCZ strength and area associated with ENSO phases is not as clear as in the SPCZ orientation. In spite of marginal changes in the SPCZ strength and area related to ENSO phases, the SPCZ strength becomes a bit stronger during El Niño and weaker during La Niña, which is represented in individual models and MME. The performance of the MME in simulating the variability of the SPCZ orientation, strength and area is also examined. We found that the MME reasonably predicts the observed interannual variability of the western portion of the SPCZ, with systematic and marginal shift southward. Compared to the western part of the SPCZ, the MME seems to have a limitation in predicting the variability of the eastern part. In comparison to the SPCZ orientation, the MME is not capable of predicting the strength and area of the SPCZ. The interannual variability of the SPCZ strength in the MME is systematically weaker compared to that in the analysis. By comparison with SPCZ orientation and strength, the SPCZ area is not resolved in the MME. The SPCZ is a main source of precipitation in the South Pacific, and the SPCZ predictability also influences high impact weather prediction such as tropical cyclones. Therefore, skillful predictions of seasonal variability of the SPCZ could benefit users who utilize the seasonal forecasting information for their decision making in many applicable sectors. South Pacific convergence zone (dpeaa)DE-He213 SPCZ orientation (dpeaa)DE-He213 SPCZ strength (dpeaa)DE-He213 SPCZ area (dpeaa)DE-He213 Seasonal forecasting (dpeaa)DE-He213 Multi-model ensembles (dpeaa)DE-He213 Enthalten in Climate dynamics Berlin : Springer, 1986 50(2017), 9-10 vom: 13. Juli, Seite 3237-3250 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:50 year:2017 number:9-10 day:13 month:07 pages:3237-3250 https://dx.doi.org/10.1007/s00382-017-3802-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_612 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.80 ASE AR 50 2017 9-10 13 07 3237-3250 |
| spelling |
10.1007/s00382-017-3802-y doi (DE-627)SPR004673492 (SPR)s00382-017-3802-y-e DE-627 ger DE-627 rakwb eng 550 ASE 38.80 bkl Kim, Ok-Yeon verfasserin aut Assessment of seasonal prediction of South Pacific Convergence Zone using APCC multi-model ensembles 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We have quantified and examined the South Pacific convergence zone (SPCZ) characteristics for the purpose of its seasonal prediction, by defining two orientation indices, strength and area. The multi-model ensemble (MME) tends to simulate the ENSO-associated shift of SPCZ orientation, especially for the 1-month forecast lead. The migration of the SPCZ orientation indices associated with ENSO phases is clear in the observation and the MME. The variation of the SPCZ strength and area associated with ENSO phases is not as clear as in the SPCZ orientation. In spite of marginal changes in the SPCZ strength and area related to ENSO phases, the SPCZ strength becomes a bit stronger during El Niño and weaker during La Niña, which is represented in individual models and MME. The performance of the MME in simulating the variability of the SPCZ orientation, strength and area is also examined. We found that the MME reasonably predicts the observed interannual variability of the western portion of the SPCZ, with systematic and marginal shift southward. Compared to the western part of the SPCZ, the MME seems to have a limitation in predicting the variability of the eastern part. In comparison to the SPCZ orientation, the MME is not capable of predicting the strength and area of the SPCZ. The interannual variability of the SPCZ strength in the MME is systematically weaker compared to that in the analysis. By comparison with SPCZ orientation and strength, the SPCZ area is not resolved in the MME. The SPCZ is a main source of precipitation in the South Pacific, and the SPCZ predictability also influences high impact weather prediction such as tropical cyclones. Therefore, skillful predictions of seasonal variability of the SPCZ could benefit users who utilize the seasonal forecasting information for their decision making in many applicable sectors. South Pacific convergence zone (dpeaa)DE-He213 SPCZ orientation (dpeaa)DE-He213 SPCZ strength (dpeaa)DE-He213 SPCZ area (dpeaa)DE-He213 Seasonal forecasting (dpeaa)DE-He213 Multi-model ensembles (dpeaa)DE-He213 Enthalten in Climate dynamics Berlin : Springer, 1986 50(2017), 9-10 vom: 13. Juli, Seite 3237-3250 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:50 year:2017 number:9-10 day:13 month:07 pages:3237-3250 https://dx.doi.org/10.1007/s00382-017-3802-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_612 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.80 ASE AR 50 2017 9-10 13 07 3237-3250 |
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10.1007/s00382-017-3802-y doi (DE-627)SPR004673492 (SPR)s00382-017-3802-y-e DE-627 ger DE-627 rakwb eng 550 ASE 38.80 bkl Kim, Ok-Yeon verfasserin aut Assessment of seasonal prediction of South Pacific Convergence Zone using APCC multi-model ensembles 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We have quantified and examined the South Pacific convergence zone (SPCZ) characteristics for the purpose of its seasonal prediction, by defining two orientation indices, strength and area. The multi-model ensemble (MME) tends to simulate the ENSO-associated shift of SPCZ orientation, especially for the 1-month forecast lead. The migration of the SPCZ orientation indices associated with ENSO phases is clear in the observation and the MME. The variation of the SPCZ strength and area associated with ENSO phases is not as clear as in the SPCZ orientation. In spite of marginal changes in the SPCZ strength and area related to ENSO phases, the SPCZ strength becomes a bit stronger during El Niño and weaker during La Niña, which is represented in individual models and MME. The performance of the MME in simulating the variability of the SPCZ orientation, strength and area is also examined. We found that the MME reasonably predicts the observed interannual variability of the western portion of the SPCZ, with systematic and marginal shift southward. Compared to the western part of the SPCZ, the MME seems to have a limitation in predicting the variability of the eastern part. In comparison to the SPCZ orientation, the MME is not capable of predicting the strength and area of the SPCZ. The interannual variability of the SPCZ strength in the MME is systematically weaker compared to that in the analysis. By comparison with SPCZ orientation and strength, the SPCZ area is not resolved in the MME. The SPCZ is a main source of precipitation in the South Pacific, and the SPCZ predictability also influences high impact weather prediction such as tropical cyclones. Therefore, skillful predictions of seasonal variability of the SPCZ could benefit users who utilize the seasonal forecasting information for their decision making in many applicable sectors. South Pacific convergence zone (dpeaa)DE-He213 SPCZ orientation (dpeaa)DE-He213 SPCZ strength (dpeaa)DE-He213 SPCZ area (dpeaa)DE-He213 Seasonal forecasting (dpeaa)DE-He213 Multi-model ensembles (dpeaa)DE-He213 Enthalten in Climate dynamics Berlin : Springer, 1986 50(2017), 9-10 vom: 13. Juli, Seite 3237-3250 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:50 year:2017 number:9-10 day:13 month:07 pages:3237-3250 https://dx.doi.org/10.1007/s00382-017-3802-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_612 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.80 ASE AR 50 2017 9-10 13 07 3237-3250 |
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10.1007/s00382-017-3802-y doi (DE-627)SPR004673492 (SPR)s00382-017-3802-y-e DE-627 ger DE-627 rakwb eng 550 ASE 38.80 bkl Kim, Ok-Yeon verfasserin aut Assessment of seasonal prediction of South Pacific Convergence Zone using APCC multi-model ensembles 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We have quantified and examined the South Pacific convergence zone (SPCZ) characteristics for the purpose of its seasonal prediction, by defining two orientation indices, strength and area. The multi-model ensemble (MME) tends to simulate the ENSO-associated shift of SPCZ orientation, especially for the 1-month forecast lead. The migration of the SPCZ orientation indices associated with ENSO phases is clear in the observation and the MME. The variation of the SPCZ strength and area associated with ENSO phases is not as clear as in the SPCZ orientation. In spite of marginal changes in the SPCZ strength and area related to ENSO phases, the SPCZ strength becomes a bit stronger during El Niño and weaker during La Niña, which is represented in individual models and MME. The performance of the MME in simulating the variability of the SPCZ orientation, strength and area is also examined. We found that the MME reasonably predicts the observed interannual variability of the western portion of the SPCZ, with systematic and marginal shift southward. Compared to the western part of the SPCZ, the MME seems to have a limitation in predicting the variability of the eastern part. In comparison to the SPCZ orientation, the MME is not capable of predicting the strength and area of the SPCZ. The interannual variability of the SPCZ strength in the MME is systematically weaker compared to that in the analysis. By comparison with SPCZ orientation and strength, the SPCZ area is not resolved in the MME. The SPCZ is a main source of precipitation in the South Pacific, and the SPCZ predictability also influences high impact weather prediction such as tropical cyclones. Therefore, skillful predictions of seasonal variability of the SPCZ could benefit users who utilize the seasonal forecasting information for their decision making in many applicable sectors. South Pacific convergence zone (dpeaa)DE-He213 SPCZ orientation (dpeaa)DE-He213 SPCZ strength (dpeaa)DE-He213 SPCZ area (dpeaa)DE-He213 Seasonal forecasting (dpeaa)DE-He213 Multi-model ensembles (dpeaa)DE-He213 Enthalten in Climate dynamics Berlin : Springer, 1986 50(2017), 9-10 vom: 13. Juli, Seite 3237-3250 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:50 year:2017 number:9-10 day:13 month:07 pages:3237-3250 https://dx.doi.org/10.1007/s00382-017-3802-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_612 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.80 ASE AR 50 2017 9-10 13 07 3237-3250 |
| allfieldsSound |
10.1007/s00382-017-3802-y doi (DE-627)SPR004673492 (SPR)s00382-017-3802-y-e DE-627 ger DE-627 rakwb eng 550 ASE 38.80 bkl Kim, Ok-Yeon verfasserin aut Assessment of seasonal prediction of South Pacific Convergence Zone using APCC multi-model ensembles 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We have quantified and examined the South Pacific convergence zone (SPCZ) characteristics for the purpose of its seasonal prediction, by defining two orientation indices, strength and area. The multi-model ensemble (MME) tends to simulate the ENSO-associated shift of SPCZ orientation, especially for the 1-month forecast lead. The migration of the SPCZ orientation indices associated with ENSO phases is clear in the observation and the MME. The variation of the SPCZ strength and area associated with ENSO phases is not as clear as in the SPCZ orientation. In spite of marginal changes in the SPCZ strength and area related to ENSO phases, the SPCZ strength becomes a bit stronger during El Niño and weaker during La Niña, which is represented in individual models and MME. The performance of the MME in simulating the variability of the SPCZ orientation, strength and area is also examined. We found that the MME reasonably predicts the observed interannual variability of the western portion of the SPCZ, with systematic and marginal shift southward. Compared to the western part of the SPCZ, the MME seems to have a limitation in predicting the variability of the eastern part. In comparison to the SPCZ orientation, the MME is not capable of predicting the strength and area of the SPCZ. The interannual variability of the SPCZ strength in the MME is systematically weaker compared to that in the analysis. By comparison with SPCZ orientation and strength, the SPCZ area is not resolved in the MME. The SPCZ is a main source of precipitation in the South Pacific, and the SPCZ predictability also influences high impact weather prediction such as tropical cyclones. Therefore, skillful predictions of seasonal variability of the SPCZ could benefit users who utilize the seasonal forecasting information for their decision making in many applicable sectors. South Pacific convergence zone (dpeaa)DE-He213 SPCZ orientation (dpeaa)DE-He213 SPCZ strength (dpeaa)DE-He213 SPCZ area (dpeaa)DE-He213 Seasonal forecasting (dpeaa)DE-He213 Multi-model ensembles (dpeaa)DE-He213 Enthalten in Climate dynamics Berlin : Springer, 1986 50(2017), 9-10 vom: 13. Juli, Seite 3237-3250 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:50 year:2017 number:9-10 day:13 month:07 pages:3237-3250 https://dx.doi.org/10.1007/s00382-017-3802-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_612 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.80 ASE AR 50 2017 9-10 13 07 3237-3250 |
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Enthalten in Climate dynamics 50(2017), 9-10 vom: 13. Juli, Seite 3237-3250 volume:50 year:2017 number:9-10 day:13 month:07 pages:3237-3250 |
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South Pacific convergence zone SPCZ orientation SPCZ strength SPCZ area Seasonal forecasting Multi-model ensembles |
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Kim, Ok-Yeon @@aut@@ |
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The multi-model ensemble (MME) tends to simulate the ENSO-associated shift of SPCZ orientation, especially for the 1-month forecast lead. The migration of the SPCZ orientation indices associated with ENSO phases is clear in the observation and the MME. The variation of the SPCZ strength and area associated with ENSO phases is not as clear as in the SPCZ orientation. In spite of marginal changes in the SPCZ strength and area related to ENSO phases, the SPCZ strength becomes a bit stronger during El Niño and weaker during La Niña, which is represented in individual models and MME. The performance of the MME in simulating the variability of the SPCZ orientation, strength and area is also examined. We found that the MME reasonably predicts the observed interannual variability of the western portion of the SPCZ, with systematic and marginal shift southward. Compared to the western part of the SPCZ, the MME seems to have a limitation in predicting the variability of the eastern part. In comparison to the SPCZ orientation, the MME is not capable of predicting the strength and area of the SPCZ. The interannual variability of the SPCZ strength in the MME is systematically weaker compared to that in the analysis. By comparison with SPCZ orientation and strength, the SPCZ area is not resolved in the MME. The SPCZ is a main source of precipitation in the South Pacific, and the SPCZ predictability also influences high impact weather prediction such as tropical cyclones. Therefore, skillful predictions of seasonal variability of the SPCZ could benefit users who utilize the seasonal forecasting information for their decision making in many applicable sectors.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">South Pacific convergence zone</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SPCZ orientation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SPCZ strength</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SPCZ area</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Seasonal forecasting</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multi-model ensembles</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Climate dynamics</subfield><subfield code="d">Berlin : Springer, 1986</subfield><subfield code="g">50(2017), 9-10 vom: 13. 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|
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Kim, Ok-Yeon |
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Kim, Ok-Yeon ddc 550 bkl 38.80 misc South Pacific convergence zone misc SPCZ orientation misc SPCZ strength misc SPCZ area misc Seasonal forecasting misc Multi-model ensembles Assessment of seasonal prediction of South Pacific Convergence Zone using APCC multi-model ensembles |
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550 ASE 38.80 bkl Assessment of seasonal prediction of South Pacific Convergence Zone using APCC multi-model ensembles South Pacific convergence zone (dpeaa)DE-He213 SPCZ orientation (dpeaa)DE-He213 SPCZ strength (dpeaa)DE-He213 SPCZ area (dpeaa)DE-He213 Seasonal forecasting (dpeaa)DE-He213 Multi-model ensembles (dpeaa)DE-He213 |
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ddc 550 bkl 38.80 misc South Pacific convergence zone misc SPCZ orientation misc SPCZ strength misc SPCZ area misc Seasonal forecasting misc Multi-model ensembles |
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ddc 550 bkl 38.80 misc South Pacific convergence zone misc SPCZ orientation misc SPCZ strength misc SPCZ area misc Seasonal forecasting misc Multi-model ensembles |
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ddc 550 bkl 38.80 misc South Pacific convergence zone misc SPCZ orientation misc SPCZ strength misc SPCZ area misc Seasonal forecasting misc Multi-model ensembles |
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Elektronische Aufsätze Aufsätze Elektronische Ressource |
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Assessment of seasonal prediction of South Pacific Convergence Zone using APCC multi-model ensembles |
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Assessment of seasonal prediction of South Pacific Convergence Zone using APCC multi-model ensembles |
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Kim, Ok-Yeon |
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assessment of seasonal prediction of south pacific convergence zone using apcc multi-model ensembles |
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Assessment of seasonal prediction of South Pacific Convergence Zone using APCC multi-model ensembles |
| abstract |
Abstract We have quantified and examined the South Pacific convergence zone (SPCZ) characteristics for the purpose of its seasonal prediction, by defining two orientation indices, strength and area. The multi-model ensemble (MME) tends to simulate the ENSO-associated shift of SPCZ orientation, especially for the 1-month forecast lead. The migration of the SPCZ orientation indices associated with ENSO phases is clear in the observation and the MME. The variation of the SPCZ strength and area associated with ENSO phases is not as clear as in the SPCZ orientation. In spite of marginal changes in the SPCZ strength and area related to ENSO phases, the SPCZ strength becomes a bit stronger during El Niño and weaker during La Niña, which is represented in individual models and MME. The performance of the MME in simulating the variability of the SPCZ orientation, strength and area is also examined. We found that the MME reasonably predicts the observed interannual variability of the western portion of the SPCZ, with systematic and marginal shift southward. Compared to the western part of the SPCZ, the MME seems to have a limitation in predicting the variability of the eastern part. In comparison to the SPCZ orientation, the MME is not capable of predicting the strength and area of the SPCZ. The interannual variability of the SPCZ strength in the MME is systematically weaker compared to that in the analysis. By comparison with SPCZ orientation and strength, the SPCZ area is not resolved in the MME. The SPCZ is a main source of precipitation in the South Pacific, and the SPCZ predictability also influences high impact weather prediction such as tropical cyclones. Therefore, skillful predictions of seasonal variability of the SPCZ could benefit users who utilize the seasonal forecasting information for their decision making in many applicable sectors. |
| abstractGer |
Abstract We have quantified and examined the South Pacific convergence zone (SPCZ) characteristics for the purpose of its seasonal prediction, by defining two orientation indices, strength and area. The multi-model ensemble (MME) tends to simulate the ENSO-associated shift of SPCZ orientation, especially for the 1-month forecast lead. The migration of the SPCZ orientation indices associated with ENSO phases is clear in the observation and the MME. The variation of the SPCZ strength and area associated with ENSO phases is not as clear as in the SPCZ orientation. In spite of marginal changes in the SPCZ strength and area related to ENSO phases, the SPCZ strength becomes a bit stronger during El Niño and weaker during La Niña, which is represented in individual models and MME. The performance of the MME in simulating the variability of the SPCZ orientation, strength and area is also examined. We found that the MME reasonably predicts the observed interannual variability of the western portion of the SPCZ, with systematic and marginal shift southward. Compared to the western part of the SPCZ, the MME seems to have a limitation in predicting the variability of the eastern part. In comparison to the SPCZ orientation, the MME is not capable of predicting the strength and area of the SPCZ. The interannual variability of the SPCZ strength in the MME is systematically weaker compared to that in the analysis. By comparison with SPCZ orientation and strength, the SPCZ area is not resolved in the MME. The SPCZ is a main source of precipitation in the South Pacific, and the SPCZ predictability also influences high impact weather prediction such as tropical cyclones. Therefore, skillful predictions of seasonal variability of the SPCZ could benefit users who utilize the seasonal forecasting information for their decision making in many applicable sectors. |
| abstract_unstemmed |
Abstract We have quantified and examined the South Pacific convergence zone (SPCZ) characteristics for the purpose of its seasonal prediction, by defining two orientation indices, strength and area. The multi-model ensemble (MME) tends to simulate the ENSO-associated shift of SPCZ orientation, especially for the 1-month forecast lead. The migration of the SPCZ orientation indices associated with ENSO phases is clear in the observation and the MME. The variation of the SPCZ strength and area associated with ENSO phases is not as clear as in the SPCZ orientation. In spite of marginal changes in the SPCZ strength and area related to ENSO phases, the SPCZ strength becomes a bit stronger during El Niño and weaker during La Niña, which is represented in individual models and MME. The performance of the MME in simulating the variability of the SPCZ orientation, strength and area is also examined. We found that the MME reasonably predicts the observed interannual variability of the western portion of the SPCZ, with systematic and marginal shift southward. Compared to the western part of the SPCZ, the MME seems to have a limitation in predicting the variability of the eastern part. In comparison to the SPCZ orientation, the MME is not capable of predicting the strength and area of the SPCZ. The interannual variability of the SPCZ strength in the MME is systematically weaker compared to that in the analysis. By comparison with SPCZ orientation and strength, the SPCZ area is not resolved in the MME. The SPCZ is a main source of precipitation in the South Pacific, and the SPCZ predictability also influences high impact weather prediction such as tropical cyclones. Therefore, skillful predictions of seasonal variability of the SPCZ could benefit users who utilize the seasonal forecasting information for their decision making in many applicable sectors. |
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| container_issue |
9-10 |
| title_short |
Assessment of seasonal prediction of South Pacific Convergence Zone using APCC multi-model ensembles |
| url |
https://dx.doi.org/10.1007/s00382-017-3802-y |
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| doi_str |
10.1007/s00382-017-3802-y |
| up_date |
2024-07-04T02:08:07.484Z |
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| score |
7.399646 |