How does the cumulus parameterization scheme influence the simulation of MJO propagation and structure?
Abstract In this study, eleven different cumulus parameterization schemes (CPSs) in Weather Research and Forecasting (WRF) model version 4.0 were used to test the sensitivity of Madden–Julian oscillation (MJO) eastward propagation to different CPSs and to explore the influences of different CPSs on...
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| Autor*in: |
Zhu, Xiaoyu [verfasserIn] Zhong, Zhong [verfasserIn] Zhu, Yimin [verfasserIn] Li, Yunying [verfasserIn] Hu, Yijia [verfasserIn] Ha, Yao [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Climate dynamics - Springer Berlin Heidelberg, 1986, 62(2024), 10 vom: 06. Sept., Seite 9755-9768 |
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| Übergeordnetes Werk: |
volume:62 ; year:2024 ; number:10 ; day:06 ; month:09 ; pages:9755-9768 |
| Links: |
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| DOI / URN: |
10.1007/s00382-024-07427-4 |
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| Katalog-ID: |
SPR057695407 |
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| 520 | |a Abstract In this study, eleven different cumulus parameterization schemes (CPSs) in Weather Research and Forecasting (WRF) model version 4.0 were used to test the sensitivity of Madden–Julian oscillation (MJO) eastward propagation to different CPSs and to explore the influences of different CPSs on MJO eastward propagation. The propagation and structures of the MJO-filtered outgoing longwave radiation (OLR) for the MJO active phase composites during the December–January–February (DJF) period of 2007–2016 over the Indian Ocean (IO) were diagnosed in this study. The results show that the simulated MJO propagation is sensitive to the selection of CPSs. And CPS5 (Tiedtke scheme) and CPS8 (New Tiedtke scheme) are selected for the good models composite while the CPS4 (Grell 3D ensemble scheme) and CPS9 (Grell-Devenyi ensemble scheme) are selected for the poor models composite. The good models composite provides similar MJO structures and MJO eastward propagation patterns as those observed, while poor models composite fails. The MJO propagation is highly correlated with boundary layer moisture convergence (BLMC) propagation pattern correlation coefficient (PCC), the U850 asymmetry index, the convective instability index and the new index reconstructed by the three main contributing factors, and BLMC propagation plays the most important role in simulating MJO propagation. Besides, different CPSs may result in different interactions between convective heating and moisture feedback, thus simulating different structures. The results support the trio‑interaction theory and provide a reference for the influence of CPS on the MJO eastward propagation. | ||
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| 700 | 1 | |a Hu, Yijia |e verfasserin |4 aut | |
| 700 | 1 | |a Ha, Yao |e verfasserin |4 aut | |
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10.1007/s00382-024-07427-4 doi (DE-627)SPR057695407 (SPR)s00382-024-07427-4-e DE-627 ger DE-627 rakwb eng 550 VZ 38.80 bkl Zhu, Xiaoyu verfasserin aut How does the cumulus parameterization scheme influence the simulation of MJO propagation and structure? 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this study, eleven different cumulus parameterization schemes (CPSs) in Weather Research and Forecasting (WRF) model version 4.0 were used to test the sensitivity of Madden–Julian oscillation (MJO) eastward propagation to different CPSs and to explore the influences of different CPSs on MJO eastward propagation. The propagation and structures of the MJO-filtered outgoing longwave radiation (OLR) for the MJO active phase composites during the December–January–February (DJF) period of 2007–2016 over the Indian Ocean (IO) were diagnosed in this study. The results show that the simulated MJO propagation is sensitive to the selection of CPSs. And CPS5 (Tiedtke scheme) and CPS8 (New Tiedtke scheme) are selected for the good models composite while the CPS4 (Grell 3D ensemble scheme) and CPS9 (Grell-Devenyi ensemble scheme) are selected for the poor models composite. The good models composite provides similar MJO structures and MJO eastward propagation patterns as those observed, while poor models composite fails. The MJO propagation is highly correlated with boundary layer moisture convergence (BLMC) propagation pattern correlation coefficient (PCC), the U850 asymmetry index, the convective instability index and the new index reconstructed by the three main contributing factors, and BLMC propagation plays the most important role in simulating MJO propagation. Besides, different CPSs may result in different interactions between convective heating and moisture feedback, thus simulating different structures. The results support the trio‑interaction theory and provide a reference for the influence of CPS on the MJO eastward propagation. MJO (dpeaa)DE-He213 WRF model (dpeaa)DE-He213 Cumulus parameterization scheme (dpeaa)DE-He213 Trio‑interaction theory (dpeaa)DE-He213 Boundary layer moisture convergence (dpeaa)DE-He213 Zhong, Zhong verfasserin (orcid)0000-0002-2019-0281 aut Zhu, Yimin verfasserin aut Li, Yunying verfasserin aut Hu, Yijia verfasserin aut Ha, Yao verfasserin aut Enthalten in Climate dynamics Springer Berlin Heidelberg, 1986 62(2024), 10 vom: 06. Sept., Seite 9755-9768 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:62 year:2024 number:10 day:06 month:09 pages:9755-9768 https://dx.doi.org/10.1007/s00382-024-07427-4 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO 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_72 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_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_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_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_2574 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 VZ AR 62 2024 10 06 09 9755-9768 |
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10.1007/s00382-024-07427-4 doi (DE-627)SPR057695407 (SPR)s00382-024-07427-4-e DE-627 ger DE-627 rakwb eng 550 VZ 38.80 bkl Zhu, Xiaoyu verfasserin aut How does the cumulus parameterization scheme influence the simulation of MJO propagation and structure? 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this study, eleven different cumulus parameterization schemes (CPSs) in Weather Research and Forecasting (WRF) model version 4.0 were used to test the sensitivity of Madden–Julian oscillation (MJO) eastward propagation to different CPSs and to explore the influences of different CPSs on MJO eastward propagation. The propagation and structures of the MJO-filtered outgoing longwave radiation (OLR) for the MJO active phase composites during the December–January–February (DJF) period of 2007–2016 over the Indian Ocean (IO) were diagnosed in this study. The results show that the simulated MJO propagation is sensitive to the selection of CPSs. And CPS5 (Tiedtke scheme) and CPS8 (New Tiedtke scheme) are selected for the good models composite while the CPS4 (Grell 3D ensemble scheme) and CPS9 (Grell-Devenyi ensemble scheme) are selected for the poor models composite. The good models composite provides similar MJO structures and MJO eastward propagation patterns as those observed, while poor models composite fails. The MJO propagation is highly correlated with boundary layer moisture convergence (BLMC) propagation pattern correlation coefficient (PCC), the U850 asymmetry index, the convective instability index and the new index reconstructed by the three main contributing factors, and BLMC propagation plays the most important role in simulating MJO propagation. Besides, different CPSs may result in different interactions between convective heating and moisture feedback, thus simulating different structures. The results support the trio‑interaction theory and provide a reference for the influence of CPS on the MJO eastward propagation. MJO (dpeaa)DE-He213 WRF model (dpeaa)DE-He213 Cumulus parameterization scheme (dpeaa)DE-He213 Trio‑interaction theory (dpeaa)DE-He213 Boundary layer moisture convergence (dpeaa)DE-He213 Zhong, Zhong verfasserin (orcid)0000-0002-2019-0281 aut Zhu, Yimin verfasserin aut Li, Yunying verfasserin aut Hu, Yijia verfasserin aut Ha, Yao verfasserin aut Enthalten in Climate dynamics Springer Berlin Heidelberg, 1986 62(2024), 10 vom: 06. Sept., Seite 9755-9768 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:62 year:2024 number:10 day:06 month:09 pages:9755-9768 https://dx.doi.org/10.1007/s00382-024-07427-4 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO 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_72 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_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_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_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_2574 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 VZ AR 62 2024 10 06 09 9755-9768 |
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10.1007/s00382-024-07427-4 doi (DE-627)SPR057695407 (SPR)s00382-024-07427-4-e DE-627 ger DE-627 rakwb eng 550 VZ 38.80 bkl Zhu, Xiaoyu verfasserin aut How does the cumulus parameterization scheme influence the simulation of MJO propagation and structure? 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this study, eleven different cumulus parameterization schemes (CPSs) in Weather Research and Forecasting (WRF) model version 4.0 were used to test the sensitivity of Madden–Julian oscillation (MJO) eastward propagation to different CPSs and to explore the influences of different CPSs on MJO eastward propagation. The propagation and structures of the MJO-filtered outgoing longwave radiation (OLR) for the MJO active phase composites during the December–January–February (DJF) period of 2007–2016 over the Indian Ocean (IO) were diagnosed in this study. The results show that the simulated MJO propagation is sensitive to the selection of CPSs. And CPS5 (Tiedtke scheme) and CPS8 (New Tiedtke scheme) are selected for the good models composite while the CPS4 (Grell 3D ensemble scheme) and CPS9 (Grell-Devenyi ensemble scheme) are selected for the poor models composite. The good models composite provides similar MJO structures and MJO eastward propagation patterns as those observed, while poor models composite fails. The MJO propagation is highly correlated with boundary layer moisture convergence (BLMC) propagation pattern correlation coefficient (PCC), the U850 asymmetry index, the convective instability index and the new index reconstructed by the three main contributing factors, and BLMC propagation plays the most important role in simulating MJO propagation. Besides, different CPSs may result in different interactions between convective heating and moisture feedback, thus simulating different structures. The results support the trio‑interaction theory and provide a reference for the influence of CPS on the MJO eastward propagation. MJO (dpeaa)DE-He213 WRF model (dpeaa)DE-He213 Cumulus parameterization scheme (dpeaa)DE-He213 Trio‑interaction theory (dpeaa)DE-He213 Boundary layer moisture convergence (dpeaa)DE-He213 Zhong, Zhong verfasserin (orcid)0000-0002-2019-0281 aut Zhu, Yimin verfasserin aut Li, Yunying verfasserin aut Hu, Yijia verfasserin aut Ha, Yao verfasserin aut Enthalten in Climate dynamics Springer Berlin Heidelberg, 1986 62(2024), 10 vom: 06. Sept., Seite 9755-9768 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:62 year:2024 number:10 day:06 month:09 pages:9755-9768 https://dx.doi.org/10.1007/s00382-024-07427-4 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO 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_72 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_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_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_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_2574 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 VZ AR 62 2024 10 06 09 9755-9768 |
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10.1007/s00382-024-07427-4 doi (DE-627)SPR057695407 (SPR)s00382-024-07427-4-e DE-627 ger DE-627 rakwb eng 550 VZ 38.80 bkl Zhu, Xiaoyu verfasserin aut How does the cumulus parameterization scheme influence the simulation of MJO propagation and structure? 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this study, eleven different cumulus parameterization schemes (CPSs) in Weather Research and Forecasting (WRF) model version 4.0 were used to test the sensitivity of Madden–Julian oscillation (MJO) eastward propagation to different CPSs and to explore the influences of different CPSs on MJO eastward propagation. The propagation and structures of the MJO-filtered outgoing longwave radiation (OLR) for the MJO active phase composites during the December–January–February (DJF) period of 2007–2016 over the Indian Ocean (IO) were diagnosed in this study. The results show that the simulated MJO propagation is sensitive to the selection of CPSs. And CPS5 (Tiedtke scheme) and CPS8 (New Tiedtke scheme) are selected for the good models composite while the CPS4 (Grell 3D ensemble scheme) and CPS9 (Grell-Devenyi ensemble scheme) are selected for the poor models composite. The good models composite provides similar MJO structures and MJO eastward propagation patterns as those observed, while poor models composite fails. The MJO propagation is highly correlated with boundary layer moisture convergence (BLMC) propagation pattern correlation coefficient (PCC), the U850 asymmetry index, the convective instability index and the new index reconstructed by the three main contributing factors, and BLMC propagation plays the most important role in simulating MJO propagation. Besides, different CPSs may result in different interactions between convective heating and moisture feedback, thus simulating different structures. The results support the trio‑interaction theory and provide a reference for the influence of CPS on the MJO eastward propagation. MJO (dpeaa)DE-He213 WRF model (dpeaa)DE-He213 Cumulus parameterization scheme (dpeaa)DE-He213 Trio‑interaction theory (dpeaa)DE-He213 Boundary layer moisture convergence (dpeaa)DE-He213 Zhong, Zhong verfasserin (orcid)0000-0002-2019-0281 aut Zhu, Yimin verfasserin aut Li, Yunying verfasserin aut Hu, Yijia verfasserin aut Ha, Yao verfasserin aut Enthalten in Climate dynamics Springer Berlin Heidelberg, 1986 62(2024), 10 vom: 06. Sept., Seite 9755-9768 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:62 year:2024 number:10 day:06 month:09 pages:9755-9768 https://dx.doi.org/10.1007/s00382-024-07427-4 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO 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_72 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_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_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_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_2574 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 VZ AR 62 2024 10 06 09 9755-9768 |
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10.1007/s00382-024-07427-4 doi (DE-627)SPR057695407 (SPR)s00382-024-07427-4-e DE-627 ger DE-627 rakwb eng 550 VZ 38.80 bkl Zhu, Xiaoyu verfasserin aut How does the cumulus parameterization scheme influence the simulation of MJO propagation and structure? 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this study, eleven different cumulus parameterization schemes (CPSs) in Weather Research and Forecasting (WRF) model version 4.0 were used to test the sensitivity of Madden–Julian oscillation (MJO) eastward propagation to different CPSs and to explore the influences of different CPSs on MJO eastward propagation. The propagation and structures of the MJO-filtered outgoing longwave radiation (OLR) for the MJO active phase composites during the December–January–February (DJF) period of 2007–2016 over the Indian Ocean (IO) were diagnosed in this study. The results show that the simulated MJO propagation is sensitive to the selection of CPSs. And CPS5 (Tiedtke scheme) and CPS8 (New Tiedtke scheme) are selected for the good models composite while the CPS4 (Grell 3D ensemble scheme) and CPS9 (Grell-Devenyi ensemble scheme) are selected for the poor models composite. The good models composite provides similar MJO structures and MJO eastward propagation patterns as those observed, while poor models composite fails. The MJO propagation is highly correlated with boundary layer moisture convergence (BLMC) propagation pattern correlation coefficient (PCC), the U850 asymmetry index, the convective instability index and the new index reconstructed by the three main contributing factors, and BLMC propagation plays the most important role in simulating MJO propagation. Besides, different CPSs may result in different interactions between convective heating and moisture feedback, thus simulating different structures. The results support the trio‑interaction theory and provide a reference for the influence of CPS on the MJO eastward propagation. MJO (dpeaa)DE-He213 WRF model (dpeaa)DE-He213 Cumulus parameterization scheme (dpeaa)DE-He213 Trio‑interaction theory (dpeaa)DE-He213 Boundary layer moisture convergence (dpeaa)DE-He213 Zhong, Zhong verfasserin (orcid)0000-0002-2019-0281 aut Zhu, Yimin verfasserin aut Li, Yunying verfasserin aut Hu, Yijia verfasserin aut Ha, Yao verfasserin aut Enthalten in Climate dynamics Springer Berlin Heidelberg, 1986 62(2024), 10 vom: 06. Sept., Seite 9755-9768 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:62 year:2024 number:10 day:06 month:09 pages:9755-9768 https://dx.doi.org/10.1007/s00382-024-07427-4 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO 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_72 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_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_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_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_2574 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 VZ AR 62 2024 10 06 09 9755-9768 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR057695407</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20241008064645.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">241008s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00382-024-07427-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR057695407</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00382-024-07427-4-e</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">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">38.80</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhu, Xiaoyu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">How does the cumulus parameterization scheme influence the simulation of MJO propagation and structure?</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</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">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In this study, eleven different cumulus parameterization schemes (CPSs) in Weather Research and Forecasting (WRF) model version 4.0 were used to test the sensitivity of Madden–Julian oscillation (MJO) eastward propagation to different CPSs and to explore the influences of different CPSs on MJO eastward propagation. The propagation and structures of the MJO-filtered outgoing longwave radiation (OLR) for the MJO active phase composites during the December–January–February (DJF) period of 2007–2016 over the Indian Ocean (IO) were diagnosed in this study. The results show that the simulated MJO propagation is sensitive to the selection of CPSs. And CPS5 (Tiedtke scheme) and CPS8 (New Tiedtke scheme) are selected for the good models composite while the CPS4 (Grell 3D ensemble scheme) and CPS9 (Grell-Devenyi ensemble scheme) are selected for the poor models composite. The good models composite provides similar MJO structures and MJO eastward propagation patterns as those observed, while poor models composite fails. The MJO propagation is highly correlated with boundary layer moisture convergence (BLMC) propagation pattern correlation coefficient (PCC), the U850 asymmetry index, the convective instability index and the new index reconstructed by the three main contributing factors, and BLMC propagation plays the most important role in simulating MJO propagation. Besides, different CPSs may result in different interactions between convective heating and moisture feedback, thus simulating different structures. 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Zhu, Xiaoyu |
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Zhu, Xiaoyu ddc 550 bkl 38.80 misc MJO misc WRF model misc Cumulus parameterization scheme misc Trio‑interaction theory misc Boundary layer moisture convergence How does the cumulus parameterization scheme influence the simulation of MJO propagation and structure? |
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550 VZ 38.80 bkl How does the cumulus parameterization scheme influence the simulation of MJO propagation and structure? MJO (dpeaa)DE-He213 WRF model (dpeaa)DE-He213 Cumulus parameterization scheme (dpeaa)DE-He213 Trio‑interaction theory (dpeaa)DE-He213 Boundary layer moisture convergence (dpeaa)DE-He213 |
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how does the cumulus parameterization scheme influence the simulation of mjo propagation and structure? |
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How does the cumulus parameterization scheme influence the simulation of MJO propagation and structure? |
| abstract |
Abstract In this study, eleven different cumulus parameterization schemes (CPSs) in Weather Research and Forecasting (WRF) model version 4.0 were used to test the sensitivity of Madden–Julian oscillation (MJO) eastward propagation to different CPSs and to explore the influences of different CPSs on MJO eastward propagation. The propagation and structures of the MJO-filtered outgoing longwave radiation (OLR) for the MJO active phase composites during the December–January–February (DJF) period of 2007–2016 over the Indian Ocean (IO) were diagnosed in this study. The results show that the simulated MJO propagation is sensitive to the selection of CPSs. And CPS5 (Tiedtke scheme) and CPS8 (New Tiedtke scheme) are selected for the good models composite while the CPS4 (Grell 3D ensemble scheme) and CPS9 (Grell-Devenyi ensemble scheme) are selected for the poor models composite. The good models composite provides similar MJO structures and MJO eastward propagation patterns as those observed, while poor models composite fails. The MJO propagation is highly correlated with boundary layer moisture convergence (BLMC) propagation pattern correlation coefficient (PCC), the U850 asymmetry index, the convective instability index and the new index reconstructed by the three main contributing factors, and BLMC propagation plays the most important role in simulating MJO propagation. Besides, different CPSs may result in different interactions between convective heating and moisture feedback, thus simulating different structures. The results support the trio‑interaction theory and provide a reference for the influence of CPS on the MJO eastward propagation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract In this study, eleven different cumulus parameterization schemes (CPSs) in Weather Research and Forecasting (WRF) model version 4.0 were used to test the sensitivity of Madden–Julian oscillation (MJO) eastward propagation to different CPSs and to explore the influences of different CPSs on MJO eastward propagation. The propagation and structures of the MJO-filtered outgoing longwave radiation (OLR) for the MJO active phase composites during the December–January–February (DJF) period of 2007–2016 over the Indian Ocean (IO) were diagnosed in this study. The results show that the simulated MJO propagation is sensitive to the selection of CPSs. And CPS5 (Tiedtke scheme) and CPS8 (New Tiedtke scheme) are selected for the good models composite while the CPS4 (Grell 3D ensemble scheme) and CPS9 (Grell-Devenyi ensemble scheme) are selected for the poor models composite. The good models composite provides similar MJO structures and MJO eastward propagation patterns as those observed, while poor models composite fails. The MJO propagation is highly correlated with boundary layer moisture convergence (BLMC) propagation pattern correlation coefficient (PCC), the U850 asymmetry index, the convective instability index and the new index reconstructed by the three main contributing factors, and BLMC propagation plays the most important role in simulating MJO propagation. Besides, different CPSs may result in different interactions between convective heating and moisture feedback, thus simulating different structures. The results support the trio‑interaction theory and provide a reference for the influence of CPS on the MJO eastward propagation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract In this study, eleven different cumulus parameterization schemes (CPSs) in Weather Research and Forecasting (WRF) model version 4.0 were used to test the sensitivity of Madden–Julian oscillation (MJO) eastward propagation to different CPSs and to explore the influences of different CPSs on MJO eastward propagation. The propagation and structures of the MJO-filtered outgoing longwave radiation (OLR) for the MJO active phase composites during the December–January–February (DJF) period of 2007–2016 over the Indian Ocean (IO) were diagnosed in this study. The results show that the simulated MJO propagation is sensitive to the selection of CPSs. And CPS5 (Tiedtke scheme) and CPS8 (New Tiedtke scheme) are selected for the good models composite while the CPS4 (Grell 3D ensemble scheme) and CPS9 (Grell-Devenyi ensemble scheme) are selected for the poor models composite. The good models composite provides similar MJO structures and MJO eastward propagation patterns as those observed, while poor models composite fails. The MJO propagation is highly correlated with boundary layer moisture convergence (BLMC) propagation pattern correlation coefficient (PCC), the U850 asymmetry index, the convective instability index and the new index reconstructed by the three main contributing factors, and BLMC propagation plays the most important role in simulating MJO propagation. Besides, different CPSs may result in different interactions between convective heating and moisture feedback, thus simulating different structures. The results support the trio‑interaction theory and provide a reference for the influence of CPS on the MJO eastward propagation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| score |
7.3992968 |