Climatological diagnostics and subseasonal‐to‐seasonal predictions of Madden–Julian Oscillation events
The Madden–Julian Oscillation (MJO) is the dominant mode of tropical intraseasonal variability, which serves as a primary source of subseasonal‐to‐seasonal (S2S) predictability. Noticeably, MJO is not always a regularly recurring cycle but is characterized by discrete episodes. In this study, consid...
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| Vydáno v: | International journal of climatology Ročník 43; číslo 5; s. 2449 - 2464 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Chichester, UK
John Wiley & Sons, Ltd
01.04.2023
Wiley Subscription Services, Inc |
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| ISSN: | 0899-8418, 1097-0088 |
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| Abstract | The Madden–Julian Oscillation (MJO) is the dominant mode of tropical intraseasonal variability, which serves as a primary source of subseasonal‐to‐seasonal (S2S) predictability. Noticeably, MJO is not always a regularly recurring cycle but is characterized by discrete episodes. In this study, considering the quasi‐consecutive actives and eastward propagating features, a standard metric is proposed to identify MJO events based on the real‐time multivariate MJO (RMM) index. The re‐identification of historical MJO events reveals that there were 5.4 MJO events each year since 1981, and the average duration of each event is about 31.5 days. More MJO events tend to occur in the boreal winter and spring, with stronger intensity, longer duration and faster propagating speed than those in the boreal summer. Furthermore, MJO events are more likely to initiate in Phases 2 and 5, with a longer lifetime than those initiating in other phases. The amplitude and propagation characteristics of MJO events are strongly modulated by the dominant modes of sea surface temperature interannual variability with a strong regional dependence. Based on hindcast datasets of six S2S models, the prediction skill for the MJO is evaluated in the perspective of individual events. The longest leading time of the skilful prediction for individual MJO events ranges from 11 to 17 days, far below the traditional recognition. This result could be attributed to the apparent prediction barrier of MJO initiation, that is, a rapid decrease in prediction skill when predictions are carried out before the initiation of MJO events. In addition, the prediction skills of MJO events depends on interannual variabilities, with relatively higher skills under the conditions of the El Niño and Indian Ocean basin warming. These findings may shed light on the complexity and challenges of profoundly understanding and skilfully predicting MJO events.
A standard metric is proposed to re‐identified all the historical MJO events since 1981 and their typical climatological characteristics are thoroughly explored. The prediction skills of six most advanced S2S models for MJO events are far below the traditional recognition, which may attribute to an apparent MJO initiation prediction barrier. |
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| AbstractList | The Madden–Julian Oscillation (MJO) is the dominant mode of tropical intraseasonal variability, which serves as a primary source of subseasonal‐to‐seasonal (S2S) predictability. Noticeably, MJO is not always a regularly recurring cycle but is characterized by discrete episodes. In this study, considering the quasi‐consecutive actives and eastward propagating features, a standard metric is proposed to identify MJO events based on the real‐time multivariate MJO (RMM) index. The re‐identification of historical MJO events reveals that there were 5.4 MJO events each year since 1981, and the average duration of each event is about 31.5 days. More MJO events tend to occur in the boreal winter and spring, with stronger intensity, longer duration and faster propagating speed than those in the boreal summer. Furthermore, MJO events are more likely to initiate in Phases 2 and 5, with a longer lifetime than those initiating in other phases. The amplitude and propagation characteristics of MJO events are strongly modulated by the dominant modes of sea surface temperature interannual variability with a strong regional dependence. Based on hindcast datasets of six S2S models, the prediction skill for the MJO is evaluated in the perspective of individual events. The longest leading time of the skilful prediction for individual MJO events ranges from 11 to 17 days, far below the traditional recognition. This result could be attributed to the apparent prediction barrier of MJO initiation, that is, a rapid decrease in prediction skill when predictions are carried out before the initiation of MJO events. In addition, the prediction skills of MJO events depends on interannual variabilities, with relatively higher skills under the conditions of the El Niño and Indian Ocean basin warming. These findings may shed light on the complexity and challenges of profoundly understanding and skilfully predicting MJO events.
A standard metric is proposed to re‐identified all the historical MJO events since 1981 and their typical climatological characteristics are thoroughly explored. The prediction skills of six most advanced S2S models for MJO events are far below the traditional recognition, which may attribute to an apparent MJO initiation prediction barrier. The Madden–Julian Oscillation (MJO) is the dominant mode of tropical intraseasonal variability, which serves as a primary source of subseasonal‐to‐seasonal (S2S) predictability. Noticeably, MJO is not always a regularly recurring cycle but is characterized by discrete episodes. In this study, considering the quasi‐consecutive actives and eastward propagating features, a standard metric is proposed to identify MJO events based on the real‐time multivariate MJO (RMM) index. The re‐identification of historical MJO events reveals that there were 5.4 MJO events each year since 1981, and the average duration of each event is about 31.5 days. More MJO events tend to occur in the boreal winter and spring, with stronger intensity, longer duration and faster propagating speed than those in the boreal summer. Furthermore, MJO events are more likely to initiate in Phases 2 and 5, with a longer lifetime than those initiating in other phases. The amplitude and propagation characteristics of MJO events are strongly modulated by the dominant modes of sea surface temperature interannual variability with a strong regional dependence. Based on hindcast datasets of six S2S models, the prediction skill for the MJO is evaluated in the perspective of individual events. The longest leading time of the skilful prediction for individual MJO events ranges from 11 to 17 days, far below the traditional recognition. This result could be attributed to the apparent prediction barrier of MJO initiation, that is, a rapid decrease in prediction skill when predictions are carried out before the initiation of MJO events. In addition, the prediction skills of MJO events depends on interannual variabilities, with relatively higher skills under the conditions of the El Niño and Indian Ocean basin warming. These findings may shed light on the complexity and challenges of profoundly understanding and skilfully predicting MJO events. |
| Author | Wu, Jie Jia, Xiaolong Ren, Hong‐Li Zhang, Peiqun |
| Author_xml | – sequence: 1 givenname: Jie orcidid: 0000-0002-7806-6718 surname: Wu fullname: Wu, Jie organization: Nanjing University of Information Science and Technology – sequence: 2 givenname: Hong‐Li surname: Ren fullname: Ren, Hong‐Li email: renhl@cma.gov.cn organization: Chinese Academy of Meteorological Sciences – sequence: 3 givenname: Xiaolong surname: Jia fullname: Jia, Xiaolong email: jiaxl@cma.gov.cn organization: Nanjing University of Information Science and Technology – sequence: 4 givenname: Peiqun surname: Zhang fullname: Zhang, Peiqun organization: National Climate Center |
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| Cites_doi | 10.1175/MWR-D-13-00059.1 10.1175/1520-0477-77.6.1274 10.1175/JCLI-D-19-0013.1 10.1175/2010BAMS2816.1 10.1038/s41467-021-23406-3 10.1007/s13351-020-9153-3 10.1002/qj.224 10.1080/16742834.2015.1116217 10.1016/j.dynatmoce.2016.06.001 10.1007/s00382-013-1859-9 10.1007/s00382-018-4369-y 10.1029/2020GL091930 10.1029/2019JD031537 10.1007/s00382-018-4492-9 10.1175/BAMS-D-12-00026.1 10.1175/BAMS-D-19-0037.1 10.1175/JCLI-D-12-00823.1 10.1029/2020MS002368 10.1175/JCLI-D-14-00744.1 10.1175/BAMS-D-18-0270.1 10.1002/2016RG000538 10.5194/gmd-14-2977-2021 10.1029/2004RG000158 10.1175/BAMS-D-21-0124.1 10.1175/BAMS-D-18-0326.1 10.1175/JCLI-D-17-0545.1 10.1175/JCLI-D-15-0102.1 10.1175/BAMS-83-11-1631 10.1126/sciadv.aax0220 10.1029/2019JD030911 10.1007/s13351-014-3087-6 10.1175/JCLI-D-12-00074.1 10.1016/j.dynatmoce.2017.04.002 10.1029/2018JD028506 10.1007/s13351-018-8078-6 10.1007/s13351-014-3041-7 10.1029/2021JD036011 10.1175/BAMS-D-16-0017.1 10.1175/MWR-D-13-00301.1 10.1175/JCLI-D-18-0210.1 10.5194/gmd-12-1573-2019 10.1007/s11802-015-2381-y 10.1007/s00382-018-4423-9 10.1175/1520-0493(2004)132<1917:Aarmmi>2.0.Co;2 10.1093/nsr/nwaa006 |
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| SubjectTerms | climatological characteristics El Nino El Nino phenomena Interannual variability Madden-Julian oscillation Madden–Julian Oscillation (MJO) MJO initiation Ocean basins Ocean warming prediction barrier Predictions Sea surface Sea surface temperature Seasonal variations Skills subseasonal‐to‐seasonal (S2S) prediction Surface temperature Variability |
| Title | Climatological diagnostics and subseasonal‐to‐seasonal predictions of Madden–Julian Oscillation events |
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