Prediction of the Central Indian Ocean Mode in S2S Models
Prediction of precipitation during the Indian summer monsoon (ISM) is a persistent scientific challenge. The central Indian Ocean (CIO) mode was proposed as a subseasonal climate mode over the tropical Indian Ocean, and it has a close relation with monsoon intraseasonal oscillations (MISO) during th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-04-01
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| Series: | Frontiers in Marine Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2022.880469/full |
| _version_ | 1828383380749680640 |
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| author | Jianhuang Qin Lei Zhou Lei Zhou Baosheng Li Ze Meng |
| author_facet | Jianhuang Qin Lei Zhou Lei Zhou Baosheng Li Ze Meng |
| author_sort | Jianhuang Qin |
| collection | DOAJ |
| description | Prediction of precipitation during the Indian summer monsoon (ISM) is a persistent scientific challenge. The central Indian Ocean (CIO) mode was proposed as a subseasonal climate mode over the tropical Indian Ocean, and it has a close relation with monsoon intraseasonal oscillations (MISO) during the ISM both in observations and simulations. In this study, the prediction skill of the CIO mode in the subseasonal-to-seasonal (S2S) air–sea coupled models is examined. The ECMWF and UKMO models display significantly higher skills for up to about 2 and 3 weeks, respectively, which are longer than other S2S models. The decline of the CIO mode prediction skill is due to the reduced signal of subseasonal zonal winds at 850 hPa over the tropical central Indian Ocean (especially along the equator; 5°S–5°N, 70°E–85°E). Therefore, a better simulation of tropical subseasonal zonal winds is required to improve the CIO mode prediction in models, and the improvement will benefit a better MISO simulation and a higher prediction skill during the ISM. |
| first_indexed | 2024-12-10T04:47:24Z |
| format | Article |
| id | doaj.art-d4cdd6d9cd6e48c785f9d388e53d0e25 |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-12-10T04:47:24Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-d4cdd6d9cd6e48c785f9d388e53d0e252022-12-22T02:01:42ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452022-04-01910.3389/fmars.2022.880469880469Prediction of the Central Indian Ocean Mode in S2S ModelsJianhuang Qin0Lei Zhou1Lei Zhou2Baosheng Li3Ze Meng4College of Oceanography, Hohai University, Nanjing, ChinaSchool of Oceanography, Shanghai Jiao Tong University, Shanghai, ChinaSouthern Marine Science and Engineering Guangdong Laboratory, Zhuhai, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, ChinaSchool of Oceanography, Shanghai Jiao Tong University, Shanghai, ChinaPrediction of precipitation during the Indian summer monsoon (ISM) is a persistent scientific challenge. The central Indian Ocean (CIO) mode was proposed as a subseasonal climate mode over the tropical Indian Ocean, and it has a close relation with monsoon intraseasonal oscillations (MISO) during the ISM both in observations and simulations. In this study, the prediction skill of the CIO mode in the subseasonal-to-seasonal (S2S) air–sea coupled models is examined. The ECMWF and UKMO models display significantly higher skills for up to about 2 and 3 weeks, respectively, which are longer than other S2S models. The decline of the CIO mode prediction skill is due to the reduced signal of subseasonal zonal winds at 850 hPa over the tropical central Indian Ocean (especially along the equator; 5°S–5°N, 70°E–85°E). Therefore, a better simulation of tropical subseasonal zonal winds is required to improve the CIO mode prediction in models, and the improvement will benefit a better MISO simulation and a higher prediction skill during the ISM.https://www.frontiersin.org/articles/10.3389/fmars.2022.880469/fullthe central Indian Ocean modeprediction skillsubseasonal-to-seasonal (S2S) predictionsignal-to-noise ratio (S/N ratio)subseasonal zonal winds |
| spellingShingle | Jianhuang Qin Lei Zhou Lei Zhou Baosheng Li Ze Meng Prediction of the Central Indian Ocean Mode in S2S Models Frontiers in Marine Science the central Indian Ocean mode prediction skill subseasonal-to-seasonal (S2S) prediction signal-to-noise ratio (S/N ratio) subseasonal zonal winds |
| title | Prediction of the Central Indian Ocean Mode in S2S Models |
| title_full | Prediction of the Central Indian Ocean Mode in S2S Models |
| title_fullStr | Prediction of the Central Indian Ocean Mode in S2S Models |
| title_full_unstemmed | Prediction of the Central Indian Ocean Mode in S2S Models |
| title_short | Prediction of the Central Indian Ocean Mode in S2S Models |
| title_sort | prediction of the central indian ocean mode in s2s models |
| topic | the central Indian Ocean mode prediction skill subseasonal-to-seasonal (S2S) prediction signal-to-noise ratio (S/N ratio) subseasonal zonal winds |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2022.880469/full |
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