ENSO hindcast skill of the IAP-DecPreS near-term climate prediction system: comparison of full-field and anomaly initialization
Model initialization is a key process of climate predictions using dynamical models. In this study, the authors evaluated the performances of two distinct initialization approaches—anomaly and full-field initializations—in ENSO predictions conducted using the IAP-DecPreS near-term climate prediction...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2018-01-01
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| Series: | Atmospheric and Oceanic Science Letters |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/16742834.2018.1411753 |
| _version_ | 1818350997820407808 |
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| author | Qian SUN Bo WU Tian-Jun ZHOU Zi-Xiang YAN |
| author_facet | Qian SUN Bo WU Tian-Jun ZHOU Zi-Xiang YAN |
| author_sort | Qian SUN |
| collection | DOAJ |
| description | Model initialization is a key process of climate predictions using dynamical models. In this study, the authors evaluated the performances of two distinct initialization approaches—anomaly and full-field initializations—in ENSO predictions conducted using the IAP-DecPreS near-term climate prediction system developed by the Institute of Atmospheric Physics (IAP). IAP-DecPreS is composed of the FGOALS-s2 coupled general circulation model and a newly developed ocean data assimilation scheme called ‘ensemble optimal interpolation-incremental analysis update’ (EnOI-IAU). It was found that, for IAP-DecPreS, the hindcast runs using the anomaly initialization have higher predictive skills for both conventional ENSO and El Niño Modoki, as compared to using the full-field initialization. The anomaly hindcasts can predict super El Niño/La Nina 10 months in advance and have good skill for most moderate and weak ENSO events about 4–7 months in advance. The predictive skill of the anomaly hindcasts for El Niño Modoki is close to that for conventional ENSO. On the other hand, the anomaly hindcasts at 1- and 4-month lead time can reproduce the major features of large-scale patterns of sea surface temperature, precipitation and atmospheric circulation anomalies during conventional ENSO and El Niño Modoki winter. |
| first_indexed | 2024-12-13T18:30:44Z |
| format | Article |
| id | doaj.art-9d88d875952b4eaca8a5a86f338d807e |
| institution | Directory Open Access Journal |
| issn | 1674-2834 2376-6123 |
| language | English |
| last_indexed | 2024-12-13T18:30:44Z |
| publishDate | 2018-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Atmospheric and Oceanic Science Letters |
| spelling | doaj.art-9d88d875952b4eaca8a5a86f338d807e2022-12-21T23:35:30ZengKeAi Communications Co., Ltd.Atmospheric and Oceanic Science Letters1674-28342376-61232018-01-01111546210.1080/16742834.2018.14117531411753ENSO hindcast skill of the IAP-DecPreS near-term climate prediction system: comparison of full-field and anomaly initializationQian SUN0Bo WU1Tian-Jun ZHOU2Zi-Xiang YAN3Chengdu University of Information TechnologyInstitute of Atmospheric Physics, Chinese Academy of SciencesInstitute of Atmospheric Physics, Chinese Academy of SciencesNanjing University of Information Science and TechnologyModel initialization is a key process of climate predictions using dynamical models. In this study, the authors evaluated the performances of two distinct initialization approaches—anomaly and full-field initializations—in ENSO predictions conducted using the IAP-DecPreS near-term climate prediction system developed by the Institute of Atmospheric Physics (IAP). IAP-DecPreS is composed of the FGOALS-s2 coupled general circulation model and a newly developed ocean data assimilation scheme called ‘ensemble optimal interpolation-incremental analysis update’ (EnOI-IAU). It was found that, for IAP-DecPreS, the hindcast runs using the anomaly initialization have higher predictive skills for both conventional ENSO and El Niño Modoki, as compared to using the full-field initialization. The anomaly hindcasts can predict super El Niño/La Nina 10 months in advance and have good skill for most moderate and weak ENSO events about 4–7 months in advance. The predictive skill of the anomaly hindcasts for El Niño Modoki is close to that for conventional ENSO. On the other hand, the anomaly hindcasts at 1- and 4-month lead time can reproduce the major features of large-scale patterns of sea surface temperature, precipitation and atmospheric circulation anomalies during conventional ENSO and El Niño Modoki winter.http://dx.doi.org/10.1080/16742834.2018.1411753Near-term climate prediction systemENSO predictionanomaly initializationfull-field initializationcoupled general circulation model |
| spellingShingle | Qian SUN Bo WU Tian-Jun ZHOU Zi-Xiang YAN ENSO hindcast skill of the IAP-DecPreS near-term climate prediction system: comparison of full-field and anomaly initialization Atmospheric and Oceanic Science Letters Near-term climate prediction system ENSO prediction anomaly initialization full-field initialization coupled general circulation model |
| title | ENSO hindcast skill of the IAP-DecPreS near-term climate prediction system: comparison of full-field and anomaly initialization |
| title_full | ENSO hindcast skill of the IAP-DecPreS near-term climate prediction system: comparison of full-field and anomaly initialization |
| title_fullStr | ENSO hindcast skill of the IAP-DecPreS near-term climate prediction system: comparison of full-field and anomaly initialization |
| title_full_unstemmed | ENSO hindcast skill of the IAP-DecPreS near-term climate prediction system: comparison of full-field and anomaly initialization |
| title_short | ENSO hindcast skill of the IAP-DecPreS near-term climate prediction system: comparison of full-field and anomaly initialization |
| title_sort | enso hindcast skill of the iap decpres near term climate prediction system comparison of full field and anomaly initialization |
| topic | Near-term climate prediction system ENSO prediction anomaly initialization full-field initialization coupled general circulation model |
| url | http://dx.doi.org/10.1080/16742834.2018.1411753 |
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