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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Published in | Atmospheric and oceanic science letters = Daqi-he-haiyang-kexue-kuaibao Vol. 11; no. 1; pp. 54 - 62 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Beijing
Taylor & Francis
02.01.2018
KeAi Publishing Communications Ltd Elsevier KeAi Communications Co., Ltd |
Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | USDOE Office of Electricity (OE), Advanced Grid Research & Development. Power Systems Engineering Research 2017YFA0604201 |
ISSN: | 1674-2834 2376-6123 |
DOI: | 10.1080/16742834.2018.1411753 |