气候变化领域集成服务门户(CCPortal): Impact of the tropical Pacific SST biases on the simulation and prediction of Indian summer monsoon rainfall in CFSv2, ECMWF-System4, and NMME models

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DOI	10.1007/s00382-020-05555-1
	Impact of the tropical Pacific SST biases on the simulation and prediction of Indian summer monsoon rainfall in CFSv2, ECMWF-System4, and NMME models
	Pillai P.A.; Rao S.A.; Srivastava A.; Ramu D.A.; Pradhan M.; Das R.S.
发表日期	2021
ISSN	0930-7575
卷号	56 期号:2021-05-06
英文摘要	Present study analyses the role of tropical Pacific sea surface temperature (SST) biases in simulating the mean and inter-annual variability of Indian summer monsoon rainfall (ISMR) using the hindcasts from Monsoon Mission CFSv2 (MMCFS) and ECMWF-SYSTEM4 (ES4) prediction systems. ES4 simulates the mean and annual cycle of ISMR better than MMCFS when initialized with February initial conditions (3-month lead, Feb IC). At the same time, interannual variability (IAV) and skill (correlation between the ensemble mean hindcast and observations) are the highest (least) for MMCFS (ES4). May IC (0-month lead) hindcasts of both the models have similar mean, annual cycle, IAV and skill. Further analysis shows that ES4_Feb IC hindcasts exhibit a strong cold bias (~ 2 °C) in the equatorial central Pacific, which is close to zero for MMCFS_Feb IC. Meanwhile, the 0-month lead (May IC) hindcast has a similar cold bias (~ 1.5 °C) in the equatorial Pacific for both the model hindcasts. Thus, model hindcasts with a better mean ISMR are having a strong cold bias in the equatorial Pacific and that having very less SST bias has higher IAV and skill for ISMR simulations. Additional investigation using North American National Multi-Model Ensemble Project (NMME) models is carried out and the following conclusion on the different roles of Pacific mean state bias is drawn. Hindcasts with strong cold SST bias in the tropical Pacific (ES4 hindcast and May IC hindcasts of MMCFS) tend to mimic the teleconnections associated with La Nina conditions reducing the dry bias over India, resulting in mean ISMR closer to the observed value, still less than the observed mean. At the same time, due to the same strong cold bias, the El Nino-Southern Oscillation (ENSO) induced rainfall and circulation pattern in the Pacific are weak and extended further northwestward. This weakens the ENSO induced heat sources over the Indian Ocean and monsoon region, resulting in reduced ENSO-ISMR teleconnections. These factors result in a poor simulation of interannual variability and skill of ISMR. On the other hand, model hindcasts with less cold bias in the equatorial Pacific (MMCFS_Feb IC) suffer from strong dry bias over India; however, the skill of ISMR is higher as a result of strong ENSO-Monsoon teleconnections. Thus the study confirms the differential role of SST bias in the tropical Pacific in simulating mean and IAV of ISMR in seasonal prediction models. A close to observed mean SST in the tropical Pacific and proper ENSO-Monsoon teleconnection is essential for the better skill of ISMR in the present generation seasonal prediction models. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.
英文关键词	ENSO; ISMR; Seasonal prediction; SST bias; Teleconnection
来源期刊	Climate Dynamics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/183369
作者单位	Monsoon Mission, Ministry of Earth Sciences, Indian Institute of Tropical Meteorology, Pune, 411008, India
推荐引用方式 GB/T 7714	Pillai P.A.,Rao S.A.,Srivastava A.,et al. Impact of the tropical Pacific SST biases on the simulation and prediction of Indian summer monsoon rainfall in CFSv2, ECMWF-System4, and NMME models[J],2021,56(2021-05-06).
APA	Pillai P.A.,Rao S.A.,Srivastava A.,Ramu D.A.,Pradhan M.,&Das R.S..(2021).Impact of the tropical Pacific SST biases on the simulation and prediction of Indian summer monsoon rainfall in CFSv2, ECMWF-System4, and NMME models.Climate Dynamics,56(2021-05-06).
MLA	Pillai P.A.,et al."Impact of the tropical Pacific SST biases on the simulation and prediction of Indian summer monsoon rainfall in CFSv2, ECMWF-System4, and NMME models".Climate Dynamics 56.2021-05-06(2021).