气候变化领域集成服务门户(CCPortal): Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF

CCPortal

DOI	10.1007/s00382-019-04897-9
	Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF
	Wang Y.; Counillon F.; Keenlyside N.; Svendsen L.; Gleixner S.; Kimmritz M.; Dai P.; Gao Y.
发表日期	2019
ISSN	0930-7575
起始页码	5777
结束页码	5797
卷号	53 期号:2020-09-10
英文摘要	This study demonstrates that assimilating SST with an advanced data assimilation method yields prediction skill level with the best state-of-the-art systems. We employ the Norwegian Climate Prediction Model (NorCPM)—a fully-coupled forecasting system—to assimilate SST observations with the ensemble Kalman filter. Predictions of NorCPM are compared to predictions from the North American Multimodel Ensemble (NMME) project. The global prediction skill of NorCPM at 6- and 12-month lead times is higher than the averaged skill of the NMME. A new metric is introduced for ranking model skill. According to the metric, NorCPM is one of the most skilful systems among the NMME in predicting SST in most regions. Confronting the skill to a large historical ensemble without assimilation, shows that the skill is largely derived from the initialisation rather than from the external forcing. NorCPM achieves good skill in predicting El Niño–Southern Oscillation (ENSO) up to 12 months ahead and achieves skill over land via teleconnections. However, NorCPM has a more pronounced reduction in skill in May than the NMME systems. An analysis of ENSO dynamics indicates that the skill reduction is mainly caused by model deficiencies in representing the thermocline feedback in February and March. We also show that NorCPM has skill in predicting sea ice extent at the Arctic entrance adjacent to the north Atlantic; this skill is highly related to the initialisation of upper ocean heat content. © 2019, The Author(s).
英文关键词	Advanced data assimilation; EnKF; ENSO; NorCPM; Sea ice extent; Seasonal prediction; SST
语种	英语
scopus关键词	climate modeling; climate prediction; data assimilation; El Nino-Southern Oscillation; Kalman filter; sea ice; sea surface temperature; seasonal variation; Arctic Ocean; Atlantic Ocean; Atlantic Ocean (North)
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/145887
作者单位	Nansen Environmental and Remote Sensing Center, Bjerknes Centre for Climate Research, Bergen, Norway; Geophysical Institute, University of Bergen and Bjerknes Centre for Climate Research, Bergen, Norway; Potsdam Institute for Climate Impact Research, Potsdam, Germany; Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China; Nansen-Zhu International Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
推荐引用方式 GB/T 7714	Wang Y.,Counillon F.,Keenlyside N.,et al. Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF[J],2019,53(2020-09-10).
APA	Wang Y..,Counillon F..,Keenlyside N..,Svendsen L..,Gleixner S..,...&Gao Y..(2019).Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF.Climate Dynamics,53(2020-09-10).
MLA	Wang Y.,et al."Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF".Climate Dynamics 53.2020-09-10(2019).