Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1007/s00382-019-04897-9 |
Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF | |
Wang, Yiguo1; Counillon, Francois1; Keenlyside, Noel1,2,3; Svendsen, Lea2,3; Gleixner, Stephanie4; Kimmritz, Madlen1; Dai, Panxi5; Gao, Yongqi1,6 | |
2019-11-01 | |
发表期刊 | CLIMATE DYNAMICS |
ISSN | 0930-7575 |
EISSN | 1432-0894 |
出版年 | 2019 |
卷号 | 53页码:5777-5797 |
文章类型 | Article |
语种 | 英语 |
国家 | Norway; Germany; Peoples R China |
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 Nino-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. |
英文关键词 | Seasonal prediction Advanced data assimilation EnKF SST NorCPM ENSO Sea ice extent |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000493469900036 |
WOS关键词 | TO-INTERANNUAL PREDICTION ; CLIMATE PREDICTABILITY ; EQUATORIAL PACIFIC ; EL-NINO ; THERMOCLINE DEPTH ; ENSO PREDICTION ; FORECASTS ; SYSTEM ; MODEL ; SST |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/187924 |
专题 | 气候变化 |
作者单位 | 1.Bjerknes Ctr Climate Res, Nansen Environm & Remote Sensing Ctr, Bergen, Norway; 2.Univ Bergen, Geophys Inst, Bergen, Norway; 3.Bjerknes Ctr Climate Res, Bergen, Norway; 4.Potsdam Inst Climate Impact Res, Potsdam, Germany; 5.Peking Univ, Sch Phys, Dept Atmospher & Ocean Sci, Beijing, Peoples R China; 6.Chinese Acad Sci, Inst Atmospher Phys, Nansen Zhu Int Res Ctr, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Wang, Yiguo,Counillon, Francois,Keenlyside, Noel,et al. Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF[J]. CLIMATE DYNAMICS,2019,53:5777-5797. |
APA | Wang, Yiguo.,Counillon, Francois.,Keenlyside, Noel.,Svendsen, Lea.,Gleixner, Stephanie.,...&Gao, Yongqi.(2019).Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF.CLIMATE DYNAMICS,53,5777-5797. |
MLA | Wang, Yiguo,et al."Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF".CLIMATE DYNAMICS 53(2019):5777-5797. |
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