The prediction on the 2015/16 El Nino event from the perspective of FIO-ESM

被引:19
作者
Song Zhenya [1 ,2 ]
Shu Qi [1 ,2 ]
Bao Ying [1 ,2 ]
Yin Xunqiang [1 ,2 ]
Qiao Fangli [1 ,2 ]
机构
[1] State Ocean Adm, Inst Oceanog 1, Qingdao 266061, Peoples R China
[2] Qingdao Natl Lab Marine Sci & Technol, Lab Reg Oceanog & Numer Modeling, Qingdao 266237, Peoples R China
基金
中国国家自然科学基金;
关键词
El Nino; prediction; FIO-ESM; Ensemble Adjusted Kalman Filter assimilation; MODEL; RAINFALL; MONSOON;
D O I
10.1007/s13131-015-0787-4
中图分类号
P7 [海洋学];
学科分类号
0707 ;
摘要
Recently atmospheric and oceanic observations indicate the tropical Pacific is at the El Nino condition. However, it's not clear whether this El Nino event of this year is comparable to the very strong one of 1997/98 which brought huge influence on the whole world. In this study, based on the Ensemble Adjusted Kalman Filter (EAKF) assimilation scheme and First Institute of Oceanography-Earth System Model (FIO-ESM), the assimilation system is setup, which can provide reasonable initial conditions for prediction. And the hindcast results suggest the skill of El Nino-Southern Oscillation (ENSO) prediction is comparable to other dynamical coupled models. Then the prediction for 2015/16 El Nino by using FIO-ESM is started from 1 November 2015. The ensemble results indicate that the 2015/16 El Nino will continue to be strong. By the end of 2015, the strongest strength is very like more than 2.0 degrees C and the ensemble mean strength is 2.34 degrees C, which indicates 2015/16 El Nino event will be very strong but slightly less than that of 1997/98 El Nino event (2.40 degrees C) calculated relative a climatology based on the years 1992-2014. The prediction results also suggest 2015/16 El Nino event will be a transition to ENSO-neutral level in the early spring (FMA) 2016, and then may transfer to La Nina in summer 2016.
引用
收藏
页码:67 / 71
页数:5
相关论文
共 36 条
[21]   Diversity and distribution of nearshore barnacle cyprids in southern California through the 2015-16 El Nino [J].
Hagerty, Malloree L. ;
Reyns, Nathalie ;
Pineda, Jesus ;
Govindarajan, Annette F. .
PEERJ, 2019, 7
[22]   Different Influences of Southeastern Indian Ocean and Western Indian Ocean SST Anomalies on Eastern China Rainfall during the Decaying Summer of the 2015/16 Extreme El Nino [J].
Chen, Jiepeng ;
Yu, Jin-Yi ;
Wang, Xin ;
Lian, Tao .
JOURNAL OF CLIMATE, 2020, 33 (13) :5427-5443
[23]   Enhanced response of global wetland methane emissions to the 2015-2016 El Nino-Southern Oscillation event [J].
Zhang, Zhen ;
Zimmermann, Niklaus E. ;
Calle, Leonardo ;
Hurtt, George ;
Chatterjee, Abhishek ;
Poulter, Benjamin .
ENVIRONMENTAL RESEARCH LETTERS, 2018, 13 (07)
[24]   A new perspective of the 2014/15 failed El Nino as seen from ocean salinity [J].
Chi, J. ;
Du, Y. ;
Zhang, Y. ;
Nie, X. ;
Shi, P. ;
Qu, T. .
SCIENTIFIC REPORTS, 2019, 9 (1)
[25]   Higher population genetic diversity within the algal symbiont Durusdinium in Pocillopora verrucosa from Mexican Pacific reefs correlates with higher resistance to bleaching after the El Nino 2015-16 event [J].
Angeles Cardenas-Alvarado, Maria ;
Nava, Hector ;
Gonzalez-Rodriguez, Antonio ;
Maldonado-Lopez, Yurixhi ;
Rodriguez-Lanetty, Mauricio .
MARINE ECOLOGY-AN EVOLUTIONARY PERSPECTIVE, 2021, 42 (04)
[26]   Mixed-layer Heat Budget in Western and Eastern Tropical Pacific Ocean during El Nino Event in 2015/2016 [J].
Kusuma, Willy Anta ;
Nur, Muhammad ;
Khakim, Mokhamad Yusup Nur ;
Iskandar, Iskhaq .
MAKARA JOURNAL OF SCIENCE, 2020, 24 (01) :10-16
[27]   Severe Heat Stress Resulted in High Coral Mortality on Maldivian Reefs following the 2015-2016 El Nino Event [J].
Bessell-Browne, Pia ;
Epstein, Hannah E. ;
Hall, Nora ;
Buerger, Patrick ;
Berry, Kathryn .
OCEANS-SWITZERLAND, 2021, 2 (01) :233-245
[28]   Unusual Rainfall in Southern China in Decaying August during Extreme El Nino 2015/16: Role of the Western Indian Ocean and North Tropical Atlantic SST [J].
Chen, Jiepeng ;
Wang, Xin ;
Zhou, Wen ;
Wang, Chunzai ;
Xie, Qiang ;
Li, Gang ;
Chen, Sheng .
JOURNAL OF CLIMATE, 2018, 31 (17) :7019-7034
[29]   Understanding the Low Predictability of the 2015/16 El Niño Event Based on a Deep Learning Model [J].
Wang, Tingyu ;
Huang, Ping ;
Yang, Xianke .
ADVANCES IN ATMOSPHERIC SCIENCES, 2024, 41 (07) :1313-1325
[30]   Data assimilation of Soil Moisture and Ocean Salinity (SMOS) observations into the Mercator Ocean operational system: focus on the El Nino 2015 event [J].
Tranchant, Benoit ;
Remy, Elisabeth ;
Greiner, Eric ;
Legalloudec, Olivier .
OCEAN SCIENCE, 2019, 15 (03) :543-563