logo

Chinese Science Bulletin, Volume 64, Issue 1: 73-78(2019) https://doi.org/10.1360/N972018-00913

Outlook for El Niño and the Indian Ocean Dipole in autumn-winter 2018–2019

More info
  • ReceivedSep 2, 2018
  • AcceptedNov 6, 2018
  • PublishedDec 4, 2018

Abstract

El Niño-Southern Oscillation (ENSO) in the equatorial Pacific Ocean and the Indian Ocean Dipole (IOD) in the equatorial Indian Ocean are two major natural variabilities on seasonal and inter-annual timescales. In this study, the Flexible Global Ocean-Atmosphere-Land System Model, finite volume version 2 (FGOALS-f2), sub-seasonal to seasonal (S2S) climate prediction system, was used to make a seasonal prediction for autumn and winter 2018–2019. The FGOALS-f2 S2S prediction system was developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS), and is run on China's Tianhe-2 supercomputer located at the National Supercomputer Center in Guangzhou, China. The model used in the prediction system is CAS FGOALS-f2, which is a next-generation climate system model of LASG-IAP, representing the interaction between the atmosphere, oceans, land, and sea ice. The seasonal prediction products from this system have been submitted to and used operationally by the National Climate Center of the China Meteorological Administration, as well as the National Marine Environmental Forecasting Center of China, since June 2017. The FGOALS-f2 S2S prediction system has achieved 37 a retrospective forecasts (reforecasts) covering the period 1981–2017. The reforecast experiments include 24 ensemble members, while the real-time prediction uses 35 ensemble members. The latest prediction results, in July 2018, reveal that: (1) A positive IOD will persist through autumn and winter 2018–2019, and the peak phase will be in October with an amplitude of approximately 0.4°C. Based on the 37 a reforecasts predicted from each July 20th, the one-month-lead prediction skill of the IOD is 0.82 in the IOD prediction of July, and the five-month-lead prediction skill is 0.56. (2) In the equatorial Pacific Ocean, the prediction results reveal a Moderate El Niño is under development, and Niño3.4 index values may reach approximately 1.3°C. Based on the 37 a reforecasts predicted from each July 20th, the one-month lead prediction skill of the Niño3.4 index is 0.97 in the ENSO prediction of July, and the six-month lead prediction skill is 0.83. (3) The Moderate El Niño and positive IOD may induce a weak China winter monsoon, characterized by a warm winter. For North China, the meteorological conditions are expected to have adverse effects on atmospheric diffusion; while for South China, warm and wet conditions are likely to prevail, since the lower-level jet of the Indian Ocean is predicted to strengthen.

To better predict the equatorial sea surface temperature anomalies in the eastern Pacific and Indian oceans, the FGOALS-f2 S2S climate prediction team will continue to update the prediction results on the 20th of every month, releasing them (with respect to ENSO, the IOD, climate, and average monthly weather in China) on the website, and report the latest prediction results via the WeChat public platform.


Funded by

国家自然科学基金(91737306)

国家自然科学基金(41675100)

国家自然科学基金(91437219)

国家自然科学基金(91637312)

中国科学院A类战略性先导科技专项(XDA19070401)

中国科学院A类战略性先导科技专项(XDA11010402)


Acknowledgment

感谢中国科学院南海海洋研究所王东晓研究员为本文提供了很多有益的建议以及审稿专家富有建设性的修改建议.


References

[1] Yuan Y, Yang S. Impacts of different types of El Niño on the East Asian climate: Focus on ENSO Cycles. J Clim, 2012, 25: 7702-7722 CrossRef Google Scholar

[2] Wu N G, Lin L X, Li T R, et al. Causality analysis of the cryogenic freezing rain and snow weather in Guangdong Province at the beginning of 2008 (in Chinese). Guangdong Meteor, 2018, 30: 4–7 [吴乃庚, 林良勋, 李天然, 等. 2008年初广东罕见低温雨雪冰冻天气的成因初探. 广东气象, 2008, 30: 4–7]. Google Scholar

[3] Zheng F, Zhu J, Zhang R H, et al. Successful prediction for the super El Niño event in 2015 (in Chinese). Bull Chin Acad Sci, 2016, 2: 251–257 [郑飞, 朱江, 张荣华, 等. 2015年超级厄尔尼诺事件的成功预报. 中国科学院院刊, 2016, 2: 251–257]. Google Scholar

[4] Song W L, Yuan Y. Uncertainty analysis of climate prediction for the 2015/2016 winter under the background of El Niño events (in Chinese). Meteorol Mon, 2017, 43: 1249–1258 [宋文玲, 袁媛. 强厄尔尼诺背景下2015/2016年冬季气候预测的不确定性分析. 气象, 2017, 43: 1249–1258]. Google Scholar

[5] Saji N H, Goswami B N, Vinayachandran P N, et al. A dipole mode in the tropical Indian Ocean. Nature, 1999, 401: 360–363. Google Scholar

[6] Liu Y F, Yuan H Z, Guan Z Y. Effects of ENSO on the relationship between Indian Ocean Dipole and China summer rainfall (in Chinese). J Trop Meteorol, 2008, 24: 502–506 [刘宣飞, 袁慧珍, 管兆勇. ENSO对IOD与中国夏季降水关系的影响. 热带气象学报, 2008, 24: 502–506]. Google Scholar

[7] Liu X F, Yuan H Z. Relationship between Indian Ocean Dipole and autumn rainfall in China (in Chinese). Nanjing Inst Meteorol, 2006, 29: 644–649 [刘宣飞, 袁慧珍. 印度洋偶极子与中国秋季降水的关系. 南京气象学院学报, 2006, 29: 644–649]. Google Scholar

[8] Wu G, Liu H, Zhao Y, et al. A nine-layer atmospheric general circulation model and its performance. Adv Atmos Sci, 1996, 13: 1-18 CrossRef ADS Google Scholar

[9] Bao Q, Wu G, Liu Y, et al. An introduction to the coupled model FGOALS1.1-s and its performance in East Asia. Adv Atmos Sci, 2010, 27: 1131-1142 CrossRef ADS Google Scholar

[10] Bao Q, Lin P, Zhou T, et al. The Flexible Global Ocean-Atmosphere-Land system model, Spectral Version 2: FGOALS-s2. Adv Atmos Sci, 2013, 30: 561-576 CrossRef ADS Google Scholar

[11] Zhou L, Bao Q, Liu Y, et al. Global energy and water balance: Characteristics from Finite-volume Atmospheric Model of the IAP/LASG (FAMIL1). J Adv Model Earth Syst, 2015, 7: 1-20 CrossRef ADS Google Scholar

[12] Oleson K W, Lawrence D M, Bonan G B, et al. Technical Description of Version 4.0 of the Community Land Model (CLM). NCAR Technical Note NCAR/TN-478+STR. 2010, doi: 10.5065/D6FB50WZ.2010. Google Scholar

[13] Smith R, Jones P, Briegleb B, et al. The Parallel Ocean Program (POP) Reference Manual, Ocean Component of the Community Climate System Model (CCSM). Los Alamos National Laboratory Technical Report, LAUR-10-01853, 141. 2010. Google Scholar

[14] Kobayashi S, Ota Y, Harada Y, et al. The JRA-55 reanalysis: General specifications and basic characteristics. J Meteorol Soc Jpn, 2015, 93: 5-48 CrossRef Google Scholar

[15] Harada Y, Kamahori H, Kobayashi C, et al. The JRA-55 reanalysis: Representation of atmospheric circulation and climate variability. J Meteorol Soc Jpn, 2016, 94: 269-302 CrossRef Google Scholar

[16] Behringer D W, Xue Y. Evaluation of the global ocean data assimilation system at NCEP: The Pacific Ocean. Eighth symposium on integrated observing and assimilation systems for atmosphere, oceans, and land surface. AMS 84th Annual Meeting, Washington State Convention and Trade Center, Seattle, Washington. 2004. 11–15. Google Scholar

[17] Barnston A G, Tippett M K, L'Heureux M L, et al. Skill of Real-Time Seasonal ENSO Model Predictions during 2002–11: Is Our Capability Increasing?. Bull Amer Meteor Soc, 2002, 93: 631-651 CrossRef ADS Google Scholar

  • Figure 1

    The outlook of Niño3.4 index in autumn and winter (from November 2018 to January 2019) by FGOALS-f2 climate prediction system. The black fine line is the Niño3.4 index calculated by observed SST. The red dotted line is the Niño3.4 index predicted by FGOALS-f2 seasonal prediction system with 35 ensemble members and the black thick solid line is their averaged result. The blue dotted line is the connection line between observation and prediction. Units: °C

  • Figure 2

    Prediction of spatial distribution of sea surface temperature (SST) anomalies in autumn and winter (from November 2018 to January 2019) by FGOALS-f2 prediction system. Units: °C

  • Figure 3

    Outlooks of spatial distribution of Indian Ocean Dipole (IOD) in autumn and winter (from November 2018 to January 2019) by FGOALS-f2 climate prediction system. Units: °C

  • Figure 4

    Prediction of spatial distribution of surface (2 m) air temperature anomalies (a) and U-V wind anomalies at 850 hPa in autumn and winter (b) (from November 2018 to January 2019) by FGOALS-f2 climate prediction system

Copyright 2019 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备18024590号-1