2018～2019年秋冬季厄尔尼诺和印度洋偶极子的预测
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摘要
热带太平洋厄尔尼诺/南方涛动(ENSO)和热带印度洋的偶极子(IOD)是全球季节到年际尺度的重要自然变率.本研究利用中国科学院大气物理研究所FGOALS-f2季节内-季节预测系统开展2018/2019年秋冬季气候异常预测.基于该预测系统从2017年7月起进行的预测显示:(1) 2018年秋冬季节IOD维持正位相,正IOD事件在10月达到最强,比常年偏高0.4℃;(2) 2018年秋冬季节赤道中东太平洋将逐渐发展成一次中等强度的厄尔尼诺事件,Ni?o3.4指数冬季达到1.3;(3) 2018年中国冬季风强度可能偏弱,冬季大部分地区气温较常年偏高,冷空气活动较弱,据此推测北方地区气象条件不利于大气污染物扩散,而南方受印度洋低层气流偏强的影响,出现暖湿气候特征.
El Ni?o-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 S2 S 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 S2 S 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 20 th, 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 Ni?o is under development, and Ni?o3.4 index values may reach approximately 1.3°C. Based on the 37 a reforecasts predicted from each July 20 th, the one-month lead prediction skill of the Ni?o3.4 index is 0.97 in the ENSO prediction of July, and the six-month lead prediction skill is 0.83.(3) The Moderate El Ni?o 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 S2 S climate prediction team will continue to update the prediction results on the 20 th 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.
El Ni?o-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 S2 S 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 S2 S 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 20 th, 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 Ni?o is under development, and Ni?o3.4 index values may reach approximately 1.3°C. Based on the 37 a reforecasts predicted from each July 20 th, the one-month lead prediction skill of the Ni?o3.4 index is 0.97 in the ENSO prediction of July, and the six-month lead prediction skill is 0.83.(3) The Moderate El Ni?o 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 S2 S climate prediction team will continue to update the prediction results on the 20 th 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.
引文
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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
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]
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]
8 Wu G X,Liu H,Zhao Y C,et al.A nine-layer atmospheric general circulation model and its performance.Adv Atmos Sci,1996,13:1-18
9 Bao Q,Wu G X,Liu Y M,et al.An introduction to the coupled model FGOALS1.1-s and its performance in East Asia.Adv Atmos Sci,2010,27:1131-1142
10 Bao Q,Lin P F,Zhou T J,et al.The flexible global ocean-atmosphere-land system model,spectral Version 2:FGOALS-s2.Adv Atmos Sci,2013,30:561-576
11 Zhou L J,Bao Q,Liu Y M,et al.Global energy and water balance:Characteristics from Finite Volume Atmospheric Model of the IAP/LASG(FAMIL1).J Adv Model Earth Sys,2015,7:1-20
12 Oleson K W,Lawrence D M,Bonan G B,et al.Technical Description of Version 4.0 of the Community Land Model(CLM).NCARTechnical Note NCAR/TN-478+STR.2010,doi:10.5065/D6FB50WZ.2010
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
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
15 Harada Y,Kamahori H,Kobayashi C,et al.The JRA-55 reanalysis:Representation of atmospheric circulation and climate variability.JMeteorol Soc Jpn,2016,94:269-302
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
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 Am Meteorol Soc,2012,93:631-651
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]
3 Zheng F,Zhu J,Zhang R H,et al.Successful prediction for the super El Ni?o 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]
4 Song W L,Yuan Y.Uncertainty analysis of climate prediction for the 2015/2016 winter under the background of El Ni?o 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]
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
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]
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]
8 Wu G X,Liu H,Zhao Y C,et al.A nine-layer atmospheric general circulation model and its performance.Adv Atmos Sci,1996,13:1-18
9 Bao Q,Wu G X,Liu Y M,et al.An introduction to the coupled model FGOALS1.1-s and its performance in East Asia.Adv Atmos Sci,2010,27:1131-1142
10 Bao Q,Lin P F,Zhou T J,et al.The flexible global ocean-atmosphere-land system model,spectral Version 2:FGOALS-s2.Adv Atmos Sci,2013,30:561-576
11 Zhou L J,Bao Q,Liu Y M,et al.Global energy and water balance:Characteristics from Finite Volume Atmospheric Model of the IAP/LASG(FAMIL1).J Adv Model Earth Sys,2015,7:1-20
12 Oleson K W,Lawrence D M,Bonan G B,et al.Technical Description of Version 4.0 of the Community Land Model(CLM).NCARTechnical Note NCAR/TN-478+STR.2010,doi:10.5065/D6FB50WZ.2010
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
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
15 Harada Y,Kamahori H,Kobayashi C,et al.The JRA-55 reanalysis:Representation of atmospheric circulation and climate variability.JMeteorol Soc Jpn,2016,94:269-302
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
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 Am Meteorol Soc,2012,93:631-651
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