A new nudging scheme for the current operational climate prediction system of the National Marine Environmental Forecasting Center of China

Xunshu Song; Xiaojing Li; Shouwen Zhang; Yi Li; Xinrong Chen; Youmin Tang; Dake Chen

doi:10.1007/s13131-021/1857-4

Volume 41 Issue 2

Feb. 2022

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Xunshu Song, Xiaojing Li, Shouwen Zhang, Yi Li, Xinrong Chen, Youmin Tang, Dake Chen. A new nudging scheme for the current operational climate prediction system of the National Marine Environmental Forecasting Center of China[J]. Acta Oceanologica Sinica, 2022, 41(2): 51-64. doi: 10.1007/s13131-021/1857-4

Citation:

Xunshu Song, Xiaojing Li, Shouwen Zhang, Yi Li, Xinrong Chen, Youmin Tang, Dake Chen. A new nudging scheme for the current operational climate prediction system of the National Marine Environmental Forecasting Center of China[J]. Acta Oceanologica Sinica, 2022, 41(2): 51-64. doi: 10.1007/s13131-021/1857-4

Citation:

Xunshu Song, Xiaojing Li, Shouwen Zhang, Yi Li, Xinrong Chen, Youmin Tang, Dake Chen. A new nudging scheme for the current operational climate prediction system of the National Marine Environmental Forecasting Center of China[J]. Acta Oceanologica Sinica, 2022, 41(2): 51-64. doi: 10.1007/s13131-021/1857-4

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A new nudging scheme for the current operational climate prediction system of the National Marine Environmental Forecasting Center of China

doi: 10.1007/s13131-021/1857-4

Xunshu Song^{1, 2
,
,},
Xiaojing Li^{1, 2},
Shouwen Zhang^{4, 6},
Yi Li^{5, 6},
Xinrong Chen^{4, 6},
Youmin Tang^{3, 1},
Dake Chen¹

1.
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
2.
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, China
3.
Environmental Science and Engineering, University of Northern British Columbia, Prince George V2N 4Z9, Canada
4.
National Marine Environmental Forecasting Center, Beijing 100081, China
5.
College of Oceanography, Hohai University, Nanjing 210098, China
6.
Key Laboratory of Marine Hazards Forecasting of Ministry of Natural Resources, Hohai University, Nanjing 210098, China

Funds: The National Natural Science Foundation of China under contract No. 41690124; the Scientific Research Fund of the Second Institute of Oceanography, Ministry of Natural Resources under contract No. JG2007; the National Natural Science Foundation of China under contract Nos 42006034, 41690120 and 41530961; the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) under contract No. 311021009.

More Information

Corresponding author: Xunshu Song, E-mail: songxs@sio.org.cn
Received Date: 2021-02-26
Accepted Date: 2021-05-11

Available Online: 2021-12-07

Publish Date: 2022-02-01

Abstract

Abstract

A new nudging scheme is proposed for the operational prediction system of the National Marine Environmental Forecasting Center (NMEFC) of China, mainly aimed at improving El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) predictions. Compared with the origin nudging scheme of NMEFC, the new scheme adds a nudge assimilation for wind components, and increases the nudging weight at the subsurface. Increasing the nudging weight at the subsurface directly improved the simulation performance of the ocean component, while assimilating low-level wind components not only affected the atmospheric component but also benefited the oceanic simulation. Hindcast experiments showed that the new scheme remarkably improved both ENSO and IOD prediction skills. The skillful prediction lead time of ENSO was up to 11 months, 1 month longer than a hindcast using the original nudging scheme. Skillful prediction of IOD could be made 4–5 months ahead by the new scheme, with a 0.2 higher correlation at a 3-month lead time. These prediction skills approach the level of some of the best state-of-the-art coupled general circulation models. Improved ENSO and IOD predictions occurred across all seasons, but mainly for target months in the boreal spring for the ENSO and the boreal spring and summer for the IOD.
- climate prediction system,
- initialization,
- prediction skill,
- ENSO,
- IOD

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References(50)

References

[1]	Berrisford P, Dee D P, Poli P, et al. 2011. The ERA-Interim archive, version 2.0. https://www.ecmwf.int/node/8174[2011-11/2020-09]
[2]	Chen Dake, Cane M A, Kaplan A, et al. 2004. Predictability of El Niño over the past 148 years. Nature, 428(6984): 733–736. doi: 10.1038/nature02439
[3]	Chen Dake, Cane M A, Zebiak S E, et al. 2000. Bias correction of an ocean-atmosphere coupled model. Geophysical Research Letters, 27(16): 2585–2588. doi: 10.1029/1999GL011078
[4]	Chen Dake, Lian Tao, Fu Congbin, et al. 2015. Strong influence of westerly wind bursts on El Niño diversity. Nature Geoscience, 8(5): 339–345. doi: 10.1038/ngeo2399
[5]	Chen Xingrong, Wang Hui, Zheng Fei, et al. 2020. An ensemble-based SST nudging method proposed for correcting the subsurface temperature field in climate model. Acta Oceanologica Sinica, 39(3): 73–80. doi: 10.1007/s13131-020-1568-2
[6]	Chen Dake, Zebiak S E, Busalacchi A J, et al. 1995. An improved procedure for EI Niño forecasting: Implications for predictability. Science, 269(5231): 1699–1702. doi: 10.1126/science.269.5231.1699
[7]	Doi T, Storto A, Behera S K, et al. 2017. Improved prediction of the Indian Ocean Dipole mode by use of subsurface ocean observations. Journal of Climate, 30(19): 7953–7970. doi: 10.1175/JCLI-D-16-0915.1
[8]	Feng Rong, Duan Wansuo. 2014. The spatial patterns of initial errors related to the “winter predictability barrier” of the Indian Ocean Dipole. Atmospheric and Oceanic Science Letters, 7(5): 406–410. doi: 10.1080/16742834.2014.11447198
[9]	Feng Rong, Duan Wansuo, Mu Mu. 2014. The “winter predictability barrier” for IOD events and its error growth dynamics: Results from a fully coupled GCM. Journal of Geophysical Research: Oceans, 119(12): 8688–8708. doi: 10.1002/2014JC010473
[10]	Hu Shineng, Fedorov A V. 2019. The extreme El Niño of 2015–2016: the role of westerly and easterly wind bursts, and preconditioning by the failed 2014 event. Climate Dynamics, 52(12): 7339–7357. doi: 10.1007/s00382-017-3531-2
[11]	Huang Ronghui, Wu Yifang. 1989. The influence of ENSO on the summer climate change in China and its mechanism. Advances in Atmospheric Sciences, 6(1): 21–32. doi: 10.1007/BF02656915
[12]	Jourdain N C, Gupta A S, Taschetto A S, et al. 2013. The Indo-Australian monsoon and its relationship to ENSO and IOD in reanalysis data and the CMIP3/CMIP5 simulations. Climate Dynamics, 41(11): 3073–3102
[13]	Kug J S, Sooraj K P, Jin Feifei, et al. 2009. Impact of Indian Ocean Dipole on high-frequency atmospheric variability over the Indian Ocean. Atmospheric Research, 94(1): 134–139. doi: 10.1016/j.atmosres.2008.10.022
[14]	Leutbecher M, Palmer T N. 2008. Ensemble forecasting. Journal of Computational Physics, 227(7): 3515–3539. doi: 10.1016/j.jcp.2007.02.014
[15]	Li Yi, Chen Xingrong, Tan Jing, et al. 2015. An ENSO hindcast experiment using CESM. Haiyang Xuebao (in Chinese), 37(9): 39–50
[16]	Lim E P, Hendon H H, Zhao Mei, et al. 2017. Inter-decadal variations in the linkages between ENSO, the IOD and south-eastern Australian springtime rainfall in the past 30 years. Climate Dynamics, 49(1): 97–112
[17]	Lin Hai, Gagnon N, Beauregard S, et al. 2016. GEPS-based monthly prediction at the Canadian meteorological centre. Monthly Weather Review, 144(12): 4867–4883. doi: 10.1175/MWR-D-16-0138.1
[18]	Ling Tiejun, Wang Zhanggui, Wang Bin, et al. 2009. Assimilation modeling by using CCSM3 model. Haiyang Xuebao (in Chinese), 31(6): 9–21
[19]	Liu Huafeng, Tang Youmin, Chen Dake, et al. 2017. Predictability of the Indian Ocean Dipole in the coupled models. Climate Dynamics, 48(5): 2005–2024
[20]	Luo Jingjia, Behera S, Masumoto Y, et al. 2008. Successful prediction of the consecutive IOD in 2006 and 2007. Geophysical Research Letters, 35(14): L14S02
[21]	Luo Jingjia, Masson S, Behera S, et al. 2005. Seasonal climate predictability in a coupled OAGCM using a different approach for ensemble forecasts. Journal of Climate, 18(21): 4474–4497. doi: 10.1175/JCLI3526.1
[22]	Merryfield W J, Lee W S, Boer G J, et al. 2013. The Canadian seasonal to interannual prediction system. Part I. Models and initialization. Monthly Weather Review, 141(8): 2910–2945. doi: 10.1175/MWR-D-12-00216.1
[23]	Philander S G. 1990. El Nino, La Nina, and the Southern Oscillation. San Diego, CA, USA: Academic Press
[24]	Rao S A, Luo Jingjia, Behera S K, et al. 2009. Generation and termination of Indian Ocean Dipole events in 2003, 2006 and 2007. Climate Dynamics, 33(6): 751–767. doi: 10.1007/s00382-008-0498-z
[25]	Ren Hongli, Jin Feifei, Song Lianchun, et al. 2017. Prediction of primary climate variability modes at the Beijing Climate Center. Journal of Meteorological Research, 31(1): 204–223. doi: 10.1007/s13351-017-6097-3
[26]	Ropelewski C F, Halpert M S. 1987. Global and regional scale precipitation patterns associated with the El Niño/southern oscillation. Monthly Weather Review, 115(8): 1606–1626. doi: 10.1175/1520-0493(1987)115<1606:GARSPP>2.0.CO;2
[27]	Saha S, Moorthi S, Wu Xingren, et al. 2014. The NCEP climate forecast system version 2. Journal of Climate, 27(6): 2185–2208. doi: 10.1175/JCLI-D-12-00823.1
[28]	Saha S, Nadiga S, Thiaw C, et al. 2006. The NCEP Climate Forecast System. Journal of Climate, 19(15): 3483–3517. doi: 10.1175/JCLI3812.1
[29]	Saji N H, Goswami B N, Vinayachandran P N, et al. 1999. A dipole mode in the tropical Indian Ocean. Nature, 401(6751): 360–363
[30]	Saji N H, Yamagata T. 2002. Structure of SST and surface wind variability during Indian Ocean Dipole mode events: COADS observations. Journal of Climate, 16(16): 2735–2751
[31]	Shi Li, Hendon H H, Alves O, et al. 2012. How predictable is the Indian Ocean Dipole?. Monthly Weather Review, 140(12): 3867–3884
[32]	Song Xunshu, Chen Dake, Tang Youmin, et al. 2018. An intermediate coupled model for the tropical ocean-atmosphere system. Science China: Earth Sciences, 61(12): 1859–1874. doi: 10.1007/s11430-018-9274-6
[33]	Tan Xiaoxiao, Tang Youmin, Lian Tao, et al. 2020. Effects of semistochastic westerly wind bursts on ENSO predictability. Geophysical Research Letters, 47(14): e2019GL086828
[34]	Tang Youmin, Kleeman R, Moore A M. 2004. SST Assimilation experiments in a tropical Pacific Ocean model. Journal of Physical Oceanography, 34(3): 623–642. doi: 10.1175/3518.1
[35]	Tang Youmin, Zhang Ronghua, Liu Ting, et al. 2018. Progress in ENSO prediction and predictability study. National Science Review, 5: 826–839. doi: 10.1093/nsr/nwy105
[36]	Trenberth K E. 1984. Some effects of finite sample size and persistence on meteorological statistics. Part I. Autocorrelations. Monthly Weather Review, 112(12): 2359–2368. doi: 10.1175/1520-0493(1984)112<2359:SEOFSS>2.0.CO;2
[37]	Vinayachandran P N, Francis P A, Rao S A. 2009. Indian Ocean Dipole: processes and impacts. In: Mukunda N, ed. Current Trends in Science. Bangalore, India: Indian Academy of Sciences, 569–589
[38]	Wajsowicz R C. 2005. Potential predictability of tropical Indian Ocean SST anomalies. Geophysical Research Letters, 32(24): L24702. doi: 10.1029/2005GL024169
[39]	Wajsowicz R C. 2007. Seasonal-to-interannual forecasting of tropical Indian Ocean sea surface temperature anomalies: Potential predictability and barriers. Journal of Climate, 20(13): 3320–3343. doi: 10.1175/JCLI4162.1
[40]	Wang Huijun. 2002. The instability of the East Asian summer monsoon–ENSO relations. Advances in Atmospheric Sciences, 19(1): 1–11. doi: 10.1007/s00376-002-0029-5
[41]	Wu Tongwen, Song Lianchun, Li Weiping, et al. 2014. An overview of BCC climate system model development and application for climate change studies. Journal of Meteorological Research, 28(1): 34–56
[42]	Wu Yanling, Tang Youmin. 2019. Seasonal predictability of the tropical Indian Ocean SST in the North American multimodel ensemble. Climate Dynamics, 53(5): 3361–3372
[43]	Xiao Mingzhong, Zhang Qiang, Singh V P. 2015. Influences of ENSO, NAO, IOD and PDO on seasonal precipitation regimes in the Yangtze River Basin, China. International Journal of Climatology, 35(12): 3556–3567. doi: 10.1002/joc.4228
[44]	Xie Shangping, Hu Kaiming, Hafner J, et al. 2009. Indian Ocean capacitor effect on Indo-Western Pacific climate during the summer following El Niño. Journal of Climate, 22(3): 730–747. doi: 10.1175/2008JCLI2544.1
[45]	Zhang Shouwen, Song Chunyang, Wang Hui, et al. 2018. Evaluation of the hindcasting main SSTA modes of the global key regions based on the CESM forecasting system. Haiyang Xuebao (in Chinese), 40(9): 18–30
[46]	Zhang Qiang, Xu Chongyu, Jiang Tong, et al. 2007. Possible influence of ENSO on annual maximum streamflow of the Yangtze River, China. Journal of Hydrology, 333(2–4): 265–274
[47]	Zhang Ronghua, Zebiak S E, Kleeman R, et al. 2005. Retrospective El Niño forecasts using an improved intermediate coupled model. Monthly Weather Review, 133(9): 2777–2802. doi: 10.1175/MWR3000.1
[48]	Zhao Mei, Hendon H H. 2009. Representation and prediction of the Indian Ocean Dipole in the POAMA seasonal forecast model. Quarterly Journal of the Royal Meteorological Society, 135(639): 337–352. doi: 10.1002/qj.370
[49]	Zheng Fei, Fang Xianghui, Zhu Jiang, et al. 2016. Modulation of Bjerknes feedback on the decadal variations in ENSO predictability. Geophysical Research Letters, 43(24): 12560–512568
[50]	Zheng Fei, Zhu Jiang, Zhang Ronghua, et al. 2006. Improved ENSO forecasts by assimilating sea surface temperature observations into an intermediate coupled model. Advances in Atmospheric Sciences, 23(4): 615–624. doi: 10.1007/s00376-006-0615-z