Skip to main content
SpringerLink
Log in

Evaluation on data assimilation of a global high resolution wave-tide-circulation coupled model using the tropical Pacific TAO buoy observations

  • Published:
Acta Oceanologica Sinica Aims and scope Submit manuscript

Abstract

In order to evaluate the assimilation results from a global high resolution ocean model, the buoy observations from tropical atmosphere ocean (TAO) during August 2014 to July 2015 are employed. The horizontal resolution of wave-tide-circulation coupled ocean model developed by The First Institute of Oceanography (FIOCOM model) is 0.1°×0.1°, and ensemble adjustment Kalman filter is used to assimilate the sea surface temperature (SST), sea level anomaly (SLA) and Argo temperature/salinity profiles. The simulation results with and without data assimilation are examined. First, the overall statistic errors of model results are analyzed. The scatter diagrams of model simulations versus observations and corresponding error probability density distribution show that the errors of all the observed variables, including the temperature, isotherm depth of 20°C (D20), salinity and two horizontal component of velocity are reduced to some extent with a maximum improvement of 54% after assimilation. Second, time-averaged variables are used to investigate the horizontal and vertical structures of the model results. Owing to the data assimilation, the biases of the time-averaged distribution are reduced more than 70% for the temperature and D20 especially in the eastern Pacific. The obvious improvement of D20 which represents the upper mixed layer depth indicates that the structure of the temperature after the data assimilation becomes more close to the reality and the vertical structure of the upper ocean becomes more reasonable. At last, the physical processes of time series are compared with observations. The time evolution processes of all variables after the data assimilation are more consistent with the observations. The temperature bias and RMSE of D20 are reduced by 76% and 56% respectively with the data assimilation. More events during this period are also reproduced after the data assimilation. Under the condition of strong 2014/2016 El Niño, the Equatorial Undercurrent (EUC) from the TAO is gradually increased during August to November in 2014, and followed by a decreasing process. Since the improvement of the structure in the upper ocean, these events of the EUC can be clearly found in the assimilation results. In conclusion, the data assimilation in this global high resolution model has successfully reduced the model biases and improved the structures of the upper ocean, and the physical processes in reality can be well produced.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Anderson J E, Riser S C. 2014. Near-surface variability of temperature and salinity in the near-tropical ocean: observations from profiling floats. Journal of Geophysical Research: Oceans, 119(11): 7433–7448

    Google Scholar 

  • Balmaseda M, Anderson D. 2009. Impact of initialization strategies and observations on seasonal forecast skill. Geophysical Research Letters, 36(1): L01701

    Article  Google Scholar 

  • Bennett A F, Chua B S, Harrison D E, et al. 1998. Generalized inversion of tropical atmosphere-ocean data and a coupled model of the tropical Pacific. Journal of Climate, 11(7): 1768–1792

    Article  Google Scholar 

  • Bhowmick S A, Agarwal N, Ali M M, et al. 2016. Role of ocean heat content in boosting post-monsoon tropical storms over Bay of Bengal during La-Niña events. Climate Dynamics,: doi: 10.1007/s00382-016-3428-5

    Google Scholar 

  • Chen Jinnian, Lv Xinyan, Hu Dunxin. 2005. Variable properties of the equatorial undercurrent in the pacific and its anomalous warm water eastward propagation. Advances in Water Science (in Chinese), 16(6): 792–798

    Google Scholar 

  • Chowdary J S, Harsha H S, Gnanaseelan C, et al. 2017. Indian summer monsoon rainfall variability in response to differences in the decay phase of El Niño. Climate Dynamics, 48(7-8): 2707–2727

    Article  Google Scholar 

  • Firing E, Lukas R, Sadler J, et al. 1983. Equatorial undercurrent disappears during 1982-1983 El Niño. Science, 222(4628): 1121–1123

    Article  Google Scholar 

  • Fu Weiwei, Zhu Jiang, Yan Changxiang, et al. 2009. Toward a global ocean data assimilation system based on ensemble optimum interpolation: altimetry data assimilation experiment. Ocean Dynamics, 59(4): 587–602

    Article  Google Scholar 

  • Gao Chuan, Zhang Ronghua. 2017. The roles of atmospheric wind and entrained water temperature (Te) in the second-year cooling of the 2010-12 La Niña event. Climate Dynamics, 48(1-2): 597–617

    Article  Google Scholar 

  • Guan Bingxian. 1986. Current structure and its variation in the equat-orial area of the western north Pacific Ocean. Chinese Journal of Oceanology and Limnology, 4(3): 239–255

    Article  Google Scholar 

  • Hayes S P, Mangum L J, Picaut J, et al. 1991. TOGA-TAO: a moored array for real-time measurements in the tropical Pacific Ocean. Bulletin of the American Meteorological Society, 72(3): 339–347

    Article  Google Scholar 

  • Henocq C, Boutin J, Reverdin G, et al. 2010. Vertical variability of near-surface salinity in the tropics: consequences for L-band radiometer calibration and validation. Journal of Atmospheric and Oceanic Technology, 27(1): 192–209

    Article  Google Scholar 

  • Jiang Jingzhong. 1993. A event of Pacific equatorial undercurrent inversion during El Niño. Donghai Marine Science (in Chinese), 11(1): 1–9

    Google Scholar 

  • Keppenne C L, Rienecker M M. 2003. Assimilation of temperature into an isopycnal ocean general circulation model using a parallel ensemble Kalman filter. Journal of Marine Systems, 40-41: 363–380

    Article  Google Scholar 

  • Kimoto M, Yoshikawa I, Ishii M. 1997. An ocean data assimilation system for climate monitoring (gtspecial issueltdata assimilation in meteology and oceanography: theory and practice). Journal of the Meteorological Society of Japan: Series II, 75(1): 471–487

    Article  Google Scholar 

  • Masuda S, Awaji T, Sugiura N, et al. 2003. Improved estimates of the dynamical state of the north pacific ocean from a 4 dimensional variational data assimilation. Geophysical Research Letters, 30(16): 1868

    Article  Google Scholar 

  • McPhaden M J. 2002. El Niño and La Niña: causes and global consequences. In: Munn T, ed. Encyclopedia of Global Environmental Change. Chichester, UK: John Wiley and Sons, 353–370

    Google Scholar 

  • Moore A M. 1991. Data assimilation in a quasi-geostrophic openocean model of the gulf stream region using the adjoint method. Journal of Physical Oceanography, 21(3): 398–427

    Article  Google Scholar 

  • Oke P R, Larnicol G, Fujii Y, et al. 2015. Assessing the impact of observations on ocean forecasts and reanalyses: Part 1. Global studies. Journal of Operational Oceanography, 8(S1): S49–S62

    Article  Google Scholar 

  • Paek H, Yu Jinyi, Qian Chengcheng. 2017. Why were the 2015/2016 and 1997/1998 extreme El Niños different? Geophysical Research Letters, 44(4): 1848–1856

    Google Scholar 

  • Parent L, Testut C E, Brankart J M, et al. 2003. Comparative assimilation of Topex/Poseidon and ERS altimeter data and of Tao temperature data in the tropical Pacific Ocean during 1994-1998, and the mean sea-surface height issue. Journal of Marine Systems, 40-41: 381–401

    Article  Google Scholar 

  • Qiao Fangli, Ma Jian, Xia Changshui, et al. 2006. Influences of the surface wave-induced mixing and tidal mixing on the vertical temperature structure of the Yellow and East China seas in summer. Progress in Natural Science, 16(7): 739–746

    Article  Google Scholar 

  • Qiao Fangli, Yang Yongzeng, Xia Changshui, et al. 2008. The role of surface waves in the ocean mixed layer. Acta Oceanologica Sinica, 27(3): 30–37

    Google Scholar 

  • Qiao Fangli, Yuan Yeli, Deng Jia, et al. 2016. Wave-turbulence interaction-induced vertical mixing and its effects in ocean and climate models. Philosophical Transactions of the Royal Society: A. Mathematical, Physical and Engineering Sciences, 374(2065): 20150201

    Article  Google Scholar 

  • Qiao Fangli, Yuan Yeli, Ezer T, et al. 2010. A three-dimensional surface wave-ocean circulation coupled model and its initial testing. Ocean Dynamics, 60(5): 1339–1355

    Article  Google Scholar 

  • Qiao Fangli, Yuan Yeli, Yang Yongzeng, et al. 2004. Wave-induced mixing in the upper ocean: distribution and application to a global ocean circulation model. Geophysical Research Letters, 31(11): L11303

    Article  Google Scholar 

  • Ren Hongli, Liu Ying, Zuo Jinqing, et al. 2016. The new generation of ENSO prediction system in Beijing Climate Centre and its predictions for the 2014/2016 super El Niño event. Meteorological Monthly, 42(5): 521–531

    Google Scholar 

  • Salau O R, Akinyemi S A. 2015. The impacts of El Niño/southern oscillation on changing precipitation over the tropical Pacific. International Journal of Environmental Sciences, 5(5): 995–1010

    Google Scholar 

  • Shi Qiang, Pu Shuzhen, Su Jie, et al. 1999. Investigation of main current system and equatorial planetary waves in the tropical Pacific during twice untypical El Niño events. Haiyang Xuebao (in Chinese), 21(4): 27–34

    Google Scholar 

  • Shu Qi, Qiao Fangli, Bao Ying, et al. 2014. Assessment of arctic sea ice simulation by FIO-ESM based on data assimilation experiment. Haiyang Xuebao (in Chinese), 37(11): 33–40

    Google Scholar 

  • Shu Qi, Qiao Fangli, Song Zhenya, et al. 2011. Improvement of MOM4 by including surface wave-induced vertical mixing. Ocean Modelling, 40(1): 42–51

    Article  Google Scholar 

  • Stammer D, Köhl A, Awaji T, et al. 2010. Ocean information provided through ensemble ocean syntheses. In: Proceedings of Ocean Obs’09: Sustained Ocean Observations and Information for Society. Venice, Italy: ESA Publication, 920–930

    Chapter  Google Scholar 

  • Sun Chaojiao, Rienecker M M, Rosati A, et al. 2007. Comparison and sensitivity of odasi ocean analyses in the tropical pacific. Monthly Weather Review, 135(6): 2242–2264

    Article  Google Scholar 

  • Torma P, Krámer T. 2017. Modeling the effect of waves on the diurnal temperature stratification of a shallow lake. Periodica Polytechnica Civil Engineering, 61(2): 165–175, doi: 10.3311/PPci.8883

    Google Scholar 

  • Vidard A, Anderson D L, Balmaseda M. 2007. Impact of ocean observation systems on ocean analysis and seasonal forecasts. Monthly Weather Review, 135(2): 409–429

    Article  Google Scholar 

  • Wang Hongna, Chen Jinnian, He Yijun. 2009. Variations of Equatorial Undercurrent and its relationship with ENSO cycle. Haiyang Xuebao (in Chinese), 31(3): 1–11

    Google Scholar 

  • Wang Ou, Fukumori I, Lee T, et al. 2004. Eastern equatorial Pacific Ocean T-S variations with El Niño. Geophysical Research Letters, 31(4): L04305

    Google Scholar 

  • Wen Na, Liu Zhengyu, Liu Yinghui. 2015. Direct impact of El Niño on East Asian summer precipitation in the observation. Climate Dynamics, 44(11-12): 2979–2987

    Article  Google Scholar 

  • Wu Lichuan, Rutgersson A, Sahlée E. 2015. Upper-ocean mixing due to surface gravity waves. Journal of Geophysical Research: Oceans, 120(12): 8210–8228, doi: 10.1002/2015JC011329

    Google Scholar 

  • Xuan Jiliang, Huang Daji, Zhou Feng, et al. 2012. Application of data assimilation to synoptic temperature mapping of the coastal ocean survey. Oceanologia et Limnologia Sinica (in Chinese), 43(1): 17–26

    Google Scholar 

  • Xue Yan, Wen Caihong, Yang Xiaosong, et al. 2017. Evaluation of tropical Pacific observing systems using NCEP and GFDL ocean data assimilation systems. Climate Dynamics, 49(3): 843–868

    Article  Google Scholar 

  • Yin Xunqiang, Qiao Fangli, Shu Qi. 2011. Using ensemble adjustment Kalman filter to assimilate Argo profiles in a global OGCM. Ocean Dynamics, 61(7): 1017–1031

    Article  Google Scholar 

  • Yin Xunqiang, Qiao Fangli, Yang Yongzeng, et al. 2010. An ensemble adjustment Kalman filter study for Argo data. Chinese Journal of Oceanology and Limnology, 28(3): 626–635

    Article  Google Scholar 

  • Yin Xunqiang, Qiao Fangli, Yang Yongzeng, et al. 2012. Argo data assimilation in ocean general circulation model of northwest Pacific Ocean. Ocean Dynamics, 62(7): 1059–1071

    Article  Google Scholar 

  • Yuan Yuan, Gao Hui, Jia Xiaolong, et al. 2016. Influences of the 2014-2016 super El Niño event on climate. Meteorological Monthly (in Chinese), 42(5): 532–539

    Google Scholar 

  • Yuan Yeli, Hua Feng, Pan Zengdi, et al. 1991. LAGFD-WAM numerical wave model-I. Basic physical model. Acta Oceanologica Sinica, 10(4): 483–488

    Google Scholar 

  • Zhai Panmao, Yu Rong, Guo Yanjun, et al. 2016. The strong El Niño in 2015/2016 and its dominant impacts on global and China’s climate. Acta Meteorologica Sinica (in Chinese), 74(3): 309–321

    Google Scholar 

  • Zhang Ronghua, Levitus S. 1996. Structure and evolution of interannual variability of the tropical Pacific upper ocean temperature. Journal of Geo physical Research: Oceans, 101 (C9): 20501–20524

    Article  Google Scholar 

  • Zhang Ronghua, Levitus S. 1997. Interannual variability of the coupled tropical Pacific ocean-atmosphere system associated with the El Niño-Southern Oscillation. Journal of Climate, 10(6): 1312–1330

    Article  Google Scholar 

  • Zuo T, Chen Jinnian, Wang Hongna. 2014. Impact of the central Pacific zonal wind divergence and convergence on the central Pacific El Niño event. Acta Oceanologica Sinica, 33(11): 85–89

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, 266100, China

    Junqiang Shi

  2. The First Institute of Oceanography, State Oceanic Administration, Qingdao, 266061, China

    Junqiang Shi, Xunqiang Yin, Qi Shu, Bin Xiao & Fangli Qiao

  3. Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266237, China

    Xunqiang Yin, Qi Shu, Bin Xiao & Fangli Qiao

  4. Key Laboratory of State Oceanic Administration for Marine Sciences and Numerical Modeling, The First Institute of Oceanography, State Oceanic Administration, Qingdao, 266061, China

    Xunqiang Yin, Qi Shu, Bin Xiao & Fangli Qiao

Authors
  1. Junqiang Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xunqiang Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qi Shu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bin Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fangli Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fangli Qiao.

Additional information

Foundation item: The National Program on Global Change and Air-sea Interaction of China under contract No. GASI-IPOVAI-05; the National Natural Science Foundation of China-Shandong Joint Fund for Marine Science Research Centers of China under contract No. U1606405; the International Cooperation Project on the China-Australia Research Centre for Maritime Engineering of Ministry of Science and Technology, China under contract No. 2016YFE0101400; the Aoshan Talents Program under contract No. 2015ASTP; the Transparency Program of Pacific Ocean-South China Sea-Indian Ocean supported by Qingdao National Laboratory for Marine Science and Technology China under contract No. 2015ASKJ01.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shi, J., Yin, X., Shu, Q. et al. Evaluation on data assimilation of a global high resolution wave-tide-circulation coupled model using the tropical Pacific TAO buoy observations. Acta Oceanol. Sin. 37, 8–20 (2018). https://doi.org/10.1007/s13131-018-1196-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13131-018-1196-2

Keywords

Search

Navigation