The impact of ocean data assimilation on seasonal predictions based on the National Climate Center climate system model

Wei Zhou; Jinghui Li; Fanghua Xu; Yeqiang Shu; Yang Feng

doi:10.1007/s13131-021-1732-3

Volume 40 Issue 5

May 2021

Turn off MathJax

Article Contents

Article Navigation > Acta Oceanologica Sinica > 2021 > 40(5): 58-70

Wei Zhou, Jinghui Li, Fanghua Xu, Yeqiang Shu, Yang Feng. The impact of ocean data assimilation on seasonal predictions based on the National Climate Center climate system model[J]. Acta Oceanologica Sinica, 2021, 40(5): 58-70. doi: 10.1007/s13131-021-1732-3

Citation:

Wei Zhou, Jinghui Li, Fanghua Xu, Yeqiang Shu, Yang Feng. The impact of ocean data assimilation on seasonal predictions based on the National Climate Center climate system model[J]. Acta Oceanologica Sinica, 2021, 40(5): 58-70. doi: 10.1007/s13131-021-1732-3

Citation:

Wei Zhou, Jinghui Li, Fanghua Xu, Yeqiang Shu, Yang Feng. The impact of ocean data assimilation on seasonal predictions based on the National Climate Center climate system model[J]. Acta Oceanologica Sinica, 2021, 40(5): 58-70. doi: 10.1007/s13131-021-1732-3

PDF( 10860 KB)

The impact of ocean data assimilation on seasonal predictions based on the National Climate Center climate system model

doi: 10.1007/s13131-021-1732-3

Wei Zhou^{1, 2
,
,},
Jinghui Li³,
Fanghua Xu³,
Yeqiang Shu^{2, 4},
Yang Feng^{2, 4}

1.
Equipment Public Service Center, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China
2.
Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
3.
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
4.
State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China

Funds: The National Key Research and Development Program of China under contract Nos 2016YFA0602102 and 2016YFC1401702; the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) under contract No. GML2019ZD0306; the National Natural Science Foundation of China under contract No. 41306005; CAS Pioneer Hundred Talents Program Startup Fund by South China Sea Institute of Oceanology under contract No. Y9SL011001.

More Information

Corresponding author: E-mail: zhouwei@scsio.ac.cn
Received Date: 2020-04-25
Accepted Date: 2020-07-23

Available Online: 2021-05-20

Publish Date: 2021-05-01

Abstract

Abstract

An ensemble optimal interpolation (EnOI) data assimilation method is applied in the BCC_CSM1.1 to investigate the impact of ocean data assimilations on seasonal forecasts in an idealized twin experiment framework. Pseudo-observations of sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), temperature and salinity (T/S) profiles were first generated in a free model run. Then, a series of sensitivity tests initialized with predefined bias were conducted for a one-year period; this involved a free run (CTR) and seven assimilation runs. These tests allowed us to check the analysis field accuracy against the “truth”. As expected, data assimilation improved all investigated quantities; the joint assimilation of all variables gave more improved results than assimilating them separately. One-year predictions initialized from the seven runs and CTR were then conducted and compared. The forecasts initialized from joint assimilation of surface data produced comparable SST root mean square errors to that from assimilation of T/S profiles, but the assimilation of T/S profiles is crucial to reduce subsurface deficiencies. The ocean surface currents in the tropics were better predicted when initial conditions produced by assimilating T/S profiles, while surface data assimilation became more important at higher latitudes, particularly near the western boundary currents. The predictions of ocean heat content and mixed layer depth are significantly improved initialized from the joint assimilation of all the variables. Finally, a central Pacific El Niño was well predicted from the joint assimilation of surface data, indicating the importance of joint assimilation of SST, SSH, and SSS for ENSO predictions.
- global ocean data assimilation,
- EnOI,
- twin experiments

FullText(HTML)

References(36)

References

[1]	Alves O, Hudson D, Balmaseda M, et al. 2011. Seasonal and decadal prediction. In: Schiller A, Brassington G B, eds. Operational Oceanography in the 21st Century. Netherlands: Springer, 513–542
[2]	Behringer D W, Ji Ming, Leetmaa A. 1998. An improved coupled model for ENSO prediction and implications for ocean initialization: Part I. the ocean data assimilation system. Monthly Weather Review, 126(4): 1013–1021. doi: 10.1175/1520-0493(1998)126<1013:AICMFE>2.0.CO;2
[3]	Boulanger J P, Menkes C. 1995. Propagation and reflection of long equatorial waves in the Pacific Ocean during the 1992−1993 El Niño. Journal of Geophysical Research: Atmospheres, 100(C12): 25041–25060. doi: 10.1029/95JC02956
[4]	Boutin J, Chao Y, Asher W E, et al. 2016. Satellite and in situ salinity: understanding near-surface stratification and subfootprint variability. Bulletin of the American Meteorological Society, 97(8): 1391–1407. doi: 10.1175/BAMS-D-15-00032.1
[5]	Breugem W P, Chang P, Jang C J, et al. 2008. Barrier layers and tropical Atlantic SST biases in coupled GCMs. Tellus A: Dynamic Meteorology and Oceanography, 60(5): 885–897. doi: 10.1111/j.1600-0870.2008.00343.x
[6]	Carton J A, Giese B S. 2008. A reanalysis of ocean climate using simple ocean data assimilation (SODA). Monthly Weather Review, 136(8): 2999–3017. doi: 10.1175/2007MWR1978.1
[7]	Chambers D P, Ries J C, Urban T J. 2003. Calibration and verification of Jason-1 using global along-track residuals with TOPEX: special issue: Jason-1 calibration/validation. Marine Geodesy, 26(3–4): 305–317. doi: 10.1080/714044523
[8]	Counillon F, Bethke I, Keenlyside N, et al. 2014. Seasonal-to-decadal predictions with the ensemble Kalman filter and the Norwegian Earth System Model: a twin experiment. Tellus A: Dynamic Meteorology and Oceanography, 66(1): 21074. doi: 10.3402/tellusa.v66.21074
[9]	Fu Weiwei, She Jun, Zhuang Shiyu. 2011. Application of an Ensemble Optimal Interpolation in a North/Baltic Sea model: Assimilating temperature and salinity profiles. Ocean Modelling, 40(3–4): 227–245. doi: 10.1016/j.ocemod.2011.09.004
[10]	Fu Xiouhua, Yang Bo, Bao Qing, et al. 2008. Sea surface temperature feedback extends the predictability of tropical intraseasonal oscillation. Monthly Weather Review, 136(2): 577–597. doi: 10.1175/2007MWR2172.1
[11]	Griffies S M, Gnanadesikan A, Dixon K W, et al. 2005. Formulation of an ocean model for global climate simulations. Ocean Science, 1(1): 45–79. doi: 10.5194/os-1-45-2005
[12]	Griffies S M, Harrison M J, Pacanowski R C, et al. 2003. A technical guide to MOM 4. Princeton, NJ, USA: Geophysical Fluid Dynamics Laboratory, 8542
[13]	Griffies S M, Winton M, Donner L J, et al. 2011. The GFDL CM3 coupled climate model: characteristics of the ocean and sea ice simulations. Journal of Climate, 24(13): 3520–3544. doi: 10.1175/2011JCLI3964.1
[14]	Guan L, Kawamura H. 2004. Merging satellite infrared and microwave SSTs: Methodology and evaluation of the new SST. Journal of Oceanography, 60(5): 905–912. doi: 10.1007/s10872-004-5782-x
[15]	Ji Ming, Reynolds R W, Behringer D W. 2000. Use of TOPEX/poseidon sea level data for ocean analyses and ENSO prediction: some early results. Journal of Climate, 13(1): 216–231. doi: 10.1175/1520-0442(2000)013<0216:UOTPSL>2.0.CO;2
[16]	Masutani M, Schlatter T W, Errico R M, et al. 2010. Observing System Simulation Experiments. In: Lahoz W, Khattatov B, Menard R, eds. Data Assimilation. Berlin, Heidelberg: Springer
[17]	Miyazawa Y, Miyama T, Varlamov S M, et al. 2012. Open and coastal seas interactions south of Japan represented by an ensemble Kalman filter. Ocean Dynamics, 62(4): 645–659. doi: 10.1007/s10236-011-0516-2
[18]	Miyoshi T, Sato Y, Kadowaki T. 2010. Ensemble kalman filter and 4D-Var Intercomparison with the Japanese operational global analysis and prediction system. Monthly Weather Review, 138(7): 2846–2866. doi: 10.1175/2010MWR3209.1
[19]	Oke P R, Brassington G B, Griffin D A, et al. 2008. The Bluelink ocean data assimilation system (BODAS). Ocean Modelling, 21(1-2): 46–70. doi: 10.1016/j.ocemod.2007.11.002
[20]	Oke P R, Sakov P, Cahill M L, et al. 2013. Towards a dynamically balanced eddy-resolving ocean reanalysis: BRAN3. Ocean Modelling, 67: 52–70. doi: 10.1016/j.ocemod.2013.03.008
[21]	Oke P R, Sakov P, Corney S P. 2007. Impacts of localisation in the EnKF and EnOI: experiments with a small model. Ocean Dynamics, 57(1): 32–45. doi: 10.1007/s10236-006-0088-8
[22]	Oke P R, Schiller A, Griffin D A, et al. 2005. Ensemble data assimilation for an eddy-resolving ocean model of the Australian region. Quarterly Journal of the Royal Meteorological Society, 131(613): 3301–3311. doi: 10.1256/qj.05.95
[23]	Pan Chudong, Zheng Lianyuan, Weisberg R H, et al. 2014. Comparisons of different ensemble schemes for glider data assimilation on West Florida Shelf. Ocean Modelling, 81: 13–24. doi: 10.1016/j.ocemod.2014.06.005
[24]	Peng Shiqiu, Zeng Xuezhi, Li Zhijin. 2016. A three-dimensional variational data assimilation system for the South China Sea: preliminary results from observing system simulation experiments. Ocean Dynamics, 66(5): 737–750. doi: 10.1007/s10236-016-0946-y
[25]	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
[26]	Vernieres G, Kovach R, Keppenne C, et al. 2014. The impact of the assimilation of Aquarius sea surface salinity data in the GEOS ocean data assimilation system. Journal of Geophysical Research: Oceans, 119(10): 6974–6987. doi: 10.1002/2014JC010006
[27]	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
[28]	Wu Tongwen, Yu Rucong, Zhang Fang, et al. 2010. The Beijing Climate Center atmospheric general circulation model: Description and its performance for the present-day climate. Climate Dynamics, 34(1): 123–147. doi: 10.1007/s00382-008-0487-2
[29]	Xu Fanghua, Oey L Y. 2014. State analysis using the Local Ensemble Transform Kalman Filter (LETKF) and the three-layer circulation structure of the Luzon Strait and the South China Sea. Ocean Dynamics, 64(6): 905–923. doi: 10.1007/s10236-014-0720-y
[30]	Xu Fanghua, Oey L Y, Miyazawa Y, et al. 2013. Hindcasts and forecasts of Loop Current and eddies in the Gulf of Mexico using local ensemble transform Kalman filter and optimum-interpolation assimilation schemes. Ocean Modelling, 69: 22–38. doi: 10.1016/j.ocemod.2013.05.002
[31]	Yan Changxiang, Zhu Jiang, Li Rongfeng, et al. 2004. Roles of vertical correlations of background error and T-S relations in estimation of temperature and salinity profiles from sea surface dynamic height. Journal of Geophysical Research: Oceans, 109(C8): C08010. doi: 10.1029/2003JC002224
[32]	Zhang S, Rosati A, Harrison M J. 2009. Detection of multi- decadal oceanic variability by ocean data assimilation in the context of a “perfect” coupled model. Journal of Geophysical Research: Oceans, 114(C12): C12018. doi: 10.1029/2008JC005261
[33]	Zhang Xuefeng, Zhang Shaoqing, Liu Zhengyu, et al. 2015. Parameter optimization in an intermediate coupled climate model with biased physics. Journal of Climate, 28(3): 1227–1247. doi: 10.1175/JCLI-D-14-00348.1
[34]	Zheng Fei, Zhu Jiang. 2010. Coupled assimilation for an intermediated coupled ENSO prediction model. Ocean Dynamics, 60(5): 1061–1073. doi: 10.1007/s10236-010-0307-1
[35]	Zheng Fei, Zhu Jiang. 2015. Roles of initial ocean surface and subsurface states on successfully predicting 2006−2007 El Niño with an intermediate coupled model. Ocean Science, 11(1): 187–194. doi: 10.5194/os-11-187-2015
[36]	Zhou Wei, Chen Mengyan, Zhuang Wei, et al. 2016. Evaluation of the tropical variability from the Beijing Climate Center’s real-time operational global ocean data assimilation system. Advance in Atmospheric Sciences, 33(2): 208–220. doi: 10.1007/s00376-015-4282-9