Predictability of the upper ocean heat content in a Community Earth System Model ensemble prediction system

Ting Liu Wenxiu Zhong

Ting Liu, Wenxiu Zhong. Predictability of the upper ocean heat content in a Community Earth System Model ensemble prediction system[J]. Acta Oceanologica Sinica, 2024, 43(1): 1-10. doi: 10.1007/s13131-023-2239-x
Citation: Ting Liu, Wenxiu Zhong. Predictability of the upper ocean heat content in a Community Earth System Model ensemble prediction system[J]. Acta Oceanologica Sinica, 2024, 43(1): 1-10. doi: 10.1007/s13131-023-2239-x

doi: 10.1007/s13131-023-2239-x

Predictability of the upper ocean heat content in a Community Earth System Model ensemble prediction system

Funds: The National Key R&D Program of China under contract No. 2020YFA0608803; the Scientific Research Fund of the Second Institute of Oceanography, Ministry of Natural Resources under contract No. QNYC2101; the National Natural Science Foundation of China under contract No. 42105052; the Fund of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) under contract No. SML2021SP310; the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) under contract No. 311021001.
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  • Figure  1.  Anomaly correlation coefficients of OHC with lead month.

    Figure  2.  Persistence coefficients of OHC with lead month.

    Figure  3.  Anomaly correlation coefficients (ACC) for the area-averaged OHC in the Pacific Ocean (a), Indian Ocean (b), and Atlantic Ocean (c). The “EP”, “WP”, “NP”, “SP”, “SI”, “NI”, “SA” and “NA” represent the eastern tropical Pacific (10°S–10°N, 170°W–90°W), the western tropical Pacific (10°S–10°N, 120°E–180°), the northeastern subtropical Pacific (20°N–60°N, 180°–120°W), the southern Pacific (20°S–60°S, 180°–90°W), the southern Indian Ocean (10°S–40°S, 40°E–120°E), the northern Indian Ocean (10°N–25°N, 50°E–110°E), the southern Atlantic Ocean (10°S–60°S, 80°W–0°), and the northern Atlantic Ocean (10°N–60°N, 60°W–0°), respectively.

    Figure  4.  Anomaly correlation coefficients (ACC) of the area-averaged OHC with the lead and target months in different oceans. a. The western tropical Pacific (10°S–10°N, 120°E–180°); b. the eastern Pacific (10°S–10°N, 170°W–90°W); c. the northern Pacific (20°N–60°N, 180°–120°W); d. the southern Pacific (20°S–60°S, 180°–90°W); e. the northern Indian Ocean (10°N–25°N, 50°E–110°E); f. the southern Indian Ocean (10°S–40°S, 40°E–120°E); g. the northern Atlantic Ocean (10°N–60°N, 60°W–0°); h. the southern Atlantic Ocean (10°S–60°S, 80°W–0°).

    Figure  5.  Phase-locking features of the area-averaged OHC in different oceans. a. The western tropical Pacific (10°S–10°N, 120°E–180°); b. the eastern Pacific (10°S–10°N, 170°W–90°W); c. the northern Pacific (20°N–60°N, 180°–120°W); d. the southern Pacific (20°S–60°S, 180°–90°W); e. the northern Indian Ocean (10°N–25°N, 50°E–110°E); f. the southern Indian Ocean (10°S–40°S, 40°E–120°E); g. the northern Atlantic Ocean (10°N–60°N, 60°W–0°); h. the southern Atlantic Ocean (10°S–60°S, 80°W–0°).

    Figure  6.  Potential correlation of OHC with lead month.

  • Behringer D, Xue Yan. 2004. Evaluation of the global ocean data assimilation system at NCEP: The Pacific Ocean. In: Proceedings of the Eighth Symposium on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface. Seattle, WA: American Meteorological Society
    Berrisford P, Dee D P, Poli P, et al. 2011. The ERA-Interim Archive: Version 2.0. ERA Report Series, 1: 23
    Bersch M. 2002. North Atlantic Oscillation-induced changes of the upper layer circulation in the northern North Atlantic Ocean. Journal of Geophysical Research: Oceans, 107(C10): 3156, doi: 10.1029/2001JC000901
    Branstator G, Teng Haiyan. 2010. Two limits of initial-value decadal predictability in a CGCM. Journal of Climate, 23(23): 6292–6311, doi: 10.1175/2010JCLI3678.1
    Cai Wenju, Santoso A, Wang Guojian, et al. 2015. ENSO and greenhouse warming. Nature Climate Change, 5(9): 849–859, doi: 10.1038/nclimate2743
    Cai Wenju, Wu Lixin, Lengaigne M, et al. 2019. Pantropical climate interactions. Science, 363(6430): eaav4236, doi: 10.1126/science.aav4236
    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
    Chen Dake. 2010. Coupled data assimilation for ENSO prediction. In: Gan Jianping, ed. Advances in Geosciences: Volume 18: Ocean Science (OS). New Jersey: World Scientific Publishing Company, 45–62
    Clarke A J, Van Gorder S. 2003. Improving El Niño prediction using a space-time integration of Indo-Pacific winds and equatorial Pacific upper ocean heat content. Geophysical Research Letters, 30(7): 1399
    DelSole T. 2004. Predictability and information theory. Part I: Measures of predictability. Journal of the Atmospheric Sciences, 61(20): 2425–2440, doi: 10.1175/1520-0469(2004)061<2425:PAITPI>2.0.CO;2
    Ham Y G, Kim J H, Luo Jingjia. 2019. Deep learning for multi-year ENSO forecasts. Nature, 573(7775): 568–572, doi: 10.1038/s41586-019-1559-7
    Jackson L C, Peterson K A, Roberts C D, et al. 2016. Recent slowing of Atlantic overturning circulation as a recovery from earlier strengthening. Nature Geoscience, 9(7): 518–522, doi: 10.1038/ngeo2715
    Jia Liwei, DelSole T. 2011. Diagnosis of multiyear predictability on continental scales. Journal of Climate, 24(19): 5108–5124, doi: 10.1175/2011JCLI4098.1
    Jian Zhimin, Wang Yue, Dang Haowen, et al. 2022. Warm pool ocean heat content regulates ocean–continent moisture transport. Nature, 612(7938): 92–99, doi: 10.1038/s41586-022-05302-y
    Jin Feifei. 1997. An equatorial ocean recharge paradigm for ENSO. Part I: Conceptual model. Journal of Atmospheric Sciences, 54(7): 811–829, doi: 10.1175/1520-0469(1997)054<0811:AEORPF>2.0.CO;2
    Josey S A, Sinha B. 2022. Subpolar Atlantic Ocean mixed layer heat content variability is increasingly driven by an active ocean. Communications Earth and Environment, 3(1): 111, doi: 10.1038/s43247-022-00433-6
    Kleeman R. 2002. Measuring dynamical prediction utility using relative entropy. Journal of the Atmospheric Sciences, 59(13): 2057–2072, doi: 10.1175/1520-0469(2002)059<2057:MDPUUR>2.0.CO;2
    Kleeman R, Tang Youmin, Moore A M. 2003. The calculation of climatically relevant singular vectors in the presence of weather noise as applied to the ENSO problem. Journal of the Atmospheric Sciences, 60(23): 2856–2868, doi: 10.1175/1520-0469(2003)060<2856:TCOCRS>2.0.CO;2
    Li Jianping, Ding Ruiqiang. 2008. Temporal-spatial distributions of predictability limit of short-term climate. Chinese Journal of Atmospheric Sciences (in Chinese), 32(4): 975–986
    Li Jianping, Ding Ruiqiang. 2013. Temporal-spatial distribution of the predictability limit of monthly sea surface temperature in the global oceans. International Journal of Climatology, 33(8): 1936–1947, doi: 10.1002/joc.3562
    Li Shujun, Zhang Liping, Wu Lixin. 2017. Decadal potential predictability of upper ocean heat content over the twentieth century. Climate Dynamics, 49(9–10): 3293–3307, doi: 10.1007/s00382-016-3513-9
    Liu Minghong, McPhaden M J, Ren Hongli, et al. 2022a. Oceanic heat content as a predictor of the Indian Ocean Dipole. Journal of Geophysical Research: Oceans, 127(12): e2022JC018896, doi: 10.1029/2022JC018896
    Liu Ting, Song Xunshu, Tang Youmin, et al. 2022b. ENSO predictability over the past 137 years based on a CESM ensemble prediction system. Journal of Climate, 35(2): 763–777, doi: 10.1175/JCLI-D-21-0450.1
    Lorenz E N. 1965. A study of the predictability of a 28-variable atmospheric model. Tellus, 17(3): 321–333, doi: 10.1111/j.2153-3490.1965.tb01424.x
    McPhaden M J. 2003. Tropical Pacific Ocean heat content variations and ENSO persistence barriers. Geophysical Research Letters, 30(9): 1480, doi: 10.1029/2003GL016872
    McPhaden M J, Zebiak S E, Glantz M H. 2006. ENSO as an integrating concept in earth science. Science, 314(5806): 1740–1745, doi: 10.1126/science.1132588
    Piecuch C G, Ponte R M, Little C M, et al. 2017. Mechanisms underlying recent decadal changes in subpolar North Atlantic Ocean heat content. Journal of Geophysical Research: Oceans, 122(9): 7181–7197, doi: 10.1002/2017JC012845
    Ren Hongli, Jin Feifei. 2013. Recharge oscillator mechanisms in two types of ENSO. Journal of Climate, 26(17): 6506–6523, doi: 10.1175/JCLI-D-12-00601.1
    Robson J, Sutton R, Lohmann K, et al. 2012. Causes of the rapid warming of the North Atlantic Ocean in the mid-1990s. Journal of Climate, 25(12): 4116–4134, doi: 10.1175/JCLI-D-11-00443.1
    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
    Seleznev A, Mukhin D. 2023. Improving statistical prediction and revealing nonlinearity of ENSO using observations of ocean heat content in the tropical Pacific. Climate Dynamics, 60(1–2): 1–15, doi: 10.1007/s00382-022-06298-x
    Shankar D, Vinayachandran P N, Unnikrishnan A S. 2002. The monsoon currents in the north Indian Ocean. Progress in Oceanography, 52(1): 63–120, doi: 10.1016/S0079-6611(02)00024-1
    Shukla J. 1981. Dynamical predictability of monthly means. Journal of the Atmospheric Sciences, 38(12): 2547–2572, doi: 10.1175/1520-0469(1981)038<2547:DPOMM>2.0.CO;2
    Song Xunshu, Li Xiaojing, Zhang Shouwen, et al. 2022. A new nudging scheme for the current operational climate prediction system of the National Marine Environmental Forecasting Center of China. Acta Oceanologica Sinica, 41(2): 51–64, doi: 10.1007/s13131-021-1857-4
    Stickler A, Brönnimann S, Valente M A, et al. 2014. ERA-CLIM: Historical surface and upper-air data for future reanalyses. Bulletin of the American Meteorological Society, 95(9): 1419–1430, doi: 10.1175/BAMS-D-13-00147.1
    Tang Youmin, Chen Dake, Yang Dejian, et al. 2013. Methods of estimating uncertainty of climate prediction and climate change projection. In: Singh B R, ed. Climate Chang-Realities, Impacts over Ice Cap, Sea Level and Risks. Rijeka: IntechOpen
    Tang Youmin, Zhang Ronghua, Liu Ting, et al. 2018. Progress in ENSO prediction and predictability study. National Science Review, 5(6): 826–839, doi: 10.1093/nsr/nwy105
    Wu Xiaofen, Liu Zenghong, Liao Guanghong, et al. 2015. Variation of Indo-Pacific upper ocean heat content during 2001–2012 revealed by Argo. Acta Oceanologica Sinica, 34(5): 29–38, doi: 10.1007/s13131-015-0664-1
    Zhang Ronghua, Gao Chuan, Feng Licheng. 2022. Recent ENSO evolution and its real-time prediction challenges. National Science Review, 9(4): nwac052, doi: 10.1093/nsr/nwac052
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出版历程
  • 收稿日期:  2023-07-19
  • 录用日期:  2023-08-07
  • 网络出版日期:  2023-10-10
  • 刊出日期:  2024-01-01

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