Vol.34, NO.1, 2023
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Article Navigation >  Journal of Applied Meteorological Science  > 2023  >  34(1): 27-38
Zhang Zhichao, Zhou Fang, Zhang Haoxin, et al. Predication of typical winter circulation systems based on BCC_CSM1.1m model. J Appl Meteor Sci, 2023, 34(1): 27-38. DOI:  10.11898/1001-7313.20230103.
Citation: Zhang Zhichao, Zhou Fang, Zhang Haoxin, et al. Predication of typical winter circulation systems based on BCC_CSM1.1m model. J Appl Meteor Sci, 2023, 34(1): 27-38. DOI:  10.11898/1001-7313.20230103.
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Predication of Typical Winter Circulation Systems Based on BCC_CSM1.1m Model

DOI: 10.11898/1001-7313.20230103
  • 1.

    Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, NUIST, Nanjing 210044

  • 2.

    Xia County Meteorological Bureau of Shanxi Province, Yuncheng 044400

  • 3.

    Chinese Academy of Meteorological Sciences, Beijing 100081

  • Received Date: 2022-08-06
  • Rev Recd Date: 2022-10-14
  • Publish Date: 2023-01-31
    Abstract
  • Accurate prediction of East Asian winter climate has become an important topic in climate research. Coupled ocean-atmosphere dynamical model prediction systems have made great progress. It can offer overall outstanding performance, and become the major tool of dynamical climate prediction. The seasonal prediction performance of BCC_CSM1.1m model has been systematically evaluated. It's found that although the model can predict temperature, precipitation, snow cover, and Asian monsoon to some extent, there are still great challenges in the prediction of East Asian winter climate. It is important to analyze the possible causes of model biases and reveal the source of its predictability. Based on the hindcasts of BCC_CSM1.1m, time correlation coefficient and root mean square error are analyzed to evaluate the prediction skills of 3 typical East Asian winter circulation systems, including Siberian high (SH), Aleutian low (AL) and East Asian winter monsoon (EAWM). Then the predictability sources are also examined through time series analysis and pattern correlation coefficient. The results show that the prediction of sea level pressure in tropical region is better than that in the middle and high latitude region. Due to the influence of El Niño and Southern Oscillation (ENSO) and its remote teleconnection, the sea level pressure prediction over the ocean is better than that over the continent, which results in better prediction skills of AL and EAWM compared to SH. Further analysis shows that the elimination of super El Niño years leads to lower prediction skills of AL and EAWM. The correlation between sea level pressure in Eurasia and ENSO is less than that in tropical and north Pacific regions, indicating that ENSO is an important source of predictability of AL and EAWM. It is also found that soil temperature at 0-10 cm in Siberia is an important factor affecting the simultaneous and later SH, which suggests that the predictability of the SH may come from the shallow soil temperature. After removing super El Niño years, the prediction skill of SH is altered greatly, which reflects the modulation of ENSO on SH prediction. The model can overestimate the linear relationship between SH and ENSO, and lead to a poor SH prediction skill. Moreover, the prediction of EAWM depends on the accurate prediction of SH and AL, and its prediction skill is restricted by the poor SH prediction skills to some extent.
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    References(34)

  • Fig. 1  Prediction skills of SHI, ALI and EAWMI initiated from Dec to Aug(LM0-LM4)

    (the dashed line and dotted line denote the levels of 0.05 and 0.01, respectively)

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    Fig. 2  TCC skills in winter sea level pressure initiated from Dec to Sep(LM0-LM3) in BCC_CSM1.1m

    (red, green, and blue boxes denote regions of SHI, ALI and EAWMI, dotted area denotes TCC passing the test of 0.05 level)
    (a)Dec(LM0), (b)Nov(LM1), (c)Oct(LM2), (d)Sep(LM3)

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    Fig. 3  Observational and predicted SHI, ALI and EAWMI initiated from Dec to Aug(LM0-LM4)

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    Fig. 4  TCC between Niño3.4 index and sea level pressure in observation and model prediction initiated from Dec to Aug(LM0-LM4)

    (red, green, and blue boxes denote the regions of SHI, ALI and EAWMI, dotted area denotes TCC passing the test of 0.05 level)
    (a)observation, (b)initiated in Dec(LM0), (c)initiated in Nov(LM1), (d)initiated in Oct(LM2), (e)initiated in Sep(LM3), (f)initiated in Aug(LM4)

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    Fig. 5  Scatter plots of PCC skill against absolute Niño3.4 index and its linear fitting line for SHI(a), ALI(b) and EAWMI(c) region initiated in Nov

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    Fig. 6  TCC between observed and BCC_CSM1.1m predicted winter SHI from Oct to Nov(LM2-LM1) and 0-10 cm soil temperature in Dec and Jan

    (red box denotes region of SHI, dotted area denotes TCC passing the test of 0.05 level)
    (a)observed winter SHI and soil temperature in Dec, (b)observed winter SHI and soil temperature in Jan, (c)winter SHI initiated in Oct and soil temperature in Dec, (d)winter SHI initiated in Oct and soil temperature in Jan, (e)winter SHI initiated in Nov and soil temperature in Dec, (f)winter SHI initited in Nov and soil temperature in Jan

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    Fig. 7  Scatter plots of PCC skill against soil temperature anomaly and its linear fitting line for SHI region initiated in Nov(LM1)(a) and Dec(LM0)(b)

    (hollow stars denote the strong El Niño year)

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  • [1]
    Li Y, Wang G F. Design and implementation of meteorological disaster risk management system. J Appl Meteor Sci, 2022, 33(5): 628-640. doi:  10.11898/1001-7313.20220510
    [2]
    Dong S, Xiao Z N. The persistent impact of winter Arctic Oscillation on the East Asian surface air temperature. J Appl Meteor Sci, 2015, 26(4): 422-431. doi:  10.11898/1001-7313.20150404
    [3]
    Wang Z Q, Xu Y, Zhou B T. Evaluation of the CMIP5 models in simulating the change of the East Asian winter monsoon indices and their relationship with the wintertime atmospheric circulation and temperature. Chinese J Geophys, 2017, 60(9): 3315-3324. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201709004.htm
    [4]
    Liu S, Sui B, Tu G, et al. The East Asian winter monsoon background on the variation of winter air temperature in Northeast China. J Appl Meteor Sci, 2014, 25(1): 11-21. http://qikan.camscma.cn/article/id/20140102
    [5]
    Chen W, Yang S, Huang R H. Relationship between stationary planetary wave activity and the East Asian winter monsoon. J Geophys Res Atmos, 2005, 110: D14110.
    [6]
    Yan H, Yang H, Yuan Y, et al. Relationship between East Asian winter monsoon and summer monsoon. Adv Atmos Sci, 2011, 28(6): 1345-1356. doi:  10.1007/s00376-011-0014-y
    [7]
    Fan G, Lv F, Zhang J, et al. A possible way to extract a stationary relationship between ENSO and the East Asian winter monsoon. Atmos Ocean Sci Lett, 2020, 13(4): 294-300. doi:  10.1080/16742834.2020.1733918
    [8]
    Ding Y H. Build-up, air mass transformation and propagation of Siberian high and its relation to cold surge in East Asia. Meteor Atmos Phys, 1990, 44(1): 281-292.
    [9]
    Cohen J, Saito K, Entekhabi D. The role of the Siberian high in Northern Hemisphere climate variability. Geophys Res Lett, 2001, 28(2): 299-302. doi:  10.1029/2000GL011927
    [10]
    Guirguis K, Gershunov A, Schwartz R, et al. Recent warm and cold daily winter temperature extremes in the Northern Hemisphere. Geophys Res Lett, 2011, 38: L17701.
    [11]
    Pickart R S, Macdonald A M, Moore G W K, et al. Seasonal evolution of Aleutian low pressure systems: Implications for the North Pacific subpolar circulation. J Phys Oceanogr, 2009, 39(6): 1317-1339. doi:  10.1175/2008JPO3891.1
    [12]
    Rodionov S N, Overland J E, Bond N A. The Aleutian low and winter climatic conditions in the Bering Sea. Part I: Classification. J Climate, 2005, 18(1): 160-177. doi:  10.1175/JCLI3253.1
    [13]
    Qian W H, Zhang H N, Zhu Y F, et al. Interannual and interdecadal variability of East Asian areas and their impact on temperature of China in winter season for the last century. Adv Atmos Sci, 2001, 18(4): 511-523. doi:  10.1007/s00376-001-0041-1
    [14]
    Wu T W, Song L C, Liu X W, et al. Progress in developing the short-range operational climate prediction system of China National Climate Center. J Appl Meteor Sci, 2013, 24(5): 533-543. doi:  10.3969/j.issn.1001-7313.2013.05.003
    [15]
    Tang H Q, Zeng G, Huang Y. An assessment of the tropical Pacific latent heat flux simulated by BCC_CSM1.1(m). J Appl Meteor Sci, 2016, 27(4): 463-472. doi:  10.11898/1001-7313.20160409
    [16]
    Wu J, Ren H L, Zhang S, et al. Evaluation and predictability analysis of seasonal prediction by BCC second-generation climate system model. Chinese J Atmos Sci, 2017, 41(6): 1300-1315. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201706013.htm
    [17]
    Zhou X, Li Q Q, Sun X B, et al. Simulation and projection of temperature in China with BCC_CSM1.1 model. J Appl Meteor Sci, 2014, 25(1): 95-106. http://qikan.camscma.cn/article/id/20140110
    [18]
    Cheng F, Li Q P, Shen X Y, et al. Evaluation of Eurasian snow cover fraction prediction based on BCC_CSM1.1m. J Appl Meteor Sci, 2021, 32(5): 553-566. doi:  10.11898/1001-7313.20210504
    [19]
    Zhang D Q, Sun F H, Zhang Y C. Evaluation of seasonal prediction for summer rainfall in China based on BCC second-generation short-range climate forecast system. Plateau Meteor, 2019, 38(6): 1229-1240. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201906010.htm
    [20]
    Zhou F, Ren H L, Hu Z Z, et al. Seasonal predictability of primary East Asian summer circulation patterns by three operational climate prediction models. Quart J Roy Meteor Soc, 2020, 146(727): 629-646.
    [21]
    Tian B, Ren H L. Diagnosing SST error growth during ENSO developing phase in the BCC_CSM1.1(m) prediction System. Adv Atmos Sci, 2022, 39(3): 427-442.
    [22]
    Hasanean H M, Almazroui M, Jones P D, et al. Siberian high variability and its teleconnections with tropical circulations and surface air temperature over Saudi Arabia. Climate Dyn, 2013, 41(7): 2003-2018.
    [23]
    Chen Y, Zhai P. Interannual to decadal variability of the winter Aleutian Low intensity during 1900-2004. Acta Meteor Sinica, 2011, 25(6): 710-724.
    [24]
    Shi N, Lu J J, Zhu Q G. East Asian winter/summer monsoon intensity indices with their climatic change in 1873-1989. Journal of Nanjing Institute of Meteorology, 1996, 19(2): 168-177. https://www.cnki.com.cn/Article/CJFDTOTAL-NJQX199602002.htm
    [25]
    Shao P C, Li D L. Classification and comparison of East Asian winter monsoon indices. J Meteor Sci, 2012, 32(2): 226-235. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKX201202016.htm
    [26]
    Yang H Q, Fan K, Tian B Q, et al. Why is the November Siberian high intensity more predictable by NCEP-CFSv2 model. Chinese J Atmos Sci, 2021, 45(4): 697-712. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK202104001.htm
    [27]
    Wang B, Lee J Y, Kang I S, et al. Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction(1980-2004). Climate Dyn, 2009, 33(1): 93-117.
    [28]
    Shi S W, Zhi H, Lin P F, et al. Contrasting salinity interannual variations in the tropical Pacific and their effects on recent El Niño events: 1997/1998, 2014/2015, and 2015/2016. Chinese J Atmos Sci, 2020, 44(5): 1057-1075. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201701003.htm
    [29]
    Liu M H, Ren H L, Zhang W J, et al. Influence of super El Niño events on the frequency of spring and summer extreme precipitation over eastern China. Acta Meteor Sinica, 2018, 76(4): 539-553. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201804004.htm
    [30]
    Ren H L, Jin F F, Song L C, et al. Prediction of primary climate variability modes at the Beijing Climate Center. J Meteor Res, 2017, 31(1): 204-223.
    [31]
    Ding Y, Krishnamurti T N. Heat budget of the Siberian high and the winter monsoon. Mon Wea Rev, 1987, 115(10): 2428-2449.
    [32]
    National Research Council. Assessment of Intraseasonal to Interannual Climate Prediction and Predictability. National Academies Press, 2010: 192.
    [33]
    Cheng Y B, Ren H L, Tan G R. Empirical orthogonal function-analogue correction of extra-seasonal dynamical prediction of East-Asian summer monsoon. J Appl Meteor Sci, 2016, 27(3): 285-292. doi:  10.11898/1001-7313.20160303
    [34]
    Xie S, Sun X G, Zhang S P, et al. Precipitation forecast correction in South China based on SVD and machine learning. J Appl Meteor Sci, 2022, 33(3): 293-304. doi:  10.11898/1001-7313.20220304
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    • Received : 2022-08-06
    • Accepted : 2022-10-14
    • Published : 2023-01-31
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