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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    • Received : 2022-08-06
    • Accepted : 2022-10-14
    • Published : 2023-01-31
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