Vol.32, NO.5, 2021
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Article Navigation >  Journal of Applied Meteorological Science  > 2021  >  32(5): 553-566
Cheng Fei, Li Qiaoping, Shen Xinyong, 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.
Citation: Cheng Fei, Li Qiaoping, Shen Xinyong, 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.
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Evaluation of Eurasian Snow Cover Fraction Prediction Based on BCC-CSM1.1m

DOI: 10.11898/1001-7313.20210504
  • 1.

    Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044

  • 2.

    National Climate Center, Beijing 100081

  • 3.

    Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai), Zhuhai 519082

  • 4.

    Tianjin Institute of Meteorological Science, Tianjin 300074

  • Received Date: 2021-02-25
  • Rev Recd Date: 2021-05-27
  • Publish Date: 2021-09-30
    Abstract
  • The model ability to predict Eurasian snow cover fraction (SCF) is evaluated by using the hindcast data during 1984-2019 from the Beijing Climate Center (BCC) Climate Prediction System version 2 (CPSv2), developed based on Climate System Model BCC-CSM1.1m. The SCF reanalysis data from National Snow and Ice Data Center (NSIDC) and other common variables reanalysis datasets are also used against the model forecasts. The prediction skills of Eurasian SCF in January and April are investigated, which separately represent the snow cover situation of winter and spring. The possible causes of model prediction errors are also discussed partly using the simulation data of two BCC climate models, BCC-CSM1.1m and BCC-CSM2-MR, respectively participating the phase 5 of Coupled Model Intercomparison Project (CMIP5) and phase 6 (CMIP6). Empirical orthogonal function (EOF), spatial and temporal correlation analysis, statistical test and other common methods are also adopted. The results show that, BCC-CSM1.1m is capable of forecasting the SCF in Eurasia two months ahead. However, the prediction skill varies both in space and time. In comparison with January, the model shows a better prediction skill both in climatology and interannual variability of Eurasian SCF in April. The prediction skill is highest in western Europe in January and in western Siberia in April. Lower-than-observed SCF are found in most areas of Eurasia except Tibetan Plateau in the predictions for LM0 (0 lead month). This coherent negative biases hardly varies with longer lead time in January, while the biases in key area of April reverse to positive and gradually increase. Analysis indicates that the SCF biases in January and April are positively related with those of precipitation and negatively related with those of surface temperature in the model. Moreover, since the corelated region between the precipitation biases and SCF biases reduces to some small areas in contrast with the surface temperature, the biases of SCF in the model exhibit closer relationship with surface temperature biases. In addition, comparing simulations from two BCC models, it's also found that the systematic biases originated from model resolution, parameterization scheme, etc. are also fundamental factors, which can explain the obvious underestimation of SCF in high latitude where observed SCF is nearly 100%.
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    References(39)

  • Fig. 1  Observation and prediction of SCF in Jan

    (the black dashed rectangles represent winter key area(WKA))
    (a)mean SCF for observation, (b)standard deviation for observation, (c)climatological biases for LM0, (d)standard deviation for LM0, (e)climatological biases for LM2, (f)standard deviation for LM2

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    Fig. 2  The same as in Fig. 1, but for Apr

    (the black dashed rectangle represent spring key area(SKA))

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    Fig. 3  Spatial distribution of temporal correlations between predictions and observation for SCF

    (the black grids denote the areas exceeding 0.05 level(Student's t-test))
    (a)prediction for LM0 in Jan, (b)prediction for LM0 in Apr, (c)prediction for LM1 in Jan, (d)prediction for LM1 in Apr, (e)prediction for LM2 in Jan, (f)prediction for LM2 in Apr

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    Fig. 4  Anomalies of observed and forecasted SCF and spatial correlation between them

    (a)anomalies in Jan, (b)spatial correlation in Jan, (c)anomalies in Apr, (d)spatial correlation in Apr

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    Fig. 5  EOF modes and corresponding principal components(PCs) of observed and forecasted Eurasian SCF for different LM in Jan

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    Fig. 6  The same as in Fig. 5, but for Apr

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    Fig. 7  Climatological semi-annual(Dec to next May) cycle of SCF for observation and forecasts averaged over eight selected regions

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    Fig. 8  Regional averaged TCC skills for SCF forecasts

    (two dashed lines up and down in each panel represents the 0.01 and 0.05 level(Student's t-test),respectively, selected regions are the same as in Fig. 7)

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    Fig. 9  Correlation coefficients between SCF biases and precipitation biases, 2 m temperature biases for LM0,respectively

    (the bold black line is the contour representing 0.05 level(Student's t-test))

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    Fig. 10  Spatial distribution of simulated climatology biases of SCF

    (simulation minus observation)(black dashed rectangles in Fig. 10a and Fig. 10c represent WKA while those in Fig. 10b and Fig. 10d represent SKA)
    (a)Jan using BCC-CSM1.1m, (b)Apr using BCC-CSM1.1m, (c)Jan using BCC-CSM2-MR, (d)Apr using BCC-CSM2-MR

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    • Received : 2021-02-25
    • Accepted : 2021-05-27
    • Published : 2021-09-30
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