Vol.32, NO.5, 2021
Turn off MathJax
Article Contents
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.
shu

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%.
    • FullText(HTML)
    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

    DownLoad: Full-Size Img PowerPoint

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

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

    DownLoad: Full-Size Img PowerPoint

    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

    DownLoad: Full-Size Img PowerPoint

    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

    DownLoad: Full-Size Img PowerPoint

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

    DownLoad: Full-Size Img PowerPoint

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

    DownLoad: Full-Size Img PowerPoint

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

    DownLoad: Full-Size Img PowerPoint

    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)

    DownLoad: Full-Size Img PowerPoint

    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))

    DownLoad: Full-Size Img PowerPoint

    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

    DownLoad: Full-Size Img PowerPoint
  • [1]
    Blanford H F.On the connexion of the Himalaya snowfall with dry winds and seasons of drought in India. Proc Roy Soc London, 1884, 37:3-22. doi:  10.1098/rspl.1884.0003
    [2]
    Cohen J. Snow cover and climate. Weather, 1994, 49(5): 150-156. doi:  10.1002/j.1477-8696.1994.tb05997.x
    [3]
    Essery R. Seasonal snow cover and climate change in the Hadley Centre GCM. Ann Glaciol, 1997, 25: 362-366. doi:  10.3189/S0260305500014282
    [4]
    Cohen J, Rind D. The effect of snow cover on the climate. J Climate, 1991, 4(7): 689-706. doi:  10.1175/1520-0442(1991)004<0689:TEOSCO>2.0.CO;2
    [5]
    Xia K, Wang B, Li L, et al. Evaluation of snow depth and snow cover fraction simulated by two versions of the flexible global ocean-atmosphere-land system model. Adv Atmos Sci, 2014, 31(2): 407-420. doi:  10.1007/s00376-013-3026-y
    [6]
    Lu M M, Wu R G, Yang S, et al. Relationship between Eurasian cold-season snows and Asian summer monsoons: Regional characteristics and seasonality. Trans Atmos Sci, 2020, 43(1): 93-103. https://www.cnki.com.cn/Article/CJFDTOTAL-NJQX202001010.htm
    [7]
    Zhang R H, Zhang R N, Zuo Z Y. An overview of wintertime snow cover characteristics over China and the impact of Eurasian snow cover on Chinese climate. J Appl Meteor Sci, 2016, 27(5): 513-526. doi:  10.11898/1001-7313.20160501
    [8]
    Li W J, Zhang R N, Sun C H, et al. Recent research advances on the interannual-interdecadal variations of drought/flood in South China and associated causes. J Appl Meteor Sci, 2016, 27(5): 577-591. doi:  10.11898/1001-7313.20160507
    [9]
    Zuo Z, Zhang R, Wu B, et al. Decadal variability in springtime snow over Eurasia: Relation with circulation and possible influence on springtime rainfall over China. Int J Climatol, 2012, 32(9): 1336-1345. doi:  10.1002/joc.2355
    [10]
    Tang J, Wu B Y. Inter-decadal shift of East Asian summer monsoon in the early 1990s. J Appl Meteor Sci, 2012, 23(4): 402-413. doi:  10.3969/j.issn.1001-7313.2012.04.003
    [11]
    Zhang R H, Wu B Y, Zhao P, et al. The decadal shift of the summer climate in eastern China in late 1980s and its possible causes. Acta Meteor Sinica, 2008, 66(5): 698-706. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB200805004.htm
    [12]
    Wu B, Yang K, Zhang R. Eurasian snow cover variability and its association with summer rainfall in China. Adv Atmos Sci, 2009, 26(1): 31-44. doi:  10.1007/s00376-009-0031-2
    [13]
    Ding Y H, Li Y, Wang Z Y, et al. Interdecadal variation of Afro-Asian summer monsoon: Coordinated effects of AMO and PDO oceanic modes. Trans Atmos Sci, 2020, 43(1): 20-32. https://www.cnki.com.cn/Article/CJFDTOTAL-NJQX202001005.htm
    [14]
    Chen H S, Sun Z B. The effects of Eurasian snow cover anomaly on winter atmospheric general circulation Part Ⅰ. Observational studies. Chinese Journal of Atmospheric Sciences, 2003, 27(3): 304-316. doi:  10.3878/j.issn.1006-9895.2003.03.02
    [15]
    Chen X F, Song W L. Analysis of relationship between snow cover on Eurasia and Tibetan Plateau in winter and summer rainfall in China and application to prediction. Plateau Meteorology, 2000, 19(2): 216-223. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX200002010.htm
    [16]
    Chen X F, Song W L. Circulation analysis of different influence of snow cover over the Tibetan Plateau and Eurasia in winter on summertime droughts and floods of China. Chinese Journal of Atmospheric Sciences, 2000, 24(5): 586-592. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK200005001.htm
    [17]
    Wu R, Kirtman B P. Observed relationship of spring and summer East Asian rainfall with winter and spring Eurasian snow. J Climate, 2007, 20(7): 1285-1304. doi:  10.1175/JCLI4068.1
    [18]
    Ye D Z. Some physical factors of long-term forecast. Meteorological Monthly, 1975, 1(3): 10-12. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX197503005.htm
    [19]
    Li D L, Wang C X. Research progress of snow cover and its influence on China climate. Trans Atmos Sci, 2011, 34(5): 627-636. doi:  10.3969/j.issn.1674-7097.2011.05.014
    [20]
    Zhu C W, Liu B Q, Zuo Z Y, et al. Recent advances on sub-seasonal variability of East Asian summer monsoon. J Appl Meteor Sci, 2019, 30(4): 401-415. doi:  10.11898/1001-7313.20190402
    [21]
    Chen L J, Zhao J H, Gu W, et al. Advances of research and application on major rainy seasons in China. J Appl Meteor Sci, 2019, 30(4): 385-400. doi:  10.11898/1001-7313.20190401
    [22]
    Peings Y, Douville H. Influence of the Eurasian snow cover on the Indian summer monsoon variability in observed climatologies and CMIP3 simulations. Climate Dyn, 2010, 34(5): 643-660. doi:  10.1007/s00382-009-0565-0
    [23]
    Saha S K, Pokhrel S, Chaudhari H S. Influence of Eurasian snow on Indian summer monsoon in NCEP CFSv2 freerun. Climate Dyn, 2013, 41(7/8): 1801-1815. doi:  10.1007/s00382-012-1617-4
    [24]
    Furtado J C, Cohen J L, Butler A H, et al. Eurasian snow cover variability and links to winter climate in the CMIP5 models. Climate Dyn, 2015, 45(9/10): 2591-2605. doi:  10.1007/s00382-015-2494-4
    [25]
    Barnett T P, Dümenil L, Schlese U, et al. The effect of Eurasian snow cover on regional and global climate variations. J Atmos Sci, 1989, 46(5): 661-686. doi:  10.1175/1520-0469(1989)046<0661:TEOESC>2.0.CO;2
    [26]
    Wu T W, Song L C, Li W P, et al. An overview on progress in Beijing climate center climate system model-Its development and application to climate change studies. Acta Meteor Sinica, 2014, 72(1): 13-29. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201401002.htm
    [27]
    Liu X, Wu T, Yang S, et al. Performance of the seasonal forecasting of the Asian summer monsoon by BCC_CSM1.1(m). Adv Atmos Sci, 2015, 32(8): 1156-1172. doi:  10.1007/s00376-015-4194-8
    [28]
    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. https://www.cnki.com.cn/Article/CJFDTOTAL-YYQX201401011.htm
    [29]
    Tang H Q, Zeng G, Huang Y. An assessment of the tropical pacific latent heat flux simulated by BCC_CSM 1.1(m). J Appl Meteor Sci, 2016, 27(4): 463-472. doi:  10.11898/1001-7313.20160409
    [30]
    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
    [31]
    Wu J, Ren H L, Zhang S, et al. Evaluation and predictability analysis of seasonal prediction by BCC second-generation climate system model. Chinese Journal of Atmospheric Sciences, 2017, 41(6): 1300-1315. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201706013.htm
    [32]
    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 systerm. Plateau Meteorology, 2019, 38(6): 1229-1240. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201906010.htm
    [33]
    He Q, Zuo Z, Zhang R, et al. Prediction skill and predictability of Eurasian snow cover fraction in the NCEP climate forecast system version 2 reforecasts. Int J Climatol, 2016, 36(12): 4071-4084. doi:  10.1002/joc.4618
    [34]
    Song M H, Wu T W, Zhang Y, et al. Evaluation on simulated snow depth over Qinghai-Tibetan Plateau with BCC-CSM(m) model during recent 30 years and its impact on precipitation in summer. Plateau Meteorology, 2020, 39(1): 15-23. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX202001002.htm
    [35]
    Wang Y J, Ren H L, Wang L. Study of seasonal-interannual climate predictions of temperature and snow depth over the third pole. Adv Earth Sci, 2021, 36(2): 198-210. https://www.cnki.com.cn/Article/CJFDTOTAL-DXJZ202102008.htm
    [36]
    Bamzai A S, Shukla J. Relation between Eurasian snow cover, snow depth, and the Indian summer monsoon: An observational study. J Climate, 1999, 12(10): 3117-3132. doi:  10.1175/1520-0442(1999)012<3117:RBESCS>2.0.CO;2
    [37]
    Guo Q, Liu X W, Wu T W, et al. Verification and correction of East China summer rainfall prediction based on BCC_CSM. Chinese Journal of Atmospheric Sciences, 2017, 41(1): 71-90. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201701006.htm
    [38]
    Wu T, Lu Y, Fang Y, et al. The Beijing climate center climate system model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geosci Model Dev, 2019, 12(4): 1573-1600. doi:  10.5194/gmd-12-1573-2019
    [39]
    Li Y D, Wu T W, Liu X W, et al. The impact of initial conditions on soil moisture predictability in early summer in Eastern China. J Appl Meteor Sci, 2018, 29(4): 423-435. doi:  10.11898/1001-7313.20180404
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中
  • -->

Catalog

    Figures(10)

    Get Citation
    PDF
    XML
    Article views (2168) PDF downloads(84) Cited by()
    • Received : 2021-02-25
    • Accepted : 2021-05-27
    • Published : 2021-09-30
    Journal Online
    Contact

    © 2020 Journal of Applied Meteorological Science   京ICP备15006967号-1

    Supported by Beijing Renhe Information Technology Co. Ltd

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return