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BCC-CSM1.1m对欧亚积雪覆盖的预测评估

成菲 李巧萍 沈新勇 柳艳菊 汪靖

成菲, 李巧萍, 沈新勇, 等. BCC-CSM1.1m对欧亚积雪覆盖的预测评估. 应用气象学报, 2021, 32(5): 553-566. DOI:  10.11898/1001-7313.20210504..
引用本文: 成菲, 李巧萍, 沈新勇, 等. BCC-CSM1.1m对欧亚积雪覆盖的预测评估. 应用气象学报, 2021, 32(5): 553-566. DOI:  10.11898/1001-7313.20210504.
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.

BCC-CSM1.1m对欧亚积雪覆盖的预测评估

DOI: 10.11898/1001-7313.20210504
资助项目: 

国家“第二次青藏高原综合科学考察研究”项目 2019QZKK0102

国家“第二次青藏高原综合科学考察研究”项目 2019QZKK0208

中国科学院战略性先导科技专项 XDA20100304

国家自然科学基金项目 41790471

公益性行业(气象)科研专项 GYHY201406001

详细信息
    通信作者:

    沈新勇, 邮箱: shenxy@nuist.edu.cn

Evaluation of Eurasian Snow Cover Fraction Prediction Based on BCC-CSM1.1m

  • 摘要: 利用基于BCC-CSM1.1m模式建立的第2代季节预测模式系统1984—2019年历史回算数据,客观评估该模式对1月和4月欧亚积雪覆盖率(snow cover fraction,SCF)气候态和年际变化的预测技巧,分析模式预测偏差产生的可能原因。结果表明:BCC-CSM1.1m模式在超前0~2个月对欧亚大陆SCF具有一定预测技巧,对4月SCF的预测能力明显高于1月,1月预测技巧在欧洲西部地区最高,4月在西西伯利亚地区最高。SCF的预测结果在除青藏高原外的大范围地区表现为系统性偏低,预测偏差在1月随着起报时间的增长没有明显变化,而在4月随着起报时间的增长,关键区偏差由负转正并逐渐增大。分析表明,SCF预测偏差与模式中近地面气温的预测偏差有直接关系。除此之外,SCF的预测偏差部分源于模式本身的系统性偏差,模式分辨率以及参数化方案可能是预测结果在积雪覆盖率接近100%的高纬度地区明显偏低的原因。
  • 图  1  1月SCF的观测与预测

    (图中黑色虚线框是定义的冬季关键区)
    (a)观测的气候平均值, (b)观测的标准差, (c)LM0的预测偏差, (d)LM0的标准差, (e)LM2的预测偏差, (f)LM2的标准差

    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

    图  2  图 1相同,但是为4月

    (黑色虚线框是定义的春季关键区)

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

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

    图  3  SCF的TCC预测技巧空间分布

    (黑色打点区域表示相关系数达到0.05显著性水平)
    (a)LM0预测1月, (b)LM0预测4月, (c)LM1预测1月, (d)LM1预测4月, (e)LM2预测1月, (f)LM2预测4月

    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

    图  4  关键区观测和预测的SCF距平值以及二者的空间相关系数

    (a)1月距平值, (b)1月空间相关系数, (c)4月距平值, (d)4月空间相关系数

    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

    图  5  观测和模式不同LM时预测的1月SCF的EOF特征向量及其对应的时间系数序列(PC)

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

    图  6  图 5相同,但为4月

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

    图  7  12月—次年5月观测和模式预测的SCF在不同地区的气候平均值

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

    图  8  SCF的TCC预测技巧区域平均

    (图中上下两条虚线分别表示相关系数达到0.01和0.05显著性水平,区域范围同图 7)

    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)

    图  9  LM0时SCF的预测偏差分别与同期降水和气温的预测偏差之间的相关系数

    (黑色实线表示相关系数达到0.05显著性水平)

    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))

    图  10  模式模拟的SCF偏差分布

    (模拟值与观测值之差)(黑色虚线框分别表示冬季关键区和春季关键区)
    (a)BCC-CSM1.1m模拟的1月,(b)BCC-CSM1.1m模拟的4月,(c)BCC-CSM2-MR模拟的1月,(d)BCC-CSM2-MR模拟的4月

    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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    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
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  • 收稿日期:  2021-02-25
  • 修回日期:  2021-05-27
  • 刊出日期:  2021-09-30

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