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Evaluation of Eurasian Snow Cover Fraction Prediction Based on BCC-CSM1.1m
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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。
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关键词:
- 积雪覆盖率;
- BCC-CSM1.1m;
- 预测技巧;
- 评估
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%.-
Key words:
- snow cover fraction(SCF);
- BCC-CSM1.1m;
- prediction skill;
- evaluation
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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 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 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 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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