| BCC-CSM1.1m对欧亚积雪覆盖的预测评估 |
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| 引用本文： | 成菲,李巧萍,沈新勇,柳艳菊,汪靖.BCC-CSM1.1m对欧亚积雪覆盖的预测评估[J].应用气象学报,2021,32(5):553-566. |
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| 作者姓名： | 成菲 李巧萍 沈新勇 柳艳菊 汪靖 |
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| 作者单位： | 1.南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心, 南京 210044 |
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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 |
| 收稿时间： | 2021-02-25 |
Evaluation of Eurasian Snow Cover Fraction Prediction Based on BCC-CSM1.1m |
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| Affiliation: | 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 2100442.National Climate Center, Beijing 1000813.Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai), Zhuhai 5190824.Tianjin Institute of Meteorological Science, Tianjin 300074 |
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| 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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