选择美国环境预报中心(NCEP)第二代气候预报系统CFSv2预报长度为9个月的业务化预报资料、美国NOAA气候预测中心(CPC)的全球逐日最高和最低地表气温资料、美国环境预报中心的NCEP/DOE再分析资料,评估了CFSv2系统对2015年11月21-27日发生在中国的寒潮过程的预报能力。结果表明:分辨率较高的CPC资料和分辨率比较粗的NCEP/DOE资料对这次寒潮过程的演变特征、降温幅度和温度距平都展现的很清楚,这次寒潮过程造成的降温主要发生在中国东部地区,同时里海和黑海以北地区也明显降温,降温明显的还有亚洲北部北冰洋沿岸地区;西伯利亚到我国东部地区比常年明显偏冷,西欧地区和伊朗高原气温偏低,欧亚大陆其他区域特别是北部地区气温是偏高的;CFSv2对这次速冻寒潮过程的整体降温和温度距平提前0,5,10和15天的都有一定得预报能力,但提前20天时预报能力已经较差,整体来讲,CFSv2对寒潮降温过程空间形势分布的预报比温度距平场的好,但与观测值的绝对误差比温度距平场的大;CFSv2对这次寒潮过程造成的我国华北华南及附近区域的降温预报偏低,而对俄罗斯西伯利亚、蒙古和我国东北地区的降温预报偏高,此外对里海和黑海以北地区的降温预报也明显偏低;CFSv2对整个欧亚陆地主体区域寒潮过程温度距平的预报偏低,而对周边海洋区域及其邻近海岸地区的预报偏高。
The operational forecast data of the second generation climate prediction system (CFSv2) with the length of 9 months and the NCEP/DOE reanalysis data from National Centers for Environmental Prediction (NCEP), and the global daily highest and lowest surface temperature data from the NOAA Climate Prediction Center (CPC) in the USA are selected, the forecasts of the CFSv2 system to a cold wave process occurred in China from 21 to 27 November 2015 are evaluated. The results show that the CPC data with high resolution and the NCEP/DOE data with coarse resolution are both very clear to show the evolution characteristics, temperature drop and temperature anomaly of this cold wave process. The temperature drops caused by the cold wave occurred mainly in the eastern part of China, and in the north of the Caspian Sea and the Black Sea at the same time. The temperature drops is also evident in the Arctic Ocean coast of northern Asia. The temperatures between Siberia and the eastern part of China are obviously colder than usual, the temperatures in Western Europe and the Iranian Plateau are lower, and the temperatures in other regions of Eurasia, especially in the north, are higher. The CFSv2 has certain abilities to forecast the whole temperature drops and temperature anomalies of this rapid freezing cold wave for 0, 5, 10 and 15 d in advance, but the prediction ability is poor 20 d in advance. On the whole, the CFSv2 can predict the spatial distribution of temperature drops better than the temperature anomalies, but the absolute errors with the observed values of temperature drops are greater than that of the temperature anomalies. The temperature drops from the CFSv2 forecasts are obviously low than the ones from the observation by the cold wave in North China, South China and nearby areas, but higher in Siberia, Mongolia and Northeast China. In addition, the temperature drops from the CFSv2 forecasts in the north of the Caspian Sea and the Black Sea is also obviously low than the ones from the observation. The temperature anomalies from the CFSv2 forecasts are obviously low than the ones from the observation by the cold wave in the whole Eurasian land area, but higher in the surrounding ocean area and the adjacent coastal area.
[1]Becker E, Dool H, 2016. Probabilistic seasonal forecasts in the North American multimodel ensemble:A baseline skill assessment[J]. Journal of Climate, 29:3015-3026. DOI:10.1175/JCLI-D-14-00862.1.
[2]Cellitti M P, Walsh J E, Rauber R M, et al, 2013. Extreme cold air outbreaks over the United States, the polar vortex, and the large-scale circulation[J]. Journal of Geophysical Research:Atmospheres, 111:D02114. DOI:10.1029/2005JD006273.
[3]Jiang Z H, Ma T T, Wu Z W, 2012. China cold wave duration in a warming winter:Change of the leading mode[J]. Theoretical and Applied Climatology, 110(1/2):65-75. DOI:10.1007/s00704-012-0613-2.
[4]Li Q P, Ding Y H, Dong W J, et al, 2007. A numerical study on the winter monsoon and cold surge over East Asia[J]. Advances in Atmospheric Sciences, 24(4):664-678. DOI:10.1007/s00376-007-0664-y.
[5]Ma T T, Wu Z W, Jiang Z H, 2012. How does cold wave frequency in china respond to a warming climate?[J]. Climate Dynamics, 39:2487-2496. DOI:10.1007/s00382-012-1354-8.
[6]Qu W J, Wang J, Zhang X Y, et al, 2015. Effect of cold wave on winter visibility over eastern China[J]. Journal of Geophysical Research:Atmospheres, 120:2394-2406. DOI:10.1002/2014JD021958.
[7]Riddle E, Amy H, Butler J, et al, 2013. CFSv2 ensemble prediction of the wintertime Arctic Oscillation[J]. Climate Dynamics, 41:1099-1116. DOI:10.1007/s00382-013-1850-5.
[8]Saha S, Moorthi S, Wu X, et al, 2014. The NCEP climate forecast system Version 2[J]. Journal of Climate, 27(6):2185-2208.
[9]Sillmann J, Thorarinsdottir T, Keenlyside N, et al, 2017. Understanding, modeling and predicting weather and climate extremes:Challenges and opportunities[J]. Weather and Climate Extremes, 18:65-74. DOI:https://doi.org/10.1016/j.wace.2017.10.003.
[10]丁一汇, 2017.中国气候[M].北京:科学出版社有限责任公司, 1-55.
[11]樊威伟, 马伟强, 郑艳, 等, 2018.青藏高原地面加热场年际变化特征及其与西风急流关系研究[J].高原气象, 37(3):591-601. DOI:10.7522/j.issn.1000-0534.2017.00062.
[12]康志明, 金荣花, 鲍媛媛, 2010. 1951-2006年期间我国寒潮活动特征分析[J].高原气象, 29(2):420-428.
[13]郎杨, 2015. CFSv2在中国区域的季节干旱可预报性研究[D].北京: 北京师范大学, 1-40.
[14]林爱兰, 吴尚森, 1998.近40多年广东省的寒潮活动[J].热带气象学报, 14(4):337-343.
[15]罗亚丽, 2012.极端天气和气候事件的变化[J].气候变化研究进展, 8 (2):90-98.
[16]马晓青, 丁一汇, 徐海明, 等, 2008. 2004/2005年冬季强寒潮事件与大气低频波动关系的研究[J].大气科学, 32(2):380-394.
[17]钱维宏, 张玮玮, 2007.我国近46年来的寒潮时空变化与冬季增暖[J].大气科学, 31(6):1266-1278.
[18]任福民, 高辉, 刘绿柳, 等, 2014.极端天气气候事件监测与预测研究进展及其应用综述[J].气象, 40(7):860-874.
[19]陶亦为, 代刊, 董全, 等, 2017. 2016年1月寒潮天气过程极端性分析及集合预报检验[J].气象, 43(10):1176-1185.
[20]王遵娅, 丁一汇, 2006.近53年中国寒潮的变化特征及其可能原因[J].大气科学, 30(6):1068-1076.
[21]魏凤英, 2008.气候变暖背景下我国寒潮灾害的变化特征[J].自然科学进展, 18(3):289-295.
[22]伍红雨, 杜尧东, 2010. 1961-2008年华南区域寒潮变化的气候特征[J].气候变化研究进展, 6(3):192-197.
[23]肖贻青, 2017.乌拉尔山阻塞与北大西洋涛动的关系及其对中国冬季天气的影响[J].高原气象, 36(6):1499-1511. DOI:10.7522/j.issn.1000-0534.2016.00109.
[24]徐宗学, 刘琳, 杨晓静, 2017.极端气候事件与旱涝灾害研究回顾与展望[J].研究探讨, 27(1):66-74.
[25]章焕, 范广洲, 张永莉, 等, 2018.青藏高原土壤湿度对一例高原涡影响的数值模拟[J].高原气象, 37(4):886-898. DOI:10.7522/j.issn.1000-0534.2018.00004.
[26]中国国家发展改革委员会, 2013.国家适应气候变化战略[EB/OL].中国政府网.[2018-11-09]. <a href="http://www.gov.cn/gzdt/att/att/site1/20131209/001e3741a2cc140f6a8701.pdf">http://www.gov.cn/gzdt/att/att/site1/20131209/001e3741a2cc140f6a8701.pdf</a>.
[27]周宁芳, 贾小龙, 2018. NCEP CFSv2对北半球夏季中高纬阻塞高压的预测检验[J].高原气象, 37(2):469-480. DOI:10.7522/j.issn.1000-0534.2017.00036.