利用北京气候中心(BCC)次季节-季节(Sub-seasonal to Seasonal, S2S)预测系统20年(1994 -2013年)回报试验数据, 在评估BCC S2S预测系统对中国西南地区夏季降水次季节预报性能基础上, 进而采用基于奇异值分解(Singular Value Decomposition, SVD)的误差订正方案对预测结果进行订正。结果表明: BCC S2S预测系统对西南地区夏季降水的次季节预报技巧随起报时间的提前不断下降, 在起报时间提前10天以内具有一定预报技巧, 而在起报时间提前10天以上基本无技巧, 同时存在明显的区域性和年际差异。采用SVD误差订正方案能够较好改善BCC S2S系统对西南地区夏季降水的次季节预测水平, 起报时间提前0~10、 11~20、 21~30天原始预测结果与观测间的异常相关系数分别为0.50, 0.31和0.25, 订正后分别提高至0.70, 0.75和0.70, 同时订正后的预测结果与观测间的空间相关系数在起报时间提前0~10天提高了0.3左右, 尤其对起报时间提前11~30天的预测结果改进更加明显, 空间相关系数提高了0.6左右。
The capability of the Beijing Climate Center (BCC) sub-seasonal to seasonal (S2S) prediction system in forecasting the summer precipitation in Southwestern China has been evaluated based on the 20-year (1994 -2013) hindcast data.Meanwhile, the singular value decomposition (SVD) bias correction scheme was subsequently adopted to improve the forecast skill of the BCC S2S prediction system.The results show that the skill of BCC S2S prediction system in forecasting the summer precipitation over Southwestern China was decreases continuously with the early forecast lead time.It shows certain forecasting skills within 10 days ahead of the starting time, but basically has no skills exceeding 10 days ahead of the starting time.In addition, the forecast skill exhibits obvious regional and inter-annual differences.The sub-seasonal prediction skill of the summer precipitation in Southwestern China could be well improved by adopting the SVD bias correction scheme.The abnormal correlation coefficients between the original prediction results and observations are 0.50, 0.31 and 0.25 with the forecast lead time of 0~10, 11~20, 21~30 days, which are increased to 0.70, 0.75 and 0.70 after the correction, respectively.Moreover, in comparison with the spatial correlation coefficient (SCC) between the bias original prediction results and observations, the SCC between the bias corrected prediction results and observations is increased by about 0.3 at the forecast lead time of 0~10 days, especially for the prediction results with the forecast lead time of 11~30 days, the spatial correlation coefficient for the bias corrected results is significantly increased by 0.6 compared to the original results.
[1]Feddersen H, Navarra A, Ward M N, 1999.Reduction of model systematic error by statistical correction for dynamical seasonal predictions[J].Journal of Climate, 12: 1974-1989.DOI: 10.1175/1520-0442(1999)0122.0.CO; 2.
[2]Gong Z, Dogar M M, Qiao S, al et, 2018.Assessment and correction of BCC_CSM's performance in capturing leading modes of summer precipitation over North Asia[J].International Journal of Climatology, 38(5): 2201-2214.DOI: 10.1002/joc.5327.
[3]Jie W, Vitart F, Wu T, al et, 2017.Simulations of the Asian summer monsoon in the sub‐seasonal to seasonal prediction project (S2S) database[J].Quarterly Journal of the Royal Meteorological Society, 143(706): 2282-2295.DOI: 10.1002/qj.3085.
[4]Kug J S, Lee J Y, Kang I S, 2008.Systematic error correction of dynamical seasonal prediction of sea surface temperature using a stepwise pattern project method[J].Monthly Weather Review, 136(9): 3501-3512.DOI: 10.1175/2008MWR2272.1.
[5]Liu X, Wu T, Yang S, al et, 2014.Relationships between interannual and intraseasonal variations of the Asian-western Pacific summer monsoon hindcasted by BCC_CSM1.1 (m) [J].Advances in Atmospheric Sciences, 31(5): 1051-1064.DOI: 10.1007/s00376-014-3192-6.
[6]Liu X, Wu T, Yang S, al et, 2017.MJO prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center[J].Climate Dynamics, 48(9-10): 3283-3307.DOI: 10.1007/s00382-016-3264-7.
[7]Liu X, Wu T, Yang S, al et, 2015.Performance of the seasonal forecasting of the Asian summer monsoon by BCC_CSM1.1 (m) [J].Advances in Atmospheric Sciences, 32(8): 1156-1172.DOI: 10.1007/s00376-015-4194-8.
[8]Lorenz E, 1975.Climatic predictability[J].The Physical Basis of Climate and Climate Modelling, 132-136.
[9]Mariotti A, Ruti P M, Rixen M, 2018.Progress in subseasonal to seasonal prediction through a joint weather and climate community effort[J].npj Climate and Atmospheric Science, 1(1): 636-646.DOI: 10.1038/s41612-018-0014-z.
[10]Olaniyan E, Adefisan EA, Oni F, al et, 2018.Evaluation of the ECMWF sub-seasonal to seasonal precipitation forecasts during the peak of West Africa monsoon in Nigeria[J].Frontiers in Environmental Science, 6, 4.DOI: 10.3389/FENVS.2018.00004.
[11]Richardson R S, Bidlot J, Ferranti L, al et, 2014.Evaluation of ECMWF forecasts, including 2012-2013 upgrades[J].ECMWF Technical Memoranda, 710: 1-49.
[12]Saha S, Moorthi S, Wu X, al et, 2014.The NCEP Climate Forecast System Version 2[J].Journal of Climate, 27(6): 2185-2208.DOI: 10.1175/JCLI-D-12-00823.1.
[13]Vitart F, Robertson A W, 2018.The sub-seasonal to seasonal prediction project (S2S) and the prediction of extreme events[J].npj Climate and Atmospheric Science, 1(1): 3549-3560.DOI: 10.1038/s41612-018-0013-0.
[14]Vitart F, Ardilouze C, Bonet A, al et, 2017.The subseasonal to seasonal (S2S) prediction project database[J].Bulletin of the American Meteorological Society, 98(1): 163-173.DOI: 10.1175/BAMS-D-16-0017.1.
[15]Vitart F, 2017.Madden-Julian Oscillation prediction and teleconnections in the S2S database[J].Quarterly Journal of the Royal Meteorological Society, 143(706): 2210-2220.DOI: 10.1002/qj. 3079.
[16]Wang Q, Huang A, Zhao Y, al et, 2016.Evaluation of the precipitation seasonal variation over eastern China simulated by BCC_ CSM model with two horizontal resolutions[J].Journal of Geophysical Research: Atmospheres, 121(14): 8374-8389.DOI: 10.1002/2016JD024959.
[17]Wang Z, Duan A, Wu G, 2014.Time-lagged impact of spring sensible heat over the Tibetan Plateau on the summer rainfall anomaly in East China: case studies using the WRF model[J].Climate Dynamics, 42(11-12): 2885-2898.DOI: 10.1007/s00382-013-1800-2.
[18]White C J, Carlsen H, Robertson A, al et, 2017.Potential applications of subseasonal-to-seasonal (S2S) predictions[J].Meteorology Atmospheric, 24(3): 315-325.DOI: 10.1002/met.1654.
[19]Wu T W, Li W P, Ji J J, et a1, 2013.Global carbon budgets simulated by the Beijing Climate Center Climate System Model for the last century[J].Journal of Geophysical Research Atmospheres, 118(10): 4326-4347.DOI: 10.1002/jgrd.50320.
[20]Zheng F, Zhu J, Zhang R H, al et, 2006.Ensemble hindcasts of SST anomalies in the tropical Pacific using an intermediate coupled model[J].Geophysical Research Letters, 33(19): L19604, DOI: 10.1029/2006GL026994.
[21]单机坤, 梁潇云, 吴统文, 等, 2018.BCC-CSM1.1(m)模式对于夏季亚洲-太平洋涛动的模拟[J].地球物理学报, 61(1): 106-118.DOI: 10.6038/cjg2018K0553.
[22]董敏, 吴统文, 左群杰, 等, 2018.气候系统模式对南亚高压气候特征的模拟比较研究[J].高原气象, 37(2): 455-468.DOI: 10. 7522/j.issn.1000-0534.2017.00051.
[23]秦正坤, 林朝晖, 陈红, 等, 2011.基于EOF/SVD的短期气候预测误差订正方法及其应用[J].气象学报, 69(2): 289-296.DOI: 10.11676/qxxb2011.024.
[24]宋敏红, 吴统文, 张宇, 等, 2020.近30年BCC-CSM(m)模拟高原积雪状况评估及其对夏季降水的影响[J].高原气象, 39(1): 15-23.DOI: 10.7522/j.issn.1000-0534.2019.00076.
[25]苏海晶, 王启光, 杨杰, 等, 2013.基于奇异值分解对中国夏季降水模式误差订正的研究[J].物理学报, 62(10): 494-503.DOI: 10.7498/aps.62.109202.
[26]舒建川, 蒋兴文, 黄小梅, 等, 2019.中国西南夏季降水预测的统计降尺度建模分析[J].高原气象, 38(2): 349-358.DOI: 10. 7522/j.issn.1000-0534.2018.00078.
[27]孙建奇, 马洁华, 陈活泼, 等, 2018.降尺度方法在东亚气候预测中的应用[J].大气科学, 42(4): 806-822.DOI: 10.3878~.issn. 1006-9895.1801.17266.
[28]唐红玉, 李永华, 何慧根, 等, 2017.前期环流相似法在重庆延伸期天气过程预报中的应用[J].气象科技, 45(1): 72-78.DOI: 10.19517/j.1671-6345.20160084.
[29]王琳, 任宏利, 陈权亮, 等, 2017.基于逐步回归模态投影方法的BCC气候系统模式ENSO预报订正[J].气象, 43(3): 294-304.DOI: 10.7519/j.issn.1000-0526.2017.03.005.
[30]魏凤英, 2007.现代气候统计诊断与预测技术(2版)[M].北京: 气象出版社, 10-28.
[31]吴捷, 任宏利, 张帅, 等, 2017.BCC二代气候系统模式的季节预测评估和可预报性分析[J].大气科学, 41(6): 1300-1315.DOI: 10.3878/j.issn.1006-9895.1703.16256.
[32]吴统文, 宋连春, 李伟平, 等, 2014.北京气候中心气候系统模式研发进展—在气候变化研究中的应用[J].气象学报, 72(1): 12-29.DOI: 10.11676/qxxb2013.084.
[33]伍丽泉, 李清泉, 丁一汇, 等, 2019.BCC_CSM1.1气候模式年代际试验对北极涛动季节回报能力的初步评估[J].气候变化研究进展, 15 (1): 1-11.DOI: 10.12006/j.issn.1673-1719.2018.004.
[34]徐敏, 罗连升, 程智, 等, 2018.MRI-CGCM模式气候预测回报试验在东亚夏季的检验和降尺度订正[J].气象学报, 76(1): 32-46.DOI: 10.11676/qxxb2017.075.
[35]张丹琦, 孙凤华, 张耀存, 2019.基于BCC第二代短期气候预测模式系统的中国夏季降水季节预测评估[J].高原气象, 38(6): 1229-1240.DOI: 10.7522/j.issn.1000-0534.2018.00149.
[36]郑然, 刘嘉慧敏, 马振峰, 2019.年际增量方法在西南夏季降水预测中的应用[J].气象学报, 77(3): 489-496.DOI: 10.11676/qxxb2019.027.