厄尔尼诺/拉尼娜事件对区域气温的影响与预测——以沈阳地区为例
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摘要
利用1961—2015年国家气象信息中心沈阳站的日平均气温资料、美国国家海洋和大气管理局(National Oceanic and Atmospheric Administration,NOAA)提供的多变量ENSO指数(multivariate ENSO index,MEI)资料等,在分析沈阳地区气温月际变化的基础上,结合厄尔尼诺/拉尼娜事件对其影响特征,利用线性倾向估计和非线性自回归(nonlinear auto regressive models with exogenous inputs,NARX)神经网络模型分别对沈阳地区2011—2015年的气温进行预测。结果表明,1961—2015年共计660个月中,沈阳地区11月—3月气温的变异系数在20%以上,远大于其他月份。1961—2015年的厄尔尼诺/拉尼娜事件往往在秋冬季达到最大强度,或为导致沈阳地区11月—3月气温变异增强的原因之一。厄尔尼诺事件结束之后的春季,沈阳地区气温偏低的概率逾70%。沈阳地区气温随MEI变化的线性倾向值为0.98,决定系数为0.98且通过了0.01的可信度检验。利用MEI对沈阳地区的气温进行同期和时滞预测,NARX的预测结果均优于一元线性回归模型。当气温滞后MEI16个月时,两者的相关系数达到最大且通过了0.01的显著性检验,此时回归模型预测的相关系数为0.59,较同期预测提升了79%;NARX预测的均方误差(mean-square error,MSE)为0.49,较同期预测降低了36%,相关系数为0.86,较同期预测提升了8%。
Based on the analysis of inter-monthly temperature variation in Shenyang, using the daily mean temperature data from National Meteorological Information Center during 1961-2015,the multivariate ENSO index(MEI) data from NOAA,and combined with the influence of El Ni?o/La Ni?a events on its characteristics, we have predicted the temperature from 2011 to 2015 in Shenyang by linear propensity and nonlinear autoregressive(NARX) neural network model. The results showed that in the 660-months from 1961 to 2015, the variation coefficient of temperature from November to March was more than 20%, which was much higher than that in other months. The El Ni?o/la Ni?a events in 1961-2015 tended to reach their maximum intensity in autumn and winter. That may one of the reasons for the increased variation of temperature from November to March. In the spring after the El Ni?o event, the probability of lower temperatures was more than 70 %. The linear trend value of the temperature change with MEI was 0.98, and the determination coefficient was 0.98 and passed the credibility test of 0.01. Synchronous and delay predicting of temperature were carried out by MEI. The prediction results of NARX were better than the one-dimensional linear regression model. When the temperature delay MEI for 16 months, the correlation coefficient between them reached the maximum and passed the significance test of 0.01. At this time, the correlation coefficient predicted by the regression model was 0.59, which was 79% higher than the corresponding prediction. The mean square error(MSE) predicted by NARX was 0.49, 36% lower than the corresponding prediction, and the correlation coefficient was 0.86, 8% higher than the corresponding prediction.
Based on the analysis of inter-monthly temperature variation in Shenyang, using the daily mean temperature data from National Meteorological Information Center during 1961-2015,the multivariate ENSO index(MEI) data from NOAA,and combined with the influence of El Ni?o/La Ni?a events on its characteristics, we have predicted the temperature from 2011 to 2015 in Shenyang by linear propensity and nonlinear autoregressive(NARX) neural network model. The results showed that in the 660-months from 1961 to 2015, the variation coefficient of temperature from November to March was more than 20%, which was much higher than that in other months. The El Ni?o/la Ni?a events in 1961-2015 tended to reach their maximum intensity in autumn and winter. That may one of the reasons for the increased variation of temperature from November to March. In the spring after the El Ni?o event, the probability of lower temperatures was more than 70 %. The linear trend value of the temperature change with MEI was 0.98, and the determination coefficient was 0.98 and passed the credibility test of 0.01. Synchronous and delay predicting of temperature were carried out by MEI. The prediction results of NARX were better than the one-dimensional linear regression model. When the temperature delay MEI for 16 months, the correlation coefficient between them reached the maximum and passed the significance test of 0.01. At this time, the correlation coefficient predicted by the regression model was 0.59, which was 79% higher than the corresponding prediction. The mean square error(MSE) predicted by NARX was 0.49, 36% lower than the corresponding prediction, and the correlation coefficient was 0.86, 8% higher than the corresponding prediction.
引文
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[27]宗海锋.两个典型ENSO季节演变模态及其与我国东部降水的联系[J].大气科学,2017,41(6):1264-1283.
[2]SINGER S F.Nature not human activity,rules the climate[M].Cambridge:Cambridge University Press,2008:10-18.
[3]LIU J J,BOWMAN K W,SCHIMEL D S,et al.Contrasting carbon cycle responses of the tropical continents to the 2015-2016 El Ni?o[J].Science,2017,358(6360):191.
[4]翟盘茂,李晓燕,任福民.厄尔尼诺[M].北京:气象出版社.2003,3-4.
[5]ALEXANDER M A,BLADéI,NEWMAN M,et al.The atmospheric bridge:the influence of ENSO teleconnections on Air-Sea interaction over the global oceans[J].JClimate,2002,15(16):2205-2231.
[6]KIM J W,AN S I,JUN S Y,et al.ENSO and East Asian winter monsoon relationship modulation associated with the anomalous northwest pacific anticyclone[J].Clim Dyn,2017,49(4):1157-1179.
[7]ANDERSON W,SEAGER R,BAETHGEN W,et al.Life cycles of agriculturally relevant ENSO teleconnections in North and South America[J].Int J Climatol,2017,37(8):3297-3318.
[8]HENSON C,MARKET P,LUPO A,et al.ENSO and PDO-related climate variability impacts on Midwestern United States crop yields[J].Int J Biometeorol,2017,61(5):857-867.
[9]GELCER E,FRAISSE C,DZOTSI K,et al.Effects of El Ni?o southern oscillation on the space-time variability of agricultural reference index for drought in midlatitudes[J].Agr Forest Meteorol,2013,174/175:110-128.
[10]李清泉,丁一汇.1991-1995年El Ni?o事件的特征及其对中国天气气候异常的影响[J].气候与环境研究,1997(2):66-80.
[11]袁媛,李崇银,杨崧.与厄尔尼诺和拉尼娜相联系的中国南方冬季降水的年代际异常特征[J].气象学报,2014,72(2):237-255.
[12]翟盘茂,余荣,郭艳君,等.2015/2016年强厄尔尼诺过程及其对全球和中国气候的主要影响[J].气象学报,2016,74(3):309-321.
[13]刘和平,刘军臣.厄尔尼诺与黄淮地区冬夏气温[J].河南气象,2003,26(3):25.
[14]杨明珠,陈丽娟,宋文玲.黑潮区海温对中国北方初霜冻日期的影响研究[J].气象,2013,39(9):1125-1132.
[15]杨东,王慧,程军奇,等.近50年青海省气候变化特征及其与ENSO的关系[J].生态环境学报,2013,22(4):547-553.
[16]陈丽娟,袁媛,杨明珠,等.海温异常对东亚夏季风影响机理的研究进展[J].应用气象学报,2013,24(5):521-532.
[17]王美娜,杨志勇,郑苗苗.试论El Nino事件对沈阳气候的影响[J].安徽农业科学,2011,39(6):3523-3526.
[18]徐迪,任保华,郑建秋,等.中国东北地区冬季气温趋势及反相模态分析[J].气象科学,2017,37(1):127-133.
[19]殷红,张美玲,辛明月,等.近50年沈阳气温变化与城市化发展的关系[J].生态环境学报,2011,20(3):544-548.
[20]徐建华.计量地理学[M].北京:高等教育出版社,2006.
[21]魏凤英.现代气候统计诊断与预测技术[M].北京:气象出版社,2007:37-38.
[22]WOLTER K,TIMLIN M S.El Ni?o/Southern Oscillation behaviour since 1871 as diagnosed in an extended multivariate enso index(MEI.ext)[J].Int J Climatol,2011,31(7):1074-1087.
[23]王小川,史峰,郁磊,等.MATLAB神经网络43个案例分析[M].北京:北京航空航天大学出版社,2013.
[24]中国气象局.厄尔尼诺/拉尼娜事件判别方法:QX/T370-2017[S].北京:气象出版社,2017.
[25]MONASTERSKY R.Monster el ni?o probed by meteorologists[J].Nature,2016,529(7586):267-268.
[26]YEH S W,KUG J S,DEWITTE B,et al.El Ni?o in a changing climate[J].Nature,2009,461(7263):511-514..
[27]宗海锋.两个典型ENSO季节演变模态及其与我国东部降水的联系[J].大气科学,2017,41(6):1264-1283.