QX气象Meteorological Monthly1000-0526气象编辑部中国北京qx-44-6-73710.7519/j.issn.1000-0526.2018.06.002P461论文ArticlesMJO对我国降水影响的季节调制和动力-统计降尺度预测Seasonal Modulation of MJO's Impact on Precipitation in China and Its Dynamical-Statistical Downscaling Prediction吴捷WUJie
Based on the China regional grid precipitation dataset CN05.1 and EAR-Interim reanalysis data, the seasonal modulation of the impact of Madden-Julian Oscillation (MJO) on China's precipitation anomaly is studied, and a dynamical-statistical downscaling model which focuses on extended-range precipitation forecast is established based on the MJO prediction by numerical model. The results show that the impact of MJO on precipitation anomaly is modulated by seasonal cycle obviously. When the MJO convection is active in tropical Indian Ocean, the above normal precipitation area advance northward along with the changes of seasons. When the MJO convection is active in the maritime continent, precipitation in eastern China and Tibet Plateau is abnormally less in autumn and winter, but this situation is gradually weakened or even reversed in spring and summer. The position and amplitude variation of MJO convection and basic state (especially the subtropical westerly jet) lead to different extra-tropical circulation responses, which are the main causes for these seasonal variations. The model verification suggests that the prediction skill of target pentad RMM index based on BCC_AGCM2.2 can extend to 18 days. In addition, a seasonal rol-ling MJO dynamical-statistical downscaling precipitation prediction model is established based on the forecasted RMM indices by dynamical model. The independent sample tests show that the dynamical-statistical model achieves higher skills in predicting the low-frequency precipitation anomaly than the direct output of BCC_AGCM2.2 in MJO high impact area during long lead time (10-20 d). The improvement is more obvious in the MJO active period. These findings could provide new thoughts for the MJO interpretation.
MJO(Madden-Julian Oscillation)是热带大气季节内变率(ISV)的主要模态(Madden and Julian, 1971;1972),也是次季节时间尺度上最具可预报性的模态(Gottschalck et al,2010)。它主要表现为热带地区行星尺度对流和环流相互耦合并向东传播的30~80 d准周期振荡现象(Zhang,2005),对热带地区的重要天气和气候事件,如台风群发(Fu and Hsu, 2011)、南亚地区的极端降水(Xavier et al,2014)、南海夏季风爆发(林爱兰等,2016)和El Niño事件发展(McPhaden,1999)等起到重要作用。尽管MJO对流异常主要发生在热带地区(15°S~15°N),但其伴随的异常加热能够激发从热带到中高纬的遥相关波列(如太平洋北美型PNA)从而对热带以外地区的环流和降水产生重要影响(Donald et al,2006;Cassou,2008;李崇银等,2014;2012;Seo and Lee, 2017)。因此MJO也被看作联系天气和气候的桥梁(Zhang,2013)。
近年来,针对MJO与我国气候异常联系也开展了一系列研究(林爱兰等,2008;任宏利和沈雨旸,2016),发现MJO对冬季我国东部地区(Jia et al,2011;贾小龙和梁潇云,2011;Yao et al,2015)、春季华南(李文铠等,2014)和云南地区(李汀等,2012;吕俊梅等,2012;牛法宝等,2012)、夏季西南(李永华等,2016)和长江流域(Zhang et al,2009)的降水异常均存在不同程度影响,这主要是由于MJO的大尺度对流加热所激发的Rossby波列对副热带高压位置(严欣和琚建华,2016)、东亚地区经向环流(刘冬晴和杨修群,2010;白旭旭等,2012)和副热带急流入口区的上升运动(Jeong et al,2008)进行调制造成的。然而,上述研究主要是针对MJO对某个特定季节和地区的气候异常影响进行研究,还缺乏考虑了气候年循环后MJO对不同地区、不同季节影响的差异对比的综合分析。一方面,MJO自身存在着显著的季节性特征(Adames et al,2016),例如MJO对流中心从冬到夏会从赤道附近移到赤道以北;另一方面,东亚地区作为世界上最为显著的季风区,气候态基本气流也存在显著季节差异,对热带地区热源强迫的响应也会有所不同(Jin and Hoskins, 1995;Henderson et al,2017)。因此,有必要从连续变化的角度来研究MJO对我国降水影响的季节调制,并进一步分析其具体成因。
目前,随着动力模式在初始化方案、集合方法和对流参数化过程等方面的改进,其对MJO的预报性能已有明显提高(任宏利等,2015),国际上的主要业务科研模式,如ECWMF(Vitart,2014)、GFDL(Xiang et al,2015)和CFSv2(Wang et al,2014)对MJO的预报技巧已稳定超过20 d,成为MJO业务预报的主流工具。我国学者也利用气候模式对MJO预测开展了相关研究(Zhao et al,2015;Ren et al,2016;Liu et al,2017)。然而,由于降水的复杂性,动力模式对延伸期时段降水的直接预报仍然存在着很多问题。我国持续性的强降水事件往往与大气低频信号的活跃存在密切联系(魏蕾等,2017;肖莺等,2017);已有研究尝试利用季节内振荡(ISO)信号与降水的关联性建立模型,以期利用ISO信息改善延伸期(10~30 d)降水和低温过程的预测(梁萍和丁一汇,2012;Lee et al,2017;陈官军等,2017)。为此,本文将MJO作为进行延伸期预报的重要可预报性来源(丁一汇和梁萍,2010),结合模式的动力预测与统计关系建立考虑季节演变的MJO对我国降水预报的动力-统计降尺度模型,将MJO信号直接释用于延伸期尺度降水预报中。
本文使用的降水资料为逐日的中国区域格点化观测数据集CN05.1,其水平分辨率为0.25°×0.25°。该数据集基于我国境内2400余个台站的观测资料,通过“距平逼近”方法插值建立(吴佳和高学杰,2013),在模式检验和极端事件的分析中得到了广泛应用(田芝平和姜大膀,2013;Hsu et al, 2016)。此外,本文使用ERA-Interim逐日再分析资料(分辨率为1.5°×1.5°)描述大尺度环流特征,包括纬向风(u)、经向风(v)、比湿(q)等要素;使用美国国家海洋和大气管理局(NOAA)提供的全球逐日向外长波辐散(outgoing longwave radiation,OLR)场(分辨率为2.5°×2.5°)表征热带大尺度对流特征。上述所用资料范围为1980—2016年,气候态取1981—2010年共30 a的平均值。
Wheeler and Hendon(2004)定义的一对实时多变量MJO指数(real-time multivariate MJO indices,简称RMM指数)来表征MJO的活动特征。该指数基于热带地区(15°S~15°N)逐日的850 hPa纬向风(U850)、200 hPa纬向风(U200)和OLR场的资料,首先减去其多年气候态的0~3波以去除季节循环,之后减去前期120 d异常值的平均以去除年际变率,最后进行经向平均(15°S~15°N)、除以各自要素的标准差,并投影到多变量联合EOF的前两个模态上,即可得到两个RMM指数,分别记为RMM1和RMM2。由这两个RMM指数构成的空间位相图上较为方便准确地表征MJO的对流位置和活动状况,在科研和业务中得到了极为广泛的使用(Gottschalck et al,2010;贾小龙等,2012)。近年来,国家气候中心发展建立了ISV/MJO监测预测业务系统(IMPRESS2.0)(任宏利等,2015;吴捷等,2016;Ren et al,2017),实时提供MJO监测预测的数据和图形产品,本文所用RMM指数的监测预测数据均由该系统提供(http://cmdp.ncc-cma.net/Monitoring/cn_mjo_impress.php)。]]>
模式资料
本文所用预测资料由国家气候中心第二代大气环流模式(BCC_AGCM2.2)提供。该模式水平分辨率由一代模式的T42提升至T106,垂直方向上分为26层(Wu et al,2010),并在此基础上建立了第二代月动力延伸模式业务系统(DERF2.0)(吴统文等,2013)。模式的大气初始场采用NCEP 1日4次的再分析资料,海表温度初始场采用NOAA的最优插值分析资料(OISST),回报试验从1983年开始,采用滞后平均法(LAF)每日生成4个样本(00、06、12、18时,世界时),对未来55 d进行预报。本文基于BCC_AGCM2.2模式输出的U850、U200和OLR预报场,将每天4个起报样本进行平均得到的逐日RMM指数预报。采用模式1991—2010年共20 a的逐日回报资料作为模式自身的气候态,从而剔除模式的系统偏差。具体模式预测的RMM指数的计算方法可参考Wu et al(2016)。
首先,利用传统的二元线性回归方法分析RMM指数所表征的MJO活动状况对我国同期降水的影响,对回归因子方差贡献的显著性进行f检验(施能,2002)(图 1)。需要注意的是,按照RMM指数划分的MJO位相和对流位置(图 1c, 1d),当RMM1指数为正时,主要对流区位于海洋性大陆(MC)地区;RMM2指数则主要反映热带印度洋和西太平洋的对流偶极子分布。为了更加突出印度洋上的MJO对流活跃特征,便于与传统合成分析研究的结果(Zhang et al,2009;Jia et al,2011)进行对比和物理解释,对RMM2×(-1)进行回归。当该指数为正时,热带印度洋对流活跃而西太平洋对流抑制,后文如无特殊说明也进行相应处理。如图 1所示,我国东部大部分地区的降水与RMM2×(-1)指数呈正相关,即MJO对流位于印度洋(2~3位相)时,我国南方和长江流域降水偏多;MJO对流位于西太平洋(6~7位相)时,我国南方和长江流域降水偏少。而当MC对流位于MC地区时,仅在高原东部出现零星的少雨区。
Regression of pentad precipitation anomalies (a, b, unit: mm·d-1) and tropical OLR anomalies (b, d, unit: W·m-2) against RMM1 index and RMM2×(-1) index
(网格和打点覆盖区域表示通过0.05显著性水平检验)
]]> (Results of meshed and stippled areas for precipitation and OLR have passed the 0.05 significance level test)
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四个季节典型候RMM1指数(a, c, e, g)和RMM2×(-1)指数(b, d, f, h)滑动回归的滞后1候的我国逐候降水异常(单位:mm·d-1)
Slide regression of lag-1 pentad precipitation anomalies (unit: mm·d-1) against RMM1 index (a, c, e, g) and RMM2×(-1) index (b, d, f, h) of 4 typical pentads
(网格覆盖区域表示通过0.05显著性水平检验)
]]>(Results of meshed and stippled areas have passed the 0.05 significance level test)
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The time-latitude sector of slide regression of lag-1 pentad precipitation anomalies (unit: mm·d-1) against RMM1 index (a) and RMM2×(-1) index (b) averaged between 105°E and 125°E; (c, d) same as Figs. 3a and 3b, but for slide correlation coefficients
Slide regression of lag-1 pentad precipitation anomalies (shading, unit: mm·d-1) and water vapor flux anomaly of the whole layer integration (vector, unit: g·m-1·s-1) against RMM1 index (a, c) and RMM2×(-1) index (b, d)
(阴影和黑色矢量表示通过0.05显著性水平检验)
]]> (The shadings for precipitation and thick black vectors for water vapor flux have passed the 0.05 significance level test)
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由于大气中的水汽主要集中在低层,整层的异常水汽通量也主要受到低层环流异常的控制,因此我们进一步以700 hPa为代表层分析冬、夏两季低层环流对MJO对流的响应特征。图 5给出在两个典型候滑动回归的异常OLR场、700 hPa风场、流函数场和气候平均的700 hPa纬向风场(同样经过前后9候的滑动)。如图所示,在冬季,MJO对流中心在赤道附近,响应也类似经典的Gill型(Gill,1980),此时气候背景场的副热带西风急流较强,且向西伸展到欧亚大陆边缘,急流附近热带外环流对MJO对流的响应较强(Adames and Wallace, 2014;Henderson et al,2017)。当对流位于MC地区时,我国南方受到异常气旋式环流北侧的偏东风控制,导致来自印度洋的水汽减弱;对流进一步激发北太平洋副热带西风急流附近的气旋环流异常,导致我国北方受到气旋西侧偏北气流控制,东亚冬季风加强,我国东部大部分地区均降水偏少(图 5a);而当对流位于印度洋时,西太平洋地区对流减弱,对应我国南海地区有一西伸脊,副热带高压偏西偏南,其外侧的西南气流输送水汽到我国华南地区,导致降水增多。在夏季,MJO对应的对流异常整体北移,且呈现出东北—西南走向,而气候场上西风急流向东北方向收缩,强度减弱,导致MJO所激发的热带外遥相关波列和环流异常明显减弱。然而,此时RMM2指数对应的西北太平洋的对流负异常显著增强,从而在其西北侧激发了一个闭合的反气旋环流,其位置也较冬季的西伸脊明显偏北,使得副热带高压偏西偏强,加强了其西北侧的水汽输送,在30°N以北表现出一条暖式切变线,导致我国华南、江南西部和黄淮、江汉地区降水偏多。
Slide regression of simultaneous OLR anomalies (shading, unit: W·m-2) and lag-1 wind (vector, unit: m·s-1) and stream function (contour, unit: 105 m2·s-1) anomalies at 700 hPa against RMM1 index (a, c) and RMM2×(-1) index (b, d)
(红色等值线代表 700 hPa>8 m·s-1气候平均的纬向风,阴影和黑色矢量表示通过0.05显著性水平检验)
]]> (Red contours indicate the climatically averaged zonal wind at 700 hPa which is larger than 8 m·s-1, shadings for OLR and thick black vectors for wind have passed the 0.05 significance level test)
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图 6则进一步给出MJO对流位置、基本气流和环流响应的连续变化特征。由图可见,RMM1指数对应的MC地区的对流异常从冬到夏明显北抬,然而由于西风急流的减弱和北抬,热带外环流响应的范围迅速缩小,导致RMM1指数所对应的我国降水异常响应的范围在冬季较大而夏季缩小;RMM2指数对应的菲律宾附近的负对流异常从冬到夏位置略有北抬,强度显著增强,激发的其西北侧反气旋式环流的脊线从15°N北移到22.5°N,对应我国异常雨带的北移。值得注意的是,在夏季菲律宾附近的对流异常激发了一个季节内经向波列,其波长约为20~25个纬距,即PJ(太平洋-日本)波列或称为EAP(东亚-太平洋)遥相关型(Huang and Li, 1987; Nitta,1987;吴捷等,2013),可将热带的对流信号带到热带外50°N以北的地区。综上所述,由于MJO对流和气候态基本气流均表现出明显的季节差异,在两者的共同作用下,热带外环流对MJO的响应也明显地受到季节循环的调制,在不同季节展现出有差异的连续变化的特征,改变了水汽输送通道,从而对我国降水异常产生不同的调制作用。
The time-latitude sector of slide regression of simultaneous OLR anomalies (shading, unit: W·m-2) and lag-1 wind anomalies (vector, unit: m·s-1) at 700 hPa against RMM1 index (a) and RMM2×(-1) index (b) averaged between 105°E and 125°E
Same as Fig. 2, but for the slide regression of lag-1 pentad simulated precipitation anomalies (unit: mm·d-1) against RMM indices forecasted 10 d in advance by BCC_AGCM2.2
图 2中观测型的空间相关系数)
]]>Fig. 8 and Fig. 2)
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独立样本检验期间(2011—2016年)MJO动力-统计降尺度模型对我国逐候降水距平预报的相关技巧评分:(a, c, e, g)全部样本,(b, d, f, h)MJO活跃时对应样本
Correlation skill of pentad precipitation anomalies forecasted by the dynamical-statistical model during the independent sample verification: (a, c, e, g) all samples, (b, d, f, h) samples of active MJO
(网格覆盖区域表示通过0.05显著性水平检验)
]]>(Results of meshed areas have passed the 0.05 significance level test)
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Correlation skill of pentad precipitation anomalies forecasted by the DERF2 model (a-d) and the skill difference between MJO dynamic-statistic model (MDSM) and DERF2 (e-h) during the independent sample verification (2011-2016)
(网格覆盖区域表示通过0.05显著性水平检验)
]]> (Results of meshed areas have passed the 0.05 significance level test)
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本文主要从延伸期预报的角度出发,对MJO信号与我国降水异常的线性关系进行讨论。需要注意的是,本文虽然提取了降水的季节内低频变化分量,但一般也是非正态分布的,基于回归方法所建立的动力-统计预报模型只能解释MJO和低频降水之间的线性关联部分,主要适用于MJO信号较强时的高影响区,如何采用更好的统计手段建立两者的非线性联系,是需要继续探索的方向。此外,如何考虑MJO和其他季节内的主要模态,如BSISO(Hsu et al,2016; Chen and Zhai, 2017)、NAO(Cassou,2008)、中高纬ISO(孔晓宇等,2017)对我国降水的协同影响,则是需要进一步解决的问题。
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