Impacts of ENSO on autumn rainfall over Yellow River loop valley in observation: Possible mechanism and stability

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1 JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES, VOL. 118, , doi: /jgrd.50264, 2013 Impacts of ENSO on autumn rainfall over Yellow River loop valley in observation: Possible mechanism and stability Chun Li 1 and Gang Zeng 2 Received 13 August 2012; revised 7 February 2013; accepted 8 February 2013; published 25 April [1] The Yellow River loop valley (YRLV) is one of major agricultural production zones in many Chinese dynasties. Predicting rainfall variability in the YRLV is important for the society and the economy. This study demonstrates impacts of El Niño Southern Oscillation (ENSO) on autumn rainfall in the YRLV based on statistical analyses of instrumental rainfall in China and reanalyzed SST and atmospheric data. Results indicate that the autumn rainfall anomaly in the YRLV may be partially controlled by the ENSO-like sea surface temperature (SST) pattern. The YRLV rainfall is below normal (above normal) in the El Niño (La Niña) developing years. This impact is mediated by an atmospheric response to the ENSO-like SST anomaly forcing via a westward Rossby wave. Furthermore, Niño3.4 index may be used as a factor for autumn rainfall in the YRLV region with a lead of 4 5 months. However, the relationship between the autumn YRLV rainfall and ENSO does not persist and has gone through decadal weakening since the end of 1980s via a decadal response of atmospheric circulation to ENSO. The decadal change of the relationship between the autumn YRLV rainfall and ENSO may be modulated by climate decadal shifts. Citation: Li, C., and G. Zeng (2013), Impacts of ENSO on autumn rainfall over Yellow River loop valley in observation: Possible mechanism and stability, J. Geophys. Res. Atmos., 118, , doi: /jgrd Introduction [2] The Yellow River loop valley (YRLV) is one of the major agricultural production regions in Chinese history. Predicting rainfall variability in the YRLV is very important for social and economic benefits, but few studies focused on it. Zhong et al. [2006] analyzed some characteristics of the summer YRLV rainfall. Li and Ma [2011] showed a coupled mode of summer rainfall in the YRLV and sea surface temperature (SST) in the northeast subtropical Pacific. This anomalous SST pattern is named as tropical Pacific meridional mode (TPMM) by Chiang and Vimont [2004]. Li et al. [2011] further demonstrated that TPMM-like SST influences summer rainfall in the YRLV via a long-journey atmospheric Rossby wave train from observation and model simulation. [3] In the YRLV, rainfall in September and October accounts for about 20% of the total annual precipitation, 1 Physical Oceanography Laboratory and Key Laboratory of Ocean- Atmosphere Interaction and Climate in Universities of Shandong, Ocean University of China, Qingdao, China. 2 Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China. Corresponding author: C. Li, Physical Oceanography Laboratory and Key Laboratory of Ocean-Atmosphere Interaction and Climate in Universities of Shandong, Ocean University of China, Qingdao , China. (lichun7603@ouc.edu.cn) American Geophysical Union. All Rights Reserved X/13/ /jgrd while rainfall in November is less than 2%. Therefore, autumn season is defined as September October (SO), not traditional September October November (SON). It is necessary to study autumn rainfall in the YRLV because of its vital roles in crop maturity and harvest. In the past, many studies focused on the influences of El Niño Southern Oscillation (ENSO) on summer monsoon and rainfall over China [e.g., Fu and Teng, 1988; Huang and Wu, 1989; Ju and Slingo, 1995; Zhang et al., 1996; Tao and Zhang, 1998; Li, 2008; Li and Ma, 2011]. But few attentions were paid to the variability and mechanisms of the autumn YRLV rainfall. Gong and Wang [1999] noted that impacts of ENSO on precipitation over eastern China in winter and autumn are more notable than those in summer. Li et al. [2000] analyzed some covariant character of ENSO and autumn rainfall anomaly in northwestern China based on rainfall in five provinces and SST data during , and Li et al. [2001] analyzed the temporal and spatial characters of autumn rainfall anomaly in northwestern China based on rainfall data in four provinces during Wu et al. [2003] studied the evolution of ENSO-related rainfall anomalies in East Asia and also demonstrated that ENSO can explain about 15% 20% of autumn rainfall variance in the western north China during an ENSO developing year. Previous studies apparently revealed a robust influence of ENSO on autumn rainfall in the northwestern China, but the mechanism and stability of ENSO influencing autumn rainfall over the northwestern China were not much discussed. Therefore, this paper will focus on the relationship of ENSO autumn YRLV rainfall and give a relatively systematic study on its possible mechanism and temporal 3110

2 stability using observational data in the past 60 years. Our finding is that the autumn YRLV rainfall can be partially controlled by ENSO-like SST variability via a westward Rossby wave and that the covariant relationship of ENSO and the autumn YRLV rainfall has gone through decadal weakening since 1990s. [4] This paper is organized as follows. A brief description of data and methods is given in section 2. In section 3, the covariant mode of ENSO and the autumn YRVL rainfall is demonstrated. The possible mechanism of ENSO YRLV rainfall teleconnection is discussed in section 4. Section 5 demonstrates the potential predictability of the autumn YRLV rainfall based on ENSO. The stability of ENSO YRLV rainfall relationship is addressed in section6.summaryanddiscussionareprovidedinthe last section. 2. Data and Methods [5] Monthly rainfall at 160 weather stations of China, global SST, and National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) atmospheric reanalysis data are used to demonstrate impacts of ENSO on the autumn YRLV rainfall. The monthly 160 station rainfall is adopted from the data center of Chinese Meteorological Administration (CMA). This rainfall data set, from January 1951 to December 2012, has been used extensively [e.g., Lau and Weng, 2001;Li et al., 2011; Li and Ma, 2011, 2012]. The monthly SST data are taken from the National Ocean and Atmospheric Administration (NOAA) extended reconstructed SST (ERSST), provided by the NOAA-Cooperative Institute for Research in Environmental Sciences (NOAA-CIRES) Climate Diagnostics Center [Smith and Reynolds, 2003]. The NOAA ERSST (version 3) data have 2 2 resolution and cover the period of In addition, monthly Kaplan SST and Hadley Center Sea Ice and SST (HadISST) data are also used to examine the reliability of relationship between ENSO and the autumn YRLV rainfall, similar with Li and Ma [2012]. Kaplan Extended SST (version 2) data, taken from NOAA-Earth System Research Laboratory (NOAA-ESRL), have 5 5 resolution, and cover the period of [Kaplan et al., 1998]. HadISST data, taken from Met Office Hadley Center observation data sets, have 1 1 resolution and cover the period of [Rayner et al., 2003]. In order to study the linkage between ENSO and the autumn YRLV rainfall, the monthly atmospheric reanalysis data (including geopotential height, sea level pressure, air temperature, horizontal wind, and specific humidity), taken from the National Center for Environmental Prediction/ National Center for Atmospheric Research (NCEP/ NCAR) reanalysis, have resolution and cover the period of [Kalnay et al., 1996]. In order to unify the time span, all data used here are from January 1951 to December Because this study focuses on the interannual relationship of ENSO and autumn rainfall in the YRLV and its decadal evolution, the long-term linear trends and decadal variability of all data are removed before analyses. [6] To identify the covariant relationship between rainfall and SST anomalies, singular value decomposition (SVD) analysis is employed following Li and Ma [2012] with a focus on boreal autumn instead of winter. It should be noted that autumn is defined as September October (SO), not the traditional September October November (SON). Both correlation and regression analyses are applied to confirm the linkage between ENSO and the autumn YRLV rainfall. To examine the stability of the ENSO YRLV rainfall mode, running correlation with 21 year sliding window is also used. 3. ENSO and YRLV Rainfall Mode [7] The leading SVD mode reveals intimate connection of rainfall variability in the YRLV with ENSO-like SST anomaly (Figure 1), which accounts for 38.4% of squared covariability between SO rainfall in China and global SST. The SST pattern shows El Niño like SST anomalies, with warmer SST in the tropical central eastern Pacific surrounded by colder SST in the tropical western Pacific, the midlatitude North and South Pacific (Figure1a).TheElNiño like SST pattern explains 17.3% of global SST variance. Coupled with El Niño like SST anomalies, the autumn rainfall anomaly in China demonstrates negative correlation in most parts of China, except for the northeastern and southeastern China with positive correlation, but significant negative correlation area exists in the YRLV region (Figure 1b). The rainfall pattern explains 9.7% of rainfall variability in China, but 30%-40% of the regional rainfall variability in the YRLV (not shown). The corresponding time coefficients have interannual variability (Figure 1c) and are highly correlated at a correlation coefficient of For simplicity, here we define the time coefficients of the SVD mode as the principal component (PC) and use subscripts s and r representing SST and rainfall. [8] To examine the robustness of the results of the SVD analysis, we repeat the SVD analyses using different regional SSTs, for example, the tropical Pacific, whole Pacific, and Indo-Pacific areas (not shown). Despite of different SST regions, the leading SVD modes are nearly similar with Figure 1. We also repeat the SVD analyses using Kaplan SST and Hadley Center SST to examine dependence on SST data. Results also show nearly same leading SVD modes (not shown). This implies that the covariant mode of the autumn YRLV rainfall ENSO is robust and independent of SST regions and data. [9] To further verify the robustness of the ENSO and YRLV rainfall covariance, we define Niño3.4 SST index averaged over 170 W 120 W and 5 S 5 N representing ENSO and YRLV rainfall index averaged over precipitation in 20 weather stations (shown in Figure 1b, i.e., Hohhot, Baotou, Shaanxi dam, Xi an, Tianshui, Minxian, Hanzhong, Taiyuan, Linfen, Yulin, Yan an, Xifengzhen, Lanzhou, Zhongning, Xinchuan, Xining, Linxia, Maduo, Wuwei, and Zhangye weather stations) (Figure 2a). The Niño3.4 index is significantly correlated with the YRLV rainfall index at a correlation coefficient of Correlation of negative YRLV rainfall index and global SST shows El Niño like SST anomaly pattern (Figure 2b) spatially correlated at a correlation coefficient of 0.96 with SVD SST pattern (Figure 1a). Correlation of Niño3.4 index and rainfall over China shows significant negative correlation centralized in the YRLV and weak 3111

3 positive correlation in the northeastern and southeastern China (Figure 2c) spatially correlated at a correlation coefficient of 0.97 with SVD rainfall pattern (Figure 1b). In addition, Niño3.4 index is highly correlated at a correlation coefficient of 0.93 with SVD PCs and YRLV rainfall index also highly correlated at a correlation coefficient of 0.84 with SVD PCr. These statistical results suggested that SO rainfall in the YRVL is below normal (above normal) in the El Niño (La Niña) developing years. Here we define the leading SVD mode as ENSO YRLV rainfall mode. 4. Possible Mechanism of ENSO YRLV Rainfall Teleconnection [10] What teleconnection conveys the impact of ENSO to the YRLV region and causes local rainfall anomaly? To address this question, we regress SO geopotential height, sea level pressure (SLP), and total moisture transport on August September (AS) Niño3.4 SST index (Figures 3, 4). [11] Regressions of geopotential height on normalized AS Niño3.4 SST index show a circumglobal wave train-like along the jet stream in the midlatitude Northern Hemisphere. The wave train displays a quasi-barotropic structure but is more clear at upper layers than at low layers (Figure 3), similar to teleconnection pattern of TPMM SST anomaly influence on the summer YRLV rainfall [Li et al., 2011; Li and Ma, 2011]. But its significant centers are mainly restricted in North Africa Asian (NAA) jet region from the North Africa extending to the North Pacific. In midlayer of troposphere at 500 hpa, the wave train is identified with significant low trough anomalies centralized in the East Asia southern Japan and Aleutian Islands (Figure 3b). The similar teleconnection pattern also exists in upper troposphere at 300 hpa, but significant negative geopotential height anomaly over the East Asia Japan stretching westward to the North Africa continent (Figure 3a). In low troposphere and sea level pressure, the wave train teleconnection also exists but is weak (Figures 3c and 3d). As seen in the middle and upper troposphere, the circumglobal wave train is restricted within westerly zonal jet (Figure 3a), which implies influence of waveguide in westerly zonal jet. But its significant regions are only observed in the NAA region. The atmospheric teleconnection is also similar to the atmospheric response to diabatic heating near the Philippine related with anticyclonic wind anomalies in the South China Sea (Figure 5), as relayed impacts of El Niño [Wu et al., 2003]. [12] Moisture is a vital factor for rainfall. In boreal autumn, total moisture is majorly transported by southerly winds to the west subtropical high in the northwestern Pacific (Figure 4a). For eastern China, moisture is majorly originated from the northwestern Pacific and Indian Ocean, while the YRLV locates in the northwestern edge of moisture transport, regulated by the northwestern Pacific subtropical high (Figure 4a). Regression of total moisture on AS Niño3.4 SST index shows that the YRLV is a moisture divergence (convergence) region in the El Niño (La Niña) developing years (Figure 4b), corresponding to the weakened (strengthened) northwestern Pacific subtropical high and local ridge-trough dipole anomalies over the west and east sides of the YRLV region (Figure 3b). This leads to less (more) rainfall in the YRLV region in the El Niño (La Niña) developing years (Figures 1b and 2c). [13] What is a possible physical mechanism controlling the atmospheric teleconnection and weakening the northwestern Pacific subtropical high? It is a conceivable westward Rossby wave triggered by ENSO event [Wang et al., 2000; Shaman and Tziperman, 2007]. To demonstrate this mechanism, tropospheric temperature is regressed against AS Niño3.4 SST index (Figure 5). Results show Gill-like pattern responses to El Niño with eastward Kelvin wave along the equator and westward Rossby wave north and south of the equator, accompanied by the equatorial anomalous westerly wind in the Pacific Ocean and easterly wind in the Indian Ocean. The significant cold temperature anomalies, coupled with negative geopotential height thickness anomalies (not shown), lie along the NAA jet. The regression pattern of tropospheric temperature is similar to composite maps of summer temperature in the upper troposphere for El Niño minus La Niña event years [Shaman and Tziperman, 2007]. [14] Shaman and Tziperman [2007] pointed out that SST warming associated with El Niño events can cause convective anomalies along intertropical convergence zone (ITCZ) over (c) Figure 1. Spatial heterogeneous pattern of the leading SVD mode of AS SST, SO rainfall over China, and (c) their corresponding time coefficients. Contour s interval is 0.1. Shaded area with light (thick) gray exceeds 0.05 (0.01) confidence level by a Student s t test. 3112

4 (c) Figure 2. Time series of AS Niño3.4 and SO YRLV rainfall indices. Correlation patterns of SST with YRLV rainfall index and (c) rainfall with Niño3.4 SST index (CI = 0.1). Shaded area with light (thick) gray exceeds 0.05 (0.01) confidence level by a Student s t test, respectively. (c) (d) Figure 3. SO regression with AS Niño3.4 SST index: Z300 (CI = 2 m), Z500 (CI = 2 m), (c) Z850 (CI = 2 m), and (d) SLP (CI = 0.2 hpa). Shading with light (thick) denotes correlation significant at 0.05 (0.01) level. In Figure 3a, thickened contours represent climatological zonal jet at 300 hpa (CI = 20 and 30 m/s). 3113

5 Figure 5. SO regression of tropospheric ( hpa) temperature (CI = 0.1 C) and 850 hpa wind velocity (vectors, m/s) with AS Niño3.4 SST index. Shading with light (thick) denotes correlation significant at 0.05 (0.01) level. Thickened contours represent climatological zonal jet at 300 hpa (CI = 20, 30 m/s). Figure 4. Total moisture transport climatology (10 4 g m kg 1 s 1 ) and subtropical high at 700 hpa (3150 gpm contour). Regression of total moisture transport with AS Niño3.4 SST index and its magnitude (contours are 2, 4, 6, 8 and gmkg 1 s 1 ). the equatorial Pacific and then induce divergence in upper troposphere and vorticity turbulences propagating westward. The westward propagation of the Rossby wave is confirmed by the two sensitive experiments via setting high damping (Sponge layers) in the west and east of the equatorial forcing. The results show that high damping in the west eliminates vorticity response in NAA jet, while high damping in the east has no effect on the vorticity response in NAA jet. These westward Rossby waves manifest as positive vorticity anomalies along NAA jet, companied with cold temperature anomalies in NAA jet. In addition, Wu et al. [2003] pointed out that anticyclonic wind anomalies, as response to El Nino event, first appear over the South China Sea in autumn of the El Niño developing year (Figure 5). The anticyclonic wind anomalies are also trigged by the El Nino event through a westward stationary Rossby wave train [Wang et al., 2000] and induce diabatic heating anomaly to contribute to the wave train [Wu et al., 2003], as relayed forcing of the El Nino event. Therefore, the atmospheric teleconnection is a westward Rossby wave response to ENSO event. 5. Predictability of YRLV Rainfall Based on ENSO [15] Analyses above confirm covariance between ENSO-like SST anomaly and the autumn YRLV rainfall through a westward Rossby wave. So it is important that whether autumn YRLV rainfall anomaly can be predicted by ENSO-like SST variability. If yes, how long can be predicted ahead? To assess the potential predictability, we repeat the SVD analyses between the global SST and SO rainfall over China, but with the SST leading the rainfall by 2 4 months. These lagged SVD analyses show the similar pattern with that at 1-month lag (not shown). Lagged correlation of the SO YRLV rainfall index and monthly Niño3.4 index also shows the predictability of the SO YRLV rainfall (Figure 6). The correlation coefficients shift from weak negative in previous winter to positive gradually and exceed 99% significant confidence level in late spring (April May). The significance of positive correlation begins in April May season, rises up to maximum in simultaneous September October season, and persists to later winter season. The results of the lagged correlation analyses suggest that the SO YRLV rainfall can be predicted by Niño3.4 SST index with about 4 5 months ahead. [16] In order to understand the 4 5 months predictable time scale, we calculate the autocorrelations of the Niño3.4 SST index to examine persistence of ENSO-like SST anomaly (Table 1) following Li et al. [2011]. The persistence of the Niño3.4 SST anomaly is similar to the Niño3 SST anomaly [Li and Ma, 2012]. The autocorrelation statistically significant, exceeding the 99% level is highlighted. To evaluate the significance of the autocorrelation coefficients, the degree of freedom is estimated following Bartlett [1935]. The length of the highlighted column for a particular month can be regarded as a measure of SST memory starting from that month [Lau and Yang, 1996]. It can be seen that the Niño3.4 SST anomalies originated from later spring (May) can persist to winter. However, starting in early spring and last winter, the significant autocorrelation of Niño3.4 index could not stride across spring barrier of ENSO prediction [Webster and Yang, 1992], consistent with the current status of ENSO prediction skill [Jin et al., 2008]. Therefore, the predictability of autumn YRLV rainfall should depend on the skill of ENSO prediction. 6. Stability of ENSO YRLV Rainfall Mode [17] To further examine the stability of the ENSO-like SST and the YRLV rainfall covariance, we calculate the 3114

6 Table 1. Autocorrelations of the Niño3.4 SST Index for as Functions of the 12 Calendar Months With Lagged Time of 1 12 Months a Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Feb 0.97 Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0.17 a Autocorrelation coefficients significant at the 99% confidence level are denoted by bold font. 21 year running correlation for PCs and PCr of the leading SVD mode Niño3.4 and the negative YRLV rainfall indices, SVD PCs and the negative YRLV rainfall index, SVD PCr and Niño3.4 index, respectively (Figure 7). In these cases, the correlations show obvious decadal variations. For SVD PCs and PCr, the running correlation appears to be higher before than after the middle of 1970s but remains stably significant. For Niño3.4 and the negative YRLV rainfall indices, the correlation is significant before 1990s and then shifts to be insignificant. It just so happens that AMO reverses its phase again from cold to warm in the end of 1980s [Sutton and Hodson, 2005]. The correlation of SVD PCs and the negative YRLV rainfall index is similar to the correlation of Niño3.4 and the negative YRLV rainfall indices. The correlation of SVD PCr and Niño3.4 index is similar to the correlation of SVD PCs and PCr, but relatively weaker than later. These differences imply that the leading SVD modes represent larger spatial and temporal covariance between SST and SO rainfall in China (Figure 1). Therefore, relationship of the ENSO YRLV rainfall has gone through a significant decadal change. [18] To further examine decadal stability of the autumn YRLV rainfall predictability, the 21 year running lagged correlation of the YRLV rainfall index with monthly Niño3.4 SST index is calculated (Figure 8). The potential predictability of the autumn YRLV rainfall displays a decadal change from about 4 to 5 months before 1976/1977 Pacific climate regime shift to about 2 3 months from Figure 6. Lagged correlations of rainfall temporal coefficients of the leading SVD mode with monthly Niño3.4 SST index for the past six decades. Dashed line denotes correlation significant at confidence level. Figure 7. The 21-year running correlation of (black solid line) SVD PCs and PCr, (black dashed line) Niño3.4 and negative YRLV rainfall indices, (gray solid line) SVD PCs and nagtive YRLV rainfall index, and (gray dashed line) SVD PCr and Niño3.4 index. Long dashed lines denote correlation significant at 0.01 and confidence levels, respectively. 3115

7 Figure 8. The 21-year running lagged correlations of SO YRLV rainfall index and monthly Niño3.4 index. Contours interval is 0.1, and contours of 0, 0.1, and 0.2 are omitted. Shading from light to thick denotes correlations significant at 0.05, 0.01, and levels, respectively. 1976/1977 Pacific climate regime shift to the end of 1980s and then seems to possibly rehabilitate the potential predictability of about 4 5 months, corresponding to 1988/1989 Pacific climate shift [Yeh et al., 2011]. These implicate possible influence of decadal climate variability on the covariant relationship of ENSO YRLV rainfall. [19] Running correlation of Niño3.4 YRLV rainfall indices shows an obvious contrast before and after the end of 1980s. The correlation coefficient of Niño3.4 YRLV rainfall indices is 0.69 (0.45) for the period of ( ). To demonstrate possible different mechanism, we repeat regression analyses of SO geopotential height and AS Niño3.4 index for and , respectively. Due to their barotropic structure, here we only show the regression of geopotential height at 500 hpa (Figures 9a and 9b). [20] For , the atmospheric teleconnection pattern also shows a circumglobal wave train, which is similar with Figure 3b, but the anomalous low trough belt from the East Asia to the North Atlantic through the North Pacific and America is separated into several low troughs by three weak anomalous high ridges in the western North Pacific, the western North America, and the North Atlantic (Figure 9a vs. Figure 3b). Negative geopotential height anomalies centralized in the East Asia south Japan, Aleutian Islands, the eastern North America, the eastern Atlantic-Europe, and the northern Asia, while weak positive geopotential height anomalies centralized in the central Asia, Kuril Islands, and the North Atlantic, a relatively strong positive anomalous geopotential height belt lies over the Arctic region, extending from the North Europe to the western North America. The atmospheric pattern is highly correlated with regression pattern of geopotential height at 500 hpa against YRVL rainfall index ( 0.85). For the local YRVL region, the low trough and high ridge lie in the west and the east of the YRLV, which induce divergence of water vapor transport (Figure 9c) and then cause less rainfall in the YRLV region (Figure 9e). [21] Compared with the period of , atmospheric teleconnection shows a dramatic difference in the period of (Figure 9a vs. Figure 9b). During this period, a prominent feature of atmospheric pattern is a wave train from the North Atlantic through the Eurasian continent to the western North Pacific, which is nearly out of phase with atmospheric pattern for In the North Atlantic, low trough and high ridge resemble characteristic of North Atlantic Oscillation (NAO). In the North Europe and the Arctic region, atmospheric anomaly shifts to low trough during the period of from high ridge during the period of Dipole anomalies of high ridge and low trough on both sides of the YRVL region are enhanced and shift northwestward, which restricts the water vapor transport to the Yangtze River valley (Figure 9d) and causes less rainfall in the Yangtze River valley, northern of the YRLV and northwestern China(Figure9f). [22] Why are the atmospheric responses so different during the two periods of and ? To further examine this, the tropospheric temperature and 850 hpa wind are also regressed on AS Niño3.4 index for and (Figure 10), respectively. In the tropics, outstanding features of the tropospheric temperature patterns are Gill-like pattern response to El Niño both for and for , which is similar with the response for (Figure 10 vs. Figure 5), consistent with Shaman and Tziperman [2007], but the Kelvin wave is likely stronger for than for In the extratropics of the Northern Hemisphere, however, the tropospheric temperature patterns have a dramatic change, nearly out of phases (Figure 10 vs. Figure 10b). For , negative tropospheric temperature anomalies exist in the northeastern America and the southwestern Europe, and positive tropospheric temperature anomalies exist in the northwestern Europe and the North Atlantic, corresponding to saddle-like anomalous geopotential pattern over the eastern North America to the west Europe through the North Atlantic (Figure 9a). In the North Atlantic, the tropospheric temperature anomalies share negative NAO-like pattern for , consistent with the negative NAO-like geopotential height anomalies (Figure 9b). In the NAA jet region, the temperature anomaly is stronger in the west part than in the east part and contracts southward in the east part. The negativepositive-negative wave-like temperature anomalies from south to north in the Asian continent form two anomalous temperature fronts, corresponding to a saddle-like anomalous geopotential patterns (Figure 9a), and favoring significant less rainfall in the YRLV region (Figure 9e). For , however, temperature anomalies share negative NAO-like pattern in the North Atlantic (Figure 10b), corresponding to negative NAO-like geopotential height anomalies there (Figure 9b). In the NAA jet region, the temperature anomaly is stronger in the east part than in the west 3116

8 (c) (d) (e) (f) Figure 9. Regression of SO geopotential height at (left) 500 hpa (CI = 2 m) on AS Niño3.4 index for and (c, d) Same as in Figures 9a and 9b, but for total water vapor transport (CI = gmkg 1 s 1 and 0 is omitted). Correlations of SO rainfall in China and AS Niño3.4 index for (e) and (f) In Figures 9a, 9b, 9e, and 9f, shading with light (thick) denotes correlation significant at 0.05 (0.01) level. part and apparently shifts northward in the east part, accompanied by zonal jet in the East Asia west Pacific shifting northward about 2 latitudes, and then forms stronger temperature front with positive temperature anomaly in Russia (Figure 10b). These temperature anomalous patterns cause the enhancement and northwestward shift of low trough (high ridge) anomaly in East Asia Japan (Russia) (Figure 9b) and then lead to less rainfall region shifting the northwestward to the north part of the YRVL region and northwestern China (9f). Therefore, the geopotential height anomalies are closely coupled with anomalous temperature. The changes of the regression patterns imply the modulation of decadal climate background on the covariant relationship of the ENSO YRLV rainfall mode. But the modulation mechanism needs further study and is out of this paper s scope. 7. Summary and Discussion [23] Based on instrumental rainfall in China, atmospheric and SST reanalyses data, impacts of ENSO on the autumn YRLV rainfall are investigated with statistical methods. Results show that ENSO and the autumn YRLV rainfall 3117

9 Figure 10. Same as Figure 5, but for and anomalies are a covariant mode via atmospheric response to ENSO-like SST variability. ENSO-like SST anomaly triggers anticyclonic wind anomalies in South China Sea Philippine and NAA jet anomalous response through an atmospheric Rossby wave westward. The anticyclone and NAA jet anomalies can cause local northwest-southeast high ridge and low trough dipole atmospheric circulation anomalies in both sides of the YRLV region. It is the dipole anomalies that lead to divergence (convergence) and increase (reduce) autumn YRLV rainfall for the developing year of El Niño (La Niña) events. Niño3.4 index may be used as a predictor for predicting autumn YRLV rainfall anomaly ahead of 4 5 months. [24] However, the covariant relationship between ENSO and the autumn YRLV rainfall anomaly does not persist and has gone through a decadal evolution. Variability of SO YRLV rainfall is mainly associated with SST anomalies in the tropical Pacific for (Figure 11a), while with SST anomalies not only in the tropical Pacific but also in the North Pacific and Atlantic for (Figure 11b). Meanwhile, regression of SST on Niño3.4 index also shows that the signal of ENSO variability is mainly restricted in the tropical Pacific for (Figure 11c), while it is not only restricted in the tropical Pacific but also related with SST variability in the North Pacific and in the North Atlantic for (Figure 11d). For , regression patterns of SST on the YRLV rainfall and Niño3.4 indices resemble the Pacific decadal oscillation (PDO) and AMO to some extent. The significant (insignificant) correlation periods between Niño3.4 and YRVL rainfall indices are corresponding to cold (warm) phases of AMO. The predictable length of the YRLV rainfall based on Niño3.4 index becomes shorter after the middle of 1970s than before it, which happens to be the corresponding Pacific climate shift. This may reflect an important role of decadal mean climate in its modulation of the influence of ENSO on the YRLV rainfall anomaly. Of course, our finding will help to understand that variability and predictability of the autumn YRLV rainfall are controlled by not only ENSO but also its modulation of the decadal climate changes. This work also serves as a reference for prediction of other elements and recommends that we should pay enough attention to the modulation of climate decadal change on interannual variability. Furthermore, the modulation mechanism still needs more studies in the future. (c) (d) Figure 11. Correlation of SO YRLV rainfall index and AS SST for and (c, d) Same as Figures 11a and 11b, but for SO Niño3.4 index. Shading with light (thick) denotes correlation significant at 0.5 (0.01) level. 3118

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