A Far-Reaching Footprint of the Tropical Pacific Meridional Mode On

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A Far-Reaching Footprint of the Tropical Paciﬁc Meridional Mode on the Summer Rainfall over the Yellow River Loop Valley

CHUN LI Physical Oceanography Laboratory, Ocean University of China, Qingdao, and LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

LIXIN WU Physical Oceanography Laboratory, Ocean University of China, Qingdao, China

PING CHANG College of Geosciences, Texas A&M University, College Station, Texas

(Manuscript received 13 May 2010, in ﬁnal form 18 November 2010)

ABSTRACT

Over thousands of years, the vicissitudes of the Yellow River and surrounding geographic environment form a unique river loop in northwestern China, namely, the Yellow River Loop Valley. Results from both observations and a climate model indicate that the summer rainfall over the Yellow River Loop Valley region can be remotely controlled by sea surface temperature fluctuations in the eastern subtropical North Pacific. This far-reaching teleconnection is achieved by an atmospheric wave train emanating from the eastern subtropical North Pacific, with a long traveling journey from the western to the Eastern Hemisphere. Fur- thermore, it is found that the SST forcing pattern resembles the Tropical Pacific Meridional Mode, a mode characterized by an interhemispheric temperature gradient and shift of the intertropical convergence zone in the eastern tropical Pacific.

1. Introduction oceanic variability, snow cover over the Tibetan Plateau, and land surface processes. Located in the middle of the Yellow River Valley, the El Nin˜ o–Southern Oscillation (ENSO) has profound Loop region is one of the major agricultural production influences on the interannual variability of the global zones in many Chinese dynasties. Predicting the rainfall climate (Webster et al. 1998) including the East Asian over the Yellow River Loop Valley (YRLV) is of signif- summer monsoon (e.g., Fu and Teng 1988; Ju and Slingo icant societal and economic benefit, but few studies so far 1995; Zhang et al. 1996; Tao and Zhang 1998). Studies have been devoted to this specific area. Zhong et al. (2006) have suggested that ENSO can affect East Asian climate analyze some characteristics of the summer rainfall over through a Rossby wave–mediated Pacific–East Asian the YRLV, but what determines its variability remains teleconnection in the lower troposphere (Wang et al. unclear. In general, the summer rainfall over the eastern 2000; Xie et al. 2009). Based on a singular value de- part of China is influenced predominantly by the East composition (SVD) analysis, Lau and Weng (2001) found Asian monsoon system, and its year-to-year variations that the summer rainfall variability over China is associ- may involve multiple processes, including surrounding ated with several different global sea surface temperature (SST) modes, and the 1997/98 rainfall anomalies over China can be explained by the mode representing the growing phase of the El Ninõ and the mode representing Corresponding author address: Dr. Lixin Wu, Physical Oceanography the transition from El Ninõ and La Ninãphases. Laboratory, Ocean University of China, 5 Yushan Road, Qingdao 266003, China. The studies mentioned above have pointed out the E-mail: [email protected] important role of the central-eastern tropical Pacific

DOI: 10.1175/2010JCLI3844.1

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FIG. 1. The SVD modes of summer SST over the Paciﬁc and rainfall over China: (a) SVD1 SST, (b) SVD1 rainfall, (c) SVD1 PCs, (d) SVD2 SST, (e) SVD2 rainfall, and (f) SVD2 PCs. Contour interval for SST and rainfall is 0.1 with 0 and 60.1 contours omitted. Shaded areas exceed the 95% signiﬁcant level based on a Student’s t test.

coupled ocean–atmosphere interaction in summer rain- observations and model simulations, we found that fall variability over China. Recent studies have identified the TPMM can profoundly affect the summer rainfall a mode characterized by meridional SST gradient cou- over the YRLV. This far-reaching teleconnection is pled with a shift of the intertropical convergence zone achieved by an atmospheric wave train emanating from (ITCZ) in the eastern tropical–subtropical Pacific, the eastern subtropical North Pacific (ESNP), with a namely, the Tropical Pacific Meridional Mode (TPMM; long traveling journey from the western to the Eastern Chiang and Vimont 2004). This mode differs from the Hemisphere. zonal ENSO mode in the seasonal phase lock, time The paper is organized as follows. Observational ev- scales, and coupled ocean–atmosphere processes and idences are presented in section 2. Section 3 presents can trigger an ENSO event (Chiang and Vimont 2004; modeling results. The paper is concluded with a sum- Chang et al. 2007; Wu et al. 2007, 2010). Here, from both mary and some discussions.

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FIG. 2. As in Fig. 1, but for SVD3.

2. Observational evidence as the principal component (PC). The second SVD mode, accounting for 17.9% of the total squared covariance, is The rainfall data used in this study are based on a 160- characterized by anomalous warm SST in the western station monthly rainfall dataset over the mainland of tropical–subtropical Pacific and wetter-than-normal con- China for the period of 1951–2001 (from the National ditions over the Yangtze River valley (Figs. 1d and 1e). Climate Center in China). This dataset has been also The corresponding PCs correlate at 0.68 (Fig. 1f). These used by Lau and Weng (2001) in their analysis. The two modes have been identified and discussed in many SST data are taken from the National Oceanic and At- previous studies (e.g., Weng et al. 1999; Lau and Weng mospheric Administration’s (NOAA) extended recon- 2001; Li 2008). structed SST (ERSST) (Smith and Reynolds 2003). The The third SVD mode explains 13.5% of the total atmospheric data used are from the National Centers squared covariance between the rainfall and SST anom- for Environmental Prediction–National Center for At- alies, and explains 10.4% and 5.9% of the variances of mospheric Research (NCEP–NCAR) reanalysis (Kalnay SST and rainfall, respectively. The SST pattern shows a et al. 1996). To be consistent with the precipitation data, wave train structure in the eastern North Pacific (Fig. 2a), all data used here are from 1951 to 2001. with cold anomalies in the tropical and extratropical North Pacific and warm anomalies stretching from the a. Covariability of the Pacific SST and rainfall over Gulf of Alaska southwestward to the central subtropi- the YRLV cal North Pacific, resembling the TPMM (Chiang and Vimont 2004). Coupled with the SST pattern, the het- To examine the relationship between the spatial and erogeneous rainfall pattern shows positive correlations temporal variability of the rainfall over China and SST predominantly localized in the YRLV and some nega- anomalies, we first perform a SVD analysis on the sum- tive correlations in far eastern China between the mer rainfall over China and simultaneous SST anomalies Yangtze River and the Yellow River Valley (Fig. 2b). by following Lau and Weng (2001), but with a focus on The PCs of the rainfall and SST are correlated at 0.72 the Pacific basin instead of the global SST. and display pronounced interannual–decadal variations The first SVD mode is characterized by an El Nin˜ o– (Fig. 2c). like SST pattern and drier-than-normal conditions over To assess the time scales of the coupled ESNP SST– North China (Figs. 1a and 1b). This mode accounts for YRLV rainfall mode, we perform the Morlet wavelet 28.1% of the total squared covariance with the corre- transform for the PCs of the coupled mode. The wavelet sponding time series correlated at 0.77 (Fig. 1c). For analysis reveals a pronounced decadal peak around simplicity, here we define the time series of a SVD mode 10 years for both the SST and the rainfall variations (Fig. 3).

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FIG. 3. Wavelet analysis for SVD3 PCs: (a) PC1 wavelet spectra, (b) PC1 wavelet coefficients, (c) PC2 wavelet spectra, and (d) PC2 wavelet coefficients. In (b) and (d), shading denotes negative values. In (a) and (c), the dashed lines represent the 95% significant level.

Besides 10-yr variability, the rainfall also exhibits sub- rainfall, respectively (Fig. 4). For the SST pattern, the stantial biennial variability, while the SST also exhib- wavelike cold–warm–cold SST anomalies from the its bidecadal variability. Overall, the coupled mode is tropical to extratropical North Pacific remain signifi- distinguished by the decadal covariability between the cant (Fig. 4a), which is very similar to Fig. 2a. For the ESNP SST and the YRLV rainfall. The decadal vari- rainfall pattern, the dominant feature is the strong cor- ability identified here is consistent with the decadal var- relation in the YRLV (Fig. 4b), which is very similar to iation of the TPMM (Wu et al. 2010) as well as the North Fig. 2b. There are 2 PCs that are significantly correlated Pacific basin-scale variation (Qiu et al. 2007). at 0.75 (Fig. 4c). Compared to SVD analyses under with The ESNP SST–YRLV rainfall mode may be con- and without ENSO (Fig. 2 versus Fig. 4), the coupled taminated by the ENSO signals (Wang et al. 2000; Xie ESNP SST–YRLV rainfall mode is a mode independent et al. 2009). To eliminate the ENSO influences, we sub- from the impacts of ENSO. It is also noted that some tract the linear regressions of both rainfall and SST negative rainfall anomalies exists in the Yangtze River anomalies against the prior Spring [March–May (MAM)] valley, which are likely attributed to warm SST anom- Ninõ-3 SST index from the data and then repeat the SVD alies in the western tropical Pacific (Huang and Sun analysis. The coupled mode emerges as the second SVD 1992). mode, which explains 18.0% of the total squared co- To further demonstrate the covariability between variance between the rainfall and SST anomalies, and the summer rainfall over the YRLV and the SST in explains 11.0% and 6.7% of the variances of SST and the ESNP, we average the summer rainfall anomalies

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FIG. 4. As in Fig. 2, but for the SVD2 of SST and rainfall, with ENSO signals are statistically eliminated by subtracting the linear regressions against the Nin˜ o-3 SST index averaged over 58S–58N, 1508–908W in spring (MAM). observed by 13 weather stations (Huhehaote, Baotou, the TPMM (Chiang and Vimont 2004). To demonstrate Xiaba, Tianshui, Minxian, Yulin, Yanan, Xifengzhen, that, we first remove the ENSO signal from the SST Lanzhou, Yinchuang, Xining, Linxia, and Wuwei) in as described above, and then we perform empirical the YRLV and correlate them with the global SST anomalies (Fig. 5a). The correlation pattern in the Pa- cific shows a great similarity with the SVD SST mode (Fig. 2a), displaying a north–south dipole in the eastern tropical and subtropical North Pacific. This strongly suggests a potential link between the YRVL rainfall and the eastern tropical–subtropical North Pacific SST variations. The emergence of correlation in the eastern equatorial Pacific may also indicate a potential mediation of ENSO in the covariability between the summer YRLV rainfall and the TPMM. In this regard, we again eliminate the ENSO influences statistically by subtracting the corresponding linear regression against the Nin˜ o-3 SST index from both the SST and the rainfall anomalies and repeat the above correlation analysis (Fig. 5b). In this case, the correlation pattern remains similar to that in the presence of ENSO influences (Fig. 5b versus Fig. 5a). Indeed, the correlation in the ESNP becomes even stronger. This indicates that the ENSO may not be a necessary mediator for the ESNP SST– YRLV rainfall covariability. The wavelet analysis of the time series of the ESNP SST–YRLV rainfall under re- moval of ENSO (Fig. 4c) further indicates that this coupled mode displays similar decadal (;10 years) covariability as that in the presence of ENSO influences FIG. 5. Correlation of the global SST with the YRLV summer rainfall anomaly: (a) with ENSO and (b) without ENSO influences. (not shown). Contour intervals for SST and rainfall are 0.1 with 0 and 60.1 The SST SVD pattern in the eastern tropical sub- contours omitted. Correlations exceeding 0.28 are statistically tropical North Pacific region has a strong projection on significant at the 95% level.

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FIG. 6. (a) Leading EOF mode of the eastern tropical–subtropical North Paciﬁc summer SST (shaded and white contours with interval at 0.18C), surface wind (arrows, m s21) and di- vergence anomalies (thin black contours, 1026 s21) (obtained by regression against the PC of the leading SST EOF mode), and climatological total precipitation (thick black contours with 5 mm day21 intervals for values .20 mm day21). (b) Normalized PC of SST EOF1. (c) Cor- relation map of the summer rainfall over China and the leading PC of SST EOF1 with 0.1 contour interval with 0 and 60.1 contours omitted. Shaded areas exceed the 95% signiﬁcant level based on a Student’s t test.

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1970s and after the early 1980s and slightly weaker in between. Overall, despite some decadal variations, the correlation between the ESNP SST and the YRLV rainfall remains significant. b. Teleconnection from the ESNP to the YRLV What are the possible mechanisms conveying the remote influences of the ESNP SST to the YRLV rainfall? It is conceivable that anomalous heating associated with the shift of the mean ITCZ induced by the ESNP SST variability can trigger Rossby wave trains to influence remote regions; to demonstrate that, the summer geopotential height anomalies at 300 hPa are regressed against the PC of the TPMM (Fig. 8a). A circumglobal FIG. 7. An 11-yr running correlation for PCs of SVD mode be- wave train is identified, with the eastern subtropical tween Pacific SST and rainfall over China: (a) SVD3 with ENSO signal (shown in Fig. 1c) and (b) SVD2 without ENSO signal North Pacific, the eastern United States–western Atlan- (shown in Fig. 2c). Dashed and solid lines represent the 95% and tic, the northeastern Atlantic–western Europe, west- 99% significant levels, respectively. central Asia, northeast Asia, and the North Pacific being positively correlated and with central North America, southern Europe stretching to north-central Asia, and the orthogonal function (EOF) analysis of the eastern eastern North Pacific being negatively correlated (Fig. 8a). tropical–subtropical summer SST. The leading EOF This circumglobal teleconnection pattern resembles the mode is distinguished by a meridional SST gradient with fifth EOF mode of the summer Northern Hemispheric strong SST anomalies in the subtropical region and some geopotential height (300 hPa) variations (not shown). It modest opposite anomalies in the equator (Fig. 6a). The is noted that a southeast–northwest pressure dipole rides regression of the surface wind against the PC demon- over the YRLV. The warm moist air associated with the strates a weakening (strengthening) of the northeastern downstream anomalous anticyclone encounters the up- trade winds associated with a positive (negative) me- stream anomalous cyclone over the YRLV, setting up ridional SST gradient (Fig. 6a). The presence of warm sufficient conditions to enhance the rainfall in this area anomalies north of the equator sets up an anomalous (Fig. 8b). In addition, the convergence of moisture over negative meridional pressure gradient that causes the the north Indian continent will enhance the summer southwesterly geostrophic wind anomalies to decelerate rainfall there (Ding and Wang 2005), which is out of this the northeastern trade winds, thus inducing anoma- paper’s scope. lous convergence northward and a poleward shift of the This global-scale teleconnection pattern also shares mean ITCZ (Fig. 6a). The PC exhibits some interannual– some similarities with the summer circumglobal tele- decadal variability (Fig. 6b), and is significantly correlated connection mode identified in an earlier study by Ding with the PCs of the SST–rainfall SVD mode (Fig. 2c), with and Wang (2005), although different indices are used. spatial correlation coefficients at 0.90 and 0.70, re- In their study, the heating sources associated with the spectively. We further correlate the PC of the TPMM Indian summer monsoon and ENSO variability are sug- with the summer rainfall over China and reproduce the gested to play a critical role in maintaining this global- anomalous rainfall pattern dominated by anomalies over scale summer teleconnection. A recent study by Ding the YRLV and far eastern China between the Yellow et al. (2011) further demonstrates that the shift of ITCZ River and the Yangtze River (Fig. 6c). The maximum over the east Pacific may be another source triggering correlation is about 0.5 right at the northernmost of the the global-scale teleconnection pattern, although it is Loop, indicating about 25% of the local rainfall vari- somewhat weaker than that of the Indian summer mon- ability (a local maximum of 55%) can be explained re- soon. Our analysis here provides support for this ESNP motely by the TPMM. forcing mechanism, which can substantiate the sum- To further examine the stability of the coupled ESNP mer circumglobal teleconnection. The circumglobal tele- SST and the YRLV rainfall covariability, we calculate connection triggered by the ESNP SST is indicative of an 11-yr running correlation for PCs of the ESNP SST potential Rossby wave propagation along some atmo- and the YRLV rainfall SVD mode with and without spheric waveguide in the Northern Hemisphere. ENSO influences, respectively (Fig. 7). In both cases, To further study the propagation of the Rossby waves, the correlations appear to be significant before the early we calculate the stationary Rossby wave activity flux based

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FIG. 8. (a) Regression map of 300-hPa geopotential height against the normalized PC of SST EOF1 with 2-m contour interval and with shaded areas exceeding the 95% signiﬁcant level by a Student’s t test. (b) As in (a), but for total moisture transport in g (m s)21. (c) Stationary wave activity ﬂuxes calculated from (a) in m2 s22.

on the regressed geopotential height anomaly (Fig. 8a) are characterized by troughs over the central North (Plumb 1985). The wave activity flux over the Northern Pacific and Atlantic and ridges over North America and Hemisphere appears to indicate an eastward wave path north Africa–southern Europe (Fig. 9a). The stationary from the eastern North Pacific to eastern Asia (Fig. 8c). Rossby wave group velocity core is about 30 m s21 To demonstrate the characteristics of the station- (Fig. 9b) and it takes about 2 weeks to span a circumglobal ary Rossby, we calculate the Rossby wavenumber and circle in the midlatitude. As shown in Fig. 9c, the Rossby group velocity based on the climatological geopotential wavenumber is between 4 and 8 in the midlatitudes of the height at 300 hPa following Ding and Wang (2005). The Northern Hemisphere with a meridional average of 6, method was introduced by Hoskins and Ambrizzi (1993). consistent with the wave pattern revealed by the re- The 300-hPa climatological geopotential height fields gression of the 300-hPa geopotential height anomalies

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The above analyses may suggest that the summer rainfall over the YRLV may be predicted based on the SST anomalies over the eastern subtropical North Pacific by about 2–3 months ahead. However, it remains elusive why the prediction time scale is only about 2–3 months. To address this question, we examine the persistency barrier following Chen et al. (2007) to calculate the autocorrelations of the SST index of the TPMM (Table 1). The TPMM SST index is obtained by projecting monthly SST anomalies onto the SST SVD mode (Fig. 2a). In Table 1, the autocorrelations statistically significant above the 95% level are highlighted, and the freedom of the time series (estimated by following Bartlett 1935) is shown in brackets. 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 TPMM SST anomalies initiated in late winter and early summer can persist 6–12 FIG. 9. (a) Climatological 300-hPa geopotential height in JJA. (b) Group velocity of stationary Rossby wave action (m s21). (c) months, while in late summer and early winter it can Stationary Rossby wavenumber based on 300-hPa climatological persist 3–5 months. Therefore, a late-winter barrier exists zonal flow. for the SST anomalies initiated from August to December. It is this barrier that limits the predictive time scale of the YRLV rainfall anomalies based on the TPMM SST against the PC of the ESNP SST (Fig. 8a). A comparison anomalies. of Figs. 9 and 8 suggests that the Rossby wave propagates eastward approximately along the zonal jet waveguide. 3. Modeling results c. Potential predictability of the summer rainfall over The observational analyses above suggest that the the YRLV ESNP SST may be used as a predictor of the summer rainfall in the YRLV with a lead of about one season. To The above analysis indicates that the relationship of further assess this potential predictability, we design a covariability between the ESNP SST and the YRLV set of AGCM experiments with the ECHAM5 model. rainfall is robust in summer. However, can we predict The ECHAM5 model is developed at the Max Planck the summer rainfall over the YRLV based on the ESNP Institute for Meteorology, which originally evolved from SST anomaly? To assess the potential predictability, we the spectral weather prediction model of the European repeat the SVD analysis between the Pacific SST and Centre for Medium-Range Weather Forecasts (ECMWF) summer rainfall over China, but with the SST leading the (Roeckner et al. 2003). The model employs a spectral rainfall by 1–3 months. dynamical core. A hybrid coordinate system is used in the The SVD mode of the Pacific SST averaged from May vertical direction with the sigma coordinates at the lowest to July and the YRLV summer (June–August) rainfall model level gradually transforming into pressure co- explains 12.9% of total squared covariance, and exhibits ordinates in the lower stratosphere. The model used here a similar pattern with that at 0 lag (Fig. 10). Despite has T42 resolution and 19 vertical levels. The detailed similarities, it is also noted that the ESNP SST anomalies description of the ECHAM5 model is given by Roecker are somewhat weaker and the equatorial SST anomalies et al. (2003). are stronger (Fig. 10a versus Figure 2a). The summer The model successfully captures the climatological rainfall pattern over China remains virtually the same as summer geopotential height with troughs in the central that at 0 lag (Fig. 10b). The PCs are significantly corre- North Pacific and Atlantic (Fig. 11a), a stationary Rossby lated (0.70) (Fig. 10c). The SVD mode of the Pacific wave group velocity with a maximum of about 35 m s21 April–June SST and the YRLV summer rainfall also in the midlatitude of the Northern Hemisphere (Fig. 11b), exhibits a similar pattern with that at 0 lag, although the and a stationary Rossby wavenumber with a meridional amplitudes of the SST anomalies are much reduced mean of 5–7 (Fig. 11c). (Figs. 10d and 10e). The corresponding PCs are also To dynamically assess the relationship between the significantly correlated (0.71). ESNP SST anomalies and the summer YRLV rainfall,

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FIG. 10. As in Fig. 1, but with SST leading rainfall over China by (a),(c) 1 month and (b),(d) 2 months.

two sets of ensemble AGCM experiments are carried. For the fixed positive SST anomaly forcing in the ESNP, In the first experiment, the AGCM is forced by a fixed the ensemble-mean response of the summer rainfall over SST anomaly from May to September over the ESNP China is largely localized over the YRLV extending to the (Fig. 12a). The SST anomaly has the same pattern and eastern part of Inner Mongolia (Fig. 12b). The positive magnitudes as the TPMM mode in the eastern sub- summer [June–August (JJA)] rainfall anomaly has a max- tropical North Pacific (Fig. 6a). The experiment consists imum amplitude of 0.6 mm day21 right over the YRLV. In of 10-member ensemble runs, with each run starting addition, drier-than-normal conditions are also found in from a different initial state. In the second experiment, the southeastern flank of the wetter region (Fig. 12b). In the AGCM is forced by the observed SST anomalies from general, the ensemble-mean rainfall response is similar 1956 to 2001 over the ESNP. This set of experiments to the rainfall pattern of the SVD mode (Fig. 2b). The consists of 13-member ensemble runs. We will focus on ensemble-mean response of the geopotential height at the summer atmospheric teleconnection and the rainfall 300 hPa displays a wave pattern with a wavenumber 6 over the YRLV. in the midlatitudes of the Northern Hemisphere, with

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TABLE 1. Autocorrelation of the TPMM index for the period 1951–2001 as functions of the 12 calendar months with a lag time of 1–12 months. Autocorrelation values signiﬁcant at the 95% level are denoted by bold font. The degrees of freedom of the time series are shown in parentheses.

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Feb 0.79(10) Mar 0.61(19) 0.84(8) Apr 0.55(22) 0.73(13) 0.86(7) May 0.50(25) 0.66(17) 0.72(14) 0.89(5) Jun 0.40(30) 0.56(22) 0.61(20) 0.81(9) 0.86(7) Jul 0.37(32) 0.51(24) 0.61(20) 0.77(11) 0.80(10) 0.86(7) Aug 0.31(36) 0.48(27) 0.55(23) 0.68(16) 0.66(18) 0.74(13) 0.85(8) Sep 0.31(35) 0.39(31) 0.37(32) 0.49(26) 0.54(23) 0.55(23) 0.59(21) 0.69(16) Oct 0.18(42) 0.31(35) 0.40(30) 0.54(23) 0.64(18) 0.60(20) 0.63(19) 0.57(22) 0.69(16) Nov 0.12(46) 0.21(41) 0.34(34) 0.51(26) 0.53(24) 0.55(23) 0.58(22) 0.53(24) 0.53(24) 0.86(7) Dec 0.06(48) 0.21(41) 0.29(37) 0.46(28) 0.47(27) 0.50(26) 0.55(23) 0.47(27) 0.47(27) 0.68(16) 0.85(7) Jan 0.17(42) 0.33(33) 0.46(27) 0.52(24) 0.44(28) 0.43(29) 0.52(24) 0.48(27) 0.37(32) 0.49(26) 0.62(20) 0.77(12) Feb 0.28(36) 0.35(32) 0.38(30) 0.27(36) 0.29(35) 0.35(32) 0.23(40) 0.19(41) 0.34(33) 0.43(29) 0.54(24) Mar 0.32(33) 0.43(27) 0.26(36) 0.38(31) 0.39(31) 0.30(36) 0.14(44) 0.25(38) 0.31(36) 0.35(34) Apr 0.43(27) 0.31(33) 0.33(33) 0.38(31) 0.24(39) 0.13(44) 0.29(36) 0.28(38) 0.30(37) May 0.23(38) 0.21(40) 0.25(38) 0.12(45) 0.17(42) 0.31(35) 0.31(36) 0.30(36) Jun 0.16(43) 0.15(43) 0.01(51) 0.12(45) 0.24(38) 0.24(39) 0.20(41) Jul 0.13(44) 0.05(49) 0.11(45) 0.20(41) 0.13(45) 0.11(46) Aug 20.08(47) 20.04(49) 0.15(43) 0.08(46) 0.12(45) Sep 0.01(51) 0.12(45) 0.09(47) 0.13(44) Oct 0.08(47) 0.03(50) 0.07(48) Nov 20.11(45) 20.06(48) Dec 20.10(45)

positive anomalies over the eastern North Pacific, the circumglobal wave train, which correlates with the ob- western North Atlantic, western Europe, eastern China– served (Fig. 8a) at 0.6 and 0.7, respectively. Furthermore, western Pacific, and with negative anomalies over North it is found that none of the EOF modes in the control America, the eastern North Atlantic, northeastern Asia, simulation in which the AGCM is forced by climatolog- and in the vicinity of the Alaska peninsula (Fig. 12c). In ical SST (not shown) resembles the circumglobal tele- general, the simulated dynamic response is similar to connection pattern. This suggests an important role of the the observed atmospheric circumglobal teleconnection (Fig. 8a). Therefore, the modeling results provide strong support for the relationship between the ESNP SST and summer rainfall in the YRLV region derived from the statistical analyses. With the observed historical SST forcing over the ESNP, the ensemble-mean response of the rainfall over the YRLV significantly correlates with the observed rainfall anomalies, with a maximum correlation at 0.40 (Fig. 13a) and a regional-mean correlation at 0.33 (Fig. 13b), which exceed the 95% significance level using a Student’s t test. This suggests the ESNP SST can impact the rainfall over the YRLV. To examine the Rossby wave teleconnection mechanism, we calculate the regression of simulated geopotential height anomalies at 300 hPa against summer SST anomalies over the ESNP (Fig. 13b) and also the leading EOF of the simulated summer geopotential height at 300 hPa (Fig. 13c). Despite some notable differences, both the regression and the leading EOF mode show a similar FIG. 11. As in Fig. 9, but for ECHAM5 model.

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4. Summary and discussion From both observations and climate model simulations, we found that summer rainfall over the YRLV can be significantly impacted by the Tropical Pacific Meridi- onal Mode. The migration of the mean ITCZ associated with the fluctuations of the northeastern trade winds can induce anomalous heating anomalies, which can trigger an atmospheric wave train to convey the ESNP SST influence to the YRLV region. The atmospheric teleconnection bears strong similarities to the summer circumglobal teleconnection identified by Ding and Wang (2005). Our study here suggests the summer rainfall over the YRLV can be partly predicted by the ESNP SST variations. Then the question is what are the mechanisms driving SST changes in the ESNP? One important forcing is the North Pacific air–sea interaction. The winter North Pacific atmospheric circulation change can induce an SST anomaly in the ESNP in spring, which can be subse- quently amplified by wind–evaporation–SST (WES) feedback (Xie and Philander 1994) or seasonal footprint feedback (Vimont et al. 2001; Chiang and Vimont 2004; Chang et al. 2007; Wu et al. 2007, 2010). Studies have in- dicated that the TPMM peaks in early summer, in contrast to fall for ENSO. This so-called seasonal footprint process (Vimont et al. 2001) can extend the predictability of the summer rainfall over the YRLV by a few months. Our study here also provides strong support for the TPMM forcing mechanism for the summer global-scale atmospheric teleconnection (Ding et al. 2011). Past studies have identified the important role of ENSO and the Indian summer monsoon variability in driving this teleconnection (Ding and Wang 2005). Our AGCM experiments also indicate that the TPMM can affect the summer rainfall in the north Indian Ocean (not shown), consistent with the observed stationary wave activity flux in that region associated with the TPMM (Fig. 8c). Therefore, it is possible that the TPMM can influence rainfall over the North Indian Ocean, which can further affect rainfall over northern China. Given the strong coupling between the TPMM and the north–south migration of the ITCZ in summer, the induced diabetic heating can sustain the summer global-scale atmospheric FIG. 12. (a) Forcing pattern of ESNP model simulated SST teleconnection mode. (0.18C contour interval with 0 and 0.1 contours omitted); (b) rainfall (0.2 mm day21) over China; and (c) 300-hPa geopotential height (2-m contour interval) responses to the imposed ESNP SST Acknowledgments. This work is supported by the forcing. Shaded areas exceed the 95% significant level. National Science Foundation of China (NSFC) Distin- guished Young Investigator Project (40788002). Chun Li Tropical Pacific Meridional Mode in triggering the sum- is supported by the Open Research Program of LASG mer circumglobal teleconnection pattern. The stationary and NSFC Grants (40906003, 40830106). We are indebted Rossby wave action flux analysis confirms that the Rossby to both reviewers for their constructive comments, which wave energy is dispersed eastward rather than westward greatly improved the paper in many aspects. Discussions (Shaman and Tziperman 2007). with Drs. Guoxiong Wu and Bin Wang were helpful.

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FIG. 13. (a) Correlation between the simulated summer rainfall and the observed over the YRLV. (b) Time series of the observed (dashed black) and simulated (gray and solid black) summer rainfall anomalies averaged over the YRLV [dashed black box in (a)]. The solid black curve denotes the ensemble mean of the model-simulated rainfall anomalies. (c) Regression of the simulated ensemble-mean 300-hPa geopotential height anomalies against the SST over the ESNP in summer. (d) Leading EOF (13.2%) of the simulated ensemble-mean 300-hPa geopotential height in summer. Shaded areas in (c) exceed the 95% signiﬁcance level.

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