J. Meteor. Res.  2015, Vol. 28 Issue (2): 214-227   PDF    
http://dx.doi.org/10.1007/s13351-015-4103-1
The Chinese Meteorological Society
0

Article Information

XIAO Ziniu, SHI Wenjing, YANG Ping. 2015.
Possible Causes of the Interdecadal Transition of the Somali Jet Around the Late 1990s
J. Meteor. Res., 28(2): 214-227
http://dx.doi.org/10.1007/s13351-015-4103-1

Article History

Received October 15, 2014;
in final form December 11, 20141
Possible Causes of the Interdecadal Transition of the Somali Jet Around the Late 1990s
XIAO Ziniu1,2, SHI Wenjing1,2,4 , YANG Ping3    
1 Department of Atmospheric Science, Nanjing University of Information Science & Technology, Nanjing 210044, China;
2 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;
3 China Meteorological Administration Training Center, Beijing 100081, China;
4 Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison, Madison, WI 53706, USA
ABSTRACT:This observational study demonstrates that the Somali jet (SMJ) experienced a notable interdecadal transition in not only its lower-level parts (< 850 hPa) but also its higher-level parts (850-600 hPa) in the late 1990s. The results also show that the jet at higher level is more significantly related to East Asian monsoon rainfall than that at lower level. Thus, a new whole-layer SMJ (WSMJ) index which includes variations of the higher-level jet is defined based on the average meridional wind speed at five levels (1000-600 hPa). The interdecadal transition of the SMJ can be mainly attributed to the meridional thermal contrast anomalies near the equator which are associated with the three-pole pattern of the southern Indian Ocean.
Keywordsinterdecadal transition     Somali jet     index     Asian summer monsoon     southern Indian Ocean     sea surface temperature    
1. Introduction

The low-level monsoon circulation over the westernIndian Ocean is characterized by southeasterlytrade winds in the Southern Hemisphere,a strong and narrow cross-equatorial flow(CEF)off the coast of Somalia, and southwesterly winds over the Arabian Sea.The low-level cross-equatorial flow,often known as theSomali jet(SMJ),is an important channel for mass,momentum, and heat exchange between the Northern and Southern hemispheres, and acts as a “bridge” forweather and climate between the hemispheres. Thislow-level strong current is most pronounced at about700 hPa,has its core at approximately 925 hPa(Qian et al., 1987; Chakraborty et al., 2009; Pu and Cook, 2010), and was discovered early in the mid 1960s(Bunker,1965; Joseph and Raman, 1966). Findlater(1969a,b)first showed that the SMJ located between38◦ and 55◦E originates from trade wind easterlies overthe southern Indian Ocean,crosses the equator alonga narrow longitudinal belt over the Somali coast, and turns towards India as a westerly current close to theArabian Sea. SMJ changes on multiple timescales,not just seasonally,but also interannually and interdecadally(Boos and Emanuel, 2009; Pu and Cook, 2010). Although the SMJ is the strongest CEF,themagnitude of its interannual variation is smaller thanthat of other CEFs in the Eastern Hemisphere(Lei and Yang, 2008; Tang et al., 2009). The interdecadalvariation of the SMJ is greater than its year-to-year variations(Zhu,2012). Shi et al.(2007)found thatthe strength of the SMJ increases,on average,by 0.25m s−1 per decade.

As part of the monsoon system(Ding,2005),theintensity of the SMJ was found to be positively relatedto the amount of rainfall in most regions of India,especiallyin the monsoon regions on both interannual and interdecadal timescales(Findlater,1977; Cadet and Desbois, 1981; Halpern and Woiceshyn, 2001; Cong et al., 2007). A stronger SMJ was related to southwesterlywind anomalies over the Arabian Sea,which canbring more water vapor to the Indian summer monsoonregion(Chakraborty et al., 2009) and thus morerainfall over most regions of India. Krishnamurti et al.(1976)also considered that cross-equatorial winds arestronger during a strong Indian monsoon than duringa weak one. Traditionally,the energy of the SMJ willbe dispersed northeastward,bringing large amountsof water vapor to the East Asian summer monsoon regions(Shi et al., 2001; Wang and Xue, 2003; Wang and Yang, 2008; Shi and Xiao, 2013; Dai and Xiao, 2014),but no strong connection has been found between theSMJ and East Asian summer rainfall on interannualtimescales(Lei and Yang, 2008; Zhu,2012). However,on the interdecadal timescale,the link between SMJintensity and East Asian summer rainfall shows a positivecorrelation over north of the Yangtze River,buta negative correlation over south of the Yangtze River(Zhu,2012). In addition,the variability of the SMJis also associated with the location of the western Pacificsubtropical high,ENSO, and the Pacific decadaloscillation(PDO; Chen et al., 2005; Wang and Yang, 2008).

Several factors have been linked to SMJ formation,including the East African Mountains,the betaeffect,l and -sea contrast,baroclinicity in the boundarylayer, and diabatic heating(Krishnamurti et al., 1976; Hart,1977; Bannon, 1979a,b; Krishnamurti and Wong, 1979; Bannon,1982; Li et al., 2006; Xu et al., 2010). Using a primitive equation model with specifiedzonal flow,orography, and diabatic heating,Rodwell and Hoskins(1995)determined that the l and -sea contrastcaused by orography plays an important role inSMJ strength variability. Chakraborty et al.(2009)suggested that the SMJ can occur even in the absence of African orography. Therefore,the cause and effectcorrelation between the thermal state of the oceans and SMJ variation remain a matter of debate.

Recently,the interdecadal shift of climate occurringin the late 1990s has become a focus of study. Forexample,Wu et al.(2012)found that September Arcticsea ice extent(SIE)showed a pronounced negativetrend over the past two decades, and summer(July–September)Arctic surface wind pattern experiencedan interdecadal shift in the late 1990s. Additionally,sea surface temperatures(SSTs)in North Atlantic and summer atmospheric circulation over Eurasia also exhibitedan interdecadal shift in the late 1990s(Honda et al., 2009; Wu et al., 2013). Actually,some studiesfound that the vertical structure of the SMJ alsoshowed an interdecadal variation around 1995(Qiu and Sun, 2013; Xie et al., 2013; Qiu et al., 2014). Zhu(2012)suggested that the Somali CEFs above 850 hPashowed a strong upward trend in the late 1990s,basedon analysis of data for the period 1950–2010.

Despite many previous studies,open questions remain and further research is required. To characterizethe strength of the SMJ,a jet index has previouslybeen defined as the area-average of the JJA(June–July–August)925- or 850-hPa meridional wind speedaround Somalia(Findlater,1969a; Li and Lou, 1987;Wang and Xue, 2003; Lin et al., 2008; Wang and Yang, 2008). However,little attention has been paid to thehigher-level component of the SMJ(i.e.,the southerlyjet above 850 hPa). Therefore,in this study,we focuson the vertical structure of the SMJ,especiallythe variation of the higher-level jet. To characterizethe strength of the SMJ,we established a new jet indexbased on the whole-layer meridional wind speed, and investigated its influence on the Asian summermonsoon. Finally,we also evaluated possible reasonsfor the interdecadal transition of the vertical structureof the SMJ around 1995. The data used in thisstudy were mainly from the NCEP/NCAR reanalysisdataset(Kalnay et al., 1996). As satellite data wereincorporated into the reanalysis after the late 1970s(Sterl,2004),which increases the reliability of the reanalysis,we focus here on the period 1979–2013.

The remainder of the paper is organized as follows.Section 2 outlines the data used in this study. Section 3 describes the temporal features and verticalstructure of the SMJ during the period 1979–2013,aswell as its connection with the Asian summer monsoon.Possible reasons for the interdecadal transitionof the SMJ vertical structure in the late 1990s are presentedin Section 4, and Section 5 presents the conclusions.

2. Data and approach

The primary atmospheric parameters consideredin this study are wind speed(m s−1) and air temperature(℃)at 17 st and ard pressure levels,as well asvertical wind speed(Pa s−1)at 12 st and ard pressurelevels,obtained from the NCEP/NCAR reanalysisdataset(Kalnay et al., 1996)on a monthly timescale and with a spatial resolution of 2.5◦ × 2.5◦. Becauseof substantial differences between the NCEP/NCARreanalysis and the ECMWF reanalysis products,particularlyaround the SMJ region(Annamalai et al., 1999),monthly wind data from the higher-resolution(1.5◦ × 1.5◦)ECMWF Interim Re-Analysis(ERAInterim;Simmons et al., 2007)were also used for comparison.

The precipitation datasets include:(1)monthlymean precipitation reconstruction data(PREC; Chen et al., 2002; 2.5◦ × 2.5◦; mm day−1)provided bythe National Oceanic and Atmospheric Administration(NOAA)/Climate Prediction Center(CPC), and (2)the monthly mean Global Precipitation ClimatologyCenter monitoring product(GPCC; Rudolf et al., 1994; 1.0◦ × 1.0◦; mm)data. We obtained monthlymean SST data(℃)from the NOAA extended reconstructedSST 2◦ × 2◦ dataset(Smith and Reynolds, 2004). All datasets cover the period 1979–2013. Summeris defined as the average from June to August.Statistical treatment of the data was based on correlationanalysis,composite analysis, and sliding correlationanalysis verified by Student’s t-test.

3. Variations in summer SMJ intensity and itsinfluence on the Asian summer monsoon 3.1 Temporal evolution and vertical structure

Located over the equator at 40◦–55◦E,the SMJ is the strongest of the Eastern Hemisphere CEFs and is most prominent during JJA. To reflect the temporalevolution and vertical structure of SMJ intensity,the areal mean(40◦–55◦E at the equator)meridionalwind speed and anomalies in summer at different levelswere calculated. This averaging region was selected tocapture the maximum southerly wind speed over theeastern coast of Kenya. Figure 1a shows that the SMJhas maximum wind speeds(> 10 m s−1)at about 925hPa, and extends from surface to 600 hPa(Gao and Xue, 2006; Boos and Emanuel, 2009; Chakraborty et al., 2009). From the late 1990s,the vertical spread ofthe stronger SMJ(> 2 m s−1)significantly increasedto above 600 hPa. As shown in Fig. 1a,the higherlevel(850,700, and 600 hPa)jet intensities were persistentlylower than normal before the late 1990s,butabove normal after that. By contrast,variations inthe lower-level(1000 and 925 hPa)jet intensity followedan opposite pattern. Although the SMJ wasmuch stronger at 925 hPa(the maximum meridionalwind speed was over 10 m s−1)than at 700 hPa(themaximum meridional wind speed was about 3 m s−1),the st and ard deviation(SD)of the latter was muchhigher(925 hPa,SD = 0.53,700 hPa,SD = 1.53; Fig. 1b). The amplitude of the variation in the higher-leveljet is much greater than that in the lower-level jet,aspointed out by Qiu and Sun(2013).

Fig. 1.(a)Areal mean meridional wind speed(contour; interval 1 m s−1) and anomalies(shaded)at different levelsin summer over the SMJ region(40◦–55◦E at the equator)between 1979 and 2013.(b)Vertical profile of the st and arddeviation of the jet at different levels.(c)Normalized time series of the summer WSMJ index(solid curve),the jet indexat 925 hPa(dashed curve), and their difference(grey bar; WSMJ index minus jet index at 925 hPa)for 1979–2013.

To better characterize the overall strength of theSMJ at five levels(from 1000 to 600 hPa),a wholelayerSMJ(WSMJ)index was defined as the area and vertical average of the JJA meridional wind speed between1000 and 600 hPa over the area 40◦–55◦E atthe equator. The time series of the WSMJ index(solidcurve)is shown in Fig. 1c. Compared with the WSMJindex,the time series(dashed curve)of the jet indexat 925 hPa(as traditionally defined in previous studies;e.g.,Tang et al., 2009) and the difference(bar)between the two indices,are also displayed in Fig. 1c.The WSMJ index and the jet index at 925 hPa bothexhibit apparent interannual and interdecadal variations.However,there are great differences betweenthem(shown by the bars),which are caused by thedifferent methods used to calculate the two indices.TheWSMJ index is below normal before the late 1990s and above normal after that,while the jet index at 925hPa just reverses. There is an interdecadal transitionpoint around the late 1990s in the vertical structureof the SMJ.

This interdecadal transition can be further confirmedby using the dominant mode(that explainsabout 55.1% of the total variance)of the time-altitudecross-section of the SMJ intensity anomalies(Fig. 1a; shaded)through an empirical orthogonal function(EOF)analysis based on the NCEP/NCAR reanalysisdata(Fig. 2). This analysis shows that the higherlevel and lower-level jet changes are out of phase. Asfor the corresponding time coefficient(Fig. 2b),thereis evident interdecadal variability. The time series isnegative from the 1980s to the late 1990s,but positivefrom the late 1990s to the present. In combinationwith Fig. 2a,it is suggested that the lower-level jetintensities are decreasing while higher-level jet intensitiesare increasing.

Fig. 2.(a)First EOF of the JJA meridional wind anomaliesaveraged over 40◦–55◦E and (b)PC1 of the EOF decomposition(bar) and its 9-yr running mean(curve).

To isolate the decadal signal,the 9-yr runningmeans of the jet intensity anomalies at different levelsfrom the NCEP/NCAR reanalysis and ERA-Interimdatasets are displayed in Figs. 3a and 3b,respectively.Decadal variations of the jet intensities at different levelsare similar to those shown by Qiu and Sun(2013) and Qiu et al.(2014),who examined the vertical structureof the SMJ in summer. By comparison,the magnitudesof the NCEP/NCAR jet intensities are muchlarger than those of the ERA-Interim jet intensities atany level. At the higher levels(850–600 hPa),their spatial distributions are similar. While there are obviousdifferences at lower levels(< 850 hPa),commonfeatures are also visible. As stated by Pu(2012),manyfactors may contribute to the differences among thereanalysis datasets; for example,different sources ofobservations,physical parameterizations used in models and assimilation methods.

Fig. 3. Altitude-time cross-section of the 9-yr running mean summer SMJ intensity anomalies from(a)NCEP/NCARreanalysis and (b)ERA-Interim..

To investigate the linkage between the lower- and higher-level jets,correlations among all combinationsof jet indices at 1000,925,850,700, and 600 hPa werecalculated(Table 1). The correlation coefficient between jet indices at 1000 and 925 hPa is 0.892,significantat the 99% confidence level. Furthermore,correlationcoefficients among jet indices at 850,700, and 600 hPa all exceed the 99% confidence level,exceptfor that between 850 and 600 hPa,which exceeds the95% confidence level. Therefore,in this study,thelower-level jet is between 1000 and 925 hPa,while thehigher-level jet is between 700 and 600 hPa. The transitionlayer is 850 hPa.

Table 1. Correlation coefficients among jet indices at 1000,925,850,700, and 600 hPa
Note: Values significant at the 95% and 99% confidence levelsare in italics and bold,respectively.

Equally notable is that the correlation coefficientbetween the lower- and higher-level jets is negative,implying that their variations are out of phase,whichis consistent with the above analysis. The jet at thetransition layer is insignificantly related to the lowerleveljet.

3.2 Correlation with East Asian summer monsoon rainfall

The analysis above reveals that the jet intensityat higher levels also shows significant year-to-yearchanges. To emphasize the significance of the higher-level jet,we compared the relative roles of the jet intensitiesat five levels(1000,925,850,700, and 600hPa)in connection with the East Asian summer monsoonrainfall. Their relationships on the interannualtimescale are discussed here. A Gaussian filter witha window width of 9 yr was used to isolate the interannualtimescale. The results(figures omitted)showthat positive correlations(strong jet with strong precipitation, and vice versa)extend from the middlereaches of the Yellow-Yangtze River basin. However,larger coherent regions of strong positive correlationover the Yellow-Yangtze River basin are more evidentat the higher level than at the lower level.

When theWSMJ index,which includes variationsof the higher-level jet, and the summer precipitationdataset from GPCC(Fig. 4a) and NCEP/NCAR(Fig. 4b)are correlated,similar patterns emerge and thepositive correlation areas are largest. We defined aYellow-Yangtze River basin precipitation index basedon the correlations shown in Figs. 4a and 4b,averagingJJA rainfall data from GPCC over 29◦–36◦N,107◦–117◦E. The jets at 925 and 700 hPa represent thelower- and higher-level jets,respectively. The statisticalrelationship between Yellow-Yangtze River basinrainfall and SMJ intensity is further verified in Figs.4c–e,which shows the interannual variations of the jetindex at 925(Fig. 4c) and 700 hPa(Fig. 4d),theWSMJ index(Fig. 4e), and the Yellow-Yangtze basinprecipitation indices. Compared with the jet index at925 hPa,the relationship between the jet index at 700hPa and precipitation indices are more highly correlated,with a correlation coefficient of 0.527 that exceedsthe 99% confidence level. Interannual variationsof the WSMJ index are also significantly correlatedwith Yellow-Yangtze River basin precipitation,with acorrelation coefficient of 0.594,also exceeding the 99%confidence level. Traditionally,the jet at 925 hPa hasbeen thought to be more important than the jet atother levels because it is the core of the SMJ. However,our comparison shows that the jet index at 925hPa has a weaker connection with the examined rainfallthan does the WSMJ index. This may be becausethe wind near the surface may be more subject to thenoise,such as heat exchange,surface friction,large terrain(Tibetan Plateau), and so on,causing less watervapor to be brought into the East Asian monsoonregion by the lower-level jet than by the higher-leveljet.

Fig. 4. Correlations between theWSMJ index and the summer precipitation from(a)GPCC data and (b)NCEP/NCARreconstructed precipitation data during 1979–2013 for the 9-yr high-pass filtered data. Light to dark shadings indicate90%,95%, and 99% confidence levels. The time series of Yellow-Yangtze River basin precipitation index(dashed linewith open squares in(c),(d), and (e))from the GPCC data and the jet index at 925(c; grey bar) and 700 hPa(d; greybar),as well as the WSMJ index(e; grey bar),the correlation coefficients of their combinations are shown in the righttop of(c),(d), and (e).(f)Moving correlation of Yellow-Yangtze River basin precipitation index with the jet index at925(solid line with dot) and 700 hPa(solid line with hollow triangle),as well as the WSMJ(solid line with square)indices,obtained by using a 9-yr window; the horizontal dashed lines show the 90%,95%, and 99% significance levels.

Figure 4f shows the 9-yr sliding correlation betweenthe precipitation index and the jet index at 925 and 700 hPa,as well as the WSMJ index. The slidingcorrelation is unstable and insignificant before the late1990s,but becomes stable and significant thereafter,especially for the jet index at 700 hPa and the WSMJindex. This increased correlation after the late 1990smay be due to the strengthened higher-level jet. Aswe all know,there are four branches of the East Asianmonsoon moisture transport channel: 1)the moistureairflow crossing the equator by SMJ; 2)the southeastmonsoon moisture airflow from the southwest sideof the western Pacific subtropical high; 3)the crossequatorialmoisture airflow through the South ChinaSea along 105◦E; and 4)the moisture airflow broughtby the midlatitude westerly disturbance. The fourbranches converge in the Yangtze and Huaihe Rivervalley and then flow to Korean Peninsula and Japan(Tao and Cheng, 1985; Tao,1987; Huang et al., 1998).Therefore,the strengthened higher-level jet can augmentthe contribution of SMJ to the moisture transportto East Asia,which results in a closer relationshipbetween rainfall and the WSMJ.

To develop an underst and ing of the underlyingatmospheric circulation anomalies associated with theconnection between the WSMJ index and Yellow-Yangtze River basin precipitation during JJA,we examinedthe wind speed anomalies at 700 hPa overEast Asia and the vertical circulation along 107◦–117◦E associated with the WSMJ index(Fig. 5).The climatological wind at 700 hPa in JJA(Fig. 5a)shows that the Yellow-Yangtze region is located atthe junction of the westerlies and southwesterlies. Asshown in Figs. 5b and 5c,because of the increasedWSMJ intensity,the western Pacific subtropical high(WPSH)strengthens and shifts southward and westward, and the Yellow-Yangtze River basin is controlledby the consistent upward movement of the whole troposphere.Then,the strengthened WPSH causes thesouthwesterlies that originate from the SMJ to shift northward and westward,causing strengthening of thesouthwesterly flow in the Yellow-Yangtze River basin.This means that more moisture is transported into theYellow-Yangtze River basin and increased rainfall isexpected to occur.

Fig. 5.(a)Climatological summer wind speed(m s−1)at 700 hPa for 1979–2013. Simultaneous correlations of theWSMJ index with(b)the 700-hPa wind anomaly(vector) and precipitation anomaly(shaded),as well as(c)the verticalcirculation(consisting of meridional wind and vertical p-velocity; vector) and vertical p-velocity(contour and shaded)along 107◦–117◦E for 1998–2010 in JJA. Light to dark shadings indicate 90%,95%, and 99% confidence levels. Theblack bold arrows denote values significant at the 95% confidence level. All data were high-pass filtered by using a 9-yrwindow. The grey shadings in(a) and (b)indicate the domain of the Tibetan Plateau.
4. Causes of the interdecadal turning of SMJvertical structure 4.1 Thermal contrast near the Somalia coast

Our discussion in Section 3.1 showed that there appears to be an interdecadal transition point inthe vertical structure of the SMJ around the late1990s,with the lower-level(higher-level)jet stronger(weaker)before the late 1990s and weaker(stronger)afterwards. However,what has caused the inversechange between the lower- and higher-level jets in thelate 1990s?

To investigate the possible mechanism,we calculatedthe composite difference of air temperature and vertical circulation consisting of meridional wind and vertical velocity along 40◦–55◦E for the 1998–2010mean minus the 1980–1992 mean during JJA(Fig. 6). Clearly,for the post-1995 period,in the tropics,thereare meridional thermal contrast anomalies,with thewarm anomaly centered around 10◦S inclined northwardwith altitude(Fig. 6a). The north(south)ofequator is relatively cold(warm)below 700 hPa,whileexactly the reverse is the case above 700 hPa. As a resultof these meridional thermal contrast anomalies,the heating differential and the pressure differentialbetween north and south of the equator are formed(Murakami et al., 1970)such that descent anomaliesnorth of the equator and ascent anomalies south of theequator,with two anomaly cells,occur(Fig. 6b). One is in the higher troposphere(at about 400 hPa) and theother is in the lower troposphere(at about 850 hPa).This implies that the northern summer Hadley cell,as stated by Oort and Rasmussen(1970),consistingof ascent near 10◦N and descent near 25◦S becomesweaker. Then,the lower tropospheric cell results inthe higher-level southerly anomalies and lower-levelnortherly anomalies over the SMJ region. For the pre-1995 period,this situation is simply reversed.

Fig. 6. Composite differences(1998–2010 mean minus 1980–1992 mean)for(a)air temperature at different levels(contour interval 0.2◦C), and (b)vertical circulation(vector; consisting of meridional wind speed(m s−1) and verticalp-velocity(Pa s−1)) and vertical p-velocity(Pa s−1; shaded)along 40◦–55◦E near the SMJ region in JJA. The verticalp-velocity has been multiplied by –100. Areas encircled by black bold dashed lines denote differences significant at the95% confidence level.

The contribution of the meridional thermal contrastanomalies near the equator to the interdecadaltransition of the inverse change between the lower- and higher-level jets is further confirmed in Fig. 7,whichshows a cross-section of the altitude-time evolutionof the meridional thermal contrast over the tropics,as well as the associated EOF analysis. The meridionalthermal contrast is defined as the difference betweenthe areal mean air temperature averaged over0◦–15◦N,40◦–55◦E and that averaged over 15◦S–0◦,40◦–55◦E. During 1979–2013,the meridional thermal contrast near the SMJ region during the boreal summerdecreased at lower levels but increased markedlyat higher levels,which is very similar to the variationcharacteristic in the vertical structure of the SMJ. Itsinterdecadal transition point was also around 1995.

Fig. 7.(a)Cross-section of altitude-time evolution of the meridional thermal contrast(defined as the air temperaturedifference averaged over 0◦–15◦N,40◦–55◦E minus that averaged over 0◦–15◦S,40◦–55◦E in JJA),(b)the first EOFdecomposition of(a), and (c)PC1 of the EOF decomposition(bar) and its 9-yr running mean(curve).
4.2 Roles of the southern Indian Ocean SST

The above analysis reveals that the meridionalthermal contrast anomalies near the equator may beresponsible for the interdecadal transition of the inversechange between the lower- and higher-level jetsin the late 1990s; but what causes the the meridionalthermal contrast anomalies near the equator? With alarge heat capacity and thermal inertia,the oceans’ influenceon the atmospheric circulation is persistent,inthe sense that the SST anomalies at the previous seasonscan affect the atmospheric circulation at the subsequentseasons. Therefore,it is necessary to considerthe role of the ocean in spring. To develop a betterunderst and ing of the dominant modes of variability,anomalies were decomposed by using the empirical orthogonalfunction(EOF)technique for the southernIndian Ocean basin between 55◦S and the equator.These SST anomalies(SSTAs)are departures from amonthly mean climatology for the period 1979–2013.The second EOF mode,which explains about 14.7% ofthe total variance,shows a three-pole pattern orientedin the northeast-southwest direction(Fig. 8a). In addition,the time coefficient has strengthened since 1995(Fig. 8b),which indicates that the three-pole pattern in the southern Indian Ocean also experienced an interdecadalchange around the late 1990s.

Fig. 8.(a)Spatial pattern of EOF2 of the Indian OceanSSTA for MAM(March,April,May) and (b)PC2 of theEOF decomposition.

Figures 9a and 9b show the regression map of thesouthern Indian Ocean SST in spring and summer,respectively,against the first principle component(PC1)of the meridional thermal contrast near the SMJ region.To allow us to consider the influence of the SSTwarming trend,the linear trend was removed from theSST data. During the two seasons,the SSTA distributionsare similar to the well-defined three-poleSSTAs in the southern Indian Ocean. The resemblancebetween the three-pole pattern of the regression plots and the second EOF mode of the southern IndianOcean SSTA in Fig. 8 suggests that the meridionalthermal contrast anomalies near the equator are possiblycaused by the three-pole pattern of the southernIndian Ocean,which can persist from spring to thefollowing summer.

Fig. 9. Regression of the Indian Ocean SSTA in(a)spring and (b)summer against PC1 of the meridional thermalcontrast near the SMJ region for the detrended SST data. Areas covered by black plus signs denote values significant at95% confidence level.

The importance of the three-pole pattern is supportedby the regressions of the air temperature and wind anomalies in JJA at 925 and 700 hPa against thePC2 of the three-pole pattern in the southern IndianOcean(Fig. 10). As shown in Figs. 10a and 10b,therelatively warm(cold)anomaly south(north)of theequator leads to cross-equatorial northerly anomaliesat 925 hPa,while the relatively warm(cold)anomalynorth(south)of the equator leads to cross-equatorialsoutherly anomalies at 700 hPa. The wind anomaliesat 700 hPa are much stronger than those at 925 hPa,which may have led to the stronger high-level crossequatorialsoutherlies over the SMJ regions after 1995.

Fig. 10. Regressions of the air temperature(shaded) and wind(vector)anomalies in JJA at(a)925 and (b)700hPa against PC2 of the Indian Ocean SSTA for MAM. Area coverd by green dots and black bold arrows denote valuessignificant at the 95% confidence level.
5. Conclusions

This study has demonstrated that the correlationshipof higher-level SMJ intensity(which has a largeryear-to-year amplitude than the lower-level jet)withthe East Asian summer monsoon is higher than that ofthe lower-level jet. Consequently,we defined a new jetindex,the WSMJ index,based on the average meridionalwind at all levels. The WSMJ is closely related tothe East Asian summer monsoon rainfall on interannualtimescales,especially after the late 1990s,withpositive correlations over the Yellow-Yangtze Riverbasin. The increased WSMJ intensity strengthens and shifts the WPSH southward and westward,causingthe Yellow-Yangtze River basin to be controlled bythe consistent upward movement and the southwesterlyanomalies through the whole troposphere. Moremoisture is transported to the Yellow-Yangtze Riverbasin and more precipitation occurs.

Variations in the vertical structure of the SMJshow that the higher- and lower-level jet changes areout of phase with an interdecadal transition occurringaround the late 1990s. Probable causes of the inversechange between the lower- and higher-level jets in thelate 1990s were analyzed in detail. During the by themeridional thermal contrast anomalies near the equa-tor,with the warm anomaly centered around 10◦S and inclined northward with altitude. Consequently,relatively cold(warm)anomalies north of the equator and warm(cold)anomalies south of the equator at925(700)hPa caused lower-level(higher-level)crossequatorialnortherly(southerly)anomalies. The secondEOF mode of the southern Indian Ocean SSTAin spring shows a three-pole pattern and the time coefficientof it also experienced an interdecadal changein the late 1990s. Thus,the meridional thermal contrastanomalies near the coast of Somalia may be attributedto this three-pole pattern,which can persistfrom spring to the following summer. The regressionmap of the southern Indian Ocean SST in spring and summer against the PC1 of the meridional thermalcontrast also supports this conclusion. Our studiesway help to improve the underst and ing of the SMJ’sestablishment and formation,as well as its impact onthe Asian summer monsoon.

References
Annamalai, H., J. M. Slingo, K. R. Sperber, et al., 1999: The mean evolution and variability of the Asian summer monsoon: Comparison of ECMWF and NCEP-NCAR reanalyses. Mon. Wea. Rev., 127, 1157-1186.
Bannon, P. R., 1979a: On the dynamics of the East African jet. I: Simulation of mean conditions for July. J. Atmos. Sci., 36, 2139-2152.
Bannon, P. R., 1979b: On the dynamics of the East African jet. II: Jet transients. J. Atmos. Sci., 36, 2153-2168.
Bannon, P. R., 1982: On the dynamics of the East African jet. III: Arabian sea branch. J. Atmos. Sci., 39, 2267-2278.
Boos, W. R., and K. A. Emanuel, 2009: Annual intensification of the Somali jet in a quasi-equilibrium framework: Observational composites. Quart. J. Roy. Meteor. Soc., 135, 319-335.
Bunker, A. F., 1965: Interaction of the Summer Monsoon Air with the Arabian Sea: Preliminary Analysis. Commercial Printing Press, 3-16.
Cadet, D., and M. Desbois, 1981: A case study of a fluctuation of the Somali jet during the Indian summer monsoon. Mon. Wea. Rev., 109, 182-187.
Chakraborty, A., R. S. Nanjundiah, and J. Srinivasan, 2009: Impact of African orography and the Indian summer monsoon on the low-level Somali jet. Int. J. Climatol., 29, 983-992.
Chen, M., P. Xie, J. E. Janowiak, et al., 2002: Global land precipitation: A 50-yr monthly analysis based on gauge observations. J. Hydrometeor., 3, 249-266.
Chen Bing, Guo Pinwen, and Xiang Yuchuan, 2005: Relationship between summer cross-equatorial flows and ENSO. J. Nanjing Inst. Meteor., 28, 36-43. (in Chinese)
Cong Jing, Guan Zhaoyong, and Wang Lijuan, 2007: Interannual (interdecadal) variabilities of two crossequatorial flows in association with the Asian summer monsoon variations. J. Nanjing Inst. Meteor., 30, 779-785. (in Chinese)
Dai Wei and Xiao Ziniu, 2014: Multi-time scale variation characteristics of Somali jet and its contact with precipitation in China. J. Trop. Meteor., 30, 368- 376. (in Chinese)
Ding Yihui, 2005: Advanced Synoptic Meteorology. China Meteorological Press, Beijing, 585 pp. (in Chinese)
Findlater, J., 1969a: A major low-level air current near the Indian Ocean during the northern summer. Quart. J. Roy. Meteor. Soc., 95, 362-380.
Findlater, J., 1969b: Inter-hemispheric transport of air in the lower troposphere over the western Indian Ocean. Quart. J. Roy. Meteor. Soc., 95, 400-403.
Findlater, J., 1977: Observational aspects of the low level cross equatorial jet stream of the western Indian Ocean. Pure Appl. Geophys., 115, 1251-1262.
Gao Hui and Xue Feng, 2006: Seasonal variation of the cross-equatorial flows and their influences on the onset of South China Sea summer monsoon. Climatic Environ. Res., 11, 57-68. (in Chinese)
Halpern, D., and P. M. Woiceshyn, 2001: Somali jet in the Arabian Sea, El Niño, and Indian rainfall. J. Climate, 14, 434-441.
Hart, J. E., 1977: On the theory of the East African low level jet stream. Pure Appl. Geophys., 115, 1263-1282.
Honda, M., J. Inous, and S. Yamane, 2009: Influence of low Arctic sea-ice minima on anomalously cold Eurasian winters. Geophys. Res. Lett., 36, L08707, doi: 10.1029/2008GL037079.
Huang Ronghui, Zhang Zhenzhou, Huang Gang, et al., 1998: Characteristics of the water vapor transport in East Asian monsoon region and its difference from that in South Asian monsoon region in summer. Chinese J. Atmos. Sci., 22, 460-469. (in Chinese)
Joseph, P. V., and P. L. Raman, 1966: Existence of low-level westerly jet stream over Peninsular India during July. Indian J. Meteor. Geophys., 17, 407- 410.
Kalnay, E., M. Kanamitsu, R. Kistler, et al., 1996: The NCEP/NCAR 40-yr reanalysis project. Bull. Amer. Meteor. Soc., 77, 437-471.
Krishnamurti, T. N., J. Molinari, and H. L. Pan, 1976: Numerical simulation of the Somali jet. J. Atmos. Sci., 33, 2350-2362.
Krishnamurti, T. N., and V. Wong, 1979: Planetary boundary-layer model for the Somali jet. J. Atmos. Sci., 36, 1895-1907.
Lei Xiaochun and Yang Xiuqun, 2008: Interannual variation characteristic of Eastern Hemispheric crossequatorial flow and its contemporaneous relationships with temperature and rainfall in China. J. Trop. Meteor., 24, 127-135. (in Chinese)
Lin Meijing, Fan Ke, and Wang Huijun, 2008: Somali jet changes under the global warming. Acta Meteor. Sinica, 22, 502-510. (in Chinese)
Li Puzhong and Lou Guanghu, 1987: A study on the passages of cross-equatorial current during the southern monsoon. Chinese J. Atmos. Sci., 11, 313-319. (in Chinese)
Li Xiaofeng, Guo Pinwen, Dong Lina, et al., 2006: Onset process of summer Somali jet and the possible influenced mechanism. Trans. Atmos. Sci., 29, 599-605. (in Chinese)
Murakami, T., R. Godbole, and R. R. Kelkar, 1970: Numerical simulation of the monsoon along 80°E. Pro. Conf. Summer Monsoon of Southeast Asia, Navy Wea. Res. Fac., Norfolk, Va., 39-51.
Oort, A. H., and E. M. Rasmusson, 1970: On the annual variation of the monthly mean meridional circulation. Mon. Wea. Rev., 98, 423-442.
Pu, B., and K. H. Cook, 2010: Dynamics of the West African westerly jet. J. Climate, 23, 6263-6276.
Pu, B., 2012: Role of the West African westerly jet in Sahel rainfall variations. J. Climate, 25, 2880-2896.
Qian Yongfu, Wang Qianqian, Dong Yiping, et al., 1987: Numerical experiment of Somali jet. Chinese J. Atmos. Sci., 11, 176-184. (in Chinese)
Qiu Jinjing and Sun Zhaobo, 2013: Variation characteristics of the vertical structure of the summer Somali cross-equatorial flow and its relationship with East Asian summer monsoon activity. Chinese J. Atmos. Sci., 37, 1129-1142. (in Chinese)
Qiu Jinjing, Sun Zhaobo, and DengWeitao, 2014: The interdecadal variations of the vertical structure of the summer Somali cross-equatorial flow. Acta Meteor. Sinica, 72, 318-336, doi: 10.11676/qxxb2014.005. (in Chinese)
Rodwell, M. J., and B. J. Hoskins, 1995: A model of the Asian summer monsoon. Part II: Cross-equatorial flow and PV behavior. J. Atmos. Sci., 52, 1341- 1356.
Rudolf, B., H. Hauschild, W. Ruth, et al., 1994: Terrestrial precipitation analysis: Operational method and required density of point measurements. Global Precipitation and Climate Change, Dubois, M., and M. Desalmand, Eds., 173-186.
Shi Neng, Feng Guolin, Gu Junqiang, et al., 2007: The climatological variation of global cross-equatorial flow for the period of 1948-2004. J. Trop. Meteor., 23, 326-332. (in Chinese)
Shi Ning, Shi Danping, and Yan Mingliang, 2001: The effects of cross-equatorial current on South China Sea monsoon onset and drought/flood in East China. J. Trop. Meteor., 17, 405-415. (in Chinese)
Shi Wenjing and Xiao Ziniu, 2013: Variation of the cross-equatorial moisture transport in Somali and its impact on China early summer rainfall in nearly 60 years. Meteor. Mon., 39, 39-45. (in Chinese)
Smith, T. M., and R. W. Reynolds, 2004: Improved extended reconstruction of SST (1854-1997). J. Climate, 17, 2466-2477.
Simmons, A., S. Uppala, D. Dee, et al., 2007: ERAInterim: New ECMWF reanalysis products from 1989 onwards. ECMWF Newsletter, 110, 25-35.
Sterl, A., 2004: On the (in)homogeneity of reanalysis products. J. Climate, 17, 3866-3873.
Tang Bi, Guo Pinwen, and Yang Liping, 2009: Interannual variation of summer cross-equatorial flow in lower troposphere of Eastern Hemisphere. Trans. Atmos. Sci., 32, 98-305. (in Chinese)
Tao, S. Y., and L. X. Chen, 1985: The East Asian summer monsoon. Proceedings of the International Conference on Monsoon in the Far East. Tokyo, Nov. 5-8, 1-11.
Tao, S., 1987: A review of recent research on the East Asian summer monsoon in China. Monsoon Meteorology, Chang, C. P., and T. N. Krishnamurti, Eds.Oxford University Press, 60-92.,
Wang Huijun and Xue Feng, 2003: Interannual variability of Somali jet and its influences on the interhemispheric water vapor transport and on the East Asian summer rainfall. Chinese J. Geophys., 46, 18-25. (in Chinese)
Wang Weiping and Yang Xiuqun, 2008: Variation of Somali jet and its impact on East Asian summer monsoon and associated China rainfall anomalies. J. Meteor. Sci., 28, 139-146. (in Chinese)
Wu, B., J. Overland, and R. D'Arrigo, 2012: Anomalous Arctic surface wind patterns and their impacts on September sea ice minima and trend. Tellus A, 64, 18590.
Wu, B. Y., R. H. Zhang, R. D'Arrigo, et al., 2013: On the relationship between winter sea ice and summer atmospheric circulation over Eurasia. J. Climate, 26, 5523-5536, doi: 10.1175/JCLI-D-12-00524.1.
Xie Lei, Sun Zhaobo, Li Zhongxian, et al., 2013: Vertical structure of summer Somali cross-equatorial flow and its relationship with South Asian monsoon. J. Meteor. Sci., 33, 37-42. (in Chinese)
Xu Zhongfeng, Qian Yongfu, and Fu Congbin, 2010: The role of land-sea distribution and orography in the Asian monsoon. Part II: Orography. Adv. Atmos. Sci., 27, 528-542.
Zhu Yali, 2012: Variations of the summer Somali and Australian cross-equatorial flows and the implications for the Asian summer monsoon. Adv. Atmos. Sci., 29, 509-518.