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--> --> -->The South Asian High (SAH) is the strongest and most stable atmospheric circulation system in the upper troposphere during boreal summer, and its formation and evolution are closely related to the seasonal enhancement of tropical convection near the Philippines (Liu et al., 2013, 2017a) and the diabatic heating of the TP (Flohn, 1957; Gao et al., 1982). Recent studies have also revealed the importance of the thermal effect from the IP to atmospheric circulation during boreal summer. The difference in the thermal conditions of these two plateaus can cause anomalies in air temperature in the upper-level troposphere, resulting in the east-west oscillation of the summertime SAH at an interannual time scale. The SAH located to the west is called the "Iranian mode", when the IP is warmer than normal; and the eastward located SAH is called the "Tibetan mode", when the TP is warmer than normal, also named the "warm-preference property" of the SAH (Qian et al., 2002a, 2002b; Wu et al., 2004). In addition, compared to the diabatic heating of the plateaus, the latent heat (LH) of condensation associated with monsoon rainfall is larger in quantity and can heat the upper-level troposphere directly; therefore, LH plays a more important role in strengthening the SAH. (Zhang et al., 2016) noted positive feedback between condensation heating associated with the EASM precipitation and the SAH variability: higher condensation associated with a stronger EASM heats the eastern flank of the summertime SAH; then, geopotential heights on the western flank of the SAH are elevated by a westward propagating Rossby wave; and finally, the whole SAH is strengthened. As two subsystems of the Asian summer monsoon, the negative correlation between the Indian summer monsoon (ISM) rainfall and EASM rainfall could cause a northwest-southeast (NW-SE) shift of the summer SAH on an interannual time scale (Wei et al., 2015). In addition to the land-sea thermal contrast, the thermal effect of the IP and TP on the generation and evolution of the ISM and EASM should not be neglected. A higher TPSH in late spring can strengthen the intensity of the EASM (Duan and Wu, 2003; Duan et al., 2003) and bring forward the onset of the ISM by suppressing air convection over northeastern India (Zhang et al., 2015); and a higher IPSH mainly contributes to a strengthening of the ISM (Wu et al., 2012), and vice versa. Considering the effects of IPSH and TPSH on the onset and intensity of the Asian summer monsoon, as well as the close connection between these two plateaus, analysis of concurrent SH anomalies over the IP and TP and the effect on subsequent Asian summer monsoons and the SAH is necessary.
Variation in the location of the SAH at the interannual time scale greatly affects summer weather and climate in China and South Asia. Undoubtedly, the climate condition——especially the thermal situations of high elevations like the IP and TP——also influence the location of the SAH. From the viewpoint of the climate mean, the core of the SAH in May is located over the Indochina Peninsula (Liu et al., 2017a). It then moves northward and westward with the evolution of the seasons, reaching its northernmost position in August, which is coincident with the seasonal continental warming, especially over the IP and TP, and then retreats southeastward in boreal autumn. Generally speaking, SH always outweighs LH over the IP; while over the TP, SH peaks in May and outweighs LH except for June to September (Zhang et al., 2017). The effect of SH, especially TPSH, on atmospheric circulation, is weakened after the onset of the Asian summer monsoon, when LH from condensation accompanying monsoon rainfall gradually becomes a dominant factor. Besides, the relationship between late springtime SH and the early summer SAH deserves further investigation in a prediction sense. Different from separately analyzing the isolated thermal effects of the TP and IP in previous studies, we explore the combined impact of late springtime SH anomalies over the TP and IP on the early summertime SAH, the aim being to provide a reference for summer climate prediction in China.
The rest of this paper is structured as follows: Section 2 briefly describes the data and methods used. In section 3, we present the interannual variation of the SAH in June and the anomalous atmospheric circulation during the same period. The dominant feature of concurrent SH anomalies over the IP and TP in late spring and its relationship with the interannual variation of the SAH is discussed in section 4. In section 5, we explore the possible mechanisms causing the concurrent SH anomalies over the IP and TP in boreal late spring that affect the early summertime SAH. And finally, conclusions and a discussion are presented in section 6.
2.1. Data
The data used in this study include monthly mean geopotential height, surface sensible heat flux and 6-h means for air temperature, zonal and meridional wind speeds, vertical velocities and specific humidity from the European Center for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim), which has a horizontal resolution of 1.5° × 1.5° and 37 vertical levels. The monthly mean precipitation data are from the Global Precipitation Climatology Project (GPCP) dataset, which has a 2.5°× 2.5° horizontal resolution. All the data are from 1979 to 2014.2
2.2. Methods
Statistical methods, such as empirical orthogonal function (EOF), singular value decomposition (SVD), correlation, and composite analyses, are used in this study. The area for the IP is represented by 101 grids (in ERA-Interim), with terrain height more than 300 m in the region (25.5°-40.5°N, 49.5°-70.5°E). The TP is represented by 116 grids (in ERA-Interim), with a terrain height higher than 3000 m over the domain (25.5°-40.5°N, 75°-105°E), which is similar to the definition from (Zhang et al., 2017). The core of the SAH is defined by a 12500-gpm isoline at 200 hPa; its ridgeline is located where zonal wind speeds u equal 0 m s-1 and ? u/? y>0, according to (Liu and Wu, 2004), and the center of the SAH is defined as the point where geopotential height is maximal at 200 hPa. Considering the relatively short time series of the data (36 years), we focus on the relationship between SH anomalies over the TP and IP and variations of the SAH, on the interannual time scale. All indexes defined in this study, which characterize variations in the SAH and SH over the two plateaus, are high-pass filtered over nine years.The apparent heat source (Q1), apparent moisture sink (Q2) and their vertical integrations in an air column, $\langle Q_1\rangle$ and $\langle Q_2\rangle$, are calculated based on the 6-h mean datasets from ERA-Interim and follow the formulas from (Yanai et al., 1973): \begin{eqnarray} Q_1&=&C_p\left[\frac{\partial T}{\partial t}+{V}\cdot\nabla T+\left(\frac{p}{p_0}\right)^\kappa\omega\frac{\partial\theta}{\partial p}\right] ;\ \ (1)\\ Q_2&=&-L\left[\frac{\partial q}{\partial t}+{V}\cdot\nabla q+\omega\frac{\partial q}{\partial p}\right] ;\ \ (2)\\ \langle{Q_1}\rangle&=&\frac{1}{g}\int_{p_{\rm t}}^{p_{\rm s}}{Q_1}dp ;\ \ (3)\\ \langle{Q_2}\rangle&=&\frac{1}{g}\int_{p_{\rm t}}^{p_{\rm s}}{Q_2}dp . \ \ (4)\end{eqnarray} Here, T is air temperature; θ is potential temperature; q is specific humidity; V is the orizontal wind vector, ω is vertical velocity in pressure coordinates, p0=1000 hPa, \(\kappa=R/C_p\), R and Cp are the gas constant and specific heat at a constant pressure for dry air, respectively; L is the latent heat of condensation; and p s and p t are the pressures at the surface and the top layer (i.e., 1 hPa), respectively. It can be deduced that the latent heat of condensation is the primary contributor to atmospheric heat sources when the quantity of $\langle Q_2\rangle$ is similar to $\langle Q_1\rangle$.
3.1. Definition of the SAH index and its interannual variation in June
The climatological ridgeline of the SAH in June (i.e., the zonal dashed green curves in Fig. 1) lies over the subtropical Asian continent between 18°N and 30°N, and the center of the SAH (i.e., the cross point of the dashed green lines in Fig. 1) is located to the south of the Himalaya. The spatial patterns of the geopotential height anomalies at 200 hPa in early boreal summer can be mainly classified into two modes according to the climatological ridgeline and location of the center of the SAH: geopotential height anomalies that are generally uniformly positive or negative within the SAH domain (EOF1, figure not shown), and geopotential height anomalies in the northwestern part of the SAH that are opposite to those in the southeastern part of the SAH (EOF2, Fig. 1a). The spatial pattern of EOF2 displays a northwest-southeast (NW-SE) shift of the SAH at the interannual time scale, which is associated with a negative correlation between the ISM rainfall and EASM rainfall and has received much attention (Wei et al., 2015). In this study, we focus on EOF2 of the SAH and its relationship with the thermal conditions of the IP and TP in late spring.
Figure1. (a) Second EOF mode for 200-hPa geopotential height (Z200) in June (shaded), in which the climatological core of the SAH (Z200) from 1979 to 2014 is shown by the black contour 12 500 gpm. The dashed black boxes indicate the NW and SE regions of the SAH from which the SAH index is calculated. The horizontal dashed green curve indicates the ridge line of the SAH, and the cross point of the dashed green lines indicate the center of the SAH (the maximum of Z200). Blue curve in (a) represents the Tibetan Plateau. (b) Time series of EOF2 (PC2) for Z200 and SAH6 NW-SE.To better describe the NW-SE shift of the SAH, we define an SAH index based on the spatial pattern of the EOF2 shown in Fig. 1. The SAH index is the difference in standardized area-averaged 200-hPa geopotential height ["Nor" in Eq. (5) represents standardization and "Z200" represents 200-hPa geopotential height] between the northwest region and the southeast region of the SAH core (dashed black boxes in Fig. 1). We define the June SAH index accordingly to accurately describe the NW-SE shift in the SAH based on its climate mean location as follows: \begin{eqnarray} {\rm SAH6}_{\rm NW-SE}&=&{\rm Nor}[{\rm Z200}_{\rm NW}]-{\rm Nor}[{\rm Z200}_{\rm SE}] . \ \ (5)\end{eqnarray}
Z200 NW represents the regional mean of 200-hPa geopotential height covering 25.5°N-31.5°N and 63°E-90°E, and Z200 SE represents the regional mean of 200-hPa geopotential height covering 17°N-23°N and 93°E-120°E. The temporal evolution of SAH6 NW-SE is closely related to the second principal component (PC2) of the SAH EOF2 in June, with a correlation coefficient as high as 0.88, statistically significant at the 99.9% confidence level. The advantage of the SAH index defined in this study is that it can exhibit the NW-SE shift of the SAH quite well (Fig. 1b) but is not dependent on the domain size that contains the SAH, which is a weakness in the EOF analysis.
The interannual variation of the SAH location (SAH6 NW-SE) is shown in Fig. 1b. During 1979-2014, 12 years with a standardized SAH6 NW-SE above 0.5 are selected to represent the northwestward-located SAH (abbreviated to NW-SAH hereafter) cases, and nine years with standardized SAH6 NW-SE values below -0.5 are chosen for the southeastward-located SAH (abbreviated to SE-SAH hereafter) cases.
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3.2. Relationships between SAH variation and the anomalies of atmospheric circulation in Asian monsoon regions
As the strongest atmospheric general circulation system in the upper troposphere during boreal summer, the SAH covers most Asian summer monsoon regions. The variation in the SAH is closely associated with the anomalous atmospheric circulation spanning West to East Asia during the same period. Composite analysis is utilized to examine the dominant features in atmospheric circulation anomalies at 200 hPa, 500 hPa and 850 hPa.In the NW-SAH cases in June (Figs. 2a, c and e), a positive anomaly center of geopotential height at 200 hPa overlying between the border of the IP and the TP contributes to the northwestward shift in the SAH (Fig. 2a). At 500 hPa, as seen in Fig. 2c, a negative anomaly center in geopotential heights and an anomalous cyclone over the Indian subcontinent contributes to the strengthening of lower-level air convection. Positive anomalies in geopotential heights are found over the IP, TP and Northeast Asia, which could cause northward movement in the western Pacific subtropical high (WPSH) in the mid-troposphere (figure not shown). The ISM is stronger, and there is an anomalous southwesterly flow toward India and a cyclonic wind shear at 850 hPa over the northern Indian subcontinent, resulting in an anomalous convergence in total column moisture flux, implying a positive anomaly in precipitation and LH release. At the same time, an anomalous anticyclone and the divergence of total column moisture flux occur over the western Pacific Ocean to the south of Japan. In addition, anomalous southerly winds at 850 hPa over eastern China bring an increase in water vapor from the South China Sea, which is consistent with the anomalous convergence of total column moisture flux over the Yangtze-Huaihe river basin (Fig. 2e). When the location of the SAH is located to the southeast in June (SE-SAH cases), the anomalous circulation patterns at all levels and vertical integrated moisture flux show almost contrary features with that in the NW-SAH cases (Figs. 2b, d and f); both the ISM and EASM are weaker, and the location of the WPSH moves southward. Weakened air convection over the northern Indian subcontinent and the Yangtze-Huaihe river basin, with anomalous divergence in total column moisture flux, are responsible for the decrease in rainfall over these regions (figure not shown).
Figure2. June anomaly composites of geopotential height (contour; units: gpm) and horizontal wind (vector; units: m s-1) at (a, b) 200hPa, (c, d) 500hPa and (e, f) anomaly composites of horizontal wind (vector; units: m s-1) at 850hPa and divergence of vertical integrated moisture flux (shaded; unit: 10-5 s-1) for years with a standardized SAH6 NW-SE (a, c, e) higher than 0.5 or (b, d, f) lower than -0.5. Stippled areas and blue vectors indicate where relevant anomalies are statistically significant above the 90% confidence level. Grey shading in each panel represents the Tibetan Plateau.The relationship between springtime SH anomalies over the IP or TP and summertime SAH variations is explored separately before we analyze the synergistic impact of springtime SH anomalies over both plateaus on the variation of the SAH in the following summer. A partial correlation analysis is employed to eliminate signals from SH anomalies over the IP (TP) when analyzing correlations between SH anomalies over the TP (IP) and the NW-SE shift of the SAH. It should be noted that the PC1 of SH over the TP has a remarkable trend, which is related to the strengthening trend of the SAH's intensity (figure not shown). This paper focuses on the correlation between the SAH and SH over the IP and TP at the interannual time scale, and therefore the EOF1 of SH is not discussed here. The partial correlation coefficients between the PC2 of SH over the TP (May, eliminated signals from PC1 or PC2 of SH over the IP), PC2 of SH over the IP (May, eliminated signals from PC2 of SH over the TP) and SAH6 NW-SE are 0.33, 0.53 and 0.42, respectively, all of which are statistically significant at the 95% confidence level. However, all these partial correlation coefficients are smaller than the correlation coefficient (0.54) between May SVD1 IPTP-SH and SAH6 NW-SE. This implies that the synergistic impact of May SH anomalies over the IP and TP on SAH variations in June is more remarkable than the impact of May SH anomalies over each single plateau. In addition, the interannual variations of SVD1 IPTP-SH in May and SAH6 NW-SE are not always consistent with each other (Fig. 3b), which implies that factors besides the elevated thermal effect on these two plateaus may also have some influence on the variation in location of the SAH.
Figure3. (a) The first mode of the SVD analysis of the surface sensible heat fluxes over the IP and TP (SVD1 IPTP-SH) in May, in which areas encircled by contours indicate correlations significant at the 99% confidence level. (b) Temporal evolution of the standardized time series for May SVD1 IPTP-SH and SAH6 NW-SE. (c, d) Composite difference in surface sensible heat fluxes (units: W m-2) over the IP and TP in May (c) and June (d) between years with a standardized May SVD1 IPTP-SH higher than 0.5 and lower than -0.5.The spatial pattern of the May SVD1 IPTP-SH shows that the TPSH anomaly is generally negatively correlated with the IPSH anomaly (Fig. 3a), especially in the northern IP (north of 32°N) and south-central TP (south of 34°N). In years with positive SVD1 IPTP-SH in May, IPSH is primarily stronger, while TPSH is weaker than normal (Fig. 3c), and the opposite is true for negative SVD1 IPTP-SH in May. Therefore, the significant positive correlation between May SVD1 IPTP-SH and SAH6 NW-SE indicates that the SAH in June is northwestward-located (southeastward-located) when IPSH is higher (lower) and TPSH is lower (higher) than normal in May. This relationship can also be demonstrated by composite analysis based on SVD1 IPTP-SH in May. The 12500-gpm isoline at 200 hPa in June is obviously northwestward-located (southeastward-located) during years with an SVD1 IPTP-SH above 0.5 (below -0.5). However, the composite for the SAH in July based on May SVD1 IPTP-SH does not exhibit any obvious deviation from its climatological state; there is only a slight westward shrinkage (eastward extension) at the eastern edge of the SAH during strong (weak) May SVD1 IPTP-SH years (figure not shown). It can be inferred that, with increasing rainfall in monsoon regions after the onset of the Asian summer monsoon, the thermal effect from SH anomalies over the two plateaus on the SAH is weakened, while LH from condensation accompanying Asian monsoon rainfall gradually becomes a dominant factor of influencing the variation of the SAH. The above analyses show that the negative correlation between TPSH and IPSH in May is closely related to the NW-SE shift of the SAH in June but has a weak relationship with the SAH in July.
Composite analysis based on the May SVD1 IPTP-SH time series shows that the SH anomaly over the IP and TP in May can persist till June (Figs. 3c and d). This surface thermal "memory" may be the result of soil enthalpy "memory" (Hu and Feng, 2004) and the feedback mechanism between IPSH and TPSH (Liu et al., 2017b).
Composite analysis shows that, in the years with positive anomalies of SVD1 IPTP-SH in May, IPSH is stronger and TPSH is weaker than normal (Fig. 3c). The stronger IPSH strengthens low-level air ascending motion around most parts of the IP and over the western TP below 200 hPa (Fig. 4c). An anomalous cyclone is located over the south of the IP and, accordingly, southwesterly flow to India brings more moisture to northern India and a cyclonic wind shear exists over northern India and the northern BOB at 850 hPa (Fig. 4a). The greater convergence of moisture flux and anomalous upward movement over the southwest TP is possibly responsible for the higher rainfall and lower SH over that region. A somewhat contrary situation is true for the composite with negative anomalies of May SVD1 IPTP-SH (Figs. 4b and d).
Figure4. (a, b) May anomaly composites of horizontal winds (vector; units: m s-1, blue vectors indicate wind anomalies are significant above the 90% confidence level) at 850 hPa and divergence of total column moisture flux (shaded; units: 10-5 s-1, stippled areas indicate moisture flux anomalies are significant above the 95% confidence level). (c, d) May anomaly composites of zonal-vertical motion along the 31.5°N cross section (units: m s-1 for u and -100 Pa s-1 for ω, color shadings indicate anomalies are significant above the 95% confidence level). Composites are calculated for years with a standardized May SVD1 IPTP-SH (a, c) higher than 0.5 or (b, d) lower than -0.5. Thick black curve in (a, b) represents the Tibetan Plateau and black shading in (c, d) represents the topography profile along 31.5°N.Since the anomalies of SH over the IP and TP in June are similar to those in May (Figs. 3c and d), composite anomalous atmospheric circulation similar to that in May is expected in June. There is an anomalous cyclone at 850 hPa between 15°N and 30°N spanning from West Asia, northern India, to the northern BOB (Fig. 5a), and the strengthened Somali jet contributes to anomalous southwesterly winds over the western Indian subcontinent, carrying more water vapor from the Indian Ocean to eastern India and the northern BOB. This anomalous low-level circulation is responsible for the convergence of total column moisture flux over the eastern India subcontinent and northern BOB, as well as the anomalous ascending motion over northern India from the IP to the western TP (Fig. 5c). Of note is that the anomalous descending motion over the eastern TP is accompanied by moisture divergence, meaning the weaker TPSH during May leads to less rainfall in the eastern TP in June due to its weakened heat pump effect. Over East Asia, an anomalous southerly flow and anticyclone to the east of Taiwan carry more water vapor to eastern China from the South China Sea and western Pacific. These two vapor flows merge over the Yangtze-Huaihe river basin, resulting in an anomalous convergence of total column moisture flux there (Fig. 5a). In years with lower SVD1 IPTP-SH values in May (Figs. 5b and d), an anomalous anticyclone and divergence in total column moisture flux exists over northeastern India and the northern BOB, which implies that the ISM in June is weakened; the anomalous cyclone over Northeast Asia and northerly winds weaken the monsoon over East China to the north of the Yangtze River valley in June (Fig. 5b). As for vertical motion, there is anomalous descent over a wide region from the western IP to northeastern India and the Yangtze River valley (Fig. 5d).
Figure5. As in Fig. 4 but for the June anomaly composite according to the May SVD1 IPTP-SH.To verify the influence of concurrent SH anomalies over the IP and TP on the aforementioned anomalous atmospheric circulation and NW-SE shift of the SAH, the vertical vorticity equation (Wu and Liu, 1998; Liu et al., 2001, Liu et al., 2013) is used to investigate the vorticity generation and diabatic heating: \begin{eqnarray} \frac{\partial\zeta}{\partial t}+{V}\cdot\nabla\zeta+\beta\upsilon&=&-(f+\zeta)\nabla\cdot{V}+ \frac{f+\zeta}{\theta_z}\frac{\partial Q_1}{\partial z}+\nonumber\\ &&\frac{1}{\theta_z}\left(\frac{\partial u}{\partial z}\frac{\partial Q_1}{\partial y}- \frac{\partial v}{\partial z}\frac{\partial Q_1}{\partial x}\right) . \ \ (6)\end{eqnarray} Here, ζ is vertical vorticity, u is zonal wind, \(\upsilon\) is meridional wind, f is geostrophic parameter and β is the meridional gradient of f, θz is the vertical gradient of θ. Other variables in Eq. (6) are consistent with preceding definitions. On the right-hand side of Eq. (6), the latter two terms represent the effects of the vertical and horizontal gradients of diabatic heating. Because the magnitude of [(f+ζ)/θz](? Q1/? z) (10-10 s-2) is one order of magnitude larger than that of (1/θz)[(? u/? z)(? Q1/? y)-(? v/? z)(? Q1/? x)] (10-11 s-2), the vorticity generated from diabatic heating can be represented by [(f+ζ)/θz](? Q1/? z), which is the vertical shear of apparent heat source. For other terms in the equation, the advection term V·?ζ on the left-hand side is balanced by the vorticity divergence term -(f+ζ)?· V on the right-hand side, and ?ζ/? t is neglected when the monthly mean is the major concern. Therefore, Eq. (6) can be simplified as \begin{equation} \beta\upsilon\propto\frac{f+\zeta}{\theta_z}\frac{\partial Q_1}{\partial z} . \ \ (7)\end{equation} That is to say, in a steady state, southerly flow is associated with a positive vertical gradient of Q1, and vice versa. When IPSH is stronger in May, which means the upward heat diffusion above the ground surface and low-level Q1 is intensified, considering the surface heat diffusion is almost zero (Wu et al., 2007), the near-surface (800 hPa) vertical gradient of Q1 [(f+ζ)/θz](? Q1/? z) is strengthened over a wide region from the IP to northern India (Fig. 6a), which is responsible for the anomalous southerly over that region in May (Fig. 4a). Similarly, when stronger IPSH in June generates larger low-level Q1, there is an anomalous positive gradient of Q1 [(f+ζ)/θz](? Q1/? z) at 800 hPa over most regions from the IP to northern India (Fig. 6b), which is responsible for the anomalous low-level southerly over the Arabian Sea and total moisture convergence over northern India and the southwestern TP (Fig. 5a). Furthermore, more convergence of moisture flux in northern India is coincident with more ISM rainfall in June and generates stronger Q1 in the mid-troposphere, which is associated with LH release over northern India (Fig. 6c). The negative anomaly of the vertical gradient of Q1 above 400 hPa over northern India and the southwestern TP is responsible for the anomalous northerly over that region and the stronger anticyclone at 200 hPa (Fig. 2a) to the west of the intensified heating center (Liu et al., 2001), and hence the northwestward shift of the SAH in June.
Figure6. Composite differences in [(f+ζ)/θz](? Q1/? z) at 800 hPa (units: 10-10 s-2) in (a) May and (b) June, and (c) differences in the pressure-longitude cross section of Q1 (shaded; units: 10-10 s-2 W m-2) and [(f+ζ)/θz](? Q1/? z) (contours; units: 10-10 s-2) along 31.5°N in June between years with a May SVD1 IPTP-SH higher than 0.5 and lower than -0.5. Stipples in (a, b) indicate differences are significant above the 95% confidence level. Grey shading in (a, b) represents the Tibetan Plateau, black shading in (c) represents the topography profile along 31.5°N.In order to validate the conjecture about the aforementioned relationships among ISM rainfall, atmospheric circulation, and surface heating, the correlation coefficients of May SVD1 IPTP-SH with $\langle Q_1\rangle$ and $\langle Q_2\rangle$ in June are calculated. Both are significantly positive in northern and eastern India and negative over the tropical Indian Ocean, and their spatial distributions are quite similar (Fig. 7). The remarkable consistency between the distribution of $\langle Q_1\rangle$ and $\langle Q_2\rangle$ in June implies that the intensified release of LH is the primary component in atmospheric heat source over northeastern India, which is associated with anomalous low-level circulation and is traced back to the surface thermal anomaly in May. From the above analysis, anomalies in atmospheric heat source associated with ISM rainfall in June may be a link between concurrent SH anomalies over the IP and TP in May and the NW-SE shift of the SAH in June.
Figure7. Spatial distributions of correlation coefficients between (a) May SVD1 IPTP-SH and June $\langle Q_1\rangle$, (b) May SVD1 IPTP-SH and June $\langle Q_2\rangle$. Light and heavy shadings indicate correlations are significant at the 90% and 99% confidence level respectively. Thick black curve represents the Tibetan Plateau.The concurrent late springtime surface thermal anomaly over the TP and IP, which is characterized by a negative correlation between TPSH and IPSH in May and persistence till June, is closely related to the variation of the SAH in June. This synergistic effect of concurrent late springtime thermal anomalies over the two plateaus is more efficient in affecting the variation of the SAH than each single surface thermal anomaly over either the TP or IP. By modulating the intensity of the ISM and air convection over northern India in May and June, the concurrent thermal contrast between the TP and IP in May causes an anomalous release in LH over northern India, which changes the vertical distribution of atmospheric heat source. Finally, an anomalous anticyclone in the upper troposphere over northwestern India results in a NW-SE shift of the SAH in June. When IPSH is stronger (weaker) and TPSH is weaker (stronger) than normal in May and persists till June, there is an anomalous cyclonic (anticyclonic) circulation over the IP and northern India at low levels. Consistent with the strengthened (weakened) low level cross-equatorial southerly flow over the western equatorial Indian Ocean and the Indian subcontinent, synergistic concurrent thermal anomalies over the two plateaus strengthen (weaken) the ISM in June. At the same time, the weaker (stronger) TP thermal effect strengthens (weakens) atmospheric convection over northeastern India. Under the composite surface thermal conditions of both the IP and TP, the release of LH over northern India is strengthened (weakened), the decreased (increased) vertical gradient of Q1 above 400 hPa generates an anomalous anticyclone (cyclone) on the western side of the anomalous heating center, and therefore the SAH in June shifts northwestward (southeastward).
Whether concurrent SH anomalies over the IP and TP in May lead to the NW-SE shift of the SAH in July is still inconclusive. Although feedback between atmospheric circulation in June and the release of LH associated with precipitation is, to some extent, responsible for the NW-SE shift of the SAH in July, the July composite for the SAH based on May SVD1 IPTP-SH does not exhibit any obvious deviation from its climatological state (figure not shown). This is partly due to the time-limited "memory" of the spring surface thermal anomaly over the IP and TP. Furthermore, the atmospheric circulation and associated rainfall is much stronger in July, as the Asian monsoon reaches its mature stage, which outweighs the surface thermal anomaly in the previous spring.
There are other issues regarding the variation in the SAH and Asian summer monsoon worth exploring, in addition to thermal anomalies over the TP and IP. Compared with land surface heating, forcing from the oceans also modulates the Asian summer monsoon; for example, ISM rainfall is significantly correlated with Indian Ocean sea surface temperature and moisture flux in the preceding winter and spring (Li et al., 2001). The EASM is also greatly affected by ENSO (Lau and Nath, 2006). It seems that, in years when the concurrent thermal effect of the IP and TP and the NW-SE shift of the SAH are not in-phase, oceanic forcing plays a dominant role in the variation of the SAH. Therefore, comprehensively considering the effects of land and ocean forcing will improve our understanding regarding variations in the SAH and summer rainfall in Asia. Further investigation through numerical simulations would give us new insights into the relationship between springtime thermal anomalies over the IP and TP and subsequent summertime variations in the SAH and the Asian monsoon.
