The South Asian High (SAH) or Asian (summer) monsoon anticyclone is one of the most pronounced circulation patterns in the Northern Hemisphere (NH) and emerges through diabatic heating in the South Asian monsoon region during boreal summer. Horizontally, the SAH covers large parts of southern Asia and the Middle East (black contours in Fig. ). It is located on the edge of the tropics and subtropics, vertically spanning from around 300 to 70 hPa see Fig. 2 in, i.e. approximately the whole upper troposphere and lower stratosphere (UTLS) region. Despite the closed anticyclonic flow often shown in climatological analysis, the circulation system exhibits strong variability in strength and location .

Apart from the highly variable synoptic behaviour of the SAH, found that the longitudinal distribution of the SAH centre location – as identified by the geopotential height maximum along the ridgeline (see green line in Fig. a) – is bimodal. Using pentad (5 day) mean data, they have found two preferred modes of the SAH at 100 hPa and have coined the terms Iranian Mode (IM) and Tibetan Mode (TM) according to the two peaks (at 55–65 and 82.5–92.5∘ E respectively) of the bimodal distribution (see cyan bars in Fig. a).

This bimodality has been attributed to the so-called warm preference of the SAH (i.e. the SAH centre is located on top of an anomalously warm air column in the troposphere; see Fig. 2 in ), and argued that the TM corresponds to diabatic heating of the Tibetan Plateau (TP) and the IM corresponds to adiabatic heating in the free troposphere and diabatic heating of the Iranian Plateau (IP). This heat preference is also supported by , who focus on the seasonal variation of the SAH, referring to the high pressure system that moves to the western Pacific during winter (see also and references therein).

Consequently, following studies seized the suggestion of this bimodality e.g., while others tried to link their results to this finding (see details in Sect. ). The classification of the SAH into two modes has also found its way into textbooks on the monsoon system e.g..

In this study we investigate the location and movement of the SAH at 100 hPa, with particular focus on the bimodality, by employing seven reanalyses, including four high-resolution (model resolution < 1∘) reanalysis data sets. A deeper understanding of the SAH has two major impact areas: first, on the regional scale the location of the SAH was found to be connected to precipitation anomalies in Asia (flood–drought areas) and was found to be a predictor of monsoonal spills seeand references therein. Second, on the global scale, the SAH features trace gas anomalies, e.g. of CO , H2O , HCN , HCFC22 and O3 , which can ultimately reach the stratosphere . Recent studies have successfully demonstrated the impact of the Asian summer monsoon on the composition of the extratropical lower stratosphere over Europe as measured during the TACTS/ campaign . Consequently, detailed knowledge of the location and movement of the SAH is necessary to be able to understand how trace gas anomalies build up and to quantify the amount of trace gases injected into stratosphere. Because in situ measurements at UTLS levels in the Asian monsoon and neighbouring regions are sparse, aircraft measurement campaigns have been specifically designed to investigate transport processes within the anticyclone and consequent outflow from the anticyclone .

The questions we want to address in this study are (1) is there bimodality of the SAH centre location at 100 hPa? (2) How can the “climatological” bimodality be connected to the movement on synoptic timescales? (3) What causes the movement of the SAH on a synoptic, seasonal and climatological basis?

The remainder of this paper is structured as follows: in Sect. , we present the data and methods used in this study. Sect.  deals with question (1). Questions (2) and (3) are addressed in Sects.  and . Finally, we discuss our results in Sect.  and end with a conclusion (Sect. ).

Simple model studies have shown that constant diabatic heating in South Asia causes a mean UTLS circulation to its north-west, which resembles the climatological SAH see. As diabatic heating in the southern monsoon region is largely caused by the latent heat release due to convection, we use OLR as a proxy for convective activity and consequently for diabatic heating.

Figure shows the temporal evolution of ERA-I geopotential (averaged over 20–40∘ N) during the summer months of 1983 and 1987. Choosing these two years is arbitrary; however, they are useful in illustrating common and individual features of the monsoon season. The green lines indicate the location of the SAH centre as diagnosed via the method described in Sect.  based on daily (light green) and pentad (dark green) data from ERA-I. Additionally, we have included mean OLR in the region 15–30∘ N (main convective region south of the SAH) from NOAA at the levels 190 and 180 W m-2 (black contours).

In Fig.  the lowest OLR values are mostly confined to the area 75–105∘ E and are mostly located east of the highest geopotential height values. East of the OLR minimum we can observe eastward migration of high geopotential, associated with eastward eddy shedding of the anticyclone. A strong shedding event is observed in mid-region August 1983 (turquoise star in Fig. a). West of the OLR minimum region, the core of the anticyclone usually propagates westward. Another feature that is visible in the Hovmöller diagrams is the splitting of the anticyclone, e.g. ∼ 10 July 1983, ∼ 10 July 1987 and the end of July 1987 (indicated by arrows in Fig. ). Splitting events and the development of high geopotential values close to the OLR forcing are often a cause for “jumps” of the location of the SAH centre (dark green line in Fig. ).

Figure  shows geopotential height along the ridgeline during June to August 1983 for individual pentads, monthly mean and seasonal mean data. We note that in 1983, based on daily means, all reanalyses show a distribution of the SAH centre that has one strong centre at approximately 60∘ E and a weaker maximum at approximately 95∘ E (not shown). The SAH centre is located over the TP in June and over the IP in July and August (coloured dashed lines in Fig.  and purple dots in Fig. a). The seasonal mean shows a maximum over the IP (dashed black line in Fig. ). The effect, which leads to different distributions with respect to varying timescales, can be inferred from the ridgelines for the months June and August in Fig. : during June the maximum in the TP is weak, whereas during August the maximum over the IP is pronounced. Nevertheless, both months contribute equally to the distribution of the SAH based on monthly mean data, whereas based on seasonal mean data, the peak will be detected over the IP only.

In 1987 the east mode is found based on seasonal data and a rather smoothed out distribution is found based on the daily analysis.

To illustrate the climatological connection between OLR and the SAH, we display the JJA climatology (1979–2013, i.e. the overlapping time period of NOAA OLR and ERA-I data) of the SAH location at 100 hPa (dashed black line) and of OLR (orange contours) in Fig. . Additionally, we show the climatology of vertical velocities at 100 hPa as diagnosed from ERA-I (colour shading) and the low level winds (grey vectors), e.g. identifying the Somali Jet, which brings moisture from the Arabian Sea to India .

The deep convective region is located to the south-east of the climatological location of the SAH (dashed black contour in Fig. ), with the lowest OLR values (below 180–190 W m-2) over the Bay of Bengal. Upward (downward) winds are located on the eastern (western) side of the SAH in agreement with .

The mean seasonal evolution of the SAH location and strength together with OLR during May–September (1979–2013) is shown in Figs. a and . Until approximately mid-July the area of strong convective activity extends north-westward and retreats south-eastward later. In a similar way the location of the SAH moves north-westward during the build up of the SAH and south-eastward during the decay phase of the SAH (shifting ∼ 30∘ longitudinally and ∼ 10–15∘ latitudinally). The seasonal east–west shift can also be found in daily precipitation data from GPCP during the period 1997–2013 (see Fig. b), and the seasonal northward migration of precipitation has been noted in previous studies e.g.their Fig. 3. We note that in contrast to the expansion of low OLR and high precipitation values, the region of lowest OLR and highest precipitation (∼ 90∘ E) in Fig.  does not shift notably.

To further study the relation of convection (OLR) to the SAH we investigate the temporal correlation of NOAA OLR with geopotential at 100 hPa from ERA-I on subseasonal timescales. Therefore, we calculate time lag correlations of OLR averaged over the region 70–130∘ E × 15–30∘ N (i.e. the deep convective region on the south-eastern border of the SAH) with geopotential averaged over 20–40∘ N. Before calculating the correlations based on data from May to September 1979–2013, the data were deseasonalised and oscillations with a period of less than 10 days were removed. The results of the time lag analysis are shown in Fig. . At around time lag zero the maximum anticorrelation (< -0.50) is found at approximately 75–85∘ E and moves westward with increasing time lag. Approximately 3 days later the maximum anticorrelation (< -0.45) is found around 45∘ E. East of the instantaneous response region the maximum anticorrelation travels eastward more slowly, e.g. the maximum anticorrelation at a time lag of 4 days (< -0.35) can be found at approximately 90–95∘ E.

The north-west to south-east movement found in the seasonal cycle of the SAH can also be identified on the interannual timescale. Table  shows the correlation of longitude and latitude occurrence of the SAH centre for the seven reanalysis data sets. The correlation coefficients are calculated based on the seasonal mean and monthly mean data during 1979–2014 (1979–2010 for CFSR and 1979–2013 for JRA-25). For the latter the multiannual mean of each month has been subtracted in order to deseasonalise the data (in the following this will be referred to as deseasonalised monthly mean data).

For seasonal mean and deseasonalised monthly mean data all reanalyses show that westward (eastward) movement of the SAH is related to northward (southward) movement. The separate analysis of June, July and August yields that this relationship is strong during June and July (significant on the 10 % level in all reanalyses). In August, however, the connection is weaker and becomes insignificant for most reanalyses (on the 10 % significance level, weakest anticorrelation of -0.08 found in MERRA data).

In summary, we have found that the SAH's location and strength are notably related to the location and strength of convection located on its south-eastern border (on climatological, seasonal and subseasonal timescales). This connection is especially prominent in the mean seasonal evolution. Moreover, the seasonal north-west to south-east movement of the SAH is also evident in the seasonal mean and in the deseasonalised monthly mean data in summer, leading to the hypothesis that changes in the location of convection are related to the movement of the SAH on these timescales as well. This hypothesis will be tested via composite analyses in the next section (Sect. ).

Regardless of the existence of two preferred spatial modes of the SAH, it is of great interest to identify signatures that are associated with an eastward or westward location of the SAH centre. The days, months, summers and years with a rather (see exact definition later) westward (eastward) location of the SAH centre will be termed western (eastern) days, months and summers.

A similar method has been applied by , who analysed satellite measurements (of O3, H2O and CO) with respect to the location of the SAH as diagnosed by NCEP-1 daily data. These composites show a dipole pattern in the distribution of trace gas anomalies where positive (negative) values of tropospheric (stratospheric) tracers are collocated with the current location of the SAH. This illustrates how trace gas anomalies follow the movement of the SAH see also.

The importance of the location of the SAH centre can be inferred from Fig. , which displays geopotential height composites (grey contours) at 100 hPa together with anomalies of the vertical velocities at 100 hPa for west (Fig. a) and east (Fig. b) summers during 1979–2013. A year belongs to the west (east) composite if the seasonal (JJA) SAH centre is located more than 7.5∘ to the west (east) of the multiannual mean location of the SAH centre (resulting in eight summers for each composite). During western years the SAH tends to be slightly stronger and negative anomalies in the vertical velocities (i.e. relative upward transport of the order of 25 % of the maximum climatological values; see Fig. ) in the centre region are found. This could be an indicator of stronger confinement and enhanced upward transport during western summers. For the eastern summers, the anomalies are exactly reversed, i.e. regions of anomalous upward motion during western years show anomalous downward motion during eastern years and vice versa.

To identify the signatures associated with the longitudinal location of the SAH, we will use composite differences of OLR (NOAA) and precipitation (GPCP). In detail, the respective data are split according to the location of the SAH centre in ERA-I data into western and eastern composites. Finally, the two composites are subtracted from each other (we show results as west minus east).

We will present analyses based on seasonal mean, monthly mean and daily data from June to August. To separate the effect of the seasonal cycle (see Figs.  and ) from subseasonal processes within the monthly mean and daily data, we use the deseasonalised monthly and daily means of OLR, precipitation and surface temperature (for daily data the June to August period of the smoothed seasonal cycle based on May to September data from 1979 to 2013 was removed). Accordingly, the split into eastern and western phases is done with respect to ±7.5∘ deviation from the multiannual summer mean, from the multiannual monthly means or from the smoothed seasonal cycle (see dashed line in Fig. ) of the longitudinal position of the SAH centre.

The following results are also supported (qualitatively) if we split according to ±1σ, where σ represents the multiannual seasonal, multiannual monthly or multiannual daily standard deviation. Similarly, splitting the data into western (< 67.5∘ E) and eastern (> 80∘ E) phase or location over the IP and TP leads to comparable results. This might be due to the fact that subseasonal variations dominate the seasonal variations. For the seasonal data the three methods give almost the same composites (see Fig. d, mean location of 73.5∘ and standard deviation of ∼ 9∘) and hence similar results.

Figure  shows composite differences of OLR and precipitation with respect to different timescales (for daily data there is no precipitation composite because daily resolved GPCP data are only available from October 1996 onward). For seasonal, monthly and daily data the western (eastern) composite is comprised of 8, 39 and 1222 (8, 38, 1087) data points respectively. Areas that do not reach the 10 % significance level are dotted.

Two main regions that show significant differences between western and eastern phases in OLR and in precipitation are India and the western Pacific. In detail, OLR is lower (higher) during western (eastern) periods over India and the Arabian Sea. For the western Pacific the reverse connection is found. Furthermore, the results from precipitation data are in agreement with the results from OLR data, i.e. lower (higher) OLR values are accompanied by more (less) rainfall. Additionally, there is less OLR in the deep tropics, indicating more convective rainfall in this region during western phases. This is most pronounced in the monthly data (Fig. c and d). In comparison with seasonal and monthly mean data (Fig. a and c) there is an important difference for the daily data (Fig. e) because negative OLR values stretch farther from west to east at approximately 20–30∘ N.

The analysis for June, July and August separately (not shown) shows that in June and July, the significant differences are mostly in agreement with the results found for the monthly JJA data. In August, however, almost no significant differences of precipitation can be found over India.

In short, we have found that the western–eastern location of the SAH is connected to opposing anomalies of convection and precipitation over India and over the western Pacific with respect to daily, monthly and seasonal data. Stronger (weaker) precipitation over India (the western Pacific) is related to a more westward location of the SAH centre.

The comparison of the daily location of the SAH centre during JJA 1979–2014 as diagnosed from NCEP-1 and ERA-I shows that NCEP-1 exhibits strong bimodality in its longitudinal location in agreement with, whereas ERA-I shows only a pronounced signature over the IP. This difference is also visible in the long-term climatology, i.e. there is bimodality in the NCEP-1 climatology of geopotential at 100 hPa.

In the analysis of seven reanalysis data sets (CFSR, ERA-I, JRA-25, JRA-55, MERRA, NCEP-1, NCEP-2) with respect to the location of the SAH we find that only NCEP-1 produces a pronounced maximum over the TP and a distinct minimum in the region 67.5–80∘ E, i.e. between the IP and the TP. Furthermore, only NCEP-1 and NCEP-2 show a sharp peak over the IP.

Although there are differences between all reanalyses, NCEP-2 and especially NCEP-1 are outliers regarding the distribution of the SAH centre at 100 hPa. The reanalysis data sets that show the best agreement regarding the location of the SAH centre are CFSR, ERA-I and JRA-55.

The analysis of individual years shows strong interannual variability in the location of the SAH. This variability limits the application of the findings for the long-term mean to single years and vice versa. For example, the distribution of low PV at 380 K in 2006 as shown in (see their Fig. 15), which is based on MERRA data, exhibits high values on the western side (∼ 30–70∘ E). This is in good agreement with the distribution of the SAH centre location at 100 hPa in 2006, which is rather shifted to the west in all reanalyses, except for NCEP-1 (not shown).

Based on monthly mean data over the period JJA 1979–2014 (1979–2010 for CFSR and 1979–2013 for JRA-25) we have found that all reanalyses (except for MERRA) show two regions of increased probability, which lie over the IP and TP (see Fig. c). However, as for the daily data, based on the monthly data NCEP-1 (NCEP-2) shows a more pronounced TM (IM) than ERA-I, JRA-55 and JRA-25. After analysing the months June, July and August separately we have found that in the latter three reanalyses two centres of activity can be found in June and July (weaker IM in June and stronger IM in July), whereas in August the distribution is rather smooth for ERA-I and JRA-55 (not shown).

Based on seasonal data we can identify bimodality in NCEP-1 and NCEP-2. As before, NCEP-1 (NCEP-2) shows a strong TM (IM) and a weaker IM (TM). Additionally, JRA-25 shows low occurrences of the SAH in the region between the IP and TP. CFSR, ERA-I, JRA-55 and MERRA show a spread-out distribution over the region 55–90∘ E with a single peak at ∼ 70–80∘ E, depending on the reanalysis.

Possible reasons for the different distributions as given by the reanalyses with respect to varying timescales (except for NCEP-1 and NCEP-2, which show consistent distributions on all timescales) are as follows: (1) the method of locating the SAH centre picks the highest local maximum for individual samples and does not take the relative strength of the current SAH centre into account. (2) It is rather unlikely that the SAH centre is located west of 55∘ E or east of 92.5∘ E for many subsequent days, hence there is almost no occurrence of the SAH in this region based on monthly means. However, on a daily and pentad basis locations to the west of the IP and to the east of the TP have a significant weight (see Fig. ), thus putting weight to the IM or TM on a monthly basis when the SAH resides rather to the west or east. (3) The bimodality in the monthly data might be influenced by the seasonal cycle.

The reasons for the differences in the SAH centre location between the reanalyses are attributable to the underlying model, the assimilation technique and the observational data, which are assimilated by the different reanalyses. While it is not possible for us to disentangle the relative influence of these sources, some hints might be as follows: numerous changes have been made from NCEP-1 to NCEP-2. These changes affect the thermal and orographic forcing of the IP and TP as well as the diabatic heating associated with tropical convection, e.g. through changed boundary conditions, updated physics and smoothed orography . These changes have a large impact on the distribution of the SAH centre location (see Fig. ). This emphasises the impact of model properties on the SAH centre location. Since some of the features described above are only shown by NCEP-1 and NCEP-2 the importance of the data assimilation scheme (e.g. inclusion of TOVS and ATOVS temperature profiles for NCEP-1 and NCEP-2 vs. radiance data directly for the other reanalyses) might be inferred. However, this could also be related to the closeness of the underlying model. During the time period 2004–2013, for which advanced observational data, which were not included in NCEP-1 and NCEP-2, are available, similar distributions for daily and pentad data are found. Together with the distribution presented in Fig.  for the period 1980–1994, this indicates that the time period chosen does not influence our results qualitatively. This in turn brings us to the hypothesis that the transition between different observational data sets, which are included in the reanalyses, are of minor importance.

have found large differences in the climatologies of diabatic heating rates among different reanalyses. These diabatic heating rate differences (and connected differences on shorter timescales) can be expected to have an impact on the distributions of the SAH centre location (on daily and pentad basis) with respect to the various reanalyses. The most prominent difference in the distribution of the SAH centre location is that the clear bimodality on short timescales found in previous studies (mostly based on NCEP-1 data) is not visible in most recent reanalyses.

We cannot answer the question of which reanalysis represents the reality best. Nevertheless, the fact that modern reanalyses do not produce the bimodality of the SAH centre location with respect to daily, pentad and seasonal data strongly suggests that the bimodality found on these timescales using NCEP-1 data is an artefact of this particular reanalysis.

Previous studies that address the bimodality of the SAH have mostly focused on the 100 hPa level (see Table ). To see how robust our results are, we employed ERA-I on the 395 K level. The SAH centre location was defined as the maximum of the Montgomery stream function along the ridgeline. We found that the PDFs of the SAH centre location with respect to daily and monthly data are similar to ERA-I on 100 hPa. For the seasonal mean data we have found that the distribution changes in favour of the TM and IM, i.e. for seasonal data 12, 10 and 14 years are located in the IP, mid-region and TP region respectively.

To assess what drives the variability of the SAH we show the movement of the SAH centre and the temporal evolution of geopotential at 100 hPa from ERA-I. We find that geopotential often moves to the west and that less regular shedding occurs to the east of the SAH centre. The mean seasonal evolution of convection (in the South Asian tropical region, here 70–130∘ E × 15–30∘ N) and mean seasonal location of the SAH centre as diagnosed by ERA-I show a clear connection (see Figs.  and ): as the region of low OLR and strong precipitation (deep convective region) extends north-westward during the build-up phase of the SAH, the SAH and its centre move north-westward as well. Once the region of strong convection withdraws south-eastward, the SAH centre follows accordingly. This is in agreement with and the climatologies of geopotential height and OLR during JJA shown in Figs.  and and was discussed based on monthly data for the retreat phase of the SAH in . The smoothed mean seasonal movement of the SAH as diagnosed from NCEP-1 behaves accordingly, but with a slightly smaller longitudinal range of approximately ∼ 25∘ compared to ∼ 30∘ for ERA-I.

A time lag analysis, linking convective activity in the tropics and the evolution of geopotential at 100 hPa on subseasonal timescales, shows that the instantaneous response of the geopotential field to convective activity is located on the western edge of the forcing region, again in agreement with results of . Overall, the evolution of geopotential and its connection to OLR (convection) is in agreement with findings based on MERRA data from , who link divergence associated with deep convection to the evolution of the area of low potential vorticity (PV) at 360 K (see their Figs. 6 and 8). When performing the time lag analysis with an averaging area of OLR, which extends farther to the north, we found weaker anticorrelations. Furthermore, when averaging OLR over the TP (approximately 70–105∘ E × 30–40∘ N) only, we get positive correlations of OLR with geopotential height located over the TP (in contrast to negative correlations, which associate reduced OLR with increased convection and strengthening of the SAH). Two possible reasons for this are (1) the SAH is not powered but maybe fed by the convection over the TP with respect to trace gases . (2) (Low) OLR in this region is not a reliable measure for convection due to the height of the TP. This might be enhanced by sampling biases due to the sun-synchronous orbit of the NOAA satellites. These biases are more important over land because convection has a stronger diurnal cycle over land than over sea .

Composites of eastern and western summers during 1979–2013 and corresponding anomalies of the vertical velocities at 100 hPa indicate the possible connection of a more eastward or westward location of the SAH with altered stratosphere–troposphere exchange in the monsoon region. The analysis of composite differences of OLR and precipitation for western and eastern summers (months), based on the location of the SAH centre, yields anomalies of convection and precipitation between these summers (months). There is notably more convection over the Arabian Sea (and India) and consequently more precipitation over India when the SAH centre is located more westward. In contrast, during eastern summers and months there is more convective activity and stronger precipitation over the western Pacific. Additionally, during the western summers and months negative surface temperature anomalies can be found over India, which is probably connected to stronger precipitation. Furthermore, in the monthly analysis we have found that these signatures mostly come from the months of June and July.

As the composite analysis of convection and precipitation, with respect to seasonal mean and monthly mean data, suggests a connection between the SAH location and the ISM (measured by IMD's AIRI), we display the relationship of the seasonal SAH centre location (from ERA-I) and the seasonal AIRI in Fig. a. Figure b shows the correlation coefficients of the location of the SAH centre with the AIRI over the time period of June to August 1979–2013 for the seven reanalyses (1979–2010 for CFSR). For the calculation of the monthly mean correlation coefficients the data were deseasonalised (i.e. multiannual monthly means were subtracted from the SAH location and the AIRI time series).

All reanalyses show stronger anticorrelations based on seasonal mean than on monthly mean data, except for CFSR and NCEP-2. The latter does not produce a significant anticorrelation on the seasonal timescale. Moreover, for the separate months the connection between the SAH location and the ISM in June and July is stronger than in August (except for JRA-25, June -0.32 vs. -0.33 in August) and gets insignificant, on the 10 % level, for CFSR, JRA-55, MERRA, NCEP-1 and NCEP-2. This is in agreement with the findings from the composite analysis.

The Asian monsoon system and especially the ISM is influenced strongly by ENSO see, e.g.,. Hence, we analysed the impact of ENSO on the connection between seasonal SAH centre location and ISM by calculating partial correlations between the SAH's longitudinal location from ERA-I and the AIRI index with respect to the seasonal Niño 3.4 index from CPC. Partial correlations were calculated with respect to the lagged seasonal Niño 3.4 starting with DJF before the summer monsoon and ending with NDJ following the monsoon period. These partial correlations were ranging from -0.44 to -0.51, which is comparable to the simple correlation coefficient between the two indices. The lagged correlations between the SAH's location and Niño 3.4 were below 0.1 and not significant.

The relationship between the ISM and the SAH is also supported by the findings of , who linked the west–east and north-west to south-east displacement of the SAH on interannual timescales with the strength of the ISM. In detail they used two indices, the SAHI14 and the SAHI15 (see caption of Fig. ), as given by geopotential from ERA40 on 200 hPa (based on JJA data during 1958–2002), and found correlation coefficients of -0.49 for the SAHI14 and -0.64 between the SAHI15 and the rainfall over India (note that a more positive SAHI corresponds to a more eastward location of the SAH). We calculated the correlation of the SAHI14 and the SAHI15 for ERA-I geopotential at 100 hPa and AIRI from the IMD as -0.57 and -0.61 respectively. Additionally, performed idealised model studies and showed the importance of latent heat release over India to the location of the SAH on interannual timescales. also address the possible influence of ENSO on the SAH's longitudinal location and come to a similar conclusion that ENSO does not play a major role for western–eastern shifts of the SAH on interannual timescales.

However, in contrast to the findings of , who identify an opposing heating or cooling source over the Yangtze River valley using GPCC data from 1958 to 2002, we do not find a significant connection between rainfall in this region and the location of the SAH, but rather with convective activity over the western Pacific. The corresponding excess or deficit precipitation over the western Pacific was also found in some of the AOGCM-based precipitation composites, which were presented in (their Fig. 4).

The composite difference of OLR based on daily data shows results similar to the seasonal and monthly data. To check how robust the signatures are, we have built composites based on the SAH centre location at 100 hPa from NCEP-1. In general, the results match those from when the splitting is performed based on ERA-I. Especially for the daily (1344 west, 1305 east) and monthly (35 west, 39 east) mean data the results match well. For the seasonal data (9 west, 10 east) the signatures in OLR and precipitation over India are shifted farther towards the southern slopes of the Himalayas.

As plateau heating has been discussed as a driver for the SAH shift e.g., we have performed the composite analysis on reanalysis surface temperature data for ERA-I and NCEP-1 (not shown). The results show that during western periods surface temperatures are usually lower (up to -1.25 K) over India, likely associated with heavier rainfall in this region. With respect to the IP (TP), positive (negative) anomalies in surface temperatures get more pronounced on shorter timescales. This is especially the case for ERA-I data, where only the composite based on daily data shows a clear and significant negative anomaly over the TP during western phases, whereas for NCEP-1 data the TP and IP anomalies are strong regardless of the timescale. This might be hinting that the significance of heating associated with the two plateaus is of more importance on shorter timescales and in NCEP-1 compared to ERA-I data.

The drawback of using reanalysis surface temperature data, especially in the TP (and probably IP) region where observational data are scarce, is that this variable is strongly influenced by the model itself at least for NCEP-1 see. Hence, the signatures in surface temperatures over the TP might not be reliable and thus reflect the models' connection of surface temperature over the TP with the location of the SAH.

In this study the movement and drivers of the SAH during the period 1979–2014 are investigated using observational and reanalysis data. Special attention is brought to the subject of bimodality, i.e. the two preferred modes of the SAH's centre location over the Tibetan and Iranian plateaus.

We find that bimodality with respect to daily, pentad and seasonal data at 100 hPa is only found in NCEP-1 and NCEP-2; however, they are not consistent with each other. Although we cannot rule out that NCEP-1 or NCEP-2 simulate the distribution of the SAH centre correctly, the other reanalyses – including most recent ones (e.g. ERA-I and JRA-55) – do not support the notion of bimodality as two designated centres of activity. This is of special interest because a couple of studies have conducted analyses based on this concept. Thus, it might be useful to investigate if their findings are affected when more recent reanalyses are being used. Furthermore, it might limit the conclusions drawn from these studies, i.e. the drivers associated with the two modes might reflect the model properties rather than the actual atmospheric situation and thus might vary from reanalysis to reanalysis. Finally, using a more recent reanalysis might enhance results found in composite differences, e.g. with respect to satellite-measured trace gases as in .

With respect to the drivers of the SAH, we find that shifts in convection are a main cause for the shift in the location of the SAH (on various scales): we connected the mean seasonal evolution of the SAH to the seasonal cycle of convection in the tropical region adjacent to the SAH. A modified extension of the composite analysis performed in showed that the ISM and convection over the western Pacific are related to the longitudinal position of the SAH centre. Hence, the location of the SAH might be related to different boundary layer source regions and in turn affect consequent transport (maybe into the stratosphere; see Fig. ).

We note that on top of the influence through the location of convection, internal variability – i.e. instability of the anticyclone and subsequent westward movement or splitting – plus external forcing described by influence the location of the SAH on the synoptic timescales. Additional influences from the orography and heating of the TP and IP might also modulate the location of the SAH e.g.and references therein. The relative importance of the thermal forcing of the TP and convection in the South Asian region on the synoptic movement of the SAH is still an open point and difficult to evaluate from reanalysis and observational data only.

We acknowledge the institutions listed in Table 1 for the production and dissemination of reanalysis data.