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  • View in gallery

    A map detailing how to calculate the correlation coefficients between the interface between the Indian summer monsoon and the East Asian summer monsoon, and the MAM sea surface temperature contrast index. For example the distance between C0 and C1 is 2.0° lon, between C0 and C2 is 2 × 2.0° lon, and between C0 and Cn is n × 2.0° lon.

  • View in gallery

    The correlation coefficients between the IIE and the MAM SST configuration series, dependent on the location of the rectangles in Fig. 1. The y axis denotes the location of the western rectangle, and the x axis the location of the eastern rectangle. The shaded areas denote the correlation coefficients between the IIE and SSTs averaged over the western rectangle minus the SSTs averaged over the eastern rectangle. The rectangle lengths are (a) 20°, (b) 30°, (c) 40°, (d) 50°, (e) 60°, and (f) 70° lon. All rectangle widths are 22° lat. The green line denotes the critical value of the correlation coefficients that are significant, at the 95% confidence level.

  • View in gallery

    (a) Normalized SSTSM index and index of the IIE. (b) The 3-month running correlation coefficients between the IIE index and the TIOI (blue), the TCWPI (green), and the SST configuration index (red). In (b), the thin and thick blue horizontal lines denote the threshold values that pass the significance test, at the 90% and 95% confidence levels, respectively.

  • View in gallery

    (a) Spring SSTs regressed onto the spring SSTSMI. (b) Summer SSTs regressed onto the summer SSTSMI. (c) Spring SSTs regressed onto the IIEI. (d) Summer SSTs regressed onto the IIEI. The shaded areas, from light to dark, denote the positive (red) and negative (blue) correlation coefficients that are significant at the 90%, 95%, and 99% confidence levels, respectively. The two green rectangles denote the sea areas used to construct the SSTSMI.

  • View in gallery

    Summer atmospheric circulations regressed onto the (a)–(c) spring SSTSMI and (d)–(f) IIEI, for (a),(d) sea level pressure and surface winds, (b),(e) tropospheric (850–250 hPa) temperature and surface winds, and (c),(f) precipitation and surface winds. The shaded areas, from light to dark, denote the positive (red) and negative (blue) correlation coefficients that are significant at the 90%, 95%, and 99% confidence levels, respectively. Only the winds significant above the 95% confidence level (vectors; m s−1) are plotted. The contour intervals are 10 Pa in (a),(d); 0.05 K in (b),(e); and 0.3 mm day−1 in (c),(f).

  • View in gallery

    The IIE from 18° to 28°N with 2° interval regressed onto the spring SSTSMI. The asterisk denotes the corresponding correlation coefficient that is significant at the 95% confidence level, and the hollow circles show the corresponding correlation coefficients that are significant at the 99% confidence level.

  • View in gallery

    (a)–(c) Summer atmospheric circulation anomalies for 1997 and (d)–(f) the anomalies reconstructed using the spring SSTSMI, for (a),(d) sea level pressures (Pa) and surface winds, (b),(e) tropospheric (850–250 hPa) temperature (K) and surface winds, (c),(f) and the precipitation (mm day−1) and surface winds. The units of the horizontal surface winds (vectors) are m s−1.

  • View in gallery

    As in Fig. 7, but for 1998.

  • View in gallery

    The sea surface temperature (SST) anomalies (°C) superimposed as perturbations on the climatological SSTs for the (a) positive and (b) negative SSTMI model runs.

  • View in gallery

    The differences of the simulated sea level pressures (Pa) and horizontal winds (m s−1; vectors) at 1000 hPa in (a) MAM, (b) April–June (AMJ), (c) May–July (MJJ), and (d) JJA. (e)–(h) As in (a)–(d), but for difference of the simulated tropospheric (850–250 hPa) temperature (K) and horizontal winds at 1000 hPa, with positive SSTSMI model runs minus the negative SSTSMI runs. Only the winds significant at the 95% confidence level are plotted. The thick lines denote that the difference is significant at the 95% confidence level.

  • View in gallery

    The location of the IIE; the red line denotes observations of the IIE’s position, and the green one shows the IIE’s position according to the historical runs. The blue solid (dashed) line denotes the IIE’s position in SE1 (SE2).

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The Variability of the Indian–East Asian Summer Monsoon Interface in Relation to the Spring Seesaw Mode between the Indian Ocean and the Central-Western Pacific

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  • 1 Department of Atmospheric Sciences, Yunnan University, and Yunnan Key Laboratory of International Rivers and Transboundary Eco-security, Kunming, China
  • 2 Department of Atmospheric Sciences, Yunnan University, Kunming, China
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Abstract

The interannual zonal movement of the interface between the Indian summer monsoon and the East Asian summer monsoon (IIE), associated with the spring sea surface temperature (SST) seesaw mode (SSTSM) over the tropical Indian Ocean (TIO) and the tropical central-western Pacific (TCWP), is studied for the period 1979–2008. The observational analysis is based on Twentieth Century Reanalysis data (version 2) of atmospheric circulations, Extended Reconstructed SST data (version 3), and the Climate Prediction Center Merged Analysis of Precipitation. The results indicate that the IIE’s zonal movement is significantly and persistently correlated with the TIO–TCWP SSTSM, from spring to summer. The results of two case studies resemble those obtained by regression analysis. Experiments using an atmospheric general circulation model (ECHAM6) substantiate the key physical processes revealed in the observational analysis. When warmer (colder) SSTs appear in the TIO and colder (warmer) SSTs occur in the TCWP, the positive (negative) SSTSM forces anomalous easterly (westerly) winds over the Bay of Bengal (BOB), South China Sea (SCS), and western North Pacific (WNP). The anomalous easterly (westerly) winds further result in a weakened (strengthened) southwest summer monsoon over the BOB and a strengthened (weakened) southeast summer monsoon over the SCS and WNP. This causes the IIE to shift farther eastward (westward) than normal.

Corresponding author address: Dr. Jie Cao, Department of Atmospheric Sciences, Yunnan University, Kunming, 650091, China. E-mail: caoj@ynu.edu.cn

Abstract

The interannual zonal movement of the interface between the Indian summer monsoon and the East Asian summer monsoon (IIE), associated with the spring sea surface temperature (SST) seesaw mode (SSTSM) over the tropical Indian Ocean (TIO) and the tropical central-western Pacific (TCWP), is studied for the period 1979–2008. The observational analysis is based on Twentieth Century Reanalysis data (version 2) of atmospheric circulations, Extended Reconstructed SST data (version 3), and the Climate Prediction Center Merged Analysis of Precipitation. The results indicate that the IIE’s zonal movement is significantly and persistently correlated with the TIO–TCWP SSTSM, from spring to summer. The results of two case studies resemble those obtained by regression analysis. Experiments using an atmospheric general circulation model (ECHAM6) substantiate the key physical processes revealed in the observational analysis. When warmer (colder) SSTs appear in the TIO and colder (warmer) SSTs occur in the TCWP, the positive (negative) SSTSM forces anomalous easterly (westerly) winds over the Bay of Bengal (BOB), South China Sea (SCS), and western North Pacific (WNP). The anomalous easterly (westerly) winds further result in a weakened (strengthened) southwest summer monsoon over the BOB and a strengthened (weakened) southeast summer monsoon over the SCS and WNP. This causes the IIE to shift farther eastward (westward) than normal.

Corresponding author address: Dr. Jie Cao, Department of Atmospheric Sciences, Yunnan University, Kunming, 650091, China. E-mail: caoj@ynu.edu.cn

1. Introduction

The Asian summer monsoon (ASM) comprises two subsystems, the Indian summer monsoon (ISM) and the East Asian summer monsoon (EASM), and is the most energetic monsoon in the world (Tao and Chen 1987). There is an interface between the ASM’s two subsystems (Jin and Chen 1982). The variation in the interface between the ISM and EASM (IIE) can synthetically mirror the effects of the ISM and EASM on the climate in East Asia, especially that around the interface. For example, in relation to the IIE moving more eastward than its normal position, the summer rainfall significantly increases between the lower reach of the Yangtze River and the lower reach of the Yellow River, and vice versa (Cao et al. 2012). Therefore, studying the IIE may offer a scientific basis for improving the ability of forecasting flooding or droughts and promote sustainable socioeconomic development of the countries affected by the ASM (Zhu et al. 2014; He and Zhu 2015).

Discrepancies in past research of the IIE exist. Jin and Chen (1982) and Gao et al. (2005) found that the IIE was near 95°–100°E, whereas Wang and Lin (2002) showed that the ISM, EASM, and western North Pacific summer monsoon (WNPSM) meet near Indochina and Yunnan–Guizhou Plateau, and Wang et al. (2003a) suggested that the IIE was located at 105°E, through their definitions of the ISM and EASM domains (40°–105°E and 105°–160°E, respectively).

There was no clear determination of the IIE position until Cao et al. (2012) quantitatively defined the IIE index. They found the IIE’s climatological location, which presents a wave pattern near 100°E, from 18° to 28°N (the red line in Fig. 11 in this study), and primarily studied the IIE’s interannual variability. Tao et al. (2015) found the release of latent heat, exerted by the low-frequency variability of anomalous land–sea thermal contrasts, to be one of the most important physical processes that correlated with the zonal movement of the IIE.

In terms of the relationship between sea surface temperature (SST) configuration and the ASM, some studies have shown that the east–west SST contrasts between the Indian and Pacific Oceans are intimately connected with the ASM. For example, Kawamura (1998) performed an ensemble of three 40-yr parallel atmospheric general circulation model (AGCM) simulations with observed SSTs; they found that east–west SST gradient anomalies, between the northern Indian Ocean (NIO) and the warm pool region east of the Philippines, were associated with extreme summers in East Asia. Kawamura et al. (2001) reproduced the zonal gradient of summer SST anomalies in relation to the ASM intensity, with an ocean general circulation model. They adopted a wind–evaporation feedback mechanism to explain the persistence of cold SST anomalies over the NIO and South China Sea (SCS), and used a wind and latent heat release feedback mechanism to explain the persistence of warm SST anomalies over the western North Pacific (WNP). Such a positive feedback atmosphere–ocean mode excites a Pacific–Japan teleconnection pattern, bringing about an unusually hot summer in the vicinity of Japan. Using a linear equatorial beta-plane model, Terao and Kubota (2005) suggested that in summers following El Niño events, east–west SST differences between the Indian and Pacific Oceans led to an anticyclonic anomaly over the WNP. Ohba and Ueda (2006) conducted experiments with an AGCM to assess the relative importance of SST anomalies in the remote NIO versus those in situ in the WNP. They found that WNP monsoon rainfall is sensitive to the spatial distribution of NIO SST anomalies, as well as the in situ WNP SST anomalies. Wu et al. (2010) determined the relative contribution of cold-anomaly SST over the WNP or Indian Ocean to the maintenance of the WNP anticyclonic anomaly. Recently, Cao et al. (2013) found that, in comparison with the winter ENSO, the spring SST contrasts between the tropical Indian Ocean (TIO) and tropical western Pacific (TWP) can be better predictors of EASM rainfall anomalies.

The studies reviewed above suggest that the SST configurations between the Indian and Pacific Oceans may exert an impact on the IIE’s variability, through influencing the ASM first. However, the relationship between Indian Ocean–Pacific SST configurations and the interannual variability of the IIE’s zonal movement is not clear. The present study aimed to establish whether the Indian Ocean–Pacific SST configurations are significantly connected with the IIE and, if so, to identify the significant and representative SST configuration, and the possible physical processes involved. These aims were addressed using observational diagnostics and numerical modeling.

In section 2, the datasets and ECHAM6 AGCM are described. Section 3 presents an observational diagnosis: definitions of a tropical Indian Ocean index (TIOI) and a tropical central-western Pacific (TCWP) index (TCWPI), calculations of an Indian Ocean–central-western Pacific SST seesaw mode (SSTSM) index (SSTSMI), and also an analysis of the possible physical processes by which the TIO–TCWP SSTSM induces the IIE anomalies. Section 4 verifies the IIE anomalies in response to the TIO–TCWP SSTSM using an AGCM. Section 5 provides a summary and further discussion.

2. Data and methods

a. Observational data

The Twentieth Century Reanalysis data (version 2) of atmospheric circulation during 1979–2008 are from NOAA/OAR/ESRL Physical Sciences Division (Compo et al. 2006, 2011; Whitaker et al. 2004). The horizontal resolution of the data is 2.0° × 2.0°. The SST data, provided by the Extended Reconstructed SST version 3 (ERSST.v3), have the same resolution as the atmospheric circulation data (Smith et al. 2008). The Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) over the period 1979–2008 is used (Xie and Arkin 1997); the CMAP horizontal resolution is 2.5° × 2.5°. The IIE index and the IIE position at each latitude during 1979–2008 from Cao et al. (2012) are used. There, the IIE is defined the partial deviation of equivalent potential temperature, with respect to longitude being equal to zero, over the region 18°–28°N, 90°–110°E, the physical meaning of which is that the two monsoon air masses fully mix and their sources cannot be discriminated from each other. The IIE index is defined as the time coefficient corresponding to the first empirical orthogonal function mode. When the IIE index is higher than the normal, the IIE’s position at each latitude shifts farther eastward than normal, and vice versa (Cao et al. 2012). In this study, spring refers to March–May (MAM), and summer to June–August (JJA).

To reveal the correlation between the IIE and SST configurations, and correspondingly obtain the highest correlation coefficient in an absolute sense, we construct a SST configuration series and calculate the correlation coefficient between the IIE and the spring SST configuration series. Figure 1 shows a map that details how to calculate the correlation coefficients between the IIE and the spring SST configuration series; the first SST configuration series is constructed using the spring SSTs averaged over rectangle , minus the spring SSTs over rectangle , year by year from 1979 to 2008, and the correlation coefficient between the IIE and the first spring SST configuration series can then be calculated. Then, the rectangle is moved eastward to rectangle , so the second SST configuration series is constructed using the spring SSTs averaged over rectangle , minus the spring SSTs over rectangle , year by year over the same period; the correlation coefficient between the IIE and the second spring SST configuration series is then obtained. Repeating the process described above, the (n + 1)th SST configuration indices are constructed using the spring SSTs averaged over rectangle , minus the spring SSTs over , year by year over the same period; the correlation coefficients between the IIE and the (n + 1)th spring SST configuration series are then obtained. Given that the widths of the two rectangles are both 22° in latitude (10°S–12°N), and that there are six different scales, from 20° to 70° in longitude (at 10° intervals) for their lengths, the correlation coefficients between the IIE and the spring SST configuration series can then be correspondingly obtained.

Fig. 1.
Fig. 1.

A map detailing how to calculate the correlation coefficients between the interface between the Indian summer monsoon and the East Asian summer monsoon, and the MAM sea surface temperature contrast index. For example the distance between C0 and C1 is 2.0° lon, between C0 and C2 is 2 × 2.0° lon, and between C0 and Cn is n × 2.0° lon.

Citation: Journal of Climate 29, 13; 10.1175/JCLI-D-15-0839.1

b. AGCM

The AGCM used in this study is the ECHAM6, which was recently released by the Max Planck Institute for Meteorology in Germany (Giorgetta et al. 2013). In ECHAM6 the land surface model (JSBACH) was extended by dynamic vegetation (Brovkin et al. 2009; Reick et al. 2013); the surface albedo scheme was improved, with a weighted average of the ice albedo and the water albedo changing with the solar zenith angle (Roeckner et al. 2012); and the representation of shortwave radiative transfer, the height of the model top, the model tuning, and convective triggering were also modified (Hagemann et al. 2013; Stevens et al. 2013). These improvements mean that ECHAM6 provides a better representation of the mean climate than ECHAM5 (Stevens et al. 2013). An ECHAM6 version with triangular truncation at zonal wavenumber 63 (T63; equivalent to 1.875° on a Gaussian grid) and 47 hybrid sigma–pressure levels in the vertical grid is adopted in this study. The historical run of ECHAM6 spans a total of 27 years, from 1979 to 2005. The corresponding model data were downloaded from http://www-pcmdi.llnl.gov/.

3. Observational analysis

a. Correlation between the IIE and spring TIO–TCWP SST configuration

Figure 2 shows the distribution of the correlation coefficients calculated; there is only one center with significant correlation coefficient in each figure part. The center value of the correlation coefficients is 0.50, for the SSTs averaged over 10°S–12°N, 50°–70°E, minus the SSTs averaged over 10°S–12°N, 160°E–180°; 0.47 for the SSTs averaged over 10°S–12°N, 50°–80°E, minus the SSTs averaged over 10°S–12°N, 152°E–178°W; 0.44 for the SSTs averaged over 10°S–12°N, 50°–90°E, minus the SSTs averaged over 10°S–12°N, 146°E–174°W; 0.43 for the SSTs averaged over 10°S–12°N, 50°–100°E, minus the SSTs averaged over 10°S–12°N, 140°–170°W; 0.41 for the SST averaged over 10°S–12°N, 50°–110°E, minus the SSTs averaged over 10°S–12°N, 132°–168°W; and 0.39 for the SSTs averaged over 10°S–12°N, 50°–120°E, minus the SSTs averaged over 10°S–12°N, 120°E–170°W. The results of the correlation analysis imply that the TIO–TCWP SST configuration may be in a seesaw mode.

Fig. 2.
Fig. 2.

The correlation coefficients between the IIE and the MAM SST configuration series, dependent on the location of the rectangles in Fig. 1. The y axis denotes the location of the western rectangle, and the x axis the location of the eastern rectangle. The shaded areas denote the correlation coefficients between the IIE and SSTs averaged over the western rectangle minus the SSTs averaged over the eastern rectangle. The rectangle lengths are (a) 20°, (b) 30°, (c) 40°, (d) 50°, (e) 60°, and (f) 70° lon. All rectangle widths are 22° lat. The green line denotes the critical value of the correlation coefficients that are significant, at the 95% confidence level.

Citation: Journal of Climate 29, 13; 10.1175/JCLI-D-15-0839.1

Because the corresponding results were not appreciably changed with substantially changing the scopes of the averaging regions—for example, the change in length of the two rectangles from 20° to 70° in longitude, or the change in width of the two rectangles from 10°S–12°N to 10°S–10°N (not shown)—for generality, in this study we choose the SSTs averaged over the TIO (10°S–12°N, 50°–100°E) and TCWP (10°S–12°N, 140°–170°W) to further analyze the relationship between the IIE and the TIO–TCWP SST configuration. The SSTs averaged over the TIO are defined as the TIOI, while those averaged over the TCWP are the TCWPI. The SSTSMI is the TIOI minus the TCWPI.

Figure 3a is the normalized SST seesaw mode index and IIE index. These time series in Fig. 3a do not present any linear trends or interdecadal variabilities, but they exhibit a significant feature with interannual variability. Calculations of the 3-month running correlation coefficients between the IIE and the MAM–SON TIOIs, TCWPIs, and SSTSMIs during 1979–2008 show that significant positive correlations exist between the IIE and SSTSMIs, from the previous boreal spring to current boreal summer (Fig. 3b). However, positive correlation coefficients between the IIE and TIOI, and negative correlation coefficients between the IIE and TCWPI, keep their sign from MAM to JJA but are not significant, even at the 90% confidence level. These results suggest that the positive correlations between the IIE and TIO–TCWP SSTSM exist for longer and are more significant, compared to the individual SST anomalies over the TIO or TCWP. There is a significant relationship between the IIE and TIO–TCWP SSTSMs, especially in spring. When the SSTs over the TIO are warmer (colder) and the SSTs over the TCWP are colder (warmer), a positive (negative) SSTSMI occurs; this indicates that the spring seesaw mode over TIO–TCWP forms. The positive (negative) spring seesaw mode will exert its effect on the IIE’s zonal movement.

Fig. 3.
Fig. 3.

(a) Normalized SSTSM index and index of the IIE. (b) The 3-month running correlation coefficients between the IIE index and the TIOI (blue), the TCWPI (green), and the SST configuration index (red). In (b), the thin and thick blue horizontal lines denote the threshold values that pass the significance test, at the 90% and 95% confidence levels, respectively.

Citation: Journal of Climate 29, 13; 10.1175/JCLI-D-15-0839.1

b. Anomalous circulation patterns in response to the spring TIO–TCWP SSTSM and the IIE

To show the TIO–TCWP SST anomalies in relation to the anomalous SSTSMI, the TIO–TCWP SSTs are regressed onto the simultaneous SSTSMI values (Figs. 4a,b). Figure 4a clearly shows a significant seesaw mode over the TIO–TCWP. The positive-anomaly SST pole dominates over the TIO; here, most of SST anomalies, with a maximum >0.6°C, are significant at the 95% confidence level. The negative-anomaly SSTs prevail over the TCWP, where the SST anomalies below −0.9°C over the central area are even significant at the 99% confidence level. Figure 4b has a similar pattern to Fig. 4a. Figures 4c and 4d show that the TIO–TCWP SSTs are regressed onto the index of the interface between the Indian summer monsoon and the East Asian summer monsoon (IIEI). It can be seen that Figs. 4c and 4d share the similar pattern with Figs. 4a and 4b. There are negative correlation coefficients over the TIO and positive correlation coefficients over the TCWP. However, these correlation coefficients become weaker than in Figs. 4a and 4b. These results agree well with Fig. 3b. The regression results suggest that the SSTSMI does reflect the large-scale east–west SST seesaw mode between the TIO and TCWP. The TIO–TCWP SSTSM rather than the SST anomalies over an individual ocean may exert its impact on the IIE’s position through the EASM and ISM.

Fig. 4.
Fig. 4.

(a) Spring SSTs regressed onto the spring SSTSMI. (b) Summer SSTs regressed onto the summer SSTSMI. (c) Spring SSTs regressed onto the IIEI. (d) Summer SSTs regressed onto the IIEI. The shaded areas, from light to dark, denote the positive (red) and negative (blue) correlation coefficients that are significant at the 90%, 95%, and 99% confidence levels, respectively. The two green rectangles denote the sea areas used to construct the SSTSMI.

Citation: Journal of Climate 29, 13; 10.1175/JCLI-D-15-0839.1

The sea level pressures (SLPs) and surface winds (Fig. 5a), the tropospheric temperature and surface winds (Fig. 5b), and the precipitation and surface winds (Fig. 5c) are regressed onto the spring SSTSMI, and the positions of the IIE from 18° to 28°N with 2° intervals are regressed onto the spring SSTSMI for analysis. In Fig. 5a, the SLP anomalies are lower than −10 Pa across the whole of the TIO. Significant negative anomalies appear around 30°–10°S, 80°–100°E, with less than −40 Pa being the center value. There are off-equatorial maxima, with greater than 50-Pa SLP anomalies in both subtropics of the central-western Pacific Ocean. In terms of the surface winds, the significant anomalous surface easterlies are mainly confined to the eastern TIO, and then across most of the tropical central-western region of the Pacific. The anomalous northeasterly winds driven by the equatorial low pressure (Fig. 5a), by surface friction, enhance a surface divergence that results in anomalous anticyclonic circulations over the Bay of Bengal (BOB), SCS, and WNP and further weakens the deep convection around 10°–15°N, 110°E–170°W (Figs. 5b,c). Meanwhile, the higher-than-normal levels of water vapor are transported by the southerly anomalies on the western flank of the anomalous anticyclone over the SCS and WNP and converge over the middle–lower reaches of the Yangtze River in China; this increases the summer precipitation over that region. Accompanied by the release of latent heat, higher-than-normal tropospheric temperatures occur over the middle–lower reaches of the Yangtze River in China (Fig. 5b). The self-consistent anomalies of the surface horizontal winds, tropospheric temperatures, and precipitation suggest that the northeasterly anomalies over the BOB weaken the southwest summer monsoon. Meanwhile, the southerly anomalies over western SCS enhance the southeast summer monsoon; these anomalous anticyclonic circulations, noted in previous studies (e.g., Wang et al. 2000, 2003b; Terao and Kubota 2005; Xie et al. 2009; Du et al. 2009), develop from the BOB to the international date line.

Fig. 5.
Fig. 5.

Summer atmospheric circulations regressed onto the (a)–(c) spring SSTSMI and (d)–(f) IIEI, for (a),(d) sea level pressure and surface winds, (b),(e) tropospheric (850–250 hPa) temperature and surface winds, and (c),(f) precipitation and surface winds. The shaded areas, from light to dark, denote the positive (red) and negative (blue) correlation coefficients that are significant at the 90%, 95%, and 99% confidence levels, respectively. Only the winds significant above the 95% confidence level (vectors; m s−1) are plotted. The contour intervals are 10 Pa in (a),(d); 0.05 K in (b),(e); and 0.3 mm day−1 in (c),(f).

Citation: Journal of Climate 29, 13; 10.1175/JCLI-D-15-0839.1

The SLP, surface winds, tropospheric temperatures, and precipitation levels regressed onto the IIE index (Figs. 5d–f) show the anomalous circulation patterns in relation to the IIE index; it is worth noting that the atmospheric circulation anomalies in Figs. 5d–f, especially those over the BOB–SCS–WNP and the middle–lower reaches of the Yangtze River in China, share almost the same patterns as those in Figs. 5a–c, respectively. However, the area of significance and intensity of the atmospheric circulation anomalies regressed onto IIE index decrease by different amounts to some extent, compared to those regressed onto the spring SSTSMI.

Considering the anomalous circulation patterns associated with the spring SSTSMI and those associated with IIE together (Figs. 5 and 6), when the positive spring SSTSMI occurs, anomalous easterlies located at south of the SCS around 15°N force out a more intense western Pacific subtropical high (WPSH) with a farther south and west position through enhancing the easterlies that prevail over the SCS during summer; also, an anomalous convergence zone, from 22°N, 100°E northeastward to 30°N, 140°E, occurs at the north flank of the WPSH (Fig. 5). Meanwhile, the anomalous easterlies extended from the SCS to the BOB weaken the southwesterlies that prevail over the BOB, and force an anomalous divergence zone between 15° and 28°N (Fig. 5). The anomalous convergence zone attracts the air mass over 22°–30°N, 105°–130°E moving eastward to the relative lower pressure region, and the anomalous divergence zone repels the air mass over 15°–28°N, 85°–100°E moving eastward from the relative higher pressure region. Under the simultaneous action of the anomalous convergence zone and the anomalous divergence zone, the IIE finally shifts eastward. In fact, the most significant displacement (>0.18° of longitude) of the IIE occurs at 26°N, which corresponds well with the positions of the anomalous convergence zone and the anomalous divergence zone (Figs. 5 and 6). If the spring SSTSMI is smaller than normal, the opposite atmospheric circulation patterns are forced, by the negative TIO–TCWP SSTSM, and the IIE shifts farther westward than its normal position.

Fig. 6.
Fig. 6.

The IIE from 18° to 28°N with 2° interval regressed onto the spring SSTSMI. The asterisk denotes the corresponding correlation coefficient that is significant at the 95% confidence level, and the hollow circles show the corresponding correlation coefficients that are significant at the 99% confidence level.

Citation: Journal of Climate 29, 13; 10.1175/JCLI-D-15-0839.1

c. Case studies

According to the SSTSMI in Fig. 3a and the anomalous regression patterns in Fig. 5, the SLP, surface winds, tropospheric temperature and precipitation anomalies associated with a spring SSTSMI can be reconstructed for any given year. Here, we choose 1997 and 1998 as two case studies of the possible influences of the spring TIO–TCWP SSTSM on atmospheric circulations, in relation to an extreme IIE. In 1997 (1998), the normalized IIE index is −1.7 (2.1) and the normalized SSTSMI is −1.6 (2.9); in these years, the normalized SSTs averaged over the TIO are −0.23 (2.83), and the normalized SSTs averaged over the TCWP are 1.33 (−0.24). These data indicate that the IIE shifted farther eastward (westward) under a positive (negative) SSTSM, over the TIO–TCWP.

In 1997, the circulation anomalies (Figs. 7a–c) mirror those anomalous features in Figs. 5a–c, respectively. The surface westerly anomalies are confined from the BOB to the international date line, and the northeasterly anomalies flow from 20°–45°N, 150°–170°E, into the low pressure region; these strengthen a surface convergence anomaly over 10°–20°N, 120°–170°E, and further enhance a deep convection from the northern SCS southeastward to the central Philippine Sea (Figs. 7a,c). Positive SLP anomalies, with maxima higher than 50 Pa, dominate the middle–lower reaches of the Yangtze River in China, and suppress convection there. In respect of the anomalous releases of latent heat, the center value of the tropospheric temperatures that exceed 0.4 K occurs over the Philippine Sea, while that of the tropospheric temperatures below −0.4 K occurs over the middle–lower reaches of the Yangtze River in China (Fig. 7b). The anomalous surface horizontal winds, tropospheric temperatures, and precipitation levels suggest that the westerly anomalies over the BOB strengthen the southwest summer monsoon, and the westerly anomalies over the Philippine Sea weaken the southeast summer monsoon. The anomalous configuration of the atmospheric circulations leads to a westward shift of the IIE. The circulation anomalies in 1998 (Figs. 8a–c) are opposite to those in 1997, but resemble those in Figs. 5a–c, respectively. As a result, the enhanced easterly winds over the SCS and weakened southwesterly winds over the BOB cause the IIE to shift eastward (Cao et al. 2012).

Fig. 7.
Fig. 7.

(a)–(c) Summer atmospheric circulation anomalies for 1997 and (d)–(f) the anomalies reconstructed using the spring SSTSMI, for (a),(d) sea level pressures (Pa) and surface winds, (b),(e) tropospheric (850–250 hPa) temperature (K) and surface winds, (c),(f) and the precipitation (mm day−1) and surface winds. The units of the horizontal surface winds (vectors) are m s−1.

Citation: Journal of Climate 29, 13; 10.1175/JCLI-D-15-0839.1

Fig. 8.
Fig. 8.

As in Fig. 7, but for 1998.

Citation: Journal of Climate 29, 13; 10.1175/JCLI-D-15-0839.1

The circulation anomalies reconstructed using the spring SSTSMI in 1997 (Figs. 7d–f) and 1998 (Figs. 8d–f) are very similar to the observed counterparts, but with smaller magnitudes. In fact, the explained variances for the SLP, zonal and meridional winds, and the tropospheric temperature, which are calculated using the method adopted by Weng et al. (2011), exceed 60% over the key region 0°–30°N, 90°–150°E. Although the explained variances for rainfall in 1997 and 1998 are lower than those for the atmospheric circulations, their values still exceed 30% (Table 1). The relatively large values of the total atmospheric circulation variances explained suggest that the spring TIO–TCWP SSTSM may be one of most important factors that causes the atmospheric circulation anomalies over 0°–30°N, 90°–150°E, resulting in the interannual variability of the IIE’s location.

Table 1.

The explained variance (%) of the circulation anomalies over 0°–30°N, 90°–150°E.

Table 1.

4. Model results

According to the diagnostic results obtained above, we carried out two sensitivity experiments. In the first sensitivity experiment (SE1), the MAM SST anomalies were averaged, using four positive-anomaly SSTSMI years (1983, 1987, 1998, and 2000; Fig. 3a), and then added to the climate SSTs over the TIO (10°S–12°N, 50°–100°E) and TCWP (10°S–12°N, 140°E–170°W), during MAM (Fig. 9a). The anomalous SST pattern shares a positive seesaw structure, similar to that over the TIO and TCWP in Fig. 4. In the second sensitivity experiment (SE2), the MAM SST anomalies were averaged, using six negative-anomaly SSTSMI years (1986, 1994, 1995, 1997, 2002, and 2004; Fig. 3a), and then added to the climate SSTs over the TIO and TCWP (as per SE1; Fig. 9b). Although the negative pole over the TIO is weaker than the positive pole over the TCWP in an absolute sense, the SST anomalies have opposite signs over the two regions. So, the negative SSTSMI almost mirrors the anomalous SST structure in Fig. 9a. In the SE1 and SE2 runs, the SST anomalies are kept unchanged in March, April, and May, and the climate SSTs are used in the other nine months, when the model is integrated for 30 yr. We analyzed the last 29 years (model run years 2–30) in the two sensitivity experiments, after eliminating the first model year, as the model spinup time. The simulated differences of SLP, horizontal winds, and tropospheric temperatures calculated during the positive SSTSMI run (SE1), minus the corresponding one from the negative SSTSMI run (SE2), are analyzed.

Fig. 9.
Fig. 9.

The sea surface temperature (SST) anomalies (°C) superimposed as perturbations on the climatological SSTs for the (a) positive and (b) negative SSTMI model runs.

Citation: Journal of Climate 29, 13; 10.1175/JCLI-D-15-0839.1

Figure 10 shows the simulated differences of the SLPs, horizontal winds, and tropospheric temperatures in 3-month running averages from MAM to JJA. All of them share a typical Matsuno–Gill pattern (Matsuno 1966; Gill 1980). During MAM (Fig. 10a), the relatively lower SLPs, with two off-equatorial SLP minima below −150 Pa, originate in the TIO, and extend into the western Pacific. They are accompanied by two cyclonic anomalies over the central-western Indian Ocean (CWIO) at both sides of the equator and strong surface easterly anomalies over the equatorial central-western Pacific. Two off-equatorial SLP maxima, exceeding 100 hPa, are located at the central-western Pacific (CWP). The two stronger SLP centers couple with two anticyclonic anomalies at both sides of the equator. Between the couple of cyclonic anomalies over the CWIO and the couple of anticyclonic anomalies over the CWP, an anomalous C-shaped pattern occurs with northeasterly anomalies north and northwesterly anomalies south of the equator. The tropospheric temperature anomalies forced by the TIO-WCWP SST seesaw mode are much closer to a standard Matsuno–Gill pattern (Fig. 10e). Consistent with Fig. 10a, two positive centers (>0.3 K) of tropospheric temperature anomalies appear symmetrically over the Arabian Sea and the southwestern Indian Ocean, and two negative centers occur symmetrically, at either side of the equator, over the CWP with the center values being below −0.5 K, respectively. During April–June (Figs. 10b,f), the strong surface easterly anomalies keep unchanged over the equatorial central-western Pacific to a large degree, but the couple of cyclonic anomalies over the CWIO, the couple of anticyclonic anomalies over the CWP, and the anomalous C-shaped wind anomalies tend to slightly move westward with enhanced intensities. During May–July and June–August, these anomalous atmospheric patterns except the equatorial central-western Pacific (Figs. 10c,d,g,h) keep moving westward. Meanwhile, their intensities continue to increase. For example, comparing Figs. 10d or 10f with Figs. 10a or 10e, it can be clearly seen that the C-shaped wind anomalies, which are very similar to the corresponding figures in Du et al. (2009, their Figs. 11a and 11b), move from around 100°E in MAM westward to around 70°E in JJA, and their intensities in JJA tend to increase twice as much as those in MAM. The anticyclonic couple over CWP also moves from around 150°E in MAM westward to around 120°E in JJA. These simulated results imply a positive feedback between atmosphere and sea.

Fig. 10.
Fig. 10.

The differences of the simulated sea level pressures (Pa) and horizontal winds (m s−1; vectors) at 1000 hPa in (a) MAM, (b) April–June (AMJ), (c) May–July (MJJ), and (d) JJA. (e)–(h) As in (a)–(d), but for difference of the simulated tropospheric (850–250 hPa) temperature (K) and horizontal winds at 1000 hPa, with positive SSTSMI model runs minus the negative SSTSMI runs. Only the winds significant at the 95% confidence level are plotted. The thick lines denote that the difference is significant at the 95% confidence level.

Citation: Journal of Climate 29, 13; 10.1175/JCLI-D-15-0839.1

Previous studies indicated that a Rossby wave, forced by an anticyclonic atmospheric circulation over the southeast tropical Indian Ocean (IO), during an El Niño developing year, deepens the thermocline when it propagates westward to the southwestern IO (SWIO). The SSTs warm up, since the Rossby wave suppresses upwelling in the thermocline dome of the SWIO (Masumoto and Meyers 1998; Yu et al. 2005). In the following spring, a SST gradient forms, due to the warming in the SWIO; this, in turn, anchors an asymmetric atmospheric circulation pattern across the equator, through wind–evaporation–SST feedback (Kawamura et al. 2001; Wu et al. 2008), with northeasterly (northwesterly) wind anomalies north (south) of the equator (Du et al. 2009). During the summer, the SWIO warming-induced northeasterly anomalies weaken the southwest summer monsoon and cause SST warming north of the equator, by reducing loss of the latent heat flux (Du et al. 2009). Such an internal air–sea interaction over the TIO is crucial to sustaining TIO warming through the summer. The development and westward expansion of the C-shaped wind anomalies and the couple of anticyclonic anomalies reproduce the persistence of the significant correlation between IIE index and SSTSM index in Fig. 3b, and resemble those in Figs. 5a and 5b. These AGCM results imply that the atmospheric anomalies, induced by the SSTSM, in turn can impact the SSTs, which is similar to the SWIO mechanism presented by previous studies (Kawamura et al. 2001; Wu et al. 2008; Du et al. 2009).

The IIE’s position in the historical run is very similar to that observed, suggesting that the ECHAM6 has a good capacity to model the IIE. In good agreement with the anomalous circulation patterns (Figs. 10d,h), the southwest summer monsoon is weakened by the northeasterly anomalies over the BOB, and the southeast summer monsoon is strengthened by the southerly anomalies over the western SCS, and easterly anomalies over the southern SCS. The seesaw configuration between the southwest summer monsoon and the southeast summer monsoon cause the IIE to move farther eastward than in the historical run. With the opposite circulation pattern, the enhanced southwesterly summer monsoon and the weakened southeasterly summer monsoon cause the IIE to move farther westward than in the historical run (Fig. 11). These results suggest that the spring TIO–TCWP SSTSM forces out an anomalous Rossby wave and an Kelvin wave, and then the westward propagation of the anomalous Rossby wave mainly induces the variability of the IIE’s zonal movement by directly influencing the horizontal wind anomalies around 20°N in the BOB–SCS–WNP.

Fig. 11.
Fig. 11.

The location of the IIE; the red line denotes observations of the IIE’s position, and the green one shows the IIE’s position according to the historical runs. The blue solid (dashed) line denotes the IIE’s position in SE1 (SE2).

Citation: Journal of Climate 29, 13; 10.1175/JCLI-D-15-0839.1

5. Summary and discussion

In this study, the correlation coefficients between the IIE and MAM SST configuration series at different rectangle lengths scales were calculated. The correlation results at all six different rectangle lengths indicate that the interannual variability of the IIE is significantly related to the MAM TIO–TCWP SSTSM. After defining the TIOI and TCWPI, the SSTSMI, which reflects the SST seesaw mode between the TIO and TCWP, was constructed (as TIOI minus TWCPI). In comparison with the very weak relationship between the IIE and the individual SSTs over the TIO or TCWP, significantly positive correlations between the IIE and the SSTSMI can persist from a previous spring to current summer. The MAM TIO–TCWP SSTSM forces anomalous zonal winds over the BOB–SCS–WNP, which induces a seesaw pattern between the southwest and southeast summer monsoons. Then the seesaw pattern exerts its effect on the interannual variability of the IIE’s location. These results agree well with those obtained by Cao et al. (2012). When the TIO SSTs are relatively high and the TWCP SSTs are relatively low, during boreal spring, a positive SSTSMI over the TIO–TCWP forms. In response to the positive (negative) SSTSM, anomalous easterlies (westerlies) appear over the BOB–SCS–WNP. The anomalous zonal winds further weaken (strengthen) the southwest summer monsoon over the BOB, but strengthen (weaken) the southeast summer monsoon over the SCS–WNP. Consistent with the configuration between the southwest and southeast summer monsoons, the IIE shifts farther eastward (westward) than normal. The modeling results based on the ECHAM6 confirm the key physical process revealed above.

The relationship between the spring Indian Ocean–central-western Pacific SSTSM and the IIE anomalies, as revealed by observational diagnosis and numerical experiments, can be explained (to a large degree) using the Matsuno–Gill mechanism. There is covariance between the Kelvin wave response to the TIO–TCWP SSTSM and the Rossby wave response to the anomalous diabatic heating of the rainfall anomalies over the Philippine Sea; this influences the interannual variability of the IIE’s zonal movement by changing the intensity and position of the WPSH (Figs. 5 and 10). When an anomalous heat source controls the TIO and an anomalous cold source controls the TCWP, a positive SSTSM forms and causes an equatorial Kelvin wave, illustrated by anomalous easterly winds from the BOB to the western Pacific; this then causes a Rossby wave, illustrated by an anomalous anticyclone with increasing SLPs, over the SCS–TCWP, by constraining a zonally elongated convection over the Philippine Sea. The mixture of the Kelvin and Rossby waves further intensifies the WPSH and drives it farther southward (Huang and Li 1987; Nitta 1987; Huang and Sun 1992; Terao and Kubota 2005; Xie et al. 2009). Accompanied by the enhanced WPSH, which is farther south than normal, the mei-yu front, located at the north of the WPSH, synchronously shifts southward and controls the middle–lower reaches of the Yangtze River in China. This anomalous circulation configuration is favorable for the eastward shift of the IIE. On the contrary, when an anomalous heat source controls the TCWP and an anomalous cold source controls the TIO, a negative SSTSM forms and causes the opposite conditions. This physical process, associated with the interannual variability of the IIE’s zonal movement, agrees well with previous studies (Cao et al. 2012, 2013; Tao et al. 2015).

The persistence of significant correlation coefficients between the IIE index and the TIOI, TCWPI, and SSTSMI from spring to summer (Fig. 3b), and AGCM experiments results (Fig. 10) associated with the development and maintenance of the SSTs and wind anomalies over TIO pole and TCWP pole, can be explained by the WES feedback similar to the SWIO mechanism developed by Xie et al. (2009) and Du et al. (2009).

Tao et al. (2015) suggested that the release of latent heat over southern China significantly influences the interannual variability of the IIE’s zonal movement. The results of the regression and case studies here (Figs. 5, 7, and 8) are consistent with their results. However, the negative SLP anomalies, with positive tropospheric temperatures over the middle–lower reaches of the Yangtze River, do not appear in the simulated results (Figs. 9c,d). This may be because 1) the anomalous convergence/divergence zone over the middle–lower reaches of the Yangtze River may be caused by other factors besides the spring TIO–TCWP SSTSM; and/or 2) the convective parameterization in the ECHAM6 may not efficiently reflect the real convection activities, so the numerical model has a relatively lower capacity to simulate the summer rainfall over the middle–lower reaches of the Yangtze River. Further studies are needed to explore and clarify this problem in the future.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (U1502233, 41375097, and 41305078).

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