The monthly SST data were obtained from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) (Rayner et al., 2003). This dataset provides global SST fields on a 1° × 1° latitude/longitude grid and covers the period from 1870 to the present. The area-weighted average of summer [June−September (JJAS) total] monsoon precipitation over India can be represented by the All-India monsoon rainfall (AIMR) index (Mooley and Parthasarathy, 1984; Parthasarathy et al., 1994), which was estimated by gauge-based rainfall observations from 306 stations over 30 meteorological subdivisions spanning the period 1871−2014. To illustrate the spatial distributions of terrestrial precipitation, we used a high resolution (i.e., a horizontal resolution of 0.5° × 0.5°) gauge-based precipitation dataset (1951−2007) developed by the Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) project (Yatagai et al., 2012). The advantages of the APHRODITE precipitation dataset lie in that it was derived from a dense network of rain gauge observations covering the whole Asian monsoon region (15°S−55°N, 60°−150°E), and produced by an improved interpolation scheme. This study also used the Climate Prediction Center Merged Analysis of Precipitation (CMAP) (Xie and Arkin, 1997) and Global Precipitation Climatology Project (GPCP), version 2.3, combined precipitation dataset (Adler et al., 2003). These two globally gridded analyses of monthly precipitation incorporate gauge observations and satellite estimates at a 2.5° × 2.5° latitude/longitude resolution and are available from 1979 to the present. For other monsoon-related atmospheric variables, we used the Twentieth Century Reanalysis, version 2, dataset (Compo et al., 2011), spanning 1871−2012 at a 2° × 2° spatial resolution. This reanalysis dataset was generated by applying a state-of-the-art National Centers for Environmental Prediction atmospheric general circulation model and an ensemble Kalman filter data assimilation system. Note that the CMAP and GPCP precipitation products and the Twentieth Century Reanalysis dataset are provided by the National Oceanic and Atmospheric Administration’s Office of Oceanic and Atmospheric Research Earth System Research Laboratory, Physical Science Division, Boulder, Colorado, USA, and are available from its website (https://www.esrl.noaa.gov/psd/).
To separate the EP and CP types of ENSO, we applied a previously derived procedure as a combination of linear regression and empirical orthogonal function (EOF) analysis (Kao and Yu, 2009; Yu and Kim, 2010; Yu et al., 2012). First of all, the monthly SST anomalies regressed with the Niño1+2 (0°−10°S, 80°−90°W) index or Niño-4 (5°S−5°N, 160°E−150°W) index were removed from the original data. We then performed the EOF analysis on the residual SST anomalies excluding the influence from the eastern Pacific Niño1+2 region (the central Pacific Niño-4 region) to extract the dominant pattern of the CP (EP) ENSO. The monthly EP and CP ENSO indices were defined as the leading principal component (PC) time series corresponding to the two leading EOF spatial patterns obtained from this combined regression-EOF method.
For the purpose of diagnosing the relative roles of the EP and CP types of ENSO governing the SASM, we produced correlation maps of monsoon-related atmospheric fields against these two types of ENSO indices. The correlation patterns of SST during the developing summer of ENSO events with the corresponding EP and CP ENSO indices are shown in Fig. 1. We also conducted composite analyses to examine the characteristics of circulation and precipitation corresponding to the two types of El Niño. Because there are arguments that the SST patterns between the two types of La Niña are much less distinctive (Kug et al., 2009; Kug and Ham, 2011), we focused on composite analyses for the two types of El Niño events. The El Niño events used for composite analyses were selected based on the criterion that a threshold of +0.5°C for the Oceanic Niño Index is met for at least five consecutive and overlapping three-month seasons from the developing summer to the decaying spring. The Niño-3.4 SST anomaly indices and the AIMR indices in the developing summer of these selected El Niño events are listed in Table 1. The El Niño events during the developing summer were classified into the EP type (CP type) when the JJAS-averaged EP (CP) ENSO index is greater than the CP (EP) ENSO index.
Figure 1. Correlation patterns of SST (units: °C) with (a) the EP ENSO index and (b) the CP ENSO index in the summer season (JJAS) during the 1950−2012 period. Regions where the correlations are statistically significant at the two-tailed α = 0.05 level are filled with colors. Interannual time series of (c) the EP ENSO index (red) and (d) the CP ENSO index (green) compared with the Niño-3.4 SST anomaly index (blue) in the summer season during the 1950−2012 period. These series have been standardized to have zero mean and unit standard deviation.
Years Major El Niño Events (1950−2012) Niño-3.4 SST Anomaly (°C) Standardized Niño-3.4 Index Type AIMR (mm) Standardized AIMR Index 1951 0.71 1.08 EP 738.7 −1.20 1953 0.49 0.75 EP 922.8 0.98 1957 0.79 1.21 EP 788.5 −0.61 1958 0.22 0.33 CP 889.1 0.58 1963 0.75 1.14 CP 857.7 0.21 1965 1.08 1.65 EP 709.2 −1.54 1968 0.35 0.54 CP 754.5 −1.01 1969 0.46 0.71 CP 831.0 −0.10 1972 1.12 1.70 EP 652.8 −2.21 1976 0.37 0.57 EP 856.6 0.20 1977 0.47 0.72 CP 883.0 0.51 1979 0.24 0.36 EP 707.7 −1.56 1982 1.07 1.63 EP 735.1 −1.24 1986 0.31 0.47 CP 742.9 −1.15 1987 1.48 2.27 CP 697.0 −1.69 1991 0.55 0.84 CP 785.2 −0.65 1994 0.39 0.60 CP 952.7 1.34 1997 1.77 2.70 EP 871.4 0.37 2002 0.73 1.12 CP 661.9 −2.10 2004 0.51 0.78 CP 744.7 −1.12 2006 0.31 0.48 CP 869.9 0.36 2009 0.65 0.99 CP 667.6 −2.04
Table 1. The Niño-3.4 SST anomaly indices and the AIMR indices in the developing summer (JJAS) of El Niño events during the 1950−2012 period. The El Niño types during the developing summer are identified based on the relative values of the EP and CP ENSO indices.