The Intraseasonal Variability of the Indian Summer Monsoon Using TMI Sea Surface Temperatures and ECMWF Reanalysis

Nicholas P. Klingaman Walker Institute for Climate System Research, Department of Meteorology, University of Reading, Reading, United Kingdom

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Hilary Weller Walker Institute for Climate System Research, Department of Meteorology, University of Reading, Reading, United Kingdom

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Julia M. Slingo Walker Institute for Climate System Research, Department of Meteorology, University of Reading, Reading, United Kingdom

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Peter M. Inness Walker Institute for Climate System Research, Department of Meteorology, University of Reading, Reading, United Kingdom

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Abstract

The northward-propagating intraseasonal (30–40 day) oscillation (NPISO) between active and break monsoon phases exerts a critical control on summer-season rainfall totals over India. Advances in diagnosing these events and comprehending the physical mechanisms behind them may hold the potential for improving their predictability. While previous studies have attempted to extract active and break events from reanalysis data to elucidate a composite life cycle, those studies have relied on first isolating the intraseasonal variability in the record (e.g., through bandpass filtering, removing harmonics, or empirical orthogonal function analysis). Additionally, the underlying physical processes that previous studies have proposed have varied, both among themselves and with studies using general circulation models.

A simple index is defined for diagnosing NPISO events in observations and reanalysis, based on lag correlations between outgoing longwave radiation (OLR) over India and over the equatorial Indian Ocean. This index is the first to use unfiltered OLR observations and so does not specifically isolate intraseasonal periods. A composite NPISO life cycle based on this index is similar to previous composites in OLR and surface winds, demonstrating that the dominance of the intraseasonal variability in the monsoon climate system eliminates the need for more complex methods (e.g., time filtering or EOF analysis) to identify the NPISO. This study is also among the first to examine the NPISO using a long-period record of high-resolution sea surface temperatures (SSTs) from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager. Application of this index to those SSTs demonstrates that SST anomalies exist in near quadrature with convection, as suggested by recent coupled model studies. Analysis of the phase relationships between atmospheric fields and SSTs indicates that the atmosphere likely forced the SST anomalies. The results of this lag-correlation analysis suggest that the oscillation serves as its own most reliable—and perhaps only—predictor, and that signals preceding an NPISO event appear first over the Indian subcontinent, not the equatorial Indian Ocean where the events originate.

Corresponding author address: Nicholas Klingaman, Department of Meteorology, University of Reading, P.O. Box 243, Reading, Berkshire, RG6 6BB, United Kingdom. Email: n.p.klingaman@rdg.ac.uk

Abstract

The northward-propagating intraseasonal (30–40 day) oscillation (NPISO) between active and break monsoon phases exerts a critical control on summer-season rainfall totals over India. Advances in diagnosing these events and comprehending the physical mechanisms behind them may hold the potential for improving their predictability. While previous studies have attempted to extract active and break events from reanalysis data to elucidate a composite life cycle, those studies have relied on first isolating the intraseasonal variability in the record (e.g., through bandpass filtering, removing harmonics, or empirical orthogonal function analysis). Additionally, the underlying physical processes that previous studies have proposed have varied, both among themselves and with studies using general circulation models.

A simple index is defined for diagnosing NPISO events in observations and reanalysis, based on lag correlations between outgoing longwave radiation (OLR) over India and over the equatorial Indian Ocean. This index is the first to use unfiltered OLR observations and so does not specifically isolate intraseasonal periods. A composite NPISO life cycle based on this index is similar to previous composites in OLR and surface winds, demonstrating that the dominance of the intraseasonal variability in the monsoon climate system eliminates the need for more complex methods (e.g., time filtering or EOF analysis) to identify the NPISO. This study is also among the first to examine the NPISO using a long-period record of high-resolution sea surface temperatures (SSTs) from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager. Application of this index to those SSTs demonstrates that SST anomalies exist in near quadrature with convection, as suggested by recent coupled model studies. Analysis of the phase relationships between atmospheric fields and SSTs indicates that the atmosphere likely forced the SST anomalies. The results of this lag-correlation analysis suggest that the oscillation serves as its own most reliable—and perhaps only—predictor, and that signals preceding an NPISO event appear first over the Indian subcontinent, not the equatorial Indian Ocean where the events originate.

Corresponding author address: Nicholas Klingaman, Department of Meteorology, University of Reading, P.O. Box 243, Reading, Berkshire, RG6 6BB, United Kingdom. Email: n.p.klingaman@rdg.ac.uk

1. Introduction

a. Monsoon intraseasonal variability

Summer monsoon rains over the Indian subcontinent provide a critical source of water for the area and its extensive agricultural production. Many regions of India depend on the established phase of the monsoon [June–September (JJAS)] for more than 85% of their annual rainfall (Mooley and Parthasarathy 1984; Webster et al. 1998). While the monsoon exhibits substantial interannual variability, some of which may be predictable from teleconnections (e.g., ENSO; Shukla and Paolino 1983; Webster and Yang 1992; Slingo and Annamalai 2000; Yang and Lau 2006), the monsoon’s intraseasonal variability is arguably of greater magnitude and hence of crucial importance for monsoon predictions and their application (Webster et al. 1998; Waliser et al. 1999). This intraseasonal variability is dominated by a 30–40-day oscillation between periods of enhanced and reduced precipitation over much of the subcontinent, which correspond to maxima in convection over India and the equatorial Indian Ocean (EqIO), respectively (Hartmann et al. 1992; Annamalai and Slingo 2001). These periods are often referred to as monsoon “active” and “break” phases. Analyses of satellite-derived cloudiness data (Yasunari 1979; Sikka and Gadgil 1980) and of field campaign measurements (Krishnamurti and Subrahmanyam 1982) have shown that individual active and break events propagate northward from the eastern EqIO (EEqIO) into the Bay of Bengal (BoB) and over India with a speed of 1–2 m s−1, or about 1°–2° latitude day−1 (Gadgil 1990; Lawrence and Webster 2002; Webster and Hoyos 2004). For a full review of the interannual and intraseasonal variability of the Indian monsoon, see Yang and Lau (2006) and Waliser (2006), respectively.

Some have suggested that this northward-propagating intraseasonal oscillation (NPISO) is a northward extension of the Madden–Julian oscillation (MJO; Madden and Julian 1971, 1972), since the phenomena show similar periods (e.g., Yasunari 1979; Julian and Madden 1981; Madden 1986). Wang and Rui (1990) identified an independent northward-propagating mode in which convection grew in situ in the EEqIO, which showed little equatorial eastward movement, and which comprised a significant fraction of the NPISO events considered. Lawrence and Webster (2002) conducted a similar study over a longer, 25-yr record and found that the in situ mode accounted for fewer than 25% of NPISO events, while the majority exhibited MJO-like eastward propagation. In contrast, Salby and Hendon (1994) and Wang et al. (2005) showed that the MJO was too weak and irregular in boreal summer (Madden 1986; Hendon and Salby 1994) to maintain the NPISO. Wang et al. attributed the oscillatory nature of the NPISO to a self-induction mechanism whereby break conditions over India reinitiated equatorial convection over the western EqIO (WEqIO). It is therefore unclear whether the relationship between the MJO and the NPISO is one of coexistence and interaction or one of strict causality. Together, the MJO, the NPISO, and a westward-propagating 10–20-day mode in the northwestern tropical Pacific are known as the boreal summer intraseasonal variability (BSISV; Wang and Xie 1997; Annamalai and Sperber 2005).

b. Northward propagation of intraseasonal events; atmosphere–ocean interactions

In an early study using a two-layer, zonally symmetric model, Webster (1983) stressed the importance of interactions between ground hydrological processes and the atmosphere in destabilizing the lower troposphere over land. Webster indicated that the oscillation between enhanced convection over the EEqIO and India was due to the competing influences of oceanic and land surface heat sources. After each active event, evaporative feedbacks and moisture depletion over the land surface led to suppressed convection and a break event. Srinivasan et al. (1993) ran the same model and concluded that the parameterization of the ground hydrological processes affected the NPISO period. By contrast, Ferranti et al. (1999) found that removing land–atmosphere feedbacks in an atmospheric general circulation model (AGCM) did not change the spatial structure of NPISO events, but did make the oceanic convective regime (i.e., break events over India) more probable.

Wang and Rui (1990) examined a decade of satellite-derived outgoing longwave radiation (OLR) data and suggested that convection in the EqIO generated Rossby waves that redirected convection poleward in each hemisphere, with the boreal summer mean state favoring the northward branch. Supported by observations and basic numerical experiments, Wang and Xie (1997) concluded that NPISO events appeared to propagate northward because of the northwestward tilt of an eastward-moving convective front, which extended from the equatorial western Pacific to 20°N at Indian longitudes. Rossby waves excited by the equatorial Kelvin–Rossby wave packet gave the convective front a characteristic “horseshoe” shape.

Kemball-Cook and Wang (2001) examined the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data and indicated that NPISO events during the established monsoon resulted from Rossby waves generated in the EEqIO. The authors emphasized coupled atmosphere–ocean processes in driving convection northward. Lawrence and Webster (2002) analyzed the same data and explained the northward propagation as the interaction between eastward-propagating convection in the WEqIO and the boreal summer background state. In the Northern (Southern) Hemisphere, SSTs were warmer (cooler) and the lower-tropospheric flow was southwesterly (southeasterly), which preferentially enhanced the Northern Hemisphere Rossby wave response to equatorial convection (Gill 1980) and led convection northward. Using a zonally symmetric model, Drbohlav and Wang (2005) concluded that the vertical advection of the mean easterly vertical wind shear triggered barotropic divergence in the free troposphere north of the convection, which in turn caused moisture convergence in the atmospheric boundary layer (ABL) and shifted convection northward. Others have attributed break events to the advection of Southern Hemisphere air with anomalously negative potential vorticity (Rodwell 1997), to Rossby waves generated by convectively stable anomalies in the BoB (Krishnan et al. 2000), and to negative convective thermal–relaxation feedbacks by which convective activity regulates itself (Goswami and Shukla 1984).

Air–sea coupled processes have recently gained credibility as potential mechanisms for maintaining and strengthening the intraseasonal oscillation (Waliser 2006). Vecchi and Harrison (2002) analyzed 3 yr of SST data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and found that cool SST anomalies in the northern BoB led break events by 7–10 days. Later studies with coupled general circulation models (CGCMs) suggested that NPISO events were internal atmospheric modes that atmosphere–ocean feedbacks could only amplify and modulate (Fu et al. 2003; Fu and Wang 2004; Rajendran and Kitoh 2006), as may be the case for the MJO. Fu et al. (2003) found that convection during active events forced intraseasonal SST anomalies through radiative fluxes and evaporation, and that these SST anomalies controlled convective activity by influencing lower-tropospheric stability. The SST anomalies and convection existed in near quadrature, with positive (negative) SST anomalies leading enhanced (suppressed) convection by about 10 days, as in the MJO (Woolnough et al. 2000). Similarly, Rajendran and Kitoh (2006) found that positive SST anomalies occurred northward of an active event, the surface ocean having been warmed by increased radiation and decreased wind-driven evaporation during the preceding break event. The warm SSTs enhanced the near-surface moisture convergence initially caused by the convection itself, as in Drbohlav and Wang (2005), and eventually shifted the convective maximum northward. Once the surface winds strengthened and the deep convection reduced the incident radiation, the SSTs cooled and eventually suppressed the convection.

c. Previous indices of the intraseasonal oscillation

Most studies that have defined active and break events in observational records or reanalysis data have employed satellite-derived OLR filtered by one or several methods (e.g., Annamalai and Slingo 2001; Vecchi and Harrison 2002; Annamalai and Sperber 2005). Others have used 850-mb winds (Webster et al. 1998), the position of the mean monsoon trough (Joseph and Sijikumar 2004), and rainfall (e.g., Fu and Wang 2004). OLR data can be used as a proxy for deep convective activity (Graham and Barnett 1987). Vecchi and Harrison (2002) defined an index for intraseasonal activity, calculated as the difference between normalized, area-averaged daily OLR anomalies (from a monthly climatology) that had been smoothed with a 7-day boxcar filter. The index also had its 50-day running mean removed, “to focus on sub-seasonal variability” (Vecchi and Harrison 2002). Krishnan et al. (2000) defined break periods using only the OLR anomalies over India. Annamalai and Sperber (2005) filtered the OLR data with a 20–100-day Lanczos filter before using cyclostationary empirical orthogonal function (EOF) analysis to construct an idealized life cycle.

d. The purpose of the present study

This study seeks to demonstrate that a simple, OLR-based index that does not specifically isolate intraseasonal variability can identify NPISO active and break events. We will define a simple index for particularly pronounced active and break events, in which convection over India is teleconnected with convection over the EEqIO (section 3). Further, we will construct a composite active and break event from reanalysis data and investigate the means by which events propagate from the equator to the subcontinent (section 4). If our index is robust, the composite events will show strong similarities to previous composites based on time-filtered indices or EOF analysis.

Furthermore, using high-resolution, daily SSTs from the TMI, we will examine to what degree the surface ocean responds to NPISO events and whether there is any indication of surface–ocean processes feeding back onto the atmosphere. Finally, we will derive an idealized NPISO life cycle from our composites (section 4e) and summarize our key conclusions (section 5).

2. Datasets and methods

To construct our composite events, we employed atmospheric and SST data during JJAS for each year from 1998 to 2005. This period was constrained by the availability of the TMI SST data. Longer periods of data were used to construct climatologies (section 2c) and for the lag-correlation analysis (section 3a).

a. Atmospheric data

We used daily OLR data from the National Oceanic and Atmospheric Administration–Cooperative Institute for Research in Environmental Sciences (NOAA–CIRES) Climate Diagnostics Center (CDC). (The data are available online at http://www.cdc.noaa.gov.) The CDC has interpolated the Advanced Very High Resolution Radiometer (AVHRR) readings onto a 2.5° × 2.5° global grid and filled missing data following the procedure in Liebmann and Smith (1996).

Estimates of 10-m winds (U10), latent heat flux (LHF), sensible heat flux (SHF), and downward shortwave radiation (DSR) were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses and operational analyses. The 40-yr ECMWF Re-Analysis data (ERA-40) were used for the period 1998–2001, after which the ERA-40 project ended. ECMWF operational analyses were used for the period 2002–05. Both datasets use the same 1.125° × 1.125° global grid. DSR, SHF, and LHF should be considered only as probable estimates and not as exact values, as these derived quantities depend heavily on the parameterizations employed in the ECMWF model used for the reanalysis. Daily means were calculated from 6-hourly wind analyses, while DSR, SHF, and LHF data were available only as daily values.

For precipitation, we used the pentad-mean, enhanced version of the NCEP Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP). (The data are available online at http://www.cdc.noaa.gov.) The enhanced version includes precipitation data from the NCEP–NCAR reanalysis, but otherwise uses the same source datasets and a similar assimilation method to that described for the monthly mean dataset in Xie and Arkin (1996).

We estimated total precipitation over the Indian subcontinent using the daily All-India Rainfall (AIR) dataset from the Indian Institute for Tropical Meteorology (IITM). The data were derived from a real-time rain gauge network that covers the mainland Indian peninsula. The processing algorithm changed slightly in 2004 when the IITM moved from a roaming to a fixed-position rain gauge network.

b. Sea surface temperature data

The TMI uses a microwave sounder that is able to read SST through clouds with an accuracy on the order of 0.5°C (Wentz 1998; Wentz et al. 2000). This feature is particularly important in the tropics, and makes the TMI product far superior in this region to the NCEP SST analyses (Reynolds and Smith 1994) that many previous studies have used for observational studies and model validation (e.g., Vecchi and Harrison 2000; Annamalai and Sperber 2005; Rajendran and Kitoh 2006). Harrison and Vecchi (2001) performed a spectral analysis and concluded that the TMI data were more accurate than the NCEP analyses when both were compared to moored observations in the Arabian Sea and the BoB. The TMI data contained as much as 4 times more power on intraseasonal time scales than the NCEP SST analyses (Senan et al. 2001; Sengupta and Ravichandran 2001; Sengupta et al. 2001). Bhat et al. (2004) found that the TMI product overestimated the intraseasonal variability in the BoB by approximately 30%, an error that appeared most substantially as a cold bias during active events. The weekly NCEP analyses contained virtually no intraseasonal variability, however, clearly making the TMI product more useful for examining the intraseasonal oscillation.

We used version 3a of the optimally interpolated (OI) TMI dataset from Remote Sensing Systems (RSS). (The data are available online at http://www.remss.com.) From 2002, RSS blended the TMI data with the Advanced Microwave Scanning Radiometer (AMSR-E) instrument on board the National Aeronautics and Space Administration’s (NASA) Aqua satellite. The RSS processing techniques reduce interinstrument inhomogeneities and improve accuracy when validated against in situ buoys and ships, while the availability of two instruments reduces the occurrence of missing data in the OI product. RSS placed all daily analyses onto a 0.25° × 0.25° grid using the optimal interpolation technique in Reynolds and Smith (1994).

c. Climatologies

The climatologies in this study were computed by first taking daily means for each day, using data from the period 1901–2005 for all-India rainfall, 1980–2005 for OLR and winds, and 1998–2005 for SSTs. These daily means were then filtered with a 30-day low-pass Butterworth filter to remove high-frequency variability and produce a daily climatology. The Butterworth filter was chosen for this study because it is particularly well-suited to short time series with sharp edges. The former concern applies here as our all-India rainfall data covered only JJAS; the second concern applies as at the onset of the monsoon many quantities (e.g., SST, precipitation, surface winds, etc.) undergo substantial changes in a short time. Krishnamurti and Subrahmanyam (1982) used this filter to examine intraseasonal variability during the 1979 Meeting for the Monsoon Experiment (MONEX), and that study provides a more-detailed discussion of the filter. Figure 1 compares the AIR for a sample year (1980) against the daily climatological AIR for 1901–2005 and the 30-day Butterworth-filtered daily climatology. The filter smoothes the daily mean rainfall while retaining the monsoon onset and retreat. A daily climatology is used instead of a monthly mean climatology to better resolve the consistent, seasonal monsoon features (Krishnan et al. 2000).

d. 10-m wind and SST climatologies

JJAS climatologies were created for 10-m wind (U10) and SST by taking the mean of the daily climatology over JJAS and are shown in Fig. 2. During the established monsoon, the mean U10 is southeasterly in the Southern Hemisphere and southwesterly in the Northern Hemisphere, with the maximum cross-equatorial flow in the low-level “Somali jet” at 50°–60°E (Webster et al. 1998; Schott and McCreary 2001). On the equator, U10 is weak and variable in the zonal direction; the cross-equatorial flow is substantially stronger and more consistent. The climatological wind stress curl is anticyclonic over the open ocean between 0° and 10°N and cyclonic north of 10°N. Climatological SSTs are warmest in the central and eastern EqIO, where U10 is slack, with a secondary maximum in the coastal BoB. The strong low-level winds of the Somali jet substantially cool the SSTs along the African coast and in the Arabian Sea.

3. The NPISO index

a. Lead–lag correlations of OLR

During the Indian monsoon, intraseasonal OLR variability in the Indian and western Pacific Oceans exhibits a quadrapole structure comprising one OLR dipole in each ocean, with one pole over the equator and the other in the Northern Hemisphere tropics (Krishnan et al. 2000; Annamalai and Slingo 2001; Annamalai and Sperber 2005); anomalously high OLR over India is correlated with low OLR over the equatorial Indian Ocean, high OLR over the equatorial western Pacific, and low OLR over the northwestern tropical Pacific (i.e., off the coast of southeast China). Annamalai and Slingo (2001) hypothesized that this structure was caused by the three modes of variability that compose the BSISV (i.e., the MJO, the NPISO, and the westward-propagating 10–20-day variability), and the cyclostationary EOF analysis of Annamalai and Sperber (2005) demonstrated that the quadrapole was reasonably robust throughout JJAS. Vecchi and Harrison (2002) used the difference in OLR across the Indian Ocean in order to capture solely the NPISO and thereby to construct an index of active and break events.

To derive an index for intraseasonal events associated with rainfall over the subcontinent and convection over the EqIO, we calculated lag correlations between area-averaged OLR anomalies in the two boxes used by Vecchi and Harrison (2002): one over India (10°–30°N, 65°–85°E hereafter “Box I”) and one over the eastern EqIO (10°S–5°N, 75°–95°E, hereafter “Box EEq”). The correlations were performed on daily OLR anomalies from a daily climatology over JJAS 1980–2005. We also included daily AIR anomalies (again over JJAS 1980–2005) from a daily climatology to ensure that our index captured anomalous convection and precipitation. For comparison with Vecchi and Harrison (2002), we included the instantaneous OLR difference, calculated as the anomaly in Box I minus the coincident anomaly over Box EEq. No time filtering was performed on the anomalies.

We calculated the number of independent samples (degrees of freedom) for each correlation in Fig. 3 using the method in Livezey and Chen (1983), which is based on autoregressions of the time series used in the correlation and is a modified form of the technique in Davis (1976). Each correlation in Fig. 3 has at least 120 degrees of freedom, and so a value of 120 degrees of freedom is assumed for all correlations.

Lag correlations between the OLR anomalies in Box I and Box EEq (Fig. 3) indicate that the intraseasonal oscillation has a half-period of 15–20 days, or a period of 30–40 days, which is in line with other studies (e.g., Hartmann et al. 1992; Annamalai and Slingo 2001). While the instantaneous OLR difference displays its maximum anticorrelation with AIR anomalies at zero lag, the anticorrelation between the anomalies in Box I and Box EEq at zero lag is only 0.10, which is not significant at the 5% level. (Significance is based on a two-tailed Student’s t test on means. The null hypothesis is that OLR anomalies in Box I and Box EEq show no correlation at zero lag.) The strong correlation between the OLR difference and the AIR at zero lag likely stems from the individual correlations between OLR in each box and the AIR: Box I anomalies lead anomalous AIR by 2–4 days, while Box EEq anomalies lag anomalous AIR by 2–4 days. In other words, while the OLR anomalies in each box are significantly correlated with the all-India rainfall anomalies at a lag near coincidence, the two boxes are not significantly correlated with each other at coincidence.

The two boxes have statistically significant correlations at two intervals: when Box EEq leads Box I by 10 days with the same sign, and when Box I leads Box EEq by 6 days with the opposite sign. The 10-day interval suggests that predictability of the monsoon intraseasonal variability exists in the equatorial ocean: the signal appears first in the EEqIO and is followed by a signal of the same sign over India approximately 10 days later. This correlation stresses the northward propagation of events from the equator to the Indian subcontinent. By contrast, the 6-day interval implies that predictability of the monsoon intraseasonal variability exists over the Indian subcontinent: the signal appears first over India and is followed by a signal of the opposite sign about 6 days later in the EEqIO. This correlation emphasizes the theories that the land and ocean “compete” for convection (Webster 1983) and that the NPISO functions by self-induction (Wang et al. 2005), such that periods of enhanced (reduced) convection over India suppress (enhance) convection over the equatorial ocean.

To determine which interval better describes the spatial and temporal characteristics of the NPISO, we will compare composite active and break events constructed using an intraseasonal index based on each interval.

b. Constructing the index and the composites

For each interval, we defined a daily index for NPISO activity based on the maximum correlation (for the 10-day interval) or anticorrelation (for the 6-day interval) between the OLR anomalies in Box I and Box EEq and the maximum anticorrelation between each box and the AIR anomalies. For the 10-day interval, this index is defined for each day t as
i1520-0442-21-11-2519-eq1
where the OLR values are the unfiltered, area-averaged anomalies.
For the 6-day interval, the index is defined as
i1520-0442-21-11-2519-eq2

Note that these indices are based on Box I OLR anomalies at day t−3 because of the 3-day lag between convection and precipitation (Fig. 3); the indices describe precipitation, not convection. The implications of using unfiltered OLR anomalies to extract NPISO events will be discussed further in section 5. The indices are negative (positive) for active (break) events over India and show similar correlations with unfiltered AIR anomalies as the Vecchi and Harrison (2002) index, which used the same boxes to area average the OLR anomalies but relied upon temporal filtering (via removing a 50-day running mean) to isolate intraseasonal variability (Table 1).

We identified pronounced, intense active (break) events as periods when an index was less (greater) than its mean minus (plus) one standard deviation for at least 5 consecutive days. The 5-day constraint was imposed to reject events associated with high-frequency (i.e., 10–20 day) variability. Previous studies have used constraints of similar length (e.g., Krishnan et al. 2000). Events beginning before 20 June were rejected so that the composite would not include the monsoon onset. Both indices identified 10–15 events of each type, fewer than the three active–break cycles per year noted by Webster et al. (1998). This is because our indices highlight only those events that showed a definite teleconnection between India and the EqIO, and also because of the strict temporal constraints imposed on the index. Our set of events provides enough degrees of freedom to establish the statistical significance of our results, however, and so we will retain the temporal constraints to promote uniformity among the events. Despite the minimum-length criterion of 5 days, the mean duration over India of the active and break events was more than 9 and 8 days, respectively, for both indices (Table 2).

We constructed a composite active and a composite break event for each event based on the arithmetic mean of all events. Each composite is centered (“day 0”) on the onset of enhanced or reduced precipitation over India, and we refer to days before (after) the onset with negative (positive) numbers. Several of the figures in section 4 are organized in triads. In these figures, “triad 0” represents the mean of day −1, day 0, and day +1; “triad +1” represents the mean of day +2, day +3, and day +4; and so on.

4. The composite break event

Although both composite active and composite break events were constructed, for the sake of brevity only the break composite results will be shown. The composite active event displays similar spatial and temporal patterns in all fields to the composite break event, with anomalies of comparable magnitude but of opposite sign. When performing a Student’s t test on means to determine statistical significance, the break composite is compared against an artificial composite with the same number of events but with a mean anomaly of zero at every grid point. This technique is also used in Kemball-Cook and Wang (2001). There are therefore 20 (26) degrees of freedom for the break composite based on the 10- (6-) day interval.

a. Radiative fluxes

Break composite OLR anomalies for each interval indicate that the 6-day interval provides a superior representation of the spatial and temporal evolution of the NPISO (Fig. 4) than the 10-day interval (Fig. 5). When using the 6-day interval, positive OLR anomalies indicative of suppressed convection clearly propagate northward from the eastern equatorial Indian Ocean to the subcontinent, beginning in triad −4 and continuing until the break event becomes established in triad 0. These anomalies can be seen at 5°N in triad −3, 10°N in triad −2, and extending to 20°N in triad 0, a propagation speed in-line with the 1°–2° latitude day−1 from observations (e.g., Gadgil 1990). Positive OLR anomalies persist until triad +2 in the Arabian Sea and triad +3 in the Bay of Bengal. Negative OLR anomalies associated with the following active event organize in the EEqIO in triad +1 and propagate northward over the four subsequent triads. The transition from a strong break to a strong active event takes 15–20 days, again giving a period of 30–40 days for the intraseasonal oscillation. While the index prescribes the appearance of the break and active events, it does not require that they propagate northward, which indicates that this propagation is a robust feature of the composite. The break composite for the 6-day interval therefore describes not only the anticorrelation in OLR between India and the EEqIO, but also the spatiotemporal evolution of events as they propagate from the equator to India.

The break composite based on the 10-day interval displays little or no northward propagation; positive OLR anomalies merely “jump” from the equatorial ocean in triad −2 to the Indian subcontinent in triad −1. While an active event does form in the EEqIO as the break event becomes established over India in triad +1, the active event does not propagate northward as seen in the composite for the 6-day interval. Despite the fact that the 10-day interval suggested northward propagation (i.e., a signal appears first over the equatorial ocean and then over India 10 days later), no such propagation can been seen in Fig. 5. The 10-day interval describes only the anticorrelation in OLR between India and the EEqIO, not the evolution of the NPISO.

Based on these results, only the composite based on the 6-day interval will be considered further in this study. The implications of these results for predicting the NPISO will be discussed in section 5.

When using the 6-day interval, statistically significant (at the 5% level) northward propagation can be observed at all Indian Ocean longitudes in the composite break event (Figs. 6a–c). Anomalies are generally stronger and propagation is typically more coherent over eastern India and the BoB, while over the Arabian Sea the anomalies propagate northward more quickly as the entire event moves northwest.

Little significant zonal propagation can be seen in the composite break event, which is not surprising as we did not specify any zonal propagation in our index. Anomalies move quickly eastward along the equator (Fig. 7a), while some westward propagation can be seen in the anomalies averaged over 15°–25°N (Fig. 7c).

Anomalies in surface DSR display a similar pattern to the OLR anomalies (Figs. 6d–f). The positive DSR anomalies show clear northward propagation from the equator to India. While India experiences increased DSR (i.e., reduced cloudiness), an area of reduced DSR (i.e., enhanced cloudiness) develops in the western EqIO and intensifies as it moves quickly eastward (Fig. 7d). The equatorial active event also propagates northward during this time, most coherently in the BoB (Fig. 6f). This northwestward tilt of the negative DSR anomalies is consistent with the composite event constructed by Lawrence and Webster (2002). Meanwhile, the Indian break event moves north from the Indian peninsula and into the foothills of the Himalaya in the central longitude band (Fig. 6e), where it stalls and dissipates, as observed by Sikka and Gadgil (1980) for active events.

b. Surface winds

We will discuss only those break composite circulation anomalies that are significant at the 5% level (Fig. 8; significance at the 5% level is indicated by color contours of divergence). Regions of anomalous cyclonic and anticyclonic circulation are annotated on Fig. 8 with a “C” or an “A,” respectively.

Two weeks before the break event begins, westerly anomalies strengthen the climatological U10 (Fig. 2) along the monsoon trough (Fig. 8a). Southeasterly anomalies along the coast of Sumatra may force oceanic upwelling there and a depressed thermocline in the EEqIO. (The effects of the anomalous U10 on anomalous SSTs will be explored in section 4d.) By triad −2, the equatorial anomalous easterlies have reached 10°N, bringing the anomalous divergence associated with suppressed convection. One triad later, easterly anomalies have weakened the climatological westerlies in the Somali jet region.

At triad 0, anomalous northeasterlies and strong anomalous divergence can be seen over central and western India (Fig. 8e). Offshore anomalies also exist along the western coast of Southeast Asia, suggesting that the two regions experience break events simultaneously. The equatorial U10 anomalies have become westerly and divergent, indicating an active event there. By triad +3 two anomalous cyclones are moving toward the subcontinent from the equator, signaling the beginning of the next active event. After triad +4 the anomalies weaken and are no longer significant at the 5% level.

As in Kemball-Cook and Wang (2001), the anticyclonic anomalies in the EEqIO resemble Rossby waves generated by equatorial convection. Positive OLR anomalies were observed in the EEqIO 7–10 days before the active event began (Fig. 6c), which corresponds to the strengthening of the two anticyclones in the BoB. Wang and Xie (1997) and Lawrence and Webster (2002) concluded that the boreal summer mean state favored generation of a Rossby cell north of the equator, with the former study emphasizing the easterly wind shear with height in the Northern Hemisphere, and the latter emphasizing surface heat fluxes and boundary layer frictional convergence. The wind anomalies during the composite break event are analogous to those in Rodwell (1997): the northeasterly anomalies along the western coast of India imply that the monsoon flow turns southeast and avoids the subcontinent during break events. This pattern favors equatorial westerly anomalies and convergence. Krishnan et al. (2000) showed a comparable pattern for 850-mb winds, attributable to Rossby waves initiated by suppressed convection in the BoB.

c. Surface heat fluxes

Substantial anomalies in SHF (defined as positive into the atmosphere) are found only over land in the composite break event (Figs. 6g–i). A decrease in the transfer of heat from the surface into the atmospheric boundary layer (ABL) leads weakened precipitation over India by 10–15 days (Fig. 6h). The SHF decrease is likely caused by negative DSR anomalies during the preceding active event (Fig. 6e). Negative SHF anomalies stabilize the ABL, creating favorable conditions for the descent and suppressed convection associated with monsoon breaks. Positive SHF anomalies during and after the reduced precipitation over India indicate an increase in insolation and surface warming. The increase in the heat flux to the atmosphere is a negative feedback on the break event, destabilizing the ABL and priming the atmosphere for the deep convection accompanying the following active event. This mechanism is consistent with Webster (1983).

The surface LHF (defined as positive into the atmosphere) influences SSTs and low-level moisture availability; reduced evaporation lags reduced convection by 3–5 days in the Arabian Sea and the Bay of Bengal (Fig. 6l), although the anomalous evaporation in both basins displays a short lead with anomalous precipitation over India. The reduced evaporation appears to be due to the weakened westerlies in these basins in triads −2 and −1 and to a cooling of the upper ocean by decreased insolation and increased evaporation during the preceding active event. The reduced transfer of heat and moisture into the atmosphere can stabilize the ABL and suppress convection at Indian latitudes. Anomalies in SHF make the dominant contribution over land, while LHF anomalies dominate over the ocean. These anomalies imply that SSTs in the northern Arabian Sea and BoB warm (cool) significantly during break (active) events, both from reduced (enhanced) evaporation and increased (decreased) insolation (section 4a). Meanwhile, EEqIO SSTs likely cool (warm) by the same mechanism.

d. Sea surface temperatures

One of this study’s key contributions is the use of daily, high-resolution SST data from the TMI instrument to investigate the surface-ocean response to NPISO events. Cool SST anomalies in the northern Arabian Sea and the northern Bay of Bengal lead the arrival of the break event over India by approximately 10 days (Fig. 9b). This cooling is far more pronounced in the Arabian Sea, where longitude-averaged anomalies are on the order of 0.5°C (Fig. 10a). In the equatorial Indian Ocean, SSTs warm in triad −1 and triad 0 following the break conditions there (i.e., increased insolation and slack winds). This warming occurs only after the break event has already shifted north to India (Fig. 4), suggesting a lag of about 10 days for the SSTs to warm following a break event. A similar lag can be seen in the Bay of Bengal during the break event over India, as the positive SST anomalies there do not reach their peak until triad +3 and triad +4, or 7–12 days after the break event begins. As the succeeding active event forms over the equatorial Indian Ocean in triad +1 (Fig. 4), the positive SST anomalies disappear and are replaced with negative anomalies that peak in triad +4. Assuming a 30-day NPISO period, the 10-day lead implies that these SST anomalies are nearly in quadrature with Indian precipitation, which agrees with the coupled model studies of Fu et al. (2003) and Rajendran and Kitoh (2006).

Clear, consistent northward propagation can be observed in each longitude band (Fig. 10). Ten days after the break event begins, the SSTs in the Bay of Bengal have warmed by 0.5°–1.0°C and the statistically significant negative anomalies in the western longitude bands have disappeared. We have already shown that the break phase is a period of increased insolation, weakened U10, and decreased evaporation from the ocean at Indian latitudes, which implies that the SST anomalies are likely linked to local atmospheric feedbacks. Studies with a one-dimensional mixed layer model (Shinoda and Hendon 1998) and a full ocean GCM (Schiller and Godfrey 2003) have shown that insolation anomalies are more important than LHF anomalies in driving Indian Ocean SST variability. Anomalous wind stress provides a driving force for SST variability only at local spatial scales (Shinoda and Hendon 1998); basinwide, the wind stress is less critical than either the anomalous heat flux or the anomalous insolation (Han 2005).

Anticyclonic wind stress curl can force downwelling in the ocean and, if the thermocline is shallow, this can lead to SST warming. The curl of the anomalous winds could therefore contribute to SST warming during break events. During the composite break event (Fig. 8), anticyclonic curl anomalies were found in the BoB from triad 0 to triad +2. This could lead to anomalous downwelling in the BoB, but the open BoB is not a region of climatological upwelling in JJAS (Shetye et al. 1991; Prasanna Kumar et al. 2002) so this anomalous downwelling is unlikely to influence the SST. No anticyclonic curl was detected in the Arabian Sea, which showed the greatest prebreak SST warming. Overall, the wind stress curl appears to have less of an impact on the SSTs than the radiative and evaporative forcing.

e. Atmosphere–ocean interactions and the idealized life cycle

Sections 4c and 4d provide ample evidence for atmosphere–ocean interactions during the NPISO, primarily through thermal and radiative fluxes and convective thermal–relaxation feedbacks. Using reanalysis and observations alone, however, it is difficult to determine whether the atmosphere or the ocean forces these interactions. Recent modeling studies have suggested that the NPISO is an intrinsic atmospheric mode and that observed SST anomalies are primarily responses to radiative and evaporative forcing (e.g., Fu et al. 2003; Waliser et al. 2003; Wang et al. 2003; Rajendran and Kitoh 2006). Similarly, Hendon and Glick (1997) examined 7 yr of reanalysis data and suggested that intraseasonal SST anomalies in the Indian Ocean were driven by anomalies in LHF and DSR.

To diagnose the direction of atmosphere–ocean interactions in our composites, we calculated area averages of each variable in section 4 over eight boxes that represent key regions in the monsoon domain (Table 3). Two boxes [northern India (NI) and southern India (SI)] include no or very little ocean, while the other six are either mostly or all ocean.

Most of the boxes show a similar progression among the variables when only the significant (at the 5% level) anomalies are plotted for the composite break event (Fig. 11). Using the Southern Arabian Sea (SAS) box as an example, strengthened winds (southwesterly anomalies) and an increased sensible heat flux into the atmosphere occur 15–20 days before the break event begins over India (Fig. 11c). Cool SSTs follow soon thereafter. The cool SSTs are followed by reduced evaporation, increased insolation, and reduced precipitation beginning at day −10; these anomalies suggest the beginning of a break event. Along with weakened winds, these anomalies warm the ocean surface, leading to the termination of the cool SST anomalies and to warm anomalies in the northern Bay of Bengal (NBoB) and southern Bay of Bengal (SBoB) boxes. Comparable patterns can be seen in the other oceanic boxes (Figs. 11d–g), although anomalies are weaker and shorter-lived in the WEqIO box, where the NPISO itself is less well defined. Warm (cool) SSTs lag increased (decreased) insolation, anomalous heat fluxes into (out of) the ocean, slack (strengthened) winds, and decreased (increased) evaporation.

It appears that the SST anomalies are a response to, rather than a cause of, the atmospheric anomalies. This has been the conclusion of several studies of the MJO, including Woolnough et al. (2000). While the SST anomalies may feed back onto convection through affecting boundary layer stability ahead of the convective anomalies, the SST anomalies themselves are most probably a passive response to the atmospheric forcing during the previous NPISO phase.

The composite break and active events constructed in this study can be summarized into an idealized life cycle (Fig. 12). This describes a complete NPISO cycle—with a period of roughly 30 days—and is based on the most frequent series of phase relationships among the area-averaged anomalies. It is important to note that the anomalies do not always occur in the order shown; that the lead and lag times can vary from event to event; and that the arrows do not imply causal relationships, only the most probable sequence of anomalies. From Fig. 11 it is evident that the longest time lags involve the SST anomalies, which often lag enhanced or reduced precipitation by about 10 days. Similar lag times have been found in coupled GCM simulations (e.g., Fu et al. 2003; Rajendran and Kitoh 2006) and for the MJO (Woolnough et al. 2000). The anomalies in radiation, precipitation, and evaporation often occur within several days of one another.

5. Summary and discussion

This study examined lag correlations among unfiltered anomalies of all-India rainfall, OLR over India, OLR over the eastern equatorial Indian Ocean, and the instantaneous difference between the latter two. The boxes used for the area-averaged OLR were the same as those in Vecchi and Harrison (2002). Contrary to the index used in that study, the present study found that the anomalous OLR over India was not significantly anticorrelated (at the 5% level) with the coincident OLR over the EEqIO. The two regions were significantly anticorrelated when the Indian OLR anomalies led by 6 days and significantly correlated when the equatorial anomalies led by 10 days. We defined an NPISO index based on each interval that did not specifically isolate intraseasonal variability, which to our knowledge is a unique feature among OLR-based intraseasonal indices.

Composite active and break events were constructed from an analysis of JJAS OLR data over 1998–2005. In these composites, OLR and DSR anomalies showed coherent northward propagation of suppressed (enhanced) convection for the composite break (active) event (Figs. 6a–f), although our index specified no such propagation. The EEqIO displayed the opposite phase of the oscillation to India. The direction and speed of propagation was consistent with many previous NPISO studies, including Sikka and Gadgil (1980) and Lawrence and Webster (2002). Westward movement of OLR and DSR anomalies at Indian latitudes suggested the involvement of Rossby waves in the propagation of active and break events, again in accordance with past studies (e.g., Wang and Xie 1997; Krishnan et al. 2000; Kemball-Cook and Wang 2001).

SHF (Figs. 6g–i) and LHF anomalies (Figs. 6j–l) demonstrated that anomalous fluxes of heat into (from) the ABL help to (de)stabilize the lower troposphere ahead of and during the break (active) events. An analysis of U10 patterns revealed low-level divergence (convergence) ahead of the suppressed (enhanced) convection. Our results are consistent with the development of convective instability by the conditional instability of the second kind (CISK) mechanism outlined in Wang and Xie (1998). The 10-m circulation patterns broadly agreed with those of Kemball-Cook and Wang (2001) for active events and with Krishnan et al. (2000) for break events.

This study made use of high-resolution, daily TMI SST analyses, which are far superior to the weekly NCEP SST data used by many previous studies, particularly in the tropics. SST anomalies were nearly in quadrature with precipitation, with warm (cool) SSTs in the northern Arabian Sea and BoB leading the onset of enhanced (reduced) precipitation over India by about 10 days (Figs. 9 and 10). The composite events showed the NPISO to be associated with a 0.5°–1.0°C variation in the SST; the variability associated with individual events would likely be greater than for the composite. When area-averaged SST anomalies were considered together with area-averaged anomalies in the atmospheric variables, we determined that the changes in SST were most probably a response to atmospheric forcing (Fig. 11). The role of atmosphere–ocean coupling in NPISO predictability needs further investigations with the most recent, highest-resolution GCMs. A high vertical resolution in the upper ocean has been shown to greatly improve model representations of intraseasonal SST variability (Bernie et al. 2005).

It is remarkable that our composites agree so well with those presented in previous studies, given the comparatively simple method we used to define our NPISO index. Our index was calculated from daily, unfiltered OLR anomalies against a daily, low-pass-filtered climatology. We constructed coherent NPISO composites without bandpass filtering, removing harmonics, EOF analysis, or any of the other techniques upon which past studies have relied to highlight intraseasonal variability. We did not require our events to propagate in any direction, yet our composites showed strong northward propagation in many variables and some equatorial eastward propagation in OLR and downward solar radiation. While we feel that the success of our index is due in part to the detailed cross-correlation analysis we conducted in section 3a, much of the composites’ coherence is due to the dominance of NPISO-like variability during the monsoon season. In other words, the fact that we generated our composites without removing any variability from our data speaks to the degree to which the NPISO controls the variability of the Indian Ocean Basin climate system during northern summer.

From the lag-correlation analysis and the composite events constructed here, it seems clear that there are no concrete precursors for NPISO events over India. This is highlighted by the failure of the 10-day interval (section 3a) to produce a reasonable NPISO life cycle. The 10-day interval assumed that one could predict the OLR over India from the OLR over the equatorial ocean 10 days prior. Active and break events have been repeatedly observed to propagate northward from the equator to India on time scales of approximately 2 weeks (e.g., Gadgil 1990; Webster et al. 1998; Lawrence and Webster 2002). When we constructed composite active and break events based on this relationship, however, no such propagation could be seen. India and the equatorial Indian Ocean displayed the opposite phase of the oscillation, but events merely “jumped” from one terminus to the other.

A composite event with realistic propagation was constructed only when we assumed that the intraseasonal oscillation began with a signal over India. In the 6-day interval, the OLR over India predicts the OLR over the equatorial ocean 6 days later. The success of this relationship in describing NPISO-like events indicates that the only reliable predictor for the NPISO over India is the current state of the oscillation itself over the subcontinent. Focusing on the continental terminus initially seems counterintuitive, as NPISO events propagate from the equatorial ocean and dissipate over the subcontinent. Yet our findings reinforce the self-induction mechanism for the NPISO proposed by Wang et al. (2005) and Wang et al. (2006), in which the dissipation of convection over India helps to reinitiate convection over the equatorial ocean. Therefore, we hypothesize that the 10-day interval failed because that it assumed that the critical terminus of the oscillation was in the equatorial Indian Ocean, while the 6-day interval succeeded because it assumed that the critical terminus was the Indian subcontinent. Further research is necessary to provide anything more than hypotheses concerning the predictability of the NPISO.

We have constructed a realistic composite of the NPISO from an exceptionally simple OLR-based index that showed a high correlation with Indian rainfall. With high-resolution TMI SST data, we have strengthened the results of several coupled model studies, which suggested that the NPISO is an internal atmospheric mode that the surface ocean could modulate and amplify, but not force. Our idealized life cycle draws together several mechanisms proposed in past studies into a single, consistent system supported by reanalysis data and observations. Furthermore, the success of our index in which the state of the NPISO over India predicts the oscillation’s state over the equatorial Indian Ocean—and the failure of our index that assumed the opposite relationship—suggests that the state of the intraseasonal oscillation over India may be the only reliable predictor for the oscillation’s future behavior.

Acknowledgments

NPK was supported by a scholarship from the Marshall Aid Commemoration Commission. HW, JMS, and PMI were supported by and are members of the Climate Directorate of the National Centre for Atmospheric Science. We thank Dr. George Kiladis and two anonymous reviewers for their invaluable advice and comments on a previous version of this manuscript. The daily all-India rainfall dataset was provided by the Indian Institute for Tropical Meteorology. Microwave optimally interpolated SST data were produced by Remote Sensing Systems and sponsored by National Oceanographic Partnership Program (NOPP), the NASA Earth Science Physical Oceanography Program, and the NASA REASoN DISCOVER Project. (Data are available online at www.remss.com.)

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Fig. 1.
Fig. 1.

A comparison of daily AIR (mm) for 1 yr (1980; solid line), the daily mean AIR (1901–2005; dashed–dotted line), and the 30-day Butterworth-filtered daily mean (dashed line).

Citation: Journal of Climate 21, 11; 10.1175/2007JCLI1850.1

Fig. 2.
Fig. 2.

Climatologies of U10 (m s−1; vectors) and SST (°C; colors) during JJAS over 1980–2005 for winds and 1998–2005 for SST. The reference vector is 3 m s−1 and the contour interval is 1°C.

Citation: Journal of Climate 21, 11; 10.1175/2007JCLI1850.1

Fig. 3.
Fig. 3.

Lag correlations between daily anomalies of (diamonds) AIR and OLR anomalies in Box I; (crosses) AIR and OLR anomalies in Box EEq; (stars) AIR anomalies and the instantaneous difference between anomalies in Box I and Box EEq; and (triangles) OLR anomalies in Box I and Box EEq. All time series include data from JJAS 1980–2005. The long dashed lines give the 95% confidence interval, with degrees of freedom calculated as in Livezey and Chen (1983).

Citation: Journal of Climate 21, 11; 10.1175/2007JCLI1850.1

Fig. 4.
Fig. 4.

(a)–(l) Evolution of OLR anomalies (W m−2) during the composite break event using the 6-day lag index with triads indicated. Triad 0 represents the onset of reduced precipitation over India. Filled circles indicate where the anomalies are significant at the 5% level. The contour interval is 10 W m−2 for anomalies. Positive values represent an increase in OLR emitted from the atmosphere to space.

Citation: Journal of Climate 21, 11; 10.1175/2007JCLI1850.1

Fig. 5.
Fig. 5.

As in Fig. 4, but for the composite break event using the 10-day lead index.

Citation: Journal of Climate 21, 11; 10.1175/2007JCLI1850.1

Fig. 6.
Fig. 6.

For the composite break event, longitude-averaged anomalies in (a)–(c) OLR (W m−2), (d)–(f) DSR (W m−2), (g)–(i) SHF (W m−2), and (j)–(l) LHF (W m−2). In all figures, day 0 is the beginning of reduced precipitation over India. The filled black circles indicate where the anomalies are significant at the 5% level. The dashed black lines trace the propagation of minima or maxima in OLR for easy comparison with other quantities. OLR and DSR use the top color bar; SHF and LHF use the bottom color bar.

Citation: Journal of Climate 21, 11; 10.1175/2007JCLI1850.1

Fig. 7.
Fig. 7.

For the composite break event, latitude-averaged anomalies in (a)–(c) OLR (W m−2) and (d)–(f) DSR (W m−2). The filled black circles indicate where the anomalies are significant at the 5% level.

Citation: Journal of Climate 21, 11; 10.1175/2007JCLI1850.1

Fig. 8.
Fig. 8.

Anomalies of U10 (vectors; m s−1) and their divergence (colors; s−1; positive for divergence) for the composite break event, plotted for the triads in Fig. 4. Divergence is shown only where either the anomalous zonal or meridional winds are significant at the 5% level, based on a Student’s t test on means. The annotations indicate significant anticyclonic (e.g., “A1”) and cyclonic (e.g., “C1”) circulation anomalies.

Citation: Journal of Climate 21, 11; 10.1175/2007JCLI1850.1

Fig. 9.
Fig. 9.

SST anomalies (°C) for the composite break event plotted by triads as in Fig. 4.

Citation: Journal of Climate 21, 11; 10.1175/2007JCLI1850.1

Fig. 10.
Fig. 10.

(a)–(c) Longitude-averaged SST anomalies (°C) for the composite break event, plotted as in Fig. 6. The white areas north of 20°N represent land points, but are shown so that the figure may be easily compared with the other latitude–time figures in this study. The dashed lines represent the propagation of minima or maxima in OLR, as in Fig. 6. The contour interval is 0.1°C and the color bar at the bottom applies to all panels.

Citation: Journal of Climate 21, 11; 10.1175/2007JCLI1850.1

Fig. 11.
Fig. 11.

Phase relationships between area-averaged anomalies for the composite break event in OLR (OL), DSR (SR), LHF (LA), SHF (SH), SST (SS), precipitation (PR), and zonal and meridional components of U10 (u and υ, respectively; positive westerly and southerly) for boxes (a) NI, (b) SI, (c) NAS, (d) SAS, (e) NBoB, (f) SBoB, (g) WEqIO, and (h) EEqIO. The boxes are as defined in Table 3. The star symbol indicates the time when the maximum anomaly occurred in the variable listed on the horizontal axis. The lines indicate the range of time over which the anomaly is significant at the 5% level.

Citation: Journal of Climate 21, 11; 10.1175/2007JCLI1850.1

Fig. 12.
Fig. 12.

A diagram of the idealized NPISO life cycle as viewed from a fixed point, derived from the composite active and break events in this study. The arrows indicate the direction of the cycle. The larger boxes to the right of the arrow represent quantitative anomalies, while the smaller boxes to the left represent our qualitative interpretations of those anomalies. The diamonds indicate approximate lag times between certain anomalies. The atmospheric boundary layer is denoted ABL. All of the changes given are in terms of an anomaly from the climatological value. As the NPISO is a continuous oscillation during the established phase of the monsoon, there is no defined starting point in the cycle. The NPISO has a period of approximately 30 days.

Citation: Journal of Climate 21, 11; 10.1175/2007JCLI1850.1

Table 1.

Pearson correlation coefficients for the NPISO index defined in section 3b and the index of Vecchi and Harrison (2002) against unfiltered AIR anomalies. The correlations are based on the period JJAS 1980–2005.

Table 1.
Table 2.

Summary statistics for the active and break events identified by our index: the number of active and break events, their mean length, and the standard deviation of that length

Table 2.
Table 3.

Names and locations for the boxes defined to create area averages of the variables considered in section 4. For brevity, the text refers to the boxes by their abbreviations.

Table 3.
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