Role of Indian and Pacific SST in Indian Summer Monsoon Intraseasonal Variability

15 JUNE 2011 A C H U T H A V A R I E R A N D K R I S H N A M U R T H Y 2915 Role of Indian and Pacific SST in Indian Summer Monsoon Intraseasonal Variability DEEPTHI ACHUTHAVARIER* Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, Virginia V. KRISHNAMURTHY Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, Virginia, and Center for Ocean Land Atmosphere Studies, Institute of Global Environment and Society, Calverton, Maryland (Manuscript received 28 January 2010, in final form 30 November 2010) ABSTRACT Three regionally coupled experiments are conducted to examine the role of Indian and Pacific sea surface temperature (SST) in Indian summer monsoon intraseasonal variability using the National Centers for Environmental Prediction s Climate Forecast System, a coupled general circulation model. Regional coupling is employed by prescribing daily mean or climatological SST in either the Indian or the Pacific basin while allowing full coupling elsewhere. The results are compared with a fully coupled control simulation. The intraseasonal modes are isolated by applying multichannel singular spectrum analysis on the daily precipitation anomalies. It is found that the amplitude of the northeastward-propagating mode is weaker when the air sea interaction is suppressed in the Indian Ocean. The intraseasonal mode is not resolved clearly when the Indian Ocean SST is reduced to daily climatology. Intraseasonal composites of low-level zonal wind, latent heat flux, downward shortwave radiation, and SST provide a picture consistent with the proposed mechanisms of air sea interaction for the northward propagation. The Pacific SST variability does not seem to be critical for the existence of this mode. The northwestward-propagating mode is obtained in the cases where the Indian Ocean was prescribed by daily mean or daily climatological SST. Intraseasonal SST composites corresponding to this mode are weak. 1. Introduction Several studies have classified the Indian summer monsoon intraseasonal variability into two main oscillations, with periods broadly falling around 30 60 and 10 20 days (Krishnamurti and Bhalme 1976; Yasunari 1979; Hartmann and Michelsen 1989; Lau and Chan 1986; Annamalai and Slingo 2001). Krishnamurti and Bhalme (1976) noted that a quasi-biweekly oscillation exists in many elements of the monsoon system, such as surface pressure, monsoon rainfall, low-level winds, and monsoon cloudiness. Yasunari (1979) reported a similar result * Current affiliation: Center for Ocean Land Atmosphere Studies, Institute of Global Environment and Society, Calverton, Maryland. Corresponding author address: Deepthi Achuthavarier, COLA, Institute of Global Environment and Society, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705. E-mail: deepthi@cola.iges.org in the cloudiness data. Hartmann and Michelsen (1989), Yasunari (1979), and Lau and Chan (1986) reported the presence of a 30 50-day oscillatory signal. The 10 20- day oscillation is generally associated with westwardpropagating events entering the Indian land region from the Bay of Bengal. The 30 60-day oscillation is associated with the northward propagation of cloudiness and rainfall from the equatorial Indian Ocean to the Indian subcontinent. Recently, applying multichannel singular spectrum analysis (MSSA) on the daily outgoing longwave radiation (OLR) data over the domain 208S 358N, 408 1008E for the period 1975 2002, Krishnamurthy and Shukla (2008, hereafter KS08) showed that the summer intraseasonal variability consists of two dominant nonlinear oscillations with periods centered at 45 and 28 days. In the 45-day oscillation, the convective anomalies originate over the equatorial Indian Ocean, propagate slightly eastward, and then northward to the Indian subcontinent. The most notable feature of this oscillation is the large-scale DOI: 10.1175/2010JCLI3639.1 Ó 2011 American Meteorological Society

2916 J O U R N A L O F C L I M A T E VOLUME 24 structure extending from 608 to 1608E in a northwest southeast orientation. The spatial structure of the 28-day mode shows a quadrupole structure in its peak phase with same sign anomalies over India and the Maritime Continent and opposite sign anomalies over the Indian Ocean and China Sea. The 28-day mode has westward propagation over the latitudes 108 258N and northward propagation over the Indian and western Pacific regions. The life cycles of the 45- and 28-day modes were shown to correspond to the active and break cycles over India, respectively. A relevant question in this topic is about the role of sea surface temperature (SST) in the intraseasonal oscillations (ISOs). Several studies have examined the role of SST on the winter intraseasonal oscillation or the Madden Julian oscillation (MJO) [see the review by Hendon (2005)]. Because of the lack of observational data over the Arabian Sea and the Bay of Bengal, and probably because of the more complex nature of the oscillation, relatively fewer studies have been published on the role of SST in the summer intraseasonal variability. Using the high-resolution SST measurements from the Tropical Rainfall Measuring Mission (TRMM), Sengupta et al. (2001) showed that the intraseasonal fluctuations in SST have large horizontal structures similar to the ISO signal in convection and amplitudes of about 0.6 0.8 K. Vecchi and Harrison (2002) reported that the basinwide average amplitude of the SST anomalies in the Bay of Bengal is about 1 2 K and that the air sea interaction could be an important factor in the evolution of active and break cycles. Using observations and reanalysis, Klingaman et al. (2008a) suggested that there exists a coherent intraseasonal oscillation in SST in association with the convection. The lead lag relationship between SST and atmospheric fields indicated that the SST anomalies are generated by the atmospheric fluxes. The role of SST in the above-mentioned 45- and 28-day modes has been examined by Krishnamurthy and Kirtman (2009). Their results showed that these modes have weak but statistically significant correlations of about j0.2j with the daily SST. Their intraseasonal composites of the SST corresponding to the OLR phases of the 45-day mode showed similar spatial patterns as of convection and had amplitudes of about 0.1 0.15 K. These observational results indicate that SST may play an important role in the summer intraseasonal variability, particularly in the 45-day mode. Despite this observational evidence, controlled general circulation model (GCM) experiments are necessary to verify the importance of SST variability in the summer intraseasonal oscillations. Relatively fewer studies have examined this problem. Using a hybrid coupled GCM, Fu et al. (2003) and Fu and Wang (2004) showed that the summer intraseasonal variability was better simulated in the coupled model compared to an atmospheric model forced with daily SST. Their results suggested that the intraseasonal SST anomalies were generated because of the atmospheric fluxes into the ocean. Similar results were reported by Zheng et al. (2004), using the Geophysical Fluid Dynamics Laboratory (GFDL) coupled GCM, and by Rajendran and Kitoh (2006), with the Meteorological Research Institute coupled GCM. Conversely, Klingaman et al. (2008b) simulated realistic intraseasonal oscillations by forcing the Hadley Centre Atmospheric Model with daily high-resolution-observed SST data. Bellon et al. (2008) showed that coupling did not modify the phase and amplitude of the intraseasonal oscillations in their idealized model with aquaplanet configuration. Therefore, the available results in the literature are largely inconclusive on the role of SST variability in the monsoon intraseasonal oscillations. Using a coupled GCM, this study investigates the role of SST variability and air sea interaction in the Indian Ocean in the 45- and 30-day intraseasonal modes. Additionally, the role of Pacific SST variability in the summer intraseasonal modes is also investigated. The study addresses the following questions: 1) What is the role of the Indian Ocean in the 45- and 30-day modes? That is, is coupled air sea interaction necessary for the simulation of these modes? Can the model reproduce the intraseasonal modes when forced with either the daily mean or the daily climatological SST in the Indian Ocean? and 2) What is the role of Pacific SST variability in the summer intraseasonal modes? An earlier study (Achuthavarier and Krishnamurthy 2011) has identified the summer intraseasonal modes in a long simulation of the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) model that are equivalent to the 45- and 30-day modes in the observations. In this study, regionally coupled simulations are performed using the CFS to examine the role of SST variability in the Indian and Pacific basins in these intraseasonal modes. Section 2 describes the model, experiment design, and the analysis methodology. The climatological fields of the model simulations are presented in section 3. The intraseasonal modes and their propagation features are discussed in section 4. Section 5 provides a summary and discussion. 2. Methodology a. Model and experiments The numerical experiments in this study are conducted using the CFS, the current operational coupled GCM at NCEP (Saha et al. 2006). The atmospheric component of the CFS is a coarse-resolution version of the NCEP s Global Forecast System (GFS) (Moorthi et al. 2001) and the ocean component is the Modular Ocean Model

15 JUNE 2011 A C H U T H A V A R I E R A N D K R I S H N A M U R T H Y 2917 Experiment name TABLE 1. Experimental design of GCM simulations. Prescribed SST domain (ocean grid points within the region) Prescribed SST type Control Fully coupled NA IO-VSST 308S 308N, 408 1208E Daily mean IO-CSST 308S 308N, 408 1208E Daily climatology PO-CSST 308S 508N, 1208E 908W Daily climatology version 3 (MOM3) developed by GFDL in Princeton (Pacanowski and Griffies 1998). The GFS has a spectral triangular truncation of 62 waves in the horizontal (equivalent to 200-km Gaussian grid) and a finite differencing in the vertical with 64 sigma layers. The atmospheric and oceanic components exchange daily average quantities of heat and momentum fluxes once a day without any flux correction. The atmosphere ocean coupling is effective between 658Sand 508N, while observed climatological SST is prescribed poleward of this region. The sea ice extent is prescribed from the observed climatology. In this study, the role of SST variability in the Indian and Pacific Oceans is investigated by performing regionally coupled simulations of the CFS. Regional coupling is a numerical simulation framework commonly used to examine the sensitivity of the phenomenon of interest to the SST variability in a specific region (e.g., Lau and Nath 2000; Huang and Shukla 2007). One way to implement this is by prescribing the SST in a certain oceanic region, while allowing full air sea coupling elsewhere. Three such regionally coupled simulations are performed (Table 1). In all the cases, the SST fields are obtained from the last 30 yr of a 50-yr long, fully coupled simulation of the CFS, which will be referred to as the control (Pegion and Kirtman 2008). In the first experiment, the Indian Ocean forced by daily varying SST (IO-VSST), the daily mean SST from the control simulation is prescribed in the Indian Ocean (308S 308N, 408 1208E), while the rest of the ocean basins are fully coupled. Therefore, in this experiment, the atmosphere is allowed to respond to the Indian Ocean SST while the influence of atmosphere on the SST is suppressed. Comparison of this run with the control allows one to specifically examine the impact of coupled air sea feedbacks in the Indian Ocean on monsoon variability. By prescribing the daily mean SST, any difference between the control and the IO-VSST can be attributed to the air sea coupling. A second experiment, the Indian Ocean forced by daily climatological SST (IO-CSST), in which the Indian Ocean is kept passive by prescribing daily climatology of SST from the control simulation has also been performed. Comparison of the IO- CSST and IO-VSST may indicate the role of subseasonal and higher-frequency SST variability in the intraseasonal oscillation. Finally, to examine the role of Pacific SST on intraseasonal modes, a third experiment is conducted by prescribing climatological SST from the control simulation in the region 308S 508N, 1208E 908W (PO-CSST). In all the experiments, the model is restarted from the control run on 1 January of the twenty-first simulation year, using perturbed atmospheric initial conditions and appropriate SST configurations. The new simulations are run continuously for 30 yr. The transition between the prescribed and coupled regions is smoothed by applying a 108-wide buffer zone, where the SST values are obtained by linear interpolation. b. Analysis The intraseasonal oscillations are isolated by applying MSSA on the daily anomalies of precipitation, a method used in the observations (KS08) as well as in the control simulation. Ghil et al. (2002) provides a detailed description of the mathematical and computational procedures of the MSSA. The MSSA is similar to the extended empirical orthogonal function analysis, meaning both are based on the eigenanalysis of the lagged covariance matrix. One main difference between the two methods is that longer lag windows are usually employed in the MSSA. The computational procedure of the MSSA involves forming the lagged covariance matrix of order (M, N 2 M 1 1) for each of the L spatial points, where N is the number of time intervals and M is the length of the lag window chosen. The lagged matrices are stacked together to form a trajectory matrix of order (L 3 M, L 3 M), an eigenanalysis of which yields L 3 M eigenvectors. The eigenvectors contain M sequences of spatial maps, which are referred as the space time EOFs (ST-EOFs). The space time principal components (ST-PCs), each of length N 2 M 1 1, are obtained by projecting the original data onto the corresponding ST-EOFs. The component of the original data corresponding to each eigenvalue can be reconstructed by combining the ST-PC and its respective ST-EOF in a least squares sense and is referred to as the reconstructed component (RC). Oscillatory signals are resolved in two successive modes with the same or close by eigenvalues, whose ST-PCs are in phase quadrature. The propagation of the oscillatory modes is represented by computing their phase composites based on the methodology of Moron et al. (1998). The space time structure of the oscillation is described using phase composites obtained by averaging the RC in equal intervals of the phase u in the interval (0, 2p). The phase composites represent the evolution of one cycle of the oscillation in an average sense. The MSSA is applied to 30 yr of daily anomalies of precipitation over the region 358S 358N, 408 1608E. As outlined in the case of the control run (Achuthavarier and Krishnamurthy 2011), first, an MSSA of lag window

2918 J O U R N A L O F C L I M A T E VOLUME 24 length of 181 days is performed over all days of the year (for 30 yr) (MSSA-1) to isolate the equivalent of the 45-day mode or the northeastward-propagating intraseasonal oscillation. This methodology is slightly different from what is employed in KS08 with observations. The present analysis is not restricted to the daily data in the June September (JJAS) season, and the lag window is increased to 181 days. The inclusion of all days of the year and the use of the longer lag window are based on the behavior of the intraseasonal oscillation in the CFS. It is found that the northeastward-propagating intraseasonal mode in the CFS control run is significantly slower compared to the observations and has a period around 106 days (Achuthavarier and Krishnamurthy 2011), which was isolated by performing the MSSA with a lag window of 181 days and all days of the year (MSSA-1). The spatial structure and propagation features of the 106-day mode have striking similarities with those of the 45-day mode of the observations, suggesting that the 106-day mode is the model counterpart of the observed 45-day mode. The validity of this mode has been examined carefully in Achuthavarier and Krishnamurthy (2011) by comparing the amplitude of the mode with total precipitation anomalies. Additionally, it is verified that this mode is not the semiannual cycle in the data, by removing the annual and semiannual signals using harmonic analysis and repeating the MSSA on the resulting anomalies. The above discussion justifies the use of the MSSA-1 in the present study. Second, to isolate the northwestward-propagating 28-day mode, the MSSA was performed on the filtered daily anomalies of the JJAS season. The filtered data can be obtained either by removing the RCs corresponding to the 106-day mode or by employing a 20 100-day bandpass filter. It was found that both methods yielded nearly identical results for this mode. The MSSA of the filtered daily anomalies restricted to the JJAS season will be referred to as MSSA-2. The MSSA-2 was necessary because the 28-day mode has low fractional variance in the MSSA-1. Daily anomalies were computed by subtracting the daily climatology from the daily mean values. A 5-day running-mean filter was applied to the daily anomalies to remove high-frequency fluctuations. Daily anomalies mentioned hereafter are 5-day running means. 3. Climatology In this section, the summer monsoon (JJAS) climatologies of precipitation and wind shear in the three regionally coupled simulations are compared with those of the control. The purpose of this section is to verify that the regionally coupled runs have the mean climate comparable to that in the control. This assures that any differences between the intraseasonal variability of the regionally coupled runs and the control are not due to differences in the simulation of the mean climate. Figure 1 shows that JJAS precipitation climatology of the experiments compares well with that of the control. In all three experiments, the main features of the monsoon climatology, such as the maxima on either side of the Indian peninsula and the rain shadow region in the southeast India, are well captured. However, the central Indian rainfall is underestimated in all the runs. The difference between the experiments and control is less than 2 mm day 21, except over certain regions in the IO-CSST. Figure 2 presents the JJAS climatology of easterly vertical wind shear, which is computed as [u(200 hpa) 2 u(850 hpa)]. Previous studies have suggested that an easterly vertical wind shear in the zonal mean wind is a critical factor in promoting the northward propagation of intraseasonal oscillations (Kemball-Cook et al. 2002). It has been suggested that the westward-propagating intraseasonal oscillations arise as a result of Rossby wave emanation from the western Pacific as a Gill-type response to convection in that region (Wang and Xie 1997). The mean easterly vertical wind shear was found to be important for the emanation of Rossby waves. Figure 2 shows that the mean easterly vertical wind shear in the regionally coupled simulations is similar to that in the control. It is, therefore, argued that the differences in the intraseasonal variability in the experiments and the control, which will be presented later, are not due to the lack of mean easterly wind shear. 4. Northeastward propagation a. Spatial structure and propagation In this section, the northeastward-propagating mode, equivalent to the 45-day mode in the observations as presented by KS08 (see their Fig. 4), in the regionally coupled runs is examined. Since this mode in the control run has a period of 106 days, it was necessary to use the MSSA-1 (as defined in section 2b) to isolate this mode. In the control run, the eigenmodes 3 and 4 constitute the 106-day mode, which shows northward and eastward propagation characteristics. To identify the corresponding intraseasonal mode in the regionally coupled runs, the exact same procedure is employed on the daily anomalies of precipitation, the results of which are discussed below. Results from the control run are reproduced from Achuthavarier and Krishnamurthy (2011), wherever appropriate, for comparison purposes. It is found that the IO-VSST and PO-CSST simulations capture the northeastward-propagating mode, while the IO-CSST does not capture this mode clearly, as explained below. In the IO-VSST simulation, eigenmodes 8 and 9 are resolved as an oscillatory pair with a period around 106 days. The oscillatory nature of these modes is verified by

15 JUNE 2011 A C H U T H A V A R I E R A N D K R I S H N A M U R T H Y 2919 FIG. 1. The (a) JJAS seasonal climatology of rainfall (mm day 21 ) for 30 yr of the control. Difference in JJAS seasonal climatology in comparison with the control for (b) IO-VSST, (c) IO-CSST, and (d) PO-CSST simulations. examining the phase shift of their ST-PCs and by computing the power spectra of the ST-PCs. The combined variance explained by modes 8 and 9 is 1.2%, which is comparable to the fractional variance of the 106-day mode in the control run (1%). A similar oscillatory component was not clearly resolved when the Indian Ocean was prescribed with climatological SST (IO-CSST). This point will be further elaborated in section 4b. In the PO-CSST simulation, the first two eigenmodes constitute an oscillatory pair (of fractional variance 1.4%) with a period of around 85 days, which will be shown as the northeastward propagation in that simulation. The spatial structure and the life cycle of the northeastward propagation are examined by computing the phase composites (Fig. 3). Each composite is in a 458-interval in phase that corresponds to 13 days for the 106-day mode of control and IO-VSST runs, and 10 days for the 85-day mode of the PO-CSST run. For brevity, only the first half of a complete cycle is shown. The composite life cycle of the oscillation shows that positive anomalies appear near the Indian peninsula between 708 and 1008E in phase 1, and strengthen and propagate northward to about 208N through phases 2 and 3. Negative precipitation anomalies start to form over the equatorial Indian Ocean by phases 3 and 4. The second half of the oscillation is the opposite of what is shown in phases 1 4. During the peak phase of the oscillation, a large-scale pattern of anomalies is seen that extends from 708 to 1608E with maxima on either side of the Indian peninsula and West Pacific. Comparing the phase composites of the IO-VSST simulation with that of the control run, the spatial structure of the northeastward propagation is seen to be captured in the IO-VSST. However, the amplitudes of the anomalies are weaker in the IO-VSST. Particularly, the rainfall maxima over the Arabian Sea, Bay of Bengal, and West Pacific are weaker. It is also noted that the similarity of the phase composites of the control and the IO-VSST is mainly over the northern parts of the domain, while over the equatorial Indian

2920 J O U R N A L O F C L I M A T E VOLUME 24 FIG. 2. As in Fig. 1, but for vertical shear of zonal wind u (m s 21 ). Ocean and Maritime Continent, features of the winter intraseasonal oscillations can be detected. It is, therefore, speculated that the eigenmodes 8 and 9 are likely to be a mixed mode of variability that combines both the summer and the winter intraseasonal oscillations. The IO-VSST anomalies present over the Maritime Continent are larger in amplitude compared to the 106-day summer mode found in the control run. No other oscillatory signals were resolved in the IO-VSST run, as opposed to the control run, where two pairs of oscillations with a period around 100 days were obtained, one of which clearly showed characteristics of the winter intraseasonal oscillation. A simple way to examine whether this mode represents both the summer and winter oscillations is to add the phase composites of the summer and winter modes obtained in the control run and examine whether the resulting pattern is similar to the phase composites of the eigenmodes 8 and 9 shown in Fig. 3 (middle panel). When this analysis was performed, it confirmed the above hypothesis (figure not shown). Comparison of the phase composites of the control and PO-CSST runs shows that the northeastward-propagating mode is captured well in the PO-CSST run. The anomalies over the equatorial Indian Ocean, Arabian Sea, and Bay of Bengal are simulated well in the PO-CSST. However, the maxima over the West Pacific region are not captured well, which is noticeable in the peak phase of the oscillation (phases 2 and 3). It can be argued that this is due to the climatological SST prescribed over the Pacific Ocean. Eastward and northward propagations of this mode are examined in Fig. 4 by averaging the phase composites over the latitudes 58S 108N and over the longitudes 608 1008E, respectively. The eastward propagation is evident from 608 to 1008E and from 1208 to 1608E both in the control and in the IO-VSST simulations. In the PO-CSST, the eastward anomalies are weaker east of 1008E, although the eastward signal is somewhat visible. The northward propagation is present from the equator to 208N in all three simulations. b. IO-CSST It was mentioned in the previous subsection that the IO- CSST simulation did not capture the northeastward propagation clearly. This section further examines the MSSA

15 JUNE 2011 ACHUTHAVARIER AND KRISHNAMURTHY 2921 FIG. 3. Phase composites of the northeastward-propagating intraseasonal mode in the (left) control, (middle) IO-VSST, and (right) PO-CSST simulation run. Each panel in a column represents a phase identified by the phase number at the top right. Units are in mm day21. results of the IO-CSST run. The power spectra of the ST-PCs from the MSSA-1 of the IO-CSST run shows that modes 4, 5, 6, 7, and 8 have peaks around 173, 167, 124, 108, and 75 days, respectively, while the spectra of modes 1 3 are red in nature. This is different from the spectra of the other runs, where at least a single pair of oscillatory modes was obtained. Since, no oscillatory signal is resolved as a pair with nearly degenerate eigenvalues and periods, it is concluded that the IO-CSST simulation may not capture a clear northeastward-propagating mode.

2922 J O U R N A L O F C L I M A T E VOLUME 24 FIG. 4. Northeastward-propagating mode: longitude-phase cross sections of the phase composites averaged between 58S and 108N for the (a) control, (b) IO-VSST, and (c) PO- CSST simulations. The X axis is longitude and Y axis is phase angle in degrees. Latitudephase cross sections of the phase composites averaged between 608 and 1008E for the(d) control, (e) IO-VSST, and (f) PO-CSST simulations. The X axis is phase angle in degrees and Y axis is latitude. Units are in mm day 21.Contoursof61.5 and 61.25 mm day 21 are not included in the shading. The results in this section suggest that an underlying SST variability in the intraseasonal time scale in the Indian Ocean is necessary for the northeastward-propagating mode, since the IO-CSST run could not capture this mode. In the IO-VSST run, the northeastward-propagating mode in precipitation is probably a response of subseasonalscale SST anomalies in the Indian Ocean. This suggests that the air sea interaction in the control improves the simulation of this mode, which is further explored in the next section. c. Air sea interaction Previous studies have suggested that the air sea interaction in the intraseasonal time scale produces SST anomalies through two processes (see, e.g., Hendon 2005). In one process, the reduced cloudiness over the suppressed convective region increases downward shortwave radiation(dswr)atthesurface,whichinturnwarmsthe ocean surface. In another process, easterly anomalies present over the reduced convective region act to reduce

15 JUNE 2011 A C H U T H A V A R I E R A N D K R I S H N A M U R T H Y 2923 FIG. 5. (left to right) Phase composites of the zonal wind u (m s 21 ) at 850 mb, latent heat flux (W m 22 ), DSWR at the surface (W m 22 ), and SST (K) anomalies corresponding to the northeastward-propagating intraseasonal mode in the control run. Each panel in a column represents a phase identified by the phase number at the top right. the total wind speed (climatological winds are westerly), which reduces evaporation and aids in generating warm SST anomalies. These two processes generate warm SST anomalies north of the enhanced convection. Similarly, reduced surface DSWR and westerly anomalies can be present north of the suppressed convection, which can generate cold SST anomalies there. The air sea interaction associated with the 106-day mode is examined by computing the phase composites of the u winds at 850 hpa, the surface latent heat flux, the surface DSWR, and the SST. The composites are made by averaging the daily anomalies of the above-mentioned variables based on the phases of the 106-day mode in precipitation. Figures 5 and 6 show such phase composites for the control and the IO-VSST runs, respectively. Comparing the precipitation composites (first panel in Fig. 3) and Fig. 5, westerly anomalies are present in the Arabian Sea and Bay of Bengal, collocated with the enhanced precipitation anomalies. During phases 1 3, positive latent heat flux anomalies are present on either

2924 JOURNAL OF CLIMATE VOLUME 24 FIG. 6. As in Fig. 5, but for the IO-VSST run. side of the Indian peninsula, collocated with the enhanced convection and associated increased total wind speed. The phase composites of the surface DSWR show positive (negative) anomalies during the suppressed (enhanced) convective phases. Finally, in the SST composites, warm SST anomalies are present just north of the enhanced precipitation, and cold anomalies develop soon after the convective anomalies leave a region. The SST anomalies, although weak in amplitude, have a large-scale structure similar to the precipitation anomalies, which is evident particularly in phases 1 and 4. The SST anomalies may appear smaller in comparison with some of the previous studies (e.g., Webster et al. 2002). It must be emphasized that the SST anomalies presented here are composite values based on all events that have occurred in 30 yr as opposed to an individual event. When compared with the study by Krishnamurthy and Kirtman (2009) that shows the composite SST anomalies corresponding to the observed 45-day mode (their Fig. 9), the model s SST amplitude is consistent with observations. Similar composite maps for the IO-VSST simulation show consistent pictures of zonal winds, latent heat flux, and the surface DSWR. However, the SST composites show two main differences. First, the anomalies are weaker in comparison

15 JUNE 2011 A C H U T H A V A R I E R A N D K R I S H N A M U R T H Y 2925 with the control run. Second, the phase lag between precipitation and SST, as seen in the control run, is not present in the IO-VSST. To examine the phase relationship between precipitation and SST, Hovmöller diagrams of the phase composites of the precipitation and SST are prepared by averaging over the longitudes 608 1008E for the control and IO-VSST simulations (Fig. 7). Lead lag composites of intraseasonal oscillation by different methods have been presented by Fu and Wang (2004) and Klingaman et al. (2008a) and others. The precipitation fields in Fig. 7 are identical to what is presented in Figs. 4d and 4e. In the control simulation, SST leads the precipitation by about 708, which corresponds to about 20 days. At a certain time or phase, warm (cold) SST anomalies are present north of the enhanced (reduced) precipitation. In the IO-VSST simulation, the atmosphere simply responds to the SST anomalies and therefore increased precipitation coincides with warm SST and vice versa. This indicates that the atmospheric fluxes are likely to induce SST fluctuations in the time scale of the intraseasonal oscillations, and these may be important for the correct simulation of the summer intraseasonal oscillations. 5. Northwestward propagation In this section, the northwestward-propagating mode of the summer intraseasonal variability simulated in the three regionally coupled runs is examined. The MSSA-2 performed on the daily precipitation anomalies of the control run isolated eigenmodes 4 and 5 as the 30-day northwestward-propagating mode, which is equivalent to the 28-day mode in the observations (Fig. 5 in KS08). The 30-day mode was obtained in the IO-VSST and IO-CSST simulations as eigenmodes 1 and 2, while this mode was not clearly resolved in the PO-CSST simulation. The phase composites of the 30-day mode from the IO- VSST and IO-CSST simulations are compared with those of the control run in Fig. 8. The phase intervals are 458 or about 3.5 days long. As shown in Fig. 3, only half of an oscillatory cycle is shown for brevity. The characteristic feature of the 30-day mode is a quadrupole-like pattern in precipitation with anomalies of the same sign over the Indian subcontinent and Maritime Continent and that of the opposite sign over the equatorial Indian Ocean and South China Sea regions (the feature is not strictly quadrupole with alternating signs of anomalies, but there are four centers of action). A somewhat similar pattern can be seen in the phases 1 and 4 in the control run, although the anomalies over the Maritime Continent and equatorial Indian Ocean are less evident. The phase composites of this mode for the IO-VSST and IO-CSST simulations are consistent with the control run. Hovmöller diagrams FIG. 7. Northeastward-propagating mode: latitude-phase cross sections of the phase composites of precipitation (mm day 21 ) (shaded) and SST (K) (contours) averaged between 608 and 1008E for the (top) control and (bottom) IO-VSST simulations. The X axis is phase angle in degrees. of the phase composites by averaging over 58S 108N and 608 1008E are presented in Fig. 9. They indicate that the westward and northward propagations of this mode are captured well in the IO-VSST and IO-CSST runs, respectively. However, one point to note is that the slight eastward propagation seen east of 1008E in the observed 28-day mode (KS08) is not captured well by the model. The model in general shows westward propagation, which is the main feature of this mode from the observed wavenumber frequency analysis (KS08, their Fig. 9d), between 58 and 108S and108 and 258N. It is noteworthy that the model captures this mode even when the Indian Ocean is prescribed with climatological SST. The results suggest that the SST anomalies in the Indian Ocean may not be critical for the existence of this mode. The SST anomalies corresponding to the 30-day mode in the control and the IO-VSST simulations are presented by computing the phase composites of the SST corresponding to the precipitation phases in Fig. 9 (Fig. 10). The amplitude of the SST anomalies for this mode is weaker

2926 JOURNAL OF CLIMATE VOLUME 24 FIG. 8. Phase composites of the northwestward-propagating intraseasonal mode in the (left) control, (middle) IO-VSST, and (right) IO-CSST simulations. Each panel in a column represents a phase identified by the phase number at the top right. Units are in mm day21. compared to that of the northeastward-propagating mode (see Fig. 5). The fact that the IO-VSST run was able to reproduce this mode fairly well suggests that the air sea interaction processes may not be critical for this mode. 6. Summary and discussion This study examined the relative roles of SST in the Indian and Pacific Oceans in the summer intraseasonal

15 JUNE 2011 A C H U T H A V A R I E R A N D K R I S H N A M U R T H Y 2927 FIG. 9. Northwestward-propagating mode: longitude-phase cross sections of the phase composites averaged between 58S and 108N. for the (a) control, (b) IO-VSST, and (c) IO-CSST simulations. The Y axis is phase angles in degrees. Latitude-phase cross sections of the phase composites averaged between 608 and 1008E for the (d) control, (e) IO-VSST, and (f) IO-CSST simulations. The X axis is phase angles in degrees. Units are in mm day 21. Contours of 61.5 mm day 21 are not included in the shading. modes using three regionally coupled simulations of the CFS model. The intraseasonal modes from the regionally coupled runs were compared with those from a fully coupled control run. The regionally coupled simulations are the Indian Ocean prescribed with daily mean SST (IO- VSST), the Indian Ocean prescribed with daily climatological SST (IO-CSST), and the Pacific Ocean prescribed with daily climatological SST (PO-CSST). The control simulation has two intraseasonal modes of periods 106 and 30 days, which have northeastward and northwestward propagations, respectively. The intraseasonal modes were isolated by applying the MSSA on the daily precipitation anomalies over the South Asian monsoon region. The northeastward propagation was obtained in both the IO-VSST and the PO-CSST runs, while it was not clearly resolved in the IO-CSST run. The northeastwardpropagating mode in the IO-VSST compares well with that in the control, in its spatial structure, period, and propagation characteristics, although the amplitudes of the anomalies are weaker, especially over the Arabian Sea and Bay of Bengal. It was also found that this mode is resolved in eigenmodes 8 and 9 in the IO-VSST, while it

2928 J O U R N A L O F C L I M A T E VOLUME 24 FIG. 10. Phase composites of the SST (K) corresponding to the northwestward-propagating intraseasonal mode in the (left) control and (right) IO-VSST simulations. Each panel in a column represents a phase identified by the phase number at the top right. was resolved in modes 3 and 4 in the control. The low ranking and the reduced amplitude of the intraseasonal mode in the IO-VSST can be attributed to the lack of air sea interaction in that experiment. The fact that the IO- CSST run fails to capture this mode seems to suggest that underlying SST variability in the intraseasonal time scale is necessary for the existence of this mode. The role of air sea interaction was examined by computing the composite

15 JUNE 2011 A C H U T H A V A R I E R A N D K R I S H N A M U R T H Y 2929 life cycles of the zonal wind at 850 hpa, the latent heat flux, the surface DSWR, and the SST. In the control run, where air sea interaction is present, the SST leads precipitation by about 20 days. The wind, latent heat flux, and shortwave radiation anomalies form a consistent picture, suggesting that they help in generating the SST anomalies. In the IO-VSST run, although the above-mentioned atmospheric variables show similar evolution as in the control, they cannot influence the SST anomalies by the design of the experiment. Therefore, the precipitation and SST anomalies are in phase, which is unlike in the observations. The results from the PO-CSST run shows that the northeastward mode is captured without any Pacific SST variability. It is, however, noted that the large-scale structure of this mode, particularly the anomalies over the West Pacific region, is not well captured. The northwestward-propagating 30-day mode was obtained in the IO-VSST and IO-CSST runs, while it was absent in the PO-CSST run. This result and the SST phase composites corresponding to this mode show that the SST anomalies in the Indian Ocean are not likely to be important for the simulation of this mode. However, since the PO-CSST run fails to capture this mode, it may be argued that the SST variability in the West Pacific is important for the simulation of this mode. The findings in this study are consistent with previous studies, in that the air sea interaction in the Indian Ocean appears to improve the northeastward-propagating intraseasonal mode. However, further investigation is necessary to answer several questions that emerged from these results. First, it is unclear why the intraseasonal mode in the CFS has a period of 106 days. The precipitation SST phase lag is about 20 days for this mode, while it is about 10 days in the observations. Further investigation is needed to understand whether the slow oscillation is related to the precipitation SST phase lag. Because of the unrealistic period of the northeastward-propagating mode in the CFS, any extrapolation of the results to the real world should be viewed with caution. The slow propagation may have resulted in larger SST anomalies, since convection stays at a location for a longer period. For example, Duncan and Han (2009) have shown that the SST response is larger for the 30 90-day intraseasonal variability compared to that of the 10 30-day variability. This implies that, had the model simulated a 45-day oscillation, as in observations, the SST response would be even smaller. Second, the role of SST variability and air sea interaction specifically in the West Pacific region needs to be studied for the 30-day mode. This study has shown that the 30-day mode is absent when the entire Pacific is forced with the climatological SST, and it is speculated that this is due to the lack of SST variability in the West Pacific. It will be interesting to investigate if the West Pacific specifically has any role in the genesis of some of these oscillatory cycles. Additional experiments that suppress the West Pacific SST variability alone are necessary for a conclusive answer to this question. Acknowledgments. This research was supported the National Science Foundation (Grants ATM-0332910, ATM-0830062, and ATM-0830068), the National Oceanic and Atmospheric Administration (Grants NA04OAR4310034 and NA09OAR4310058), and the National Aeronautics and Space Administration (Grants NNG04GG46G and NNX09AN50G). The computing resources provided by the National Center for Atmospheric Research for conducting the numerical experiments in this study are gratefully acknowledged. The authors thank Kathy Pegion for the control simulation data and Ben Kirtman for his helpful discussions. This work formed a part of Deepthi Achuthavarier s Ph.D. thesis, which was submitted to George Mason University. REFERENCES Achuthavarier, D., and V. Krishnamurthy, 2011: Daily modes of South Asian summer monsoon variability in the NCEP Climate Forecast System. Climate Dyn., 36, 1941 1958, doi:10.1007/ s00382-010-0844-9. Annamalai, H., and J. M. Slingo, 2001: Active/break cycles: Diagnosis of the intraseasonal variability of the Asian summer monsoon. Climate Dyn., 18, 85 102. Bellon, G., A. H. Sobel, and J. Vialard, 2008: Ocean atmosphere coupling in the monsoon intraseasonal oscillation: A simple model study. J. Climate, 21, 5254 5270. Duncan, B., and W. Han, 2009: Indian Ocean intraseasonal sea surface temperature during boreal summer: Madden-Julian Oscillation versus submonthly forcing and processes. J. Geophys. Res., 114, C005002, doi:10.1029/2008jc004958. Fu, X., and B. Wang, 2004: Differences of boreal summer intraseasonal oscillations simulated in an atmosphere ocean coupled model and an atmosphere-only model. J. Climate, 17, 1263 1271.,, T. Li, and J. P. McCreary, 2003: Coupling between northward-propagating, intraseasonal oscillations and sea surface temperature in the Indian Ocean. J. Atmos. Sci., 60, 1733 1753. Ghil, M., and Coauthors, 2002: Advanced spectral methods for climatic time series. Rev. Geophys., 40, 1003, doi:10.1029/2000rg000092. Hartmann, D. L., and M. L. Michelsen, 1989: Intraseasonal periodicities in Indian rainfall. J. Atmos. Sci., 46, 2838 2862. Hendon, H., 2005: Air sea interaction. Intraseasonal Variability in the Atmosphere-Ocean Climate System, W. K. M. Lau and D. E. Waliser, Eds., Springer-Praxis, 223 246. Huang, B., and J. Shukla, 2007: Mechanisms for the interannual variability in the tropical Indian Ocean. Part I: The role of remote forcing from the tropical Pacific. J. Climate, 20, 2917 2936. Kemball-Cook, S., B. Wang, and X. Fu, 2002: Simulation of the intraseasonal oscillation in the ECHAM-4 model: The impact of coupling with an ocean model. J. Atmos. Sci., 59, 2923 2942. Klingaman, N. P., P. M. Inness, H. Weller, and J. M. Slingo, 2008a: The importance of high-frequency sea surface temperature

2930 J O U R N A L O F C L I M A T E VOLUME 24 variability to the intraseasonal oscillation of Indian monsoon rainfall. J. Climate, 21, 6119 6140., H. Weller, J. M. Slingo, and P. M. Inness, 2008b: The intraseasonal variability of the Indian summer monsoon using TMI sea surface temperatures and ECMWF reanalysis. J. Climate, 21, 2519 2539. Krishnamurthy, V., and J. Shukla, 2008: Seasonal persistence and propagation of intraseasonal patterns over the Indian monsoon region. Climate Dyn., 30, 353 369., and B. P. Kirtman, 2009: Relation between Indian monsoon variability and SST. J. Climate, 22, 4437 4458. Krishnamurti, T. N., and H. N. Bhalme, 1976: Oscillations of a monsoon system. Part I. Observational aspects. J. Atmos. Sci., 33, 1937 1954. Lau, K.-M., and P. H. Chan, 1986: Aspects of the 40 50 day oscillation during the Northern summer as inferred from outgoing longwave radiation. Mon. Wea. Rev., 114, 1354 1367. Lau, N.-C., and M. J. Nath, 2000: Impact of ENSO on the variability of the Asian Australian monsoons as simulated in GCM experiments. J. Climate, 13, 4287 4309. Moorthi, S., H.-L. Pan, and P. Caplan, 2001: Changes to the 2001 NCEP operational MRF/AVN global analysis/forecast system. NWS Tech. Procedures Bull. 484, 14 pp. Moron, V., R. Vautard, and M. Ghil, 1998: Trends, interdecadal and interannual oscillations in global sea-surface temperatures. Climate Dyn., 14, 545 569. Pacanowski, R. C., and S. M. Griffies, 1998: MOM 3.0 manual. NOAA/Geophysical Fluid Dynamics Laboratory Rep., 638 pp. Pegion, K., and B. P. Kirtman, 2008: The impact of air sea interactions on the simulation of tropical intraseasonal variability. J. Climate, 21, 6616 6635. Rajendran, K., and A. Kitoh, 2006: Modulation of tropical intraseasonal oscillations by ocean atmosphere coupling. J. Climate, 19, 366 391. Saha, S., and Coauthors, 2006: The NCEP Climate Forecast System. J. Climate, 19, 3483 3517. Sengupta, D., B. N. Goswami, and R. Senan, 2001: Coherent intraseasonal oscillations of ocean and atmosphere during the Asian summer monsoon. Geophys. Res. Lett., 28, 4127 4130. Vecchi, G. A., and D. E. Harrison, 2002: Monsoon breaks and subseasonal sea surface temperature variability in the Bay of Bengal. J. Climate, 15, 1485 1493. Wang, B., and X. Xie, 1997: A model for the boreal summer intraseasonal oscillation. J. Atmos. Sci., 54, 72 86. Webster, P. J., and Coauthors, 2002: The JASMINE pilot study. Bull. Amer. Meteor. Soc., 11, 1603 1630. Yasunari, T., 1979: Cloudiness fluctuations associated with the Northern Hemisphere summer monsoon. J. Meteor. Soc. Japan, 57, 227 242. Zheng, Y., D. E. Waliser, W. F. Stern, and C. Jones, 2004: The role of coupled sea surface temperatures in the simulation of the tropical intraseasonal oscillation. J. Climate, 17, 4109 4134.