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Modulation of Tropical Intraseasonal Oscillations by Ocean–Atmosphere Coupling K. RAJENDRAN
AND
A. KITOH
Meteorological Research Institute, Tsukuba, Ibaraki, Japan (Manuscript received 20 January 2005, in final form 20 August 2005) ABSTRACT The impact of ocean–atmosphere coupling on the structure and propagation characteristics of 30–60-day tropical intraseasonal oscillations (TISOs) is investigated by analyzing long-term simulations of the Meteorological Research Institute coupled general circulation model (CGCM) and its stand-alone atmospheric general circulation model (AGCM) version forced with SSTs derived from the CGCM and comparing them with recent observation datasets [Global Precipitation Climatology Project (GPCP) precipitation, 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40), and Reynolds SST]. Composite events of (i) eastward propagating Madden–Julian oscillations (MJOs) during boreal winter and (ii) northward propagating intraseasonal oscillations (NPISOs) during boreal summer, constructed based on objective criteria, show that the three-dimensional structure, amplitude, and speed of propagation, and the phase relationship among surface fluxes, SST, and convection, are markedly improved in the CGCM simulation. Consistent with the frictional wave conditional instability of the second kind mechanism, successive development of low-level convergence to the east (north) of deep convection was found to be important for eastward (northward) propagation of MJO (NPISO). Complex interaction between large-scale dynamics and convection reveals the importance of atmospheric dynamics and suggests that they are intrinsic modes in the atmosphere where coupling is not essential for their existence. However, as in observations, realistic coupling in the CGCM is found to result in the evolution of TISOs as coupled modes through a coherent coupled feedback process. This acts as an amplifying mechanism for the existing propagating convective anomalies and plays an important modifying role toward a more realistic simulation of TISOs. In contrast, the simulated TISOs in its atmosphere-alone component lack many of the important features associated with their amplitude, phase, and life cycle. Thus, a realistic representation of the interaction between sea surface and the atmospheric boundary layer is crucial for a better simulation of TISOs.
1. Introduction The dominant mode of low-frequency intraseasonal variability in the Tropics occurs on a 30–60-day time scale and is referred to as the tropical intraseasonal oscillation (TISO). Over the near-equatorial regions, 30–60-day signals evident in large-scale circulation strongly coupled with fluctuations in convection originate in the Indian Ocean and move eastward across the Maritime Continent into the western Pacific. This recurrent, broadband phenomenon is known as the Madden–Julian oscillation (MJO: Madden and Julian 1971, 1972, 1994) and accounts for a large portion of the total variability in both large-scale circulation and convec-
Corresponding author address: Dr. K. Rajendran, Meteorological Research Institute/JMA, 1-1 Nagamine, Tsukuba, Ibaraki 305 0051, Japan. E-mail:
[email protected]
© 2006 American Meteorological Society
tion over the tropical Eastern Hemisphere. Although there is no obvious change in the average period with seasons (Anderson et al. 1984), the seasonal dependence in amplitude is significant with the MJO being more prevalent in boreal winter than other seasons (Hartmann and Gross 1988). During boreal summer, in addition to the eastward propagation, pronounced northward propagations of organized convection and associated large-scale circulation are evident over the Asian monsoon region (Yasunari 1979, 1981; Sikka and Gadgil 1980; Krishnamurti and Subrahmanyam 1982; Murakami and Nakazawa 1985; Srinivasan and Smith 1996). For example, over the Indian region, these propagations of organized convection begins in the equatorial Indian Ocean and extends up to the foothills of the Himalayas at an average speed of 1° day⫺1 (Murakami 1976; Gadgil and Srinivasan 1990). These 30–60-day propagations appear every year and are closely associated with the active
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and break phases of the South Asian monsoon (Lau and Chan 1986). The strength and duration of active/ break phases and associated northward propagations significantly account for the seasonal anomaly during a particular year (Gadgil 2003). Several studies suggest that the two modes of TISOs, namely, the MJO and the northward propagating intraseasonal oscillation (NPISO), are related and the NPISO over the Asian monsoon region is an internal component of MJO (e.g., Krishnamurti et al. 1985; Lau and Chan 1986; Madden 1986; Chen et al. 1988; Wang and Xie 1997; Maloney and Hartmann 1998; Lawrence and Webster 2002). However, the difference in the properties of the underlying surface and the difference in land–sea distribution result in slightly different characteristics in the two modes. For example, eastward propagations occur over the equatorial oceans while the northward propagating mode propagates from the equatorial ocean to land. While the former occurs throughout the year, the latter occurs only in boreal summer. Consequently, the mechanisms for the two modes were also proposed based on different processes. For the eastward propagation, most proposed mechanisms were based on equatorial wave dynamics (e.g., Lau and Peng 1987; Hendon 1988; Wang and Rui 1990; Salby et al. 1994; Hendon and Salby 1994; Jones and Weare 1996; Maloney and Hartmann 1998), with some of them addressing the importance of ocean– atmosphere interaction (e.g., Flatau et al. 1997; Woolnough et al. 2000). In contrast, the atmosphere–land interaction was proposed as a mechanism to explain NPISO (e.g., Webster 1983; Goswami and Shukla 1984; Gadgil and Srinivasan 1990; Wang and Rui 1990; Nanjundiah et al. 1992; Srinivasan et al. 1993; Wang and Xie 1997; Kemball-Cook et al. 2002; Jiang et al. 2004). Despite these differences, most of the proposed mechanisms for both of the modes emphasize the importance of the energy exchange between the atmosphere and the underlying surfaces through boundary layer processes. Intraseasonal variability is also observed in SST, latent heat flux, sensible heat flux, and radiation at the ocean surface over the Tropics (Krishnamurti et al. 1988; Zhang and McPhaden 1995; Zhang 1996; Hendon and Glick 1997; Sengupta et al. 2001). Further, the SST fluctuation on TISO time scales is found to be largely explained by variation in surface fluxes (e.g., Zhang 1996; Hendon and Glick 1997; Lau and Sui 1997; Sperber et al. 1997; Shinoda et al. 1998; Jones et al. 1998). This implies that active ocean–atmosphere interaction is embedded in TISOs and indicates the presence of a dominant feedback mechanism in which the ocean plays an important role in defining the TISO character-
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istics. Consistently, the atmosphere-alone components of several GCMs were found to have significant shortcomings in representing the MJO (Slingo et al. 1996; Waliser et al. 2003) and NPISO (e.g., Kang et al. 2002; Rajendran et al. 2002). Motivated by these findings, coupled modeling studies using greatly simplified ocean models and moderate ocean models to ocean GCMs, addressing the significance of SST and ocean– atmosphere coupling on TISOs, have been initiated. These studies revealed that TISOs involve a coherent interaction among SST, convection, and surface fluxes. Hence, coupling of an atmospheric GCM to some form of ocean model in which SSTs can vary in response to changes in surface fluxes by intraseasonal variation of tropical convection was found to improve the model’s ability to reproduce many observed aspects of TISOs. However, due to the complex interaction among largescale dynamics, convection, and air–sea interaction (Inness et al. 2003; Rajendran et al. 2004b) the simulation of TISO appears to be a challenging task, even for coupled GCMs. Among theoretical and modeling studies addressing the importance of SST and/or coupling for MJO, Flatau et al. (1997) investigated the impact of SST on MJO simulation using an aquaplanet version of an AGCM with a simple empirical representation of an ocean mixed layer. Their coupled model produced a stronger and slower MJO, suggesting the importance of coupled feedback. This study proposed the air–sea convective intraseasonal interaction (ASCII), which is basically the wave–conditional instability of the second kind (CISK) modified by ocean surface heat fluxes as the mechanism for the MJO. Wang and Xie (1997), using a simplified linear coupled model, found that the coupling produces SST anomalies that lead the convective anomalies and act to destabilize the atmospheric moist Kelvin wave and reduce its phase speed to make it closer to observation. Waliser et al. (1999) showed that MJO simulation is improved in an AGCM coupled to a slab ocean model compared to the simulation with the same model that used prescribed SST. The improvements in simulated MJO are attributed to the feedbacks with SST. However, the model systematic error in the mean state is found to affect the performance of a coupled GCM (CGCM) in representing many aspects of the MJO (Hendon 2000; Inness et al. 2003), which may have been influenced by basic-state biases in the individual components of the coupled model. For example, Hendon (2000) investigated the impact of air– sea coupling associated with the MJO in an AGCM coupled to an ocean mixed layer model and found little impact of the coupling on the simulated MJO. The lack of significant impact of the coupling was primarily due
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to the deficiencies in latent heat flux, which did not show proper phase relationship with convection owing to the biases in the simulated basic state. Further, the reduction of systematic error of the mean state has been shown to improve the simulation of MJO in a coupled GCM (Inness et al. 2003). Fu et al. (2003) studied the impact of air–sea coupling on NPISO using simulations from a hybrid coupled model in the tropical domain of the Indian and Pacific Oceans and its atmosphere-alone version. In their model, the best representative northward propagating event evolved as a coupled mode and was found to be more realistic compared to its AGCM version. Later, Fu and Wang (2004) showed improvements in the three-dimensional structure of the simulated NPISO in the hybrid coupled model due to coupling. Rajendran et al. (2004a) investigated the role of coupling on the structure and propagation of NPISO using the second Meteorological Research Institute (MRI) coupled GCM (CGCM2) and its atmosphere-alone version. Their analysis based on composite events demonstrated the significant role of ocean–atmosphere coupling in modulating the strength, horizontal structure, and propagation characteristics of NPISO. Using the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM) 2.0 and CCSM2, Sperber (2004) showed the impact of ocean– atmosphere coupling on improving the simulation of MJO and suggested that rectifying the systematic model error is important for understanding the underlying ability of the model in representing MJO. Zheng et al. (2004) studied the role of coupled SSTs on the simulation of boreal winter and summer TISOs using the Geophysical Fluid Dynamics Laboratory (GFDL) coupled GCM and demonstrated that the realistic phase relationship between SST and precipitation improved the simulation of TISOs in the coupled model. These studies have suggested the need of coupled models for TISO studies and the importance of their skill in representing the basic state realistically. Under this scenario of the model-dependent nature of the previous results and the varying nature of the air–sea coupling used, the present study extends our previous work to examine the role of coupling on the simulation of the three-dimensional structure and propagation characteristics of prominent TISOs, namely, the MJO and NPISO, using the MRI CGCM2. This is carried out under a new analysis framework where the impact of coupling on the horizontal and vertical structure and propagation characteristics of both summer and winter modes of TISOs are investigated and the dominant mechanism for their propagations are studied using recently available observations [e.g., Reynolds SST,
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Global Precipitation Climatology Project (GPCP) precipitation, and the 40-yr ECMWF Re-Analysis (ERA40)]. The ability of the MRI CGCM in simulating the MJO is yet to be investigated, and in this study, in addition to this aspect, we examine the role of coupling on TISOs through comparisons with observations and with the atmosphere-alone component of the same model forced with monthly SSTs derived from the CGCM. This implies that any difference in TISO simulation by the models can be attributed to the difference in the coupling process. The additional importance of this analysis is to document the model’s skill for applications such as the Intergovernmental Panel on Climate Change (IPCC) in which characteristics of the simulation of the present-day climate and its variability provide a context for interpreting the simulations under climate change scenarios. We briefly discuss the details of the model, experiments, and datasets in the next section. The simulation of mean background state and climatological characteristics are presented in section 3. Three-dimensional structure and propagation characteristics of MJO and NPISO are discussed in section 4. In section 5, we summarize the results and discuss their implications.
2. Model, experiments, and datasets We used a version (MRI CGCM2) of the global ocean–atmosphere coupled GCM developed at MRI (Yukimoto et al. 2001) for the present study. The ocean model is a Bryan–Cox-type general circulation model (OGCM) with a global domain. The horizontal grid spacing is 2.0° latitude and 2.5° longitude. Between 4°S and 4°N the meridional grid spacing is set as 0.5° in order to better resolve equatorial oceanic waves. The ocean model has 23 vertical levels with the bottom at 5000 m. The uppermost layer has a thickness of 5.2 m. Parameterized subgrid mixing processes using viscosities and diffusivities are specified as follows: the horizontal viscosity coefficient is 1.6 ⫻ 105 m2 s⫺1 and vertical viscosity coefficient is 1 ⫻ 10⫺4 m2 s⫺1. A Gent and McWilliams (1990) eddy mixing parameterization is used with isopycnal mixing coefficient 2 ⫻ 103 m2 s⫺1 and diapycnal mixing coefficient 1 ⫻ 10⫺5 m2 s⫺1. To simulate the surface mixed layer, vertical turbulence viscosity and diffusivity following Mellor and Yamada (1982) and Mellor and Durbin (1975) are modeled in addition to the isopycnal mixing. A convective adjustment through mixing of unstable columns is applied when vertical stratification becomes unstable. Solar radiation penetrates seawater with absorptivity of 10-mdepth e-folding decay, which heats several tens of meters of the surface seawater.
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The atmospheric component of the model (AGCM) has been developed based on a version of the operational weather forecasting model of the Japan Meteorological Agency (JMA) MRI/JMA98 at a horizontal resolution of T42. The vertical specification consists of a 30-layer pressure hybrid coordinate with the top at 0.4 hPa. Some of the physical process schemes are replaced with those of the original JMA version (Shibata et al. 1999). An Arakawa–Schubert scheme with a prognostic mass flux formulation similar to that of Randall and Pan (1993) is used for cumulus parameterization. The atmosphere and the ocean interact with each other by exchanging fluxes of heat, freshwater, and momentum at the sea surface. The fluxes are exchanged every 24 hours in the model. The detailed coupling scheme of the model can be found in Yukimoto et al. (2001).
a. The model integrations The CGCM is integrated for 350 years with flux adjustment under constant CO2 level. To keep the model climatology of the CGCM close to observations, the flux adjustment for heat and freshwater is applied globally, and that for wind stress is applied in the equatorial region between 12°S and 12°N. These flux adjustment values are obtained during a long spinup, and are used throughout the simulation. In this study, the 30-yr simulation from model year 181 to year 210 is analyzed. The model output were stored at 6-h intervals where fluxes are 6-h averages and variables such as wind and temperature are snapshots. The AGCM is forced with sea ice distribution and daily SSTs interpolated from monthly CGCM SSTs following Taylor’s correction method (Taylor et al. 2000) to match the monthly means of daily interpolated SSTs with monthly mean CGCM SSTs. The CGCM SSTs are used, on one hand, to study the impact of using SSTs from a long coupled run as a surrogate for observed SST under an Atmospheric Model Intercomparison Project (AMIP)-type condition and, on the other, to minimize errors associated with AGCM response to high frequency signals in daily SSTs. Thus, while the air–sea fluxes are determined through consistent coupled air–sea interaction in the CGCM, the imposed SST in the AGCM does not reflect in situ air–sea fluxes, which play a significant role in driving the atmospheric motion. The 30-yr CGCM simulation from model year 181 to year 210 is used as boundary conditions for integrating the AGCM.
b. Validation datasets Daily precipitation interpolated from the GPCP (Xie et al. 2003) pentad dataset available on a 2.5° ⫻ 2.5°grid
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for the period 1979–2002 is used for validating simulated daily mean precipitation (estimated from 6-hourly outputs). For space–time spectral analysis, a GPCP daily precipitation dataset (Huffman et al. 2001) on a 1.0° ⫻ 1.0° grid for the period 1997 to 2003 is also used. Daily mean circulation fields for the period 1979–2002 are estimated from the 6-hourly ERA-40 reanalysis from 1958 to 2002 (Simmons and Gibson 2000). The primary reference and a full report documenting the dataset for the reanalysis can be found online (see information at http://www.ecmwf.int/research/era/). Daily SSTs interpolated from Reynold’s weekly SSTs for the period 1982–2002 (Reynolds et al. 2002) are used for validating model simulated SSTs (daily means are computed from 6-hourly outputs). To separate the TISO variability from other intraseasonal modes, a 241-point 20–90-day Lanczos bandpass filter (Duchon 1979) is applied to the datasets. Analyses of the two TISO modes are conducted for two different seasons owing to the pronounced seasonality in their amplitude, with an extended boreal winter from November to April (hereafter referred to as “winter”) for MJO and extended boreal summer from May to October (hereafter referred to as “summer”) for NPISO.
3. Mean background state a. Seasonal variation The representation of mean characteristics over the Asia–Pacific region is important as this forms the necessary background state on which the intraseasonal variation appears. The mean seasonal variation of the region is characterized by a reversal in lower-tropospheric winds and a marked change in rainfall. The simulated mean precipitation overlaid with 850-hPa winds and SST during winter and summer are compared with corresponding GPCP precipitation, ERA-40 reanalysis winds, and Reynolds SST in Fig. 1. Since the AGCM yields a climatological seasonal mean simulation close to that of the CGCM, we restrict the discussion of this aspect to the CGCM. Although the model shows a tendency for overestimating the maximum rainfall, the large-scale distribution and location of rainfall maxima and the strength of associated low-level circulation are reasonably well represented in the model during both seasons. In winter, the zonally oriented rainfall maximum along the near-equatorial region and the rainfall associated with the Australian–Indonesian monsoon and the South Pacific convergence zone (SPCZ) are well simulated by the model. The simulated zonal winds at lower levels in the CGCM are also close to observations with westerlies reaching 160°E in most years (not shown).
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FIG. 1. Northern Hemispheric winter and summer climatological mean (a), (b) precipitation with 850-hPa winds and (c), (d) SST from the observations and MRI CGCM simulation.
In summer, the zonally oriented rain belt over the Tropics extending from the Indian region to the West Pacific and associated circulation features are well represented in the model. The prominent components of the Asian summer monsoon, such as the welldeveloped cross-equatorial flow from the Southern Hemisphere across the East African coast and major convective centers over the Indian subcontinent and
equatorial Indian Ocean, are captured by the model. The distribution of rainfall over the Indian region comprising rainfall maxima over the monsoon trough zone, local orography, and warm equatorial Indian Ocean and relative low rainfall over the southeastern peninsular India are also well simulated. Mean Asian summer monsoon rainfall simulation by the CGCM is analyzed in detail by Rajendran et al. (2004b). The pro-
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FIG. 2. Northern Hemispheric (a) winter and (b) summer percentage of total variance of rainfall explained for a 20–90-day time scale.
nounced seasonal character from winter to summer over the Eastern Hemisphere occurs in response to the forcing due to the seasonal excursion of the solar insolation, consequent warm SST regions, and the ITCZ, aided by the existence of the regional land–sea thermal contrast during the respective summer months. It is seen that the coupled model is able to capture this aspect well with the simulation of this coherent seasonal variation of the large-scale circulation and its coupling with the lower boundary SST forcing. In spite of the flux adjustment, the coupled model retains some persistent differences in simulated SST with small positive bias over the equatorial region of the Eastern Hemisphere in winter and small cold bias around India in summer, which was found to be associated with overestimated precipitation in the CGCM (Rajendran et al. 2004b). The difference in the coupling process has a weak impact on the long-term mean model climate; therefore, the changes in the characteristics of simu-
lated TISOs are not likely due to the changes in the background climate.
b. Mean TISO variability The observed and simulated percentage of total variance explained by the 20–90-day time scale during winter and summer are shown in Fig. 2. For models, the fraction of total variance explained by the intraseasonal time scale is weaker than that observed, but the locations of maxima and the distribution are comparable to observations. However, in models, especially during winter, the region of maximum intraseasonal variance is more zonally oriented with a slight southward shift compared to observations. The distribution of total variance, where large variance is collocated with large seasonal-mean rainfall (not shown), also shows a similar southward shift in their maxima compared to observations over the equatorial region. Consequently, there is a reduction in intraseasonal variance over the equa-
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tor compared to that over off-equatorial regions. In spite of this bias, it was found that the reduction in equatorial TISO variability does not lead to any changes in our major conclusions. Further, the model shows a bimodal spatial structure over the Indian and Pacific Oceans with more than 25% of the total variance over the major convective zones explained by the TISO periodicities. The TISO variance in the CGCM compared to the AGCM is slightly higher over the seasonal convective centers such as equatorial oceans and maritime continents in winter and over the equatorial Indian Ocean, the head of the Bay of Bengal, and west Pacific in summer. The similarity in the gross features of TISO variability in the models can be associated with a small difference in the simulated basic mean state. The relationship between the strength of seasonal and intraseasonal variability is further examined. Figure 3 shows the variation of mean amplitude of the seasonal cycle with intraseasonal variation in observed and simulated precipitation during winter and summer. The amplitudes are estimated based on harmonic analysis. Harmonic analysis was applied to each year separately and the sum of the amplitudes of the first four harmonics constitutes a seasonal cycle, the sum from 5 to 18 constitutes the intraseasonal cycle, and the rest are considered as transient eddies. The scatter diagram for winter shows the relationship between mean amplitudes averaged over November–April of all years at each grid point in the domain 5°S–5°N, 70°–140°E and that for the summer shows the relationship between the mean amplitudes averaged over May–September of all years at each grid point over the Indian region (12.5°–25°N, 70°–90°E). Observations and simulations show a strong relationship with high correlation between the seasonal and intraseasonal amplitudes for both seasons, which are characterized by the dominance of the two components of TISOs. The simulations are close to observations and show only a slight difference between them in capturing this relationship. The tendency for pronounced overestimation of both seasonal and intraseasonal variability by the models during summer depends largely on the selected domain where the simulated variance on both time scales is much larger than for observations (e.g., Fig. 2b). The near-linear variation with high positive correlation suggests that strong
→ FIG. 3. Scatter diagram of mean amplitude of seasonal cycle in the precipitation vs the mean amplitude of intraseasonal variation for grid points over representative domains during (a) winter and (b) summer.
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FIG. 4. Wavenumber–frequency spectrum of observed GPCP rainfall for (a) symmetric and (b) antisymmetric components around the equator divided by the background power. The power has been averaged over latitudes 15°S–15°N. Ratios greater than the 95% significance level are shaded. Superimposed thin lines are the dispersion curves of equatorial Kelvin, n ⫽ 1 Rossby, mixed Rossby– gravity wave, and n ⫽ 1 [in (b)], 2 [in (a)] inertio–gravity waves for the equivalent depths of h ⫽ 8, 25, and 90 m. Dashed lines represent the Doppler-shifted dispersion curve with a 3 m s⫺1 easterly basic state. MJO periodicities of 30–60 days are highlighted with horizontal dashed lines.
(weak) intraseasonal variability on TISO time scales is mostly associated with strong (weak) seasonal variability. Hence, the ability to simulate the two aspects is related and a reasonable representation of the seasonal mean state is a prerequisite for a realistic simulation of intraseasonal variability.
c. Convectively coupled equatorial waves Major MJO shortcomings in the majority of GCMs are found to be their inability to represent the dominance of the 30–60-day time scale and to partition this variability relative to higher frequency variations and between eastward and westward propagating modes. Recently there has been an increased recognition of the significance of other modes of intraseasonal variation and the observed coupling of the pure shallow-waterlike equatorial waves to convection in producing prominent fluctuations in intraseasonal time scales over the equatorial Tropics (e.g., Takayabu 1994; Numaguti 1995; Wheeler and Kiladis 1999). It is also suggested that a realistic representation of convectively coupled equatorial waves is essential for the success of predictions of variabilities on all time scales.
We analyzed the spectral characteristics of the MJO based on the mean wavenumber–frequency spectra. The space–time spectra are computed following Wheeler and Kiladis (1999). Spectra were calculated for segments of 96 days using the anomaly data after removing the seasonal cycle. The wavenumber– frequency spectra are then averaged over all 96 segments between 15°S and 15°N. Background power spectra are constructed by smoothing the computed spectra many times with a 1–2–1 filter in both frequency and wavenumber. Figures 4 and 5 show the symmetric and antisymmetric power spectra (defined with respect to the equator as in Wheeler and Kiladis 1999) of observed and simulated precipitation in which the background red spectrum has been removed to emphasize the dominant spatial and time scales of tropical convection. Theoretical dispersion curves for equatorial wave modes (Matsuno 1966) are drawn for equivalent depths of 8, 25, and 90 m. The Doppler-shifted dispersion curves for the corresponding waves with a 3 m s⫺1 easterly basic state are also drawn. The observed spectra (Fig. 4) indicate that the equatorial convection is organized on preferred space and time scales that coincide
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FIG. 5. Same as in Fig. 4 but for (a), (b) CGCM and (c), (d) AGCM simulations.
with various theoretical equatorial wave modes such as the equatorial Rossby (ER), Kelvin, and mixed Rossby–gravity (MRG) waves. It shows that defined modes other than modes of large-scale tropical intraseasonal variability also exist. As previous studies suggested (Wheeler and Kiladis 1999), many of these shallow-
water equatorially trapped wave modes occur as convectively coupled signals. However, it is only the convectively coupled Kelvin wave that is expected to have a direct influence in the low-frequency intraseasonal band, with the others having frequencies higher than the defined intraseasonal limit.
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Both models show skill in representing the organization of variance and its broad correspondence to equatorial waves (Figs. 5a and 5b). But, on the lowfrequency intraseasonal time scale of 20–90 days, the large-scale eastward propagating component of tropical variability (e.g., eastward planetary wavenumbers 1–5) is better represented in the CGCM. In the 30–60-day range, observations show a broad spectral peak around wavenumbers 1–2 associated with the MJO and a sharp reduction in power at shorter and longer periods (Fig. 4). In the CGCM, although the 30–60-day eastward spectral peak is broader, spreading to longer periods, the strength and distribution of spectral variance associated with MJO is closer to observations. In the AGCM, this MJO variability is much weaker than the dominant peak at longer periods and is distributed broadly to higher wavenumbers. The representation of the westward component on the 30–60-day time scale in the CGCM is also closer to observations, although in the observation it is weaker than the eastward component with peaks confined between wavenumbers 3 and 4. In the AGCM this westward component is associated with a broader peak stretching from wavenumbers 2 to 6 with amplitude equal to that of the eastward component. However, compared to observations, both simulations show a steeper distribution of eastward symmetric spectral power at periods less than ⬃5 days, broader antisymmetric variance at periods longer than 20 days, and lack significant variance in 10–20-day periods.
4. Tropical intraseasonal oscillations a. The Madden–Julian oscillation MJO characteristics are analyzed using composite eastward propagation characteristics, wavenumber– frequency spectra, and the vertical structure. The simulation of spectral characteristics has been assessed by applying space–time spectral analysis (Hayashi 1982) on filtered precipitation. Since the MJO shows pronounced seasonality, the space–time spectra of filtered precipitation at each grid between 10°S and 10°N are estimated for the extended winter season of each individual year. The spectra are averaged over the entire grid boxes and the mean for all 29 years is then computed. Figure 6 shows the spectra of the precipitation anomaly from the observations and simulations. In the CGCM, as seen in observations, the broad spectral maximum extends from wavenumbers 1 to 3 with a peak at wavenumber 2 within an eastward time scale of about 30–60 days. Whereas in the AGCM, on MJO time scales, the maximum eastward power is at wavenumber 1 with the maximum power for higher wavenumbers located at longer periods. At wavenumber 2
FIG. 6. Mean wavenumber–frequency power spectra of filtered time–longitude precipitation data over the equatorial region during winter from the (top) observations, (middle) CGCM, and (bottom) AGCM. Spectra are computed for each latitude between 10°S and 10°N for the extended winter season of each individual year after the ends of the series are tapered to zero. Spectra are averaged over all the latitudes and then the ensemble average for all 29 years is computed.
where the observed precipitation spectrum is characterized with maximum eastward power, the amplitude is relatively less and occurs at longer periods. Also, in the CGCM there is relatively negligible power at westward intraseasonal frequencies and there is a marked increase in the eastward maximum power compared to
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JOURNAL OF CLIMATE TABLE 1. Number of MJO events for observation and simulations at different reference longitudes.
GPCP CGCM AGCM
60°E
90°E
130°E
32 26 19
38 28 20
24 28 15
the AGCM. In contrast there is comparable power for the eastward and westward components on the MJO time scale, especially for wavenumbers 1, 3, and 4. Thus, the improved spectral characteristics in the CGCM simulation imply the influence of a realistic coupling process in producing MJO variability with amplitude and time scales that are close to observations.
1) COMPOSITE
CHARACTERISTICS
The composite MJO events from observations and simulations are identified from filtered precipitation av-
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eraged over the equatorial region between 10°S and 10°N. At each longitude, the precipitation anomaly above one standard deviation is selected. For longer MJO events, the precipitation anomaly 45° to the west (east) of the reference longitude must be positive for the period between 10 and 20 days earlier (later), propagating eastward continuously between the starting and ending points. For shorter MJO events, the filtered anomaly 22.5° to the west (east) of the reference longitude must be positive for the period between 4 and 14 days earlier (later), propagating continuously eastward between the starting and ending points. The number of events selected for observation and simulation at three reference longitudes, namely, 60°, 90°, and 130°E, are given in Table 1. Figure 7 shows the Hovmöller diagrams of composite precipitation anomalies at three reference longitudes from the observations and CGCM and AGCM simulations. Although both simulations capture the essential features of the MJO and the gross propagation charac-
FIG. 7. Time–longitude composites of observed and simulated precipitation (shaded) and SST (contours) anomaly from the (a) observations, (b) CGCM, and (c) AGCM based on eastward propagating events with respect to 60°, 90°, and 130°E. The zero lag and the reference longitudes at zero lag are represented by black solid lines.
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teristics, the CGCM simulation is closer to observations. For instance, the number of coherent events is larger for the CGCM at all longitudes. Also, the propagations are more coherent, maintaining the same speed throughout the event and extending farther eastward in the CGCM. As seen in the observations, coherent and continuous eastward propagation beginning from the Indian Ocean and extending to the central Pacific exists at all reference points in the CGCM simulation, whereas in the AGCM, propagation tends to be weaker and less coherent, especially when slightly away from the reference longitudes (e.g., 130°E). In addition, in the CGCM, strong and coherent eastward propagation is evident, even at central Pacific longitudes of 150°E, whereas the number of propagating events is much fewer in the AGCM (not shown). During El Niño years, owing to the eastward extension of the warm pool and westerly winds into the central Pacific, the region over which the relationship between convection, SST, and fluxes holds also extends farther beyond the date line. However, In the CGCM, the El Niño occurrence was found to be predominantly on a biennial time scale (Rajendran et al. 2004b), which might have resulted in a larger number of eastward propagating events at central Pacific longitudes in the CGCM compared to observations. The corresponding composite SST anomalies at 60°, 90°, and 130°E are shown as contour lines in Fig. 7. At all longitudes, as seen in the observations, the CGCM SSTs show a pronounced eastward propagation, whereas the SST anomalies in the AGCM do not show any coherent propagation. In the observations and the CGCM, the intraseasonal SST anomalies propagate eastward with a near-quadrature phase relationship between precipitation maximum and maximum SST anomalies where coherent eastward propagating warm (cold) SST anomalies lead (lag) enhanced precipitation. This supports previous results and suggests a feedback role for SST in the development of the MJO in both observations and coupled models. However, in the CGCM, the phase relationship appears to occur with slightly closer lags compared to observation.
2) COUPLED
FEEDBACK PROCESSES
To bring out the coherent temporal relationships among convection, SST, and surface fluxes, composite time-lagged correlation of the equatorially averaged precipitation anomalies against anomalies of SST and surface fluxes are computed. Figure 8 shows the Hovmöller diagram of lag correlations of precipitation (PR) with SST, net heat flux at the surface (QNet), latent heat flux (LH), surface wind stress (), and lag correlation
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between LH and surface zonal wind stress (x) from observations and simulations. The correlations above the respective 95% significant levels estimated based on a local Student’s t test are shaded. Positive surface fluxes except for LH correspond to fluxes into the surface; however the positive LH anomaly corresponds to enhanced evaporation from the sea surface. Figure 8a shows that positive SST anomalies in the observations and CGCM lead enhanced PR (by ⬃8 days in CGCM and ⬃11 days in the observations) and negative SSTs follow enhanced PR by several days across the Indian Ocean, Maritime Continent, and west Pacific. These positive SST anomalies tend to occur along the leading edge slightly east of the main rising branch of the MJO in the Indian and west Pacific Oceans (Fig. 7). The lag correlations decrease eastward of 180° in both the observations and models. In the AGCM, the lag correlations are weaker compared to the CGCM, and PR anomalies are almost in phase with SST anomalies for which enhanced PR is associated with warmer SSTs (Kitoh and Arakawa 1999). Figure 8b shows that at zero lag, enhanced (reduced) PR is correlated with reduced (enhanced) QNet reaching the ocean surface. In both simulations, enhanced PR is associated with a reduction in QNet, mainly due to increased cloudiness, with strong correlation extending into the Pacific. Unlike in the AGCM, the enhanced (reduced) QNet in the CGCM leads warm (cold) SST anomalies by about 10 days (Figs. 8a and 8b). This near-quadrature phase relationship between SST and QNet anomalies in the CGCM and observations suggests that the intraseasonal SST variability over warm oceans occurs as a thermodynamic response to variations in surface flux. Whereas in the AGCM, the prescribed SST prohibits coherent interaction among fluxes, convection, and SST. Figure 8c shows that in observations, from the Indian Ocean to the date line, enhanced LH into the ocean surface (i.e., reduced evaporation) leads enhanced PR by about 12 days and reduced LH lags PR by about 4 days. In the observations and CGCM, enhanced PR is found to be associated with reduced LH. Also, up to the central Pacific enhanced PR is preceded by easterly wind anomalies and is followed by westerly wind anomalies (Fig. 8d). In the region where climatological winds are westerly at the surface, these wind anomalies correspond to reduced wind speed and wind-induced evaporation, resulting in surface warming prior to convection, and enhanced wind speed and wind-induced evaporation, resulting in surface cooling following the PR maximum. Thus, over the mean westerly wind region (up to 180°), high easterly wind stress anomalies
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FIG. 8. Hovmöller diagram of lag correlations of PR with (a) SST, (b) QNet, (c) LH, (d) and that between (e) LH and x, from the observations and CGCM and AGCM simulations. Positive lags correspond to PR anomalies preceding anomalies in the surface fields. The sign convention is such that negative correlations correspond to enhanced PR being correlated to negative SST anomalies, reduced QNet, enhanced LH (reduced evaporation), and easterly wind anomalies. Dark (light) shadings highlight positive (negative) correlations above respective 95% significant levels.
(weakening winds) and reduced evaporation lead to positive SST anomalies. Following these warm SST anomalies there is enhanced PR and reduced QNet. Westerly wind stress anomalies (stronger winds) and increased evaporation occur immediately with enhanced PR in the CGCM. In Fig. 8e, between 60°E and the date line, strong correlation at lag zero indicates that westerly x anomalies lead to enhanced evaporation (reduced LH into the ocean surface), while easterly x anomalies reduce evaporation. The lag correlations of PR with LH (Fig. 8c) along with that of LH
with x (Fig. 8e) show that to the east of the date line where the mean surface winds become easterly, the phase relationship between PR and LH breaks down. These results imply that in both the Indian and west Pacific Ocean basins the LH tends to lead the SST anomalies with reduced (enhanced) evaporation leading warm (cold) SST anomalies. Thus, the cooling of SST after the maximum PR, on one hand, is due to the reduced shortwave flux at the surface (SWNet) and hence reduced QNet associated with enhanced convection (Fig. 8b). On the other hand, westerly wind anoma-
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FIG. 9. Longitude–height profiles of composite divergence anomalies with respect to precipitation over 90° for lags of ⫺5, 0, and 5 days from ERA-40 reanalysis and CGCM and AGCM simulations. Convergence anomalies are shaded and the reference longitude is represented by the vertical dash line. Thick solid curves represent normalized moisture convergence anomalies integrated over 1000–850 hPa. Dashed (thin solid) curves represent corresponding composite SST (precipitation) anomalies.
lies where the mean wind is westerly is also associated with enhanced evaporation and thus contributes to the cooling of SST. The reverse cycle occurs during the SST warming phase. This suggests that the eastward propagation of the MJO was facilitated through successive eastward development of a strong and coherent coupled feedback process. Whereas over the east Pacific Ocean, there is no evident phase relationship between anomalies of SST and PR or between PR and or LH anomalies on this time scale (Figs. 8c and 8d). The realistic phase relationship between SST and convection anomalies in the CGCM is due to a realistic coupling process in which the convection modifies the SST through surface fluxes and the modified SST feeds back to further modulate convection through resultant changes in surface boundary anomalies (Rajendran et al. 2004a). As a result, the simulated MJO in CGCM evolves as a strongly coupled mode and has more realistic structure and propagation characteristics compared to the AGCM simulation. In the AGCM, in the absence of a realistic coupling process, there are either warm SST anomalies collocated or leading enhanced
PR by a day at all longitudes. Hence, the simulated MJO does not evolve as a coupled mode and lacks many of the observed features, which suggests that, although coupling is not essential for the existence of MJO, it plays an important role in sustaining and modulating the MJO.
3) VERTICAL
STRUCTURE
Vertical structure of the MJO is analyzed using longitude–height cross sections of composite divergence anomalies at different lags. Figure 9 shows the composite vertical structure with respect to precipitation over 90°E for lag(⫺5) to lag(⫹5). The models are successful in reproducing the baroclinic vertical structure associated with the MJO. For example, when the MJO is in the suppressed convection phase over 90°E, before lag(⫺5), anomalous divergence at 200 hPa trails the region of suppressed convection. Similarly at lag(⫹5), convergence at 200 hPa trails the region of enhanced convection. At the surface, circulations for the opposite sign exist, but with much weaker magnitude than those near the tropopause. The variation of composite pre-
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cipitation (SST) anomalies normalized with respect to the maximum is shown as a thin (dash) line in Fig. 9. In the observations and CGCM simulation, positive intraseasonal SST anomalies develop successively east of the center of convection. Near-surface convergence appearing ahead of the SST maxima strengthens and extends deeper into the lower atmosphere as SST rises before reaching a maximum ahead of the onset of convection. This implies that the presence of warmer SSTs help to set thermodynamic instability over a region of near-surface convergence. The CGCM simulation is associated with a realistic SST variation and phase relationship among SST, convection, and atmospheric dynamics with slightly closer lags among them; whereas, in the AGCM the SST variation does not show distinct minima and maxima or a coherent cycle of variation and phase relationship with precipitation. In the observations, ahead of the onset of deep convection, strong near-surface convergence appears over distant eastern longitudes. This surface convergence gradually extends up to 400 hPa around the center of deep convection at the time of maximum precipitation. Consequently, the depth of low-level convergence ahead of deep convection continuously increases westward, resulting in a pronounced westward tilt with height. This implies the dominance of a frictional convergence mechanism and the important role of lowlevel convergence in leading to eastward propagation of the MJO. However, in models, although near-surface convergence develops several days ahead and east of deep convection [e.g., lag(⫺5) and lag(0) precipitation over 90°E], it is weaker, appears over much nearer eastern longitudes, and extends to upper levels more rapidly than in observations. This preconditioning of the lower troposphere east of the center of convection is an important aspect, and the successive eastward development of low-level convergence can make the atmosphere more conducive for deep convection and subsequent eastward propagation of deep convection. Although not as coherent as in the observations, simulations reflect this aspect reasonably well. Thus, the results also support the current paradigm that the low-level convergence is the dominant mechanism for the eastward propagation of the MJO (e.g., Hendon and Salby 1994; Jones and Weare 1996). The appearance of low-level convergence increasingly near the surface is also consistent with the frictional wave–CISK mechanism proposed in previous studies (Wang and Rui 1990). Composite low-level moisture convergence anomalies integrated over 1000–850 hPa are represented by thick solid curves in Fig. 9. Both in observations and simulations, the occurrence of maximum moisture con-
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vergence coinciding with the onset of convection and its successive eastward placements are evident. In the reanalysis, over the Indian Ocean, to about lag (⫺15), anomalies of moisture divergence prevail in the lower troposphere associated with the dry phase of the MJO (not shown). As the dry phase weakens, moistening of the lower troposphere starts with the appearance of moisture convergence anomalies. Subsequently, the moisture buildup strengthens gradually, maximizes ahead of deep convection, and starts weakening after maximum precipitation as the MJO enters the subsequent dry phase. Both of the simulations capture this aspect reasonably well. Accordingly, successive eastward placement of near-surface convergence to the east of deep convection moistens the boundary layer and creates a potentially unstable condition, resulting in eastward propagation of MJO convection. The CGCM SSTs used for forcing the AGCM and the ability of the AGCM in simulating the phase relationship and the interaction between convection and atmospheric dynamics enables it to produce some coherent MJO events in spite of the absence of a realistic and self-evolving air–sea interaction. But, the coupling strengthens and improves many aspects of the simulated MJO in the CGCM. In the CGCM, the intraseasonal SST fluctuation first impacts the atmospheric dynamics through better boundary layer processes and then modulates the convection and thus evolves as a strong coupled ocean–atmosphere mode. These results indicate that the MJO is an intrinsic atmospheric mode that is significantly modulated and amplified by the interaction between the sea surface and lower boundary layer.
b. Monsoon low-frequency intraseasonal oscillation: NPISO 1) COMPOSITE
CHARACTERISTICS
Coherent northward propagations during the May– October period are identified based on the time– latitude variation of filtered precipitation. To construct a composite, all maxima in precipitation greater than one-half standard deviation at the reference latitude of 10°N are first selected. A threshold of one-half standard deviation is used because of slightly weaker amplitude of TISO variability in the simulations compared to observations. Each selected event is then examined within a 3°S–28°N domain to satisfy the following conditions: namely, the anomaly must be positive starting anywhere between 3°S and 1°N during 9–15 days earlier and ending anywhere between 22° and 28°N during 10– 16 days later with respect to the reference point and must propagate continuously northward between the
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starting and ending dates. This objective criteria takes into account the broad range of speed exhibited by lowfrequency intraseasonal oscillations and facilitates the selection of the best events within the speed range of 0.75°–1.25° day⫺1 across the domain considered. A composite event is then constructed using datasets 30 days prior to 30 days after each propagation reaches the reference latitude. The number of identified events is found to be more for observations compared to models as seen in the case of the MJO. For example, at 90°E, the number of coherent events is found to be 38, 29, and 19 for the observations, CGCM, and AGCM, respectively. The meridional propagation of composite anomalies of observed and simulated precipitation along 90°E is shown in Fig. 10. Enhanced convection begins at the equator about 10 days prior to the maximum around 10°N and extends to 25°N in about 10 days, maintaining a uniform propagation rate. Termination of propagation at the northern latitude coincides with the commencement of a fresh propagation of opposite sign over the equatorial region. The propagations are stronger with a larger number of events, faster and more coherent, with anomalies extending farther northward in the CGCM simulation compared to the AGCM. Thus, the CGCM produces realistic propagating signals with a phase speed and latitudinal extent closer to observations. Composite analysis for other Indian longitudes also yields similar characteristics associated with northward propagation. The existence of NPISO events in the AGCM, though they propagate slowly, demonstrates the importance of atmospheric dynamics in the northward advancement of convection anomalies (Wang and Xie 1997). However, the additional impact of realistic coupling appears to result in stronger, faster, and more coherent northward propagation in the CGCM. This suggests the importance of addressing the external influence of SST forcing in the proposed mechanisms for low-frequency intraseasonal oscillations. The corresponding composite anomalies of SST and 925-hPa horizontal divergence (D925) along 90°E from observations and the simulations are also shown in Fig. 10. In the observations, positive (negative) SST anomalies lead (lag) the main rising branch of the northward propagating mode. The quadrature phase relationship
→ FIG. 10. Composite filtered anomalies of rainfall (shaded), SST (black contours), and D925 ⫻ 106 (white contours at 0.4 interval, negative values corresponds to convergence) at 90°E. The number of events are 38, 29, and 19 for the observations and CGCM and AGCM, respectively.
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FIG. 11. Composite lag correlations of observed and simulated filtered precipitation with (a) SST, (b) QNet (positive: into the ocean), and (c) LH (positive: out of ocean) at 90°E. Negative lags in (a) indicate SST leads precipitation and (d) composite lag correlations of SST with . Dark (light) shadings highlight positive (negative) correlations above the respective 95% significant levels.
between SST and convection anomalies is captured by the CGCM. This relationship over the Bay of Bengal implies the use of these SST signals as a potential predictor for the monsoon active–break cycle (Vecchi and Harrison 2002). However, slowly propagating intraseasonal SST anomalies in the AGCM are weaker and lack any coherent phase relationship with convective activity. This is because the prescribed SST in AGCM acts only as a boundary forcing in which convection and surface fluxes do not play any interactive role in modulating SST. In the observations, convergence (divergence) leads the northward-moving positive (negative) convection anomalies by a few days. This suggests that the low-level divergence and absence of active convection leads to SST warming, which in turn leads to lowlevel convergence and subsequent deep convection. But, in the CGCM, this lead is much closer than in
observations, especially with only a couple of days over the off-equatorial latitudes, whereas in the AGCM the maximum convergence and maximum precipitation appears to be almost collocated. Lag correlations of PR anomalies with anomalies of SST, QNet, LH, and correlations of SST anomalies with anomalies of along 90°E from the observations and simulations are shown in Fig. 11. The correlations above the respective 95% significance levels estimated based on a local Student’s t test are shaded. In the observations and CGCM, correlations of PR with SST (Fig. 11a) show that north of the equator warmer SSTs lead enhanced convection by several days. These strong correlations indicate that the intraseasonal SST anomalies systematically feed back to the atmosphere. The surface energy balance over the oceans is largely determined by variations in SWNet and LH. Consistently, in
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FIG. 12. Latitude–height cross sections of composite divergence anomalies with respect to composite precipitation anomaly over 90°E for lags of ⫺5, 0, and 5 days from ERA-40 reanalysis and CGCM and AGCM simulations. Convergence anomalies are shaded and thick solid curve represents corresponding normalized moisture convergence anomalies integrated over 1000–850 hPa. Dashed (thin solid) curves represent corresponding composite SST (precipitation) anomalies normalized with respect to their maxima.
the observations and CGCM, correlations of PR with QNet and LH show that enhanced PR is associated with reduced QNet (Fig. 11b) and enhanced LH (Fig. 11c). But, for the AGCM, owing to the absence of an active ocean component and self-evolving SST, the lead–lag relationships are rather weak. For example, north of the equator PR and SST are almost in phase with a weak positive correlation. In addition, PR and QNet (LH) are negatively (positively) correlated. The lack of significant SST impact in the AGCM is further evident in the correlations of SST with (Fig. 11d). While in the observations and CGCM weaker (stronger) leads (lags) warmer SSTs, in the AGCM there exists only a weak positive correlation of enhanced winds lagging warmer SSTs. Furthermore, in the CGCM, as in the observations, the evolution of NPISO as a coupled mode is complete when the SST is also modulated by
fluctuations in convection, radiative, and evaporative fluxes. Such a strong coupled feedback following the ASCII mechanism was found to exist over the entire warm northern Indian Ocean during monsoons and over the warm equatorial Indian Ocean and west Pacific during winter (Rajendran et al. 2004b). Further, comparing Fig. 8 and Fig. 11, it can be seen that the CGCM is simulating coupled feedback processes better for the MJO than for NPISO.
2) VERTICAL
STRUCTURE
The latitude–height cross sections of composite divergence anomalies at different lags from the ERA-40 reanalysisand simulations are shown in Fig. 12. This depicts the development of atmospheric conditions during the evolution of NPISO relative to the precipitation maximum at the reference latitude of 10°N. The pre-
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conditioning of the lower troposphere ahead and north of the center of convection is found to be an important factor in both ERA-40 and simulations. The successive northward development of strong near-surface convergence, which then extends deeper into the lower troposphere, can induce thermodynamic instability in the presence of positive intraseasonal SST anomalies and facilitate northward propagation of enhanced convection. These signatures of enhanced low-level convergence leading deep convection occur increasingly closer to the surface [at lag(0)] in a given atmospheric column, resulting in a pronounced southward tilt with height. The corresponding composite moisture convergence anomalies integrated over 1000–850 hPa (Fig. 12) also show that strong low-level moisture convergence is located ahead and to the north of the deep convection that maximizes coinciding with precipitation. In the observation and CGCM simulation, positive intraseasonal SST anomalies develop north of the center of convection. In the presence of positive SST anomalies, the near-surface convergence strengthens and extends deeper into the lower atmosphere, inducing thermodynamic instability conducive for northward propagations. As in observations, the CGCM simulation is associated with a realistic SST variation and phase relationship among SST, convection, and atmospheric dynamics, but with closer lags among them. The gross features of the vertical structure associated with the MJO (Fig. 9) and NPISO appear to be largely similar in the reanalysis and in simulations. This indicates the possible similarity in the dominant mechanisms behind these two modes. However, there exist a couple of distinct differences. For example, northwardleading strong convergence extends deeper into the free atmospheric column and the strength of the upperlevel circulation gyres is almost equal to that in the corresponding opposite gyres in lower levels. In addition, the simulated vertical structure in the AGCM is comparable with that in the CGCM in spite of the absence of realistic intraseasonal SST variation. This implies a stronger (weaker) influence of atmospheric dynamics (air–sea coupling) for the off-equatorial summer NPISOs compared to the MJO. But, the additional impact of realistic coupling in CGCM does produce stronger NPISOs (with a larger number of coherent events) with slightly improved northward lead of lowlevel moisture convergence (Fig. 11).
3) IMPORTANCE
OF AIR–SEA INTERACTION
The temporal phase relationship between PR and different surface and dynamical fields during the composite NPISO event is summarized in Fig. 13. This is based on the extrema in the composite lag correlation
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of different fields with the PR maximum at the reference point over 15°N, 90°E for observations and simulations. The lags at which the correlation extrema greater than the 95% significance level occur and the lag range of significant correlation levels on either side of the significant extrema for each variable are highlighted. The events relative to the PR maximum at the reference point (indicated by PR⫹) are ordered from left to right of the abscissa where “⫹” indicate maxima and “⫺” indicate minima of the quantities. The positive variables are SST, moisture divergence at 925 hPa (M925), divergence at 200-hPa (D200) and 925-hPa (D925) levels, SWNet, net longwave flux (LWNet), QNet into the ocean, sensible heat flux (SH), LH fluxes out of the ocean, and . The simulated phase relationships are assessed against the corresponding relationships in the observation that is shown in the background. In the observation and CGCM, during SST warming phase, low-level wind and moisture divergence (D925⫹ and M925⫹) over the SST minimum (and SH⫺) is associated with enhanced SWNet, QNet, and LWNet due to reduced convection and cloudiness in a stable atmosphere (D200⫺). This in conjunction with reduced evaporation (LH⫺) owing to weaker surface winds (⫺) result in the SST maximum (SST⫹) that leads maximum PR by several days (12 days in observations, but with a closer lag of 8 days in the CGCM as in Fig. 13a). This is because, in the wake of the SST maximum, sensitive heat flux into the atmosphere (SH⫹) makes the lower atmosphere unstable, strengthens the lowlevel moisture and wind convergence (M925⫺ and D925⫺), and the onset of convection occurs. Analysis of the vertical structure of both TISOs reveals that the near-surface frictional convergence appears ahead of the SST maximum and extends deeper into the lower troposphere at this phase. Once convection sets in, convergence in the lower atmosphere is further increased by the convection itself, which in turn is strengthened by the enhanced convergence of moisture. Thereafter, reduced SWNet, QNet, and LWNet owing to increased cloudiness by enhanced convection (D200⫹) and increased evaporation (LH⫹) induced by stronger were found to result in the SST minimum (SST⫺) that lags maximum PR by several days (4 days in the observations, but 7 days in the CGCM). A marked feature in the CGCM simulation is the realistic evolution of phase relationships consistently throughout the SST cycle. However, the complex negative feedback process involving SST, atmospheric stability, surface fluxes, and convection operates faster (slower) during the SST warming (cooling) phase in the CGCM. Apart from that, the correlations are significant throughout the cycle (except for the SH anomaly
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FIG. 13. Phase relationship between PR and SH, M925, D925, SWNet, QNet, LWNet, D200, LH, , and SST at the reference point 15°N, 90°E during NPISO composite: SWNet, QNet, and LWNet, are positive into the ocean; SH and LH are positive out of the ocean. The events are ordered from left to right relative to the PR maximum at the reference point indicated as PR⫹, with ⫹ indicating maxima and ⫺ indicating minima of the correlations. The temporal relationships for the (a) CGCM and (b) AGCM simulations are shown against the corresponding observation in the background. The solid circles indicate lags at which the correlation maxima (minima) above (below) the 95% significance level occur for each variable from the observations and the shading indicates the corresponding lag range of 95% significant levels on either side of the significant extrema. The open squares (diamond) indicate the lags at which the correlation extrema are significant (insignificant) at 95% level for the simulations and the solid bars indicate their corresponding lag range for 95% significant levels on either side of significant extrema. Correlations below 0.1 are not shown.
in the beginning). Although the precise lag positions of the extrema slightly differ from observation, they are still within the respective observed lag range of significant correlation (Fig. 13a). Another noticeable aspect in the temporal evolution in the observations and CGCM is the feedback of SST fluctuations to the largescale atmospheric dynamics through circulation fields such as the strengthening of low-level moisture divergence and lower- and upper-level divergence. This clearly indicates that the intraseasonal SST anomalies
systematically feed back to the atmosphere by first affecting the atmospheric dynamics and then the convection. This in turn affects the surface fields that then feed back into SST fluctuations. The important outcome of this analysis is the occurrence of dominant signatures in low-level moisture convergence associated with fluctuations in SST and SH. Thus, NPISO propagation can be directly attributed to this low-level moisture convergence, first appearing due to friction. Further, in the presence of positive SST anomalies, fluctuations in SH
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strengthen lower-atmospheric instability and lead to convection. This enhanced convection and increased cyclonic vorticity at lower levels is associated with a boundary layer moisture convergence maximum located to the north. This can create a northward gradient in convective instability and lead to subsequent NPISO propagation (Lawrence and Webster 2002; Hsu et al. 2004; Jiang et al. 2004). This mechanism is also in agreement with factors identified as important for NPISO by some previous studies. For example, enhanced SH into the boundary layer contributes to the northward propagation by making the lower atmosphere unstable ahead of the ascending zone, as first proposed by Webster (1983) for NPISO over land, although the cause for the heating differs. This is also consistent with previous studies that suggested the northward propagation of deep convection could be attributed to the positive meridional gradient of atmospheric instability and convective efficiency (Nanjundiah et al. 1992; Srinivasan et al. 1993). For example, the evolution of NPISO (Figs. 11 and 13) implies this factor and the importance of atmospheric dynamics. In addition, enhanced convective activity is important in gradually reducing instability and restoring the atmosphere to a stable state through the dominant convective–thermal feedback inherent in the Tropics, resulting in fluctuations between convectively stable and unstable regimes, as proposed by Goswami and Shukla (1984), which however, did not take into account the external role of SST fluctuation. As seen in the observations and CGCM simulation, the lowfrequency SST anomalies influence convection by first changing the surface heat fluxes and thereby enhancing convective instability. In addition, north of 12°N, enhanced evaporation leads PR by a few days (Fig. 11c), which can directly induce atmospheric instability and thereby facilitate NPISO propagation. This result, in turn, is consistent with the proposed mechanism of NPISO that suggests that the important contribution for NPISO is from surface evaporation (Webster 1983; Goswami and Shukla 1984). For example, at 15°N, the evaporation maximum (LH⫹ in Fig. 13a) leads PR with significant correlation between them appearing many days earlier. However, south of 10°N the phase relationship between evaporation and PR reverses and becomes similar to that associated with the MJO over the equatorial Indian and west Pacific Oceans (Fig. 8). This is in agreement with the ASCII mechanism where precipitation leads enhanced evaporation by a few days. The marked improvement in the evolution of the coherent coupled convective–thermodynamic feedback in the CGCM is evident from the strong correlations and their temporal evolution in the coupled model through
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the full half cycle (Fig. 13). The coordination of the interaction of convection with atmospheric dynamical fields and the interaction of convection with the surface boundary anomalies are well represented in the coupled model. This produces stronger, more realistic, and coherent NPISO events that evolve as strong coupled modes with structure and propagation characteristics closer to observations. Over the equatorial and near-equatorial regions, this feedback is largely consistent with the conceptual model based on the ASCII mechanism. But, in contrast to the phase relationships proposed for ASCII and also found in the MJO (where enhanced evaporation occurs only after the PR maximum), over the off-equatorial regions maximum evaporation occurs ahead of the PR maximum (off-equatorial correlations in Fig. 11c) or shows a near-simultaneous phase relationship around 10°N. Given the uncertainties possible in ERA-40 to an extent, and in the models to a large extent, this aspect needs to be verified further. In contrast, the AGCM shows different behavior with weak and insignificant lag correlations during the SST half cycle. Owing to the absence of direct interaction of convection and surface fluxes in modulating SST, there is a complete breakdown of the coherent evolution of the phase relationships except for warm SST boundary anomalies directly modifying the largescale dynamical fields and causing the precipitation. This conveys that the given SST acts only as a boundary forcing in the AGCM. However, the ability of the AGCM in simulating the phase relationship and the interaction between convection and atmospheric dynamics enables it to produce some coherent events. Thus, the results suggest that, though NPISO is intrinsically an atmospheric mode, it is modulated and amplified to a great extent by the ocean–atmosphere coupling and associated strong feedback process.
5. Summary and discussion This study examines the role of coupling on the simulation of three-dimensional structure and propagation characteristics of prominent TISOs, namely, the MJO and NPISO. The dominant mechanisms behind their propagation are identified and compared and the relationship between the two modes is discussed. The analysis intercompares the long-term simulation of the MRI CGCM2 with observations and the atmospherealone component of the same model forced with monthly SSTs derived from the CGCM. Coherent MJO and NPISO events are selected with respect to representative reference locations based on objective criteria, and composites for the two modes are constructed
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from the selected events. These composite events are then used to analyze the structure and propagation characteristics of TISOs. The model shows a marked ability to capture most of the important aspects of the basic mean state, mean TISO variability, and organization of equatorial mean convection at different scales coupled to equatorial waves. The coupled model simulation shows improvement in the amplitude, structure, and propagation characteristics of TISOs. Near-surface convergence located to the east (north) of the deep convection appears to lead eastward (northward) propagation of the MJO (NPISO), consistent with the frictional wave–CISK mechanism proposed by previous studies (Wang and Xie 1998). The presence of positive SST anomalies enhances the initial low-level convergence that subsequently extends up to the midtroposphere. Once deep convection sets in, for the MJO in particular, the variation in cloudiness and low-level winds associated with the enhanced convection induces coherent variations in fluxes of latent heat and shortwave radiation at the ocean surface. For NPISO, as in the observations, although latent heat flux play a similar role along with convective–radiative feedback to produce an oscillation between stable and unstable regimes over the near-equatorial regions, it plays a different phase relationship with convection over offequatorial regions. Thus, changes of SST, surface fluxes, and convection imply the presence of a coherent coupled feedback, as seen in observations, in which the convection modifies the SST through fluctuations in surface fluxes and modified SST feedbacks, in turn, to modulate the convection. As suggested by previous studies (e.g., Fu and Wang 2004; Rajendran et al. 2004a; Sperber et al. 1997; Wang and Xie 1998) of the importance of SST anomalies and coupled feedback in inducing TISO propagations, the present analysis confirms that the stronger and more realistic TISOs in the CGCM are due to realistic ocean–atmosphere coupling. Whereas in the AGCM, precipitation and SST are almost in phase with weak correlation between them, indicating that the convection in AGCM is subjected to the given SST instead of its evolution through coherent coupled feedback. Here, the variation of surface fluxes with respect to enhanced convection is not able to change the prescribed SST. The similarity of the coupled feedback process during the evolution and the dominant mechanism behind the propagation for the two TISO components prompted the examination of the relationship between the two modes. The relationship of NPISOs with equatorial eastward-moving Kelvin–Rossby wave packets is reflected in the eastward propagation of the offequatorial convection in the NPISO composite during
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the boreal summer season. A Hovmöller diagram of rainfall anomalies averaged over NPISO composite events along the equator, 10°, and 20°N from lag(⫺20) to lag(⫹20) are shown in Fig. 14. In the observations and CGCM, the off-equatorial precipitation anomalies exhibit eastward propagation with phase speed similar to that along the equator. The systematic eastward movement of convection at the northern latitudes develops successively northward, following the equatorial convection during active NPISO events. This supports the hypothesis that NPISO is one aspect of an eastward MJO where the northward propagation of convection is forced by surface frictional convergence into the low pressure center of the Rossby cell that is excited by equatorial MJO convection (Lawrence and Webster 2002). The evidence in this study suggest that the TISOs are inherently atmospheric modes, generated and maintained through coherent interaction between convection and atmospheric dynamics. Accordingly, the vertical structure of TISOs reflects the significance of atmospheric dynamics during their life cycle and are associated with a pronounced vertical tilt owing to the dominance of near-surface convergence and the lead in the response of the lower boundary layer ahead of the free atmosphere. The models capture these aspects with reasonable accuracy. Consequently, some coherent MJO and NPISO events occur in the AGCM. However, these events in the AGCM are less frequent, weaker, and less coherent than the events in the CGCM, indicating that TISOs evolve at least partly as a coupled mode where coupled feedback processes play a very important role in modulating the lower boundary layer. In other words, although the coupling is not essential for TISOs, it acts as an amplifying mechanism for the existing propagating convective anomalies and plays an important modifying role toward a more realistic TISO simulation. The simulated TISO propagations in the AGCM are found to be associated with frictional wave–CISK mechanism, whereas in the CGCM the positive SST anomaly forced by latent heating and, to a lesser extent, insolation anomalies to the east (north) of convection associated with MJO (NPISO) act to reinforce convergence associated with the wave–conditional instability of the second kind mechanism operating in the model. This enhanced convergence transports more low-level moisture into the region east (north) of the convection, resulting in increased moist static energy eastward (northward) and helps to destabilize the atmosphere ahead of the eastward (northward) propagation of the MJO (NPISO) and maintain the propagation compared to the atmosphere-alone case. While this is consistent
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FIG. 14. Hovmöller diagram of precipitation anomaly averaged over NPISO composite events from the (a) observations and (b) CGCM and (c) AGCM simulations at the equator, 10°, and 20°N. The reference longitude and zero lag are shown as solid lines.
with the frictional convergence paradigm, the convergence in the boundary layer forced by intraseasonal SST fluctuation also contributes to enhance the convergence leading the deep convection. It is also found that both versions of the model fail to simulate as many TISO events as are observed and the
magnitude of TISO-related SST anomalies is smaller than for the observed anomalies. In the AGCM propagation appears to be much faster, especially at 10°N, than in the observations and CGCM. Compared to the observations, both models have much weaker amplitudes at the equator, which is consistent with the un-
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derestimation of summer intraseasonal variance (Figs. 2 and 11) along the equator by the models. These factors imply the need for further improvement in the simulation of intraseasonal variability in this GCM. This may be achieved through further improvements in parameterizations of individual components of the CGCM and improved and more frequent coupling between the ocean and atmosphere in the model. Further, there are important caveats of the present modeling framework that need to be discussed under the TISO scenario. As discussed in section 2, the SSTs are slightly different for the two versions of the model; the SSTs used for forcing the AGCM were monthly averages, whereas the SSTs in the CGCM vary daily. Hence, some of the differences in TISO variability in the AGCM can be attributed to the rectification of the possible erroneous response to the high frequency SST variability (Kitoh and Arakawa 1999). For example, Fu and Wang (2004) indicate that forcing the AGCM with daily SSTs introduces an erroneous boundary interference on the internal dynamics associated with the summer intraseasonal oscillation. Another point is that the model still shows some systematic bias, especially over mainland and southeast China (Rajendran et al. 2004b), which is found to be associated with large biases in the surface temperature simulation. These biases needs to be rectified through appropriate modification of the model land surface scheme, representation of terrain and land–sea distribution around the Maritime Continent, and boundary layer parameterization. Acknowledgments. The authors thank Dr. T. Nakazawa for his support throughout this study and Dr. S. Sajani, VSSC, India, for detailed and constructive comments on the manuscript. The authors are also grateful to Mr. O. Arakawa and Dr. S. Yukimoto for their help. We appreciate comments and suggestions provided by the anonymous reviewers, which were helpful for improving the paper. K. Rajendran is supported by the Co-operative Systems for Supporting Priority Research, Japan Science and Technology Agency. REFERENCES Anderson, J. R., D. E. Stevens, and P. R. Julian, 1984: Temporal variations of the tropical 40–50 day oscillation. Mon. Wea. Rev., 112, 2431–2438. Chen, T. C., R. Y. Tzeng, and M. C. Yen, 1988: Developement and life cycle of the Indian monsoon: Effect of the 30–50 day oscillation. Mon. Wea. Rev., 116, 2183–2199. Duchon, C. E., 1979: Lanczos filtering in one and two dimensions. J. Appl. Meteor., 18, 1016–1022. Flatau, M., P. J. Flatau, P. Phoebus, and P. P. Niler, 1997: The feedback between equatorial convection and local radiative
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