Role of the Indian Ocean in the ENSO Indian Summer Monsoon Teleconnection in the NCEP Climate Forecast System

Transcription

1 2490 J O U R N A L O F C L I M A T E VOLUME 25 Role of the Indian Ocean in the ENSO Indian Summer Monsoon Teleconnection in the NCEP Climate Forecast System DEEPTHI ACHUTHAVARIER* Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, Virginia V. KRISHNAMURTHY Center for Ocean Land Atmosphere Studies, Institute of Global Environment and Society, Calverton, Maryland, and Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, Virginia BEN P. KIRTMAN Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida BOHUA HUANG 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 23 February 2011, in final form 14 November 2011) ABSTRACT The observed negative correlation between El Niño Southern Oscillation (ENSO) and the Indian summer monsoon is not simulated by the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) coupled model. The correlation is partially restored in the simulations where the Indian Ocean (IO) sea surface temperature (SST) is prescribed with the daily mean or climatology. Comparison among the simulations suggests that ENSO-induced SST anomalies form a strong dipole pattern oriented along the zonal direction in the IO in the coupled model, preventing the ENSO signals from reaching the Indian monsoon region. In the model, the dipole develops early in the monsoon season and extends to the central equatorial IO while it is formed at the end of the season in observations. The dipole modifies low-level winds and surface pressure, and grows in a positive feedback loop involving winds, surface pressure, and SST. Examination of the mean state in the model reveals that the thermocline is relatively shallow in the eastern IO. This preconditions the ocean such that the atmospheric fluxes can easily impart fluctuations in the subsurface temperature and thereby in the SST. These results suggest that biases in the IO can adversely affect the ENSO monsoon teleconnection in a coupled model. 1. Introduction The relation between Southern Oscillation and seasonal (June September, JJAS) mean summer monsoon rainfall over India is one of the earliest observed teleconnections in global climate studies (Walker 1924). * Current affiliation: Universities Space Research Association, Columbia, Maryland. Corresponding author address: Deepthi Achuthavarier, Goddard Earth Sciences Technology and Research, B33 C118, NASA GSFC, Greenbelt, MD dvarrier@yahoo.com Observational estimates of rainfall shows that India tends to experience a below-normal monsoon during an El Niño or warm Pacific event and above-normal monsoon during a La Niña or cold Pacific event (Walker 1924; Sikka 1980; Angell 1981; Rasmusson and Carpenter 1983; Ropelewski and Halpert 1987). This relationship is often presented as a lead lag correlation plot between the monthly mean Niño-3 index and the seasonal anomalies of the Indian monsoon rainfall (IMR) index (see, e.g., Kirtman and Shukla 2000, Krishnamurthy and Kinter 2003). The Niño-3 index is the sea surface temperature (SST) anomalies averaged over the region (58S 58N, W) and IMR is the JJAS seasonal anomalies of precipitation averaged over the political boundary of DOI: /JCLI-D Ó 2012 American Meteorological Society

2 1APRIL 2012 A C H U T H A V A R I E R E T A L India, which excludes ocean grid points. The simultaneous correlation between the JJAS IMR and June Niño-3 is about The correlation is strongest (20.6) during October January, following the monsoon season. The observed negative correlation between the Indian summer monsoon and Niño-3 can be explained to some extent by the modulation of the Walker circulation (Shukla and Wallace 1983; Palmer et al. 1992; Ju and Slingo 1995; Soman and Slingo 1997). During warm Pacific events, the ascending branch of the Walker circulation shifts eastward in response to the anomalous warming in the central and eastern Pacific, resulting in subsidence and reduced rainfall over the Indo-western Pacific region. Regression and composite analyses of low-level winds with respect to the Niño-3 index indicate easterly (westerly) anomalies over the equatorial northern Indian Ocean (IO) and Arabian Sea, which weakens (strengthens) the monsoon flow during a warm (cold) Pacific event (e.g., see Lau and Wang 2006). These wind anomalies are consistent with the surface pressure anomalies over the Maritime Continent associated with an El Niño Southern Oscillation (ENSO) event. Although the regressed fields provide a generic idea of the ENSO-induced circulation anomalies, it is not clear whether they are entirely remotely forced or generated as a combination of the remote ENSO forcing and local air sea interaction in the IO. Wu and Kirtman (2004) reported that, unlike their globally coupled run, their IO-decoupled runs could not reproduce the observed ENSO monsoon link underlining the importance of air sea interaction processes in the IO. Bracco et al. (2007) also reached a similar conclusion based on their GCM experiments. Several studies have pointed out that the ENSO-induced circulation anomalies would generate SST anomalies in the Indian Ocean through cloud radiation and wind evaporation feedbacks, which in turn would influence the monsoon rainfall (Lau and Nath 1996; Klein et al. 1999; Lau and Nath 2000; Shinoda et al. 2004). Shinoda et al. (2004) showed that, in boreal summer, a developing El Niño could induce easterly anomalies in the eastern IO associated with subsidence and low-level divergence, which when combined with the climatological easterly winds would result in an increase in total wind speed. This could increase evaporative cooling and aid in generating negative SST anomalies, although the decreased cloud cover and increase in net shortwave radiation could slightly offset the surface cooling. During boreal winter, when the climatological winds are of opposing directions, the ENSO-induced anomalies act to reduce the total wind speed. Then, the reduced evaporation combined with the increased shortwave radiation warms the ocean surface. The SST anomalies in the IO either generated by the ENSO-induced atmospheric fluxes or by any independent ocean process in the IO can influence the interannual variability of the summer monsoon. Krishnamurthy and Shukla (2008) have shown that ENSO and the Indian Ocean variability may act together or against each other in certain years. The motivation for the present study comes from the observation that the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS), despite being a fully coupled GCM, fails to simulate the simultaneous negative correlation between Niño-3 and Indian summer monsoon rainfall. The objective of this study is to document the ENSO monsoon teleconnection in the CFS and to provide reasons as to why the model fails to capture this relationship in spite of having a dynamical ocean model in the IO. Mechanistic experiments are performed by decoupling the Indian Ocean, in order to understand its role in the ENSO monsoon teleconnection. The results presented here may aid in improving the simulation of the ENSO monsoon teleconnection and thereby the simulation and forecast of the Indian summer monsoon. Details of the CFS coupled model, experimental design, and analyses methodology are discussed in section 2. The ENSO monsoon relations in the model simulations are compared with observations in section 3. The interannual variability of the monsoon rainfall and the tropical SST are presented in section 4. Section 5 discusses the role of the Indian Ocean in the ENSO monsoon teleconnection in the CFS. A summary of our results is provided in section Model and methodology a. Model and experiments The model used is the NCEP CFS (Saha et al. 2006), which has NCEP s Global Forecast System (GFS; Moorthi et al. 2001) as the atmospheric component and version 3 of the Geophysical Fluid Dynamics Laboratory s (GFDL) Modular Ocean Model (MOM3; Pacanowski and Griffies 1998) as the oceanic component. The GFS has a spectral triangular truncation of 62 waves in the horizontal (equivalent to a 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, freshwater, and momentum fluxes once a day. The atmosphere ocean coupling is effective between 658S and 508N, while the observed climatological SST is prescribed poleward of this region. The sea ice extent is prescribed from the observed climatology. No flux correction is employed in the CFS. A fully coupled, 50-yr-long simulation of the CFS is described by Pegion and Kirtman (2008). This run is initialized on 1 January 1985 with atmospheric initial

3 2492 J O U R N A L O F C L I M A T E VOLUME 25 Expt 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, E, Daily mean IO-CSST 308S 308N, E Daily climatology conditions from the NCEP/Department of Energy Global Reanalysis 2 (Kanamitsu et al. 2002) and the ocean initial conditions from the Global Ocean Data Assimilation System (GODAS). Data from the last 30 yr of that simulation (control, hereafter) are extensively analyzed. In addition, two regionally coupled simulations, where the Indian Ocean is decoupled, each of length 30 yr, are performed in order to examine the role of the Indian Ocean in the ENSO monsoon teleconnection (Table 1). The regional coupling framework used here is similar to that employed by Huang and Shukla (2007), where the atmosphere ocean decoupling is achieved by prescribing the SST in the region of interest. Although a flux correction to relax the SST predicted by the ocean model in the decoupled area is often employed to avoid development of erroneous ocean currents, it is not implemented here. However, the SST fields, as predicted by the ocean model are checked to verify that they are realistic. In the first regionally coupled simulation, the daily mean SST from the control run is prescribed over the region (308S 308N, E), and full coupling is employed elsewhere. This run, hereafter referred to as IO-VSST (Indian Ocean prescribed with varying SST), retains SST interannual variability in the IO while suppressing the air sea interaction. Since this run starts from a perturbed initial condition, as described later, it is not a repeat of the control run, even though the prescribed SST in the Indian Ocean is exactly the same as in the control. In the second simulation, the interannual SST variability and air sea interaction are both suppressed by prescribing the region (308S 308N, E) with daily climatological SST from the control, and will be referred to as IO-CSST (Indian Ocean prescribed with climatological SST). In both cases, a 108-wide buffer zone is employed at the transition between the coupled and uncoupled regions, where SST values are computed by linear interpolation. In both cases, the prescribed SST is obtained from the control so that a direct comparison with the control is possible. The model is restarted from the control run on 1 January of the 21st simulation year with perturbed atmospheric initial conditions and run continuously for 30 yr. In any particular ENSO year, it is possible that some of the SST anomalies in the IO are forced by the ENSO. In the IO-VSST run, although the prescribed SST FIG. 1. Monthly mean Niño-3 index (K) for 30 yr of the control, IO-VSST, and IO-CSST runs. Correlation between CTL and IO- VSST and CTL and IO-CSST is 0.2. anomalies in the IO are partly caused by the ENSO events from the control run, they do not correspond to the ENSO variability in the Pacific in the IO-VSST run. This is because the ENSO events in the control and the IO-VSST run do not have year-to-year correspondence. Sufficient perturbations are introduced in the atmospheric initial conditions to make the Pacific SST evolution in the uncoupled runs different from that of the control. To illustrate this point, monthly mean Niño-3 results of the three runs are shown in Fig. 1. In a way, this destroys the atmospheric bridge between the Pacific and the IO, thereby permitting only the remote impact of ENSO signals that are not modified by the air sea interaction in the IO. b. Method of analysis The ENSO signal in precipitation over the Indian monsoon region is extracted from the daily data using the multichannel singular spectrum analysis (MSSA). Krishnamurthy and Shukla (2007, 2008) have successfully used this technique to isolate interannual modes from the daily data, which they referred to as seasonally persistent signals. The MSSA is the same as the more familiar extended empirical orthogonal function analysis, both involving the eigenanalysis of the lagged covariance matrix (Ghil et al. 2002). A minor distinction is that MSSA usually uses larger lags than EEOF does (e.g., Ghil et al. 2002). The term MSSA is more commonly used in the field of nonlinear dynamics, which has

4 1APRIL 2012 A C H U T H A V A R I E R E T A L provided more extensive analysis techniques. The MSSA yields M sequences of spatial maps (eigenvectors), which are referred to as the space time EOFs (ST-EOFs), where M is the lag window chosen. The space time principal components (ST-PCs), each of length N2M11, where N is the total number of time steps in the data, are obtained by projecting the original data onto the corresponding ST-EOFs. The component of the original data corresponding to an eigenvalue can be reconstructed by combining the ST-PC and its respective ST-EOF in a least squares sense, which will be referred to as the reconstructed component (RC) in the rest of the paper. A more technical discussion of the MSSA is provided by Ghil et al. (2002). The leading modes of variability in the control and the two regionally coupled runs are obtained by performing an MSSA on JJAS daily anomalies of precipitation for 30 yr, with a lag window length of 61 days. Daily anomalies are obtained by first removing the daily climatology obtained by simple averaging. In addition, tropical intraseasonal modes are removed from the daily data by isolating them using a separate MSSA with a longer lag window and by subtracting their corresponding RCs from the total anomalies. Although no such intraseasonal filtering was employed in the observed datasets (Krishnamurthy and Shukla 2007, 2008), it was necessary with the CFS data because of the existence of slower intraseasonal modes. Otherwise, in a 61-day lag-window MSSA, the intraseasonal oscillations of period days were found to contaminate the interannual signals. For all model runs, the MSSA was performed over the region 358S 358N, E. 3. ENSO monsoon correlation In this section, the ENSO monsoon teleconnections in the CFS control and experimental runs are compared with the observed relationship. The climatological features of the rainfall and SST in the IO-decoupled runs show good agreement with those of the control (figures not shown). Therefore, any difference between the control and the experimental runs can be attributed to the changes made in the IO. Figure 2 shows the lead lag correlation between the extended Indian monsoon rainfall (EIMR) index of the JJAS seasonal anomalies of precipitation and monthly means of the Niño-3 index. The EIMR index is obtained by averaging the rainfall over the region N, E. The observed relationship is computed based on the merged analysis of precipitation from Smith et al. (2010) and the Hadley Centre SST (Rayner et al. 2003) for the period As mentioned earlier, the CFS control run fails to capture the simultaneous negative correlation between the FIG. 2. Lead lag correlation between JJAS EIMR and monthly Niño-3 index for the three model simulations and the observations. Lag 0 (11, 21) represents the correlation between JJAS rainfall and July (August, June) Niño-3 and so on. Gray shading represents the JJAS season. Niño-3 and the monsoon rainfall. The correlation in JJAS is weakly positive (#0.2) in the control run while the observed data show values of 20.4 to In the CFS control, an above-normal monsoon tends to coincide with a warm Pacific event and vice versa. An interesting feature in Fig. 2 is the strong negative correlation in the simulations observed 12 months before the monsoon, which implies some potential for predictability of the seasonal mean monsoon in the CFS system. In the model, although weakly correlated with the simultaneous monsoon, the Niño-3 index appears to have a strong relation with the following monsoon. Lagged regression analysis between JJA Niño-3 and monthly precipitation, winds, and SST anomalies in the following seasons (figure not shown) suggests that this may be related to ENSOinduced anomalies in the monsoon region, particularly the basinwide cooling (warming) associated with a cold (warm) Pacific event. Further investigation is required to identify the mechanisms behind this feature, which is not the focus of this study. Both of the regionally coupled runs capture the sign of the correlation during the JJAS season, although the details of the curve are far from perfect. For example, the correlations are unrealistically large months before the monsoon season, whereas they remain close to zero in the observations. However, the fact that the correlations are of the correct sign during and after the monsoon season in the IO-decoupled runs demands further investigation. The above result is seemingly inconsistent with the findings of Wu and Kirtman (2004) and Bracco et al. (2007), who found that the decoupling of the IO deteriorates the relationship in comparison to their respective fully

5 2494 J O U R N A L O F C L I M A T E VOLUME 25 FIG. 3. JJAS rainfall variance of IO-VSST and IO-CSST normalized by that of the control. Regions where the control variance is less than 3 are masked to avoid large unrealistic ratios. coupled runs. The results of this study will show that model biases arising from the IO coupled processes can destroy the ENSO monsoon link, particularly over the EIMR region. A point to note here is that Wu and Kirtman (2004) used an anomaly coupling strategy (Kirtman et al. 2002), which largely corrected their model biases at the air sea interface. Bracco et al. (2007) also applied similar corrections in the SST. In CFS, decoupling the IO appears to have captured the remote ENSO signals correctly to some extent. However, the ENSO monsoon correlation is not perfect in the decoupled runs either. Observed data show that the ENSO signal over the Indian monsoon region (EIMR region) in boreal summer is not as strong as that over the equatorial eastern IO and the Maritime Continent (see, e.g., Krishnamurthy and Shukla 2008, Lau and Wang 2006). This indicates that the ENSO signal over the EIMR region is more susceptible to alterations by other climate signals and air sea interaction. More discussion will be provided as to why the CFS control run fails to capture the correct ENSO monsoon relation in the following sections. In the next section, the gross features of the interannual variability in the coupled and IO-decoupled runs and evidence of air sea interaction in the IO are presented. The objective there is to document the differences in the interannual variability over the monsoon region (if any) as a result of decoupling the IO. 4. Interannual variability a. Variance Figure 3 shows the variance of JJAS seasonal anomalies of precipitation for the IO-VSST and IO-CSST simulations. The variances plotted in Fig. 3 are fractional increases or decreases with respect to the control. In general, the interannual variance of the decoupled runs is larger compared to the control over the regions where SST is prescribed. The increase in variance is seen mainly to the north of the equator, in the Arabian Sea, and in the Bay of Bengal, while a reduction or little change is seen to the south (158S 08). These results are consistent with those of Wu and Kirtman (2005), who compared similar coupled and uncoupled runs. They suggested that, in a coupled simulation, air sea interaction processes such as the cloud radiation and wind evaporation feedbacks, reduce atmospheric variability to some extent. This damping affect is suppressed when decoupling the ocean and atmosphere. Another point to note is that both the IO-VSST and IO-CSST runs show similar increases in variability, which indicates that the

6 1APRIL 2012 A C H U T H A V A R I E R E T A L enhanced atmospheric variability is due to the lack of air sea interaction, but not due to the lack of SST interannual variability. In IO-CSST, both the interannual SST variability and air sea interaction are suppressed, while in IO-VSST only the latter is absent. The variability in the Pacific basin is slightly increased as a result of decoupling the IO, which must be in response to an increase in SST variability seen in that region (see Fig. 6). The increased SST variability in the Pacific will be examined in detail later in this section. Another point to note in Fig. 3 is the decrease in variability over the Indian Ocean in regions south of the equator (158S 08) in the IO-CSST simulation. This feature is absent in the IO-VSST run, which indicates that, in both the control and IO-VSST runs, large variability exists in the equatorial southern Indian Ocean. The variance in this region is closely associated with the SST interannual variability in that region (as shown later). In the IO-CSST run, in the absence of the SST interannual variability, rainfall variability is considerably reduced. This also indicates that, in the control run, the rainfall variability in the southern IO (158S 08) is largely forced by the SST anomalies. b. Air sea interaction in the IO The air sea coupling in the Indian Ocean is further examined by performing correlation analyses. Figure 4 shows a point-to-point temporal correlation of monthly anomalies of precipitation for 30 JJAS seasons (number of data points ) between the control and IO- VSST simulations. If the rainfall is forced by local SST, the IO-VSST run should produce the same variability as in the control, in which case high positive correlation should be expected in Fig. 4. In Fig. 4, however, positive values of are limited to regions over the eastern and western equatorial IO (158S 58N), indicating the importance of SST forcing in those regions. Over the rest of the Indian Ocean, particularly over the Arabian Sea and the Bay of Bengal, the correlation is less than 0.3, indicating a weaker forcing by the local SST anomalies. Figure 4 is consistent with Fig. 3 in that the SSTforced regions identified in Fig. 4 are those regions where the fraction of variance is close to 1 in Fig. 3 (top). Another method of examining the local air sea interaction is through the correlation between SST and precipitation (Wu and Kirtman 2005, 2007). The idea behind this approach is that, when the atmosphere acts only in response to the ocean, there should be simultaneous large positive correlation between the SST and the precipitation. On the other hand, when the atmospheric fluxes can significantly influence the SST through wind evaporation or cloud radiation feedbacks, one can expect a large negative correlation between precipitation FIG. 4. Point-to-point temporal correlation of monthly anomalies of precipitation for the JJAS season between the control and IO-VSST simulations. Correlation values above the 95% confidence level are shown. and SST tendency. The SST tendency is computed by forward differencing as, SST(t 2 ) SST(t 1 ), where t 1 and t 2 are two consecutive months with t 2 following t 1. Figures 5a,b show point-to-point temporal correlation between monthly anomalies of precipitation and SST in the control and IO-VSST for 30 summer seasons. In the control (Fig. 5a), where air sea interaction is allowed in all ocean basins, the correlation is strong (;0.6) over a broad region in the eastern Pacific, consistent with the observations (see Fig. 1c in Wu and Kirtman 2007). Weaker positive and negative correlations exist over the IO, particularly over the Arabian Sea and Bay of Bengal, as well as over the northern west Pacific, indicating the role of coupled processes in the SST variability there. In the IO-VSST run (Fig. 5b), however, the correlation is more positive ( ) over the IO, which is not surprising since that simulation has only an SSTforced response in the IO, by design. Consistent with the above discussion, the precipitation and SST tendency are negatively correlated over the Arabian Sea and Bay of Bengal, indicating the role of air sea coupling in that region (Fig. 5c). These findings are again consistent with the variance map presented in Fig. 3. In short, there is large rainfall variability over the southern part of the equatorial IO (158S 08), which appears to be driven by large local SST variance. Air sea coupling has a dominant role in the northern part of the IO on either side of the Indian Peninsula. Decoupling the Indian Ocean increases the rainfall variability in those regions. c. Pacific SST variability In this section, the effect of decoupling the IO on the Pacific SST variability is discussed. The standard

7 2496 J O U R N A L O F C L I M A T E VOLUME 25 FIG. 5. Point-to-point temporal correlation between monthly anomalies of precipitation and SST for the JJAS season for the (a) control and (b) IO-VSST simulations. (c) Point-to-point temporal correlation between monthly anomalies of precipitation and SST tendency for the JJAS season for the control run. The SST tendency is computed from monthly anomalies as [SST(t 2 ) SST(t 1 )], where t 1 and t 2 denote consecutive months with t 2 following t 1. deviation of JJAS seasonal SST anomalies shows that the Pacific variability is larger in the IO-VSST and IO- CSST runs compared to the control (Fig. 6). As mentioned earlier, the larger rainfall variance in the Pacific (Fig. 3) in the decoupled runs is in response to this increased SST variability. Next, we investigate whether the Pacific SST variability is connected to the ENSO monsoon relation in the model. As discussed earlier, the negative correlation between monsoon rainfall and Niño- 3 was not captured by the control but was better in the IO-decoupled runs. Previous studies have shown that a weak (strong) monsoon can exert anomalous westerlies (easterlies) in the Pacific and thereby enhance an ongoing warm (cold) Pacific event (Kirtman and Shukla 2000; Wu and Kirtman 2003). Based on that result, the increased Pacific SST variability is explained as follows. The weak ENSO monsoon correlation in the control run shows that, unlike in the observations, the monsoon has little impact on the simulated ENSO variability. The fact that the correlation is even slightly positive indicates that, in the model, a weak (strong) monsoon may impart anomalous westerlies in the Pacific during an ongoing cold (warm) event, which may reduce the ENSO SST anomalies. Conversely, in IO-VSST and IO-CSST, the ENSO monsoon correlation is negative where the monsoon-induced wind anomalies can potentially enhance an already developing Pacific event as argued in Kirtman and Shukla (2000). The above hypothesis can be tested by simple statistical calculations. Figure 7 shows lagged regressions of SST and zonal wind fields at 850 hpa on the EIMR index. Monthly anomalies of the SST and zonal wind are regressed on standardized JJA monthly anomalies of the EIMR index for 30 yr of model data (number of data points ). Lag 11 denotes that EIMR index leads the SST and wind fields by 1 month and so on. The SST

8 1APRIL 2012 A C H U T H A V A R I E R E T A L FIG. 6. Standard deviation of JJAS SST anomalies for the control, IO-VSST, and IO-CSST runs. and the zonal winds corresponding to the EIMR are markedly different between the control and the two IOdecoupled runs. In the control run, an above-normal (below normal) monsoon induces westerly (easterly) anomalies in the Pacific except over the eastern Pacific in lags 0 and 12. The SST anomalies associated with an above-normal monsoon are weakly positive in all lags. However, in IO-VSST and IO-CSST, the regressed anomalies are of the opposite sign in the equatorial Pacific. A strong (weak) monsoon is consistent with easterly (westerly) anomalies, which can enhance an already developing cold (warm) event. The corresponding SST fields are consistent with the wind. Based on Fig. 7, it is reasonable to assume that the increase in the Pacific SST variability in the decoupled runs is due to the improved ENSO monsoon teleconnection in those runs. Figure 7 also provides an explanation for the apparent discrepancy between this study and Wu and Kirtman (2005), who found that the ENSO variability was reduced by 20% as a result of decoupling the IO. Unlike the present study, their experiments were based on a coupled model that reproduced the observed negative correlation between the ENSO and monsoon rainfall. In their model, decoupling the IO destroyed the observed ENSO monsoon link and also reduced the ENSO variability. That is, in their experiments, the reduction in the ENSO variability coincided with a weak ENSO monsoon correlation, which is in fact consistent with our results.

9 2498 J O U R N A L O F C L I M A T E VOLUME 25 FIG. 7. Lagged regression of monthly anomalies of SST (shaded) and u winds (contours) at 850 hpa on the monthly JJA EIMR index for (left column) control, (middle) IO-VSST, and (right) IO-CSST. Lag 11 denotes that EIMR index leads the SST and wind fields by 1 month and so on. Dotted contour lines represent negative wind anomalies. 5. ENSO mode In this section, the reasons for the weak ENSO monsoon link in the control and its improvement in the IO-decoupled runs are investigated. For this purpose, the MSSA is applied on monsoon rainfall anomalies to obtain the ENSO mode over the monsoon region (see section 2 for details of the MSSA). The first MSSA mode

10 1APRIL 2012 A C H U T H A V A R I E R E T A L FIG. 8. The S-EOF1 of the ENSO mode for (a) control, (b) IO-VSST, and (c) IO-CSST runs. The variances explained with respect to their corresponding RCs are noted in percent values. Daily point correlations between the S-PC1 of the ENSO mode of (d) control, (e) IO-VSST, and (f) IO-CSST with their corresponding Niño-3 index. Correlation values above the 95% confidence level are shaded. in all the simulations will be shown to have strong correlation with the eastern Pacific SST anomalies and, therefore, will be referred to as the ENSO mode hereafter. The ENSO modes of the control, IO-VSST, and IO-CSST have 2.1%, 4.1%, and 3.4% of the variance of the daily data, respectively. These values, although seemingly small, are typical of analyses using daily data. In other words, the day-to-day weather dominates the overall variance; nevertheless, the technique effectively isolates the low-frequency climate signal in the presence of this high-frequency variance. The spatial structure of this mode is revealed in the 60 ST-EOF maps (eigenvectors from the MSSA), which can be visualized in an animation. Alternatively, one can perform an additional spatial EOF analysis on its corresponding RC, where the leading mode often captures almost all of the variance (Krishnamurthy and Shukla 2008). The resulting EOFs and PCs are referred to as S-EOFs and S-PCs, where S stands for spatial and they are different from the ST-EOFs and ST-PCs obtained from the MSSA. Figure 8 shows the S-EOF-1 of the RCs of the ENSO mode of the control, IO-VSST, and IO-CSST simulations. The leading EOF captures most of the variance in the ENSO mode (95%, 90%, and 96% for the control, IO-VSST, and IO-CSST, respectively) (Figs. 8a c). The corresponding PCs, when correlated with JJAS daily anomalies of SST, show strong signals in the eastern Pacific confirming their ENSO dependence (Figs. 8d f). The JJAS seasonal mean S-PCs are also strongly correlated with the Niño-3 index (figure not shown). The spatial structure of the ENSO mode is clearly different between the control and the IO-decoupled runs. In the control, most of the variance is over the equatorial Indian Ocean with a dipolelike pattern along the zonal direction. The large anomalies over the eastern IO are connected to surface convergence or divergence as a result of modulation of the Walker circulation. Unlike the observed ENSO mode (Krishnamurthy and Shukla 2008, their Fig. 7), the anomalies over the eastern IO do not extend to the Indian subcontinent and adjacent

11 2500 J O U R N A L O F C L I M A T E VOLUME 25 FIG. 9. Lead lag correlation between JJAS EIMR computed from the ENSO mode of the control, IO-VSST, and IO-CSST runs and their corresponding monthly Niño-3 index. Lag 0 (11, 21) represents the correlation between JJAS rainfall and July (August, June) Niño-3 and so on. Gray shading represents the JJAS season. oceans. Anomalies of opposite sign are present over parts of India and the Bay of Bengal. The large loading over the eastern IO is also unrealistic when compared to Niño-3-based composites of monsoon precipitation (Lau and Wang 2006). The ENSO mode has noticeable changes in the IO- VSST and IO-CSST runs, the most striking one being the extension of the strong positive pattern from the equatorial eastern IO to the Arabian Sea and parts of India. The variance in the eastern IO is somewhat reduced. The negative anomalies off the coast of Africa are also absent, eliminating the dipole structure that was present in the control. Between the IO-VSST and IO- CSST runs, the features of the ENSO mode are very similar. The fact that the ENSO mode is different in the coupled run (though unrealistic in this case) indicates the importance of coupled processes in the IO in this model. These spatial structures are consistent with our earlier observation that the ENSO monsoon correlation is more realistic in the uncoupled runs. This can be further illustrated by examining the lead lag correlation between the JJAS EIMR corresponding to the ENSO mode and total monthly Niño-3 anomalies (Fig. 9). Figure 9 is similar to Fig. 2 except that JJAS EIMR is computed from the RC1 as opposed to total anomalies. An advantage of the MSSA compared to regression or compositing techniques is that it permits the extraction of the signal, which has the same spatial and temporal dimensions as the original data. In the IO-decoupled runs, the ENSO-induced monsoon rainfall has a strong negative correlation with the simultaneous Niño-3 (about 20.8) while the correlation is positive in the control. Compared to Fig. 2, the correlation is stronger here since the rainfall anomalies are devoid of other boundaryforced influences and high-frequency atmospheric variability. To further examine the ENSO mode, JJAS monthly anomalies of SST, horizontal winds at 850 hpa, and the latent heat flux (LHF) are regressed on JJAS monthly mean S-PC1 of the ENSO mode (Fig. 10). Positive LHF values indicate upward flux (i.e., the ocean loses heat through net evaporation). In the control, the SST anomalies form a dipole in the zonal direction with positive anomalies in the east and negative anomalies in the west, consistent with the rainfall. The corresponding winds show strong convergence in the eastern IO with westerlies along the equatorial IO and easterlies in the western Pacific. The winds over the southwestern coast of India are northwesterly, opposing the southwesterly climatological flow. In IO-VSST and IO-CSST, the winds along the coast of Africa are stronger and southwesterly enhancing the mean winds. The SST anomalies are positive and more or less uniform over the IO in IO-VSST. The fact that there is no dipole in the SST in the IO-VSST verifies that there is a disconnection between the Pacific and IO variability in this run. This was in fact the very purpose of this experimental design where the atmospheric fluxes associated with the ENSO could not impart SST anomalies in the IO. One could argue that, in the IO-VSST run, the SST is exactly the same as in the CTL and therefore a dipole must be present there. Although such dipole variability existed in the IO-VSST, it must be recalled that it does not correspond to the ENSO variability in that run since there is no year-to-year correspondence between the ENSO events of the CTL and the IO-VSST. The disconnection between the Pacific and the IO in the IO-VSST is also evident in the correlation between the Niño-3 and an index over the western Indian Ocean (area average over 108S 108N, E), which is 0.6 in the CTL and 20.1 in the IO-VSST. The large positive rainfall anomalies over the Arabian Sea (Figs. 8b,c) are due to the stronger winds and large positive LHF anomalies over that region. These large values of the LHF are typical of the uncoupled simulations where SST cannot respond to the heat flux anomalies (Wu and Kirtman 2005). The improved representation of the ENSO mode in the IO-decoupled runs is mainly due to the better simulation of the ENSO-induced winds over the Arabian Sea. In the decoupled runs, surface evaporation and rainfall anomalies are large in response to the winds. Since SST cannot respond to the atmospheric fluxes, no damping mechanism exists to reduce the evaporation. It is possible that the LHF anomalies are overestimated in the IO-decoupled run. However, in the control run, the LHF over the Arabian Sea is weak, probably due to the

12 1APRIL 2012 A C H U T H A V A R I E R E T A L FIG. 10. JJAS regressed fields of (top row) SST, (middle) horizontal winds at 850 hpa, and (bottom) latent heat flux for the (left) control, (middle) IO-VSST, and (bottom) IO-CSST. Regression is done with respect to normalized JJAS monthly means of S-PC1 of the corresponding ENSO mode.

13 2502 J O U R N A L O F C L I M A T E VOLUME 25 FIG. 11. Cold warm monthly composites of SST (K), latent heat flux (W m 22 ), surface net shortwave radiation (W m 22 ), and horizontal winds at 850 hpa (m s 21 ) for June August based on the normalized JJAS seasonal mean S-PC1 of the ENSO mode in the control. negative SST anomalies there. Additionally, the remotely forced winds appear to be driven by the large SST anomalies in the eastern IO, which is absent in the IO- VSST. The LHF anomalies of the control show some signs of atmospheric control on SST. For example, negative LHF anomalies over the equatorial IO (08 108S, E) indicate reduced evaporative cooling, thereby aiding in the formation of positive SST anomalies there. Similarly, positive LHF anomalies over the Arabian Sea are consistent with negative SST anomalies there. To examine the role of the atmospheric heat fluxes on the SST anomalies, the evolution of SST, LHF, net shortwave radiation (SWR) (downward upward), and horizontal winds at 850 hpa are composited for each month separately based on the JJAS seasonal mean PC1 of the ENSO mode in the control simulation (Fig. 11). The impact of the other components in the heat flux equation (longwave radiation and sensible heat) was found to be negligible in previous studies (Lau and Nath 2003). All years when the JJAS mean of the normalized PC1isabove0.8orbelow20.8 are included in the composite. Based on that criterion, six cold and five warm events were obtained. Similar composites prepared based on the JJAS Niño-3 index are qualitatively similar to Fig. 11 (figure not shown). Negative SST anomalies begin to appear over the western IO by July and strengthen as the monsoon progresses. The positive SST anomalies over the eastern IO extend to the central IO as the season progresses. The low-level circulation along the equatorial IO is developed by July and is maintained throughout the season. In July, as the southwesterly wind sets over the western IO, large positive LHF anomalies form along the wind path. The SWR anomalies are weak or negative over the western IO except for a small region off the coast of Africa, south of the equator. In July, both the SWR and LHF anomalies seem to act together in cooling the surface. Another instance where the LHF and SWR anomalies act together to promote surface cooling

14 1APRIL 2012 A C H U T H A V A R I E R E T A L FIG. 12. Cold warm monthly composites of observed SST (K), latent heat flux (W m 22 ), and horizontal winds at 850 hpa (m s 21 ) for June August based on the normalized JJAS seasonal mean Niño-3 index from the observations. Observed SST is from the Hadley Centre and latent heat flux and wind are from the ERA-40 reanalysis, both for the period is seen over the Arabian Sea and Bay of Bengal and the southern part of IO ( S, E) in September. Over the western IO, the SWR anomalies are negative throughout the season, consistent with the increased rainfall anomalies. There are negative LHF anomalies over the central part of the IO, which however, are offset by the negative SWR anomalies. So the surface heat fluxes do not provide a complete explanation for the development of the SST anomalies. Figure 11 is compared with observations using SST from the Hadley Centre (Rayner et al. 2003) and winds and latent heat flux from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re- Analysis (Uppala et al. 2005) in Fig. 12 where monthly composites are calculated based on the JJAS Niño-3 index. If the normalized JJAS Niño-3 index is above 0.8 (below 20.8), it is considered to be a warm (cold) year. Based on this criterion, 10 warm and 8 cold events were identified during the period Figure 12 shows that the SST anomalies in the IO are much larger in the CFS compared to the observations. In the model, the warm SST anomalies in the eastern IO develop early in the monsoon season and extend to the central IO by the end of the season. In the observations, the eastern IO anomalies mature only by the end of the monsoon season, and therefore the SST anomalies are of the same sign over most of the IO during the monsoon season. The LHF and wind anomalies are also stronger in the model. While the observed LHF anomalies in the eastern IO are consistent with the warming, the atmospheric fluxes do not play a major role in generating SST anomalies in the model. Next, the role of the subsurface temperature fluctuations in the formation of the SST anomalies is examined.

15 2504 J O U R N A L O F C L I M A T E VOLUME 25 FIG. 13. As in Fig. 11, but for ocean potential temperature. Composites are averaged over (left) 08 58N and (right) 108S 08. Figure 13 shows composites of potential temperature anomalies averaged over 08 58N and 108S 08 in the control run. Composites are prepared in the same way as in Fig. 11. Figure 14 shows the corresponding temperature composites using the Simple Ocean Data Assimilation (SODA) version (Carton and Giese 2008) data for the period and based on the JJAS Niño-3 index from the Hadley Centre SST data. Comparing Figs. 13 and 14, the subsurface temperature anomalies are larger in the model. In the SODA data, positive anomalies in the eastern IO are weak and develop only by the end of the monsoon season while, in the model, a strong east west dipole is present in July. Figure 15 shows the monthly climatology of ocean temperature averaged over 108S 08 for May September for the CFS control and the SODA data. The CFS ocean data used here are for potential temperature while the SODA data are for the in situ temperature. However, the difference between the potential and in situ temperatures can be considered negligible in the upperocean layers. In JJAS, climatological winds over the western and eastern IO promote coastal upwelling off the coasts of Africa and the Indonesian islands, respectively. During a cold Pacific event, westerly anomalies in the

16 1APRIL 2012 A C H U T H A V A R I E R E T A L FIG. 14. As in Fig. 12, but for observed SODA ocean temperature for the period Composites are averaged over (left) 08 58N and (right) 108S 08. western IO increase the total wind and therefore enhance upwelling. Over the eastern IO, the northwesterly anomalies act to reduce the total wind and thereby reduce the coastal upwelling. Similarly, anomalous winds during warm Pacific events act to increase (decrease) the upwelling over the eastern (western) IO. The westerly wind anomalies that developed over the equatorial ocean also change the slope of the thermocline along the equator, which causes anomalous upwelling in the west and downwelling in the east. The mean state of the ocean vertical temperature profile from SODA data (Fig. 15) indicates deeper thermocline in the east, which suggests a minimum influence of winds on the subsurface temperature fluctuations. On the other hand, in the model, the relatively shallow thermocline in the eastern IO provides a set of conditions where surface winds can impart thermocline anomalies easily. Compared with the observations, the zonal slope of the thermocline in the model is tilted in the opposite direction along the equator. 6. Summary and discussion The present study has examined the ENSO-induced rainfall anomalies in the Indian monsoon region and the

17 2506 J O U R N A L O F C L I M A T E VOLUME 25 FIG. 15. Monthly climatology of ocean temperature for the months May September averaged over 108S 08 for (left) CFS control and (right) SODA in 8C. CFS climatology is of potential temperature and SODA climatology is of in situ temperature. impact of local air sea interaction in the NCEP CFS coupled model. The intent of the study is to understand why CFS, a fully coupled GCM, fails to simulate the observed negative correlation between the summer monsoon rainfall and the Niño-3 index. The correlation is partially restored when the IO is selectively prescribed with daily mean or daily climatological SST while full coupling is allowed elsewhere. Further, it is shown that

18 1APRIL 2012 A C H U T H A V A R I E R E T A L an overactive Indian Ocean dipole or zonal mode in the coupled model prevents the ENSO-induced anomalies from reaching India and adjacent oceanic regions. In the coupled run, larger than observed SST fluctuations form a dipole pattern in the zonal direction that matures as the monsoon season progresses. During a cold (warm) Pacific event, as early as July of the ENSO developing year, positive (negative) SST anomalies develop in the eastern IO while negative (positive) anomalies appear over the west, along the coast of Africa. This SST dipole modifies the ENSO-induced low-level winds in the equatorial IO. During a La Niña (El Niño) event, westerly (easterly) anomalies in the northwestern IO or Arabian Sea become northwesterlies (southeasterlies) as a result of the positive (negative) SST anomalies and associated low (high) pressure anomalies over the eastern IO. The SST anomalies in the western IO are formed partly by latent heat flux anomalies and partly by increased decreased upwelling. In the eastern IO, the thermocline is shallow, making it possible for the wind anomalies to enhance or reduce upwelling. Due to the climatological nature of the winds in the region (i.e., westerlies in the east, and easterlies in the west), a dipole once formed can be amplified due to air sea feedback, in which SST anomalies drive surface pressure and wind anomalies in turn reinforce the SST anomalies. These findings indicate that the IO SST variability plays an important role in the ENSO-induced anomalies in the Indian monsoon region. A strong SST dipole with positive anomalies in the east and negative anomalies in the west may suppress the effect of ENSO on the Indian monsoon region. This result is consistent with recent observational evidence reported by Krishnamurthy and Shukla (2008), who showed that in 1997, when the JJAS seasonal mean monsoon was near normal despite coinciding with a developing El Niño, the IO dipole signal acted to nullify the impact of the ENSO. While in the observations, the dipole acted to nullify the impact of the ENSO in certain years, in the CFS, a dipole is present almost always with an ENSO. Recently, Janakiraman et al. (2011) pointed out the existence of unrealistic IO dipole events in their CFS hindcast run. Lau and Nath (2000) also noted that in a coupled system, the ENSO-induced SST anomalies in the IO could act to reduce the impact of the remote signal. The improvements seen in the decoupled runs should be viewed with caution, where the local feedbacks are unrealistically shut down. Our results do not indicate that coupled processes in the IO are irrelevant, especially since the observed data show evidence of air sea interaction. Conversely, these results suggest that biases in the local air sea feedbacks in the IO can adversely affect the remote ENSO signals in the region and the ENSO monsoon correlation. Future work should address the unrealistic variability in the IO and the biases in the mean thermocline depth in the CFS. One approach would be to compare the long coupled run with the CFS hindcast, where the mean state biases are unlikely to be fully developed. Another point worth pursuing is that a dipole in the IO can nullify the impact of ENSO on the monsoon. While this study has focused on the aspect of model bias, it would be worthwhile to analyze similar cases in the observations. As mentioned earlier, observational analysis shows that 1997 was one such case. This suggests that the correct simulation of the IO climate is important for improving the seasonal forecasts of the monsoon rainfall. Acknowledgments. This research was supported by grants from the National Science Foundation (ATM , ATM , and ATM ), the National Oceanic and Atmospheric Administration (NA04OAR and NA09OAR ), and the National Aeronautics and Space Administration (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. DA would like to thank Kathy Pegion for CFS control simulation data and help in setting up the model, Edwin Schneider for CFS ocean output data, and Lakshmi Krishnamurthy for the precipitation observations. A large part of this work formed part of the Ph.D. thesis of DA submitted to George Mason University. REFERENCES Angell, J. 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