4FB 4VSGBDF 4BMJOJUZ 7BSJBCJMJUZ BOE JUT …asia-pacific journal of atmospheric sciences, 44, 2,...

ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES, 44, 2, 2008, p. 173-189

Sea Surface Salinity Variability and its Relation

to El Niño in a CGCM

Bo Young Yim1, Sang-Wook Yeh

2, Yign Noh

1, Byung-Kwon Moon

3 and Young-Gyu Park

2

1Department of Atmospheric Sciences, Yonsei University, Seoul, Korea2Korea Ocean Research & Development Institute, Ansan, Korea

3Division of Science Education/Institute of Science Education, Chonbuk National University, Jeonju, Korea(Manuscript received 22 March 2008; in final form 9 May 2008)

Abstract

We explore the characteristics of sea surface salinity (SSS) variability along with its relation to the El Niño by analyzing results from a coupled general circulation model (CGCM). The CGCM simulates realistic El Niño as well as the mean and the variability of SSS. The SSS anomaly variability is dominated on interannual timescales with its maximum variance in the western tropical Pacific, and large positive SSS anomaly events occur prior to El Niño. The SSS variability and its associated barrier layer thickness are related to the El Niño in the CGCM. Between two subsequent El Niño events a buildup of warm water is evident as indicated by a large barrier layer thickness in the western equatorial Pacific. Weak stratification due to high SSS anomalies helps to discharge the heat stored in the thick barrier layer in the western equatorial Pacific, initiating El Niño development. Further analysis is conducted to support the role of SSS variability associated with El Niño in terms of the variability of heat content anomalies.

Key words: Sea surface salinity, El Niño, CGCM, barrier layer thickness, stratification

Corresponding Author: Yign Noh, Department of Atmosp-heric Sciences/Global Environmental Laboratory, YonseiUniversity, 134 Shinchon-dong, Seodaemun-gu, Seoul 120-749, Korea.Phone : +82-2-2123-2690, Fax : +82-2-365-5163E-mail: [email protected]

1. Introduction

There is no doubt that El Niño and Southern Oscillation (ENSO) is the largest climatic phenom-enon in the tropical Pacific ocean-atmosphere cou-pled system, but its environmental impacts are be-yond the tropics through the atmospheric teleconnections. During the past two decades consid-erable progresses have been made in order to describe ENSO as a coupled ocean-atmosphere phenomenon as well as understanding its underlying physics (McPhaden et al., 2006). While the effort has con-tinued to improve our understanding of ENSO phys-ics, the dimensions of this research have expanded to explore the role of other climate phenomena asso-ciated with ENSO such as salinity variability.

Recent studies using the observations and coupled general circulation model (CGCM) simulations have paid attention on the role of salinity variability to un-derstand air-sea interactions involved in the El Niño events in the western tropical Pacific (Lukas, 1988; Godfrey and Lindstrom, 1989; Lukas and Lindstrom 1991; Shinoda and Lukas, 1995; Vialard and Delecluse, 1998a,b; Maes, 2000; Vialard et al., 2002; Delcroix and McPhaden, 2002; Maes et al., 2002a,b; Maes et al., 2005, 2006). It has been thought that sal-inity plays a very little role, but these studies suggest that salinity is not only a tracer of the interannual ENSO but could play an active role through the sub-surface processes associated with ENSO. This is be-cause the ENSO is linked to the subsurface adjust-ment of the ocean that provides the memory of the coupled ocean-atmosphere system (Wang, 2001; Wang and Fiedler, 2006). Equatorial heat buildup is an intrinsic element of El Niño development (Meinen and McPhaden, 2000) and the amount of oceanic heat in the upper layer of the warm pool can be dependent on the salinity stratification (Vialard and Delecluse,

174 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES

1998a). Observational analysis has revealed that the mixed layer in the western tropical Pacific is in-sulated from deeper cold waters by strong salinity stratification within the warm isothermal waters of the so-called “barrier layer” phenomenon (Lindstrom et al., 1987; Lukas and Lindstrom, 1991). The heat buildup is a necessary condition for El Niño develop-ment and the barrier layer in the western equatorial Pacific is important for maintaining the heat buildup. Modeling analysis is further arguing that the sea sur-face salinity (SSS) variability and its associated bar-rier layer are intrinsically linked to the dynamics of ENSO (Vialard and Delecluse, 1998a,b; Maes et al., 2005). Horizontal gradients of salinity contribute to horizontal pressure gradient and variability of the currents (Cooper, 1988; Murtugudde and Busalacchi, 1998; Roemmich et al., 1994). Salinity variability al-so could influence the thermal field, dynamic top-ography, and the vertical stability of water column.

So far these sorts of studies heavily rely on the anal-ysis of observational data such as spot oceanographic cruises, on CTD-derived gridded seasonal fields, in situ and the satellite data (Maes et al., 2006). While these observational analyses reveal a number of im-portant features of salinity variability it is difficult to identify a long-term evolution of the salinity field and its associated relationship with El Niño due to data availability issue. Several studies using ocean gen-eral circulation models have focused on the barrier layer associated with salinity stratification in ideal-ized experiments (Vialard and Delecluse, 1998a,b; Vialard et al., 2002). Besides, Maes et al. (2002, 2005) examined such issues in observations and in a CGCM. However, there is no CGCM study in inves-tigating on continuous evolution on the salinity vari-ability and its associated relationship with El Niño. In this study we explore the relationship of SSS varia-bility in the western tropical Pacific with El Niño us-ing a CGCM simulation. Since understanding the role of salinity variability is associated with the oscil-latory nature of El Niño, it is prerequisite for the CGCM to simulate El Niño properly to investigate the effects of salinity variability. The CGCM used in this study simulates realistic El Niño, which also mo-tivates this study. In this work we use data from a

CGCM simulation to investigate the role of SSS vari-ability on the El Niño with the analysis of barrier layer simulated in a CGCM.

The paper is organized as follows. Section 2 is de-voted to the coupled models’ descriptions. Section 3 describes the characteristics of SSS variability si-mulated in a CGCM and its association with ENSO and the summary is given in the section 4.

2. The coupled model

The atmospheric component of our CGCM is the Seoul National University atmospheric general cir-culation model (SNUAGCM) (Kim et al., 1998), a global spectral model with T42 resolution (approximately 2.8° long x 2.8° lat) with 20 unevenly spaced sigma-coordinate vertical levels in the model. It is based on the CCSR/NIES AGCM (Numaguti et al., 1995), but has several major changes in physical process. The SNUAGCM contains non-precipitat-ing shallow convection in diffusion type (Bonan, 1996) and the non-local PBL/vertical diffusion scheme (Holtslag and Boville, 1993) and land sur-face parameterization by National Center for Atmospheric Research (NCAR) Community Climate Model 3 (CCM3) (Bonan, 1996). The radia-tion processes are parameterized by the two stream k-distribution method (Nakajima and Tanaka, 1986). The cumulus parameterization is based on the Relaxed Arkawa- Schubert scheme (Moorthi and Suarez, 1992). The orographic gravity-wave drag is parameterized following McFarlane (1987). The SNUAGCM has been widely used in studies of the Asian monsoon (Yoo et al., 2004), El Niño events (Lee et al., 2002) and atmospheric convective activ-ities (Wang et al., 2004). It is one of the major atmos-pheric GCMs involved in the recent International Research Programme on Climate Variability and Predictability (CLIVAR) Monsoon GCM Intercom- parison Project (Kang et al., 2002; Waliser et al., 2003).

The ocean model is the Geophysical Fluid Dynamical Laboratory (GFDL) Modular Ocean Model (Rosati and Miyakoda, 1988; Pacanowski et al., 1993) version 2.2 (MOM2.2). Its domain extends

31 May 2008 Bo Young Yim et al. 175

Fig. 1. The time series of NINO3 index simulated in the CGCM for the period of 45 years (a). (b) is the same as in(a) except for the period of 1963-2007 in observations.

global from 30°S to 50°N with realistic bottom topography. The sea surface temperatures (SSTs) are relaxed to Levitus (1982) climatology along the northern and southern boundary using a Newtonian damping. The grid has 360 uniformly spaced points in longitude. The meridional resolution is varying from 1/3° within the region 10°N-10°S. The reso-lution increase around 3° near the northern and south-ern boundary. Vertically, there are 37 unevenly spaced depth levels, of which 22 levels within the up-per 275 m. The thickness increases smoothly from 7.5 m at z = 0 m to 10 m at z = 70-120 m, to 50 m at z = 600 m, and to 1000 m at z = 4800 m. The lateral mixing coefficients for eddy viscosity and diffusivity are given by AM = 1 × 104 m2s-1 and AH = 1 × 103 m2s-1, respectively. For the vertical mixing, the Noh ocean mixed layer model was used, which has been shown to reproduce the upper ocean structure realistically in various circumstances (Noh and Kim, 1999; Noh et al., 2002, 2005, 2007) and is supported by large eddy simulation results (Noh et al., 2004). The de-tailed explanation is given by Noh et al. (2002).

The atmosphere and ocean components are cou-pled without any flux corrections or restoring once a day by exchanging daily mean values of the surface heat flux, freshwater flux, momentum fluxes and SST. The atmospheric initial state is taken from a 10 year simulation with observed SST. The ocean initial state is taken from Levitus SST and salinity climatology. The simulation has been run for 50 years and all of the analysis shown here is based on the data for the last 45 years. During this period, the surface variables do not show significant long-term tendency and there is no climate drift for the entire period. A more complete description of the model and its varia-bility is presented in the Appendix. The CGCM used in this study is basically the same as that of Kug et al. (2008) and Kim et al. (2008), except horizontal do-main and vertical resolution of the ocean model.

3. Analysis

a. Simulated ENSO

In order to show the ENSO variability simulated

in a CGCM a time series of SST anomaly (SSTA) averaged in the NINO3 region (5°N-5°S, 150°W -90°W, hereafter, NINO3 SST index) from the model is shown in Fig. 1a and that from observations for the period 1963-2007 in Fig. 1b. Here, we used the ob-served monthly SST from 1963 to 2007, which were recently released by the National Climatic Data Center (Smith and Reynolds, 2004). The observed SSTA is defined as the deviation from the mean annu-al cycle calculated over the entire record (1963- 2007). The ENSO events simulated in the model are irregular and the amplitude of warm and cold events is reasonable, with peaks in the neighborhood of -2.0°C to +3.0°C. One standard deviation of the si-mulated NINO3 SST index is 0.84°C, and is slightly weaker than that of the observations (0.92°C). This is in part due to the narrow meridional scale of tropical SSTA variability simulated in the CGCM compared to the observations (not shown). The power spectrum of the NINO3 SST index is shown in Fig. 2. The NINO3 SST index yields a broad peak around 3-7 years along with relatively short time scale (~ 15 months) of variability, which is comparable to that in observations (2-7 years). In spite of some discrep-ancies the CGCM simulates realistic ENSO in terms


Fig. 2. Power spectra of the simulated NINO3 index. Thesolid curve shows the power spectra and the long-dashedcurve shows the power spectra for red noise.

of amplitude (Fig. 1a) and frequency (Fig. 2) con-sistent with observations, and it enables us to inves-tigate the relationship between the SSSA variability and El Niño.

b. Relationship between SSS anomaly variability

and ENSO

We first show the pattern of simulated SSS varia-bility in the tropical Pacific, in comparison with that of the Simple Ocean Data Assimilation (SODA) ocean reanalysis for the period 1958-2004 (Carton et al., 2005). Figure 3 shows the annual mean SSS (a) and the seasonal cycle (b) of SSS in the CGCM and SODA after averaging between 2°N and 2°S. The CGCM simulates general features of salinity dis-tribution, although the annual mean SSS in the west-ern tropical Pacific is lower than those of SODA and the seasonal cycle of SSS in the eastern tropical Pacific is stronger than those of the SODA. The west-ern tropical Pacific is marked by low salinity in the CGCM, separating the salinity lower than 35 psu west of 150°E from the high salinity in the central and eastern equatorial Pacific (Figs. 3a,b). Seasonal SSS cycle becomes stronger to the east with higher sal-inity in summer and with lower salinity in winter in the east (Fig. 3b). The amplitude of the seasonal SSS variations is around 2 psu in the equatorial Pacific,

which is larger than that of the observed climato-logical value around 1 psu (Delcroix and Hénin 1991). In the central and western equatorial Pacific a seasonal cycle is weak, and a SSS front appears be-tween the fresh western Pacific warm pool and the saltier central Pacific water, which is generally con-sistent with the results described in previous studies (Picaut et al., 1996; Delcroix and Picaut, 1997; Stoens et al., 1999).

Figure 3c compares the standard deviation of the total SSS anomaly (SSSA) from the CGCM and SODA. The anomaly is defined as the deviation from the mean annual cycle calculated over the analyzed period. The SSSA variability simulated in the CGCM is much larger than that in the SODA, but the 1979-92 standard deviation of SSS assembled using a combination of bucket, merchant ship thermosali-nograph (TSG) network (Hénin and Grelet, 1996), and CTD data (Delcroix, 1998) is similar to that of the CGCM in its magnitude (Lagerloef and Delcroix 2001). The simulated SSSA variability is low in the central and eastern equatorial Pacific. This reduced variability can be explained by upwelling processes, strong vertical mixing and a low level of precip-itation (Maes, 2000). In the western equatorial Pacific, however, the SSSA variability is large in the region of 130°E-170°E, 10°N-10°S, with a max-imum around the north of New Guinea (Fig. 3c). The maximum SSSA variability in the western equato-rial Pacific is co-located with the maximum precip-itation variability simulated in the CGCM (not shown), indicating that the simulated SSSA varia-bility primarily reflects the freshwater forcing due to precipitation in this region. These results are con-sistent with the observations reported by Maes (2000) where the maximum SSS variability is found primarily in the western Pacific warm pool based on the analyzed results using Tropical Ocean Global Atmosphere (TOGA) Tropical Atmosphere Ocean (TAO) data.

Figure 4a shows the time series of the SSSA aver-aged over the western equatorial Pacific (130°E- 170°E, 10°N-10°S, hereafter, WPSSS index) for the analyzed period. The WPSSS index shows irregular variations on interannual timescales. The WPSSS in-


Fig. 3. The annual mean sea surface salinity (SSS) averaging 2°N and 2°S (a), the seasonal cycle of SSS (b) and the standarddeviation of total SSS anomaly (c) from the CGCM (left) and SODA (right). Contour interval is 0.5 psu in (b) and 0.1psu in (c). Shading is above 0.1 psu in (c).


Fig. 4. The time series of (a) the SSSA averaged over thewestern equatorial Pacific (130°E-170°E, 10°N-10°S). (b)the simulated SSTA averaged over the NINO3 region(5°N-5°S, 210°E-270°E) for the model period 6-50 years.Vertical lines in (a) and arrows in (b) indicate a peak of pos-itive SSSA event and a subsequent El Niño, respectively. See the text for the explanation.

Fig. 5. The power spectrum of the WPSSS index simulatedin the CGCM. The solid curve shows the power spectra andthe long-dashed curve shows the power spectra for red noise. See the text for the explanation of the WPSSS index.

dex varies from 0.5 psu to -0.7 psu and its one standard deviation is 0.21 psu. For comparison with the WPSST index (Fig. 4a) we show again the time series of NINO3 SST index (Fig. 4b). It is significant that large positive SSSA events occur prior to El Niño. For example, positive SSSA event is dominant dur-ing model years 10-11, 14-16, 17-18, 21-22, 32-33, 39-40, 43-45, and 48-49 and El Niño subsequently occurs at model years 11-12, 16, 18-19, 23-24, 34, 41-42, 46, and 50. Simply put, this result indicates that positive SSSA event in the western equatorial Pacific is a precursor of El Niño. The power spectrum of the WPSSS index is also shown in Fig. 5. The domi-nant period of WPSSS index is around 3-7 years with a broad spectrum on interannual timescales, which is quiet similar to that of the NINO3 SST index as in Fig. 2. This suggests that the SSSA variability in the western equatorial Pacific is closely related to the ENSO in the CGCM.

To examine the tropical Pacific SSSA variability associated with El Niño we first compute the lag regression coefficients between tropical Pacific SSSAs and the NINO3 SST index (Figs. 6a-d).

Figures 6a-d show the regression of SSSAs versus the NINO3 SST index for lags of -18 months to zero month at six-month intervals. The SSSA varia-bility against the NINO3 SST index is limited to the lagged-time because we focus on the SSSA var-iability prior to El Niño. Note that regions in which regression coefficients between SSSAs and the NINO3 SST index exceed 90% significance are shaded. The prominent variations in SSSAs asso-ciated with El Niño are isolated in the western equa-torial Pacific. At a lag of 18 months there are pos-itive deviations of SSSAs in the western equatorial Pacific (Fig. 6a). As time progresses, the SSSA var-iability is more intensified in the western equatorial Pacific (Fig. 6b). At a lag of 6 months, the max-imum SSSA variability is shift to the off equatorial (~10°N or ~10°S) western Pacific in both hemispheres. Negative SSSAs associated with El Niño first appear in the western equatorial Pacific at around three months lag (not shown). The SSSA pattern at zero lag, which corresponds to the mature El Niño phase, is shown in Fig. 6d. The low salinity at the peak of the El Niño may be linked to increased precipitation over the same region (Mae, 2000). Figure 6 suggests that positive SSSAs appear and persist more than 12 months in the western tropical Pacific prior to El Niño. This is consistent with the


Fig. 7. The composite of high SSSA states based on periodswhen the WPSSS index exceeds one standard deviation (a). (c) is the same as in (a) except for the low SSSA statesbased on periods when the WPSSS index is less than onestandard deviation below normal. (b), (d) is the same as in(a), (c) except for corresponding SSTAs, respectively. Regions in which the composite map exceeds 90% sig-nificance are shaded.

Fig. 6. The regression of SSSAs versus the NINO3 SSTindex for lags of -18 months to zero month at six-monthintervals (a)-(d). Regions in which regression coefficientsbetween SSSAs and the NINO3 SST index exceed 90%significance are shaded.

results as in Fig. 4, i.e., positive SSSA event in the western equatorial Pacific as a precursor of El Niño.

To further examine the relationship between the SSSA variability and ENSO we computed compo-sites based on the WPSSS index. Relatively high SSSA states in the western equatorial Pacific (Fig. 7a) are based on periods when the WPSSS index ex-

ceeds one standard deviation. Similarly, low SSSA states (Fig. 7c) are based on periods when the WPSSS index is less than one standard deviation below normal. Corresponding SST anomalies (SSTAs) are also shown in Figs. 7b,d, respectively. The spa-tial patt ern of high SSSA resembles that of regressed


Fig. 8. The time-longitude sections of (a) SSTAs and (b) BLT along the equator for the period of model year 11-30. Thedash line in (a) represents negative SSTA. Contour interval is 0.5°C in (a) and 20 m in (b). The black line in (b) representsthe 26.5°C SST isotherm.


Fig. 9. The composite of anomalous BLT during the mature stage of El Niño (a). (b) is the same as in (a) except for theLa Niña. Unit is meter. Regions in which the composite map exceeds 90% significance are shaded.

SSSAs against the NINO3 SST index at a lagged time around 12-18 months (Figs. 6a,b). High values are found in the western equatorial Pacific with two branches running northeastward and south-eastward like a boomerang-shape. The corre-sponding SSTAs (Fig. 7b) are characterized by weak cold event around -0.5°C extending to the far west. As shown in the appendix the simulated ENSO in the CGCM is characterized by the west-ward propagation of the SSTA along the equator. This indicates that weak cold event extending to the far west corresponds to the decaying stage of La Niña which is associated with high SSSA state in the CGCM. As shown in Fig. 4 we argued that large positive SSSA events occur prior to El Niño. The results in Fig. 4 and Figs. 7a,b indicate that high SSSAs are prepared for a El Niño event on the decaying stage of La Niña in the western equa-torial Pacific. The spatial patterns for low SSSAs (Fig. 7c) resemble that of high SSSAs except for the sign. The corresponding SSTAs (Fig. 7d) are characterized by warm event extending to the west which is also away from the peak of simulated El Niño in the eastern equatorial Pacific in the CGCM. Unlike weak cold event as in Fig. 7b, rela-tively large amplitude of composite warm event indicates that the SSTAs corresponding to low SSSA state are on the very early stage of El Niño decaying.

c. Role of SSS variability

Here we investigate the relationship between the

barrier layer and ENSO simulated in the CGCM. Figure 8 shows the time-longitude sections of (a) SSTAs and (b) BLT along the equator for the period of model year 11-30 chosen arbitrary. The black line represents the 26.5°C SST isotherm indicating a boundary of warm water. The BLT is computed as the difference between the depth where the temperature differs from SST by 0.5°C and the depth of the mixed layer where the density differs from the surface den-sity by 0.125 kg m-3 (Maes et al., 2005). As shown in Fig. 1a, the model simulates ENSO variability with different intensities and at irregular intervals on inter-annual timescales (Fig. 8a).

The variability of BLT is dominant in the western equatorial Pacific and extends to the west of the date line (Fig. 8b). During El Niño (i.e., model years, 11-12, 16, 18-19, 23-24) 26.5°C isothermal line ex-tends to the east and the BLT is relatively thin. On the other hand, during La Niña (i.e., model years, 13-15, 17, 20-21, 24-25) a thick barrier layer is formed in the western tropical Pacific. In order to describe a more detailed variability of BLT associated with ENSO we present BLTA composites during the ma-ture stage of El Niño and La Niña in Fig. 9. There are large differences of BLT anomalies in western trop-ical Pacific during the mature stage of El Niño and La Niña. During El Niño BLTA composite is shal-lower more than 20 m in the western equatorial Pacific, while it is thicker about 5-10 m during La Niña. Changes in BLT are significantly associated with heat storage in the subsurface layer, i.e., buildup or discharge. Close inspection of BLT variability in Fig. 9 indicates that more heat is stored within the bar-


Fig. 10. The Lead-lag regression of temperature anomalies versus the BLTAs averaged over the western equatorialPacific (130°E-170°E, 10°N-10°S) along the equator for lags of -15 months to 18 months at three-month intervals(a)-(l). Regions in which regression coefficients between temperature anomalies and the BLTAs exceed 90% sig-nificance are shaded.


Fig. 11. The composite of anomalous mean vertical buoy-ancy frequency N2(z) in the upper layer during high SSSAstate. (b) is the same as in (a) except for the low SSSA state.Unit is (min)-2. Regions in which the composite map ex-ceeds 90% significance are shaded.

1) The mature stage of El Niño is defined the period whenthe NINO3 SST index exceeds one standard deviation during three successive months. Similarly, the mature stage of La Niña is defined the period when the NINO3SST index is less than one standard deviation below normal.

rier layer in the western tropical Pacific during the mature stage of La Niña than during that of El Niño1).

In order to understand how the BLT variability is associated with heat storage and El Niño in the sub-surface layer, the lead-lag regression of temperature anomalies with time series of BLTAs along the equa-tor is shown in Fig. 10. Here, the time series of the BLTAs are averaged over the western equatorial Pacific (130°E-170°E, 10°N-10°S) which corre-sponds to the regions of the WPSSS index. Note that the 0 month (Fig. 10f) and 12 months (Fig. 10j) corre-spond to BLT maximum and the mature stage of El Niño, respectively. As the BLT is thicker in the west-ern equatorial Pacific from the -15 months to 0 month, it helps to sustain warm water within the subsurface layer during La Niña. After the BLT reaches the max-imum, the heat stored within the thick barrier layer gradually migrates to the central and eastern equato-rial Pacific, initiating El Niño development (Figs. 10g-l). This result illustrates the role of the barrier layer in the western equatorial Pacific in initiating El Niño.

Remind that the composite SSTAs corresponding to high SSSA state are characterized by weak cold event which is on the decaying stage of La Niña. On the contrary, those corresponding to low SSSA state are characterized by warm event which is on the very early decaying stage of El Niño. In order to under-stand the role of SSS variability we composite the anomalous mean vertical buoyancy frequency N2(z)

(here, N2(z)= zg

∂∂− ρ

ρ is the Brunt-Väisälä frequency)

corresponding to high and low SSSA states, re-spectively (Fig. 11). The anomalous mean vertical buoyancy frequency N2(z) corresponding to high and low SSSA states has negative and positive sign at up-per levels (~120 m) in the western and central equato-rial Pacific, respectively, indicating that the vertical stratification decreases (increases) during the period

of high (low) SSSA state. The weakened vertical stratification contributes to strengthen the vertical mixing, resulting in the release of the ocean heat con-tent stored within the barrier layer in the western equatorial Pacific. These results give a hint on the role of SSS variability in relation to El Niño simulated in the CGCM. That is to say, the strengthened vertical


2) The first EOF explains 23.8% of the total HCA varianceand its spatial pattern has large variability off the equatorin both hemispheres.

Fig. 12. The second HCA EOF for the entire analyzed peri-od (a). (b) is the lead-lagged correlation of WPSSS indexand the PC time series of the second HCA EOF. Negativelags indicate the WPSSS index preceding the PC of the sec-ond EOF.

mixing due to the weakened stratification from high SSSA during the decaying stage of La Niña helps to release the heat stored within the thick barrier layer in the western tropical Pacific to the upper mixed layer. The released warm water mass migrates to the east by downwelling equatorial Kelvin waves that propagate eastward, which thus initiates El Niño. On the other hand, during the early stage of El Niño de-caying strong stratification due to low concentration SSSAs start to accumulate heat in the western equato-rial Pacific, preparing the subsequent El Niño via La Niña with a large barrier layer thickness.

In order to support the above argument we show the relationship between heat storage and SSSA var-iability in the western equatorial Pacific. We here an-alyze the variability of simulated heat content anomalies (HCAs) which is averaged temperature anomaly in the upper 250 m of the ocean in the CGCM. Figure 12a is the second HCA empirical or-thogonal function (EOF), which explains 20.8% of

the total HCA variance2). The spatial pattern of the second EOF has large positive HCAs localized along the equator in the central and eastern tropical Pacific, indicating that this mode of variability is as-sociated with ENSO. The lead-lag correlation co-efficients between the time series of the principal component (PC) of the second HCA EOF and the NINO3 SST index (not shown) indicate that this mode of HCA variability leads to the NINO3 SST at a leads of 3 months on significant confidence level. To understand the relationship between SSSA varia-bility and HCAs the lagged correlation of WPSSS index and the PC time series of the second HCA EOF is calculated (Fig. 12b). The maximum correlation between the two time series occurs around a lag of five month with positive correlation, 0.64, at the 95% confidence level. This indicates that large positive HCAs in the central equatorial Pacific follow high SSSA event in the western equatorial Pacific, result-ing from discharging heat from the west to the east due to weak stratification. This is also consistent with the result in Fig. 10.

It is interesting to note that there is no indication that the amplitude and persistent time of a thick bar-rier layer is linearly related to the ENSO amplitude and duration. For example, thick barrier layer with large amplitude persists during the period of model years 14-15, however, the subsequent El Niño, which occurs during the middle of year 16, has weak ampli-tude with short duration. On the other hand, the bar-rier layer, which is persistent during model year 17, is associated with subsequent El Niño with long last-ing and large amplitude during the period of model yeas 18-19.

4. Conclusion and Discussion

Several recent studies have paid attention to the role of salinity variability to understand air-sea inter-actions associated with ENSO in the western tropical Pacific. More work, however, is required to de-


termine the influence of the salinity variability during ENSO in both the observations and modeling. In this paper we used a CGCM simulation to document sal-inity variability and its relationship to El Niño. We first analyzed the simulated SSSA variability, and then we showed the relationship between SSSA vari-ability and ENSO simulated in the CGCM with fo-cusing on the role of SSS variability. We obtain the following results:∙The mean and seasonal cycle of simulated SSS

variability is comparable to the observations. The eastern and central equatorial Pacific is charac-terized by high mean salinity, and the western trop-ical Pacific is marked by low salinity with large variability. The SSSA variability in the western equatorial Pacific has irregular variations on inter-annual timescales.∙The prominent variations in SSSAs associated

with El Niño are isolated in the western equatorial Pacific. The composite analysis indicated that the SSTA during high SSSA state corresponds to the decaying stage of La Niña, while that during low SSSA state corresponds to the very early stage of El Niño decaying. This indicates that high and low SSSA states are not on the symmetric relationship with the corresponding SSTA states in the tropical Pacific. ∙We showed the relationship between the barrier

layer and ENSO simulated in the CGCM. During warm events the BLT is relatively thin, on the other hand, during cold events a thick barrier layer is formed over the western equatorial Pacific. There are large differences of BLT anomalies in western tropical Pacific during the mature stage of El Niño and La Niña, indicating that changes in heat stor-age in the western tropical Pacific is closely asso-ciated with the El Niño development. ∙The HCA analysis showed that high SSSA event in

the western equatorial Pacific precedes large pos-itive HCAs in the central equatorial Pacific, which suggests the westward transport of heat stored in the BLT in the western tropical Pacific to the east. The strengthened vertical mixing due to the weakened stratification from high SSSA during the decaying stage of La Niña helps to release the heat stored with-

in the thick BLT to the upper mixed layer. The re-leased warm water mass migrates to the east, thus initiating El Niño. On the other hand, during the ear-ly stage of El Niño decaying strong stratification due to low concentration SSSAs start to accumulate heat in the western equatorial Pacific, preparing the sub-sequent El Niño.Our analysis, however, showed that there is no in-

dication that the strength of BLT and SSS variability is linearly related to that of ENSO. Moreover, the SSTA corresponding to high and low SSSA state has some differences in terms of amplitude and phase. This requires more detail analysis of the subsurface ocean variability including the connection of trop-ical-extratropical interaction.

In the present paper we have clarified the role of SSSA during ENSO by analyzing the data from the newly developed CGCM. It may be necessary, how-ever, to substantiate the present result further by ana-lyzing observation data or other CGCM results. It is also important to clarify the mechanism to generate SSSA through the surface forcing and horizontal ad-vection, and the associated mixed layer process in the western tropical Pacific in the future study. Meanwhile, it is interesting to examine the role of the diurnal cycle of the fluxes at the sea surface, as its pos-sible importance has been suggested recently (Bernie et al., 2007).

Acknowledgements. This work was funded by the Korea Meteorological Administration Research and Development Program under Grant CATER 2006- 4201.

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Fig. A1. The annual mean SST for both (a) model and (b)observations. The simulated SST error map relative to theobservations (c).

Appendix

In this appendix, we present the model climatology over the Pacific ocean, the region of interest here. One key quantity by which the performance of coupled models can be evaluated is SST. We here display the simulated mean state and the seasonal cycle in the trop-ical Pacific. Strong and weak points of this coupled model can be identified in view of model’s perform-ance of tropical Pacific mean state even though mod-el’s integration is rather short. We display in Fig. A1 the annual mean SST from both model (a) and the ob-servations (b) as well as the simulated SST error map relative to the observations (c). The monthly-mean ob-served SST data is from the National Climate Data Center (Smith and Reynolds, 2004) for the period of 1900-2002. Overall, the simulated patterns compare reasonably well with the observations. However, the model has about 2°-3°C cold bias throughout the trop-ical Pacific Ocean. Cold tongue in the eastern tropical Pacific is too narrowly confined to the equator and ex-tends too far to the west and divided the warm pool in to a boomerang shape (Fig. A1b). Also the simulated western tropical Pacific warm pool does not extend to the north as the observations.

Since the seasonal cycle in the tropical Pacific in-volves complex interactions between ocean and at-mosphere, the simulation of the seasonal cycle is an ideal testbed for a CGCM. The simulated seasonal cycle of SST in the equatorial Pacific agrees reason-ably well with the observed one (Fig. A2b), although more pronounced both in amplitude and spatial ex-tension to the west. The variations in observed SST is dominated by the annual harmonic that peaks at 100°W, with the warm phase in April stronger than the cold phase in September-October. This simulated annual harmonic peak in magnitude is around 130°W while the observed peak is at 100°W and a maximum development of the cold tongue is simulated in September. The cold tongue persists long enough in-to the boreal winter than the observations. The warm-ing is simulated during April and May with a delay of 1 month compared to the observations.

The CGCM reasonably well simulate the ENSO in terms of its period and amplitude as mentioned in

the section 3. In order to provide a more detailed at the ENSO variability in the CGCM we present a lead-lagged regressed SSTA against with the NINO3.4 SST index in comparison with the ob-servations (Fig. A3). Figure A3 shows the regressed SSTA against with the NINO3.4 SST index for the lagged period of ±24 months along the equator in the CGCM (a) and the observations (b). The duration of ENSO simulated in the CGCM is relatively shorter than that of the observations. The westward migra-tion of the SSTA and the extending of the anomalies near the western boundary are readily apparent in the CGCM (Fig. A3a). In contrast to the CGCM, the obs


Fig. A2. A seasonal cycle of simulated SSTA along the equator (a). (b) is the same as in (a) except for the ob-servation during period of 1900-2002.

Fig. A3. The regressed SSTA against with the NINO3.4SST index for the lagged period of ±24 months along theequator in the CGCM (a) and the observations for the peri-od of 1900-2002 (b).

Fig. A4. The annual mean precipitation (mm day-1) for both (a) model and (b) observations. The simulated precip-itation error map relative to the observations (c).

erved SSTA is primarily a standing mode and the zo-nal extent of the SSTA is confined in the central and eastern tropical Pacific.

Figure A4 shows the annual-mean rainfall from the observations (Fig. A4a) and the CGCM (Fig. A4b) over the tropical Pacific. For the observations, we used the Climate Prediction Center Merged Analysis of Precipitation (CMAP; Xie and Arkin, 1997) data for the period of 1979-2006. The model shows strong

precipita tion in the far western tropical Pacific and the maximum precipitation center is shifted to the west in the northern Hemisphere. However, the model simulates weak precipitation along the South Pacific convergence zone (SPCZ) and northern intertropical convergence zone (ITCZ) regions compared to the observation. There is too much rainfall near New Guinea and too little rainfall along the equator in the central Pacific. The precipitation biases are largely due to local SST bias in the model: there is too little precipitation on the equator in the central Pacific where SSTs are too cold, and too much rainfall in the far west where SSTs are warm.

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