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Physical Processes Associated with the Tropical Atlantic SST Meridional Gradient ZENG-ZHEN HU Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland
BOHUA HUANG Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland, and Department of Climate Dynamics, College of Science, George Mason University, Fairfax, Virginia (Manuscript received 17 June 2005, in final form 1 March 2006) ABSTRACT The major modes of seasonal sea surface temperature (SST) meridional gradient and their connection with some regional mean SST indices in the Atlantic Ocean are examined using reanalysis data. The focus of the work is on the evolution of the dominant mode of the meridional SST gradient in boreal spring and the associated physical processes. The spatial distribution of the dominant mode in boreal spring is a seesaw pattern, reflecting the opposite variation of the meridional SST gradient between the subtropical and tropical North Atlantic, which resulted from a coherent warming or cooling with maxima along 10°–15°N. It is confirmed that this mode is dominated by the wind–evaporation–SST feedback. The feedback persists a longer time in the western Atlantic than in the eastern. The contribution to the SST variation is mainly from latent heat flux. The surface longwave and shortwave cloud radiative forcings are mainly determined by low cloud cover variations. The authors also found that the thermodynamic mode that peaked in boreal spring becomes weak in the following boreal summer. A similar thermodynamic mode appears in a northward position in boreal autumn, and its life cycle is shorter than the one in boreal spring. In contrast to the leading mode in boreal spring, it is shown that the leading mode in boreal summer is a dynamical air–sea feedback mode, reflecting a coherent warming or cooling pattern extending from the Angolan coast toward the equator in the Gulf of Guinea. The thermodynamic processes act as a negative feedback. The net surface latent heat flux anomalies are the leading damping factor, while the sensible heat flux plays the same role on a smaller scale.
1. Introduction The sea surface temperature (SST) gradient is one of the most important factors that determine the atmospheric circulation (Lindzen and Nigam 1987). Together with the deep convection in the western Pacific (Gill 1980), the east–west SST gradient in the tropical Pacific drives the equatorial easterlies linked to the Walker circulation. In the Atlantic sector, the fluctuation of the SST gradient, which is closely associated with air–sea interaction (Czaja et al. 2002; Visbeck et al. 2003), plays a central role in current theories of tropical Atlantic climate variability (TAV). Early studies demonstrated that the precipitation variability over north-
Corresponding author address: Dr. Zeng-Zhen Hu, Center for Ocean–Land–Atmosphere Studies, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705. E-mail:
[email protected]
© 2006 American Meteorological Society
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east Brazil (e.g., Hastenrath and Heller 1977; Moura and Shukla 1981) and sub-Saharan Africa (Lamb 1978; Folland et al. 1986) is associated with an asymmetric SST pattern of opposite signs straddling the climatological location of the intertropical convergence zone (ITCZ), the so-called SST dipole (e.g., Servain 1991). Wang (2002) demonstrated that both the Atlantic equatorial mode and the meridional gradient mode are linked with large-scale atmosphere circulation, such as the Walker and Hadley cells. The existence of the dipole pattern is controversial. Houghton and Tourre (1992) first pointed out that the dipole may not be a realistic physical entity because the SST fluctuations in the northern and southern branches of the dipole are largely independent. Subsequent analyses with more data (e.g., Enfield and Mayer 1997; Mehta 1998; Dommenget and Latif 2000) confirmed their result, although some of the issues are still in debate. For instance, Enfield et al. (1999) found that, syn-
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optically, simultaneous opposite SST anomalies (SSTAs) forming a dipole pattern are not frequently observed in the historical data in comparison with the occurrences of independent centers of SSTAs in either the north or the south. Using synthetic data, Dommenget and Latif (2002) further demonstrated that dipolelike patterns can be artifacts of the constraints of empirical orthogonal function (EOF) analyses. Wang (2002) indicated that the contrasted SST variation is characterized by when the tropical North Atlantic is anomalous or when the tropical South Atlantic is anomalous, or when both conditions exist nearly simultaneously and are opposite in sign. At this moment, a logical explanation of the statistical SST dipole pattern is that it is mainly a manifestation of the anomalous SST meridional gradient near the equator and the overlying atmosphere (Houghton and Tourre 1992), which can be generated by SSTAs from either the south or north. The anomalous meridional SST gradient near the equatorial Atlantic plays a significant role in forcing the overlying atmospheric circulation (e.g., Moura and Shukla 1981). Uncoupled simulations using ocean general circulation models forced with observed surface wind stress and parameterized surface heat flux also show that, away from the equator, SSTAs in the tropical Atlantic are usually forced by trade wind fluctuations (Huang et al. 1995) that modulate the surface latent heat flux (Carton et al. 1996). Using a hybrid coupled model composed of an intermediate dynamical ocean model and a statistical atmosphere, Chang et al. (1997) further demonstrated a well-defined lowfrequency oscillation on decadal time scales. In this oscillation, the positive feedback takes place through the mutual enhancement among the anomalies of the SST gradient, wind stress, and latent heat flux, which is similar to the wind speed–evaporation–SST (WES) mechanism proposed by Xie and Philander (1994) to explain the meridional asymmetry of the eastern equatorial Pacific climatology. Using a linear coupled model of the tropical Atlantic between an ocean mixed layer and a simplified dynamical atmosphere, Xie (1999) derived an unstable mode associated with the WES feedback and emphasized the extratropical influences on the time scales of the unstable mode. These previous studies have illustrated the controversy about the variability and the mechanisms of the SST gradient in the Atlantic Ocean. Given the prominence of the meridional SST gradient in the theories of TAV mechanisms, it is surprising that little attention has been paid to the direct analysis of this important field from the observations. As far as we know, major features of the SST gradient anomalies are mainly in-
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ferred from the statistical patterns of either EOF or rotated EOF (REOF) analyses of the SSTA (e.g., Servain 1991; Houghton and Tourre 1992; Enfield and Mayer 1997; Mehta 1998; Ruiz-Barradas et al. 2000; Huang et al. 2004). Features of the gradient field are usually characterized by indices based on differences of the SST averaged over quite large areas (e.g., Servain 1991; Penland and Matrosova 1998; Mélice and Servain 2003). An interesting question is whether these SSTbased modes and indices realistically reflect the usually more localized nature of the SST gradient. It will be interesting to directly study the major spatial pattern of the meridional SST gradient, instead of studying the SST itself, and to further examine the physical processes involved in the evolution and formation of the dominant pattern of the meridional SST gradient. Another issue is the seasonality of the meridional SST gradient fluctuations. The tropical Atlantic climate system displays strong seasonality, particularly for the eastern tropical ocean (Xie and Carton 2004). In boreal spring, temperatures reach their maximum when the equatorial winds are weakest and the thermocline is deepest in the east. The ITCZ is in its southernmost position. As the year progresses the equatorial trade winds are intensified. The intensified winds result in an enhanced zonal pressure gradient in the tropical ocean and a shallowed thermocline in the east, leading to seasonal cooling of SSTs in the eastern equatorial Atlantic. The minimum SSTs appear along the eastern coast of Africa in July and in the southeastern Gulf of Guinea in August. A cold tongue forms in the south of the equator and extends from the African coast to the central ocean. The band of precipitation is sharp and stretches across the entire ocean basin with largest values in the east. In the following seasons, the trade winds weaken and the temperatures in the east increase. As a result, the east and west gradient becomes smaller. The SST meridional gradient and its variability also vary with season. From the South Atlantic to about 5°–10°N, the SST meridional gradient is mostly positive (not shown). In general, the gradient and its variability in 20°S–30°N are larger in the eastern than in the western ocean. The strongest positive SST meridional gradient appears in 0°–5°N in boreal summer and the negative one along 5°–15°N in boreal spring. The largest variability of the gradient exists in the tropical eastern North Atlantic in boreal spring, which is consistent with the results in the following sections. The seasonal cycle of the tropical Atlantic climate system is determined by multifactors, such as solar radiation, African monsoon, ocean shape, and air–sea interaction (Xie and Carton 2004). Although simple models predict that relatively long time scales are associated with the thermodynamic air–
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sea feedback (Chang et al. 1997; Xie 1999), these model simulations of the evolution of the system are usually based on stationary background states. Given a more realistic background, especially the strong annual cycle in the Atlantic basin, the effectiveness of the WES mechanism may be different. Moreover, recent observational and model studies demonstrate that, besides regional air–sea interactions, external factors, such as the El Niño–Southern Oscillation (ENSO), also influence the TAV strongly (e.g., Enfield and Mayer 1997; Huang et al. 2002; Merkel and Latif 2002; Huang 2004; Tourre and White 2005). In comparison with the external forcing, the regional air–sea feedback seems to be most effective within the deep Tropics (e.g., Chang et al. 2001; Chiang et al. 2002; Huang et al. 2004). In this work, we focus on the regional air–sea feedback since the ENSO effect is relatively better known. In this study, the meridional gradient fluctuations are analyzed using historical SST analysis. We first calculate the major modes of the seasonal meridional SST gradient in the Atlantic Ocean and examine their relationship with some regional mean SST indices. The focus of the work is on the evolution of the dominant mode of the meridional SST gradient in boreal spring and summer as well as their associated physical processes. The paper is organized as follows. In section 2, the data and analysis strategy are briefly described. Section 3 presents the results of REOF analyses of the seasonal meridional SST gradient in the Atlantic Ocean and their relationship with some regional mean SST indices used in previous studies. The seasonality of the major modes of the seasonal meridional SST gradient and their relationships with the indices are also investigated in this section. Section 4 focuses on the physical processes and the mechanism associated with the dominant mode of the meridional SST gradient in the Atlantic Ocean in boreal spring. The similarity and difference of the related mode in boreal summer and autumn are also discussed by comparing it with that in boreal spring in this section. In section 5, we show the SST and wind stress evolution associated with the leading mode in boreal summer and the related physical processes. The comparison with the leading mode in the boreal spring is also included. Section 6 is a summary and some further discussion.
2. Data and analysis strategy The monthly mean atmospheric data were derived from the reanalysis of the National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) (Kalnay et al. 1996). The analyzed variables in this work include surface
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wind stress, low cloud cover, middle cloud cover, high cloud cover, latent heat flux, sensible heat flux, net surface shortwave and longwave radiations, and sea level pressure (SLP). The reconstructed SST on a 2° ⫻ 2° grid is from Reynolds et al. (2002). This work focuses on the local ocean–atmosphere interaction and its variability by largely removing the ENSO influence on the Atlantic Ocean. An effort has been made to filter out the ENSO signals from the dataset following Mestas-Nuñez and Enfield (1999). The ENSO components are subtracted from the SST field prior to doing REOF analyses. The ENSO component is defined as the first complex EOF (CEOF) of the monthly SSTA for the World Ocean between 40°S and 60°N. An ENSO component in any of the other variables is constructed by projecting the variable and its Hilbert transform onto the first complex principal component of the SST data. The ENSO influence is also removed for other variables before computing the regression. Details about how to remove the ENSO signal were given in Huang and Shukla (2005). Due to the tapering near the end points of the time series in the application of Hilbert transform, the reanalysis data and the reconstructed SST are available for the period of January 1955–December 1996 after removing the ENSO signal. The investigation is focused on interannual time scales by adopting a 10-yr high-pass filter, which is based on a fast Fourier transform (Press et al. 1992). We first conduct the REOF of the seasonal meridional SST gradient anomalies in the Atlantic Ocean (20°S to 30°N) and examine the correlations between these REOF modes and regional mean SST indices in the Atlantic Ocean used in some previous studies. The seasonality of the major modes of the seasonal meridional SST gradient and their relationships with the indices are investigated by a similar calculation using meridional SST gradient anomalies in individual seasons. The result from REOF instead of EOF calculation is displayed since REOF is more stable than EOF in revealing spatial patterns, as demonstrated by Dommenget and Latif (2002), and also because of the localized nature of the meridional SST gradient. REOF is computed by the varimax method (Richman 1986) using the first 15 EOFs, which explains more than 85% of the total variance. The covariance matrixes are weighted by the square root of the gridbox area before the EOF computation (North et al. 1982). Since the meridional SST gradient is strongest in boreal spring, we then focus on the dominant mode of the meridional SST gradient anomalies in boreal spring [March–May (MAM); hereafter, 3-month periods are denoted by the first letter of each respective month]. Consistent with
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many previous studies, we found that the dominant REOF mode in MAM is a thermodynamic mode. Based on the time series of the first REOF (REOF1), the regressions of different variables of three consecutive month means onto the time series were calculated. The regressions range from those leading the MAM REOF1 by 12 months to those lagging by 12 months. By examining the significant signals in the regressions, the physical processes and mechanisms involved in the evolution and formation of the thermodynamic mode are systematically investigated. In addition, the main features related to the leading mode in boreal summer are also demonstrated and compared with the one in boreal spring.
3. REOF analyses of seasonal meridional SST gradient Figure 1 shows the spatial patterns and their corresponding time series of the first two REOFs for the seasonal mean meridional SST gradient anomalies in the Atlantic Ocean (20°S to 30°N). The first two modes explain 10% and 9% of the total variance, respectively. The REOF1 (Fig. 1a) and REOF2 (Fig. 1b) are mainly associated with the variability around the equator and 10°N, respectively. They represent different physical structures. Due to the robust and localized feature of REOF, unlike EOF, two REOFs are physically unique even if they explain similar fractions of variance according to the conclusions of Cheng et al. (1995). Separated REOF calculations using the meridional SST gradient for different seasons show that the REOF1 mode in Fig. 1b is similar to the REOF1 mode in boreal summer (Fig. 2a) and REOF2 in boreal autumn (Fig. 2d) and winter (Fig. 2f). The REOF2 mode in Fig. 1d is analogous to the REOF1 mode in boreal autumn (Fig. 2c) and spring (Fig. 3b), and the REOF2 mode in boreal summer (Fig. 2b). The first mode (REOF1) is a seesaw pattern, which reflects the opposite variation of the meridional SST gradient to the north and south of the equator (Fig. 1b). It is associated with a warming or cooling with maximum SSTA located on the equator. When the maximum warming (cooling) is on the equator, the meridional SST gradient is similar (opposite) to the spatial pattern of REOF1. The time evolution of REOF1 is significantly correlated with regional averaged SST in the whole (20°S to 30°N) [tropical basin (TB), 0.41], the north (5° to 28°N) [north basin (NB), 0.22], and the south (20°S to 5°N) [south basin (SB), 0.34] basin indices, defined by Servain (1991; see second row in Table 1). However, the correlation with the dipole index, defined as NB–SB, is ⫺0.11, which is not significant at the 95% level. The correlations confirm that the dominant
FIG. 1. (a), (c) Time series and (b), (d) spatial pattern of REOF1 and REOF2 of seasonal mean meridional SST gradient in the Atlantic Ocean in 1955–96. The meridional gradient is defined by a centered difference of SST between the north and south. The contour interval is 0.1, the zonal line is omitted, and the shading represents values larger than 0.2 or less than ⫺0.2. The percentage of the variance explained by the REOF1 and REOF2 are 10% and 9%, respectively. Regional mean SST indices TB and NB (bars) are plotted in (a) and (c), respectively; TB and NB have significant correlations with the corresponding time series of REOF1 (0.41) and REOF2 (0.21).
variation of the seasonal SST is a coherent warming or cooling in the whole tropical Atlantic domain with more weight on the Southern Ocean (Table 1). Our analysis also found that this mode is associated with a strong zonal SST gradient (not shown), as has been pointed out by many previous studies (e.g., Zebiak 1993; Carton and Huang 1994; Huang et al. 1995; Huang and Shukla 1997; Latif and Grötzner 2000), and is sometimes referred to as the zonal mode. The second spatial pattern (REOF2) displays a see-
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FIG. 2. As in Fig. 1 but for the spatial patterns of REOF1 and REOF2 of meridional SST gradient in (a), (b) JJA, (c), (d) SON, and (e), (f) DJF.
saw variation of the seasonal mean meridional SST gradient between the subtropical and tropical North Atlantic (Fig. 1d), which reflects a coherent warming or cooling with maxima along 10°–20°N. There are marginal positive significant correlations between the time
evolution of the REOF2 and NB (0.21) or dipole (0.22) indices (see third row in Table 1). The marginal significant correlations may result from the fact that the major variation patterns of SST meridional gradient vary with seasons (Figs. 2 and 3). The tropical dipole mode
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TABLE 1. Correlations between the seasonal mean of regional averaged SST indices and the time series of REOF1 and REOF2. Significant correlations at the level of 95% are bold when the correlations are larger than 0.16 for seasonal data and larger than 0.30 for individual season data. The definitions of the indices are as follows: TB, NB, and SB are the mean of SST in 20°S–30°N, 100°W–20°E; 5°–28°N, 100°W–20°E; and 20°S–5°N, 100°W–20°E, respectively. Dipole is the difference NB ⫺ SB. Index REOF
TB
NB
SB
Dipole
REOF1_seasonal REOF2_seasonal REOF1_JJA REOF2_JJA REOF1_SON REOF2_SON REOF1_DJF REOF2_DJF REOF1_MAM REOF2_MAM
0.41 0.00 0.55 0.22 ⫺0.19 0.54 0.03 0.05 0.16 0.35
0.22 0.21 0.26 0.31 0.03 0.39 0.36 0.30 0.54 0.03
0.34 ⫺0.12 0.50 0.08 ⫺0.21 0.39 ⫺0.21 ⫺0.19 ⫺0.21 0.39
⫺0.11 0.22 ⫺0.23 0.13 0.19 ⫺0.05 0.42 0.36 0.47 ⫺0.27
is not the dominant variation in the tropical Atlantic on seasonal and interannual time scales, in agreement with previous investigations (e.g., Houghton and Tourre 1992; Enfield and Mayer 1997; Mehta 1998; Dommenget and Latif 2000). The correlations between the REOF time series and the previously defined gradient (dipole) index are comparable to those between the time series and the other indices (Table 1). In addition, it is evident that the dipole index is highly correlated with the REOF1 and REOF2 in DJF and REOF1 in MAM. Some of the REOF modes in individual seasons are significantly correlated with the regional mean SST indices (Table 1). The variation of the correlations is consistent with the spatial pattern of the corresponding REOFs. In boreal summer, REOF1 (Fig. 2a) is significantly correlated with TB (0.55) and SB (0.50), and REOF2 (Fig. 2b) has a marginally significant positive correlation with NB (0.31) (see fourth and fifth rows of Table 1). REOF1 (Fig. 2a) mainly reflects the SST variation around the equator, resulting in a significant correlation with TB. The correlation of REOF1 with SB is larger than that with NB, which is associated with this mode developing and evolving mainly in the south Tropics. REOF2 is mainly linked to the SST variation centered along 10°N. That is the reason why only NB shows a significant correlation with REOF2. The dominant mode in boreal autumn (Fig. 2c) does not have significant correlations with any of the indices (see sixth row of Table 1). That may be caused by the fact that this mode is mainly linked to SST variation farther to the north. REOF2 (Fig. 2d) in that season is significantly correlated with TB (0.54), NB (0.39), and SB
FIG. 3. (a) Time series of REOF1 (curve) and REOF2 (bar), and spatial pattern of (b) REOF1 and (c) REOF2 of SST gradient in MAM. The percentages of the variance explained by the REOF1 and REOF2 are 18% and 12%, respectively. The contour interval is 0.1, the zonal line is omitted, and the shading represents values larger than 0.2 or less than ⫺0.2.
(0.39) (seventh row of Table 1). It is a tropical mode similar to the REOF1 in JJA (Fig. 2a). Due to the variability mainly in the southeastern ocean (Fig. 3c), the REOF2 in MAM is significantly correlated with the TB and SB indices and insignificantly correlated with the NB index (last row of Table 1). From the correlations in Table 1 (eight and nineth rows of Table 1), we see some dipole features in the first two modes in boreal winter (Figs. 2e,f). The correlations between REOF1/REOF2 and NB are significantly positive, and the correlations with SB are negative. The spatial distribution of REOF1/REOF2 mainly reflects the opposite variation between the tropical and North Atlantic, which can explain why their correlations with the dipole
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FIG. 4. Regression of three-consecutive-month mean surface wind stress (vector) and SST (contour) onto REOF1 of the MAM SST gradient in Fig. 3a. The shading represents the significant regression of SST at 95% using the F test. The regression is displayed from (a) those leading REOF1 9 months to (h) those lagging 12 months. The contour interval is 0.3°C per °C (1000 km)⫺1 and the zero line is omitted.
index are larger than with any of the other indices in DJF. Figure 3 shows the time series (Fig. 3a) and spatial pattern of REOF1 (Fig. 3b) and REOF2 (Fig. 3c) of the meridional SST gradient anomalies in boreal spring
(MAM). The spatial distribution of REOF1 is a seesaw pattern, similar to that of REOF2 of the seasonal mean meridional SST gradient anomalies in Fig. 1d. That pattern, which has a significant correlation with NB (0.54, see 10th row of Table 1), mainly reflects a coherent
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warming or cooling with maxima along 10°–15°N. That is demonstrated by the regression of the threeconsecutive-month mean SST onto REOF1 of the MAM meridional SST gradient (Fig. 4), confirming that the variations associated with the all-season REOF2 are strongest in boreal spring. In the next section, we focus on this mode and investigate the associated physical processes. The main variability of REOF2 (Fig. 3c) is located in the South Atlantic Ocean, which shows a positive significant correlation with the TB and SB indices (last row of Table 1). From the correlation analyses, we see that a dipolelike SST variation pattern exists in boreal winter and spring (Table 1) but not in boreal summer and autumn.
4. Physical processes associated with the dominant mode in boreal spring a. SST and surface wind stress In the regressions, the surface wind stress evolves in a manner consistent with the SST. A cyclonic wind pattern first appears in the North Atlantic near the western coast of Africa in boreal fall associated with positive SSTA between 20° and 30°N and weakened northeast trade winds between 10° and 20°N (Fig. 4b). Meanwhile, the significant negative SSTA is initiated in the southeastern part of the ocean, which may be associated with anomalously strong southeast trade winds (Fig. 4b). By boreal winter (Fig. 4c), the northeast trade winds further weaken over the basin and a positive SSTA develops in the region from the western to the eastern Atlantic along 10°–15°N, probably initiated first by weakened upwelling along the western and eastern coasts. The southeast trade winds in the South Atlantic Ocean are also enhanced in this season. In boreal spring (Fig. 4d), the positive SSTA grows rapidly and extends to over most of the tropical North Atlantic; the negative SSTA is also strongest to its north and south, as the wind stress anomalies reach their maximum with strong cross-equatorial flow. It should be noted that the trade wind fluctuations are associated with surface latent heat flux changes. A full account of the heat flux evolution will be given in the next subsection. Here we would like to point out that the wind–SST pattern is consistent with the WES feedback (Chang et al. 1997; Xie 1999), which may account for the rapid growth of the anomalies in this season. In the following seasons, both the SSTA and wind stress anomalies weaken in the eastern Atlantic, but are more persistent in the western part of the Atlantic Ocean (Figs. 4e–g), consistent with the regions where the WES mechanism is most effective (Chang et al. 2000). It is interesting to
FIG. 5. As in Fig. 4 but for mean net surface energy (down ⫺ up). The regression is displayed from (a) those leading REOF1 6 months to (e) those lagging 6 months. The contour interval is 20 (W m⫺2) per °C (1000 km)⫺1 and the zero line is omitted.
note that a significant positive SSTA appears in the eastern subtropical Atlantic Ocean (Fig. 4h), implying a reemergence of a similar process by the next spring (Alexander et al. 1999). Overall, because of the tropical air–sea feedback, the duration is longer for the positive SSTA along 10°– 20°N than for the negative SSTA to the north and south. The significant SSTA and wind stress anomalies disappear earlier in the east than in the west Atlantic. The significant positive SSTA in the western subtropical North Atlantic lasts for more than one year, from the preceding boreal winter (Fig. 4c) to the following winter (Fig. 4g). This region is collocated with the highest climatological SST in the subtropical North Atlantic (not shown), which seems to imply that deep convection over the warm ocean, besides the surface pressure gradient, also plays a role (Chiang et al. 2001). This
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FIG. 6. As in Fig. 5 but for Ekman wind (vectors) and the divergence and convergence (contour and shading). The unit for the vectors is (m2 s⫺1) per °C (1000 km)⫺1. The contour interval for the divergence and convergence is 5 ⫻ 10⫺7m s⫺1 per °C (1000 km)⫺1. The zero line is omitted.
highest SST region is believed to be the region of highest sensitivity and the most effective region for forcing the North Atlantic Ocean from the Tropics, through stationary Rossby waves generated by diabatic heating in this region (Peng et al. 2005; Cassou et al. 2004).
b. Heat flux and surface radiation In the following analyses, the regression patterns of other variables are analyzed from those leading the MAM REOF1 by 6 months to those lagging by 6 months. The net surface energy balance (Fig. 5) is consistent with the corresponding SSTA evolution in Fig. 4. The net surface energy balance is positive before and in the boreal spring (Figs. 5a–c) in the positive SSTA regions of Fig. 4, and it is negative after that (Figs. 5d–e). As expected, the net surface energy variation leads the
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FIG. 7. As in Fig. 5 but for latent heat flux. The contour interval is 20 (W m⫺2) per °C (1000 km)⫺1.
SSTA by one season. The net surface energy flux in Figs. 5b and 5c can lead to a warming of the upper 50 m of the ocean by about 2.2°C in 90 days in the maximum flux center, which is consistent with the amplitudes of SST change in Fig. 4d. Additional calculation shows southeastward transport and divergence in the North Atlantic caused by wind-driven Ekman currents (Fig. 6). The transport and divergence of the wind-driven Ekman currents are unfavorable for and damp the development of the positive SSTA in Fig. 4. Both the net surface energy balance and the wind-driven Ekman wind confirm that the leading mode of SST meridional gradient in MAM (Fig. 3b) is mainly determined by thermodynamic processes. The net surface heat flux anomalies are mainly caused by latent heat flux (Fig. 7), which can result in warming the upper 50 m of the ocean by about 1.8°C in 90 days. Consistent with the reduction of the northeast trade winds, the latent heat flux warms the tropical and
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FIG. 8. As in Fig. 5 but for shortwave cloud radiative forcing of downward minus upward. The contour interval is 10 (W m⫺2) per °C (1000 km)⫺1.
FIG. 9. As in Fig. 5, but for longwave cloud radiative forcing of downward minus upward. The contour interval is 5 (W m⫺2) per °C (1000 km)⫺1.
subtropical North Atlantic and cools the middle latitudes of the North Atlantic and the equator in the preceding boreal winter (Fig. 7b) and spring (Fig. 7c). The amplitude of the latent heat flux reaches 40 W m⫺2 per l°C (1000 km)⫺1 of meridional SST gradient at its peak. The latent heat flux is the main driver of the SSTA from boreal spring to summer (Figs. 4d,e,b,c). The sensible heat flux anomalies also have a positive, but smaller, contribution to the SST variations (not shown). A decrease of wind speed (Fig. 4d) reduces the sensible heat transport from the ocean into the atmosphere, and the anomalous downward sensible heat flux warms the ocean. The amplitude of the sensible heat flux anomalies is just 15%–25% of that of the latent heat flux anomalies. The sensible heat flux anomalies lead to an about 0.4°C increase of the upper 50 m of the ocean temperature in 90 days. The consistency between the heat flux and SST tendency indicates that the pattern of
the meridional SST gradient (Fig. 3b) and SSTA (Fig. 4d) in boreal spring is dominated mainly by thermodynamic processes. Thus, it is appropriate to refer to this mode as a thermodynamic mode. Apart from the latent heat flux, the next largest factor determining the SSTA is the shortwave cloud radiative forcing. The solar cloud radiative forcing contributes to warming the SST from the preceding autumn (Fig. 8a) to the spring (Fig. 8c). By boreal summer, the solar cloud radiative forcing acts as a negative feedback to the positive SSTA (Fig. 8d), enhancing the damping from the latent heat flux. The net (down ⫺ up) surface longwave cloud radiative forcing (Fig. 9), acting as a damping factor, varies almost oppositely to the corresponding solar radiation (Fig. 8). The shortwave (longwave) cloud radiative forcing in MAM (Figs. 8c, 9c) can warm (cool) the upper 50 m of the ocean by about 1.4°C (0.6°C) in 90 days. Owing to the fact that
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FIG. 10. As in Fig. 5 but for low cloud cover. The contour interval is 4% per °C (1000 km)⫺1.
FIG. 11. As in Fig. 5 but for high cloud cover. The contour interval is 4% per °C (1000 km)⫺1.
the amplitude of the shortwave cloud radiative forcing (Fig. 8) is about two to three times of that of longwave cloud radiative forcing (Fig. 9), the net surface cloud radiative forcing (not shown) has a positive contribution to the SST variation.
erally collocated with positive and negative meridional SST gradients, respectively. When a maximum warming exists in the subtropical North Atlantic, the low cloud cover increases (decreases) to its south (north), and vice versa when there is maximum cooling. In addition, the low cloud cover variations extend from the open ocean to the Caribbean Sea and the northern part of the South American continent. Moreover, the ITCZ fluctuation in the eastern Atlantic seems to be mainly associated with low cloud, that is, relatively shallow convection in the lower atmosphere (Trenberth et al. 2000; Zhang et al. 2004). The anomalous distribution of low cloud cover (Fig. 10) is linked to that of net surface shortwave (Fig. 8) and longwave (Fig. 9) cloud radiative forcing. Decreased (increased) low cloud cover reflects less (more) shortwave radiation to space and enhances (reduces) the incoming shortwave radiation to the surface, which results in warming (cooling) of the ocean. Meanwhile,
c. Cloud cover and precipitation The net surface shortwave and longwave radiation anomalies are mainly determined by cloud cover change (Futyan et al. 2004; Randel and Vonder Haar 1990). It is noted that the surface cloud radiative forcing is most significantly influenced by low clouds in the tropical and subtropical ocean. Low cloud cover increases in the regions between 2°S and 8°N and decreases to the north and south during the period from the preceding boreal winter to boreal spring (Figs. 10b,c). Comparing Figs. 10b,c with Fig. 3b, we found that increasing and decreasing low cloud cover are gen-
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FIG. 12. As in Fig. 4 but for regressions onto REOF2 of JJA SST meridional gradient in Fig. 2b.
decreased (increased) low cloud cover also enhances (blocks) outgoing surface longwave radiation, which causes a cooling (warming) of the Atlantic. Since the low cloud cover mainly decreases (Figs. 10b,c) in the major positive SSTA region (Figs. 4c,d), the net surface shortwave (longwave) cloud radiative forcing has a positive (negative) contribution to the SSTA development.
Associated high cloud cover variations (Fig. 11) are similar to middle cloud cover variations (not shown), but both are different from the low cloud cover variations (Fig. 10). The reasons causing the different features of the low and middle/high cloud cover variations in association with the leading mode of SST meridional gradient in MAM are unclear. It is possible that the
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differences are related to the cloud parameterization in the model used to produce the reanalysis data. Further observation-based studies are needed to determine whether the relationship is physical or not. During the period from the preceding boreal winter to boreal summer (Figs. 11b–d), the main feature of high and middle cloud cover variations is the increase in the north and decrease in the south of the domain. Compared with Figs. 3b and 4d, the increases (decreases) of the high and middle cloud cover are mainly over the regions to the north (south) of the maximum warming, which is the regression of the surface wind stress convergence (divergence) (Fig. 4d). Deep convection, however, plays a relatively smaller role than shallow convection in influencing the processes near the surface.
d. Similar pattern in JJA and SON The spatial pattern of REOF2 of the SST meridional gradient in JJA (Fig. 2b) is similar to that of REOF1 in MAM (Fig. 3b). The corresponding regressions are displayed in Fig. 12. For the REOF2 of JJA, the associated SST and wind stress evolution (Fig. 12) is similar to the corresponding one in MAM (Fig. 4). The SST and wind stress anomalies initiate in the preceding boreal autumn (Fig. 12a), develop in the preceding boreal wind (Fig. 12b), and reach their peak in boreal spring (Fig. 12c). From boreal summer, the anomalies start to weaken (Figs. 12d–h). Besides the similarity, there are also some differences between the boreal spring and summer modes. By comparing Figs. 4 and 12, it is noticed that the disappearance of the anomalies occurs more quickly in Fig. 12 than in Fig. 4. In boreal spring, the SSTA at the western coast of North Africa is much larger in Fig. 12c than in Fig. 4d. The role of surface heat flux on the SSTA is also different. In contrast with the significantly positive contribution from surface heat flux for the boreal spring mode (Fig. 5), surface heat flux has little contribution to the SSTA associated with the boreal summer mode (Fig. 13). In boreal summer (Fig. 13c), the surface heat flux even becomes a damping in the eastern North Atlantic. Based on these features, it is concluded that the mode in boreal summer (Fig. 2b) is mainly the result of persistence of the anomalies associated with the mode in boreal spring (Fig. 3b). The spatial pattern of REOF1 of the SST meridional gradient in SON (Fig. 2c) is similar to that of the REOF1 in MAM (Fig. 3b) and REOF2 in JJA (Fig. 2b). However, in the regressions (Fig. 14), the associated SST and wind stress anomaly evolution differs from that in Figs. 4 and 12. Significant positive SSTAs first appear at the western coast of North Africa in the
FIG. 13. As in Fig. 5 but for regressions onto REOF2 of JJA SST meridional gradient in Fig. 2b.
preceding boreal winter (Fig. 14a), and then the SSTAs become smaller and less significant in the following season (Fig. 14b). By the preceding summer, significant positive and negative SSTAs are observed northward of 20°N (Fig. 14c). The anomalies reach their maximum in boreal autumn (Fig. 14d), and then the anomalies weaken quickly and significantly after boreal autumn (Figs. 14e–h). The role of the surface heat flux on the SSTA associated with the boreal autumn mode (Fig. 15) is similar to that with the boreal spring mode (Fig. 5). Both are different from that associated with the boreal summer mode (Fig. 13). The surface heat flux associated with the boreal autumn mode has a positive contribution to the SSTAs in the preceding boreal summer and autumn (Figs. 15b,c). From the above analyses, we found that the thermodynamic mode reaches its peak in boreal spring and becomes weak in the following boreal summer. A similar thermodynamic mode appears in a northward posi-
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FIG. 14. As in Fig. 4 but for regressions onto REOF1 of SON SST meridional gradient in Fig. 2c.
tion in boreal autumn, and its life cycle is shorter than the one in boreal spring.
5. Contrasting with the dominant mode in JJA Comparing with the dominant mode in boreal spring (Fig. 3a), the main variation of the SST meridional gra-
dient is confined in the region around the equator instead of the North Atlantic in the leading mode in boreal summer (Fig. 2a). These two modes are different in their evolution and related physical processes. As discussed in the previous section, the dominant mode of the meridional SST gradient in the North Atlantic in boreal spring is a thermodynamic mode, mainly af-
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FIG. 15. As in Fig. 5 but for regressions onto REOF1 of SON SST meridional gradient in Fig. 2c.
fected by the WES feedback (Chang et al. 1997; Xie 1999). Here we would like to point out that, unlike the case in spring, the variations of the dominant mode of the SST gradient in boreal summer are mainly determined by dynamical processes. The thermodynamic process is accounted for as a damping to dynamically driven anomalies. In the boreal autumn and winter preceding the peak season (Figs. 16a,b), the SSTAs are generally small, showing a pattern with positive anomalies in the North Atlantic and negative ones in the South Atlantic. In boreal winter (Fig. 16b), small positive SSTAs appear along the eastern coast of the South Atlantic around 15°S. The anomaly grows rapidly in the next season (Fig. 16c), reaching maximum warming along the coast with an amplitude of 2°C per 1°C (1000 km)⫺1 meridional SST gradient. Meanwhile, the southeast trade winds weaken over the southern tropical Atlantic with westerly anomalies dominating in the open ocean, lead-
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ing to a strong convergence near the eastern coast. Westerly anomalies along the equator depress the zonal thermocline gradient and extend SSTAs from the eastern coast into the central ocean. It is noted that, while the Atlantic anomalies are developing, negative SSTAs persist in the eastern tropical Pacific Ocean and reach their maximum in boreal spring (Fig. 16c), implying a connection between the two oceans even though major ENSO signals have been removed from this data. By boreal summer (Fig. 16d), the SSTAs and the surface wind stress convergence are further intensified, and the maximum SSTAs appear in the central equatorial ocean. The wind anomalies are tightly coupled with those of the SST near the equator and converge into the center of SSTAs from all directions. In particular, the westerly anomalies are enhanced to the west of the equatorial SST maximum, providing the positive SST–wind feedback as described by Zebiak (1993). Meanwhile, the meridional winds are driven by the SST-induced surface pressure gradient. This atmospheric response to the equatorial SSTAs probably has the effect of reversing the alongshore wind direction near the southeastern boundary, thus weakening the SSTAs along the Angolan coast. The eastern tropical ocean response to forcing is very sensitive in boreal summer since it is the season with the shallowest thermocline depth in the central and eastern equatorial ocean. In the following boreal autumn (Fig. 16e), both the SSTAs and the surface wind stress convergence weaken, while persistent anomalies occupy the central equatorial to the southeastern ocean away from the coast. By the following boreal winter (Fig. 16f), negative SSTAs have been initiated along the eastern coast and anomalous easterly winds occur in the South Atlantic. The positive SSTA is intensified in the eastern North Atlantic, possibly associated with an atmospheric cyclonic circulation farther north. In the following seasons, both the positive SSTAs and wind stress anomalies further weaken in the North Atlantic while the negative SSTAs grow slowly in the South Atlantic (Figs. 16g,h). The net surface energy balance (Fig. 17) shows that the thermodynamic processes play a damping role on the SSTAs in Fig. 16, confirming that the growth of the dominant mode of the SST gradient in boreal summer is mainly determined by dynamical processes. The net surface energy transport into the ocean is small before boreal summer (Figs. 17a,b). However, the ocean tends to lose heat to the atmosphere near the eastern coast after the SSTA is initiated. In the mature and decay phases, net heat flux is negative over positive SSTAs from boreal summer to winter (Figs. 17c–e).
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FIG. 16. As in Fig. 4 but for regressions onto REOF1 of JJA SST meridional gradient in Fig. 2a.
We looked further at the components of the net surface heat flux and found that both latent and sensible heat fluxes damp the SSTAs (not shown). The net surface heat flux anomalies are mainly the result of the latent heat flux anomalies associated with the reduced trade wind speed and air ⫺ sea temperature differences. The pattern of the sensible heat flux anomaly
(not shown) is similar to that of the latent heat flux anomaly, but its amplitude is only 10%–15% of the latter.
6. Summary and discussion In this work, we have shown the major modes of the seasonal meridional SST gradient and examined their
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FIG. 17. As in Fig. 5 but for regressions onto REOF1 of JJA SST meridional gradient in Fig. 2a.
connection with the regional mean SST indices in the Atlantic Ocean. The focus of the work is on the evolution of the dominant mode of the meridional SST gradient in boreal spring and the associated physical processes. It is found that the spatial distribution of the dominant gradient mode in boreal spring is a seesaw pattern, reflecting the opposite variation of the meridional SST gradient between the subtropical and tropical North Atlantic, which results from a coherent warming or cooling with maxima along 10°–15°N. The coherent warming is associated with weakened trade winds. The smaller trade wind speed suppresses the evaporative heat loss through the sea surface, which favors warming SST. The warm SSTA then further enhances the surface wind anomalies in the equatorial side. Overall, this wind–evaporation–SST feedback may account for the rapid development of the SSTA in the tropical North Atlantic and its southward extension in boreal spring. The anomalies seem to be triggered in the preceding
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boreal winter, when the trade winds weaken and positive SSTA develops in the region from the western to the eastern Atlantic along 10°–15°N. There are some signs that significant negative SSTA is initiated in the preceding boreal autumn in the tropical Southern Hemisphere, which is associated with the intensification of southeast trade winds. However, its contribution to the rapid development of SSTAs is not clear. The feedback persists for a longer time in the western Atlantic than in the eastern. Both positive and negative SSTAs, as well as the wind stress anomalies, reach their maximum in boreal spring and weaken in the following seasons. We also found that a similar thermodynamic mode appears in a northward position in boreal autumn, and its life cycle is shorter than the one in boreal spring. The driving force in the SST variation is mainly latent heat flux and solar cloud radiative forcing. The sensible heat flux and the longwave cloud radiative forcing have some secondary contribution to the SST variation. The surface longwave and shortwave cloud radiative forcing variations are mainly determined by low cloud cover variations. More low cloud reduces the heat loss due to the longwave radiation, but also reduces the gain from shortwave radiation. Since solar cloud radiative forcing is generally larger than the longwave cloud radiative forcing, decreasing and increasing low cloud cover are collocated with positive and negative meridional SST gradients, respectively. High and middle cloud cover variations are different from that of the low cloud cover. In contrast to the leading mode in boreal spring, it is shown that the leading mode in boreal summer is a dynamical air–sea feedback mode, reflecting a coherent warming or cooling pattern extending from the Angolan coast toward the equator in the Gulf of Guinea. The thermodynamic processes act as a negative feedback. The net surface latent heat flux anomalies are the leading damping factor, while the sensible heat flux plays the same role on a smaller scale. Besides the meridional gradient discussed above, the zonal gradient is also an important factor driving the circulation over the Atlantic Ocean. However, in boreal spring, the zonal gradient is less important than the meridional gradient for the climate variability over the Atlantic Ocean and its vicinity. The variability of the zonal gradient is mainly confined to the eastern boundary of the Atlantic (not shown), which is the result of upwelling and downwelling. The dominant mode of the zonal gradient variability in boreal spring (not shown) occurs in the development phase of the dominant mode in boreal summer.
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CORRIGENDUM ZENG-ZHEN HU Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland
BOHUA HUANG Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland, and Department of Climate Dynamics, George Mason University, Fairfax, Virginia (Manuscript received and in final form 7 May 2007)
In Hu and Huang (2006), due to a coding error, the values of the regression coefficients shown in Figs. 4–17 are incorrect. The correct values should be divided by the square root of the sample size. For the sample size of 40, the amplitudes of the regression coefficients in these figures should be 6.3 times smaller than shown in the figures. All the regression patterns and significance tests remain the same. The conclusion is not affected by this error. REFERENCE Hu, Z.-Z., and B. Huang, 2006: Physical processes associated with tropical Atlantic SST meridional gradient. J. Climate, 19, 5500–5518. Corresponding author address: Zeng-Zhen Hu, Center for Ocean–Land–Atmosphere Studies, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705. E-mail:
[email protected]
DOI: 10.1175/JCLI4356.1 © 2007 American Meteorological Society
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