1 JULY 2013
HU ET AL.
4649
Leading Modes of the Upper-Ocean Temperature Interannual Variability along the Equatorial Atlantic Ocean in NCEP GODAS ZENG-ZHEN HU AND ARUN KUMAR Climate Prediction Center, NOAA/NWS/NCEP, College Park, Maryland
BOHUA HUANG Department of Atmospheric, Oceanic, and Earth Sciences, College of Science, George Mason University, Fairfax, Virginia, and Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland
JIESHUN ZHU Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland (Manuscript received 20 August 2012, in final form 17 December 2012) ABSTRACT In this work, the authors analyze the physical mechanisms of interannual variability of the upper-ocean temperature anomaly (OTA) in the equatorial Atlantic Ocean, using ocean reanalysis from the National Centers for Environmental Prediction (NCEP) Global Ocean Data Assimilation System. The variability of equatorial Atlantic OTA is dominated by two leading modes. The first mode is characterized by same-sign variation along the thermocline with pronounced amplitude in the central and eastern equatorial Atlantic. This mode represents the modulation of the overall thermocline depth at the equator generated by net heat convergence in the equatorial ocean, with heat content first accumulated mainly in the off-equatorial northwestern Atlantic in response to anomalous wind curl associated with Atlantic meridional mode. The second leading mode shows an opposite variation between the western and eastern Atlantic. This mode is mainly driven by the zonal wind stress fluctuation confined in the southwestern tropical and equatorial Atlantic and reflects the equatorial balanced response between the zonal slope of the equatorial thermocline depth and the atmospheric zonal wind variations with pronounced surface wind and ocean anomalies in the southwestern and equatorial ocean. The different characteristics of these two modes suggest that they may occur independently. In fact, evolution of the two leading modes is approximately in quadrature, and they may also occur in sequence on interannual time scales. The two leading mode-associated air–sea interaction processes suggest that the Atlantic meridional mode and zonal mode are statistically and physically connected in their evolution.
1. Introduction Because of the significant impact of the Atlantic Ocean on the climate variability in the surrounding regions, the Atlantic sea surface temperature (SST) variability has received increasing attention in the recent decades (Hurrell et al. 2003; Xie and Carton 2004). In addition to the remote influence of El Ni~ no–Southern Oscillation (ENSO) and the North Atlantic Oscillation
Corresponding author address: Zeng-Zhen Hu, Climate Prediction Center, NOAA/NWS/NCEP, 5830 University Research Court, College Park, MD 20740. E-mail:
[email protected] DOI: 10.1175/JCLI-D-12-00629.1 Ó 2013 American Meteorological Society
on the tropical Atlantic (Enfield and Mayer 1997; Seager et al. 2000; Huang and Hu 2007; Lee et al. 2008; Hu et al. 2011, 2013), there is also self-generated local air–sea coupled variability in the equatorial Atlantic, such as the zonal mode and meridional mode in the deep tropical Atlantic (Servain et al. 1982; Zebiak 1993; Carton and Huang 1994; Xie and Carton 2004; Bunge and Clarke 2009, hereafter BC2009; L€ ubbecke et al. 2010; Richter et al. 2010). The equatorial Atlantic SST fluctuations are generally attributed to dynamical air–sea interactions associated with equatorial oceanic dynamics, and early studies emphasized the similarity of the Atlantic warm events to the Pacific El Ni~ no. For instance, Servain et al. (1982)
4650
JOURNAL OF CLIMATE
VOLUME 26
FIG. 1. Monthly mean OTA along the equator Atlantic at (a) 358 and (b) 238W of GODAS (contours) and PIRATA (shading) between ocean surface and 200 m during January 1998–December 2011. Contour interval is 0.58C. No shading areas represent data unavailable for PIRATA.
and Hirst and Hastenrath (1983) find that the SST anomalies (SSTAs) in the eastern and southeastern tropical Atlantic are significantly correlated with the zonal wind anomalies in the western equatorial Atlantic. However, unlike the Pacific El Ni~ no, the ocean wave dynamics may not be the dominant factor for the equatorial Atlantic variability, although the equatorial Atlantic could exhibit a self-sustaining oscillation involving both equatorial Kelvin waves and off-equatorial Rossby waves (e.g., Handoh and Bigg 2000). For instance, applying the Zebiak and Cane (1987) model to the tropical Atlantic domain, Zebiak (1993) found that the time scale of equatorial oscillation was around 4 yr. This time scale is longer than the cross-basin time of the equatorial oceanic waves and also the time scale expected from the delayed oscillator mechanism (Schopf and Suarez 1988; Battisti and Hirst 1989). In general, the adjustment time of the equatorial ocean to a sudden change in zonal wind near the western boundary is determined by the time of the wind-stimulated eastward propagating equatorial Kelvin wave front to
cross the basin, which is then reflected at the eastern boundary as the gravest equatorially trapped Rossby wave front propagating westward across the basin (see, e.g., Philander and Pacanowski 1981). In the equatorial Atlantic Ocean, the second baroclinic mode is dominant (e.g., Busalacchi and Picaut 1983). The Kelvin waves take about 1–2 months for the eastward propagation and the Rossby waves take about 4–5 months for westward propagation to cross the Atlantic basin. Therefore, the intrinsic oceanic adjustment time of the equatorial Atlantic is about 5–7 months, which is less than the time scale of the interannual variability. That implies that the SSTAs and zonal slope of the thermocline in the equatorial Atlantic are mostly in equilibrium with the surface wind stress on interannual time scales. This is also largely true for the Pacific ENSO. For instance, Schneider et al. (1995) argued that the zonal slope of the thermocline in the equatorial Pacific is largely in equilibrium with the surface wind stress on interannual time scales, while an imbalance exists between the zonal mean heat content and the surface wind
1 JULY 2013
HU ET AL.
4651
FIG. 2. Mean ocean temperature (contours) and standard deviation (STDV) of OTA along the equatorial Atlantic averaged in 28S–28N in GODAS during January 1979–December 2011. The contour interval is 28C.
stress. This is consistent with the evidence that the eastern equatorial oceanic heat content in the Pacific increases prior to a warm event but decreases after the peak phase of the event in both observations (Wyrtki 1985) and model simulations (Zebiak and Cane 1987). These findings formed the basis of the discharge–recharge paradigm of the ENSO, characterized by the balanced (east–west asymmetric) and imbalanced (east–west symmetric) components in the upper-ocean heat content with the latter regulating the meridional heat transport into the equatorial zone (Jin 1997a,b; Meinen and McPhaden 2000; Clarke et al. 2007; Clarke 2010; Kumar and Hu 2013). And this leads to the phase transition in an ENSO cycle. Analyzing the seasonal cycle in the tropical Atlantic, BC2009 showed that similar asymmetric and symmetric modes also exist in the equatorial Atlantic. Given their characteristics, the former can be referred to as tilt mode and latter can be referred to as warm water volume (WWV) mode. Using sea surface height (SSH) data along the equatorial Atlantic, BC2009 argued that seasonally eastward propagation of SSH is not a free equatorial Kelvin wave. Rather, it is due to the combined effect of the asymmetric (tilt) and symmetric (WWV) modes. They suggested that WWV mode is mainly driven by wind stress curl on both sides of the equator and the tilt
mode is driven by the zonal wind stress along the equator, similar to the Pacific. In this work, using a comprehensive ocean reanalysis data, we examine the leading modes of interannual variability in ocean temperature anomaly (OTA) along the equatorial Atlantic and associated physical processes. First, the spatial patterns of the first two leading modes of OTA are identified with the method of empirical orthogonal function (EOF) analysis. Then, the physical processes associated with these two modes, as well as the connections between two modes, are investigated. The paper is organized as follows: After the introduction section, observation-based reanalysis and analysis data used in this work are briefly introduced in section 2. In section 3, we present the results, followed by a summary and discussion in section 4.
2. Reanalysis and analysis data The main dataset used in this work is from the Global Ocean Data Assimilation System (GODAS; Behringer and Xue 2004). The ocean model providing for the first guess fields for analysis is based on the Geophysical Fluid Dynamics Laboratory Modular Ocean Model version 3 (MOM3), which is forced with atmospheric fluxes from the National Centers for Environmental
4652
JOURNAL OF CLIMATE
VOLUME 26
FIG. 3. The (a) PC1, (b) PC2, (c) EOF1, and (d) EOF2 of OTA along the equatorial Atlantic. The percentages of total variance explained by EOF1 and EOF2 are 30% and 17%, respectively. The thick dashed line in (c),(d) is the climatological mean D20.
Prediction (NCEP)–Department of Energy (DOE) reanalysis 2 (R-2) (Kanamitsu et al. 2002). The Modular Ocean Model version 3 (MOM3) is in a quasi-global (758S–658N) configuration with prescribed climatological sea ice. The spatial resolution is 18 (1/ 38) in zonal (meridional) direction between 108S and 108N, gradually increasing through the tropics to 18 poleward of 308S and 308N. There are 40 vertical layers with 27 layers in the upper 400 m of the ocean. The observational data are assimilated continuously through a three-dimensional variational data assimilation (3DVAR) scheme, which include in situ temperature profiles from expendable bathythermographs; Tropical Atmosphere Ocean (TAO), Triangle Trans Ocean Buoy Network, Prediction and Research Moored Array in the Tropical Atlantic (PIRATA), and Argo profiling floats; and SST based on weekly optimal interpolation (OI) SST analysis (Behringer and Xue 2004). Vertical ocean temperature profile, SST, and depth of the 208C isotherm (D20) from GODAS and surface wind stress and sea level pressure (SLP) from R-2 in January 1979– December 2011 are used in this analysis.
One concern of this analysis is about the reliability of the GODAS OTA data. Kumar and Hu (2013) have demonstrated that the mean state and its variability of ocean temperature along the equatorial Pacific in GODAS are comparable with that of TAO. Because there were much fewer observational data that were assimilated in the Atlantic than in the Pacific, it seems more necessary to verify the GODAS with observations in the equatorial Atlantic. To verify GODAS, we use ocean temperature vertical profile data along the equatorial Atlantic at two mooring stations (238 and 358W) from PIRATA (http://www.pmel.noaa.gov/pirata/) (Bourl es et al. 2008). Along with OTA in GODAS (contours), Fig. 1 shows OTA at two PIRATA mooring stations (shading) along the equator at 358 (Fig. 1a) and 238W (Fig. 1b) during January 1998–December 2011. In spite of a lot of missed data in PIRATA, we note that the pronounced OTA— such as negative ones in late 1998–99, 2002–early 2003, and 2008 and positive ones in 2004–05 at 358W (Fig. 1a), as well as negative ones in early 2004, 2007, and 2009 and
1 JULY 2013
HU ET AL.
4653
FIG. 4. Lead and lag regressions of D20 (shading), SST (contours), and surface wind stress (vectors) anomalies onto PC1 in January 1979–December 2011 for leading PC1 by (a) 15, (b) 10, (c) 5, (d) 3, (e) 2, (f) 1, and (g) 0 months and lagging PC1 by (h) 1 and (i) 3 months. Contour interval is 0.058C per STDV of PC1.
positive ones in late 2004, mid-2005, and mid-2010 at 238W (Fig. 1b)—are quite consistent between GODAS and PIRATA. This is consistent with Zhu et al. (2012a). They indicated that GODAS is one of the best analyses in representing the variability of upper-ocean heat content over the tropical Atlantic Ocean. These results suggest the reliability of GODAS OTA data along the equatorial Atlantic and also provide a basis for following analyses.
3. Results Similar to the equatorial Pacific, the mean thermocline is deeper in the western and shallower in the eastern equatorial Atlantic, forming a zonal slope along the equator. The mean D20 is around 140 m in the western and 50 m in the eastern Atlantic on the equator
(Fig. 2). The annual cycle of the thermocline depth associated with the seasonal enhancement of the cold tongue has been discussed in previous studies (e.g., BC2009). On interannual time scales, the variability of OTA is concentrated around the thermocline (Fig. 2). Compared with the variability around the thermocline, the variability is smaller in the mixed layer in the western Atlantic and also below 200 m. We concentrate on variability in the upper ocean and on interannual time scales. For this purpose, we first identify the leading modes of variability through an EOF analysis and then investigate the associated physical processes. The EOFs are computed using OTA along the equatorial Atlantic averaged between 28S and 28N in 0–459 m during January 1979–December 2011. Taking into account the uneven distribution of the vertical levels, the data at each level are weighted by the
4654
JOURNAL OF CLIMATE
VOLUME 26
FIG. 5. Zonal average (558W–108E) of positive lead and lag regressions of D20 anomaly onto PC1 in January 1979–December 2011 from the D20 leading PC1 by 21 months to lagging PC1 by 21 months. The horizontal dished line represents the equator, and the vertical one is for zero lead.
square root of its corresponding layer thickness before the covariance matrix is constructed. To focus on interannual signals, and to suppress the influence of lowerfrequency components, a 10-yr high-pass filter is applied to the OTA prior to the EOF calculation. In the following discussion, the first two leading EOF modes are discussed, which together account for about 47% of the total variances of the filtered data and are well separated from the higher-order modes (Fig. 3), according to North et al. (1982), a criterion based on the degree of separation between two consecutive eigenvalues.
a. EOF1 of the OTA: WWV mode Figure 3c depicts EOF1 of OTA of GODAS, which is characterized by a coherent variation along the thermocline of the equatorial Atlantic with pronounced variations in the central and eastern Atlantic, which explains 30% of the total variance of the filtered data. Its time series [the first principal component (PC1)] shows fluctuations at interannual time scales (Fig. 3a). This mode was referred to as symmetric or WWV mode (BC2009) because it represents the overall deepening or shoaling of the equatorial thermocline due to the net convergence into or divergence out of the equatorial Atlantic region. This is similar to the overall change of the equatorial thermocline depth during certain phases
of the discharge/recharge process in relation with the Pacific ENSO cycle (Wyrtki 1975; Jin 1997a,b; Clarke et al. 2007) and also the seasonal cycle of the equatorial Atlantic SSH (BC2009). However, unlike the Pacific discharge–recharge process, where the heat convergence/ divergence is mainly controlled by equatorial wind stress– stress curl anomalies (e.g., Jin 1997a,b; Clarke et al. 2007), the heat convergence/divergence in the Atlantic WWV fluctuation is more likely forced by processes outside the equatorial zone, particularly the large-scale air–sea interaction in the northwestern Atlantic. That will be discussed next. The off-equatorial heat source can be tracked from lead–lag regressions between the time series of the WWV mode (PC1) and the D20 anomalies, which characterizes the upper-ocean heat content anomalies. At 15-month lead relative to the WWV, some positive D20 anomaly is already developed in the northwestern tropical Atlantic (Fig. 4a), then slowly propagates into the western equatorial Atlantic along the western coast while intensifying (Figs. 4b–d). In the 2-month lead (Fig. 4e), the D20 anomaly then propagates eastward and occupies the whole equatorial Atlantic, deepening the equatorial thermocline. The positive anomaly further strengthens in the following 2 months, possibly through Bjerknes-like air–sea feedback (Figs. 4f,g) (Bjerknes
1 JULY 2013
HU ET AL.
4655
FIG. 6. Lead and lag correlation of the (a) ATL3 and (b) dipole indices with PC1 in January 1979–December 2011 from the indices leading PC1 by 24 months to lagging PC1 by 24 months.
1969; Zebiak 1993; Carton and Huang 1994; Hu and Huang 2006, 2007). In the months after the peak (Figs. 4h,i), the D20 anomaly is enhanced in the central and eastern equatorial Atlantic and expands along the eastern coast in meridional directions. By this time the equatorial D20 anomalies in the western Atlantic starts to change sign, the fronts of the D20 anomalies near the African coast have reaches 158–208 latitudes in both hemispheres and also expands westward to around 308W
near 58S and 58N, possibly as off-equatorial Rossby waves (Fig. 4i). It should be pointed out that the propagation of D20 is asymmetric in two hemispheres with less propagation northward along the eastern boundary and more propagation southward, probably due to geographical difference of the coast. As a result, net heat is transported from the northwestern Atlantic into the southeastern Atlantic through the equatorial waveguide.
4656
JOURNAL OF CLIMATE
VOLUME 26
FIG. 7. Lead and lag regressions of surface wind stress curl anomaly onto PC1 in January 1979–December 2011 for the surface wind stress curl leading PC1 by (a) 3, (b) 2, (c) 1, and (d) 0 months.
According to Illig et al. (2004), the phase speed is about 0.43 m s21 for the gravest Rossby wave of the second baroclinic mode and crossing the Atlantic basin at 58N (about 4500 km) takes about 4 months. A similar Rossby wave phase speed value (0.48 m s21) was given in Schouten et al. (2005). This is roughly the speed seen in Figs. 4f–i, when a cold anomaly propagates westward from the eastern boundary to the north of the equator and arrives at the western boundary in about 4 months. Therefore, the wave propagation is consistent with the previous works (e.g., Illig et al. 2004; Schouten et al. 2005), suggesting that the second baroclinic mode is important for the tropical Atlantic seasonal cycle. However, we should point out that the cold anomaly might be enhanced en route by the Ekman transport caused by the equatorial westerly wind anomalies, which may account for its zonally elongated structure and seemingly fast propagation from Figs. 4f and 4g. The heat transport from the northwestern Atlantic is further demonstrated in Fig. 5, which is the zonal average (558W–108E) of lead–lag positive regression of D20
onto PC1. Only using positive regression average is utilized to focus on the southward propagation of the positive D20 regression initiated in the northwestern Atlantic (Fig. 4). It is noted that the heat anomaly develops in 08– 158N at 15–21-month lead and then propagates into the equator at about 12-month lead. The heat anomaly further develops along the equator in the following months and reaches the peak in zero-month lead. Afterward, the heat anomaly propagates northward and southward along the ocean eastern boundary as seen in Fig. 4 and discussed in the previous paragraph. The results here suggest that heat can propagate from one place to another when the thermocline slope and surface wind stress are in imbalance (Schneider et al. 1995).This heat transport process is associated with a systematic evolution of the surface wind stress anomaly (vectors in Fig. 4), as well as a coherent evolution pattern of SST (contours in Fig. 4) and SLP (not shown) anomalies. During the 15–21-month lead (Figs. 4a–d), opposite SSTAs are present in the tropical North and South Atlantic, and the anomalous surface wind blows from SST negative anomaly to
1 JULY 2013
HU ET AL.
4657
FIG. 8. Lead and lag regressions (shading) of surface zonal wind stress anomaly onto PC2 and the corresponding correlations (contour) in January 1979–December 2011 for the surface zonal wind stress anomaly leading PC2 by (a) 3, (b) 2, (c) 1, and (d) 0 months. The contour interval is 0.1.
positive anomaly regions, consistent with the so-called dipole mode (Nobre and Shukla 1996; Chang et al. 1997; Xie and Carton 2004; Hu and Huang 2006). Nevertheless, the existence of the dipole pattern is controversial (Dommenget and Latif 2002). Houghton and Tourre (1992) argued 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. In fact, the dipole pattern is a seasonally dependent mode (Xie and Carton 2004). Both positive and negative SSTAs associated with the dipole mode, as well as the wind stress anomalies, reach their maximum in boreal spring and weaken in the following seasons (Hu and Huang 2006). The accumulation of the upper-ocean heat content anomalies in the northwestern tropical Atlantic is the subsurface projection of the surface meridional mode, mainly due to the surface forcing by the off-equatorial wind stress curl (e.g., Huang and Shukla 1997; RuizBarradas et al. 2000; Zhu et al. 2012b). Joyce et al. (2004) and Lee and Wang (2008) showed that anomalous
Sverdrup transports induced by the anomalous meridional wind stress curls are associated with the meridional mode transfer heat anomalies across the equator. Doi et al. (2009, 2010) showed that the wind-induced Ekman upwelling associated with the northward migration of the ITCZ cools the northern tropical Atlantic, which contributes to the decay of the meridional mode. As the dipole-like pattern weakens (Fig. 4e), the accumulated heat content anomalies near the northwest are released into the equatorial waveguide and generate an overall deepening of the equatorial thermocline. This process triggers the development of the so-called zonal mode or Atlantic Ni~ no event, which reaches its peak through air–sea feedback (Figs. 4f,g) (Zebiak 1993; Carton and Huang 1994; Huang et al. 1995; Huang and Shukla 1997; Hu and Huang 2006, 2007), when the EOF1 and SSTA are simultaneous (Fig. 4g). Afterward, similar to the associated D20 anomaly, the associated SSTA expands in meridional direction, causing the warming in the southeastern Atlantic (Figs. 4h,i). Lead and lag correlations (Fig. 6) indicate that this mode lags the Atlantic
4658
JOURNAL OF CLIMATE
VOLUME 26
FIG. 9. Lead and lag regressions of SLP (shading) and surface wind stress (vectors) anomalies onto PC2 in January 1979–December 2011 for leading PC2 by (a) 15, (b) 10, (c) 5, (d) 3, (e) 2, (f) 1, and (g) 0 months and lagging PC2 by (h) 1 and (i) 3 months.
dipole mode index by 4–6 months and is almost in phase with the Atl3 index,1 confirming the evolution displayed in Fig. 4. It is interesting to note that a major source of heat accumulation is in the northwestern Atlantic in response to the anomalous wind curl associated with the meridional mode (Figs. 4, 7). The anomalous wind curl (instead of the zonal wind anomaly) is the main driver for initiating this mode, since the amplitudes of the zonal and meridional wind anomalies are comparable in Figs. 3a–f. The negative (positive) wind curl anomaly develops in the northwestern (southwestern) tropical Atlantic (Fig. 7a).
1 The dipole index is defined as the averaged SSTA differences between (58–208N, 308–608W) and (08–208S, 308W–108E), and the ATL3 index is defined as the averaged SSTA in (2.58S–2.58N, 208W–08) (Zebiak 1993).
In the 2- and 1-month leads (Figs. 7b,c), the negative wind curl anomaly extends southward along the coast and then eastward along the equator. According to Clarke et al. (2007), the negative anomaly of surface wind curl causes an increase of the thermocline depth (D20) through divergent meridional flow and vortex stretching [see their Eqs. (2.14) and (2.20)]. These ocean adjustment processes cause the occurrence of zonal mode (Atlantic Ni~ no)–like anomalous pattern (Fig. 4). In this sense, this mode represents a connection of the meridional and zonal modes in the tropical Atlantic, as already noticed by previous researchers (e.g., Servain et al. 1999; Murtugudde et al. 2001; Chiang et al. 2002; Foltz and McPhaden 2010; Zhu et al. 2012b). Different from some of the previous studies, which ascribe the connection between the zonal and meridional modes to the equatorial zonal wind anomalies, we emphasize the off-equatorial wind anomalies in forcing the heat
1 JULY 2013
HU ET AL.
4659
FIG. 10. Lead and lag regressions of D20 (shading), SST (contours), and surface wind stress (vectors) anomalies onto PC2 in January 1979–December 2011 for leading PC2 by (a) 5, (b) 2, and (c) 0 months and (d) lagging PC2 by 2 months. Contour interval is 0.058C per STDV of PC2.
content anomalies in the northwestern Atlantic, consistent with the scenario described by Huang and Shukla (1997) and Zhu et al. (2012b). Moreover, the ocean heat accumulates off the equatorial guide and then transports into the equatorial ocean largely through the west. That is different from the recharge/discharge theory described by previous studies (e.g., BC2009), which emphasized the meridional heat transport by the local wind/wind curl anomalies on both sides of the equator, although the process is similar to their paradigm in latter stages of the evolution of the EOF1 (Figs. 4e–g).
b. EOF2 of OTA: Tilt mode The spatial pattern of EOF2 (Fig. 3d) depicts an opposite variation between the western and eastern Atlantic and the corresponding time series [the second principle component (PC2)] shows a fluctuation at interannual time scales (Fig. 3b). This asymmetric mode was referred to as tilt mode (BC2009), which explains 17% of the total variance of the filtered data. This pattern largely reflects the balanced response between the equatorial zonal wind anomalies and the zonal pressure
gradient in the upper ocean. This is confirmed by the relatively high correlations between PC2 and the zonal wind stress anomalies near the equator (contour in Fig. 8). The regression analysis shows that the associated zonal wind anomalies are largest in the southwestern and equatorial ocean (shading in Fig. 8), which are generated by the zonal pressure gradient associated with a low SLP center established in the tropical South Atlantic (Figs. 9c,d). Figure 10 shows the regression of D20 (shading), SST (contour), and surface wind stress (vector) anomalies onto PC2. It is noted that the shoaling of the equatorial thermocline in the west has a wider meridional extent (Fig. 10) than that associated with EOF1 (Fig. 4), and both zonal and meridional wind anomalies contribute to the thermocline change associated with EOF2. It is also interesting to note that, associated with the changes in the equatorial thermocline, the SSTAs are initiated first in the African coast around 158S (Fig. 10a) and later expand into the equatorial Atlantic through coupled air–sea interaction as found by Hu and Huang (2007). This is different from Florenchie et al. (2003), who
4660
JOURNAL OF CLIMATE
VOLUME 26
FIG. 11. Lead and lag correlations between PC2 and surface zonal wind stress anomaly in January 1979–December 2011 from the surface wind stress anomaly leading PC2 by 24 months to lagging PC2 by 24 months. The zonal wind stress anomaly is the average in (28S–28N, 108– 428W).
argued that the relaxation of the trade wind over the western equatorial Atlantic triggers the Benguela Ni~ no. Correspondingly, negative SLP anomalies centered near 208S strengthen and subsequently migrate equatorward (Fig. 9). The associated SLP anomaly depicts an opposite variation between the tropical North and South Atlantic with major anomalous center in the south (Fig. 9). The SLP anomaly pattern is consistent with the previous results (e.g., Hu and Huang 2007; L€ ubbecke et al. 2010; Richter et al. 2010). It is suggested that the Benguela Ni~ no event is mainly generated from perturbations in the south. The negative SLP anomaly in the tropical South Atlantic causes convergence and warming along the African coast. As indicated in Hu and Huang (2007) and similar to the processes associated with EOF1, the warming along the African coast may trigger and enhance the westerly wind anomaly along the equator (Figs. 9, 10). As a consequence, warming along the equatorial Atlantic occurs, and later it merges with the warming along the coast and becomes a warming event in the southeastern Atlantic (Figs. 10c,d). Consistent with BC2009, EOF2 is mainly driven by the zonal wind stress fluctuation along the equator (Figs. 8–11). The associated zonal wind anomaly is strong and confined in the southwestern tropical and equatorial
Atlantic, instead of only along the equator, but it is relatively weak in the eastern tropical Atlantic (Figs. 8–10). The maximum correlation occurs when zonal wind stress leads PC2 by 1 month (Fig. 11).
c. Relation between EOF1 and EOF2 Previous analysis emphasizes the independent evolution of the two leading EOF modes. Indeed, EOF1 and EOF2 seem to be associated with quite different physical processes. On the other hand, these two modes can be connected as indicated in BC2009, mainly during the equatorward transport of the heat accumulated in the northwestern tropical Atlantic. For example, the deepening of the equatorial thermocline in EOF1, associated with heat convergence into the equatorial ocean driven by north–south wind curl gradient in the western Atlantic, usually leads to an initial warming near the Gulf of Guinea. As indicated by Hu and Huang (2007) and L€ ubbecke et al. (2010), warm SSTAs generally establish first in the southeastern ocean, subsequently generating the equatorial dynamical air–sea feedback that causes the adjustment of both the surface zonal wind and the thermocline depth: that is, the development of the EOF2-like tilt pattern. At the peak of SSTA, the surface wind converges into the equatorial region with maximum
1 JULY 2013
HU ET AL.
4661
FIG. 12. Lead–lag correlations between PC1 tendency and PC2 in January 1979–December 2011 from the PC1 tendency leading PC2 by 24 months to lagging PC2 by 24 months.
SSTA (Fig. 4). Meanwhile, convergence is also observed near the eastern coast around 108–208S and is linked with positive SSTA along the coast, a typical Benguela Ni~ no event. Therefore, it is natural to find some lagged correlations between EOF1 and EOF2. On the other hand, the EOF2-type event can occur independently, without heat accumulation preconditioning in the northwestern tropical Atlantic. This is mainly the case when the fluctuation of the subtropical anticyclone in the South Atlantic perturbs the upwelling near the Angola coast (e.g., Richter et al. 2010), the equatorial zonal winds in the central and western Atlantic (Hu and Huang 2007; L€ ubbecke et al. 2010), or both. EOF2 reflects the equatorial balanced response between the ocean thermocline and the atmosphere wind anomalies with pronounced wind and D20 anomalies in the southwestern and equatorial ocean. The SSTA associated with EOF2 is initiated at the African coast and later expands into the equatorial Atlantic through air–sea interaction. Thus, EOF2 could also be driven by the zonal wind stress fluctuation confined in the southwestern tropical Atlantic. Because of the connections described above, the EOF1 and EOF2 are approximately in quadrature [see Eq. (2.2) of BC2009], as discussed by Clarke et al. (2007) for the ENSO cycle and BC2009 for the equatorial
Atlantic annual cycle. That is confirmed by the lead–lag correlations between PC1 tendency and PC2 (Fig. 12) with maximum negative correlation seen at zero lead. On the other hand, because the EOF2 pattern can also be generated independently, the correlation is not as high as shown in previous studies.
4. Summary and discussion In this work, we analyzed the first two modes of interannual variability of OTA of GODAS along the equatorial Atlantic and then examined their associated physical processes. The ocean temperature variations are mainly along the thermocline between 80 and 180 m in the west and between 10 and 130 m in the east. The first leading mode of OTA is characterized by a coherent variation along the thermocline of the equatorial Atlantic with pronounced variation in the central and eastern Atlantic, which was called symmetric or WWV modes. This mode is associated with large-scale air–sea interaction and represents the net heat convergence–divergence into the equatorial ocean that modulates the overall thermocline depth. A major source of heat accumulation is in the northwestern Atlantic, in response to the anomalous wind curl associated with the meridional mode. The negative (positive) wind curl anomaly initiates
4662
JOURNAL OF CLIMATE
in the northwestern (southwestern) tropical Atlantic. The negative wind curl anomaly extends southward along the coast and then propagates eastward along the equator, resulting in the occurrence of zonal mode–like anomalous pattern. In this paradigm, this mode represents a connection of the meridional and zonal modes in the tropical Atlantic. In the months preceding the development of this mode, the ocean heat accumulates off the equatorial guide and then is transported into the equatorial ocean largely through the west, different from the recharge– discharge theory described in previous studies (e.g., BC2009; Clarke et al. 2007). They emphasized the meridional heat transport by the local wind–wind curl anomalies on both sides of the equator. This is also different from the situation in the Pacific that the zonal wind anomaly along the equator plays a dominant role in the ENSO evolution (Jin 1997a,b; Clarke et al. 2007). EOF2 shows an opposite variation between the western and eastern equatorial Atlantic. This asymmetric mode was referred to as tilt mode and reflects the equatorial balanced response between the ocean thermocline and atmosphere wind anomalies with pronounced wind and D20 anomalies in the southwestern and equatorial ocean. The SSTA associated with EOF2 is initiated in the African coast and later expands into the equatorial Atlantic through air–sea interaction. EOF2 is mainly driven by the zonal wind stress fluctuation confined in the southwestern tropical and equatorial Atlantic, instead of only along the equator. These results indicate that EOF1 and EOF2 have different characteristics and are associated with quite different processes and also suggest that they may occur independently, although these two modes can follow each other and they are approximately in quadrature as discussed by BC2009. The two leading EOF mode associated air–sea interaction processes suggest that the Atlantic meridional mode and zonal mode are statistically and physically connected. A caveat of this study is that the tropical Atlantic variability has strong seasonality (Kushnir et al. 2006). We speculate that the two leading modes discussed in this work may also experience seasonality. That deserves further investigation. Also, the results shown in this work are based on the analysis of GODAS data, and it is necessary to verify the results using other ocean reanalysis data. Acknowledgments. We appreciate the constructive suggestions from reviewers and the editor (Dr. Eric Maloney), as well as Drs. Caihong Wen and Hui Wang. We thank the TAO Project Office of NOAA/PMEL for supplying the PIRATA data. B. Huang and J. Zhu are supported by the COLA omnibus grant from NSF (ATM-0830068), NOAA (NA09OAR4310058), and NASA (NNX09AN50G).
VOLUME 26 REFERENCES
Battisti, D. S., and A. C. Hirst, 1989: Interannual variability in a tropical atmosphere–ocean model: Influence of the basic state, ocean geometry and nonlinearity. J. Atmos. Sci., 46, 1687–1712. Behringer, D. W., and Y. Xue, 2004: Evaluation of the global ocean data assimilation system at NCEP: The Pacific Ocean. Preprints, Eighth Symp. on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface, Seattle, WA, Amer. Meteor. Soc., 2.3. [Available online at http://ams. confex.com/ams/84Annual/techprogram/paper_70720.htm.] Bjerknes, J., 1969: Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev., 97, 163–172. Bourles, B., and Coauthors, 2008: The PIRATA program: History, accomplishments, and future directions. Bull. Amer. Meteor. Soc., 89, 1111–1125. Bunge, L., and A. J. Clarke, 2009: Seasonal propagation of sea level in the equatorial Atlantic. J. Phys. Oceanogr., 39, 1069–1074. Busalacchi, A. J., and J. Picaut, 1983: Seasonal variability from a model of the tropical Atlantic Ocean. J. Phys. Oceanogr., 13, 1564–1588. Carton, J. A., and B. Huang, 1994: Warm events in the tropical Atlantic. J. Phys. Oceanogr., 24, 888–903. Chang, P., L. Ji, and H. Li, 1997: A decadal climate variation in the tropical Atlantic Ocean from thermodynamic air–sea interactions. Nature, 385, 516–518. Chiang, J. C. H., Y. Kushnir, and A. Giannini, 2002: Deconstructing Atlantic intertropical convergence zone variability: Influence of the local cross-equatorial sea surface temperature gradient and remote forcing from the eastern equatorial Pacific. J. Geophys. Res., 107, 4004, doi:10.1029/2000JD000307. Clarke, A. J., 2010: Analytical theory for the quasi-steady and lowfrequency equatorial ocean response to wind forcing: The ‘‘tilt’’ and ‘‘warm water volume’’ modes. J. Phys. Oceanogr., 40, 121–137. ——, S. Van Gorder, and G. Colantuono, 2007: Wind stress curl and ENSO discharge/recharge in the equatorial Pacific. J. Phys. Oceanogr., 37, 1077–1091. Doi, T., T. Tozuka, and T. Yamagata, 2009: Interannual variability of the Guinea Dome and its possible link with the Atlantic meridional mode. Climate Dyn., 33, 985–998. ——, ——, and ——, 2010: The Atlantic meridional mode and its coupled variability with the Guinea Dome. J. Climate, 23, 455– 475. Dommenget, D., and M. Latif, 2002: A cautionary note on the interpretation of EOFs. J. Climate, 15, 216–225. Enfield, D. B., and D. A. Mayer, 1997: Tropical Atlantic sea surface temperature variability and its relation to El Ni~ no-Southern Oscillation. J. Geophys. Res., 102, 929–945. Florenchie, P., J. R. E. Lutjeharms, C. J. C. Reason, S. Masson, and M. Rouault, 2003: The source of Benguela Ni~ nos in the South Atlantic Ocean. Geophys. Res. Lett., 30, 1505, doi:10.1029/ 2003GL017172. Foltz, G. R., and M. J. McPhaden, 2010: Interaction between the Atlantic meridional and Ni~ no modes. Geophys. Res. Lett., 37, L18604, doi:10.1029/2010GL044001. Handoh, I. C., and G. R. Bigg, 2000: A self-sustaining climate mode in the tropical Atlantic, 1995–1997: Observations and modeling. Quart. J. Roy. Meteor. Soc., 126, 807–821, doi:10.1256/ smsqj.56402. Hirst, A. C., and S. Hastenrath, 1983: Atmosphere-ocean mechanisms of climate anomalies in the Angola-tropical Atlantic sector. J. Phys. Oceanogr., 13, 1146–1157.
1 JULY 2013
HU ET AL.
Houghton, R. W., and Y. M. Tourre, 1992: Characteristics of lowfrequency sea surface temperature fluctuations in the tropical Atlantic. J. Climate, 5, 765–771. Hu, Z.-Z., and B. Huang, 2006: Physical processes associated with tropical Atlantic SST meridional gradient. J. Climate, 19, 5500– 5518. ——, and ——, 2007: Physical processes associated with tropical Atlantic SST gradient during the anomalous evolution in the southeastern ocean. J. Climate, 20, 3366–3378. ——, A. Kumar, B. Huang, Y. Xue, W. Wang, and B. Jha, 2011: Persistent atmospheric and oceanic anomalies in the North Atlantic from summer 2009 to summer 2010. J. Climate, 24, 5812–5830. ——, ——, ——, W. Wang, J. Zhu, and C. Wen, 2013: Prediction skill of monthly SST in the North Atlantic Ocean in NCEP Climate Forecast System version 2. Climate Dyn., 40, 2745– 2759, doi:10.1007/s00382-012-1431-z. Huang, B., and J. Shukla, 1997: Characteristics of interannual and decadal variability in a general circulation model of the tropical Atlantic Ocean. J. Phys. Oceanogr., 27, 1693–1712. ——, and Z.-Z. Hu, 2007: Cloud-SST feedback in southeastern tropical Atlantic anomalous events. J. Geophys. Res., 112, C03015, doi:10.1029/2006JC003626. ——, J. A. Carton, and J. Shukla, 1995: A numerical simulation of the variability in the tropical Atlantic Ocean, 1980–88. J. Phys. Oceanogr., 25, 835–854. Hurrell, J. W., Y. Kushnir, G. Ottersen, and M. Visbeck, 2003: An overview of the North Atlantic Oscillation. The North Atlantic Oscillation: Climate Significance and Environmental Impact, Geophys. Monogr., Vol. 134, Amer. Geophys. Union, 1–36. Illig, S., and Coauthors, 2004: Interannual long equatorial waves in the tropical Atlantic from a high resolution ocean general circulation model experiment in 1981–2000. J. Geophys. Res., 109, C02022, doi:10.1029/2003JC001771. Jin, F.-F., 1997a: An equatorial ocean recharge paradigm for ENSO. Part I: Conceptual model. J. Atmos. Sci., 54, 811–829. ——, 1997b: An equatorial ocean recharge paradigm for ENSO. Part II: A stripped-down coupled model. J. Atmos. Sci., 54, 830–847. Joyce, T. M., C. Frankignoul, J. Yang, and H. E. Phillips, 2004: Ocean response and feedback to the SST dipole in the tropical Atlantic. J. Phys. Oceanogr., 34, 2525–2540. Kanamitsu, M., W. Ebisuzaki, J. Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP–DOE AMIP-II Reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 1631–1643. Kumar, A., and Z.-Z. Hu, 2013: Interannual and interdecadal variability of ocean temperature along the equatorial Pacific in conjunction with ENSO. Climate Dyn., in press. Kushnir, Y., W. A. Robinson, P. Chang, and A. W. Robertson, 2006: The physical basis for predicting Atlantic sector seasonal-to-interannual climate variability. J. Climate, 19, 5949–5970. Lee, S.-K., and C. Wang, 2008: Tropical Atlantic decadal oscillation and its impact on the equatorial atmosphere–ocean dynamics: A simple model study. J. Phys. Oceanogr., 38, 193– 212. ——, D. B. Enfield, and C. Wang, 2008: Why do some El Ni~ nos have no impact on tropical North Atlantic SST? Geophys. Res. Lett., 35, L16705, doi:10.1029/2008GL034734. L€ ubbecke, J. F., C. W. B€ oning, N. S. Keenlyside, and S.-P. Xie, 2010: On the connection between Benguela and equatorial
4663
Atlantic Ni~ nos and the role of the South Atlantic anticyclone. J. Geophys. Res., 115, C09015, doi:10.1029/2009JC005964. Meinen, C. S., and M. J. McPhaden, 2000: Observations of warm water volume changes in the equatorial Pacific and their relationship to El Ni~ no and La Ni~ na. J. Climate, 13, 3551– 3559. Murtugudde, R., J. Ballabrera-Poy, J. Beauchamp, and A. Busalacchi, 2001: Relationship between the zonal and meridional modes in the tropical Atlantic. Geophys. Res. Lett., 28, 4463–4466. Nobre, P., and J. Shukla, 1996: Variations of sea surface temperature, wind stress, and rainfall over the tropical Atlantic and South America. J. Climate, 9, 2464–2479. North, G. R., T. L. Bell, R. F. Cahalan, and F. J. Moeng, 1982: Sampling errors in the estimation of empirical orthogonal functions. Mon. Wea. Rev., 110, 699–706. Philander, S. G. H., and R. C. Pacanowski, 1981: Response of equatorial oceans to periodic forcing. J. Geophys. Res., 86, 1903–1916. Richter, I., and Coauthors, 2010: On the triggering of Benguela Ni~ nos: Remote equatorial versus local influences. Geophys. Res. Lett., 37, L20604, doi:10.1029/2010GL044461. Ruiz-Barradas, A., J. A. Carton, and S. Nigam, 2000: Structure of interannual-to-decadal climate variability in the tropical Atlantic sector. J. Climate, 13, 3285–3297. Schneider, E. K., B. Huang, and J. Shukla, 1995: Ocean wave dynamics and El Ni~ no. J. Climate, 8, 2415–2439. Schopf, P. S., and M. J. Suarez, 1988: Vacillations in a coupled ocean–atmosphere model. J. Atmos. Sci., 45, 549–566. Schouten, M. W., R. P. Matano, and T. P. Strub, 2005: A description of the seasonal cycle of the equatorial Atlantic from altimeter data. Deep-Sea Res., 52, 477–493. Seager, R., and Coauthors, 2000: Causes of Atlantic Ocean climate variability between 1958 and 1998. J. Climate, 13, 2845–2862. Servain, J., J. Picaut, and J. Merle, 1982: Evidence of remote forcing in the equatorial Atlantic Ocean. J. Phys. Oceanogr., 12, 457–463. ——, I. Wainer, J. P. McCreary, and A. Dessier, 1999: Relationship between the equatorial and meridional modes of climate variability in the tropical Atlantic. Geophys. Res. Lett., 26, 458–488. Wyrtki, K., 1975: El Ni~ no—The dynamic response of the equatorial Pacific Ocean to atmospheric forcing. J. Phys. Oceanogr., 5, 572–584. ——, 1985: Water displacements in the Pacific and the genesis of El Ni~ no cycles. J. Geophys. Res., 90, 7129–7132. Xie, S.-P., and J. A. Carton, 2004: Tropical Atlantic variability: Patterns, mechanisms, and impacts. Earth Climate: The Ocean– Atmosphere Interaction, Geophys. Monogr., Vol. 147, Amer. Geophys. Union, 121–142. Zebiak, S. E., 1993: Air–sea interaction in the equatorial Atlantic region. J. Climate, 6, 1567–1586. ——, and M. A. Cane, 1987: A model El Ni~ no–Southern Oscillation. Mon. Wea. Rev., 115, 2262–2278. Zhu, J., B. Huang, and M. A. Balmaseda, 2012a: An ensemble estimation of the variability of upper-ocean heat content over the tropical Atlantic Ocean with multi-ocean reanalysis products. Climate Dyn., 39, 1001–1020, doi:10.1007/ s00382-011-1189-8. ——, ——, and Z. Wu, 2012b: The role of ocean dynamics in the interaction between the Atlantic meridional and equatorial modes. J. Climate, 25, 3583–3598.
