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Atmosphere–Ocean Coupled Variability in the South Atlantic

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Atmosphere–Ocean Coupled Variability in the South Atlantic 2904 JOURNAL OF CLIMATE VOLUME 10 Atmosphere±Ocean Coupled Variability in the South Atlantic S. A. VENEGAS,L.A.MYSAK, AND D. N. STRAUB Centre for Climate and Global Change Research and Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada (Manuscript received 9 July 1996, in ®nal form 9 April 1997) ABSTRACT The climate variability of the South Atlantic region is determined from 40 yr (1953±92) of Comprehensive Ocean±Atmosphere Data Set monthly sea surface temperature (SST) and sea level pressure (SLP) data using the empirical orthogonal function (EOF) and the singular value decomposition (SVD) analysis methods. The EOF method is applied to each ®eld separately, whereas the SVD method is applied to both ®elds simultaneously. The signi®cance of the atmosphere±ocean interaction is revealed by a strong resemblance between individual (EOF) and coupled (SVD) modes of SST and SLP. The three leading modes of coupled variability on interannual and interdecadal timescales are discussed in some detail. The ®rst coupled mode, which accounts for 63% of the total square covariance, represents a 14±16-yr period oscillation in the strength of the subtropical anticyclone, accompanied by ¯uctuations of a north±south dipole structure in the SST. The atmosphere±ocean coupling is strongest during the southern summer. The second coupled mode (20% of the total square covariance) is characterized by east±west shifts of the anticyclone center, in association with 6±7-yr period ¯uctuations of SST off the coast of Africa. The coupling depicted by this mode is weaker than that found in the ®rst and third modes. The third coupled mode (6% of the total square covariance) is characterized by north±south displacements of the anticyclone, accompanied by SST ¯uctuations over a latitudinal band in the central South Atlantic. These oscillations occur on a relatively short interannual timescale (;4 yr). As with the ®rst mode, the atmosphere±ocean coupling is strongest during the southern summer. This mode is found to be temporally and spatially correlated with the El NinÄo±Southern Oscillation phenomenon. The statistical robustness of the results is tested by using a Monte Carlo approach, which indicates that the presented results are highly signi®cant. 1. Introduction sure (SLP) ¯uctuations and their coupled variability During the past few decades there has been consid- have also been widely investigated by analyzing a va- erable effort devoted to obtaining a better understanding riety of datasets particularly over the North Atlantic and of natural climate variability on interannual to inter- the North Paci®c Oceans (Deser and Blackmon 1993; decadal timescales. To determine the mechanisms gov- Kushnir 1994; Latif and Barnett 1996; Mann and Park erning these climatic variations, it is essential to char- 1996) and by performing experiments with atmospheric, acterize the large-scale interactions between the ocean oceanic, or coupled atmosphere±ocean models (Palmer and the overlying atmosphere. A number of recent stud- and Sun 1985; Kushnir and Lau 1992; Delworth et al. ies have sought to identify such interactions and their 1993; Zebiak 1993; Peng et al. 1995; Halliwell 1996). associated timescales, by analyzing both observational Thermodynamic and wind-induced mechanisms such records and the results of climate model simulations. as air±sea heat exchange, entrainment, vertical mixing, The nature of the global air and sea surface temper- and wind stress forcing, have been frequently proposed ature variability on interannual to interdecadal time- as ways in which the atmosphere forces upper-ocean scales has been documented in both data studies (Fol- temperature anomalies (Frankignoul 1985; Wallace et land et al. 1984; Ghil and Vautard 1991; Houghton and al. 1990; Cayan 1992a; Cayan 1992b; Miller et al. 1994; Tourre 1992; Mann and Park 1993; Mann and Park Battisti et al. 1995). Other studies, however, have sug- 1994) and model simulations (Manabe and Stouffer gested that the ocean may drive atmospheric circulation 1994; Mehta and Delworth 1995). The relationships be- anomalies through large-scale changes in the thermo- tween sea surface temperature (SST) and sea level pres- haline circulation on interdecadal or longer timescales timescales (Levitus 1989; Ghil and Vautard 1991; Stocker and Mysak 1992). A few investigators have also proposed positive feedbacks between SST and the at- Corresponding author address: Dr. Lawrence A. Mysak, Centre for Climate and Global Change Research, McGill University, 805 mospheric circulation on interdecadal timescales (Deser Sherbrooke Street West, Montreal, PQ H3A 2K6, Canada. and Blackmon 1993; Latif and Barnett 1994). E-mail: [email protected] A common feature of these studies is that the major q 1997 American Meteorological Society Unauthenticated | Downloaded 10/02/21 10:40 AM UTC NOVEMBER 1997 VENEGAS ET AL. 2905 modes of large-scale variability in the Northern Hemi- sphere are characterized by interannual to interdecadal timescales. It has been generally accepted that inter- annual variability is primarily driven by the atmosphere, while interdecadal or longer-scale variability is mainly associated with changes in the ocean (Deser and Black- mon 1993; Kushnir 1994). However, recent work by Halliwell (1996) and results from this paper suggest that atmospheric forcing of the ocean on interdecadal times- cales may be more important than previously realized. As a complement to the Northern Hemisphere focus of the above studies, this paper (which is partly sum- marized in Venegas et al. 1996) investigates the natural climate variability in one region of the Southern Hemi- sphere, namely, the South Atlantic. The ®rst goal is to identify the principal modes of behavior of the SST and the overlying atmospheric circulation, in order to pro- FIG. 1. Percentage of months in which data was available at each grid point over the 480-month period analyzed. vide insight into the variability of the South Atlantic coupled atmosphere±ocean system on interannual to in- terdecadal timescales. A secondary goal is to determine pling based on the SVD analysis are discussed in section whether the South Atlantic modes of variability are con- 4. A summary and the conclusions are given in section 5. nected with any of the well-known tropical and/or Northern Hemisphere climate oscillations (e.g., El NinÄo±Southern Oscillation, North Atlantic Oscillation, 2. Data and methods Paci®c±North American pattern). The suggestion of a. Data such a connection between hemispheres on interdecadal The data employed in this study include South At- timescales was proposed in Mysak and Power (1992). lantic sea SST, sea level pressure (SLP), and vector wind These connections were further explored by Mann and extracted from the Comprehensive Ocean±Atmosphere Park (1993, 1994). Data Set (COADS) (Woodruff et al. 1987). These data Empirical orthogonal function (EOF) and singular are available as monthly means over a grid of 28 lat 3 value decomposition (SVD) analyses are used to de- 28 long, and span the period 1854±1992. The 40-yr scribe both the independent and coupled variability of period analyzed in this paper extends from 1953±92, SST and SLP in the South Atlantic. While the EOF during which time the data coverage over the South analysis method has been widely applied in geophysics Atlantic basin is fairly good. The area of interest of our research (Davis 1976; Wallace et al. 1990; Deser and study is the South Atlantic Ocean, from the equator to Blackmon 1993; En®eld and Mayer 1997), the SVD 508S, and from 708W (or South American coast) to 208E analysis method has become more commonly used only (or South African coast). The region south of 408S be- in the last decade (Lanzante 1984; Fang and Wallace tween 408W and 208E is excluded from the analysis, 1994; Peng and Fyfe 1996), although it was ®rst pro- however, due to poor data coverage (see Fig. 1). posed in a meteorological context by Prohaska (1976). The SST, SLP, and wind climatologies are calculated The interest in trying to model the type of South for each calendar month at each grid point by averaging Atlantic variability described in this paper has substan- the data over the 40-yr period. Monthly anomalies are tially increased since the creation of two recent large- then de®ned as deviations from this mean annual cycle. scale climate programs: CLIVAR (Climate Variability To reduce the noise inherent in the monthly observa- and Predictability Program) and ACCP [The Atlantic tions, the anomalies are temporally smoothed by per- Climate Change Program, described in Molinari et al. forming three-month running means (3MRM). At a giv- (1994)]. It is hoped that this observational study will en grid point, 3MRMs are computed by averaging offer new insights into the mechanisms and long-range anomalies of three consecutive months and by assigning linkages of interannual to interdecadal climate vari- that mean value to the central month. If two or more ability. These insights will also help the modeling stud- neighboring monthly anomalies are missing, the cor- ies associated with these programs. responding 3MRM is not computed and the month is The paper is organized as follows. A brief description given a missing value ¯ag. of the datasets and methods, together with an introduc- For a successful application of the EOF and SVD tion to the main South Atlantic oceanic and atmospheric analysis methods described in the next subsection, the features are given in section 2. Results from the inde- spatial gaps in the SST and SLP data are ®lled using pendent SST and SLP EOF analyses follow in section an inverse distance interpolation method (Thiebaux and 3. The results pertaining to the atmosphere±ocean cou- Pedder 1987). As a ®nal step before applying the sta- Unauthenticated | Downloaded 10/02/21 10:40 AM UTC 2906 JOURNAL OF CLIMATE VOLUME 10 tistical methods, the data from the original 28 by 28 grid are averaged onto a coarser 48 lat 3 108 long grid to reduce the size of the datasets used in our analysis.
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