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Tropical Atlantic Oceanic Variability in the CCSM4

Ernesto Muñoz

National Center for Atmospheric Research

Boulder, CO

Wilbert Weijer

Los Alamos National Laboratory and New Mexico Consortium

Los Alamos, NM
Semyon Grodsky

University of Maryland

College Park, MD
Susan C. Bates

National Center for Atmospheric Research

Boulder, CO

Submission to: Journal of Climate, Special Collection on CCSM4

Date of revision: October 2011

Corresponding author address:

Ernesto Muñoz

National Center for Atmospheric Research


P.O. Box 3000

Boulder, CO, 80307-3000

E-mail: emunoz@ucar.edu


In this study we analyze important aspects of the tropical Atlantic Ocean from the new simulations of the 4th version of the NSF-DOE coupled climate model, the Community Climate System Model (CCSM4). The data used in this study is from several different simulations, among them a set of five 20th-century simulations with different initial conditions, but similar radiative forcing. Among the features analyzed in this study are: the structure of the Atlantic warm pools; the main modes of sea surface temperature (SST) variability in the tropical South and North Atlantic; the variability of heat storage in the Benguela region; and differences between the model simulations and the observations in the tropical Atlantic. The results indicate that some of the biases of the tropical Atlantic have been reduced in CCSM4 compared to the previous version of the CCSM. Yet, there are still significant biases in the CCSM4 sea surface temperatures (SST), with a colder tropical North Atlantic (TNA) and a hotter tropical South Atlantic (TSA), that are related to biases in the wind stress. The biases in the TSA and the TNA are also reflected in the Atlantic warm pools in April and September with a warm pool volume greater than in observations in April and smaller than in observations in September. The variability of SSTs in the tropical Atlantic is well represented in CCSM4 although the leading EOF in CCSM4 shows more homogeneous warming than the leading EOF in observations. A heat budget analysis of the Benguela region indicates that the variability of SSTs is dominated by vertical advection.

1. Introduction

The tropical Atlantic is an important region in the Earth’s coupled climate system. Understanding the variability of the tropical Atlantic is not only important for the communities of tropical America and tropical Africa, but is also important for other remote regions and for the climate system in general. For example, Ding et al. (2011) and Losada et al. (2009) document the remote impacts that the tropical Atlantic has on ENSO. Furthermore, Losada et al., (2009), Kucharski et al., (2008) and Zhang and Delworth (2006) show that the tropical Atlantic has an impact on the Indian Ocean climate variability. A few recent reports summarize the advances in the understanding of the tropical Atlantic climate and its variability (e.g., Hurrell et al. 2006; Xie and Carton, 2004; Garzoli and Servain, 2003; Visbeck et al. 2001).

To date, the tropical Atlantic has been challenging to model adequately by coupled climate models. The recent availability of the CCSM4 provides an opportunity to assess the status of the simulation of the tropical Atlantic by one of the leading coupled climate models. Also, the Community Earth System Model (CESM) (of which the CCSM4 is a subset) will be used as part of the next phase of the Coupled Model Intercomparison Project (CMIP5); is therefore important to understand the CCSM4 simulation of the tropical Atlantic Ocean. In this study the following main aspects of the tropical Atlantic Ocean are analyzed from the 4th version of the NSF-DOE Community Climate System Model (CCSM4).
a. Mean Biases in the Tropical Atlantic

The annual cycle of sea surface temperature (SST) in the tropical Atlantic is tied to the annual cycle of wind stress. When the Intertropical Convergence Zone (ITCZ) is furthest from the equator in August/September, cross-equatorial flow is at its weakest seasonal state, and equatorial easterlies are at their strongest. These are upwelling favorable winds; therefore, the coldest eastern equatorial SSTs occur during this season (Fig. 2b). When the ITCZ is at its southernmost position in boreal spring, meridional cross-equatorial flow dominates the deep tropics, and a hemispheric dipole structure in SST is present. During this period, the warmest SSTs are present along the equator (see Bates, 2008; Okumura and Xie, 2006 for more details on the tropical Atlantic annual cycle).

Among the known biases of coupled models in the tropical Atlantic region are: a warm bias in the tropical southeastern Atlantic, a barrier layer thicker than observed, and relaxed zonal winds along the equator related to weaker precipitation over the Amazon region. (Doi et al. 2012; Tozuka et al. 2011; Richter et al. 2011; Wahl et al. 2011; Richter and Xie, 2008; Breugem et al. 2008; Chang et al. 2007).
b. The Atlantic Warm Pools

Warm pools (WP) have been defined as those regions of the ocean with temperatures greater than 28.5°C (Tian et al. 2001; Wang and Enfield, 2003). The Atlantic WP has a component in the northwestern tropical Atlantic spanning the Gulf of Mexico and the Caribbean Sea (i.e., the Intra-Americas Sea) that peaks during boreal summer and early fall (Wang et al., 2006). The other component of the Atlantic WP peaks during boreal winter and spring when the waters of the tropical South Atlantic reach temperatures greater than 28.5°C also forming a warm pool.

Beyond its surface manifestation and extent, the Atlantic WPs in the Tropical North Atlantic (TNA) and the Tropical South Atlantic (TSA) have vertical and horizontal profiles that are important with respect to the heat content of the upper layer of the ocean. The heat content in the TNA-WP is also available for tropical storms and hurricanes that travel through that region (Wang et al. 2006). Furthermore, the earlier CCM3 modeling study of Saravanan and Chang (2000) regarding the Caribbean sea surface temperatures (SSTs) already pointed to the Caribbean as critical in the teleconnections with the tropical Pacific. That is to say, Saravanan and Chang (2000) found that the Caribbean heat sources can affect the Pacific through an upper-level Gill-type circulation. This and other impacts of the TNA-WP were documented by Wang et al. (2008) from a model.

c. Leading modes of Tropical Atlantic Variability

Observed changes in SST, which affect the meridional gradient of SST in the tropical Atlantic, occur on a wide range of time scales. On multidecadal timescales, the Atlantic Multidecadal Oscillation (Enfield et al., 2001) is mostly confined to the North Atlantic, and possibly reflects changes in the strength of the Atlantic Meridional Overturning Circulation (Muñoz et al., 2011). At shorter, decadal timescales, the out-of-phase variations of SST in the northern and southern tropical Atlantic are self-sustained and driven by the wind-evaporation-SST feedback in the trade winds (Carton et al., 1996; Chang et al., 1997). At shorter timescales remote impacts of the El Niño-Southern Oscillation (ENSO; Enfield and Mayer, 1997) and the North Atlantic Oscillation (Czaja et al., 2002) produce different SST responses in the northern and southern tropical sectors and thus contribute to the observed lack of coherence between SST variations in the two regions.

The tropical Atlantic variability (TAV) has been observed to have a few main modes of variability that are predominant at different times of the year (Servain et al. 1990; Servain et al. 2003). One of the modes of variability is the so-called meridional mode or interhemispheric mode and is usually observed in the boreal spring (Servain et al. 1998; Mahajan et al. 2010). The meridional mode is characterized by a meridional gradient of SST anomalies from one subtropical region to its counterpart in the other hemisphere, and a pattern of surface wind anomalies from the colder subtropics to the warmer subtropics (Nobre and Shukla, 1996). Another mode of variability is the so-called zonal mode or Atlantic Niño and is predominant in the boreal summer (Tokinaga and Xie, 2011; Carton and Huang, 1994; Zebiak, 1993; Shannon et al. 1986). Yet, the identification of Tropical Atlantic modes of variability have also benefited from the use of statistical techniques such as rotated Empirical Orthogonal Functions (rEOFs). Modes of TAV by using rEOFs have been referred to as the southern tropical Atlantic (STA) pattern, the northern tropical Atlantic (NTA) pattern, and southern subtropical Atlantic (SSA) pattern (Hu et al., 2008; Bates, 2010). Previous studies have analyzed these modes and the dynamics and thermodynamics that explain their variability (Bates, 2010; Huang and Shukla, 2005; Florenchie et al. 2004; Chang et al. 1997; Carton et al. 1996; Shannon et al. 1987).

d. Heat budget of the Benguela region

A majority of coupled GCMs have SST biases in the tropical Pacific and Atlantic including notorious warm biases in the southeastern tropical basins (Zuidema et al., 2011). But the most severe warm SST bias (in excess of 5°C) occurs along the Benguela coast of southwestern Africa (e.g. Chang et al., 2007; Richter and Xie 2008). This spurious pool of abnormally warm water simulated by a majority of climate models alters large scale meridional SST gradient across the tropical Atlantic, and thus projects on the natural mode of the tropical Atlantic variability known as the meridional or inter-hemispheric mode (e.g. Xie and Carton, 2004).

Periodic changes of SST in the northern and southern tropical Atlantic meridionally displace the Intertropical Convergence Zone (ITCZ) and affect rainfall over surrounding continents in the northeastern Brazil and African Sahel (see e.g. Xie and Carton, 2004 and references therein). Observation-based analyses of SST variability in the tropical Atlantic indicate that the standard deviation of anomalous1 SST is strongest in areas adjacent to the western coast of Africa (Doi et al., 2010) and reaches a maximum in the Angola-Benguela frontal zone (referred to as the Benguela region in this paper; see e.g. Florenchie et al., 2003). The enhanced variability of SST in the Benguela region suggests that this particular region contributes significantly to the meridional gradient of tropical Atlantic SST and thus plays a role in tropical Atlantic variability. But due to complex chain of the air-sea-land feedbacks the accuracy of coupled simulations of SST in the tropical Atlantic and particularly in the Benguela region still remains a challenge.

Observations and model simulations indicate that SST in the Benguela region is affected by local and remote impacts. Florenchie et al. (2003) have suggested a link between the Benguela warm events and weakening of the zonal equatorial winds 1 to 2 months in advance, which remotely impact the Benguela region via Kelvin waves propagating eastward along the Equator and further south along the coast. On longer time scales Chang et al. (2007) and Richter and Xie (2008) have shown that abnormally weak equatorial easterly wind is responsible in part for the time mean warm bias of the Benguela SST. In addition to remote mechanisms, the impact of local meridional winds and upwelling on the Benguela SST has been demonstrated by Large and Danabasoglu (2006). Impact of local upwelling is also emphasized by Grodsky et al. (2011) who argue that adequate representation of magnitude of the southerly Benguela low-level wind jet (Nicholson, 2010) is crucial for maintaining the zonal sea level gradient in the coastal ocean, and thus cold water transport by the coastal jet of Benguela Current. Independent of its origin any warm SST bias in the Benguela region grows and expands via the positive feedbacks from marine stratocumulus clouds (e.g. Mechoso et al., 1995).

Relative impacts of local versus distant winds on the Benguela SST have been addressed in a number of recent reports (Richter et al. 2010; Rouault, 2010). In particular, Richter et al. (2010) have demonstrated based on observations and model simulations that impact of local upwelling on Benguela SST is comparable to the remote impact of the Equatorial winds. As a part of the CCSM4 evaluation in this paper we perform the heat budget analysis of the upper ocean layer in the Benguela region in order to quantify relative contributions of the air-sea heat fluxes versus heat advection terms and evaluate their relationship with changes in local and remote winds.

e. Organization of the manuscript

Section 2 lists the data used in this study. Section 3 introduces the general improvements and biases in the tropical Atlantic of the CCSM4 surface fields compared to observations and those of CCSM3 (the previous CCSM version). In Section 4 an analysis of the structure of the Atlantic Warm Pools (in the north and south tropical Atlantic) is presented based on a suite of ensemble simulations. In Section 5 the main modes of sea surface temperature (SST) variability in the tropical Atlantic are compared against those from observations. In Section 6 the variability of the tropical South Atlantic, in specific the Benguela region, is further analyzed based on the heat storage. Summaries and discussion are presented as the final section of the manuscript.

3. Model and Observational Data

a. Model data

In this study we compare the data from CCSM4 simulations against observations in the tropical Atlantic. The main CCSM4 data set analyzed is an ensemble of five 20th Century (20C) CCSM4 simulations (see Gent et al. (2011)). These CCSM4 ensemble members were run in the same manner with the exception of their initial state. Each of the CCSM4 members was initialized from the 1850 control simulation, with the five initializations chosen to represent different states of the Atlantic meridional overturning circulation (see Gent et al., 2011, this issue). The period used for the analyses in this study span from 1950 to 2005 of the CCSM4 20C simulations, i.e., the last few decades of data. Complete descriptions of these simulations are provided by Gent et al. (2011).

The CCSM4 20C ensemble simulations were compared to observations, to a CCSM3 20C ensemble mean, and to a coupled ocean-sea ice experiment forced by the CORE v2 Inter-Annual Forcing data (Large and Yeager, 2009). This ocean simulation (POP-CORE) was conducted using the 1-degree horizontal resolution version of the CCSM4 ocean model coupled to an active freely evolving dynamic-thermodynamic sea ice model (CICE). vMonthly climatological river runoff is based on Dai et al. (2003) discharge estimates. The data analyzed is from the fourth (last) forcing cycle of 60 years (1948-2007).

There were eight ensemble members in the CCSM3 simulations, and where possible, all eight members are used. One of the ensemble members did not contain monthly output and, therefore, is not used in the annual cycle comparisons. The CCSM3 ensemble members were initialized from the 1870 control simulation (the control was switched to 1850 for CCSM4) at 20-year intervals with no tie to a physical feature (see Gent et al., 2006, Table 1 for details on CCSM3 simulations). The CCSM3 simulations ended in December of 1999; therefore, the 20-year mean used in this study is from 1980-1999.

Differences between CCSM3 and CCSM4 are expected due to various changes in the model physics and the spin-up and tuning procedures, which are all intended to produce a more realistic model. Both sets of simulations are the nominal 1-degree resolution in all components; however, this resolution has increased in the atmospheric and land components from approximately 1.4 degrees in CCSM3 to approximately 1 degree in CCSM4. The depth resolution of the ocean model has also increased from 40 levels in CCSM3 to 60 levels in CCSM4, with the majority of the additional layers in the upper ocean. The dynamical core is different in the atmospheric component (Neale et al. 2011, this issue), the ice model component, with a new radiation scheme and different albedo values, produces different sea ice cover (Holland et al. 2011, this issue), and the addition of new ocean parameterizations include more ocean physics than were present in CCSM3 (Danabasoglu et al. 2011, this issue). Due to differences in model tuning and spin-up (Gent et al., 2011, this issue), the ocean in the CCSM3 20C simulations lose heat over the length of the run, while in the CCSM4 simulations it more realistically gains heat.

In the section on the Benguela heat budget, we use monthly averaged fields from the 1-degree 1850 control run of CCSM4 (Gent et al., 2011, archived as b40.1850.track1.1deg.006), which is a 1300-year simulation forced by fixed pre-industrial levels of ozone, solar, volcanic, greenhouse gases, carbon, and sulfur dioxide/trioxide. Our analysis focuses on data from a 97-year period (model years 863-959)2. A sensitivity examination has been carried out to ensure that the climatology of this particular period is similar to that of later periods.

b. Observational datasets

For the analyses of the mean biases, the observations used include: sea surface salinity (SSS) from the Polar Hydrographic Climatology (PHC2) dataset (a blending of Levitus et al. (1998) and Steele et al. (2001)), sea surface temperature (SST) from Hurrell et al. (2008), and wind stress from an uncoupled ocean-ice simulation forced with the Coordinated Ocean-ice Reference Experiments (CORE; Griffies et al. (2009), Large and Yeager (2009)), which represents the wind stress forcing of the ocean by the CORE atmospheric wind data. The time period used from the observations is chosen to match that used in the CCSM3 and CCSM4 ensemble means.

For analyses of the Atlantic warm pools, two observational data sets were used for comparison. One observational data set is the World Ocean Atlas 2009 (WOA09) climatological fields (Locarnini et al. 2010; Levitus et al. 1998) with mean temperature data for the twelve calendar months. These WOA09 monthly climatological fields are based on available observations during the period from the year 1773 to the year 2008. Also used is the observational data set developed by Ishii et al (2006) with monthly temperature data interpolated to a 1x1 degree grid. Both of these observational data sets have the same horizontal 1x1 degree grid, and the same vertical levels, with data at the surface, 10m, 20m, 30m, 50m, 75m, 100m, 125m, 150m, 200m, 250m, and at 100m intervals between 300m and 700m. (The depth of the 28.5°C isotherm does not exceed 185 meters at any time in the period analyzed from these observational products.)

An advantage of the observational product from Ishii et al (2006) (from now on Ishii) is that it provides monthly data for the recent period thereby allowing for the calculation of means based on different periods (e.g., from 1950 to 2005), whereas the Levitus climatology provides 12 monthly climatologies based on available observations for the period 1773-2008 covering more than a couple of centuries. For the warm pool estimates, the averages from the Ishii product were computed based on the period 1950-2005.

For the comparison of rotated Empirical Orthogonal Functions (rEOFs) the Extended Reconstructed SST version 3b (ERSSTv3b; Smith et al., 2008) observational data set is used.

3. Mean Biases in the Tropical Atlantic Ocean

a. SST, SSS and TAU mean biases

In general, a shift to warmer surface ocean temperatures is noted in the CCSM4 versus CCSM3 (Danabasoglu et al, 2011), mostly due to the spinup procedure effects described above. This holds true in the tropical Atlantic basin (Fig. 1a,c). The mean values from 40°S to 40°N of the tropical Atlantic biases are -0.53°C for CCSM3 and 0.61°C for CCSM4, excluding the Mediterranean Sea and Pacific Ocean. These indicate that the overall mean of the SST bias has not changed in magnitude but has flipped from an overall negative bias in CCSM3 to an overall positive bias in CCSM4. The root mean square (RMS) error of these biases over the same region does show improvement with a value of 1.52°C for CCSM3 and 1.29°C for CCSM4.

The largest SST biases are found in the southern Caribbean Sea along the coast of South America, and in the southeastern basin along the coast of Africa. The largest negative bias occurs in the southern Caribbean Sea in both CCSM3 and CCSM4 with a value of ~ -2.5 and ~ -4.0 degrees C, respectively. The largest positive bias occurs in the southeastern basin near the African coast in CCSM3 with a value of ~7.5 degrees C and in the Gulf Stream region in CCSM4 with a value of ~8.0 degrees C. The maximum value along the coast of Africa in CCSM4 is somewhat improved over CCSM3, but is still large at ~6.5 degrees C.

Sea surface salinity (SSS) in the tropical Atlantic has improved in the CCSM4 (Figs. 1b,d). The mean of the SSS biases, as calculated above, is -0.517 g/kg for CCSM3 and -0.148 g/kg for CCSM4 indicating an overall reduction of fresh biases. This is different from the global mean bias, which shows little to no improvement from CCSM3 to CCSM4 (Danabasoglu et al. 2011, this issue). The RMS error of these biases also displays improvement with a value of 1.080 g/kg for CCSM3 and 0.775 g/kg for CCSM4. The maximum value of the biases occurs in the same regions in both CCSM3 and CCSM4. The minimum bias value is located off the coast of Angola with a value of ~ -12 and ~ -6 psu in CCSM3 and CCSM4, respectively, while the largest bias value occurring at the mouth of the Amazon River with a value of ~10 and 11.3 psu in CCSM3 and CCSM4, respectively.

Large improvements in the fresh bias are apparent in the northern and southern tropics from approximately 20°S to the equator and in the eastern North Atlantic from approximately 15°N-30°N. This improvement is most likely due to a reduction of the positive precipitation bias in this region causing a reduction in the input of freshwater to the ocean (Bates et al. 2011, this issue). Salinity bias improvements are also noted in the south Caribbean Sea; however, the northern portion of the Gulf of Mexico now has a larger saline bias in CCSM4 over CCSM3. Changes in the Gulf Stream and North Atlantic Current path have reduced fresh biases in the central North Atlantic and increased them off the coast of North America. The result is a generally saltier North Atlantic (Danabasoglu et al., 2011, this issue). Persistent in both CCSM3 and CCSM4 are the excessive runoff from the Congo River, which contributes to the fresh bias in the southeast Atlantic basin, and the weak runoff from the Amazon River, which contributes to the salty bias extending from the river mouth north to Bermuda (Danabasoglu et al. 2011, this issue).

The biases in the mean state of zonal (TAUX) and meridional (TAUY) wind stress for CCSM3 and CCSM4 compared to observations are provided in Fig. 3. As will be shown later, wind stress is important to processes controlling heat content changes in the eastern equatorial Atlantic as well as to changes in the volume of the Atlantic warm pools. The overall pattern of easterlies throughout the tropics with centers near 15S and 15N and equatorward flow strongest near Africa (Fig. 3a) is captured by CCSM3 and CCSM4. The direction of the wind stress, for the most part, is correct in the model, it is the strength that causes the biases in Fig. 3 (b,c). In general, the coupled model exhibits weaker wind stress throughout the equatorial region. In the western basin, this is mostly due to weakened easterlies, and in the eastern basin, weakened southerlies are responsible for the decrease in magnitude. In the regions of strongest easterlies (centered around 15S/N), the wind stress is enhanced over CORE with largest differences in the eastern basin near the African continent. The wind stress due to easterlies in the southern Caribbean region is much too strong in both CCSM3 and CCSM4. In general, all of these biases have been reduced in CCSM4 compared to CCSM3.

b. Seasonal cycles wind stress and SST biases

In Figure 4, we present the seasonal cycle of wind stress from CORE (left panels) along with the bias for each season in the CCSM4 compared to CORE (right panels). In all seasons, wind stress is too weak in the deep tropics with bands that are too strong at higher tropical latitudes. The deep tropics biases are dominated by weakened easterlies in the west and weakened southerlies in the east, as found in the total mean bias discussed above. The largest and most widespread of these biases occurs during the Dec-Jan-Feb (DJF) and Mar-Apr-May (MAM) seasons, corresponding to the seasons when the equatorial easterlies are the weakest and monsoonal flow in the Gulf of Guinea the strongest. The weakened easterlies are related to the incorrect SST gradient along the equator, and perhaps the model is under-representing the air-sea temperature difference driving the monsoonal flow. In MAM, a large bias of southerly wind stress occurs in the central basin just north of the equator. This bias is due to the displacement of largest cross equatorial flow westward in CCSM4, while in CORE this maximum occurs in the central basin.

The annual cycle of SST along the equator in the CCSM4 has improved greatly over the CCSM3 (Fig. 2b,c,d), most likely due to improvements in wind stress forcing from the atmosphere. Most notable of these improvements is found in the warm phase in late boreal winter and spring. In CCSM3, the warm phase begins in the western basin and propagates eastward, neither of which occur in the observations, and are corrected in the CCSM4. Though the phasing in the CCSM4 lags observations by approximately half a month, the character and magnitude are quite similar.

c. Biases in the tropical North Atlantic and tropical South Atlantic

Both improvements and degradations in SST bias occur in the transition from CCSM3 to CCSM4. Of importance to the investigations of this paper are the large reduction in the cold biases centered at 20°N as well as in the Caribbean Sea and Gulf of Mexico. The warm bias of the upwelling regions is worse in CCSM4 compared to CCSM3. This too is a global feature, as the SST warm bias in CCSM3 in all the eastern basin upwelling regions has worsened in CCSM4. Both the reduction of the cold biases and the worsening of warm biases could be partly due to the overall warming in CCSM4 compared to CCSM3.

1) Tropical North Atlantic Biases

A consistent bias of overly strong easterlies in the Caribbean Sea is present in all seasons, with the largest biases in the southern region (Fig. 4, right panels). However, the difference plots in Figure 4 include the mean bias, and much this bias is present in the mean (Fig. 3b). Comparing the seasonal cycle between observations and the CCSM4, with the respective annual means removed (not shown), we note that the seasonal TAUX biases in this region are slightly too weak in Jun-Jul-Aug (JJA) and slightly too strong, but not as widespread as those in CORE in Sep-Oct-Nov (SON). Easterlies in this region may drive coastal upwelling and thus may explain the negative bias in SST (Fig. 1a).

In the north tropical Atlantic centered at 25N and next to the African coast, where northeasterly winds dominate, the magnitude of wind stress is too strong compared to CORE (Fig. 4, right panels). This is true in all seasons for zonal wind stress, and in JJA and SON for meridional wind stress (though the bias is still somewhat present in DJF and MAM). As with the Caribbean Sea, these biases are present in the mean, and when the seasonal departure from the annual mean is examined, we find the zonal wind stress bias to be the strongest in DJF and also present in MAM, and the meridional wind stress weakened in DJF and strengthened in JJA.

2) Tropical South Atlantic Biases

The gradient of SST along the equator in both CCSM3 and CCSM4 is influenced by the warm biases in the eastern basin (Fig. 2a). Observations indicate a west-east warm-to-cold gradient. CCSM3 exhibits the opposite gradient. While CCSM4 obtains approximately the right warm pool temperatures in the west, the eastern basin is still much too warm.

Meridional winds along the African coast from the southern tip to the Gulf of Guinea are important for coastal upwelling and thus affect the Benguela Niño and equatorial SST variability. In the mean, a bias of overly strong southerly winds is present from the tip of Africa to approximately 15S with southerly winds that are too weak extending from there to the Gulf of Guinea (Fig. 3b). In general, this bias is present in all seasons (Fig. 4, right panels); however, the weakened southerlies in the Gulf of Guinea are strongest and most widespread in DJF and MAM, while the strengthened southerlies in the southern portion are strongest in SON. Removing the annual mean to examine the seasonal departures, we find the more northern biases to indicate weakened wind stress in DJF, accompanied by southerlies to the south that are too strong, with the opposite sign biases occurring in JJA.

In CCSM4, a large positive bias, not present in CCSM3, occurs in the south Atlantic with its center in the eastern basin near 40S (Fig. 1a). This, too, is a global feature with warm biases appearing in all ocean southern ocean basins with centers in the eastern basin (see Danabasoglu et al, 2011, this issue, their Fig. 6a).

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