Los Alamos National Laboratory and New Mexico Consortium
Los Alamos, NM
University of Sao Paulo
Sao Paulo, Brazil
University of Maryland
College Park, MD
Cooperative Institute for Marine and Atmospheric Studies
University of Miami and NOAA-AOML
Susan C. Bates
National Center for Atmospheric Research
Submission to: Journal of Climate, Special Collection on CCSM4
Date of submission: 19 May 2011
Corresponding author address:
New Mexico Consortium
4200 West Jemez Road, Suite 301
Los Alamos, NM 87501
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; the density-dependent circulation from the subtropics to the tropics; 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. The ensemble mean and variability of the Tropical North Atlantic Warm Pool in the CCSM4 is realistic when compared to observations for the period between 1980 and 2005. 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. The subtropical waters in the coupled configuration reach the Equator mainly from the South Atlantic, whereas in an ocean-only simulation there are also contributions from the North Atlantic.
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. 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. 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. The Atlantic Warm Pools
The Atlantic warm pool (AWP) has been defined as that region of the tropical Atlantic Ocean with temperatures greater than 28.5°C (Wang and Enfield, 2003). The AWP 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. During boreal winter and spring 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 Warm Pools (AWPs) in the Tropical North Atlantic (TNA) and the Tropical South Atlantic (TSA) have a vertical profile that is important with respect to the heat content of the upper layer of the ocean. The heat content in the AWP-TNA 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 AWP-TNA were documented by Wang et al. (2008) from a model.
Anomalous SSTs in the tropical Atlantic vary 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 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), sometimes referred to as the inter-hemispheric mode. 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 two 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). 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 (Carton and Huang, 1994; Zebiak, 1993; Shannon et al. 1986). 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).
c. The Benguela region
Periodic changes of sea surface temperature (SST) in the northern and southern tropical Atlantic meridionally displace the Intertropical Convergence Zone (ITCZ) and the rainfall associated with it, impacting precipitation over northeastern Brazil (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 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 region may contribute significantly to the meridional gradient of tropical Atlantic SST and thus plays a role in tropical Atlantic variability.
In the Benguela region significant interannual variations of SST (area average of up to 3°C, e.g. Florenchie et al., 2003) are superimposed on lower-frequency variations. Observations and model simulations of Florenchie et al. (2003) suggest a link between the Benguela warm events and weakening of the Equatorial winds 1 to 2 months in advance. This remotely impacts the Benguela region via Kelvin waves propagating eastward along the Equator and further south along the coast (Zebiak, 1993). 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) has 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.
d. Density-dependent subtropical source waters
The regions of the Benguela Niño, the Americas Warm Pool, and the equator are sourced by subtropical water. Aside from local air-sea interactions and wave dynamics, the water in the regions indicated above is affected by water from remote locations in the subtropics. The Caribbean Sea, for example, is sourced by waters from both the tropical North Atlantic and the tropical South Atlantic (Kilbourne et al. 2007). The fate of the subtropical waters of the Atlantic Ocean is therefore important to understand. In fact, there is still much to be learned about the causes of variability of the Atlantic south-north pathways between different models.
Some of the Atlantic subtropical water ventilates the equator via Subtropical-Tropical Cells (STCs). The STCs are shallow overturning cells, mostly confined to the upper 500 meters, associated with the equatorial upwelling and a return flow. The ventilation process associated with STCs in the tropical Atlantic has been discussed by Wainer et al. (2006), Kroger et al. (2005), Zhang et al. (2003), Molinari et al. (2003), Snowden and Molinari (2003), and others. Once the subtropical water reaches the equator it travels eastward reaching the eastern boundary and the region of the Benguela Niño. Yet, there is other subtropical water from the South Atlantic that crosses the equator northward, ultimately reaching the Caribbean Sea and other regions of the tropical North Atlantic. The Intra-Americas Sea is also sourced by subtropical water from the North Atlantic.
d. Organization of the manuscript
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.
Section 2 of this study 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). Next, 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. Additionally, the main modes of sea surface temperature (SST) variability in the tropical Atlantic are compared against those from observations. The variability of the tropical South Atlantic, in specific the Benguela region, is further analyzed based on the heat storage. Finally, the density-dependent flow from the subtropics to the tropics is analyzed through the use of virtual floats. Summaries and discussion are presented as the final section of the manuscript.
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 (Richter et al. 2011; Wahl et al. 2011; Richter and Xie, 2008; Breugem et al. 2008; Chang et al. 2007). However, as discussed below, the CCSM4 version shows some improvement with respect to CCSM3 regarding some of these features. In this section we compare the ensemble mean of five 20th century (20C) CCSM4 simulations (see Gent et al. (2011)), from 1986 to 2005 to observations and to a CCSM3 20C ensemble mean.
In the tropical Atlantic (Figs. 1a,c) a shift to warmer surface ocean temperatures is noted in the CCSM4 versus CCSM3 (Danabasoglu et al, 2011). The mean values from 40°S to 40°N of the tropical Atlantic biases (shown in Fig. 1) are -0.53°C for CCSM3 and 0.61°C for CCSM4, excluding the Mediterranean Sea and Pacific. 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. This is a trend that is also seen globally (Danabasoglu et al., 2011). 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.
Particular regions show great improvement in SST bias. Of importance to the investigations of this paper are the large improvements in the cold bias 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. More details on this particular feature of the CCSM are discussed in Large and Danabasoglu (2006).
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. 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. 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.
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. 2. 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. It is therefore encouraging to see (in Figs. 2c and 2e) the large improvements made in the transition to CCSM4 from CCSM3 in TAUX in the northern Caribbean Sea. The TAUX in the Caribbean Sea is related to the Caribbean low-level jet (Mo et al. 2005; Muñoz et al. 2008). Both CCSM3 and CCSM4 display weakened easterlies along the equator compared to observations; however, this bias has been greatly reduced in CCSM4. Also apparent from Fig. 2 (panels c and e) is a reduction in the overly strong easterlies in the north tropical Atlantic near the coast of Africa.
Notable improvements in TAUY (Fig. 2, panels d and f) include a reduction of overly strong wind stress in the north tropical Atlantic, in particular near the coast of Africa, as well as improvement in the weaker than observed northerly wind stress near the coast of Angola and in the Gulf of Guinea. This weak northerly flow contributes to weaker upwelling in this region and therefore to the warm SST bias mentioned above, though Large and Danabasoglu (2006) cite a number of oceanic and atmospheric processes in addition to weak upwelling that cause these eastern basin SST biases.
3. The Atlantic Warm Pools
In this section the differences in the simulation of the AWPs by CCSM4 are evaluated against those estimated from observations. The data used in this section is from an ensemble of five 20th century (20C) CCSM4 simulations, from which the last few decades of data (i.e., from 1980 to 2005) are used. Complete descriptions of these simulations are provided by Gent et al. (2011, this issue).
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 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 1980 to 2005), whereas the Levitus climatology provides 12 monthly climatologies based on the period 1773-2008 covering more than a couple of centuries. In this section the averages from the Ishii product were computed based on the period from the year 1980 to the year 2005. That is to say, the means computed from the Ishii et al (2006) product correspond in time to the means computed from the ensemble of CCSM4 simulations, thereby allowing a fair comparison.
The CCSM4 temperature data is provided in a non-equidistant grid with horizontal resolution of nominally 1 degree (and finer resolution in the Tropics). The vertical resolution of the model is finer than that of the observational data sets. To do a consistent comparison of the depth of the 28.5°C isotherm (Z28.5), the model data was interpolated to the vertical levels of the observations before calculating the Z28.5 depth. Once the Z28.5 depth was calculated, the result was interpolated to the horizontal grid of the observational products at each month. These monthly values were used to calculate the long-term averages.
Figure 3 shows the month of the calendar year when the 28.5°C isotherm is deepest in the long-term mean. Although both the Ishii and Levitus (Figs. 3a-b) are products derived from observations, there is a clear distinction in much of the central tropical Atlantic. The Ishii Z28.5 depth extends across much of the tropical Atlantic throughout the year. The Levitus monthly means do not have a 28.5°C isotherm present in many areas of the tropical Atlantic. The main difference in these results most likely stems from the period covered by each of the Z28.5 estimates. As indicated before, the Ishii Z28.5 values were computed based on the period from 1980 to 2005, whereas the Levitus climatological fields are based on available data in the period from the year 1773 to 2008.
When the CCSM4 ensemble mean is compared to the Levitus and the Ishii data sets, a better agreement is obtained between CCSM4 and Ishii. The CCSM4 Z28.5 extends throughout the tropical South Atlantic similar to the Ishii Z28.5 (Fig. 4). Also, in the tropical North Atlantic the CCSM4 produces a Z28.5 depth in the Intra-Americas Sea (i.e., Caribbean Sea and Gulf of Mexico) and across the basin at about 10°N similar to the Ishii Z28.5 (Fig. 5).
One of the main differences between the CCSM4 and the Ishii product is the lack of a Z28.5 in the Caribbean in the CCSM4. Both the Levitus and the Ishii products have a Z28.5 in the Caribbean deeper in October. Even though the CCSM4 shows a Z28.5 in the northern Caribbean deeper in November, it does not extend to the southern Caribbean. In fact, this is related to the strong meridional gradient of subsurface temperatures across the Caribbean in CCSM4, which may be an indication of the strong zonal wind stress along the Caribbean, in particular in the southern Caribbean. The CCSM4 wind stress is stronger in the southern Caribbean and weaker in the north than observations (Fig. 2), which would induce stronger upwelling in southern Caribbean thereby simulating temperatures colder than observations. This relationship indicates that there are dynamical aspects setting the lack of Z28.5 in the southern Caribbean (in addition to the contributions by air-sea interactions indicated by Misra et al. (2009) and Muñoz et al. (2010)).
Another main difference between the Z28.5 from observations and from CCSM4 is the timing of the deepest Z28.5 in the tropical South Atlantic (Fig. 3). In the Benguela and Gulf of Guinea regions, the observations have the deepest Z28.5 in March, while the CCSM4 has its deepest Z28.5 in May. This indicates that the CCSM4 is staying warmer than observations during the decay of the TSA warm pool in the eastern tropical South Atlantic. Furthermore, in the model, as observed from Figs. 3d-f, the volume of water encompassed by the 28.5°C isotherm is largest in boreal spring; whereas in observations is largest in boreal summer-fall peaking in September.
In the tropical South Atlantic, the month of greater extent of Z28.5 is April (Figs. 3-4). From the ensemble mean (Fig. 4c) it can be observed that the CCSM4 has a warm bias with respect to observations. In April the R006 ensemble member (Fig. 4e) has a shallower Z28.5 than the rest of the ensemble members, and is an outlier. Nonetheless, the Z28.5 depth from the R006 member compares well to the Z28.5 from Ishii (Fig. 4a).
In September the Intra-Americas Sea Warm Pool has its greatest horizontal extent (Figs. 3, 5). Compared to Levitus, the CCSM4 ensemble mean has greater extent to the northeast of the Caribbean and across the basin at about 10°N. However, the CCSM4 compares more favorably with the Ishii data set which also has Z28.5 across the basin at about 10°N and extending to the northeast of the Caribbean. In fact, even though the CCSM4 ensemble mean is deeper than Ishii to the north of the Caribbean, the ensemble member R006 is an outlier having a shallower Z28.5 than the other ensemble members.
The departures from the mean September time series of TNA volume (km3) encompassed by the 28.5°C isotherm are shown in Figure 6. The volume was calculated from the 5°N latitude to the north and across the basin. The Pacific data was not included in the volume calculation. The Ishii observational product shows anomalously large volumes in the late 1980s and after 1994. The years of decreased volume are the mid-1980s and the early 1990s. A similar evolution is obtained from the ensemble mean with a resulting correlation of 0.70 between the observations and the CCSM4 ensemble mean (Table 1). In fact, none of the individual ensemble simulations had such a high correlation coefficient. This correlation also accounts for the positive trends (not removed) in observations and ensemble members, although attribution to either external forcing or natural decadal variability is difficult to assess. Furthermore, the disagreement in TNA warm pool volume between CCSM4 and observations in the early 2000’s should be further investigated given that there was a decrease in the number observations in the Caribbean and Gulf of Mexico in the early 2000’s and subsurface temperatures in the Caribbean Sea may not be reliable in the 2000’s. With respect to the TSA-AWP in April (Fig. 6a), there is less agreement between the interannual variability in observations and in simulations (Table 1).
4. Leading modes of Tropical Atlantic Variability
a. Data and Methods
One of the statistical techniques usually utilized to identify patterns of TAV is Empirical Orthogonal Functions (EOFs) analysis. Although EOF analysis can be used to reduce the amount of data in a data set, the EOF technique has become standard in the identification of modes of climate variability (von Storch et al. 2002). In this section we are using the standard EOF technique to identify modes of TAV in the CCSM4 SSTs, and assess the similarity between TAV in the CCSM4 and observations. Furthermore, the technique is applied to the individual ensemble members, thereby evaluating the sensitivity of TAV to different initial conditions.
The CCSM4 model data used for computing the EOFs are from the five 20C ensemble simulations. The data span the last 2.6 decades (i.e., 1980-2005) of the simulations that started in 1850. This period was chosen given the reliability of the remotely-sensed observations in this “satellite” period. The data were not detrended prior to the EOF analyses. EOFs were also calculated from observed SSTs from the Extended Reconstructed SST (ERSST) version 3b data set (Smith et al, 2008). In all cases the EOFs are computed from monthly SST anomalies from 1980 to 2005 and between 30°S and 30°N. Another set of EOF analysis was performed partitioning the data by seasons (March-April-May, July-August-September). In contrast to many previous studies, the Caribbean and Gulf of Mexico SSTs are included in these EOF analyses of the tropical Atlantic.
The first EOFs of the model SSTs are shown in Fig. 7. In all panels of Fig. 7, the loadings are positive throughout the tropical Atlantic. The Benguela and Equatorial regions in the model simulations have greater variability than their surroundings. The variability from observations is also focused on the Benguela and Equatorial regions, with another center of variability in the TNA. Yet, the EOFs from the simulations have greater loadings than the observations overall, indicating that the model simulations have enhanced variability compared to observations.
The principal components (PCs; Fig. 8) show that these patterns are a combination of interannual and decadal variability, including a trend. The PCs from the ensemble members were averaged into an ensemble mean PC (black line in Fig. 8). The ensemble mean PC has a correlation of 0.74 with the PC from observations (Table 2), which is higher than with any of the PCs from the individual ensemble members. For example, both the observations and the ensemble mean have lower values in 1982-83 and 1992-93. Also, both time series have a shift in values from low in the mid 1980’s to high in the late 1980’s to low in the early 1990’s. This higher correlation between the ensemble mean than with any ensemble member was also obtained from the AWP statistics for September (previous section).
The second EOF shows the meridional mode (Fig. 9) in all ensemble members and observations. This interhemispheric mode is characterized by a dipole structure with loadings of opposite sign in the tropical North and South Atlantic. In the observations, the 2nd EOF has greater loadings in the eastern TNA and the eastern TSA (in particular the Benguela region) and decreasing in strength to the west across the basin. The various ensemble runs represent this pattern very well given the high spatial correlation between the patterns obtained from the ensemble simulations and that obtained from observations. For example, in the TSA the 2nd EOF obtained from R007 is very similar to the 2nd EOF from observations, in that it has less variability in the equatorial Atlantic, and high variability in the Benguela region. In the TNA, all ensemble members are fairly similar to the observations. However, regarding the temporal variability from the principal components (PCs), the PC2 of observations has higher frequency than that from the ensemble mean of the model simulations (Fig. 10). Furthermore, even though the individual ensemble members display higher frequency, the correlations between the PC2 from ensemble runs (including their mean) and the PC2 from observations show values lower than 0.18 (Table 2).
5. Heat budget of the Benguela region
In this section we use approximately 100 years (model years 863-959 or 1164 months of simulation) of the control integration of the coupled Community Climate System Model version 4 (CCSM4; Gent et al., 2011) to evaluate the upper ocean heat budget in the Benguela region and to explore its relationship with local and remote forcing.
For the purposes of this study the model Benguela region is defined based on the variability of the heat content rate of change (HCR). Standard deviation of anomalous HCR reveals patterns associated with equatorial Kelvin wave and off-equatorial Rossby wave propagation (Fig. 11). In the east the high values of HCR variability extends poleward from the Equator along the coast reflecting the combined effect of local upwelling and coastal waves. In the east where the thermocline is shallower the regions of high HCR variability are roughly collocated with regions of high SST variability. This is in contrast to the western equatorial Atlantic where SST variability is relatively weak regardless of rather strong HCR variability. The model Benguela region extends from 20°S to the northern edge of the time mean SST front at 13°S (where HCR variability ≥ 250 Wm-2), and from 9°E to the coast of southwestern Africa (Fig. 11). Although the observed SST front is mostly confined to the meridional extent of the Benguela region, the model SST front is stretched further south (Fig. 11a). This produces one of the strongest and most persistent warm SST biases among various versions of the CCSM (see e.g. Chang et al., 2007). As a result the region of high SST variability is also stretched southward. The model Benguela region, as defined above, covers only the northern part of the high model SST variability zone. This selected northern part is close to the observed region of high SST variability in the Angola-Benguela front (see e.g. Florenchie et al., 2003).
Anomalous SST in the Benguela region reveals 30 warm events (SSTA>1°C) and 21 cold events (SSTA<-1°C) over the model run’s 1164 months (Fig. 12a). The maximum magnitude of area-averaged anomalous SST is around 2°C which is weaker than in observations (up to 3°C) (see Fig. 11 for the time-mean model front). The CCSM4 also produces low frequency variability with characteristic periods of 2 to 5 years and a magnitude of up to 0.5°C. This variability is remotely forced by the Equatorial Pacific as is illustrated by coherent variations of low frequency Benguela SST and anomalous SST in the NINO3 region (Fig. 12a). Correlation analysis shows that low-frequency SST warming in the Benguela region is related to a basin-scale pattern associated with a warm south tropical Atlantic, weaker than normal southeasterly trade winds, and a southward shift of the ITCZ (northerly cross-equatorial winds). All these changes are typical of development of the Atlantic meridional mode in response to ENSO (Enfield and Mayer, 1997; Xie and Carton, 2004). This forced variability contributes to the Atlantic meridional mode (discussed above in the Section 4), but the latter mode of variability involves wider spectrum of processes including those unrelated to ENSO. The spatial pattern of SST response stretching from the South African coast westward to Brazil across the southern tropical Atlantic also resembles the second EOF of anomalous mixed layer temperature found by Colberg and Reason (2007a) in model simulations forced by observed winds.
To evaluate relative impacts of heat advection and the surface fluxes on anomalous heat content we next focus on the relationship between the terms of the vertically integrated heat balance equation (1) spatially averaged over the Benguela region. In common notations the vertically-integrated heat balance equation is:
where NSHF is the net surface heat flux, and vertical integration is taken in the upper H=80m. We focus only on the terms available from the standard CCSM4 output, and so the vertical diffusion is combined with other unresolved terms and is missing from further analysis2.
Heat balance analysis automatically focuses on high frequency variability (e.g. gray line in Fig. 12a), which is emphasized by the time derivative in equation (1). Anomalous SST events in the Benguela region last for approximately 4 months (Fig. 13a). Equation (1) suggests that anomalous heat content rate of change (HCR) is positively correlated with instantaneous anomalous heat advection and surface flux. In general this is confirmed by correlation analysis (Fig. 13a), which identifies ocean heat advection as the dominant contribution to the heat budget in Benguela region. The largest influence is provided by vertical heat advection (upwelling). The magnitude of its correlation with anomalous HCR at zero lag () exceeds 0.7. Vertical advection accounts for 51% of the anomalous HCR variance. The second strongest contribution is from anomalous meridional heat advection3 (CORR()=0.5), which accounts for about 26% of the anomalous HCR variance. The impact of zonal advection is weak due to predominantly zonal orientation of isotherms in the Angola-Benguela front. Local surface flux accounts for only 12% of the anomalous HCR variance. Surface flux also provides a weak negative feedback on anomalous SST in two months after the peak of HCR via latent heat flux.
To explore possible atmospheric forcing of anomalous heat advection in the Benguela region, the area-averaged time series of anomalous vertical and meridional advection have been lag-correlated with wind stress anomalies elsewhere. This analysis illustrates that anomalously warm vertical advection in the Benguela region (reduced upwelling) occurs in phase with weakening of southeasterly trade winds (Fig. 13b). The maximum correlation is at zero lag, suggesting that impact of local upwelling dominates. This is evident in an anomalous cyclonic wind pattern driven by an anomalously weak southern Atlantic subtropical high (Fig. 13b). The anomalous wind pattern includes a northerly (downwelling) component along the coast. Although the upwelling is attenuated along a major portion of the South African coast its impact on SST is stronger in the Benguela region where the thermocline shoals. An interesting (but not yet well understood) feature of the air pressure pattern is the area of anomalously high pressure over South Africa. A zonal gradient between the high over land and the low over the ocean further accelerates anomalous downwelling winds along the coast. An increase in air pressure over the land during warm Benguela events may be linked to cooling of the land due to above-average rainfall along the coast of Angola and Namibia observed by Rouault et al. (2003). But in CCSM4 the Benguela SST does not correlate significantly with either land temperature or rainfall.
The wind pattern corresponding to anomalous meridional advection (Fig. 13c) is different from that in Fig. 13b in many aspects. The area of weaker trades does not cover the Benguela region itself. There is significant correlation with the zonal Equatorial winds that lead the meridional advection by about a month. This suggests that non-local processes translating wind impacts from the Equatorial region (such as equatorial and coastal Kelvin waves) are responsible for anomalous meridional heat advection in the Benguela region.