Source waters of the tropical Atlantic

To assess the contributions of the Atlantic subtropical waters to various regions in the tropical Atlantic, Lagrangian analyses are performed on a CCSM4 coupled model run and an ocean-only component (POP) run. Virtual floats are released off-line in three different isopycnal surfaces on the annual mean velocity field of the model outputs, where the equations of motion are integrated forward in time using a 12-hour time-step. This time-step was calibrated after several tests, to be sufficiently small for this study. Trajectories of particle-floats are then compared for the fully coupled CCSM4 run and the ocean-only component run (POP). Particles (launching sites marked by yellow squares in Fig. 14) are released in the subtropics, in the same position for both runs. The Lagrangian trajectories were computed in the following isopycnal sigma (σ_θ) surfaces: 25.5 kg/m³, 26.5 kg/m³ and 27.1 kg/m³.

The ocean-only (POP) run 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). It was forced with the CORE v2 IAF data sets Large and Yeager (2009). The data analyzed is from the fourth (last) forcing cycle of 60 years (1948-2007). Monthly climatological river runoff is based on Dai et al. (2003) discharge estimates. A weak salinity restoring (50m/4year) flux is applied globally after subtraction of the global mean.

b. Results

Figures 14a-b show that differences in density-dependent flow are particularly pronounced in the sigma 25.0 layer. This layer is within the base of the tropical thermocline, and intersects the equator near the core of the equatorial undercurrent (EUC). Along the equator, the EUC is a strong feature of the flow. In the CCSM4 the EUC is fed predominantly by the Southern Hemisphere waters through the western boundary pathway. Indeed, Hazeleger et al. (2003), Wainer et al. (2006), Molinari et al. (2003) and others have already shown that in the Atlantic most of the water reaching the equator originates from Southern Hemisphere. On the other hand, the POP (ocean-only) results show a higher contribution of the northern subtropical waters to the EUC. Also, at this level, the interior pathway from the southern subtropics is more prominent in the ocean-only results than in the CCSM4 results. The other sigma levels (26.5 and 27.1 kg/m³) do not show a contribution from the subtropics to the equator.

The Benguela region is also sourced by subtropical waters. At the σ_θ=25.0 kg/m³ layer the Benguela region is sourced mostly by the southern subtropics in both the ocean-only and the CCSM4 results (Figs. 14a-b). Yet, the CCSM4 results show σ_θ=25.0 kg/m³ subtropical water reaching farther south than that from the ocean-only results. This difference is mostly due to where the sigma 25.0 layer outcrops in the South Atlantic in either the coupled or ocean-only simulations; in the coupled run (Fig. 14a) the sigma 25.0 layer outcrops much further south than in the ocean-only simulation (Fig. 14b) allowing particles to travel farther south. In the next density category (σ_θ=26.5 kg/m³) there is subtropical water recirculating towards the Angola-Benguela system (Figs. 14c-d). This recirculation extends farther in the longitudinal direction in CCSM4 than in POP, thereby taking longer in CCSM4 for subtropical water to reach the Benguela region at this level. At the σ_θ=27.1 kg/m³ level (Figs. 14e-f), a small recirculation at the eastern boundary is also evident in the ocean-only run, but does not appear in CCSM4. This cyclonic circulation in the southeast portion of the basin is probably associated with the Angola-Benguela current system, as has been suggested by observations.

In the North Atlantic many of the floats released in either experiment travel to the Intra-Americas Sea (Caribbean Sea and Gulf of Mexico). In the shallowest sigma level (σ_θ=25.0 kg/m³) the northern subtropical flow bifurcates into a branch leading to the Caribbean Sea and another branch recirculating to the east in a cyclonic direction. In the ocean-only run some of the floats that recirculate towards the east reach the equator. This recirculation is not present at the deeper sigma levels. The branch leading to the Caribbean Sea contains floats that originated in the northwestern subtropics as part of an anticyclonic gyre. At the 26.5 sigma level this anticyclonic gyre extends farther east with floats originating in the eastern North Atlantic in both the coupled and ocean-only runs. Yet, in the coupled model, the floats released as far north as 37°N (at the 26.5 sigma level) enter this anticyclonic gyre feeding the Intra-Americas Sea, whereas in the ocean-only run the same floats travel northeastward from their launching sites towards midlatitudes. This indicates that in the coupled model the Caribbean Sea is receiving water from farther north in the North Atlantic compared to the ocean-only run. At the 27.1 sigma level the floats that reach the Caribbean Sea from the North Atlantic are those released near the eastern boundary of the North Atlantic. The floats at the 27.1 sigma level take longer to reach the Caribbean Sea in the ocean-only run than in the coupled run.

Subtropical water from the southern subtropics cross the Equator at the 26.5 and 27.1 sigma levels, also reaching the Caribbean Sea. At the 27.1 sigma level the southern subtropical waters that cross the equator do so through the North Brazil Undercurrent. These particles take longer to reach the Caribbean Sea in CCSM4 than in the POP run. Once the southern subtropical waters cross the equator, the currents separate from the western boundary towards the east as the retroflection of the North Brazil Current (NBC). After the separation, the retroflection feeds into the North Equatorial Countercurrent (NECC) and the North Equatorial Undercurrent (NEUC) flowing across the basin toward the Guinea Dome (Huttl-Kabus and Boning, 2008). This retroflection occurs further north (12°N) in the CCSM4 than in the ocean-only run, and converges into a stronger NEC closing the southern boundary of the Northern Subtropical gyre. The σ_θ=25 kg/m³ level does not show a prominent interhemispheric flow from western boundary currents.

To better quantify the differences relative to the inter-hemispheric exchange of waters in the Atlantic Ocean, the volume transport is calculated within five sigma layers at 2°N and 2°S (Fig. 15), similarly to Laurian and Drijfhout (2010). Each potential density layer represents one water mass: σ_θ = 20-23 kg/m³ represents the surface layer; σ_θ = 23-25 kg/m³ the upper layer; σ_θ = 25-27 kg/m³ the intermediate layer, σ_θ = 27-28 kg/m³ the deep layer; and σ_θ > 28 kg/m³ represents the bottom layer.

The surface layer (σ_θ > 23 kg/m³) is characterized by a much stronger flow in the coupled (CCSM4) model in both latitudinal bands. This flow, which is mostly through the interior path, is dominated by the Ekman divergence in the equatorial region and accounts for about 4-7 Sv. In the ocean–only model, this layer is almost inexistent, therefore the contribution of the Ekman divergence is entangled with the geostrophic flow in the upper layer (σ_θ = 23-25 kg/m³), resulting in a northward transport of 2Sv across 2°S and 4Sv across 2°N.

In the intermediate layer, the flow is directed northward for both models. In the ocean-only model, the intermediate flow loses strength from 2°N towards 2°S, whereas in the coupled model the intermediate flow increases in strength. It suggests that there is opposite water masses transformation between the two models, with 1Sv of water flowing from intermediate to upper depths in the ocean model, and 1Sv been transformed into intermediate depths in the coupled model.

In the deep layer, the flow is directed southward mostly through the Deep Western Boundary current (DWBC). In both models there is a decrease of the flow of about 5Sv in the equatorial region (from 2°N to 2°S). This suggests some flow being transformed into intermediate layer. In the bottom layer (σ_θ > 28 kg/m³), the flow is directed southward with a magnitude of about 2.5Sv, opposite to northward flow of the Antarctic Bottom Water. This means that this layer still captures a significant fraction of the southward flow of North Atlantic Deep Water.
7. Summary

In this study we analyze the variability in the CCSM4 with respect to some of the main aspects of the tropical Atlantic variability (TAV). Various analyses are presented and discussed covering the circulation, the variability of the heat budget in the Benguela region, the variability of sea surface temperatures, and the basic structure of the tropical Atlantic waters warmer than 28.5°C.

We have performed different sets of analyses to address differences and improvements achieved by the CCSM4 model in the upper layers of the tropical Atlantic Ocean. These results can serve as a baseline for future studies and improvements in the model. The analyses and results presented and discussed above will be useful for further evaluations of CCSM4 simulations of the tropical Atlantic climate and for predictive studies of such region. As a result of our analyses, we demonstrate that the CCSM4 improved some biases in the Tropical Atlantic, and although there are still discrepancies towards observations (such as warm biases, too deep isopycnals and too strong winds), its interannual variability agrees well with observations. Following we present a summary of our results by section.

a. Atlantic warm pools

Previous studies have analyzed the Atlantic warm pool (AWP) mostly from Intra-Americas Sea (IAS) observations of sea surface temperature (SSTs) and models other than the POP used by CCSM4. In this set of analyses the warm pool is analyzed by its vertical structure throughout the year in both the Intra-Americas Sea (or tropical North Atlantic, TNA) and the tropical South Atlantic (TSA) from the new CCSM4 simulations. Furthermore, an observational data set with subsurface temperature estimates for the recent decades is used to compare with the CCSM4 simulations spanning the period 1980-2005.

The choice of the observational data set is critical when diagnosing a model. For example, even though the Levitus temperature data set is widely used to diagnose ocean models, there are some limitations when diagnosing recent climate means and climate variability. For the AWPs in the IAS and TSA, the simulations of CCSM4 in the late 20^th century are in better agreement with the Ishii data set than with the Levitus data set.

The volume of the AWP in the tropical South Atlantic (AWP-TSA) peaks in April and in the tropical North Atlantic (AWP-IAS) peaks in September. The timing of the AWP in CCSM4 is similar to that of the observations, although the vertical structure indicates that the TSA warm pool is deeper in the CCSM4 than in observations. This deeper AWP-TSA is related to the CCSM4 warm bias in the TSA region, a common challenge to many coupled models. In the IAS the warm pool is smaller in the CCSM4 than in observations, as a result of the CCSM4 cold bias in the IAS in particular in the southern Caribbean Sea and to the northeast of the Caribbean Sea.

The ensemble spread of the warm pool is skewed towards a deeper warm pool in the TSA and a near-normal depth in the IAS. Yet, the R006 ensemble member is consistently cooler than the other ensemble runs. In time, the ensemble mean of the IAS-AWP volume in September follows the lower-frequency variability from observations including the positive trend during the period of analyses. Furthermore, in September there is greater correlation between the observations and with the ensemble mean than with any of the ensemble simulations.

b. Modes of Tropical Atlantic variability

Empirical Orthogonal Functions (EOFs) were applied to the sea surface temperature (SSTs) fields of the various ensemble simulations and to an observational data set for the period 1980-2005. The spatial patterns of the main modes of variability in the model are similar to that from the observations. However, the leading EOF (EOF1) in the simulations tends to show a tropical Atlantic warmer than in the observations. Also, the EOF1 has greater explained variance (ensemble average of 40%) than in observations (explained variance of 25%). Nonetheless, the ensemble mean of the corresponding time series (PCs) has variability in agreement with that from the observations. The second EOF (EOF2) in the model simulations and the observations is also well correlated in space, although the temporal characteristics show less agreement.

An analysis of approximately 100-year long records from the CCSM4 control run reveals realistic values of anomalous SST in the Benguela region that varies interannually to decadally. The maximum magnitude of interannual events reaches 2°C, which is somewhat smaller than in observations (up to 3°C). This lack of variability is attributed to the shape of the simulated Angola-Benguela front, which is not as sharp as it is in observations. The CCSM4 also produces interannual (2 to 5 years) variability of Benguela SST. This lower frequency variability is up to 0.5°C and is remotely forced by ENSO.

Analysis of the model heat budget in the Benguela region suggests that anomalous vertical advection accounts for about 50% of the anomalous heat content rate variance while the contribution by anomalous meridional heat advection is half as strong. Local surface flux accounts for only 12% of the anomalous HCR variance. The impact of zonal advection is weak.

Anomalously warm vertical advection in the Benguela region (reduced upwelling) occurs in phase with the weakening of southeasterly trade winds. Correlation is maximum at zero lag suggesting that the impact of local upwelling dominates. In contrast to vertical heat advection the anomalous meridional heat advection is forced by zonal equatorial winds, which lead it 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. In distinction from observations of Florenchie et al. (2003) this wave-based teleconnection is not the dominant mechanism of heat content variability in the Benguela region in CCSM4.

To analyze the source waters of the tropical Atlantic in the CCSM4 and its ocean component, the POP, Lagrangian trajectories were computed on three isopycnal surfaces: 25.5 kg/m³, 26.5 kg/m³ and 27.1 kg/m³. Analyses comparing the circulation in coupled and ocean-only simulations suggest that not only the horizontal flow characteristics are different between the coupled (CCSM4) and the ocean-only (POP) results, but also there are density-dependent flow differences as well. In the coupled run, the water that reaches the equator is predominantly from the Southern Hemisphere, through the western boundary current; whereas in the ocean-only simulation and observations there are also pathways to the equator from the Northern Hemisphere and within the interior of the South Atlantic Ocean.

From the analysis of the trajectories of the virtual particles on sigma (σ_θ) surfaces, our results show that in both hemispheres there are interior and western boundary pathways. Trajectories on the isopycnal surface σ_θ = 27.1 kg/m³ suggest that the northern pathway to the equator is absent throughout the thermocline for both runs. At a lighter density σ_θ = 26.5 kg/m³, the trajectories indicate a pathway through the western boundary to the equator in both runs. The flow in CCSM4 seems more vigorous both north and south of the basin with enhanced northward flow through the North Brazil Current system, a stronger gyre and associated transport within the western boundary.

The ocean-only integration features stronger stratification in the Atlantic Ocean, as the isopycnals generally outcrop closer to the equator than in CCSM4. The CCSM4 coupled run presents stronger winds, and anomalous positive wind stress curl in the South Atlantic, which causes a stronger Sverdrup circulation there. Consequently, the ocean gyres are more prominent in the coupled run, consistent with stronger western boundary currents. With respect to the subtropical waters reaching the Caribbean Sea, the main differences between the ocean-only and the coupled results are at the denser levels (27.1 and 26.5 kg/m3).

We thank all the scientists and software engineers who contributed to the development of the CCSM4. Computational resources were provided by the Climate Simulation Laboratory at NCAR’s Computational and Information Systems Laboratory (CISL), sponsored by the National Science Foundation and other agencies. The CCSM is also sponsored by the Department of Energy. We also wish to thank the staff of the Earth System Grid (including Gary Strand from NCAR) and Matthew Maltrud (from LANL) for the support and contribution in downloading and facilitating data used in this study. The Earth System Grid is funded by the U.S. Department of Energy (DOE). Ernesto Muñoz and Wilber Weijer were supported by the Regional and Global Climate Prediction Program of the DOE Office of Science, and by NSF-OCE award 0928473. Ilana Wainer was supported by CNPq-MCT/INCT and FAPESP. Semyon Grodsky was supported by NOAA Climate Variability and Predictability (CVP) Program. The contribution by Marlos Goes was carried out in part under the auspices of CIMAS and NOAA, cooperative agreement #NA17RJ1226.

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CAPTIONS
Figure 1. Tropical Atlantic sea surface temperature (°C, left panels) and salinity (psu; right panels) model minus observations difference distributions. This figure corresponds to Figure 6 from Danabasoglu et al (2011, this issue) with a focus on the Tropical Atlantic details. The temperature observations are from the Hurrell et al. (2008) dataset. The salinity observations are from the PHC2 dataset. The panels show the differences between observations and CCSM4 (top), or CCSM3 (bottom).
Figure 2. Tropical Atlantic zonal wind stress (dynes/cm²; left panels) and meridional wind stress (dynes/cm²; right panels). The top panels show the long-term mean from QSCAT observations. The middle and bottom panels show the model minus observations difference distributions for CCSM4 (middle) and CCSM3 (bottom).
Figure 3. (a-c) Horizontal distribution of the month of deepest 28.5°C isotherm from the long-term mean. The numbers 1 to 12 correspond to the months from January to December. The Pacific data has been masked. (d-f) Seasonal cycle of the volume (black line) of the 28.5°C isotherm between 40°S-40°N and above 250 meters of depth. The orange lines are the +/- standard deviation. The top and middle panels (a-b; d-e) are the observational products. The CCSM4 ensemble mean is used for the bottom panels (c, f).
Figure 4. Mean depth (meters) of the 28.5°C isotherm in April. The top panels (a and b) are observational products. The ensemble mean (panel c) is the mean of the simulations R005 to R009 (panels d through h).
Figure 5. Mean depth (meters) of the 28.5°C isotherm in September. The top panels (a and b) are observational products. The ensemble mean (panel c) is the mean of the simulations R005 to R009 (panels d through h).
Figure 6. Time series of the volume (10⁴ km³) encompassed by the 28.5°C isotherm a) in April south of 5°N, and b) in September north of 5°N. Shown are the departures from the long-term mean of each time series. The ensemble mean (red solid line) is the mean of the R005-R009 simulations. The Ishii time series (black solid line) is the estimate based on the observational product.
Figure 7. First EOF of monthly anomalies of sea surface temperature from CCSM4 for the period 1980-2005. EOFs are plotted for the five ensemble members (R005-R009), and the ERSSTv3b data set.

Figure 8. Principal component (PC) of 1^st EOF from monthly anomalies of sea surface temperature (SST) of the tropical Atlantic (30°N-30°S). PCs are plotted for the five ensemble members (R005-R009), the ensemble mean, and the ERSSTv3b data set. An 11-month running mean was applied to the time series for display. The explained variance is on top of each panel.

Figure 9. Second EOF of monthly anomalies of sea surface temperature from CCSM4 for the period 1980-2005. EOFs are plotted for the five ensemble members (R005-R009), and the ERSSTv3b data set.

Figure 10. Principal component (PC) of 2^nd EOF from monthly anomalies of sea surface temperature (SST) of the tropical Atlantic (30°N-30°S). PCs are plotted for the five ensemble members (R005-R009), the ensemble mean, and the ERSSTv3b data set. An 11-month running mean was applied to the time series for display. The explained variance is on top of each panel.

Figure 11. Standard deviation (STD) of anomalous heat content rate of change in the upper 80m (shading, Wm^-2), STD of anomalous SST (black contours), and time mean SST (gray contours). Box is the model Benguela region.
Figure 12. (a) Monthly (gray) and yearly smoothed (solid black) anomalous SST in the Benguela region, model NINO3 anomalous SST (dashed black) shown 9 months ahead of Benguela SST. (b) Correlation of yearly smoothed Benguela SST with SST and wind stress elsewhere.
Figure 13. (a) Lagged autocorrelation of anomalous SST and lagged correlation of anomalous heat content rate of change (HCR) with anomalous vertical (VERT), meridional (MER), zonal (ZON) heat advection, and anomalous net surface heat flux (NHF). All variables are spatially averaged over the Benguela region box and vertically integrated in the upper 80m.

(b) Lagged correlation of anomalous vertical heat advection in the Benguela region with wind stress elsewhere. Arrows show maximum correlation. Shading shows time lag (in month) corresponding to maximum correlation. Wind stress leads for positive lags. Correlations exceeding 0.3 are shown in red. Temporal regression of anomalous vertical heat advection on anomalous mean sea level pressure elsewhere at zero lag is overlain as contours. Contour values show pressure anomalies (mbar) corresponding to 100W/m^2 anomalous vertical heat advection in the Benguela region.

Figure 1. Tropical Atlantic sea surface temperature (°C, left panels) and salinity (psu; right panels) model minus observations difference distributions. These plots correspond to Figure 6 from Danabasoglu et al (2011, this issue) with a focus on the Tropical Atlantic details.