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Tropical Atlantic Biases in CCSM4
Semyon A. Grodsky1, James A. Carton1, Sumant Nigam1, and Yuko Okumura2
May 27, 2011To be submitted to the Journal of Climate, special CCSM4 issue
1Department of Atmospheric and Oceanic Science University of Maryland College Park, MD 207422 National Center for Atmospheric Research, Boulder, CO
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Abstract This paper focuses on biases in the climate of the tropical Atlantic in the 1850
control simulation of CCSM4. The biases appear in both atmospheric and oceanic
components. Mean sea level pressure is erroneously high by a few mbar in the
subtropical highs and erroneously low in the polar lows (similar to CCSM3). As a result,
surface winds in the tropics are ~1 ms-1 too strong. Excess winds cause excess evaporative
cooling and depressed SSTs north of the equator. However, south of the equator SST is
erroneously high due to the evaporative cooling effects being outweighed by the warming
effects of other factors. The region of highest SST bias is close to the coast of Southern
Africa near the mean latitude of the Benguela Front (17oS). Comparison of CCSM4 to
ocean simulations of varying resolution suggests that insufficient horizontal resolution
leads to insufficient northward transport of cool water along this coast and erroneous
southward stretching of the Benguela Front. A similar problem arises in the coupled
model if the atmospheric component produces alongshore winds that are too weak.
Erroneously warm coastal SSTs spread westward through a combination of advection and
positive air-sea feedback from marine stratocumulus clouds. This study identifies three
aspects of coupled models to improve in order to reduce bias in simulations of the
tropical Atlantic seasonal cycle: 1) large scale atmospheric pressure fields, 2) winds
along the coast of southwest Africa, and 3) coastal ocean currents along the same coast.
Improvements of the latter require local horizontal resolution finer than the 1o used in
climate models.
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1. Introduction
Because of its proximity to land and presence of coupled interaction processes the
seasonal climate of the tropical Atlantic Ocean is notoriously difficult to simulate
accurately in coupled models (Zeng et al., 1996; Davey et al., 2002; Deser et al., 2006;
Chang et al., 2007; Richter and Xie, 2008). Several recent studies have linked the
ultimate causes of these persistent biases to problems in clouds and convection in the
atmospheric component. This paper revisits the problem of biases in coupled simulations
of the tropical Atlantic through examination of simulations using the Community Climate
System Model version 4 (CCSM4, Gent et al., 2011), a coupled climate model
simultaneously simulating the earth's atmosphere, ocean, land surface and sea-ice
processes.
The predominant feature of the seasonal cycle of the tropical Atlantic is the seasonal shift
of the zonally oriented Intertropical Convergence Zone (ITCZ), which defines the
boundary between the southeasterly and northeasterly trade wind systems. As the ITCZ
shifts northward in northern summer from its annual mean latitude a few degrees north of
the equator the zonal winds along the equator intensify, increasing the zonal tilt of the
oceanic thermocline and bringing cool water into the mixed layer of the eastern
equatorial ocean (e.g. Xie and Carton, 2004). This northward shift reduces rainfall into
the Amazon and Congo basins, reducing the discharge of those Southern Hemisphere
rivers and enhancing rainfall over Northern Hemisphere river basins such as the Orinoco,
and over the northern tropical ocean. Rainfall affects sea surface salinity (SSS) which in
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turn affects SST through its impact on the upper ocean stratification and barrier layers
(Carton, 1991; Breugem et al., 2008).
The northward shift of the ITCZ also leads to a seasonal strengthening of the alongshore
winds off southwest subtropical Africa. A low–level atmospheric jet along the Benguela
coast is driven by the south Atlantic subtropical high pressure system, with topographic
enhancement of winds west of the Namibian highland (Nicholson, 2010). This coastal
wind jet drives local upwelling and the coastal branch of the equatorward Benguela
Current, causing equatorward advection of cool southern hemisphere water (e.g. Boyer et
al. 2000, Colberg and Reason, 2006; Rouault et al., 2007). The Benguela Current meets
the warm southward flowing Angola Current at around 17oS and thus shifts in the frontal
position are a cause of large ocean temperature anomalies. The reduced SSTs associated
with intensified upwelling have the effect of extending the area of the eastern ocean
covered by a low lying stratus cloud deck and thus reducing net surface solar radiation
(Zuidema et al., 2009). Long-wave cooling from the cloud tops is balanced by adiabatic
warming, i.e., subsidence. The subsidence leads to near-surface divergence, and thus
counter clockwise circulation in the Southern Hemisphere, i.e., to southerlies along the
coast (see Nigam, 1997). This suggests that a reduction in stratocumulus cover produces
anomalous northerlies along the coast which has the effect of raising SST and further
reducing cloud cover.
As the seasons progress towards northern winter the trade wind systems shift southward
and equatorial winds reduce in strength along with a reduction in the zonal SST gradient
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along the equator. It is evident from this description that the processes maintaining the
seasonal cycle of climate in the tropical Atlantic involve intimate interactions between
ocean and atmosphere. Thus, a meridional displacement of the ITCZ and the trade wind
systems is linked through wind-driven evaporation effects to a shift in the
interhemispheric gradient of SST. Such meridional shifts in the both are known to occur
every few years (the ‘meridional’ or ‘dipole’ mode, e.g. Xie and Carton, 2004).
Likewise, changes in the strength of the zonal winds and the zonal SST gradient along the
equator occur from year to year in a way which is reminiscent of the kinematics and
dynamics of ENSO. Indeed, Chang et al. (2007) points out that the existence of these
coupled feedback processes in the Atlantic may explain why the patterns of SST, wind,
and precipitation bias are quite similar from one coupled model to the next, even though
careful examination shows that the processes causing this bias may be quite different.
This paper follows examinations of bias in the earlier CCSM3 model version (described
in Collins et al. 2006a). For example, in CCSM3 Large and Danabasoglu (2006) and
Chang et al. (2007) both pointed out that major atmospheric pressure centers and all
global scale surface wind systems are stronger than observed. In the northern tropical
Atlantic this excess wind forcing results in excess SST cooling. But despite the excess
winds SST in the southeastern tropical Atlantic is too warm. In CCSM3 the SST warm
bias in the southeast has been attributed to the remote impact of anomalously weak
Walker circulations over the equatorial Atlantic (Chang et al. 2007, 2008; Richter and
Xie, 2008). The zonal-vertical Atlantic Walker circulation was shown to be too weak
during March-April-May (MAM) due to a deficit of rainfall over the Amazon basin. This
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wind-precipitation bias was also shown to be present in the atmospheric model
component, CAM3, when forced with observed SST as a surface boundary condition. In
the ocean the resulting zonal wind bias leads to an erroneous deepening of the equatorial
thermocline and warming of the cold tongue in the eastern equatorial Atlantic (this bias is
common to most of non-flux-corrected coupled simulations of the earlier generation,
Davey et al., 2002). Predictably, this warm SST bias in the eastern equatorial zone is
reduced if the model equatorial winds are strengthened (Wahl et al., 2009; Richter et al.,
2010b).
The warm SST bias in CCSM3 and many other models extends from the equatorial zone
into the tropical southeastern Atlantic where it is stronger and more persistent (Stockdale
et al. 2006; Chang et al., 2007; Huang and Hu, 2007). There erroneously warm SSTs are
subject to warm equatorial ocean temperature errors via transport by coastal waves and
the Angola Current (Florenchie et al. 2003, Richter et al. 2010a). They are also
influenced by errors in the alongshore component of the coastal winds (Richter et al.
2010 a,b). Impact of wind-driven ocean currents is also emphasized by Zheng et al.
(2011) who have examined systematic warm biases in SST in the stratus cloud region of
the southeastern Pacific in 19 coupled models from the Intergovernmental Panel on
Climate Change (IPCC) Fourth Assessment Report (AR4). Although stratus clouds are
underrepresented by those models due to the presence of warm SST bias, negative biases
in net surface heat fluxes are evident in most of the models, suggesting that warm SST
bias is caused by biases in the ocean heat transport. In particular, the Peruvian upwelling
in most models is much weaker than observed due to weaker than observed alongshore
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winds, resulting in insufficient advection of cold water. The crucial importance of the
impact on bias throughout the basin due to correcting SST along the southeastern
coastline has been demonstrated by Large and Danabasoglu (2006).
Interestingly, a warm SST bias may also be present along the southwestern coast of
Africa in forced ocean-only simulations, as discussed by Large and Danabasoglu (2006).
A reason for this bias to occur is the fact that there is a strong SST front at the latitude of
the boundary between the warm Angola and cold Benguela Current systems (which
should meet at ~17.5°S) (Rouault et al., 2007; Veitch et al., 2010). The position of this
front is maintained partly by local wind-induced upwelling and thus local wind errors
will cause errors in the position and strength of the SST front. Also, even if the local
winds are correct, the ocean model must have sufficient resolution to resolve the 40-
60km horizontal scale of the coastal current and upwelling system and associated cross-
shelf pressure gradient (Colberg and Reason, 2006). But, results from previous attempts
to improve the coupled simulations by improving spatial resolution are ambiguous.
Tonniazzo et al. (2010) have found apparent improvements of SSTs in the dynamically
similar Peruvian upwelling region using an eddy permitting 1/3o resolution ocean and
1.25o x 5/6o resolution atmosphere in the Hadley Center coupled model. But, Kirtman
(2011, personnel communication) reports a persistent warm SST bias in the Benguela
region using an eddy resolving 0.1o resolution ocean coupled with a 0.5º resolution
CAM3.5 atmosphere.
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Another potential source of bias is the impact of errors in the atmospheric hydrologic
cycle on ocean stratification through its effects on ocean salinity. In CCSM3 the
appearance of excess precipitation in the southern hemisphere and resulting erroneously
high Congo river discharge contributes to an erroneous freshening of the surface ocean
by 1.5psu, erroneous extension of oceanic barrier layers, and as a result an erroneous
warming of SST (Carton, 1991: Breugem et al., 2008). Conversely, north of the equator,
reduced rainfall results in erroneous deepening and enhanced entrainment cooling of
winter mixed layers. These processes have the effect of cooling the already cold-biased
SST (Balaguru et al., 2010).
In this study we extend our examination of seasonal bias in CCSM3 to consider its
descendent, CCSM4. Our goals are to compare the CCSM4 bias to that in the earlier
version and to explore previously reported and newly proposed mechanisms to explain
the presence of the bias.
2. Model and Data
The version of CCSM4 used in this study is the 1850 control run produced by NCAR and
archived as b40.1850.track1.1deg.006, which is a 1300 yr simulation forced by fixed
preindustrial levels of ozone, solar, volcanic, greenhouse gases, carbon, and sulfur
dioxide/trioxide. Our analysis of CCSM4 focuses on data from a 97 year period (model
years 863-959). A sensitivity examination has also been carried out to ensure that the
climatology of this particular period is similar to that of other periods.
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To understand the contributions of individual components of CCSM4 we also examine
atmospheric and oceanic components separately in other experiments carried out by
NCAR (Table 1). The atmosphere component known as the Community Atmosphere
Model, version 4 (CAM4, Neale et al., 2011); employs an improved deep convection
scheme relative to the earlier CAM3 (described in Collins et al., 2006b) by inclusion of
convective momentum transport and a dilution approximation for the calculation of
convective available potential energy (Neale et al., 2008; Neale et al., 2011). The model
has 26 vertical levels and 1.25° longitude x 1° latitude resolution that improves the T85
(approximately 1.41° zonal resolution) of CAM3. The simulation examined here (1979-
2005), archived as CAM4/AMIP, f40.1979_amip.track1.1deg.001 differs from CCSM4
in that it is forced by specified, observed monthly merged SST (described in Hurrell et
al., 2008) interpolated onto model time steps.
The ocean model component of CCSM4, uses Parallel Ocean Program version 2 (POP2)
numerics (Smith et al., 2010; Danabasoglu et al., 2011). Among other improvements
relative to the version POP1.3 used in CCSM3, POP2 implements a simplified version of
the near-boundary eddy flux parameterization of Ferrari et al. (2008), vertically-varying
thickness and isopycnal diffusivity coefficients (Danabasoglu and Marshall, 2007),
modified anisotropic horizontal viscosity coefficients with much lower magnitudes than
in CCSM3 (Jochum et al., 2008), and a modified K-Profile Parameterization with
horizontally-varying background vertical diffusivity and viscosity coefficients (Jochum,
2009). The number of vertical levels has been increased from 40 levels in CCSM3 to 60
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levels in CCSM4. The ocean component of CCSM4 is run with a displaced pole grid with
average horizontal resolution of 1.125°longitude x 0.55°latitude in midlatitudes (similar
to horizontal ocean grid of CCSM3). To explore errors in the ocean model component we
consider uncoupled ocean run using the same grid but forced by repeating annually the
Normal Year Forcing (NYF) fluxes of Large and Yeager (2009). The experiment we
examine is c40.t62x1.verif.01 and is referred in this paper as POP/NYF.
To explore the impact of ocean model resolution we examine two additional global ocean
simulation experiments, also based on the same POP2 numerics. The first, referred to
here as POP_0.25 has eddy permitting 0.4ox0.25o resolution in tropics with 40 vertical
levels (Carton and Giese, 2008). Surface fluxes are provided by the 20th Century
Reanalysis Project (CR20) version 2 of Compo et al. (2011). Data from 1980-2008 are
used to evaluate the monthly climatology from POP_0.25 run. The second, referred to as
POP_0.1/NYF, has even finer 0.1ox0.1o horizontal resolution in the tropics (Maltrud et al.,
2010). The forcing for this simulation is again the NYF fluxes of Large and Yeager
(2009). The results shown here are for year 64. Because of our interest in the seasonal
cycle we first monthly average the various atmospheric and oceanic fields, then compute
a climatological monthly cycle by averaging successive Januarys, Februarys, etc.
Because of our interest in interactions between atmosphere and ocean we focus on a few
key variables including SST, SSS, surface winds, and surface heat and freshwater fluxes.
In order to determine the presence of bias in the various simulations we compare the
model results to a variety of observation-based, or reanalysis-based data sets listed in
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Table 2. In addition, a detailed comparison is made to observations from a fixed
mooring at 10oS, 10oW, which is part of the PIRATA mooring array, maintained by a tri-
part Brazilian, French, United States collaborative observational effort (Bourles et al.,
2008). This mooring was first deployed in late-1997 and has been maintained nearly
continuously since with a suite of surface flux instruments, as well as in situ temperature
and salinity. We use two observation-based estimates of wind stress of Bentamy et al.
(2008) and Risien and Chelton (2008) derived from QuikSCAT scatterometer data. The
difference between the two is due to differences in spatial resolution and formulation of
the surface drag coefficient in the stress formulation.
3. Results
The presentation of the results is organized in the following way. In the first part of this
section we address errors in the large scale atmospheric circulation and compare them
with errors in the tropical-subtropical Atlantic SST. We will see that wind errors are
symmetric about the equator while the SST errors have an antisymmetric, dipole-like
pattern (cold north and warm south). We next examine the reasons for the dipole-like
pattern of SST errors and its link to deficiencies in the atmospheric and oceanic
components of the coupled model.
a. Gross features Bands of excessive surface subtropical high pressure systems
encircle the globe in both hemispheres in CCSM4 (Fig. 1). The error is larger in the
Atlantic sector than the Pacific and Indian sectors, and there it exceeds 4 to 5 mbar. We
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can show that the source of this error is within the atmospheric model because the
pressure pattern error is also apparent in atmospheric module, CAM4, forced with
observed SST. We can also show the persistence of this error in that it is evident even
more strongly in the earlier generation models: CCSM3 and CAM3 (Figs. 1c, 1d). The
improvement in representation of MSLP progressing from CCSM3 to CCSM4 is
particularly evident in the North Atlantic where the region of erroneously high MSLP
decreases in spatial scale and amplitude (compare Figs. 1a, 1c). However the errors have
increased somewhat in the Southern Hemisphere. The erroneously high subtropical high
pressure systems have the effect of producing erroneously strong surface westerlies in
midlatitude (by a factor of two in wind stress) and easterly surface trade winds in the
subtropics and tropics (Fig. 2). In turn, these erroneously strong winds can be expected to
produce excess evaporation and mixing giving rise, all other things being equal, to
erroneously cool SST. MSLP error in the southeastern tropics, a region where pressure is
normally low, is negative (this is also evident in the CAM4 experiment).
In spite of the fact that trade winds in CCSM4 are too intense in both hemispheres, errors
in time mean SST are hemispherically asymmetric (Fig. 3). In the northwestern tropics
SST is too cool by 1-2°C, an error consistent with the effects of wind-induced excess
latent heat loss and deeper mixed layers (by 10 Wm-2 and 20m, respectively, not shown).
In contrast, in the southeastern tropics SST error is positive and grows to > 3oC in the
coastal upwelling region of southern Africa despite the presence of excess trade winds
(Fig. 2). Comparison with the results of CCSM3 (e.g. Chang et al., 2007) shows that the
CCSM4 SST error extends even further westward across the basin. To explore the origin
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of the SST error in CCSM4 we compare it to the SST error in the CCSM4 ocean model
component when forced with representative observed surface forcing (Fig.3). The latter
also has an SST error of a couple of degrees near the southern African coast throughout
the year (Fig. 4) suggesting that the ocean component and its response to surface forcing
may contribute to the development of SST errors close to the coast.
The seasonal timing of SST errors along the southern African coast is such that they
develop in the boreal spring in both CCSM4 and in POP/NYF (Fig. 4). These errors
grow and expand northwestward through March-May and reach the equatorial region in
the boreal summer months. As the region of low MSLP error develops and expands, it is
coincident with the warming of SST, shown in the equatorial zone in Fig.5.
b. Equatorial Zone In the equatorial zone there are pronounced time mean as well
as seasonal errors. Time mean MSLP error over the equatorial Atlantic in CCSM4 is
+0.6 mb in the western basin and -0.3 mb in the eastern basin, which results in an
erroneously weak time mean eastward pressure gradient force along the equator (Fig. 6).
This error, somewhat reduced from CCSM3, is apparent, but not as pronounced in
CAM4. A striking difference between CCSM3 and CCSM4 equatorial MSLP is evident
over the continent of South America. Between the Atlantic and the South American
continent the error in CCSM3 time mean MSLP undergoes a dramatic 2 mb drop
implying a strong erroneous component to the westward pressure gradient force onto the
continent. The error in CCSM4 time mean MSLP undergoes a much smaller decrease,
implying weaker erroneous flow onto the continent. Time mean MSLP over central
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Africa is also erroneously high in the coupled models, and is not improved in CCSM4
relative to CCSM3. CAM4 and CAM3 also exhibit an erroneous time mean westward
MSLP pressure gradient force across the equatorial Atlantic, but it is less than half as
strong as in the corresponding coupled models.
Observed zonal winds along the equator are easterly but east of 0oE where the monsoonal
westerly is present (Fig. 7a). The equatorial westerly amplifies twice a year in April and
again in October giving rise to the primary and secondary SST cooling in the east in July-
August and November-December, respectively (e.g. Okumura and Xie, 2006). The
October zonal wind amplification is missing in both CAM4/AMIP and CCSM4 (Figs. 7b,
7c). But the strongest error in simulated zonal winds is associated with the April event.
CCSM4 surface zonal winds develop a striking 5 ms-1 error in boreal spring leading to
westerlies prevailing over much of the equator. Even this represents a noticeable
improvement relative to the massive surface wind errors in CCSM3 (Chang et al., 2007),
but is still much stronger than the corresponding errors in CAM4 (Fig. 7c). The timing of
this error corresponds to the seasonal northward migration of the southeasterly trade wind
system, and thus a partial interpretation is that the error partially reflects a delay in this
migration (although this does not explain why the winds actually turn eastward). The
seasonal intensification of the CCSM4 surface zonal winds in the western basin in boreal
fall is by 2 ms-1 stronger than observed (Fig. 7b). However, in boreal fall the errors are
even larger in CAM4.
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c. Conditions along the Southern African Coast As noted above, CCSM4 SST is
erroneously high along the Benguela region of the southern African coast 20oS to 13oS
(Fig. 4 and Fig. 7e). Within approximately 10o of the coast (east of 10oE) the bias in
CCSM4 SST varies seasonally by approximately 2oC and reaches a maximum (> 5oC) in
austral winter (Fig. 8). The SST bias in POP/NYF has similar ~2oC seasonal amplitude
and seasonal timing (although its time mean bias is several degrees lower), consistent
with an oceanographic origin to this effect. Meridional (alongshore) wind bias in
CCSM4 is negative (northerly) throughout the year indicating insufficient upwelling.
Strikingly, this northerly bias in coastal winds is opposite to generally enhanced
southeasterly trades present over much of the South Atlantic (see also maps of meridional
wind stress bias in Munoz et al., 2011). Seasonal variations of coastal wind bias closely
reflect the seasonal changes of SST bias with a one month lag. But the two are
inconsistent. The warm SST bias weakens in austral summer just when the lack in local
upwelling cooling enhances due to the northerly bias in coastal winds. This suggests that
at least a part of the warm Benguela SST bias originates in the ocean component due to
non-local effects related to biases in the horizontal heat transport.
Comparison of surface currents to those produced by the three different ocean component
models (Table 1) shows that CCSM4 surface currents closely resemble those of
POP/NYF with a broad and weak Benguela Current which coastal branch is almost
missing and cold flow doesn’t reach the climatological position of the Angola-Benguela
front at 17oS (Fig. 9). Instead, the Angola current dominates coastal circulation at this
latitude carrying warm equatorial water to coastal regions south of 20oS. POP_0.1/NYF
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has a stronger, more coastally trapped Benguela Current, but in this experiment as well,
ocean advection is acting to warm the coastal ocean well south of the observed
Angola/Benguela frontal position at 17oS. This southward bias in the frontal position
explains why SST bias in CCSM4 is so large near the coast in this range of latitudes. Of
the experiments we examine only POP_0.25 has both reasonable coastal branch of the
Benguela Current, and has the frontal position at approximately the correct latitude, and
thus has greatly reduced SST bias near the coast.
The vertical structure of ocean conditions along the southern African coast confirms
similarities of this aspect of CCSM4 and POP/NYF and POP_0.1/NYF (Fig. 10). All
three experiments show a strengthening of the southward Angola Current between 15-
19oS (also evident in the eddy resolving simulation of Veitch et al. 2010), and its
continuation south of 25oS. In striking contrast, POP_0.25 shows strong equatorward
transport of cool southern hemisphere water south of 20oS, extending even further
northward at surface levels. One possible explanation for the erroneous behavior of
CCSM4 and POP/NYF is the insufficiency of horizontal resolution in the ocean, below
that required to resolve baroclinic coastal Kelvin waves (approximately 40-60 km at
17oS, Colberg and Reason, 2006; or even finer scales Veitch et al., 2010). However, the
fact that the same error is evident in POP_0.1/NYF suggests the presence of an error in
surface forcing as well.
Comparison with satellite observed wind stress (Fig. 11e) shows that NYF wind stress
has an insufficiently intense Benguela wind jet, which remains offshore and thus does not
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extend to the coast. It is thus not surprising that the experiments driven by NYF wind
stress has weak, displaced coastal currents, even if the model horizontal resolution is
sufficient. In contrast the 20CR wind stress more closely resembles the magnitude of
satellite observed winds (Figs. 11a,f). This similarity explains the presence of a strong
coastal jet of Benguela Current in POP_0.25 (Figs. 9, 10).
d. Surface Shortwave Radiation The bias in downwelling shortwave radiation in
CCSM4 is similar to that in CAM4/AMIP (except in the equatorial region where the
anomalous southward shift of ITCZ contributes) and is larger than what was present in
CCSM3 particularly in the eastern ocean boundary regions (see Fig.2 in Bates et al.,
2011). Thus, in the southeast shortwave radiation is biased high by at least 20 Wm-2 and
reaches a maximum of 60 Wm-2 in austral winter and spring (when SST is seasonally
cold) due to a lack of shallow stratocumulus clouds (e.g. Cronin et al. 2006). This
regional excess of shortwave radiation is compensated for in part by excess latent heat
loss due to anomalously strong southeasterly trade winds (Fig. 2). These biases are
evident in a comparison of CCSM4 surface downward shortwave radiation (Fig. 13) and
latent heat loss (Fig. 14) with observations at the PIRATA mooring at 10oS, 10 o W.
There CCSM4 downward shortwave radiation exceeds observations by 60 Wm-2 in
August. But, the time mean excess of 33 Wm-2 is almost compensated for by the time
mean excess of latent heat loss of 30 Wm-2.
e. Precipitation and salinity CCSM4 tropical precipitation is displaced into the
southern hemisphere (Fig. 15b), consistent with the warm bias of SST in this hemisphere.
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Also, there is excess precipitation over the equatorial region of the African continent
(also apparent in CCSM3 and CAM4) leading to excess Congo River discharge by at
least a factor of two (Fig. 16). Interestingly, CCSM3 had insufficient precipitation over
the Amazon basin and thus insufficient Amazon River discharge. In CCSM4
precipitation over the Amazon basin is more realistic, and thus Amazon River discharge
more closely resembles observations, but is still biased low. These biases in precipitation
and river discharge contribute to a CCSM4 fresh bias in sea surface salinity which
becomes extreme in the eastern basin and likely contributes to the excess SST by
inhibiting vertical mixing. Fresh water bias present south of the equator is spread into the
south subtropical gyre by the South Equatorial Current. This results in lowering of the
south subtropical salinity maximum by 1 psu in CCSM4.
4. Summary
This paper revisits biases in coupled simulations of the tropical Atlantic based on an
approximately 100 yr long sample of the control CCSM4 run (model years 863-959) with
an emphasis on the causes of biases in basin-scale surface winds and in the coastal
circulation in the southeastern Atlantic boundary and its consequences for producing
biases in SST. Like its predecessor model, CCSM3, the atmospheric component of
CCSM4, known as CAM4, has abnormally intense surface subtropical high pressure
systems and abnormally low polar low pressure systems (by a few mbar), and these
biases in MSLP cause corresponding biases in surface wind stress. In particular, the trade
wind stresses are approximately 0.05 Nm-2 that is by 0.02 Nm-2 (~1 m/s) too strong in
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both CAM4 and CCSM4. As a consequence, latent heat loss is too large throughout the
basin.
In spite of erroneously strong trade winds in both hemispheres, SST bias is
hemispherically asymmetric. In the northwestern tropics time-mean SST is biased cold by
1-2°C consistent with erroneously strong northeasterly trades and excess evaporation. But
south of the equator, SST is biased cold only in the west. In the southeast SST has a
warm bias despite erroneously strong winds. This dipole-like pattern of SST bias is likely
further amplified since it projects onto a natural mode of climate variability inherent to
this basin, as suggested by Chang et al. (2007).
The existence of a warm SST bias in the southeastern tropical Atlantic is affected by
local and remote (equatorial) biases in the atmospheric model component. In this study
we focus on biases in coastal circulation along the southeastern Atlantic boundary. By
comparing the results of CCSM4 with a suite of ocean simulations with different spatial
resolutions, using different wind forcings, we find that the warm bias evident along the
coast of southern Africa has at least two additional causes. The first is the presence of
horizontal resolution which is insufficient to resolve fundamental processes of coastal
dynamics: the baroclinic coastal Kelvin Wave. The second is the erroneous weakening of
the wind field within 2o of the coast of southern Africa. The impact of either of these
errors (both of which are present in CCSM4) is to allow the warm Angola Current to
extend too far south against the opposing flow of the cold Benguela Current. The
resulting warm bias of coastal SST may expand westward through coupled air-sea
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feedbacks, e.g. due to its effect on low level clouds. Another feedback mechanism
involve the effects of excessive precipitation in the southern hemisphere on surface
salinity, and thus indirectly on SST through enhanced vertical stratification.
Acknowledgements This research was supported by the NOAA/CPO/CPV and NASA
Ocean Programs. Computing resources were provided by the Climate Simulation
Laboratory at NCAR's Computational and Information Systems Laboratory (CISL),
sponsored by the National Science Foundation (NSF) and other agencies. This research
was enabled by CISL compute and storage resources. Bluefire, a 4,064-processor IBM
Power6 resource with a peak of 77 TeraFLOPS provided more than 7.5 million
computing hours, the GLADE high-speed disk resources provided 0.4 PetaBytes of
dedicated disk and CISL's 12-PB HPSS archive provided over 1 PetaByte of storage in
support of this research project. NCAR is sponsored by the NSF.
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Table 1 Experiments used in this study
Experiment Years Forcing Resolution
CCSM4 1300yr
(863-959)
Coupled, 1850 preindustrial
gas forcing
1.25°x1° ATM
1.125°x0.5° OCN
CAM4/AMIP 1979-2005 SST (Hurrell et al., 2008) 1.25°x1°
CCSM3 1870-1999
(1949-1999)
Coupled, 20C3M run 1,
historical gas forcing
T85 (1.41°x1°)
ATM
1.125°x0.5° OCN
CAM3/AMIP 1950-2001 SST (Hurrell et al., 2008) T85
POP_0.25 1871-2008
(1980-2008)
20CR v.2 fluxes (Compo et al.,
2011).
0.4°x0.25° (OCN
model resolution
in tropics)
0.5°x0.5° output
grid
POP_0.1/NYF Model year
64
Repeating annual cycle of
Normal Year Forcing (NYF,
Large and Yeager, 2009)
0.1°x0.1°
POP/NYF Model year
10
Repeating annual cycle of
Normal Year Forcing (NYF,
Large and Yeager, 2009)
1.125°x0.5°
27
621
28
Table 2 Data sets used to evaluate seasonal bias
Variable Years Description Resolution
SST 1982-
present
optimal interpolation version 2
Reynolds et al. (2002)
1°x1°
10m Winds 1999-2009 QuikSCAT scatterometer (e.g. Liu,
2002)
0.5°x0.5°
Wind Stress 1999-2007 QuikSCAT Bentamy et al. (2008) 1°x1°
Wind Stress climatology QuikSCAT Risien and Chelton (2008) 1/4°x1/4°
Shortwave
radiation
2002-2010 Moderate Resolution Imaging Spectro-
radiometer (Pinker et al., 2009)
1°x1°
Latent heat
flux
1992-2007 IFREMER satellite-based Bentamy et al.
(2003, 2008)
1°x1°
Precipitation 1979-2010 Climate Prediction Center Merged
Analysis of Precipitation Xie and
Arkin (1997)
2.5°x2.5°
Mean sea
level
pressure
1958-2001 ERA-40 Uppala et al., 2005 2.5°x2.5°
SSS 1871-2008
Used data
1980-2008
SODA 2.2.4, Carton and Giese (2008),
Giese et al. (2010)
0.5°x0.5°
28
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Figure captions
Figure 1. Time mean bias in mean sea level pressure (mbar) in CCSM and its
atmospheric component forced by observed SST (CAM/AMIP). Top/bottom lines show
versions 4/3, respectively.
Figure 2. Time and zonal mean TAUX over the ocean from QuikSCAT (shaded),
in CCSM4, and its atmospheric component forced by observed SST (CAM4/AMIP).
Figure 3. Time mean SST bias in CCSM4 and its ocean component forced by the
normal year forcing (POP/NYF).
Figure 4. Bias in SST (degC, shading) and MSLP (mbar, contours) during four
seasons. Left column is CCSM4 data. Right column presents data from two independent
runs: SST is from a stand alone ocean model forced by the normal year forcing
(POP/NYF), MSLP is from a stand alone atmospheric model forced by observed SST
(CAM4/AMIP). Surface wind bias is also shown for the coupled run (left column).
Figure 5. Scatter diagram of time mean biases in MSLP and SST over the
equatorial Atlantic Ocean (5oS-5oN). Each symbol represents grid point value.
Figure 6. Time mean MSLP bias in the 5oS-5oN belt in (solid) CCSM and
(dashed) CAM/AMIP. Difference between the two is shaded. Top and bottom panels
present version 4 and 3 results, respectively. Ocean is marked with gray bar in panel (a).
Figure 7. Observed (a) zonal wind along the Equator and (b) meridional wind
along the western coast of southern Africa (contour interval is 1 ms-1). (b,e) CCSM4 SST
bias (shading), winds (black contours). Zonal wind bias is shown for the equatorial zonal
winds only (red contours, negative-dashed, positive-solid, contour interval is 1 m/s, zero
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contour is not shown). (c,f) The same as in (b,e) but for CAM4/AMIP winds, and
POP/NYF SST.
Figure 8. Seasonal cycle of SST bias and meridional wind (V) bias spatially
averaged over the Angola-Benguela front region (10oE-shore, 20 oS-13 oS).
Figure 9. Time mean surface currents (arrows) and SST (contours, CINT=1oC) in
(a) POP_0.25, (b) POP_0.1/NYF, (c) CCSM4, and (d) POP/NYF. Northward/southward
currents are blue/red, respectively. SST below 20oC is shown in dashed.
Figure 10. Time mean meridional currents (shading), water temperature
(contours), and meridional and vertical currents (arrows) averaged 2o off the coast. See
Table 1 for description of runs. Arrow scale represents meridional currents. Vertical
currents are magnified.
Figure 11. Time mean wind stress (arrows) and wind stress magnitude (shading)
in the Benguela region. Panel (f) shows wind stress magnitude averaged 2o off the coast
(red line in (b)). QuikSCAT wind stress in (f) is shown twice based on (solid) Bentamy et
al. (2008) and (dashed) Risien and Chelton (2008).
Figure 12 Seasonal bias in downwelling surface short wave radiation in (left)
CCSM4 and (right) CAM4/AMIP. CINT=20 Wm-2, positive/negative values are shown
by solid/dashed, respectively. Zero contour is not shown. The PIRATA mooring 10oW,
10oS location is marked by ‘+’.
Figure 13 Seasonal cycle of downwelling SWR (Wm-2) at 10oW, 10oS from
MODIS satellite retrievals, observed at the PIRATA mooring, and simulated by CCSM4
and CAM4/AMIP.
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Figure 14 Seasonal cycle of latent heat flux (LHTFL, Wm-2) at 10oW, 10oS from
IFREMER satellite retrievals of Bentamy et al. (2008), from the PIRATA mooring, and
simulated by CCSM4 and CAM4/AMIP. Observed LHTFL is calculated from the buoy
data using the COARE3.0 algorithm of Fairall et al. (2003).
Figure 15 Time mean sea surface salinity (SSS, psu, shading) and precipitation
(mm dy-1, contours). (a) SODA salinity and CMAP precipitation, (b) CCSM4 SSS and
precipitation, (c) data from two independent uncoupled runs: POP/NYF SSS and
CAM4/AMIP precipitation.
Figure 16 Time mean river runoff shown as equivalent surface freshwater flux
(mm dy-1). (a) Normal year forcing of Large and Yeager (2009), (b) CCSM4.
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Figure 1. Time mean bias in mean sea level pressure (mbar) in CCSM and its atmospheric component forced by observed SST (CAM/AMIP). Top/bottom lines show versions 4/3, respectively.
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Figure 2. Time and zonal mean TAUX over the ocean from QuikSCAT (shaded), in CCSM4, and its atmospheric component forced by observed SST (CAM4/AMIP).
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Figure 3. Time mean SST bias in CCSM4 and its ocean component forced by the normal year forcing (POP/NYF).
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Figure 4. Bias in SST (degC, shading) and MSLP (mbar, contours) during four seasons. Left column is CCSM4 data. Right column presents data from two independent runs: SST is from a stand alone ocean model forced by the normal year forcing (POP/NYF), MSLP is from a stand alone atmospheric model forced by observed SST (CAM4/AMIP). Surface wind bias is also shown for the coupled run (left column).
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Figure 5. Scatter diagram of time mean biases in MSLP and SST over the equatorial Atlantic Ocean (5oS-5oN). Each symbol represents grid point value.
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Figure 6. Time mean MSLP bias in the 5oS-5oN belt in (solid) CCSM and (dashed) CAM/AMIP. Difference between the two is shaded. Top and bottom panels present version 4 and 3 results, respectively. Ocean is marked with gray bar in panel (a).
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Figure 7. Observed (a) zonal wind along the Equator and (b) meridional wind along the western coast of southern Africa (contour interval is 1 ms-1). (b,e) CCSM4 SST bias (shading), winds (black contours). Zonal wind bias is shown for the equatorial zonal winds only (red contours, negative-dashed, positive-solid, contour interval is 1 m/s, zero contour is not shown). (c,f) The same as in (b,e) but for CAM4/AMIP winds, and POP/NYF SST.
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Figure 8. Seasonal cycle of SST bias and meridional wind (V) bias spatially averaged over the Angola-Benguela front region (10oE-shore, 20 oS-13 oS).
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Figure 9. Time mean surface currents (arrows) and SST (contours, CINT=1oC) in (a) POP_0.25, (b) POP_0.1/NYF, (c) CCSM4, and (d) POP/NYF. Northward/southward currents are blue/red, respectively. SST below 20oC is shown in dashed.
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Figure 10. Time mean meridional currents (shading), water temperature (contours), and meridional and vertical currents (arrows) averaged 2o off the coast. See Table 1 for description of runs. Arrow scale represents meridional currents. Vertical currents are magnified.
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Figure 11. Time mean wind stress (arrows) and wind stress magnitude (shading) in the Benguela region. Panel (f) shows wind stress magnitude averaged 2o off the coast (red line in (b)). QuikSCAT wind stress in (f) is shown twice based on (solid) Bentamy et al. (2008) and (dashed) Risien and Chelton (2008).
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Figure 12 Seasonal bias in downwelling surface short wave radiation in (left) CCSM4 and (right) CAM4/AMIP. CINT=20 Wm-2, positive/negative values are shown by solid/dashed, respectively. Zero contour is not shown. The PIRATA mooring 10oW, 10oS location is marked by ‘+’.
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Figure 13 Seasonal cycle of downwelling SWR (Wm-2) at 10oW, 10oS from MODIS satellite retrievals, observed at the PIRATA mooring, and simulated by CCSM4 and CAM4/AMIP.
Figure 14 Seasonal cycle of latent heat flux (LHTFL, Wm-2) at 10oW, 10oS from IFREMER satellite retrievals of Bentamy et al. (2008), from the PIRATA mooring, and simulated by CCSM4 and CAM4/AMIP. Observed LHTFL is calculated from the buoy data using the COARE3.0 algorithm of Fairall et al. (2003).
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Figure 15 Time mean sea surface salinity (SSS, psu, shading) and precipitation (mm dy-1, contours). (a) SODA salinity and CMAP precipitation, (b) CCSM4 SSS and precipitation, (c) data from two independent uncoupled runs: POP/NYF SSS and CAM4/AMIP precipitation.
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Figure 16 Time mean river runoff shown as equivalent surface freshwater flux (mm dy-
1). (a) Normal year forcing of Large and Yeager (2009), (b) CCSM4.
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