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

senya@atmos.umd.edu

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

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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

622

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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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