Download Marine stratocumulus clouds cover a considerable portion of the

Tropical Atlantic Biases in CCSM4
Semyon A. Grodsky, James A. Carton, and Sumant Nigam
March 17, 2011
To be submitted to the Journal of Climate
[email protected]
Department of Atmospheric and Oceanic Science
University of Maryland
College Park, MD 20742
Abstract This paper focuses on the tropical Atlantic biases in the control simulation of
the Community Climate System Model version 4 (CCSM4). We find that local and
remote biases in both, atmospheric and oceanic components of the coupled model
contribute. Like in previous version, CCSM3, the atmospheric component of CCSM4
(CAM4) has abnormally high (by a few mbar) mean sea level pressure (MSLP) in the
subtropical pressure highs and abnormally low MSLP in the polar lows. As a result all
global scale winds are accelerated. Wind stress in the trade winds is approximately 0.05
N/m^2 (~2 m/s) stronger. In spite of anomalously strong trade winds in the north and
south, the SST error in the tropical Atlantic changes sign across the Equator. This dipolelike SST error pattern suggests that errors in the coupled model climate may be further
amplified by projecting on the natural meridional mode of variability inherent to the
tropical Atlantic. In the northern tropics the time-mean sea surface temperature (SST) is
colder than normal by 1-2°C in line with stronger northeasterly trades and local
evaporation. Similar anomalous cooling of SST is present in the south. But, in distinction
from the north where the cold SST error is present across the entire basin, the cold SST
error in the south is limited to the west, though stronger than normal winds are basinwide there. Despite stronger winds, the SST has a warm bias in the southeastern tropical
Atlantic. By comparing higher horizontal resolution ocean simulations (0.25°x0.4°) with
simulations on a CCSM4 ocean grid (1.125°x0.55°), we find that decrease in horizontal
resolution (below the local Rossby radius of deformation) has significant impact on the
eastern boundary currents. In particular, the currents affecting the Angola-Benguela
temperature front are distorted in CCSM4. The southward Angola Current that originates
at around 5°S is shifted southward while the coastal jet of the cold Benguela Current is
replaced by a broad northward flow. These biases in the eastern boundary currents and
their meridional heat transport result in stretching of the SST front and warming of SSTs
at latitudes where cold water transported by Benguela Current is normally present. In
CCSM4 the warm SST bias along the coast of Southwestern Africa (originated in the
ocean component) is amplified and expanded into the open ocean via positive feedback
from marine stratocumulus. This study suggests that smaller biases in coupled
simulations of the tropical Atlantic may be achieved via improving the large scale
pressure fields in the atmospheric component and by more accurate simulation of the
coastal circulation in the ocean component. The latter needs locally enhanced horizontal
resolution in the ocean boundaries.
1. Introduction
Although the climate of the tropical Atlantic Ocean is mostly seasonal, its coupled
simulation still remains a problem that is evident in notorious biases in regional winds
and SST that have being present in recent generations of coupled models (Davey et al.,
2002; Deser et al., 2006; Chang et al., 2007). In the Pacific sector, better representation of
the deep-convection in the NCAR Community Atmosphere Model version 4 (CAM4) has
lead to significant improvements in the phase, amplitude and spatial patterns of El Nino
Southern Oscillation (ENSO, Neale et al., 2008). But in the Atlantic sector improvements
are not that noticeable. Due to proximity of continents the Atlantic coupled air-sea system
is impacted by land processes (e.g. Zeng et al., 1996, Richter and Xie, 2008). Thus errors
originated in any module of the Atlantic ocean-atmosphere-land system may impact each
other and grow through coupled interactions.
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Several recent studies have linked the causes for the persistent tropical Atlantic biases in
coupled simulations with problems in the atmospheric component. In particular, Chang et
al. (2007) have found that warm SST bias in the equatorial and southeastern tropical
Atlantic in CCSM3 is due to below normal zonal equatorial winds during March-AprilMay (MAM) that originate due to deficit of rainfall (lack of diabatic heating/ colder air/
higher pressure) in the Amazon. The bias in equatorial westerly winds during MAM is
also present in uncoupled atmospheric model component, CAM3, forced by observed
SST (Atmospheric Model Intercomparison Project, AMIP, style run) suggesting that the
bias is initiated in the atmospheric component. The link between equatorial zonal winds
and Amazon rainfall have been demonstrated by Chang et al. (2008) who have
reproduced the equatorial westerly bias in a model forced by diabatic heating bias over
the Amazon. In parallel, Richter and Xie (2008) have shown that erroneously weak
Atlantic Walker circulation in conjunction with deficient and excessive terrestrial
precipitation over equatorial South America and Africa, respectively, are robust across
coupled models from the third Coupled Model Intercomparison Project (CMIP3) and
their uncoupled AMIP counterparts. The causes of biases in tropical convection over the
two continents may be in boundary conditions from the land component or in the
convection scheme used by atmospheric component but further studies are needed to
evaluate their relative importance (Richter et al., 2010b). Using a simplified model Zeng
et al. (1996) have demonstrated that Atlantic Walker circulation weakens and equatorial
zonal SST gradient drops by 1C as a result of changes in land albedo and
evapotraspiration due to Amazon deforestation.
In coupled simulations, the westerly error in equatorial winds during MAM leads to
abnormal deepening of the thermocline in the east that attenuates the cold tongue in the
next season (JJA). In CCSM3 this abnormal thermocline deepening is accompanied by
reversal of the Equatorial Undercurrent (EUC) and by reversal of normally westward
gradient of equatorial SST (Chang et al., 2007). This erroneous, eastward SST gradient is
further amplified and prolonged by the Bjerknes feedback and is present in all CMIP3
models (Richter and Xie, 2008) as well as in most of the non-flux-corrected coupled
simulations analyzed by Davey et al. (2002).
The important role of valid equatorial wind stress for proper simulations of the equatorial
SST gradient has been confirmed by Wahl et al. (2011). Richter et al. (2010b) have
examined impact of the seasonal errors in equatorial winds. They have found that cold
tongue SST in JJA reduces by 3 K and warm pool SST increases by 0.5 K if modelgenerated wind stress in MMA is substituted with observed climatology. Their results
thus confirm that deficient equatorial easterly in MAM is a possible reason for the
anomalously warm JJA cold tongue.
The warm SST bias present in coupled simulations in the eastern equatorial area extends
in the tropical southeastern Atlantic where it is more persistent and less seasonally
dependent (Stockdale et al. 2006; Chang et al., 2007; Huang et al. 2007). In fact, the
largest mean SST biases develop along the eastern boundaries of subtropical gyres (Large
and Danabasoglu, 2006). Many studies have suggested that wind stress along the equator
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influences the coastal region of southwestern Africa via Kelvin waves (Florenchie et al.
2004, Richter et al. 2010a). So that biases in wind stress originated in the atmospheric
component are responsible in part for biases in the ocean response. Sensitivity
experiments of Richter et al. (2010 a,b) have shown that errors in both zonal equatorial
winds (remote impact via equatorial and coastal waves) and local off-equatorial alongshore winds (impact on local upwelling) contribute comparably to the warm SST biases
in the southeastern tropical Atlantic.
But ocean model deficiencies also contribute. In fact, the warm SST bias is present along
the southwestern coast of Africa in uncoupled ocean simulations as well (Large and
Danabasoglu, 2006). Hence the local atmospheric forcing is only a part of the problem
along eastern boundaries, and the representations of ocean upwelling and meridional heat
transport in the ocean component are other likely contributors. If the impact of
anomalously weak upwelling is corrected by setting coastal temperature and salinity
along the southeastern Atlantic boundary close to observations, the coupled model SST
improves across the entire southeastern Atlantic (Large and Danabasoglu, 2006).
Warm coastal SST bias is advected into open regions of the southeastern Atlantic by
wind-driven ocean currents (Large and Danabasoglu, 2006). In addition, the marine
stratocumulus clouds (developing over cold SSTs) provide yet another coupled oceanatmosphere mechanism for spreading warm SST anomalies off the coast. Marine
stratocumulus clouds cover significant portion of the ocean and are particularly evident
over the upwelling areas along the western coasts of continents (Zuidema et al., 2009).
Due to their high reflectivity, stratocumulus clouds play important roles in the ocean
radiation budget. Marine stratocumulus clouds affect SST not only through their radiative
shading effects, but also dynamically: 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). Not enough clouds results in weaker upwelling
favorable southerly wind along the coast and warmer SSTs. Thus, both, radiative and
dynamic effects provide positive feedback on SST.
Successful simulation of marine stratocumulus clouds still remains a challenge.
Maximum stratocumulus cover develops when SST is coldest, in particular in austral
spring in the Namibian and Peruvian stratus regions. This link to SST leads to positive
feedback (warm SST – less stratocumulus clouds) in coupled ocean-atmosphere models
(Mechoso et al., 1995; de Szoeke et al., 2010) that amplifies magnitudes and spatial
coverage of warm anomalies originated along the western coasts. Once anomalously
warm SST off the southwestern coast of Africa is in place, the reduced low-clouds over
the warm anomaly force SST warming in a larger area thus spreading the SST anomaly in
the direction of mean southeasterly winds (Huang and Hu, 2007).
Sea surface salinity (SSS) can also affect SST and air-sea interactions through its impact
on the upper ocean stratification and barrier layers (e.g. Breugem et al., 2008). Tropical
SSS biases in coupled models are generally attributed to errors in precipitation. In
CCSM3 the fresh SSS bias is the largest south of the equator (in excess of 1.5 psu) due to
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the southward shift of the ITCZ and the “double” ITCZ (Large and Danabasoglu, 2006).
Meridional shift of rainfall has significant impact on the tropical rivers discharge in the
Atlantic sector. In particular, the Congo discharge in CCSM3 more than doubles the
climatological discharge of Large and Yeager (2009). An excessive river plume produced
by anomalously strong Congo discharge affects barrier layers (BLs) in the southeastern
tropical Atlantic. BLs lead to SST warming (e.g. Breugem et al., 2008) by suppressing
vertical heat exchange between thermocline and the mixed layer and thus provide a
positive feedback on already warm SST bias in the region. Similarly in the north tropical
Atlantic, the absence of BLs (that are normally present in this area due to the Amazon
freshwater transport in the surface layers and the subduction of high haline water from
the subtropical SSS maximum) results in anomalous deepening and entrainment cooling
of the winter mixed layer that again provide a positive feedback on already cold SST bias
in the region (Balaguru et al., 2010). Surface salt flux, ( E  P ) * S , is replaced in POP by
( E  P) * S 0 where S 0 is the global mean sea surface salinity. Underestimation of the
surface salt flux in the high salinity pools produces lower salinity there, that in turn leads
to weaker barrier layers on the equatorward flanks of subtropical salinity maxima.
2. Model and Data
This research focuses on the tropical Atlantic biases in the Community Climate System
Model version 4 (CCSM4). The CCSM4 is a coupled climate model composed of four
separate models simultaneously simulating the earth's atmosphere, ocean, land surface
and sea-ice, and one central coupler component1. The CCSM4 data used in this study are
the output data of the 1300 yr control model integration (archived as
b40.1850.track1.1deg.006). This run is forced by historical ozone, solar, volcanic, green
house gases, carbon, and sulfur dioxide/trioxide. Our analysis focuses on data for 97 year
period (model years 863-959). A sensitivity examination has been carried out to ensure
that the climatology of this particular period is similar to that of other periods. Our focus
is on the performance of atmospheric and oceanic model components. Fluxes exchanged
among component models were not adjusted during this simulation.
The atmosphere component of CCSM4, Community Atmosphere Model, version 4
(CAM4); employs an improved deep convection scheme (in comparison with CAM3 of
Collins et al. 2006) by inclusion of convective momentum transport and a dilution
approximation for the calculation of convective available potential energy (Neale et al.,
2008). It is run on a 26 vertical levels, 1.25° longitude x 1° latitude grid. To separate
errors originated in the atmospheric component from those in the coupled system we also
use a stand alone CAM4 data simulated on the same grid and forced by observed SST
(CAM4/AMIP, f40.1979_amip.track1.1deg.001)
The ocean model component of CCSM4 has been updated to the Parallel Ocean Program
version 2 (POP2) of the Los Alamos National Laboratory (Smith et al., 2010). Among
other improvements the POP2 implements a simplified version of the near-boundary
eddy flux parameterization of Ferrari et al., (2008), vertically-varying thickness and
1
http://www.cesm.ucar.edu/models/ccsm4.0/
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isopycnal diffusivity coefficients (Danabasoglu and Marshall, 2007), modified
anisotropic horizontal viscosity coefficients with much lower magnitudes than in CCSM3
(Jochum et al., 2008); and modified K-Profile Parameterization that uses horizontallyvarying background vertical diffusivity and viscosity coefficients (Jochum, 2009). The
number of vertical levels has been increased from 40 levels in CCSM3 to 60 levels in
CCSM4. It is run on a displaced pole grid with average horizontal resolution of 1.125°longitude x 0.55°-latitude in midlatitudes. To separate errors originated in the ocean
component from those in the coupled system we also use a stand alone POP2/NYF
simulation run on the same grid and forced by repeating Normal Year Forcing (NYF)
fluxes of Large and Yeager (2009), (c40.t62x1.verif.01).
To explore impacts of the horizontal resolution of the ocean model component, the
CCSM4 data are compared to the Simple Ocean Data Assimilation (SODA) of Carton
and Giese (2008) with atmospheric forcing from the Twentieth Century Reanalysis
Project version 2 of Compo et al. (2011). SODA employs the same ocean model (POP2)
but is run on higher horizontal resolution grid with an average resolution of
0.25°x0.4°x40 levels with 10-m spacing near the surface. In the assimilation (SODA
2.2.4)2, a forecast produced by ocean model is corrected by contemporaneous
observations and is forced by. A parallel run, SODA_simul (SODA 2.2.0), is run on the
same grid and is forced with identical surface boundary conditions, but without data
assimilation.
For each variable the model biases are evaluated for each calendar month as the difference
between model and observed climatology. A number of observation data are used for
comparisons with the CCSM4 simulations. In choosing observations we require (if
possible) a minimum 10 year records in order to estimate a stable seasonal cycle. Our SST
data is the Reynolds et al. (2002) optimal interpolation version 2 analysis spanning late 1981
- onward. Observed 10m neutral winds are available from the QuikSCAT scatterometer (e.g.
Liu, 2002) during mid-1999-2009. For shortwave radiation (SWR) we rely on retrievals from
the Moderate Resolution Imaging Spectro-radiometer (Pinker et al., 2009), which is
available on a 1°x1° grid since mid-2002. Latent heat flux is based on the recent update of
Institut Francais pour la Recherche et l’Exploitation de la Mer (IFREMER) weekly
satellite-based turbulent fluxes of Bentamy et al. (2003, 2008) spanning 1992-2008.
Precipitation is provided by the Climate Prediction Center Merged Analysis of
Precipitation (CMAP) of Xie and Arkin (1997), which covers the period 1979 -present.
Mean sea level pressure (MSLP) is the European Center for Medium Range Weather
Forecasts ERA-40 reanalysis (Uppala et al., 2005) monthly means spanning the period
from 1958 through 2001. In-situ measurements from the PIRATA moorings in the
tropical Atlantic (Bourles et al., 2008) are also used for comparisons.
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 have
2
http://soda.tamu.edu/data.htm
5
mostly symmetric pattern (trade winds are accelerated north and south of the Equator)
while the SST errors resemble a pattern of the Atlantic meridional mode (cold north and
warm south). This dipole-like SST error pattern suggests in turn that errors in the coupled
model climate may be further amplified by projecting on the natural mode of variability
inherent to the Atlantic (Chang et al., 2007). We next examine the reasons for the dipolelike pattern of SST errors and its link with deficiencies in the atmospheric and oceanic
components of the coupled model.
Mean sea level pressure and surface winds. Over the off-equatorial latitudes, the
excessive subtropical high pressure systems circle the globe (Fig. 1). The anomalous
pressure pattern originates from imperfections in the atmospheric module that is evident
by apparent similarity of the time mean MSLP bias in CCSM4 and its atmospheric
module forced by observed SST (CAM4/AMIP, compare Figs. 1a, 1b). Errors in the
pressure fields are inherited by CAM4 from previous versions of the atmospheric
module. In particular, similar pattern of anomalously strong subtropical high pressure
systems is present in CCSM3 and its atmospheric module (CAM3/AMIP) (Figs. 1c, 1d).
The CCSM4 subtropical high systems have larger errors in the Atlantic sector where
MSLP high reaches 4-5 mbar above the normal in the north and south. In spite of these
errors, the MSLP biases in CCSM4 have been improved in comparison with CCSM3.
This improvement is particularly evident in the North Atlantic where the region of above
normal MSLP has smaller spatial extension and is lower in magnitude (comp. Figs. 1a,
1c). Improvements in MSLP are noticeable in the Northern Hemisphere while they are
less evident in the Southern Hemisphere. Moreover, the MSLP bias in the South Atlantic
in CCSM4 and CAM4/AMIP is stronger than in corresponding version 3 runs.
Anomalously high MSLP in subtropical high systems along with anomalously low MSLP
in the polar low systems result in anomalously strong meridional pressure gradient (Fig.
1), thus the middle-high latitudes zonal winds.
Persistent enhancement of the subtropical high and polar low pressure systems in both,
CCSM4 and CAM4 is reflected in simulated wind stress (Fig. 2). In fact, all global scale
wind systems are anomalously strong including north and south mid-latitude westerly
winds and northeasterly and southeasterly trade winds. The anomalously strong
atmospheric circulation affects the ocean in various ways. But, like in CCSM3, the
strength of the wind-driven gyres and interbasin exchange is in reasonable agreement
with observations, despite the generally too strong near-surface winds (Large and
Danabasoglu, 2006).
Above normal wind speed produces stronger evaporation and mixing, and thus colder
SST. But, like in CCSM3 and CAM3, the centers of the Atlantic subtropical high
pressure systems of both CCSM4 and CAM4 are slightly shifted poleward (see Chang et
al., 2007 for version 3 analysis), which is seen in the negative bias of MSLP off the West
Africa coast (Fig. 1b). This shift causes the poleward wind stress bias of 0.01 to 0.02 Nm2
(~ 1 ms-1) along the West Africa coast in the upwelling regions at 10°N-20°N and 10°S20°S, which in turn may result in warming of SST due to reduced coastal upwelling.
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SST In spite of anomalously strong trade winds in the north and south, the SST errors in
the Atlantic sector are asymmetrical with respect to the Equator (Fig. 3). In the northern
tropics the time-mean SST is too cool by 1-2°C. This cooling is consistent with
anomalously strong northern trade wind and evaporative heat loss. Similar cooling of
SST is present in the south. But, in distinction from the north where the cold SST error
stretches across the entire basin, the cold SST error in the south is limited to the west,
though stronger than normal winds are basin-wide there (Fig. 2). Despite stronger winds,
the SST has a warm bias in the southeastern tropical Atlantic. Possible reasons for this
erroneous warming are errors in the surface heat flux and wind forcing seen by the ocean
component as well as errors of the ocean component itself. As discussed in the
Introduction, a number of wind errors affect SST in the southeastern tropical Atlantic.
These include below normal local upwelling (southerly) along shore winds or remote
impacts from the equatorial Atlantic initiated by below normal equatorial easterly winds
and anomalous deepening of equatorial thermocline. But the warm SST error along the
southwestern coast of Africa is also present in the ocean component, POP/NYF, forced
by observed surface flux climatology (Fig. 3). This in turn suggests that ocean component
contributes to SST bias in the southeastern Atlantic as well. The meridional dipole-like
SST bias is persistent in all seasons (Fig. 4). Its seasonal variations are more noticeable in
the south where the warm SST bias in CCSM4 peaks in JJA and minimizes in DJF in
general correspondence with the seasonal cycle of the warm bias in the ocean component
forced by NYF.
MSLP and SST In the Atlantic, the off-tropical MSLP bias in CCSM4 and
CAM4/AMIP increase poleward representing the above normal subtropical high pressure
systems (Figs. 4, 1). In the southeastern tropics anomalously low off-coast pressure is
noticeable in CAM4/AMIP during austral spring and summer. This anomalously low
pressure is due to the poleward shift of subtropical highs Chang et al. (2007). But, in the
coupled mode the bias in off-shore pressure low is stronger in response to the warm SST
bias. As the warm SST bias grows and expands in MAM, the southeastern Atlantic
pressure low bias extends toward the equator in JJA and thus decreases the eastward
equatorial zonal air pressure gradient (e.g. Lindzen and Nigam, 1987) that further warms
equatorial SSTs in the east by attenuating the equatorial upwelling.
Anomalously weak equatorial zonal pressure gradient in previous version of the coupled
model (CCSM3) has been attributed to deficient/excessive rainfall over the
Amazon/equatorial Africa, respectively, and the impact of anomalous rainfall on
atmospheric pressure and the Atlantic Walker cell through the diabatic heating (Chang et
al., 2007; Richter and Xie, 2008). Equatorial zonal winds are governed by zonal
distribution of air pressure along the equator. So we focus on MSLP in the 5S-5N belt
over the Atlantic. From Fig. 4 we see that anomalous westward MSLP gradient over the
equatorial Atlantic originates mostly over the ocean where warm/cold SST errors
correspond to below/above normal MSLP, respectively. These biases in SST and MSLP
may amplify each other via the Bjerkness feedback. In contrast with the westward
anomalous pressure gradient over the equatorial Atlantic Ocean, the anomalous zonal
equatorial pressure gradient between adjacent land masses is opposite (eastward) and is
defined mostly by above normal MSLP over the equatorial Africa (Fig. 4).
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MSLP bias in the equatorial Atlantic Ocean is negatively correlated with local SST bias
(Fig. 5), so that warm SST errors correspond to below normal MSLP (see also Fig. 4). In
CCSM4, the ratio of MSLP bias and SST bias is around -0.5mbar/oC. The westward
MSLP bias is also present in the equatorial Atlantic in CAM/AMIP simulations (Fig.6)
but has weak values (of the order of 0.3 mbar across the basin) in both, CAM3 and
CAM4. This westward MSLP bias grows in coupled simulations due to the Bjerkness
feedback from the cold west – warm east SST bias. In fact, it is significantly stronger in
coupled simulations. Time mean anomalous MSLP difference between 40oW and 0oE is
about 0.9 mbar and 1.7 mbar in CCSM4 and CCSM3, respectively. The anomalous
westward pressure gradient in the equatorial Atlantic sector locates over the ocean, and
thus results more likely from local atmosphreric response to anomalous eastward SST
gradient rather than is produced by atmosphere-land interactions.
Equatorial zonal winds and SST Observed zonal winds along the equator are easterly
but east of 0E 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 primarily and
secondary SST cooling in the east in July-August and November-December, respectively
(e.g. Okumara 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. In CAM4/AMIP the equatorial easterly between
40W and 15W is anomalously weak by about 1 m/s in February-April but have valid
direction (Fig. 7c). This is noticeable improvement in comparison with CAM3/AMIP
(see Chang et al., 2007), in which zonal winds were reversed during boreal spring. Even
in the coupled mode the equatorial zonal winds are noticeably improved in comparison
with CCSM3. Although in boreal spring they are abnormally attenuated and switch to
westerly east of 35oW, the westerly wind speed (less than 1 m/s) is weaker than that in
CCSM3 (> 3m/s, compare with Fig. 2c in Chang et al., 2007).
Regardless of notorious improvement in the equatorial zonal winds in comparison with
CCSM3, their bias is still significant in CCSM4 (Fig. 7b). The strongest zonal wind bias
occurs in May (exceeding 5 m/s at 30oW) and results from a delay in the ITCZ northward
march and related equatorial wind strengthening. The strengthening of equatorial easterly
takes place in April in both observations and CAM4/AMIP while it is delayed by 1
month and takes place in May in CCSM4 (Figs. 7a-7c). This delay is forced by
anomalous southward SST gradient permanently present in the coupled simulation (Figs.
3, 4). Like in the naturally occurring meridional mode (e.g. Xie and Carton, 2004), the
anomalous air pressure field associated with SST bias shifts the ITCZ to the south. This,
in turn results in anomalous weakening of equatorial easterly during boreal spring and its
delayed restoration. Similar to physics involved in the zonal mode (e.g. Xie and Carton,
2004), anomalous westerly in May reduces the westward gradient of the sea level,
deepens the thermocline in the east reducing local upwelling cooling and producing
anomalously warm (>2.5oC) cold tongue in May-August. The ocean component also
contributes to the warm equatorial SST bias (up to 1oC, Fig. 7c), but the SST bias in
CCSM4 is stronger in magnitude and spatial extension.
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Coastal winds The warm SST bias in CCSM4 extends from the central and eastern
equatorial region into the southeastern tropical Atlantic. Along the coast of South Africa,
where the water temperature is impacted by local upwelling, at least a part of the warm
SST bias may be produced by below normal southerly (upwelling) wind. Winds along the
western coast of South Africa have permanent southerly component reflecting
anticyclonic atmospheric circulation around the South Atlantic subtropical pressure high.
Southerly winds are normally stronger/weaker south/north of the Benguela region and are
amplified over the warm sector of the Angola-Benguela temperature front (Fig. 7d). All
these features of alongshore winds are simulated qualitatively valid in both CAM4/AMIP
and CCSM4 (Figs. 7e, 7f). But, CCSM4 SST has significant warm bias along the coast
that peaks in the Benguela region between 20oS and 13oS. In this region the CCSM4 SST
bias has semiannual harmonics and maximizes twice a year in austral summer and winter
reaching 5oC in both seasons (Fig. 8, see also Figs. 7e, 4). But there are noticeable
differences between the seasonal variations of biases in SST and meridional wind (Fig.
8). In both, CCSM4 and CAM4/AMIP, the time mean southerly winds are below normal
in the Benguela region. Wind bias varies mostly annually and peaks in austral summer to
fall (October through April) when upwelling winds are weaker than normal by about 2
m/s. Anomalous weakening of upwelling winds in austral summer and fall is in phase
with the secondary seasonal amplification of warm SST bias in CCSM4. But the
strongest SST bias in CCSM4 occurs in austral winter when the upwelling winds are
close to normal. Noticeably, the SST bias in the uncoupled ocean run (POP/NYF) varies
mostly annually. Like in CCSM4, it peaks in austral winter but is half that strong (up to
2.5oC, Figs. 7f, 8). This suggests that at least a part of the warm Benguela SST bias
originates in the ocean component due to biases in the meridional heat transport.
Coastal currents The Angola-Benguela temperature front locates at about 17.5°S and is
maintained by the confluence of the two meridionally oriented eastern boundary currents.
Warm water is transported from the north by the Angola Current while the cold water is
transported from the south by the Benguela current (e.g. Rouault et al., 2007). This
converging current system is reasonably well represented (see Figs. 9a, 9b) by the ocean
data assimilation SODA. This is evident by only minor regional SST bias (Figs. 9e, 9f)3.
Meridional currents and temperature from SODA are in contrast with CCSM4 and
POP/NYF that use the same ocean model but on coarser grid with an average resolution
of 1.125°x0.55°x60 levels.4 Decrease in horizontal resolution has apparent impact on the
eastern boundary currents (Fig. 9c, 9d). In particular, the southward Angola Current that
originates at around 5S is weak and shifted southward while the coastal jet of cold
Benguela Current is replaced by a broad northward flow.
Biases in coastal circulation present in CCSM4 and POP/NYF along the southwestern
coast of Africa produce related biases in the upper ocean heat budget of the Benguela
region5. In SODA_simul, the maximum rate of cooling occurs in April-June following
3
SODA_simul has a weak relaxation of SST to Reynolds et al. (2002), to which the SST bias is referenced
to.
4
SODA and POP/NYF also use different forcing. We believe this difference has minor effect.
5
Note, that seasonal heat budget reflects amplitude and phase of the seasonal cycle rather than magnitude
of water temperature bias.
9
the maximum in entrainment cooling in March-April (Fig. 10a). In CCSM4, the
maximum cooling rate occurs approximately one season later in phase with the maximum
entrainment cooling in July (Fig. 10b). Despite weaker than normal southerly winds, the
time mean entrainment cooling in CCSM4 is stronger than that in SODA_simul. The
entrainment cooling in CCSM4 amplifies in May-September when the bias in the local
southerly winds weakens (Figs. 10b, 8). Shift in the seasonal phase of the maximum
cooling rate results in related shift in the seasonal phase of Benguela Nino that is
observed in austral fall but occurs 3 months later in CCSM4.
But, the strongest impact on SST comes from the differences in the meridional currents.
In SODA_simul, the meridional currents provide cooling to the Benguela region during
all seasons except in austral spring (Fig. 10a). In CCSM4, the meridional heat transport in
the region is positive and has minor seasonal variations (Fig. 10b) reflecting missing
northward coastal flow of Benguela current that is replaced by the time mean southward
flow across the Benguela region (Fig. 9c). These biases in the eastern boundary currents
and their meridional heat transport result in stretching of SST front and warming of SSTs
at latitudes where cold water transported by Benguela Current is normally present (Fig.
9g).
In CCSM4 the warm SST bias along the coast of Southwestern Africa is amplified and
expanded into the open ocean (Fig. 9e) via positive feedback from marine stratocumulus
clouds. This positive feedback acts through radiative shading (Mechoso et al., 1995) as
well as dynamically through further weakening of coastal winds (Nigam, 1997). The
crucial role of coastal water temperature bias is confirmed by Large and Danabasoglu
(2006). If coastal water temperature is set close to normal, this improves temperature
fields across entire southeastern tropical Atlantic.
SWR One of the reasons for the spatial expansion of the warm bias in coupled models is
the positive feedback between SST, clouds, and solar radiation, by which SST anomaly is
amplified and progresses seaward in the direction of southeasterly trades (Mechoso et al.,
1995; Huang and Hu, 2007). But, even with ‘perfect’ SST the atmospheric component
simulates an excessive SWR over the southeastern tropical Atlantic (Fig. 11). In CCSM4,
the SWR bias is similar to that in CAM4/AMIP (except in the equatorial region where the
anomalous southward shift of ITCZ contributes). Over the southeastern tropical Atlantic
the SWR is biased high by at least 20 W/m^2 increasing locally to 60 W/m^2 in austral
winter and spring when local SST is cold. This regional excess of solar radiation is
compensated for in part by above normal latent heat loss due to anomalously strong
southeasterly trade winds (Fig. 2).
The magnitude and seasonal changes in SWR bias is confirmed by comparison with insitu measurements at the PIRATA 10S, 10W mooring (Fig. 12). At this location the
downwelling solar radiation in CCSM4 exceeds observations by 60 W/m^2 in August
and at least by 20 W/m^2 during June-January. But, the time mean excess of SWR of 33
W/m^2 is almost compensated for by the time mean excess of latent heat loss of
30W/m^2 (Fig. 13).
10
Ocean Salinity Misrepresentation of the upper ocean salinity impacts the SST, hence the
air-sea interactions. The strongest salinity impact is achieved for fresh bias in SSS that
produces barrier layers and decreases the thermal inertia of the mixed layer bottomed by
salinity stratified layer. Precipitation biases in the tropical Atlantic are mostly produced
by the coupled response. Both, observations and CAM4/AMIP precipitation have similar
patterns and magnitude over the ocean (Figs 14a, 14c). Time mean freshwater falling on
the ocean within 2mm/dy contour is around 4 km^3/dy (observations) and around 3
km^3/dy in CAM4/AMIP. In contrast, the CCSM4 precipitation is stretched into southern
latitudes due to abnormal southward shift of the ITCZ and the double ITCZ problem (Fig.
15b). But the time mean freshwater falling on the ocean within 2mm/dy contour is close
to observations in CCSM4 (3.8 km^3/dy).
The excess precipitation over south tropical Atlantic is augmented by remote contribution
from the excess precipitation over the Congo River basin, much of which is discharged
into the coastal southeastern tropical Atlantic. Like in CCSM3 (Large and Danasaboglu,
2006), the CCSM4 Congo discharge more than doubles the observed freshwater flux of
Large and Yeager (2009) while the Amazon discharge is closer to observations but still
below normal (Fig. 15). Above normal Congo runoff produces the fresh anomaly
spanning much of the Gulf of Guinea (Fig. 10b). Anomalously fresh water is diverged off
the equator by wind driven currents, while in the equatorial belt the SSS is kept saltier by
the vertical exchange with the EUC. Fresh water bias present south of the equator is
further transported into the south subtropical gyre by the South Equatorial Current. This
results in lowering of the south subtropical salinity maximum by 1 psu.
Summary
This paper focuses on regional biases in the tropical Atlantic sector present in
approximately 100 yr long records (model years 863-959) of the control CCSM4 run.
This research shows that local and remote biases in both, atmospheric and ocean
component of the coupled model contribute.
Like in previous version of the coupled model (CCSM3), the atmospheric component of
CCSM4 (CAM4) has abnormally high MSLPs in the subtropical pressure highs and
abnormally low MSLPs in the polar lows by a few mbar, even if it forced by observed
SST (CAM4/AMIP). As a result of these MSLP biases, all global scale winds produced
by the atmospheric component are accelerated. Wind stress in the global trade winds is
approximately 0.05 N/m^2 (~2 m/s) stronger, including the Atlantic sector. Similar wind
bias is present in CCSM4.
In spite of anomalously strong trade winds in the north and south, the SST error in the
tropical Atlantic changes sign across the Equator. In the northern tropics the time-mean
SST is colder than normal by 1-2°C in line with anomalously strong northeasterly trades
and local evaporation. Similar cooling of SST is present in the south. But, in distinction
from the north where the cold SST error is present across the entire basin, the cold SST
error in the south is limited to the west, though stronger than normal winds are basinwide there. Despite these stronger winds, the SST has a warm bias in the southeastern
11
tropical Atlantic. This dipole-like SST error pattern suggests in turn that errors in the
coupled model climate may be further amplified by projecting on the natural mode of
variability inherent to the Atlantic.
By comparing POP2 0.25°x0.4° ocean simulations with simulations on a 1.125°x0.55°
grid (CCSM4 ocean grid) we have found that decrease in horizontal resolution (below the
local Rossby radius of deformation) has apparent impact on the eastern boundary
currents. In particular, the southward Angola Current that originates at around 5°S is
shifted southward while the coastal jet of cold Benguela Current is replaced by a broad
northward flow on CCSM4 grid. These biases in the eastern boundary currents and their
meridional heat transport result in stretching of SST front and warming of SSTs at
latitudes where cold water transported by Benguela Current is normally present. In
CCSM4 the warm SST bias along the coast of Southwestern Africa (originated in the
ocean component) is amplified and expanded into the open ocean via positive feedback
from marine stratocumulus clouds as well as by regional bias in incoming solar radiation.
This study suggests that smaller biases in coupled simulations of the tropical Atlantic
may be achieved primarily via addressing the problem of large scale pressure fields in the
atmospheric component and by more accurate simulation of the coastal circulation in the
ocean component. This latter issue needs usage of irregular grids with locally enhanced
resolution in the eastern and western boundaries.
Acknowledgements This research was supported by the NOAA/CPO and NASA Ocean
Programs.
References
Balaguru, K., P. Chang, and R. Saravanan, 2010: Barrier layers in the Atlantic warmpool
- Formation and influence on climate at various time-scales, Tropical Atlantic and
PIRATA-15 meeting 2-5 March 2010, Miami, Florida,
http://www.aoml.noaa.gov/phod/pne/pirata15/karthik.pdf
Bentamy, A., K.B. Katsaros, A.M. Mestas-Nuñez, W.M. Drennan, E.B. Forde, and H.
Roquet, 2003: Satellite Estimates of Wind Speed and Latent Heat Flux over the
Global Oceans. J. Climate, 16, 637–656.
Bentamy, A., L-H. Ayina, W. Drennan, K. Katsaros, A. M. Mestas-Nuñez, R. T. Pinker,
2008: 15 Years of Ocean Surface Momentum and heat Fluxes from Remotely
Sensed Observations, FLUXNEWS, 5, 14-16. Available online at
http://sail.msk.ru/newsletter/fluxnews_5_final.pdf
Bourlès, B., R. Lumpkin, M.J. McPhaden, F. Hernandez, P. Nobre, E. Campos, L. Yu, S.
Planton, A. Busalacchi, A.D. Moura, J. Servain, and J. Trotte, 2008: The Pirata
Program: History, Accomplishments, and Future Directions. Bull. Amer. Meteor.
Soc., 89, 1111–1125.
Breugem, WP, P.Chang, C.J. Jang, J. Mignot, and W. Hazeleger, 2008: Barrier layers and
tropical Atlantic SST biases in coupled GCMs. Tellus, 60A:885–897.
Carton, J.A., and B.S. Giese, 2008: A reanalysis of ocean climate using Simple Ocean
Data Assimilation (SODA), Monthly Weather Rev., 136, 2999-3017.
12
Chang CY, Carton JA, Grodsky SA, Nigam S (2007) Seasonal climate of the tropical
Atlantic sector in the NCAR Community Climate System Model 3: error structure
and probable causes of errors. J Clim 20:1053–1070
Chang CY, Nigam S, Carton JA (2008) Origin of the springtime westerly bias in
equatorial Atlantic surface winds in the Community Atmosphere Model version 3
(CAM3) simulation. J Clim 21:4766-4778
Danabasoglu, G. and J. Marshall, 2007: Effects of vertical variations of thickness
diffusivity in an ocean general circulation model. Ocean Modelling, 18, 122–141,
doi:10.1016/j.ocemod.2007.03.006.
Compo, G.P., and coauthors. 2011. The Twentieth Century Reanalysis Project. Q. J. R.
Meteorol. Soc. 137, 1–28, DOI:10.1002/qj.776.
Davey, M., M. Huddleston, K.R. Sperber et al., 2002: STOIC: A study of coupled model
climatology and variability in tropical ocean regions. Clim. Dyn., 18, 403-420.
Deser, C., A. Capotondi, R. Saravanan, and A. Phillips, 2006: Tropical Pacific and
Atlantic Climate Variability in CCSM3. J. Climate, 19, 2451-2481.
De Szoeke, S., C. Fairall, D. Wolfe, L. Bariteau, and P. Zuidema, 2010: Surface flux
observations on the southeastern tropical Pacific Ocean and attribution of SST
errors in coupled ocean-atmosphere models. J. Clim., 23, pp. 4152-4174.
Huang, B., and Z.Z. Hu (2007), Cloud SST feedback in southeastern tropical Atlantic
anomalous events, J. Geophys. Res., 112, C03015, doi:10.1029/2006JC003626.
Fairall, C.W., E.F. Bradley, J.E. Hare, A.A. Grachev, and J.B. Edson, 2003: Bulk
Parameterization of Air–Sea Fluxes: Updates and Verification for the COARE
Algorithm. J. Climate, 16, 571–591.
Ferrari, R., J. McWilliams, V. Canuto, and M. Dubovikov, 2008: Parameterization of
eddy fluxes near oceanic boundaries. J. Climate, 21, 2770–2789,
doi:10.1175/2007JCLI1510.1.
Florenchie, P., J.R.E Lutjeharms, C.J.C. Reason, S.Masson, and M.Rouault, The source
of benguela Ninos in the South Atlantic Ocean, Geoph. Res. Lett., 30, 1505,
doi:10.1029/2003GL017172, 2003.
Giese, B. S., N. C. Slowey, S. Ray, G. P. Compo, P. D. Sardeshmukh, J. A. Carton, J. S.
Whitaker, 2010: The 1918/19 El Niño. Bull. Amer. Meteor. Soc., 91, 177–183.
Jochum, M., G. Danabasoglu, M. Holland, Y.-O. Kwon, and W. Large, 2008: Ocean
viscosity and climate. J. Geophys. Res., 113, C06 017, doi:10.1029/2007JC004
515.
Jochum, M., 2009: Simulated climate impacts of latitudinal variations in diapycnal
diffusivity. J. Geophys. Res., 114, C01 010, doi:10.1029/2008JC005 030.
Large, W.G. and G. Danabasoglu, 2006: Attribution and Impacts of Upper Ocean Biases
in CCSM3. J. Climate, 19, 2325-2346.
Large, W. G., and S. G. Yeager, 2009: The Global Climatology of an Interannually
Varying Air-Sea Flux Data Set. Clim. Dyn., 33, 341-364.
Lindzen, R.S., and S. Nigam, 1987: On the role of sea surface temperature gradients in
forcing low-level winds and convergence in the tropics, J. Atmos. Sci., 44, 24182436.
Liu, W.T., 2002: Progress in scatterometer application. J. Oceanogr., 58(1), 121-136.
13
Mechoso, C. R., et al. (1995), The seasonal cycle over the tropical Pacific in coupled
ocean-atmosphere general circulation models, Mon. Weather Rev., 123, 2825–
2838.
Neale, R. B., J. H. Richter, and M. Jochum, 2008: The impact of convection on ENSO:
From a delayed oscillator to a series of events, J. Climate, 21, 5904–5924.
Nigam, S., 1997: The annual warm to cold phase transition in the eastern equatorial
Pacific: Diagnosis of the role of stratus cloud-top cooling Source, J. Clim., 10,
2447 -2467.
Okumura,Y., and S.P. Xie (2006), Some Overlooked Features of Tropical Atlantic
Climate Leading to a New Niño-Like Phenomenon. J. Climate, 19, 5859–5874
Pinker, R. T., H. Wang, and S. A. Grodsky (2009), How good are ocean buoy
observations of radiative fluxes?, Geophys. Res. Lett., 36, L10811,
doi:10.1029/2009GL037840.
Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes and W. Wang, 2002: An
improved in situ and satellite SST analysis for climate. J. Climate, 15, 1609-1625.
Richter, I., and S.P. Xie (2008), On the origin of equatorial Atlantic biases in coupled
general circulation models, Clim. Dyn., 31, 587–598, doi:10.1007/s00382-0080364-z.
Richter, I., S. K. Behera, Y. Masumoto, B. Taguchi, N. Komori, and T. Yamagata
(2010a), On the triggering of Benguela Niños: Remote equatorial versus local
influences, Geophys. Res. Lett., 37, L20604, doi:10.1029/2010GL044461.
Richter, I., S.P. Xie, A.T. Wittenberg, and Y. Masumoto, 2010b: Tropical Atlantic biases
and their relation to surface wind stress and terrestrial precipitation, Clim. Dyn.,
subm.
Rouault, M., S. Illig, C Bartholomae, C.J.C. Reason, and A. Bentamy, 2007: Propagation
and origin of warm anomalies in the Angola Benguela upwelling system in 2001,
J. Mar. Systems, 68 ,473-488, DOI: 10.1016/j.jmarsys.2006.11.010.
Smith, R., and Coauthors, 2010: The Parallel Ocean Program (POP) Reference Manual
Ocean Component of the Community Climate System Model (CCSM) and
Community Earth System Model (CESM), Los Alamos National Laboratory,
LAUR-10-01853, 23, 140pp.
Stockdale, TN, MA Balmaseda, A Vidard (2006) Tropical Atlantic SST prediction with
coupled ocean-atmosphere GCMs. J Clim 19:6047-6061.
Uppala,S.M. and Coauthors, 2005: The ERA-40 re-analysis, Q. J. Royal Meteor. Soc.,
131, 2961-3012, DOI: 10.1256/qj.04.176
Wahl, S, M. Latif, W. Park, N. Keenlyside, 2011: On the Tropical Atlantic SST warm
bias in the Kiel Climate Model. Clim. Dyn., 36, 891-906, DOI 10.1007/s00382009-0690-9.
Xie, P., and P.A. Arkin, 1997: Global Precipitation: A 17-Year Monthly Analysis Based
on Gauge Observations, Satellite Estimates, and Numerical Model Outputs. Bull.
Amer. Meteor. Soc., 78, 2539–2558.
Xie, S.-P., and J.A. Carton, 2004: Tropical Atlantic variability: patterns, mechanisms, and
impacts, in “Ocean-Atmosphere Interaction and Climate Variability”, edited by C.
Wang, S.-P. Xie, and J. A. Carton, AGU Press.
Zeng, N., R. E. Dickinson, and X. Zeng, 1996: Climatic impact of Amazon deforestation-a mechanistic model study. J. Climate, 9, 859-883.
14
Zuidema, P., D. Painemal, S. de Szoeke and C. Fairall, 2009: Stratocumulus cloud top
height estimates and their climatic implications. J. Clim., 22, pp. 4652-4666.
15
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.
16
Figure 2. Time and zonal mean TAUX from satellite observations (QuikSCAT
scatterometer), in CCSM4, and its atmospheric component forced by observed SST
(CAM4/AMIP). Zonal average is taken over the ocean only.
17
Figure 3. Time mean SST bias in CCSM4 and its ocean component forced by the normal
year fluxes (POP/NYF).
18
Figure 4. Bias in SST (degC, shading) and MSLP (mbar, contours) during four seasons.
Left column is CCSM4. Right column presents data from two independent runs: SST is
from a stand alone ocean model forced by the normal year fluxes (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).
19
Figure 5. Scatter diagram of time mean biases in MSLP and SST over the equatorial
Atlantic Ocean (5S-5N). Each symbol represents grid point value.
20
Figure 6. Time mean MSLP bias 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).
21
Figure 7. Observed (a) zonal wind along the Equator and (b) meridional wind along the
western coast of South Africa (CINT=1m/s). (b,e) CCSM4 SST bias (shading), winds
(black contours), and wind bias (red contour). Wind bias is shown for the equatorial zonal
winds only (negative-dashed, positive-solid, CINT=1 m/s, zero contour is not shown).
(c,f) The same as in (b,e) but for the two independent runs: CAM4/AMIP winds, and
POP/NYF SST.
22
Figure 8. Seasonal cycle of SST bias and meridional wind (V) bias spatially averaged
over the Benguela region (10E-shore, 20S-13S, see Fig. 5c).
23
Figure 9. Time mean surface meridional currents (shading) and SST (contours) in (a)
SODA assimilation, (b) SODA simulation, (c) CCSM4, and (d) stand alone ocean model
component forced by the Normal Year Forcing (POP/NYF). Benguela region box is
shown in panel (c). Bottom line presents SST bias for each run.
24
Figure 10. Heat budget in the upper 100m water column spatially averaged over the
Benguela region for (a) SODA simulations, (b) CCSM4. Shown are: heat content rate of
change (HCR), surface heat flux (SHF), and components of the vertically integrated heat
advection,  C p   v(T / y )dz ,  C p   u(T / x )dz ,  C p   w(T / z )dz .
25
Figure 11. Seasonal bias in downwellig short wave radiation in (left) CCSM4 and (right)
CAM4/AMIP. CINT=20 W/m^2, positive/negative values are shown by solid/dashed,
respectively. Zero contour is not shown. The PIRATA mooring 10W, 10S location is
marked by ‘+’.
26
Figure 12. Seasonal cycle of downwelling SWR (W/m^2) at 10W, 10S from MODIS
satellite retrievals, observed at the PIRATA mooring, and simulated by CCSM4 and
CAM4/AMIP.
Figure 13. Seasonal cycle of latent heat flux (LHTFL, W/m^2) at 10W, 10S from
IFREMER satellite retrievals, observed at the PIRATA mooring, and simulated by
CCSM4 and CAM4/AMIP. Observed LHTFL is calculated using the COARE3.0 of
Fairall et al. (2003) from observed air and water temperatures, humidity, and wind speed.
27
Figure 14. Time mean sea surface salinity (SSS, psu, shading) and precipitation (mm/dy,
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.
28
Figure 15. Time mean river runoff shown as equivalent surface freshwater flux
(mm/dy). (a) Normal year forcing of Large and Yeager (2009), (b) CCSM4.
29