Download Cloud Feedbacks Found to Amplify Global Warming

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VOLUME 23
The Impact of Global Warming on Marine Boundary Layer Clouds
over the Eastern Pacific—A Regional Model Study
AXEL LAUER
International Pacific Research Center, University of Hawaii at Manoa, Honolulu, Hawaii
KEVIN HAMILTON AND YUQING WANG
International Pacific Research Center, and Department of Meteorology, University of Hawaii at Manoa, Honolulu, Hawaii
VAUGHAN T. J. PHILLIPS
Department of Meteorology, University of Hawaii at Manoa, Honolulu, Hawaii
RALF BENNARTZ
Department of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, Madison, Wisconsin
(Manuscript received 9 February 2010, in final form 3 June 2010)
ABSTRACT
Cloud simulations and cloud–climate feedbacks in the tropical and subtropical eastern Pacific region in 16
state-of-the-art coupled global climate models (GCMs) and in the International Pacific Research Center
(IPRC) Regional Atmospheric Model (iRAM) are examined. The authors find that the simulation of the
present-day mean cloud climatology for this region in the GCMs is very poor and that the cloud–climate
feedbacks vary widely among the GCMs. By contrast, iRAM simulates mean clouds and interannual cloud
variations that are quite similar to those observed in this region. The model also simulates well the observed
relationship between lower-tropospheric stability (LTS) and low-level cloud amount.
To investigate cloud–climate feedbacks in iRAM, several global warming scenarios were run with boundary
conditions appropriate for late twenty-first-century conditions. All the global warming cases simulated with
iRAM show a distinct reduction in low-level cloud amount, particularly in the stratocumulus regime, resulting
in positive local feedback parameters in these regions in the range of 4–7 W m22 K21. Domain-averaged
(308S–308N, 1508–608W) feedback parameters from iRAM range between 11.8 and 11.9 W m22 K21. At
most locations both the LTS and cloud amount are altered in the global warming cases, but the changes in
these variables do not follow the empirical relationship found in the present-day experiments.
The cloud–climate feedback averaged over the same east Pacific region was also calculated from the Special
Report on Emissions Scenarios (SRES) A1B simulations for each of the 16 GCMs with results that varied
from 21.0 to 11.3 W m22 K21, all less than the values obtained in the comparable iRAM simulations. The
iRAM results by themselves cannot be connected definitively to global climate feedbacks; however, among
the global GCMs the cloud feedback in the full tropical–subtropical zone is correlated strongly with the east
Pacific cloud feedback, and the cloud feedback largely determines the global climate sensitivity. The present
iRAM results for cloud feedbacks in the east Pacific provide some support for the high end of current estimates of global climate sensitivity.
1. Introduction
Corresponding author address: Axel Lauer, IPRC/SOEST, University of Hawaii at Manoa, 1680 East-West Rd., POST Building
401, Honolulu, HI 96822.
E-mail: [email protected].
DOI: 10.1175/2010JCLI3666.1
Ó 2010 American Meteorological Society
State-of-the-art comprehensive global climate models
(GCMs) display a wide range of global and regional
sensitivity to imposed large-scale climate forcings. The
1 NOVEMBER 2010
LAUER ET AL.
equilibrium global surface temperature increase projected to result from a doubling of atmospheric CO2
concentration reported for the models in the latest Intergovernmental Panel on Climate Change (IPCC) intercomparison varies from 2.1 to 4.4 K. This range has
not narrowed appreciably compared to that found in
earlier model intercomparisons (e.g., Houghton et al.
2001). The variation in global climate sensitivity among
these GCMs is largely attributable to differences in cloud
feedbacks and feedbacks of low-level clouds in particular (e.g., Bony and Dufresne 2005; Stowasser et al. 2006;
Solomon et al. 2007; Medeiros et al. 2008).
Persistent stratocumulus decks found predominantly in
the subtropical eastern ocean margins have a major impact
on the radiation budget by reflecting incoming solar radiation (e.g., Randall et al. 1984; Hartmann and Doelling
1991). These clouds are prominent over cool ocean surfaces in regions where large-scale atmospheric subsidence
leads to the formation of sharp temperature inversions,
which trap moisture in the marine boundary layer (MBL;
e.g., Albrecht et al. 1988). The simulation of these marine
clouds has been a particular challenge for global and regional models (e.g., Bretherton et al. 2004; Wang et al.
2004a,b). This results in a particularly high uncertainty of
the climate feedback of these low-level marine clouds (e.g.,
Bony and Dufresne 2005; Solomon et al. 2007). Medeiros
et al. (2008) showed that trade wind cumuli might also play
an important role; they are also not well captured by global
climate models (Medeiros and Stevens 2009).
The radiative effect of low marine clouds is dominated
by their contribution to the planetary albedo as their
impact on outgoing longwave radiation is limited because of the small temperature difference between cloud
tops and the underlying surface. The cloud optical depth
for these low-level clouds is proportional to cloud geometrical thickness, the liquid water content (LWC), and
the size of the cloud droplets. There have been attempts
to use empirical guidance to determine how these basic
cloud properties, and hence albedo, may respond to
changes in large-scale climate.
The empirical relationship between cloud LWC and
temperature obtained by Feigelson (1978) from aircraft
measurements of midlatitude clouds for the temperature range between 2258 and 58C predicts an increase
in cloud water with temperature. Somerville and Remer
(1984) noted that if a constant cloud geometrical thickness for marine stratocumulus decks is assumed, a negative cloud–climate feedback would be implied (i.e.,
warmer temperatures would lead to more reflective
clouds). On the other hand, studies analyzing satellite
data from the International Satellite Cloud Climatology
Project (ISCCP), the Advanced Very High Resolution
Radiometer (AVHRR), and the Clouds and the Earth’s
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Radiant Energy System (CERES) indicate that cloud
optical depth of low marine clouds might be expected to
decrease with increasing temperature (Tselioudis et al.
1992; Greenwald et al. 1995; Chang and Coakley 2007;
Eitzen et al. 2008). This suggests a positive shortwave
cloud–climate feedback for marine stratocumulus decks.
In a recent paper, Clement et al. (2009) analyzed several
decades of ship-based observations of cloud cover along
with more recent satellite observations, with a focus on
the northeastern (NE) Pacific between 158 and 258N.
They found that there is a negative correlation between
cloud cover and sea surface temperature (SST) apparent
on a long time scale—again suggesting a positive cloud–
climate feedback in this region.
Bony and Dufresne (2005) analyzed 17 yr of observed
SST, top of atmosphere (TOA) net radiative flux, and
500-hPa vertical velocity (v500) from satellite data and
reanalyses, respectively. Focusing only on the ocean regions between 308S and 308N, they scaled the anomalies
in monthly mean TOA fluxes with the coincident SST
anomalies. This analysis was done for grid points in
different dynamical regimes defined by the 500-hPa vertical velocity. They showed that the cloud feedback parameter defined this way is dominated by changes to the
shortwave flux and that the feedbacks are positive and in
the range of 0–6 W m22 K21. The Bony and Dufresne
estimate of this feedback is largest in the regions with
the strongest 500-hPa subsidence (corresponding to the
regions of low-level marine clouds). Of course, as emphasized by Bony and Dufresne, the cloud responses to
natural interannual variations of SST may differ from the
response expected to large-scale forced global warming.
Cloud feedbacks have also been assessed by a number
of modeling studies using a variety of models—ranging
from single-column radiative–convective equilibrium
models to GCMs with conventional cloud parameterizations (e.g., Somerville and Remer 1984; Xu et al. 2010;
Roeckner et al. 1987; Caldwell and Bretherton 2009;
Tselioudis et al. 1998; Zhang and Bretherton 2008) or to
GCMs with superparameterizations in which a cloudresolving model (CRM) is run within each GCM column
(e.g., Wyant et al. 2009). However, state-of-the-art GCMs
display a wide range of simulated cloud–climate feedbacks in the marine stratocumulus regions. In addition,
such models generally do a poor job in simulating the
present-day climatology of marine stratocumulus clouds.
The dynamics of marine stratocumulus clouds involve
tightly coupled interactions among atmosphere, ocean,
and land making them extremely challenging for climate
models to capture (e.g., Bony and Dufresne 2005). Singlecolumn models and large-eddy simulations (LES) can
explicitly represent detailed cloud microphysics, but the
interaction between clouds and the large-scale atmospheric
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circulation in such models has to be prescribed or determined on the basis of simplified assumptions.
This paper describes a modeling study of the response
of clouds in the eastern tropical and subtropical Pacific
region to large-scale climate forcing. The eastern Pacific
region features extensive stratocumulus decks and the
cloud feedbacks in this region in climate models contribute significantly to the global mean feedbacks and
climate sensitivity (e.g., Stowasser et al. 2006). For this
investigation we used the version of the International
Pacific Research Center (IPRC) Regional Atmospheric
Model (iRAM) described in Lauer et al. (2009). Lauer
et al. (2009) also showed that iRAM is able to simulate
a reasonable present-day seasonal climatology of stratocumulus clouds in the eastern Pacific region. As part of
the present study we will show that iRAM simulates the
basic cloud climatology in the eastern Pacific better than
current GCMs. We will also show that iRAM successfully simulates the main features of the observed interannual variation of clouds in this region, including the
evolution of the clouds through the ENSO cycle.
Section 2 describes the regional climate model used in
this study and the details of the present-day and global
warming simulations. This is followed by a comparison
of modeled cloud properties over the east Pacific with
observations in section 3. Section 4 presents the results
of the cloud response to global warming. The conclusions
are summarized in section 5.
2. Model and model simulations
a. iRAM
IPRC iRAM is based on the hydrostatic primitive
equations and uses s coordinates in the vertical (Wang
et al. 2004a). All model simulations presented here were
conducted at a horizontal resolution of 0.58 3 0.58, with
the model domain covering the tropical and subtropical
eastern Pacific as well as large parts of South America
(408S–408N and 1608–508W). There are 28 model levels
from the surface up to about 10 hPa (;30 km) with 10
levels below 800 hPa. We used the final analysis data
(FNL) from the U.S. National Centers for Environmental Prediction [(NCEP); NCEP FNL Operational
Model Global Tropospheric Analyses, continuing from
July 1999, are updated daily. Dataset ds083.2 is published by the Computational and Information Systems
Laboratory (CISL) Data Support Section at the National
Center for Atmospheric Research (NCAR), Boulder,
Colorado, available online at http://dss.ucar.edu/datasets/
ds083.2/] as initial and lateral boundary conditions for
the model integrations. The FNL data with a horizontal
resolution of 18 3 18 and 26 vertical pressure levels
VOLUME 23
(NCEP–NCAR reanalysis with a horizontal resolution
of 2.58 3 2.58 and 17 pressure levels prior to the year
2000; Kalnay et al. 1996) at 6-h time intervals are interpolated linearly in time and using cubic splines to the
model grid. SSTs employed are the National Oceanic
and Atmospheric Administration (NOAA) analyses
(Reynolds et al. 2007), which are based on daily mean
satellite observations from AVHRR and the Advanced
Microwave Scanning Radiometer (AMSR) instruments.
The prognostic model variables are nudged to the NCEP
FNL analysis data within a 108 buffer zone along the
lateral boundaries. The buffer zone is not in the analyses
of the results shown here.
Grid-scale cloud processes are calculated using a
double-moment cloud microphysics scheme with a semiprognostic aerosol component considering six aerosol
species—sulfate, sea salt, soluble and insoluble organic
matter, black carbon, and mineral dust (Phillips et al.
2007, 2008, 2009)—which replaces the original singlemoment cloud microphysics module of Wang (2001). The
cloud microphysics scheme predicts the mass mixing
ratios of water vapor, cloud liquid water, cloud ice, rain,
snow, and graupel as well as the number mixing ratios
of cloud droplets and ice crystals. The size distributions
of cloud and precipitation particles are assumed to follow gamma distributions. Diffusional growth of cloud
particles and precipitation is predicted explicitly with
a linearized supersaturation scheme from the modeled
updraft and properties of cloud liquid and water vapor.
The predicted supersaturation is applied to calculate the
activation of aerosol particles at cloud base using the
aerosol activation scheme of Ming et al. (2006) or inside
the cloud when supersaturation becomes high enough.
Critical droplet diameters and supersaturations as well
as the equilibrium supersaturations of the droplets are
obtained from the k-Köhler theory using the hygroscopicity parameters k from Petters and Kreidenweis
(2007). The autoconversion of cloud droplets to rainwater is parameterized after Liu et al. (2007). We chose
the parameterization from Liu et al. (2007) over other
schemes as it results in improved agreement for our
regional model with a 50-km resolution between modeled and observed liquid water path (LWP) and cloud
cover. Primary and secondary ice nucleation (Hallet and
Mossop 1974), as well as homogeneous freezing of aerosols (Koop et al. 2000) and cloud droplets (Phillips et al.
2007), are included. All the known and empirically
quantified mechanisms for initiation of cloud droplets
and ice are represented (Phillips et al. 2007).
The cloud microphysics module is coupled to the
radiation scheme and provides effective radii of cloud
droplets and ice crystals as well as the liquid water and
ice content as input for the radiative transfer calculations.
1 NOVEMBER 2010
LAUER ET AL.
The radiation scheme is based on the radiation package
of Edwards and Slingo (1996) with improvements by
Sun and Rikus (1999). It considers four bands in the
solar spectral range and seven bands in the thermal
spectral range. Cloud amount is diagnosed from cloud
liquid water/ice content and relative humidity following
Xu and Randall (1996). Subgrid-scale convection including shallow, mid-level, and deep convection is parameterized following Tiedtke (1989) with modifications
by Gregory et al. (2000). The average entrainment rate
for shallow convection and relative mass flux at a level of
zero buoyancy (overshooting cumuli) have been adjusted
using results from large-eddy simulations (Wang et al.
2004a,b). Cloud water and cloud ice detrained at the
cloud tops are considered as an additional source of cloud
water/ice used by the grid-scale cloud microphysics (Wang
et al. 2003).
The iRAM results with double-moment cloud microphysics have been compared extensively to measurements from aircraft, ships, and satellites to evaluate the
model performance simulating clouds over the eastern
Pacific (Lauer et al. 2009). This evaluation showed that
the model is able to simulate average cloud properties
such as liquid water content, cloud droplet number concentration, cloud cover, and the radiative effect of clouds
[also referred to as cloud radiative forcing (CRF)] that
compare well with the observed climatology. Lauer et al.
(2009) also showed that the diurnal cycle of cloud liquid
water over the eastern Pacific is reasonably well simulated by iRAM.
For additional details on iRAM, we refer to Wang
(2001), Wang et al. (2004a), and the literature cited
therein. Additional details on the double-moment cloud
microphysics scheme and a model evaluation can be found
in Phillips et al. (2007, 2008, 2009) and Lauer et al. (2009),
respectively.
b. Model experiments
In the present study, we performed two kinds of experiments: a simulation of January 1997–December 2008
using observed SSTs and lateral boundary conditions
and a set of 10-yr integrations designed to simulate late
twenty-first-century conditions.
For the twenty-first-century experiments we apply what
has been termed the ‘‘pseudo-global-warming’’ (PGW)
method, which has been employed in other recent studies
to downscale global climate change projections using
a regional atmospheric model (Kimura and Kitoh 2007;
Sato et al. 2007; Knutsen et al. 2008). In the PGW
method, initial and lateral boundary conditions for the
model integration are given by the sum of 6-h reanalysis
data as used for the present-day experiment and a climate change signal based on results from a coupled global
5847
climate model (or an ensemble of such models). We
based the climate change signals used in iRAM on the
monthly averaged differences between present-day climate and projections for the end of the twenty-first
century made by GCMs included in the IPCC Fourth
Assessment Report (AR4). Specifically, the climate change
signal adopted here was computed as the difference in
10-yr means for each calendar month for the late twentieth century [1990–99 in the AR4 twentieth-century
forced runs (20C3M) and for the late twenty-first century
2090–99 in the Special Report of Emissions Scenarios
(SRES) A1B runs]. Data were obtained from the World
Climate Research Programme’s (WCRP’s) Coupled
Model Intercomparison Project phase 3 (CMIP3) data
archive (Meehl et al. 2007). Following the A1B scenario,
we increased the CO2 concentration in iRAM from
370 ppm used in the present-day run to 720 ppm. Here,
we only consider global warming perturbations of the
meteorological boundary conditions [i.e., temperature,
horizontal wind components, sea level pressure (SLP),
and humidity] and of the SST. Concentrations of trace
gases other than CO2 such as ozone or aerosols were not
changed in our global warming simulations and remained
at their present-day levels. Of course the global change
signals differ quite significantly among the CMIP–AR4
GCMs. We performed three 10-yr experiments using
1999–2008 as the base and adding different climate change
signals derived from the results of the CMIP–AR4 model
simulations:
1) IPCC AR4 ensemble mean (case A): The climate
change signal is averaged over all 19 AR4 models
(see Table 1) that provided all data needed for
specifying the climate change contribution to the
boundary conditions in iRAM. All model results are
interpolated to the 18 3 18 grid and 26 pressure
levels of the FNL data before averaging.
2) Canadian Centre for Climate Modelling and Analysis
(CCCma) Coupled General Circulation Model, version 3.1(T63) (CGCM3.1; case B): The climate change
signal is obtained from simulations with CGCM3.1
(McFarlane et al. 1992; Flato 2005) of CCCma.
Among the AR4 GCMs, the CGCM3.1 has one of
the higher global climate sensitivities and also has
a strong positive cloud–climate feedback over the
eastern Pacific (see Fig. 9 below).
3) NCAR Community Climate System Model, version
3 (CCSM3; case C): The climate change signal is
obtained from NCAR CCSM3 (Collins et al. 2006).
Among the AR4 GCMs, the CCSM3 has one of the
lower global climate sensitivities and also has a
negative cloud–climate feedback over the eastern
Pacific (see Fig. 9 below).
UKMO-HadGEM1
Parallel Climate Model version 1 (PCM1)
UKMO-HadCM3
IPSL-CM4
MIROC3.2 high-resolution version (hires)
Instituto Nazionale di Geofisica e Vulcanologia
SXG (INGV-SXG)
INM-CM3.0
GFDL Climate Model version 2.0 (GFDL-CM2.0)
GFDL-CM2.1
The Goddard Institute for Space Studies Model
E-H (GISS-EH)
GISS Model E-R (GISS-ER)
CSIRO-Mk3.0
CSIRO-Mk3.5
ECHAM5/MPI-OM
Flexible Global Ocean–Atmosphere–Land System
Model gridpoint version 1.0 (FGOALS-g1.0)
CNRM-CM3
CCSM3
Coupled General Circulation Model, version 2.3.2
(CGCM2.3.2)
CGCM3.1(T63)
Model
L’institut Pierre-Simon Laplace, France
Center for Climate System Research (University of Tokyo),
National Institute for Environmental Studies, and Frontier
Research Center for Global Change (JAMSTEC), Japan
NCAR, United States
Hadley Centre for Climate Prediction and Research/Met Office,
United Kingdom
Hadley Centre for Climate Prediction and Research/Met Office,
United Kingdom
Institute for Numerical Mathematics, Russia
Instituto Nazionale di Geofisica e Vulcanologia, Italy
1.38 3 1.98, L38
2.88 3 2.88, L26
2.58 3 3.758, L19
2.58 3 3.758, L19
1.18 3 1.18, L56
58 3 48, L21
1.18 3 1.18, L19
48 3 58, L20
2.08 3 2.58, L24
2.08 3 2.58, L24
48 3 58, L20
1.98 3 1.98, L18
1.98 3 1.98, L18
1.98 3 1.98, L31
2.88 3 2.88, L26
1.98 3 1.98, L45
1.98 3 1.98, L31
CCCma, Canada
Météo-France/Centre National de Recherches
Météorologiques, France
CSIRO Atmospheric Research, Australia
CSIRO Atmospheric Research, Australia
Max Planck Institute for Meteorology, Germany
National Key Laboratory of Numerical Modeling for
Atmospheric Sciences and Geophysical Fluid Dynamics
(LASG)–Institute of Atmospheric Physics, China
U.S. Department of Commerce/NOAA/GFDL, United States
U.S. Department of Commerce/NOAA/GFDL, United States
National Aeronautics and Space Administration (NASA)–GISS,
United States
NASA–GISS, United States
1.48 3 1.48, L26
2.88 3 2.88, L30
Resolution
(atmosphere)
NCAR, United States
Meteorological Research Institute (MRI), Japan
Host institute
TABLE 1. The IPCC AR4 models providing data for this study (WCRP CMIP3 multimodel dataset; Meehl et al. 2007).
Washington et al. (2000)
Gordon et al. (2000);
Pope et al. (2000)
Johns et al. (2006);
Martin et al. (2006)
Alekseev et al. (1998);
Galin et al. (2003)
Hourdin et al. (2006)
Hasumi and Emori (2004)
Anderson et al. (2004)
Anderson et al. (2004)
Schmidt et al. (2006);
Bleck (2002)
Schmidt et al. (2006);
Russell et al. (1995)
Roeckner et al. (1996)
Gordon et al. (2002)
Gordon et al. (2002)
Roeckner et al. (2003)
Yu et al. (2004)
McFarlane et al. (1992);
Flato (2005)
Salas-Mélia et al. (2005)
Collins et al. (2006)
Yukimoto et al. (2006)
References
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FIG. 1. Annual average TOA shortwave cloud forcing for present-day conditions from 16 IPCC AR4 models (see Table 1) and iRAM
compared with CERES satellite observations (Loeb et al. 2009).
The PGW method has some obvious limitations, notably the variability from daily to interannual periods in
the boundary conditions is necessarily the same in the
warming simulations and in the present-day simulation
(e.g., Hara et al. 2008). We would also like to note that
this study examines cloud response to a given climate
change signal only. The usage of prescribed SSTs does
not allow for possible atmosphere–ocean feedbacks. We
expect that an interactive coupling of atmosphere and
ocean could modify the climate change signals and may
thus result in a different cloud response.
3. Comparison with observations
In this section, we compare the cloud fields in our
present-day iRAM simulation with observations. We
compare 10-yr mean simulated values with observed
climatology. We also evaluate the interannual variations in the simulation, which provides an opportunity
to see how realistically the simulated clouds respond to
changes in large-scale meteorological forcing. We also
evaluate correlations between simulated low-level cloud
amount and, for instance, sea surface temperatures, lowertropospheric stability (LTS), or 500-hPa vertical velocities.
These correlations are then compared to corresponding
correlations obtained from observations.
a. Shortwave cloud forcing
Shortwave cloud forcing (SCF) at the top of the atmosphere is calculated as the difference between all-sky
and clear-sky shortwave radiation at the top of the atmosphere. Figure 1 shows a comparison of the multiyear
annual average SCF from iRAM as well as from 16 IPCC
AR4 models with multiyear (2000–05) satellite observations from CERES (Loeb et al. 2009). Observations
show a large area of small (absolute) SCF south of the
intertropical convergence zone (ITCZ) and between
the western domain boundary at 1508W and about
1008W. These weakly negative SCF values correspond
to a low average cloud amount over the warm-pool region. The size and extent of this area are reproduced by
iRAM reasonably well, although the maximum SCF
values are overestimated by about 5 W m22 compared
with the CERES observations. The satellite data show
the most negative SCF in our domain in the ITCZ and
in the two stratocumulus decks off the coasts of North
and South America. Here, iRAM overestimates the
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TABLE 2. Multiyear annual averages of cloud properties from the iRAM present-day simulation and from satellite observations. Values
given are domain averages (308S–308N, 1508–608W) over the ocean. The iRAM data used to calculate the averages are matched to the
observational periods. Low cloud amount refers to clouds below 680 hPa.
iRAM
Observations
SCF
(W m22)
CFnet
(W m22)
Total cloud
amount (%)
Low cloud
amount (%)
LWP
(g m22)
Rain rate
(mm day21)
253
246a
233
223a
48
58b
35
33b
63
62c
3.4
2.0d
a
CERES Energy Balanced and Filled climatology (EBAF) for the years 2000–05 (CERES EBAF TOA Terra Edition 1a dataset; Loeb
et al. 2009).
b
ISCCP observations for the years 1999–2007 (Rossow et al. 1996).
c
University of Wisconsin climatology based on data from SSM/I, TMI, and AMSR-E for the years 1999–2007 (O’Dell et al. 2008).
d
TMI level 3 monthly 0.58 3 0.58 profiling (3A12) for the years 1999–2008 (Kummerow et al. 2001).
magnitude of the SCF in the ITCZ by about 25%, but
the modeled SCF of the stratocumulus decks agrees well
with the observations. However, the stratocumulus deck
in iRAM over the southeastern (SE) Pacific is shifted
by about 98 (;1000 km) northwestward compared with
the observed, also seen in the simulated cloud liquid
water and cloud cover. Lauer et al. (2009) speculated
that this deficiency could be related to the model horizontal resolution, which leads to an overly smooth representation of the steep Andes.
Table 2 summarizes the multiyear mean cloud properties averaged over the ocean region of the model domain. Specifically cloud forcing, cloud amount, liquid
water path, and rain rate from iRAM are compared with
satellite observations. The details of the satellite datasets
used for comparison are given in the table along with
relevant references. The model overpredicts the average SCF over the ocean by 7 W m22 and underpredicts
the magnitude of the longwave cloud forcing (LCF) by
4 W m22 compared with satellite measurements. This
overestimation in the magnitude of SCF is mainly caused
by a too large (absolute) SCF in the ITCZ as well as an
underprediction of the extent of the region of weak SCF
associated with the warm pool. The small values of
domain-averaged LCF in the model mainly reflect an
underestimation of cirrus clouds in, and north of, the
ITCZ. The difference between domain-averaged modeled and observed total cloud forcing (CFnet) is about
210 W m22.
Figure 1 also shows that the 16 IPCC AR4 models investigated here, with the possible exception of the Met
Office (UKMO) Hadley Centre Global Environmental
Model version 1 (HadGEM1) model, fail to adequately
reproduce the large area of small magnitude of SCF over
the warm-pool region. Among the better models in reproducing observed SCF in the ITCZ and the two stratocumulus regions are the two Geophysical Fluid Dynamics
Laboratory (GFDL) models that, however, fail to reproduce the extent and position of the stratocumulus
deck in the southeastern Pacific.
b. Cloud amount, liquid water path, and rainfall
The results in Table 2 show that the domain-averaged
iRAM simulated low cloud cover (35%) is in good
agreement with satellite observations (33%). Here, lowlevel cloud amount refers to clouds below 680 hPa.
While low-level cloud amount can be averaged over all
time steps in the model, the satellite data cover only
periods not obstructed by high-level clouds. This is,
however, not expected to be a problem as the satellite
data show only small differences between low-level and
total cloud amount in the stratocumulus regions, which
are the main focus of this study. The domain-averaged
total cloud cover in iRAM is 48%, rather less than the
58% from the satellite climatology, a difference that
reflects the underprediction of the cirrus clouds in and
north of the ITCZ in iRAM. The modeled multiyear
annual average LWP over the ocean, 63 g m22, is in
good agreement with 62 g m22 determined from satellite observations. Figure 2 shows the geographical distribution of the iRAM simulated LWP compared with
observations and the 20C3M simulations in the IPCC
GCMs. The iRAM captures the observed overall pattern reasonably well, whereas the IPCC models vary
widely among themselves and none reproduces all the major features in the observed LWP. The domain-average
rainfall rate in iRAM over the ocean is 3.4 mm day21,
which is considerably higher than the 2.0 mm day21 in
the satellite-based climatology. Li and Fu (2005) showed
that the rainfall climatology from the Tropical Rainfall
Measuring Mission (TRMM) satellite data has lower
average rain rates than the Global Precipitation Climatology Project (GPCP; Huffman et al. 1997; Adler
et al. 2003), particularly over the ocean. A comparison of
the geographical pattern of long-term mean rain rate
with satellite observations (not shown) reveals that iRAM
captures the location and intensity of the rain rate over
most of the ocean reasonably well, but the belt of heavy
rainfall in the ITCZ is overestimated by iRAM in both its
meridional extent and its intensity.
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5851
FIG. 2. As in Fig. 1, but for liquid water path. Satellite observations are from the University of Wisconsin climatology (UWisc) based on
SSM/I, TMI, and AMSR-E (O’Dell et al. 2008).
c. Low-level cloud amount and lower-tropospheric
stability
Observations show that the low-level cloud cover in
tropical and subtropical regions is strongly correlated
with LTS, defined as the difference in potential temperatures u at 700 hPa and the surface (LTS 5 u700hPa – us)
(Slingo 1980, 1987; Klein and Hartmann 1993). For the
tropical and subtropical clouds over the eastern Pacific
in our model domain we find very similar correlations
between observed low-level cloud amount and LTSestimated inversion strength (Wood and Bretherton
2006). In this study, we use LTS for our analysis.
We combined monthly mean ISCCP satellite observations of low-level cloud amount (Rossow et al. 1996) with
LTS values calculated from monthly mean NCEP final
analysis data (NCEP–NCAR reanalysis before the year
2000; Kalnay et al. 1996). The ISCCP data (2.58 3 2.58) are
interpolated to the 18 3 18 grid of the FNL data. For
comparison with results from iRAM we averaged the
model data onto the 18 3 18 FNL grid. The black and blue
dots in Fig. 3a show low-level cloud amount binned by
LTS values from the combined ISCCP–NCEP dataset and
from iRAM for the years 2000–07. The mean low-level
cloud amount and its standard deviation are calculated
for all grid cells in the whole domain (308S–308N and
1508–608W) and all individual monthly means in the time
period 2000–07 within the same LTS bin. The vertical bars
show 61 standard deviation. The standard deviation
within each LTS bin will reflect spatial, annual, and interannual variability. Observations and model show an
approximately linear increase in low cloud amount with
increasing LTS. Such a linear relationship between seasonal mean LTS and low-level cloud amount for regions
in the subtropics has also been found by Klein and
Hartmann (1993). A linear fit to the observations in Fig. 3a
has a slope of 0.031 K21, which is close to the 0.030 K21
obtained for the present-day iRAM simulation. The range
of variability of cloud amount for any given LTS value is
larger in the model than in the observations. The difference
even persists when the iRAM data are averaged to 2.58
resolution to be directly comparable to the ISCCP data.
The corresponding probability density functions (PDFs)
of monthly mean LTS from the present-day iRAM simulation and NCEP FNL data are shown as the blue and
black curves in Fig. 3b. For both model and FNL data
the most common LTS values are found to be in the
range of 12–14 K. Maximum LTS densities are found
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FIG. 3. (a) Low-level cloud cover vs LTS as derived from monthly mean ISCCP satellite observations (Rossow et al.
1996) and NCEP final analysis data for the years 2000–07, respectively, compared with results from iRAM for
present-day and the global warming case A (IPCC AR4 ensemble mean). Filled circles depict mean values and bars
show standard deviation. (b) PDF of the monthly mean LTS in the model domain (308S–308N, 1508–608W) from
iRAM and NCEP final analysis data. (c) Changes in the LTS–low-level cloud amount relationship between the
(normal) year 2005 and El Niño year 1997 from iRAM and NCEP/ISCCP data. (d) Changes in the LTS–low-level
cloud amount relationship between present-day and global warming case A. For details see text.
to be at 12.8 K for NCEP FNL data and 13.2 K for the
iRAM present-day simulation. The model reproduces
the LTS PDF from NCEP FNL data for the east Pacific
region reasonably well, although the modeled distribution is wider than the observed one for small LTS values
(LTS 5 10–12 K).
Figure 3c shows the changes in the LTS–low-level
cloud amount relationship between the strong El Niño
year 1997 and the year 2005 (no El Niño or La Niña
events) from iRAM and NCEP/ISCCP data. Average
LTS and low-level cloud amount in the El Niño year
were calculated for the same grid cells and months that
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FIG. 4. Time series of anomalies in (a) monthly mean low-level cloud amount, (b) liquid water path, (c) sea surface
temperature, (d) lower-tropospheric stability, and (e) 500-hPa vertical velocity obtained from satellite observations
and NCEP final analysis data (NCEP–NCAR reanalysis before 2000; Kalnay et al. 1996) compared with results
from iRAM. The figure shows monthly means as well as 1-yr running means averaged over the southeastern Pacific
(258–58S, 1008–758W). Warm and cold ENSO episodes are shaded in light red and light blue, respectively. For details
see text.
were used to calculate low-level cloud amount for each
of the year 2005 LTS bins. LTS decreases in the El Niño
year for all bins with year 2005 LTS values larger than
14 K in the NCEP data and larger than 12.5 K in the
model data, respectively, while corresponding low-level
cloud amount decreases particularly in the LTS range
most relevant for stratocumulus clouds (LTS . 15 K).
The displacement arrows from NCEP/ISCCP and iRAM
are almost parallel within this LTS range, indicating that
the model simulates the observed changes in the LTS–
low-level cloud amount relationship during El Niño reasonably well.
d. Interannual variations in low-level cloud amount
and liquid water path
To evaluate the response of the modeled low-level
clouds to interannual variations in local thermal structure and circulation, we compare monthly mean cloud
anomalies with other observed properties in the stratocumulus regions off the coasts of North and South
America. Cloud properties of primary interest are lowlevel cloud amount and liquid water path as these are
key parameters determining the cloud radiative properties. SST and LTS reflect the local thermal structure,
while 500-hPa vertical velocities are indicative of largescale circulation changes. Figure 4 shows time series of
anomalies in monthly mean low-level cloud amount,
liquid water path, SST, LTS, and 500-hPa vertical velocities from iRAM in comparison with observations.
Monthly mean anomalies are calculated by subtracting
the average seasonal cycle calculated over the entire
period. Observed low-level cloud amounts are obtained
from ISCCP satellite data (Rossow et al. 1996); liquid
water path from the Special Sensor Microwave Imager (SSM/I), TRMM Microwave Imager (TMI), and
AMSR for Earth Observing System (AMSR-E; O’Dell
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et al. 2008); SSTs are taken from NOAA daily highresolution blended analyses (Reynolds et al. 2007); and
LTS as well as 500-hPa vertical velocity are calculated
from NCEP FNL data (NCEP–NCAR reanalysis before
2000; Kalnay et al. 1996). The time series cover the period 1997–2007 (for which we have satellite data for
liquid water path and cloud cover) and are averaged
over the southeastern Pacific stratocumulus region (258–
58S, 1008–758W, Fig. 4). The warm and cold ENSO episodes denoted by shading in Fig. 4 are based on observed
Niño-3.4 SST anomalies. Even though they are averaged
over a large domain, the monthly mean anomalies still
show significant variability. To reduce the noise introduced by the subseasonal variability we calculate 1-yr
running means shown as thick curves in Fig. 4.
SSTs in the southeastern Pacific region show strong
positive deviations from average values during the 1997/98
El Niño event and negative deviations from the average
in the subsequent cold ENSO episode (Fig. 4c). From
2001 through 2006 SST anomalies are fairly small and it
seems that the weak El Niños of 2002/03, 2004/05, and
late 2006 have at most small effects on low clouds in the
southeastern Pacific. Both model and observations show
a strong negative low-level cloud amount anomaly (28%)
during the strong El Niño event in 1997/98 and a small
positive anomaly (2%–3%) in the years 2002–04 (Fig. 4a).
During the rest of the time period 1997–2007, the 1-yr
running mean anomalies of observed low-level cloud
amount are small. This behavior is reproduced by iRAM
except for a 15-month period following the strong
El Niño event of 1997/98 where the model predicts a
positive low-level cloud amount anomaly of 4%. Anomalies in the liquid water path show a high positive correlation with low-level cloud amount anomalies (Fig. 4b).
The top part of Table 3 shows correlations of the time
series of the southeastern Pacific region mean value of
low cloud amount with other quantities averaged over
the same region (using the 1-yr running mean of all
quantities). The correlations of observed and modeled
low-level cloud amount and LWP are 0.85 and 0.89, respectively. SST is strongly anticorrelated with observed
and modeled low-level cloud amount with correlations
coefficients of 20.81 and 20.75, respectively. Figure 4d
shows a comparison of modeled LTS anomalies with
those calculated from NCEP data. The 1-yr running mean
from iRAM agrees reasonably well with the FNL data,
showing smaller than average LTS values between 1997
and 2001 and larger than average values thereafter.
Earlier observational studies have shown that LTS is
strongly correlated with subtropical low-level stratocumulus cloud fraction (Slingo 1980, 1987; Klein and
Hartmann 1993). Correlation coefficients for the modeled and FNL LTS with low-level cloud amount over the
VOLUME 23
TABLE 3. Correlations of the 1997–2007 time series for low-level
cloud amount from observations and calculated by iRAM with
liquid water path, sea surface temperature, lower-tropospheric
stability, 500-hPa vertical velocity, and sea level pressure. Quantities have been averaged over the southeast and northeast Pacific,
and a 1-yr running mean has been applied to the time series. For
details see text.
Parameter
Observations
Southeast Pacific (258–58S, 1008–758W)
LWP
0.85
SST
20.81
LTS
0.80
v500
0.37
SLP
0.69
Northeast Pacific (208–308N, 1208–1308W)
LWP
0.66
SST
20.27
LTS
0.34
v500
0.23
SLP
0.27
iRAM
0.89
20.75
0.81
0.21
0.19
0.60
20.62
0.49
0.14
0.58
southeastern Pacific stratocumulus region are 0.81 and
0.80, respectively. By contrast, 500-hPa vertical velocities
(Fig. 4e) have a much weaker correlation with low-level
cloud amount anomalies over the east Pacific stratocumulus regions during the period studied here. Table 3 also
gives the correlation coefficient with domain-averaged
SLP, which is 0.69 in observations but much smaller
(0.19) in the iRAM simulation.
We repeated this analysis for the averages over the
northeastern Pacific stratocumulus region (208–308N,
1208–1308W). In this region as well, there is a correlation
of the SST with the ENSO state, notably with anomalously warm surface waters during the 1997/98 El Niño
and cold water during 1999. The correlation coefficients
of low cloud amounts averaged over 208–308N, 1208–
1308W, with SST, LWP, LTS, SLP, and midtropospheric
vertical velocity, are given in the bottom part of Table 3.
These correlations are similar in the observations and in
the iRAM simulation.
The ability of iRAM to reproduce the interannual
variations of cloud properties in stratocumulus regions
through the ENSO cycle is much better than that of
typical current coupled GCMs. Clement et al. (2009)
showed that many global models have difficulties even
in reproducing the correct sign of the correlations between cloud properties and meteorological quantities
that we show in Table 3. Of course, the results in Fig. 4
and Table 3 involve iRAM run with prescribed SSTs
and lateral boundary conditions and might be more directly comparable to prescribed SST GCM simulations
than free-running coupled GCMs. However, the reality
is that climate change experiments in AR4 have been
performed with models that have poor representation of
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FIG. 5. The 10-yr average change in (middle) low-level cloud amount (DCA), (bottom) cloud
feedback parameters (l), and (top) the underlying global warming signals in sea surface
temperatures (DSST) for (left to right) the three global warming cases A–C compared with
present-day conditions (1999–2008). For details see text.
the mean cloud climatology in the eastern Pacific stratocumulus regions and do not reproduce the connections
between tropical and subtropical clouds and large-scale
meteorological variables (e.g., Stowasser and Hamilton
2006; Clement et al. 2009). The much better cloud representation for current climate in iRAM provides the
motivation for conducting the climate change experiments described in the next section.
means for each case [i.e., including only the last 10 yr
(1999–2008) of the control run]. Figure 5 shows changes
in low-level cloud amount as well as the imposed changes
in SST for all three global warming cases. Also shown is
the local cloud feedback parameter l calculated as the
change in net cloud forcing (CFnet) divided by the change
in surface temperature Ts:
l5
4. Global warming results
a. iRAM global warming simulations
We estimate the response of clouds to global warming by calculating the differences between each of the
three global warming cases A–C (see section 2) and our
present-day reference experiment. We compare 10-yr
DCFnet
.
DT s
(1)
The net cloud forcing is calculated as the sum of SCF
and LCF, where SCF and LCF are calculated as the
difference between the all-sky and clear-sky shortwave
and longwave radiation at the top of the atmosphere,
respectively. Negative values correspond to a cooling
effect on the climate system. Although this definition
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[Eq. (1)] of the cloud feedback parameter depends on
changes in both cloud and clear-sky properties, such as
changes in water vapor, temperature, or surface albedo
(Soden et al. 2004), it is commonly used to diagnose
global climate simulations because its calculation is
straightforward and the cloud forcing defined in this
way can be estimated in a fairly direct way from observations (Bony et al. 2006).
The spatial structure of the late twenty-first-century
SST warming patterns taken from the multimodel ensemble (case A), CGCM3.1 (case B), and CCSM3 (case
C) are rather similar, but the overall magnitude of the
warming differs quite significantly among the cases (largest for CGCM3.1, smallest for CCSM3). In each case, the
largest warming occurs in the equatorial east Pacific and
the smallest warming occurs in the southernmost part of
our model domain between 208 and 308S.
Changes in the amount of low-level marine clouds
calculated by iRAM in response to the global warming
signals (cases A–C) have similar geographical patterns
showing a strong decrease of 5%–10%, particularly in
the two stratocumulus regions, and an increase in lowlevel cloud amount in the range of 2%–8% over the
equatorial Pacific between 1508 and 1008W. Consistent
with the amplitudes of the imposed global warming
signals, case B shows the largest decrease in low-level
cloud amount in both horizontal extent and amplitude,
whereas case C has the smallest cloud response. The
local cloud feedback parameters [Eq. (1)] are shown
in the bottom panels of Fig. 5 and basically scale the
cloud changes (specifically in shortwave cloud forcing)
by the imposed SST changes. The feedback parameters
are quite similar in cases A–C and are in the range of
4–7 W m22 K21 in the stratocumulus regions, 22 to
24 W m22 K21 over the equatorial Pacific between
1508 and 1008W, and about 1 W m22 K21 over much of
the rest of the Pacific. Clement et al. (2009) estimate a
warming effect from changes in net cloud forcing because
of changes in SST in the northeast Pacific stratocumulus
region (158–258N, 1158–1458W) of about 6 W m22 K21.
This observation-based estimate compares reasonably
well to results from iRAM ranging between 4.2 and
5.9 W m22 K21 averaged over the same region (global
warming cases A–C).
As noted above, there is a strong similarity in the
feedback parameters among the cases A–C, despite the
different warming increments imposed in SST. However, it is possible that the close agreement in l may
depend on the overall geographic pattern of SST warming being similar among the three cases. This issue was
investigated in a fourth experiment in which a uniform
2-K warming was applied to the sea surface throughout
the domain and through the depth of the atmosphere
VOLUME 23
on the lateral boundaries. The l distribution in that experiment (not shown) was indeed rather different from
that seen in cases A–C (the domain-average feedback in
this uniform warming case was 3 W m22 K21).
The red dots in Fig. 3a show the dependence of lowlevel cloud amount on LTS in the global warming simulation case A. The mean relation between LTS and
low-level cloud amount is significantly different in the
perturbed climate from that in the control run. Specifically, in the warmer climate there are systematically
smaller low-level cloud amounts for any given LTS
value, except for a narrow region around LTS 5 14.5 K.
Also the slope of the linear fit to results from the global
warming scenario (0.025 K21) is somewhat smaller than
that for the present-day model results or from observations. The model results suggest that the average relation between LTS and low-level cloud amount obtained
from present-day observations over the east Pacific can
change significantly in an altered climate. Application of
simple models (e.g., Miller 1997) or parameterizations
of the boundary layer cloud amount based on the observed present-day relation between LTS and low-level
cloud cover may not be appropriate for climate change
scenarios.
The red curve in Fig. 3b shows the LTS PDF in the
global warming simulation case A—compared with the
present-day result there is a shift toward higher LTS
values. Figure 3d shows the changes in the LTS–lowlevel cloud amount relationship between our presentday simulation and global warming case A. As for the
1997/98 El Niño case discussed above (Fig. 3c), average
LTS and low-level cloud amount in the global warming
case were calculated for the same model grid cells and
months that were used to calculate low-level cloud
amount for each of the present-day LTS bins. In other
words, the blue dots for the present-day simulation in
Fig. 3d are identical with the ones in Fig. 3a, whereas the
red dots show LTS and low-level cloud amount values
averaged over the same model grid cells but for the
global warming case A. LTS increases in the global
warming scenario for all bins while low-level cloud
amount decreases for bins with present-day LTS values
smaller than 13 K or larger than 16 K. However, lowlevel cloud amount remains close to its present-day level
for the bins in the LTS range 13–16 K. Here, the increase in low-level cloud amount in the equatorial region between 1508 and 1008W (see Fig. 5) balances
approximately the decrease in low-level cloud amount
within the same LTS range over other parts of the ocean.
The general increase in average LTS is consistent with
the reduction in mean tropospheric lapse rates and increased dry stability, which are robust predictions from
current GCMs in a warmed climate.
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FIG. 6. The 10-yr mean diurnal cycle of cloud-base and cloud-top height averaged over
NE and SE Pacific for the present-day simulation and global warming case A (IPCC AR4
ensemble mean).
Figure 6 shows the mean diurnal cycles of cloudbottom and cloud-top heights for the core regions of
the stratocumulus regimes over the northeastern (208–
308N, 1208–1308W) and southeastern Pacific (258–58S,
858–958W). We define cloud-bottom height as the lowest
level between the surface and 4 km at which the monthly
mean cloud liquid water content exceeds 0.025 g kg21
and cloud-top height as the highest level at which LWC
falls below this threshold value. The results shown have
been averaged over the whole 10-yr period of the presentday simulation and the global warming scenario case A.
While the cloud-bottom heights in the stratocumulus
regions change only modestly, the average cloud-top
heights in the global warming case (A) are about 50–
100 m lower compared with those of the present-day
scenario. The vertical model resolution in the vicinity of
the cloud tops over the northeastern Pacific is about
200 m and over the southeastern Pacific about 300 m.
The overall thinning of the stratus cloud in the global
warming case is consistent with the reduction in cloud
shortwave forcing.
To provide the thermal structure context for the cloud
changes, an analysis of the vertical temperature profile
and lapse rate in the iRAM experiments is conducted.
Conventional averaging with the vertical coordinate fixed
in time and horizontal space will blur any marked feature of the vertical structure that is strongly variable in
time (and horizontal space; Birner 2006). This blur effect makes the temperature inversion atop the MBL in
the east Pacific region hard to see in a multiyear climatology. Following the approach adopted by Birner (2006)
to characterize behavior near the tropopause, we use the
inversion layer base height as a common reference level
to composite all temperature profiles. This is done by
introducing a modified vertical coordinate defined as
z 2 zB with z as altitude and zB as inversion layer base
height. The data are interpolated from model levels onto
vertical levels in z 2 zB with a cubic spline interpolation.
Profiles that do not contain a temperature inversion in
the lower troposphere (0–3 km) are not included in the
average. The inversion layer base height is taken as the
minimum temperature in this altitude range calculated
from daily mean temperature profiles. Figure 7 (left panel)
shows the results for the composited temperature profiles. We added the average of the inversion layer base
height to the vertical coordinates shown in Fig. 7. The
right panel of Fig. 7 shows the corresponding lapse rates
(2dT/dz). The mean inversion heights in the presentday iRAM simulation are about 1.4 km in the southeast
Pacific region and 0.7 km in the northeast Pacific region.
These are somewhat lower than the mean cloud-top
heights presented in Fig. 6 (1.6 km and 1.0 km) as the
mean heights for the clouds were computed including
occasions when there is no well-defined inversion and
also depend on the threshold value for monthly mean
LWC (see above). The drop in diurnal mean cloud
heights by about 100 m in the southeast Pacific and 50 m
in the northeast Pacific (Fig. 6) in the global warming
simulation is paralleled by the very similar reductions in
the mean inversion heights (Fig. 7).
In contrast, our sensitivity experiment with a uniform
2 K increase in SST throughout the domain and in atmospheric temperatures on the lateral boundaries shows
only a little change or slight increase in inversion layer
base heights in the stratocumulus regions. This suggests
that the reduction in mean tropospheric lapse rates predicted by the GCMs in a warmed climate (cases A–C) is
important for the shallowing of the marine boundary
layer in these regions.
Analysis of the entrainment rates at the top of the
boundary layer shows that entrainment in the global
warming run is reduced by 9% in the northeast and by
12% in the southeast Pacific stratocumulus region compared with the present-day simulation. Reduced entrainment could be a reason for the reduction in boundary
layer height (e.g., Stevens 2006), causing the inversion
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FIG. 7. The 10-yr mean vertical profiles of (left) T and (right) lapse rate (2dT/dz) averaged
over NE and SE Pacific for the present-day simulation and global warming case A (IPCC AR4
ensemble mean). Temperature profiles have been aligned at the inversion layer base height
prior to averaging. The arrows indicate the average inversion layer base height.
to drop and the clouds to thin. Consistent with Caldwell
and Bretherton’s (2009) hypothesis that a decreased
radiative cooling of the boundary layer in an enhanced
greenhouse case could cause the inversion to drop, we
find average turbulent kinetic energy (TKE) is less in
our global warming run in both stratocumulus regions.
In addition to less turbulence, entrainment could also
be reduced by greater inversion strength in the global
warming case particularly in the southeastern Pacific
stratocumulus region (see also Fig. 7).
b. Comparison with IPCC model results
We calculated the local feedback parameters for all
16 IPCC AR4 models that provided both TOA clear-sky
and all-sky fluxes needed to compute TOA cloud forcings [Eq. (1)]. Just as for the other aspects of the global
warming signal (see section 2), we calculate the change
in cloud forcing due to global warming by subtracting
10-yr averages for the present-day simulation (experiment 20C3M, years 1990–99) from projections for the
end of the twenty-first century (SRES scenario A1B
years 2090–99). Figure 8 shows a comparison of l from
IPCC AR4 models with the results from iRAM for
global warming case A. The geographical patterns as
well as the amplitudes of the local feedback parameters
vary widely among the IPCC AR4 models. Of the
16 IPCC models, six of them [Centre National de Recherches Météorologiques Coupled Global Climate
Model, version 3 (CNRM-CM3); Institute of Numerical
Mathematics Coupled Model, version 3.0 (INM-CM3.0);
L’Institut Pierre-Simon Laplace Coupled Model, version 4
(IPSL CM4); ECHAM5–Max Planck Institute Ocean
Model (MPI-OM); the third climate configuration of
the Met Office Unified Model (UKMO HadCM3); and
UKMO HadGEM1] simulate fairly strong positive local
feedback parameters throughout most of the stratocumulus regions. By contrast, the Commonwealth Scientific
and Industrial Research Organisation Mark version 3.5
(CSIRO-Mk3.5) and CCSM3 simulate fairly strong negative local feedback parameters in the two stratocumulus
regions. The other IPCC models have feedback parameters in the stratocumulus areas that are either quite small
(CGCM3) or that vary in sign through these regions. In
other parts of the domain shown in Fig. 8 the simulated
local feedback parameter differs greatly among the IPCC
models. While the IPCC models disagree widely among
themselves, none of the GCM simulated patterns of l
compare well with that in the iRAM simulation.
The domain-averaged l from iRAM is 1.8 W m22 K21
for global warming case A (1.9 W m22 K21 when averaged over ocean grid cells only). This positive feedback
parameter mainly reflects the decrease in shortwave cloud
forcing resulting from decreased low-level cloud amount
and liquid water path. The response of clouds to global
warming in cases B and C gives similar domain-averaged
local feedback parameters of 1.8 and 1.9 W m22 K21
(2.1 and 2.0 when averaged over ocean grid cells only),
respectively, even though the amplitude of the global
warming signals varies significantly among these cases.
The domain-averaged changes in shortwave and net
cloud forcing as well as the local feedback parameters
for all global warming cases are summarized in Table 4.
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FIG. 8. Local feedback parameter l [Eq. (1)] from 16 IPCC AR4 models and iRAM calculated from the changes in shortwave TOA
cloud forcing and surface temperatures between 10-yr means for present-day conditions and SRES A1B end of the twenty-first-century
simulations. For details see text.
The light gray bars in Fig. 9 compare the local feedback parameters from the IPCC AR4 models averaged
over the domain of the regional model (308S–308N,
1508–608W) with iRAM. The domain-averaged feedback parameter simulated by iRAM is higher than that
simulated by any of the 16 IPCC AR4 models. Out of
the 16 IPCC models, 10 simulate positive feedback
parameters for the east Pacific region, and 6 predict a
negative domain-averaged feedback parameter. The dark
gray bars in Fig. 9 show the feedback parameter for each
of the IPCC models averaged over the entire tropical–
subtropical belt (308S–308N, 08–3608). The mean feedbacks in the entire tropical–subtropical belt in each model
are fairly closely related to those for the east Pacific
domain (the correlation coefficient over the 16 models
is 0.95).
The east Pacific cloud feedbacks in the GCMs also
correlate reasonably well with the equilibrium global
climate sensitivities given in Table 8.2 of Solomon et al.
(2007). The GCMs that have the highest east Pacific cloud
feedback (and hence are closest to the iRAM result) are
the Model for Interdisciplinary Research on Climate 3.2
(MIROC3.2), IPSL-CM4, and UKMO-HadGEM1. These
(along with the medium-resolution version of MIROC3.2
not considered in this paper) are the GCMs with the
highest global climate sensitivity according to the IPCC
Table 8.2. It may also be noted that UKMO-HadGEM1
was identified by Clement et al. (2009) as the GCM that
TABLE 4. Changes in the 10-yr averages of SST, SCF, CFnet, and l for the individual global warming studies from iRAM compared with the
present-day run. The values given are averaged over all ocean grid cells within the model domain (308S–308N, 1508–608W).
Global warming experiment
DSST
(K)
DSCF
(W m22)
DCFnet
(W m22)
l
(W m22 K21)
Case A—Global warming, IPCC AR4 ensemble mean
Case B—Global warming, CCCma CGCM3.1(T63) (positive cloud feedback)
Case C—Global warming, NCAR CCSM3 (negative cloud feedback)
2.0
2.2
1.7
5.0
5.2
5.4
4.1
4.6
3.6
1.9
2.0
2.1
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FIG. 9. Annual average local feedback parameter l [Eq. (1)] in W m22 K21 for the east Pacific region and the latitude
belt 308S–308N from 16 IPCC AR4 models and calculated by iRAM.
had the most realistic cloud responses to variations in the
large-scale environment.
5. Summary and conclusions
We have examined the cloud simulations and cloud–
climate feedbacks in the tropical and subtropical eastern
Pacific region in 16 state-of-the-art coupled GCMs and
in the regional atmospheric model iRAM using prescribed boundary conditions. We find that the simulation of the mean cloud climatology for this region in the
GCMs is very poor. The cloud feedbacks to imposed
climate forcings vary widely among the GCMs in the
east Pacific and in the 308N–308S band in general. These
variations account for a large fraction of the uncertainty
in global climate sensitivity.
Following Lauer et al. (2009), we have found that
iRAM forced with observed boundary conditions simulates rather realistic mean cloud fields in the east Pacific
domain. Going beyond the earlier analysis of Lauer et al.,
we have also shown that the iRAM reproduces the observed interannual variations in cloud fields (as well as
LTS), notably correctly simulating the response of the
clouds through the 1997–99 El Niño to La Niña transition. By contrast, Clement et al. (2009) note that low
clouds in GCMs generally do not respond realistically
through the ENSO cycle.
To investigate cloud feedbacks in iRAM, three global
warming scenarios have been run with SSTs and horizontal boundary conditions meant to be appropriate for
late twenty-first-century conditions; specifically, warming
signals based on IPCC AR4 SRES A1B simulations
from 1) an ensemble mean of 19 GCMs, 2) the CGCM3.1
model, and 3) the CCSM3 model.
All three global warming cases simulated with iRAM
show a distinct reduction in low-level cloud amount particularly in the stratocumulus regime, resulting in positive
local feedback parameters in these regions in the range
of 4–7 W m22 K21. The model results suggest that the
reduction in stratocumulus clouds because of global
warming is caused by a drop in average inversion layer
base height and a consequential decrease in cloud-top
height. As the cloud-base height remains approximately
unchanged the decrease in cloud-top height causes the
stratocumulus clouds to thin and liquid water path to
decrease. This results in a less efficient reflection of solar
radiation and a reduction in shortwave cloud forcing—
domain-averaged feedback parameters from iRAM range
between 1.8 and 1.9 W m22 K21 (cases A–C).
We have analyzed the relation between monthly mean
low-level cloud cover and LTS in our iRAM simulations. The present-day simulation reproduces quite well
the long-term mean relation in observations (NCEP
data and satellite cloud retrievals). In both present-day
iRAM simulation and observations, the El Niño perturbations in cloud cover are largely accounted for by
the reduction in LTS. By contrast, in the global warming
simulation the clouds and thermal structure change in
such a way that the cloud cover versus LTS relation is
significantly different from the present-day simulation.
This suggests that the decrease in low-level cloud amount
during the 1997/98 El Niño, and the decrease because of
global warming by doubled CO2, is controlled by different physical processes as proposed by Zhu et al. (2007).
Furthermore, this shows rather dramatically the inadequacy of cloud parameterization schemes based purely on
present-day empirical relations between cloud cover and
large-scale environmental fields.
The cloud–climate feedback averaged over the east
Pacific region has also been calculated from SRES A1B
simulations for 16 AR4 GCMs. The GCM feedbacks vary
from 21.0 to 11.3 W m22 K21, which are all less than
the 11.8 to 11.9 W m22 K21 obtained in the comparable iRAM simulations. The iRAM results by themselves
1 NOVEMBER 2010
LAUER ET AL.
cannot be connected definitively to global climate feedbacks, but we have shown that among the GCMs the
cloud feedbacks averaged over 308S–308N and the equilibrium global climate sensitivity are both correlated
strongly with the east Pacific cloud feedback. To the extent that iRAM results for cloud feedbacks in the east
Pacific are credible, they provide support for the high end
of current estimates of global climate sensitivity.
Acknowledgments. This research was supported by the
Japan Agency for Marine-Earth Science and Technology
(JAMSTEC), by NASA through Grant NNX07AG53G,
and by NOAA through Grant NA09OAR4320075, which
sponsor research at the International Pacific Research
Center. This research was also supported by NOAA/
CPPA Grant NA07OAR4310257 and DOE Regional
and Global Climate Modeling (RCGM) Program Grant
ER64840. NCEP FNL data for this study are from the
Research Data Archive (RDA), which is maintained by
CISL at NCAR. NCAR is sponsored by the National
Science Foundation (NSF). NCEP–NCAR reanalysis
data have been provided by the NOAA/OAR/ESRL
PSD, Boulder, Colorado, available online from their
Web site at http://www.cdc.noaa.gov/. We acknowledge
the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s
Working Group on Coupled Modelling (WGCM) for
their roles in making available the WCRP CMIP3 multimodel dataset. Support of this dataset is provided by
the Office of Science, DOE.
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