Origins of differences in climate sensitivity, forcingand feedback in climate models

Mark J. Webb • F. Hugo Lambert •

Jonathan M. Gregory

Received: 31 October 2011 / Accepted: 8 March 2012 / Published online: 12 April 2012

� Crown Copyright 2012

Abstract We diagnose climate feedback parameters and

CO2 forcing including rapid adjustment in twelve atmo-

sphere/mixed-layer-ocean (‘‘slab’’) climate models from

the CMIP3/CFMIP-1 project (the AR4 ensemble) and fif-

teen parameter-perturbed versions of the HadSM3 slab

model (the PPE). In both ensembles, differences in climate

feedbacks can account for approximately twice as much of

the range in climate sensitivity as differences in CO2

forcing. In the AR4 ensemble, cloud effects can explain the

full range of climate sensitivities, and cloud feedback

components contribute four times as much as cloud com-

ponents of CO2 forcing to the range. Non-cloud feedbacks

are required to fully account for the high sensitivities of

some models however. The largest contribution to the high

sensitivity of HadGEM1 is from a high latitude clear-sky

shortwave feedback, and clear-sky longwave feedbacks

contribute substantially to the highest sensitivity members

of the PPE. Differences in low latitude ocean regions

(30�N/S) contribute more to the range than those in mid-

latitude oceans (30–55�N/S), low/mid latitude land (55�N/

S) or high latitude ocean/land (55–90�N/S), but contribu-

tions from these other regions are required to account fully

for the higher model sensitivities, for example from land

areas in IPSL CM4. Net cloud feedback components over

the low latitude oceans sorted into percentile ranges of

lower tropospheric stability (LTS) show largest differences

among models in stable regions, mainly due to their

shortwave components, most of which are positive in spite

of increasing LTS. Differences in the mid-stability range

are smaller, but cover a larger area, contributing a com-

parable amount to the range in climate sensitivity. These

are strongly anti-correlated with changes in subsidence.

Cloud components of CO2 forcing also show the largest

differences in stable regions, and are strongly anticorre-

lated with changes in estimated inversion strength (EIS).

This is qualitatively consistent with what would be

expected from observed relationships between EIS and

low-level cloud fraction. We identify a number of cases

where individual models show unusually strong forcings

and feedbacks compared to other members of the ensem-

ble. We encourage modelling groups to investigate unusual

model behaviours further with sensitivity experiments.

Most of the models fail to correctly reproduce the observed

relationships between stability and cloud radiative effect in

the subtropics, indicating that there remains considerable

room for model improvements in the future.

Keywords Cloud � Climate models � Climate sensitivity �Feedback � Effective forcing � Rapid adjustment �Carbon dioxide � CO2

1 Introduction

Clouds remain a major source of uncertainty in climate

model projections of future changes in global mean surface

temperatures (Randall et al. 2007; Bony et al. 2006). In the

IPCC AR4 generation of climate models, all types of

M. J. Webb (&) � J. M. Gregory

Hadley Centre, Met Office, FitzRoy Road,

Exeter EX1 3PB, UK

e-mail: mark.webb@metoffice.gov.uk

F. H. Lambert

College of Engineering, Mathematics and Physical Sciences,

University of Exeter, Exeter, UK

J. M. Gregory

National Centre for Atmospheric Science,

Reading University, Reading, UK

123

Clim Dyn (2013) 40:677–707

DOI 10.1007/s00382-012-1336-x

clouds contribute to this uncertainty, but low clouds have

been shown to make the largest contribution, mainly

through their impact on shortwave radiation (Bony and

Dufresne 2005; Webb et al. 2006; Wyant et al. 2006;

Williams and Tselioudis 2007; Medeiros et al. 2008; Soden

and Vecchi 2011).

Perturbed parameter ensembles have also been used to

explore inter-model differences in feedbacks and equilib-

rium climate sensitivity (Murphy et al. 2004; Stainforth

et al. 2005; Webb et al. 2006; Sanderson et al. 2008a, b;

Rougier et al. 2009; Joshi et al. 2008; Yokohata et al.

2010) and in transient climate change (Collins et al. 2006a,

2010; Harris et al. 2006). As in the case of the AR4

ensemble, shortwave cloud feedbacks in regions where low

level cloud changes dominate make the largest contribution

to inter-model spread in climate sensitivity (Webb et al.

2006; Yokohata et al. 2010).

A number of studies have looked for evidence to support

a dominant contribution from one type of marine boundary

cloud over another. Bony and Dufresne (2005) divided 15

atmosphere–ocean general circulation models (AOGCMs)

into high and low sensitivity groups, and showed that, over

the tropical oceans, the largest differences in cloud feed-

backs between these were found in regions of weak sub-

sidence. Medeiros et al. (2008) compared atmosphere-only

experiments from three models forced with observed SSTs

and with zonally uniform ‘aquaplanet’ SSTs with no land,

diagnosing cloud feedbacks induced by a uniform ?2K

SST perturbation. The difference in the average cloud

feedback over the tropical oceans between the realistic

NCAR and GFDL models was reproduced in the aqua-

planet configurations, despite their lack of persistent

stratocumulus.

Both of these results support the idea that trade cumulus

clouds (which cover large areas of the tropics) contribute as

much (or more) than persistent stratocumulus clouds to

inter-model spread in tropical cloud feedback. However,

Williams and Webb (2009) examined cloud feedbacks in

10 CO2 doubling experiments with atmosphere-mixed-

layer ocean ‘‘slab’’ models from the Cloud Feedback

Model Intercomparison Project, and found that stratocu-

mulus and stratocumulus/trade-cumulus transition clouds

make a larger contribution to inter-model spread in cloud

feedback than trade cumulus in those models. A weakness

of the Williams and Webb (2009) study is the fact that,

although 10 model versions were analysed, two of these

were versions of the MIROC3.2 model, and four were

versions of the Hadley Centre model, meaning that only six

distinct models were analysed [far fewer than Bony and

Dufresne (2005)]. This in part motivates the present study,

which analyses a larger set of slab models.

Another development since IPCC AR4 has been the

realisation that instantaneous CO2 forcing can lead to rapid

adjustments in the structure of the troposphere (on time-

scales of weeks rather than years), leading to equally rapid

adjustments in cloud and hence radiation at the top of the

atmosphere (Gregory and Webb (2008), hereafter GW08).

For analysis purposes these can be treated as a components

of an ‘effective’ radiative CO2 forcing (analogous to

stratospheric adjustment or indirect aerosol forcing) which

includes the effect of adjustments on short atmospheric

response time scales, in contrast to conventional climate

feedbacks which scale with global temperature, operating

on longer ocean response time scales. GW08 suggested that

tropospheric adjustment to CO2 may be responsible for

some of the model spread in equilibrium climate sensitiv-

ity. They also showed that CO2 forcing diagnosed in this

way was in good agreement with that diagnosed by dou-

bling CO2 while holding SST’s fixed (Hansen et al. 2002,

2005; Shine et al. 2003) in one model, HadSM3. Andrews

and Forster (2008) drew similar conclusions, and high-

lighted the role of cloud masking effects (Soden et al.

2004) on the cloud adjustment term.

More recently Colman and McAvaney (2011), Wyant

et al. (in press) and Watanabe et al. (2011) have analysed

cloud adjustments in individual models. Colman and

McAvaney (2011) found that a positive shortwave cloud

adjustment in a version if the Australian Bureau of Mete-

orology Research Centre (BMRC) climate model was due

to reductions in low-mid level cloud fraction associated

with enhanced heating rates, increased temperatures from

increased CO2, and associated reductions in relative

humidity. Meanwhile, Wyant et al. (in press) and Watan-

abe et al. (2011) have found positive adjustments in the

SP-CAM and MIROC models respectively, coincident with

a shallowing of the boundary layer in subtropical regions.

GW08 also examined a parameter-perturbed version of

HadSM3, and showed that its low climate sensitivity was

due to a smaller global effective CO2 forcing term than

standard HadSM3 because of shortwave cloud effects.

Geographical maps of the forcing terms showed substantial

differences in the tropics and in mid-latitudes. Doutriaux-

Boucher et al. (2009) applied a similar analysis to a fully

coupled AOGCM related to HadSM3 including an

interactive carbon cycle (HadCM3LC) and found a sub-

stantial effect on the shortwave cloud adjustment over

northern hemisphere land areas. They attributed this to

the sensitivity of stomatal conductance to CO2 increases,

which affects low level cloudiness through suppressed

evapotranspiration.

Pincus et al. (2008) and Collins et al. (2010) correlated

global feedbacks from the AR4 models with various

measures of present-day model skill, including global mean

bias and root mean square error, but found no statistically

significant relationships. Klocke et al. (2011) argue that

global measures of model skill such as these may be

678 M. J. Webb et al.

123

unrelated to climate sensitivity because they are influenced

not only by the regions controlling the sensitivity, but also

by other regions with large present-day biases. Trenberth

and Fasullo (2010) do however find a statistically signifi-

cant anti-correlation between the global climate sensitivity

and the net downward top-of-atmosphere radiation at the

top of atmosphere averaged over the Southern Hemisphere

in the AR4 models.

GW08 did not examine the contributions of different

geographical regions to the differences in forcings and

feedbacks in the multi-model ensemble. Here we apply the

analysis of GW08 to a larger ensemble of slab models to

highlight the contributions of different regions of the globe

to inter-model differences in effective forcings, feedbacks

and climate sensitivity. This also enables us to look for

relationships between regional biases and forcings and

feedbacks arising in those same regions.

Although GW08 argued that cloud adjustments occur as

a response to changes in the structure of the troposphere in

direct response to CO2 increases, the possibility remains

that some of these changes are driven by rapid land

warming. Dong et al. (2009) show evidence of a warming

in the free troposphere spreading out from land regions in a

CO2 doubling experiment with the atmosphere component

of HadSM3, and Williams et al. (2008) show evidence of

rapid warming over land in the slab versions of HadSM3

and HadGEM1. Here we use the term ‘effective forcing’ to

encompass the effects of rapid adjustments (in the tropo-

sphere or the land surface), which happen on timescales

which are short compared to the timescale of the ocean

temperature response. Defining forcing and feedback in

this way is a pragmatic approach, which diagnoses the

climate feedback parameter in a way which is more

accurately applicable to a wider range of forcings. Another

benefit of this approach is that it can be applied consistently

across models, and yields forcing and feedback values

which will reproduce the correct time variation of the

global temperature response in simple energy balance

models used to emulate AOGCMs.

This study also aims to address certain questions in rela-

tion to CFMIP-2, the second phase of the Cloud Feedback

Model Intercomparison Project. CFMIP-2 aims to make

better use of observations to evaluate clouds in climate

models, and to gain a better understanding of the physical

mechanisms responsible for the inter-model spread in cli-

mate sensitivity attributable to clouds. Plans for these dif-

ferent activities are detailed on the project website at

http://www.cfmip.net, and summarised in Bony et al. (2011)

(CLIVAR Exchanges, May 20111). CFMIP-2 includes an

activity co-organised with the GEWEX Cloud System Study

(GCSS) Boundary Layer Cloud Working Group called

CGILS (CFMIP/GCSS Intercomparison of LES and SCM,

Zhang et al. May 2010, GEWEX News2). This intercom-

parison uses a set of idealised SCM/LES forcings designed to

represent the changes in the local environment of subtropical

clouds in the warmer climate, building on the work of Zhang

and Bretherton (2008), Wyant et al. (2009) and Blossey

et al. (2009), and aims to establish whether or not idealised

SCM experiments can reproduce the different feedbacks

seen in the global models, and whether or not the cloud

resolving models show a smaller spread. For activities such

as CGILS, it is useful to know not only which type of cloud to

focus on (e.g. persistent stratocumulus, fairweather cumulus

or stratocumulus-trade cumulus transition) but also the rel-

ative importance of CO2 forced cloud adjustments and cloud

feedbacks. It is also useful to know the extent to which dif-

ferent cloud-climate responses are due to different changes

in large-scale variables such as stability and vertical velocity.

Wyant et al. (2009) investigated this question in the SP-

CAM, by sorting a number of model variables into percen-

tiles of lower tropospheric stability (LTS) over the low

latitude oceans. We apply their technique to a wider range of

models here to inform CGILS and future related activities.

An outline of the present study follows. Section 2

describes the twelve atmosphere/mixed layer ocean ‘slab’

models from CMIP3/CFMIP-1 and fifteen parameter-per-

turbed versions of HadSM3 analysed, the observations, and

the variant of the GW08 method which we employ. In Sect.

3 we compare the relative sizes of the contributions of

inter-model differences in global effective forcings and

feedbacks (and their cloud components) to the range in

climate sensitivity in both ensembles. In Sect. 4 we com-

pare the contributions from low latitude oceans (30�N/S),

mid-latitude oceans (30–55�N/S), low/mid latitude land

(55�N/S) and high latitude ocean/land (55–90�N/S), and

look for relationships between them and biases in the

control simulations compared to observed climatologies in

those same regions. Section 5 highlights some unusual

behaviours in individual models which we consider worthy

of future investigation by the modelling groups. In Sect. 6

we sort cloud components of feedback and forcing terms

over the low latitude oceans into percentile ranges of LTS,

following the approach of Wyant et al. (2009). Composite

responses of LTS, EIS [estimated inversion strength, Wood

and Bretherton (2006)] and pressure velocity at 500 hPa are

also examined to see if any relationships are present

between these large scale forcings and the cloud terms. We

also examine composites from the control simulations and

compare them with observational estimates of the equiva-

lent quantities. We present our conclusions and plans for

future work in Sect. 7.

1 See http://www.clivar.org/publications/exchanges/exchanges.php. 2 See http://www.gewex.org.

Climate sensitivity, forcing and feedback in climate models 679

123

2 Model descriptions and analysis methods

Two slab model ensembles are analysed. The first is the

AR4 multi-model ensemble combining experiments from

the CFMIP-1 and CMIP3 archives at PCMDI. These are

summarised in Table 1, and are a combination of those

analysed in Webb et al. (2006), Gregory and Webb (2008)

and Williams and Webb (2009). Selected members of the

HadSM3 perturbed physics ensemble (PPE) from Webb

et al. (2006) are also analysed. These are the slab model

equivalents of the 17 AOGCM experiments described in

Collins et al. (2010), with the exception of the standard

unperturbed experiment (for which the response immedi-

ately following CO2 doubling required for the GW08

analysis is unavailable) and one other which has data

missing.

Earth radiation budget (ERB) observational estimates

are taken from the ERBE-S4G (Harrison et al. 1990) and

ISCCP-FD (Zhang et al. 1995) datasets. The same period

(February 1985–January 1990) is used in both cases. We

note that these datasets are not pure observations and that

they should be considered to be subject to an observational

uncertainty which may be greater than the differences

between them. We also use analyses from MERRA

(Rienecker et al. 2011) and ERA40 (Uppala et al. 2005)

for the same period.

We apply the analysis method of Gregory et al. (2004)

and GW08, with some modifications. We quantify the

magnitude of climate change in the CO2 doubling experi-

ments using the change in the global-mean near surface

temperature DT relative to the control state. We can

express the climate sensitivity (DT at equilibrium) as a

function of a radiative forcing f and a climate feedback

parameter K such that f þ KDT ¼ 0. Note that K has the

opposite sign convention to the a feedback parameter used

in GW08; i.e. K ¼ �a. Thus defined, a stable system will

have a negative climate feedback parameter. Positive

increments to f and K tend to increase climate sensitivity,

making f more positive and K less negative, while negative

increments tend to decrease climate sensitivity. Gregory

et al. (2004) showed that f and K can be estimated in CO2

doubling experiments by linearly regressing the 2CO2

minus control difference of the annual-mean time series of

the net downward radiation at the top of the atmosphere (N)

against DT . The intercept of the regression line with the

line DT ¼ 0 gives an estimate for f and its slope gives an

estimate of K. The effective climate sensitivity is an esti-

mated value for the climate sensitivity defined as �f=Kusing the values from the regression. This may differ

slightly from the actual equilibrium climate sensitivity, but

we use it for consistency. To improve signal-to-noise in the

regressions we use the full time series of model outputs in

the 2CO2 experiments, ranging from 25 years in HadSM3

to 95 years in GFDL AM2.0 (GW08 used the first 20 years

only). Ordinary least squares regression is used as in

GW08, but we perform our error analysis using the case

resampling bootstrap method (Efron and Tibshirani 1993).

The annual-mean time series is randomly sampled ‘with

replacement’, producing a sample the same size as the

original time series, in which some values will be usually

be duplicated and some not sampled. The regression is

applied to this sample, and the procedure is repeated 1,000

times, to produce 95 % (2.5–97.5 %) confidence intervals

on the slope and intercept values.

Figure 1 shows a scatterplot of global forcing versus

feedback values from the AR4 ensemble, and Fig. 2 shows

the equivalent for the PPE. Statistical uncertainties in the

estimation of the forcing and feedback values for each

model are illustrated by a cloud of points sampled using the

bootstrap method. Rather than being ellipses with hori-

zontal and vertical axes of symmetry, as would be expected

for two independent variables, these clouds of points tend

Table 1 List of AR4 CMIP3/

CFMIP-1 models used in this

study

Atmospheric model References

IPSL-CM4 (CFMIP-1) Hourdin et al. (2006)

HadGEM1 (CMIP3) Martin et al. (2006), Johns et al. (2006)

MIROC3.2 medres (CMIP3) Hasumi and Emori (2004)

CCCMA CGCM4 (CFMIP-1) von Salzen et al. (2005)

HadCM3 (CFMIP-1) Pope et al. (2000), Gordon et al. (2000)

ECHAM5/MPI-OM (CFMIP-1) Roeckner et al. (2003)

MRI-CGCM2.3.2a (CMIP3) Yukimoto et al. (2006)

CSIRO-Mk3.0 (CMIP3) Gordon et al. (2002)

CCCMA CGCM3.1(T63) (CMIP3) http://www.cccma.bc.ec.gc.ca/models/

cgcm3.shtml

GFDL-CM2.0 (CMIP3) Delworth et al. (2006)

NCAR CCSM3 (CMIP3) Collins et al. (2006b)

GISS-ER (CMIP3) Schmidt et al. (2006)

680 M. J. Webb et al.

123

to be roughly aligned with and confined to be close to lines

of constant climate sensitivity. This indicates that the

uncertainties in the forcing and feedback estimates for each

model are anti-correlated. Our interpretation of this is as

follows. Firstly, the climate sensitivity for each model is

well estimated because we include all years of model data

at equilibrium. This means that the intercept of the

regression line with the line y = 0 on the Gregory plot (e.g.

Figure 1 of GW08) will be well constrained. However,

models warm fastest in the first few years after CO2 dou-

bling, so relatively few sample points are available to

constrain the estimated forcing value, allowing interannual

variability to have a bigger impact on its uncertainty. Since

the climate sensitivity is well known, any uncertainty in the

forcing will lead to the regression line ‘pivoting’ around

the intercept with the line y = 0, so that a more positive

forcing/intercept produces a more negative feedback/slope,

consistent with the anti-correlation seen in the forcing and

feedback values errors here. This estimation uncertainty

can be reduced in future model intercomparisons by

increasing the signal-to-noise ratio. In the forthcoming

CMIP5 experiments this will be achieved by running fully

coupled AOGCM experiments subject to a stronger forcing

(CO2 quadrupling rather than doubling). These experi-

ments will be run for 150 years and will equilibrate more

slowly than the slab models analysed here, providing a

larger number of sample points spread out along the

regression line. Dividing the forcing estimate for CO2

quadrupling by two will give an estimate of the forcing due

to CO2 doubling with smaller error bars. This will also

result in a smaller uncertainty in the feedback parameter, as

the pivoting effect described above will be reduced by the

smaller uncertainty in the forcing. It will be interesting to

see whether the forcings and feedbacks diagnosed in these

experiments are consistent with those under CO2 doubling.

An initial condition ensemble will also be run for each

model, staggered at one month intervals to diagnose

responses on time scales of less than one year (Doutriaux-

Boucher et al. 2009), which will provide a more accurate

estimate of the forcing intercept value, and fixed SST

experiments subject to CO2 quadrupling will also provide

forcing estimates using the Hansen et al. (2002) method.

As in GW08, we use the difference between all-sky and

clear-sky fluxes to measure the effect of clouds on CO2

forcing and feedback. This difference is often referred to as

the ‘cloud radiative forcing’, but we prefer the term cloud

radiative effect (CRE) which is analogous to ‘greenhouse

effect’. As pointed out by Soden et al. (2004), the change

in the CRE is not the same as the cloud feedback diagnosed

using the partial radiative perturbation (PRP) method (e.g.

Wetherald and Manabe 1988; Colman 2003), or approxi-

mate versions of it (e.g. Soden and Held 2006; Taylor et al.

2007). PRP defines the cloud feedback as the partial

derivative of the TOA radiative flux with respect to a

change in cloud where all other quantities (e.g. water

vapour, surface albedo) are artificially held fixed. PRP also

diagnoses non-cloud feedbacks using partial derivatives,

including the effects of climatological cloud masking, for

example where the effect of a change in surface albedo is

not seen at the TOA because of persistent cloudiness. In

contrast, the CRE method diagnoses the non-cloud feed-

backs as they would be seen in clear-sky conditions with-

out the effects of cloud masking, while the cloud masking

effects on the non-cloud feedbacks are included in the

change in CRE along with the effect of any cloud changes.

Climatological cloud masking clearly has a substantial

impact on CO2 forcing, climate feedback and climate

-2.0 -1.5 -1.0 -0.5 0.0

Feedback (W/m2/K)

0

1

2

3

4

5

Eff

ectiv

e Fo

rcin

g (W

/m2 )

Δ T=2Δ T=3Δ T=4Δ T=5Δ T=6Δ T=7

GISS-ERCCSM3GFDL AM2.0CCCMA-CGCM3.1MRI-CGCM2.3.2ACSIRO Mk3.0MPI ECHAM5UKMO-HadSM3CCCMA CGCM4MIROC3.2(medres)HadGEM1IPSL-CM4

Fig. 1 Global mean forcings and feedbacks in the AR4 slab

ensemble. Squares and coloured ticks on the axes show the best

estimates, dots show the individual bootstrap values and the diamonds

show the 95 % (2.5–97.5 %) confidence intervals. The ensemble

mean forcing and feedback values and their ranges are shown using

horizontal and vertical black lines. Sloping lines of constant climate

sensitivity are shown in black. The legend shows the names of the

models ordered from highest to lowest climate sensitivity

-2.0 -1.5 -1.0 -0.5 0.0

Feedback (W/m2/K)

0

1

2

3

4

5

Eff

ectiv

e Fo

rcin

g (W

/m2 )

ΔT=2ΔT=3ΔT=4ΔT=5ΔT=6ΔT=7

aduvbadreaadtlgadumdadumfadseoadrhjadsebadsbdadsbbadsbhadryaadrhladryeadsea

Fig. 2 As Fig. 1, but for the PPE

Climate sensitivity, forcing and feedback in climate models 681

123

sensitivity. If, say, in a particular climate model, the sea ice

feedback were to be masked by a climatological excess of

cloud, then this would have as much potential to affect the

total feedback and climate sensitivity of that model as an

unusually large or small change in cloud. We consider the

CRE method acceptable for the purpose employed here,

although it does not separate these two effects. Our reason

for using the CRE method rather than the other possibilities

is mainly a practical one, in that the other methods require

additional diagnostics which are not available in all mod-

els. We wish to include as many models in our analysis as

possible, and all-sky plus clear-sky fluxes at TOA are

universally available. In the following text we refer to the

change in the CRE as the cloud component of the total

feedback or forcing, noting that this includes the net effect

of the cloud responses and cloud masking.

Although we regress various quantities against global

mean near-surface temperature, this does not mean that we

think that clouds respond to this quantity directly. This is

more a convenient analysis method which takes advantage

of the observation that top of atmosphere radiative fluxes

do respond quite linearly with global temperature in slab

models, as shown in Figure 2 of Gregory and Webb (2008).

Since this is the case, it seems likely that those factors

which are thought to influence the large scale cloud

response (e.g. stability, subsidence, moist physics) do also

scale linearly with the system warming. Defining forcing

and feedback in this way is a pragmatic approach, which

diagnoses the climate feedback parameter in a way which

is more accurately applicable to a wider range of forcings.

Another benefit of this approach is that it yields forcing and

feedback values which will reproduce the correct time

variation of the global temperature response in simple

energy balance models used to emulate AOGCMs. Given

that global top-of-atmosphere fluxes do respond quite lin-

early with temperature increases in slab models, we think

that this approach is justified here.

3 Global forcings and feedbacks

In this section we compare the relative sizes of the con-

tributions of inter-model differences in global effective

forcings and feedbacks (and their cloud components) to the

range in climate sensitivity in both ensembles.

For the AR4 ensemble the range in effective climate

sensitivity is 2.3 K (2.7–5.0 K) (Fig. 1; Table 2). If there

were no inter-model differences in climate feedback, and

all models had a feedback value equal to ensemble mean,

then the remaining range (due to forcing differences alone)

would be 1.5 K (2.9–4.3 K). This estimate of the contri-

bution of forcing differences to the spread in climate sen-

sitivity can be seen graphically in Fig. 1 by comparing the

full range of sensitivities with those spanned by the thick

black vertical line, which covers the range of model forcing

values at the ensemble mean value of the feedback

parameter. Conversely, if there were no inter-model dif-

ferences in CO2 forcing, and all models had a forcing value

equal to the ensemble mean, then the resulting range (due

to feedback differences alone) would be 2.9 K (2.6–5.4 K).

(See the thick black horizontal line.) The range in climate

sensitivity in the presence of global feedback differences

Table 2 Global effective

forcings, feedbacks and

effective climate sensitivities

for the AR4 ensemble

The values in brackets indicate

95 % (2.5–97.5 %) confidence

intervals relative to the best

estimate. Note that the ensemble

mean climate sensitivity is not

exactly the same as the climate

sensitivity predicted by the

ensemble mean forcing and

feedback

Model Forcing, f (W m-2) Feedback, K(W m-2 K-1)

Effective climate

sensitivity, DT (K)

IPSL CM4 3.70 (-0.51, 0.43) -0.75 (-0.11, 0.13) 4.96 (-0.18, 0.25)

HadGEM1 3.13 (-0.35, 0.24) -0.67 (-0.06, 0.09) 4.68 (-0.16, 0.19)

MIROC 3.2 medres 4.27 (-0.47, 0.25) -1.04 (-0.07, 0.12) 4.11 (-0.07, 0.08)

CCCMA CGCM4 4.02 (-0.82, 0.24) -1.00 (-0.07, 0.24) 4.03 (-0.11, 0.19)

HadSM3 3.71 (-0.94, 0.34) -1.00 (-0.12, 0.28) 3.73 (-0.17, 0.23)

MPI ECHAM5 4.49 (-0.65, 0.65) -1.28 (-0.20, 0.22) 3.52 (-0.11, 0.13)

CSIRO Mk3 3.00 (-0.55, 0.26) -0.89 (-0.09, 0.19) 3.37 (-0.09, 0.18)

MRI CGCM2 3.06 (-0.89, 0.62) -0.96 (-0.20, 0.30) 3.19 (-0.10, 0.14)

CCCMA CGCM3.1 4.40 (-0.61, 0.42) -1.42 (-0.13, 0.19) 3.11 (-0.05, 0.05)

GFDL AM2.0 2.97 (-0.69, 0.26) -1.02 (-0.09, 0.24) 2.90 (-0.05, 0.06)

NCAR CCSM3.0 3.03 (-0.38, 0.16) -1.12 (-0.07, 0.16) 2.72 (-0.05, 0.06)

GISS-ER 3.64 (-0.60, 0.31) -1.35 (-0.11, 0.24) 2.69 (-0.03, 0.04)

Ensemble mean 3.62 -1.04 3.58

Effective climate sensitivity 3.48

predicted by ��f=�K

Ensemble range 1.52 0.75 2.27

682 M. J. Webb et al.

123

alone is almost twice that with global effective forcing

differences alone, indicating that feedback differences

make the largest contribution. This result is qualitatively

consistent with those obtained by earlier studies which did

not allow for the effects of rapid cloud adjustments on

radiative forcing (Webb et al. (2006) using a subset of the

models examined here and Dufresne and Bony (2008)

using the CMIP-3 AOGCMs). The contribution from

forcing differences is substantial however, and 50 % larger

than the equivalent estimate from Webb et al. (2006).

Climate sensitivity range is a statistic commonly quoted for

climate models, but it has the disadvantage of being

determined by the models at the extremes, and contains no

information on the distribution of values within. Consid-

ering ensemble variance in climate sensitivity yields fairly

similar results to those described above (Table 4).

There is an anti-correlation (r = - 0.52) between

feedbacks and forcings in the AR4 ensemble, which can be

seen in Fig. 1 as a tendency for models with more negative

feedbacks to have more positive forcing values and those

with less negative feedback values to have less positive

forcing values. This anti-correlation is not significant at the

95 % level however, and so may simply be due to a chance

distribution of feedback and forcing values arising from the

different physical assumptions in the models. The range of

climate sensitivities calculated using feedback differences

alone is larger than that with forcing and feedback differ-

ences considered together. Contributions from the cloud

components of the feedback also give a larger range than

cloud components of the feedback and forcing taken

together (Table 4). This is not the case for the clear-sky

equivalents, so we conclude that cloud effects are the main

cause.

Note that it is important not to confuse the anti-corre-

lation between the best estimates of the forcings and

feedbacks across the ensemble with the much stronger anti-

correlations seen between regression error estimates for the

forcing and feedback in each individual model. The latter is

a consequence of using the Gregory et al. (2004) method

while the former is a property of the ensemble. We know

this because Webb et al. (2006) previously found the range

in climate sensitivities calculated using feedback differ-

ences alone to be larger than that with forcing and feedback

differences in a subset of the AR4 ensemble, but using a

more conventional forcing diagnosis method which did not

use regression and made no allowance for rapid cloud

adjustment.

For the PPE, the range in effective climate sensitivity is

4.6 K (2.2–6.9 K) (Fig. 2; Table 3), which is considerably

larger than in the AR4 ensemble. One might expect that an

ensemble based on a single model would be more tightly

constrained than the AR4 ensemble (for example through

the use of a single radiative transfer code in the case of the

CO2 forcing). This is not the case here however. This is

partly due to the experimental design of the PPE, which

aims to reflect the full range of sensitivities possible with

perturbations of HadSM3, while placing constraints on top-

of-atmosphere radiative balance which are relatively weak

Table 3 As Table 2 but for the

PPEModel Forcing, f (W m-2) Feedback,

K (W m-2K-1)

Effective climate

sensitivity DT (K)

adsea 2.75 (-0.62, 0.38) -0.40 (-0.06, 0.10) 6.87 (-0.14, 0.20)

adrye 2.72 (-0.27, 0.19) -0.49 (-0.04, 0.06) 5.53 (-0.18, 0.21)

adrhl 3.23 (-0.70, 0.42) -0.67 (-0.10, 0.16) 4.81 (-0.15, 0.20)

adrya 3.48 (-0.80, 0.41) -0.74 (-0.09, 0.18) 4.72 (-0.08, 0.13)

adsbh 3.27 (-0.34, 0.49) -0.70 (-0.11, 0.08) 4.68 (-0.14, 0.14)

adsbb 3.70 (-0.54, 0.38) -0.82 (-0.08, 0.13) 4.54 (-0.08, 0.10)

adsbd 2.93 (-0.45, 0.48) -0.65 (-0.12, 0.12) 4.48 (-0.16, 0.21)

adseb 3.20 (-0.35, 0.20) -0.72 (-0.06, 0.08) 4.44 (-0.13, 0.15)

adrhj 2.84 (-0.54, 0.33) -0.72 (-0.09, 0.17) 3.97 (-0.15, 0.23)

adseo 3.48 (-0.66, 0.30) -1.03 (-0.09, 0.21) 3.39 (-0.09, 0.11)

adumf 2.80 (-1.08, 1.02) -0.92 (-0.35, 0.38) 3.03 (-0.17, 0.25)

adumd 3.16 (-0.41, 0.93) -1.11 (-0.38, 0.18) 2.86 (-0.15, 0.19)

adtlg 2.86 (-0.71, 1.15) -1.11 (-0.49, 0.33) 2.58 (-0.21, 0.39)

adrea 2.15 (-0.68, 0.85) -0.86 (-0.39, 0.33) 2.52 (-0.18, 0.30)

aduvb 2.39 (-1.58, 1.53) -1.08 (-0.76, 0.80) 2.22 (-0.22, 0.51)

Ensemble mean 3.00 -0.80 4.04

Effective climate sensitivity

predicted by ��f=�K3.75

Ensemble range 1.55 0.71 4.65

Climate sensitivity, forcing and feedback in climate models 683

123

compared to those applied to the AR4 ensemble (Sexton

et al. 2012).

The ranges in climate sensitivity due to forcing differ-

ences alone and feedback differences alone are both larger

than in the AR4 ensemble, but the two ensembles are

qualitatively consistent in the sense that inter-model dif-

ferences in feedbacks contribute roughly twice as much as

differences in forcings to the range in effective climate

sensitivity (Tables 4, 5).

Cloud and clear-sky components of the forcing and

feedback terms are also calculated, as in GW08. Cloud

components of forcing and feedback are more than

capable of explaining the full range in climate sensitivity

in the AR4 ensemble, and cloud components of the global

feedback contribute more than four times as much as

cloud components of the global forcing (Table 4). In the

PPE however, cloud components of forcing and feedback

terms alone give a range less than half the size of the

total, and cloud components of the forcing contribute

more than the cloud components of the feedback

(Table 5).

4 Regional contributions to forcings and feedbacks

Here we decompose the global mean forcings and feed-

backs and their cloud components into contributions from

low latitude ocean regions (30�N/S), mid-latitude oceans

(30–55�N/S), low-mid latitude land (55�N/S) and high

latitude ocean/land (55–90�N/S), and look for relationships

between them and regional biases in the control simula-

tions compared to observed climatologies. The boundaries

between latitude bands were chosen as far as possible to lie

between the major features of the annual mean net CRE

climatology from ERBE (not shown), and to fit in with

regions used in previous studies (e.g. Bony and Dufresne

2005). The low latitude ocean is the largest region, cov-

ering 30 % of the globe, and includes the deep convection

associated with the ITCZ and warm pool, the persistent

stratocumulus decks in the Eastern basins of the subtropical

oceans in the vicinity of California, Peru, Namibia, Aus-

tralia and the Canaries, and the trade cumulus regions. We

define mid-latitude oceans as those between (30–55�N/S),

which contain most of the cloud associated with the mid-

latitude oceanic storm tracks. The 55� boundary was cho-

sen to exclude sea ice from the mid-latitude regions, and

was chosen by examining climatologies of clear-sky

reflected shortwave radiation from ERBE. We consider

high latitude land and ocean regions poleward of 55�N/S

together. We also consider the remaining low-mid latitude

land areas as a single region. Forcing and feedback con-

tributions from each region are calculated by regressing the

regional average quantities against the global near-surface

temperature response, following the regional feedback

decomposition of Boer and Yu (2003).

To compare the contributions of the different regions to

the range in climate sensitivity, we use a similar procedure

to that described above when comparing global forcing and

feedback values. For each model, we calculate the effective

climate sensitivity using the forcing and feedback values

from the region of interest, but using ensemble mean val-

ues elsewhere. This procedure includes area weighting, so

that for example a forcing value which is 1 W m-2

stronger than the ensemble mean value over the low lati-

tude oceans will contribute an extra 0.3 W m-2 to the

global forcing value, and an extra 0.3 K to the climate

sensitivity (the ensemble mean feedback parameter being

close to -1). Although we refer to these values as contri-

butions, it is important to note that they do not add up

exactly. This is because of the non-linearity of the effective

climate sensitivity equation, which has the feedback

parameter on its denominator. Still, this procedure provides

Table 4 Contributions to the effective climate sensitivity (K) range and variance from global forcing and feedback components in the AR4

ensemble

Total range, variance Net cloud Shortwave clear Longwave clear

Feedback and forcing 2.3, 0.55 3.5, 0.99 1.5, 0.21 2.0, 0.31

Feedback only 2.9, 0.70 4.3, 1.30 1.4, 0.20 1.7, 0.30

Forcing only 1.5, 0.31 0.9, 0.11 1.0, 0.09 1.0, 0.07

Table 5 As Table 4 but for the PPE

Total range, variance Net cloud Shortwave clear Longwave clear

Feedback and forcing 4.6, 1.64 2.2, 0.49 0.7, 0.05 1.5, 0.16

Feedback only 4.8, 1.73 1.5, 0.17 1.0, 0.08 2.4, 0.35

Forcing only 1.9, 0.28 2.1, 0.36 0.5, 0.01 0.7, 0.04

684 M. J. Webb et al.

123

an indication of the relative sizes of the contributions from

the different regions.

In the AR4 ensemble, differences in forcings and feed-

backs over the low latitude oceans alone can explain a

range in climate sensitivity two thirds the size of the total

range, almost twice as much as any of the other regions

(Fig. 3a). This is more than would be expected on the basis

of area alone; if the different feedbacks and forcings were

globally uniform in the models, then the low latitude

oceans would explain only 30 %. Feedback differences in

this region contribute about twice as much as forcing dif-

ferences (Fig. 3b, c). These can in turn be largely explained

by their cloud components (Fig. 4b, c). These results are

consistent with those of Bony et al. (2006), in that they

indicate that the ocean regions between 30�N/S show a

larger inter-model range in feedback spread than the

extratropics. Contributions from these other regions are

still substantial however, and are required to account fully

for the highest model climate sensitivities, none of which

can be explained entirely by the contributions from the low

latitude oceans. In the PPE, the low-latitude oceans also

make the largest contribution, but this is only a third of the

size of the global climate sensitivity range in this case, and

contributions from mid-latitude oceans and high latitudes

are relatively more important than in the AR4 ensemble,

being required to explain all of the higher model sensitiv-

ities (Fig. 5).

Non-cloud components also contribute in some models.

For example, the largest contribution to the higher-than-

average sensitivity of HadGEM1 comes from the feedback

term in the high latitude region (Fig. 3). However, the cloud

component of the feedback term is very close to the

ensemble mean in this region (Fig. 4). This feature of

HadGEM1 is explained by a stronger than average short-

wave clear-sky feedback term (not shown). The most likely

explanation for this is a stronger than average positive sur-

face albedo feedback associated with changes to surface

snow and sea ice. (This is discussed in more detail in Sect. 5.)

We also compare the net downward top-of-atmosphere

radiative flux and the net CRE from the control simulations

with observed equivalents from ERBE and ISCCP-FD. In

the AR4 ensemble, there is no obvious relationship

between the global mean biases in these quantities and the

climate sensitivity, or the contributions from global forc-

ings or feedbacks (Figs. 3, 4). This null result is not rep-

licated in the PPE however, where there is a distinct

tendency for higher sensitivity models in the PPE to be

associated with positive biases in net downward top-of-

atmosphere radiation and net CRE in the global mean, and

for lower sensitivity models to have negative biases

(Figs. 5, 6). The climate sensitivities in the PPE are posi-

tively correlated with the global net downward radiation in

the control simulations (r = 0.91, significant at the 95 %

level). This is mainly attributable to relationships between

the control values and the cloud components of the feed-

back terms in the mid-latitude ocean and high latitude

regions. These can in turn be attributed to the shortwave

cloud components in these regions (not shown).

Possible situations in which present-day biases in clouds

might affect cloud feedbacks have been suggested in a

number of studies. For example, Williams and Tselioudis

(2007) argued that excessive cloud fraction (or frequency

of occurrence) might possibly lead to a cloud feedback

which is too strong, for a given change in cloud optical

thickness. Similarly, excessive cloud optical thickness

might lead to a cloud feedback which is too strong for a

given change in cloud fraction. Another possibility was

raised by Trenberth and Fasullo (2010), who argued that

models which underpredict cloud fraction might have more

scope for increases in cloud fraction than would be possible

in the real world in locations which are nearly overcast in

reality (for example in over the Southern Oceans).

Yokohata et al. (2010) analysed the full set of experi-

ments from which our PPE is drawn, and showed that

model versions with a larger global mean low level cloud

cover and cloud albedo tend to have stronger negative

global mean shortwave cloud responses at equilibrium and

lower climate sensitivities (see their Fig. 11). Our results

indicate that this effect is mainly due to feedbacks oper-

ating in midlatitudes. Separating the contributions from the

Northern and Southern Hemisphere mid-latitude regions

(not shown) indicates that this is due the the contribution

from the southern midlatitudes, illustrating the importance

of feedbacks in this region as highlighted by Trenberth and

Fasullo (2010). It is possible that the potential for similar

relationships might be present in AR4 ensemble, but that

these are not apparent because many of the models have

been tuned to agree with observations.

5 Unusual behaviours in individual models

Here we highlight unusual forcings and feedbacks in

individual models, focusing on the global forcings and

feedbacks which have confidence intervals distinct from

the ensemble means in Figs. 1 and 2. We also highlight any

substantial control biases which coincide with them.

The higher-than-average sensitivity of IPSL CM4 is

entirely due to its global feedback parameter, as its global

forcing value is close the the ensemble mean (Fig. 1;

Table 2). This is primarily due to cloud feedback compo-

nents which are more positive than the ensemble mean over

low latitude ocean and low/mid-latitude land areas

(Figs. 3b, 4b) which are in turn all due to the corresponding

shortwave cloud feedback components (not shown). These

are the most strongly positive in the ensemble.

Climate sensitivity, forcing and feedback in climate models 685

123

(a) AR4 Ensemble Climate Sensitivity

2

3

4

5

6

7

Clim

ate

Sens

itivi

ty (

K)

Global

range =2.3K

variance =0.55

Low Lat Ocean

1.5K

0.15

Mid Lat Ocean

0.7K

0.05

High LatLand/Ocean

0.8K

0.05

GISS-ERCCSM3GFDL 2.0 CCCMA-3MRI-2.3.2ACSIRO Mk3 ECHAM5HadSM3CCCMA-4MIROC3.2HadGEM1IPSL-CM4

Low/MidLat Land

0.8K

0.07

(b) AR4 Ensemble Contributions from Feedback

2

3

4

5

6

7

Clim

ate

Sens

itivi

ty (

K)

Global

range =2.9K

variance =0.70

Low Lat Ocean

1.4K

0.17

Mid Lat Ocean

0.8K

0.09

High LatLand/Ocean

0.9K

0.06

Low/MidLat Land

1.3K

0.11

(c) AR4 Ensemble Contributions from Forcing

2

3

4

5

6

7

Clim

ate

Sens

itivi

ty (

K)

Global

range =1.5K

variance =0.31

Low Lat Ocean

0.8K

0.07

Mid Lat Ocean

0.4K

0.02

High LatLand/Ocean

0.5K

0.02

Low/MidLat Land

0.6K

0.03

(d) AR4 Ensemble Control Net TOA Radiation Bias (W/m2)

-20

-15

-10

-5

0

5

10

Con

trol

GlobalLow Lat Ocean

Mid Lat Ocean

High LatLand/Ocean

Low/MidLat Land

Fig. 3 Climate sensitivities in the AR4 ensemble with regional contri-

butions (a). Climate sensitivities due to feedback (b) and forcing

differences (c). Net down TOA radiation biases relative to ERBE and

ISCCP FD (d). Regional values are area weighted so that inter-model

differences sum to global equivalents. Black lines denote observational

estimates on d and the climate sensitivity from the ensemble mean forcing

and feedback on a–c. Ranges (maximum–minimum) and variances are

annotated in black. Models are presented in order of climate sensitivity

686 M. J. Webb et al.

123

(a) AR4 Ensemble Cloud Component of Climate Sensitivity

2

3

4

5

6

7

Clim

ate

Sens

itivi

ty (

K)

Global

range =3.5K

variance =0.99

Low Lat Ocean

1.9K

0.25

Mid Lat Ocean

1.0K

0.09

High LatLand/Ocean

0.7K

0.06

GISS-ERCCSM3GFDL 2.0 CCCMA-3MRI-2.3.2ACSIRO Mk3 ECHAM5HadSM3CCCMA-4MIROC3.2HadGEM1IPSL-CM4

Low/MidLat Land

1.0K

0.11

(b) AR4 Ensemble Contributions from Cloud Component of Feedback

2

3

4

5

6

7

Clim

ate

Sens

itivi

ty (

K)

Global

range =4.3K

variance =1.30

Low Lat Ocean

1.6K

0.20

Mid Lat Ocean

1.5K

0.15

High LatLand/Ocean

0.8K

0.06

Low/MidLat Land

1.9K

0.27

(c) AR4 Ensemble Contributions from Cloud Component of Forcing

2

3

4

5

6

7

Clim

ate

Sens

itivi

ty (

K)

Global

range =0.9K

variance =0.11

Low Lat Ocean

0.7K

0.05

Mid Lat Ocean

0.6K

0.03

High LatLand/Ocean

0.3K

0.01

Low/MidLat Land

0.8K

0.04

(d) AR4 Ensemble Control Net CRE Bias (W/m2)

-20

-15

-10

-5

0

5

10

Con

trol

GlobalLow Lat Ocean

Mid Lat Ocean

High LatLand/Ocean

Low/MidLat Land

Fig. 4 As previous figure, but for net CRE contributions only

Climate sensitivity, forcing and feedback in climate models 687

123

(a) PPE Climate Sensitivity

2

3

4

5

6

7

Clim

ate

Sens

itivi

ty (

K)

Global

range =4.6K

variance =1.64

r =0.91

Low Lat Ocean

1.5K

0.20

Mid Lat Ocean

1.2K

0.16

0.96

High LatLand/Ocean

1.1K

0.12

aduvbadreaadtlgadumdadumfadseoadrhjadsebadsbdadsbbadsbhadryaadrhladryeadsea

Low/MidLat Land

0.5K

0.03

(b) PPE Contributions from Feedback

2

3

4

5

6

7

Clim

ate

Sens

itivi

ty (

K)

Global

range =4.8K

variance =1.73

r =0.90

Low Lat Ocean

2.6K

0.37

Mid Lat Ocean

1.2K

0.14

0.87

High LatLand/Ocean

1.4K

0.17

Low/MidLat Land

0.7K

0.04

(c) PPE Contributions from Forcing

2

3

4

5

6

7

Clim

ate

Sens

itivi

ty (

K)

Global

range =1.9K

variance =0.28

Low Lat Ocean

1.3K

0.10

Mid Lat Ocean

0.6K

0.03

High LatLand/Ocean

0.5K

0.02

Low/MidLat Land

0.4K

0.02

(d) PPE Control Net TOA Radiation Bias (W/m2)

-20

-15

-10

-5

0

5

10

Con

trol

GlobalLow Lat Ocean

Mid Lat Ocean

High LatLand/Ocean

Low/MidLat Land

Fig. 5 As Fig. 3 but for the PPE. Correlations with control biases which take values greater than r = 0.8 are annotated in black

688 M. J. Webb et al.

123

(a) PPE Cloud Component of Climate Sensitivity

2

3

4

5

6

7

Clim

ate

Sens

itivi

ty (

K)

Global

range =2.2K

variance =0.49

r =0.83

Low Lat Ocean

2.3K

0.27

Mid Lat Ocean

1.4K

0.18

0.96

High LatLand/Ocean

1.1K

0.09

aduvbadreaadtlgadumdadumfadseoadrhjadsebadsbdadsbbadsbhadryaadrhladryeadsea

Low/MidLat Land

1.5K

0.13

(b) PPE Contributions from Cloud Component of Feedback

2

3

4

5

6

7

Clim

ate

Sens

itivi

ty (

K)

Global

range =1.5K

variance =0.17

Low Lat Ocean

4.1K

0.95

Mid Lat Ocean

1.3K

0.16

0.90

High LatLand/Ocean

1.0K

0.08

0.86

Low/MidLat Land

2.4K

0.33

(c) PPE Contributions from Cloud Component of Forcing

2

3

4

5

6

7

Clim

ate

Sens

itivi

ty (

K)

Global

range =2.1K

variance =0.36

Low Lat Ocean

1.4K

0.15

Mid Lat Ocean

0.5K

0.02

High LatLand/Ocean

0.3K

0.01

Low/MidLat Land

0.8K

0.05

(d) PPE Control Net CRE Bias (W/m2)

-20

-15

-10

-5

0

5

10

Con

trol

GlobalLow Lat Ocean

Mid Lat Ocean

High LatLand/Ocean

Low/MidLat Land

Fig. 6 As Fig. 4 but for the PPE. Colours are as in Fig. 2

Climate sensitivity, forcing and feedback in climate models 689

123

The higher-than average sensitivity of HadGEM1 is also

due to its global feedback parameter (Fig. 1; Table 2). This

is partly due to positive cloud feedbacks over the mid-lat-

itude oceans (Figs. 3b, 4b) which are mainly explained by

their shortwave components (not shown). The largest con-

tribution is however from the high latitudes (Fig. 3b) and is

due to a stronger-than-average positive clear-sky shortwave

feedback, which coincides with a substantial negative bias

in the absorbed shortwave clear-sky radiation at high lati-

tudes in the control compared to observations, indicating

excessive clear-sky albedo (not shown). These results are

consistent with Johns et al. (2006), who note that sea ice

extents in the slab version of HadGEM1 are too extensive.

Sea ice extents are smaller in the fully coupled AOGCM

version of HadGEM1, and they attribute this difference to

the ice thickness distribution component of the sea ice

scheme in HadGEM1 being dependent on coupled ocean

effects which are not well represented in the slab model.

They also show that removing the ice thickness distribution

scheme from the slab model version of HadGEM1 reduces

the climate sensitivity by 1K. This brings the slab model

sensitivity much closer to that of the fully coupled version

of HadGEM1 estimated by Williams et al. (2008).

The higher-than-average sensitivity of MIROC 3.2

medres is mainly due to its strong forcing parameter

(Fig. 1; Table 2). This is largely due to the cloud compo-

nents of the forcing originating in the low and mid latitude

ocean and land regions (Figs. 3c, 4c) which are mainly

explained by their shortwave cloud components (not

shown). In this case the global cloud adjustment contrib-

utes more to the higher-than-average climate sensitivity of

MIROC3.2 than the cloud feedback component (in contrast

to IPSL CM4 where the reverse is true). This demonstrates

the fact that, although cloud components of the CO2 forc-

ing cannot explain as much of the sensitivity range as cloud

feedback components, they do have a substantial impact on

some models. Williams and Webb (2009) show evidence of

the net CRE in MIROC 3.2 medres becoming substantially

more positive at equilibrium after CO2 doubling in strato-

cumulus and trade cumulus/stratocumulus transition

regimes. Our results suggest that these are most likely due

to rapid cloud adjustments. A substantial negative net CRE

bias is present over the low latitude oceans, which is the

second largest in the ensemble, due to a shortwave CRE

bias which is the largest (not shown).

MPI ECHAM5 shows a large global forcing and

strongly negative feedback, the net effect of which is a mid-

range climate sensitivity. This cancellation is mainly due to

cloud components of forcing and feedback over the low and

mid-latitude oceans (again mainly shortwave, not shown).

CCCMA CGCM3.1 has a similar global compensation

arising in the same regions. However the feedback con-

tribution is mainly a clear-sky longwave effect, while the

forcing contribution is mainly due to the shortwave cloud

component (not shown).

The lower-than average sensitivity of GFDL AM2.0 is

mainly due to its global forcing parameter, which is the

smallest in the AR4 ensemble. This behaviour is unusually

uniform; all of the regions contribute a small amount.

However, no single forcing component dominates in any of

them.

The lower-than-average sensitivity of NCAR CCSM3.0

can also be explained mostly in terms of its small forcing

value. The largest contributions are in the mid and high-

latitude ocean regions, and are primarily due to cloud

effects (mainly the shortwave components, not shown). It is

also worth noting however that NCAR CCSM3.0 has the

strongest negative total and cloud feedback components in

the ensemble over the low latitude oceans (again mainly

due to the shortwave cloud component, not shown). A

substantial negative shortwave CRE bias (the largest in the

ensemble) is also present here (not shown). We analyse

cloud feedbacks over the low latitude oceans in more detail

in Sect. 6.

GISS-ER has the lowest sensitivity, mainly due to its

strongly negative global feedback parameter (Fig. 1;

Table 2). This is primarily due to a clear-sky feedback

component over the mid-latitude oceans and a shortwave

cloud effect over the low latitude oceans (not shown).

Regression uncertainties are larger in PPE than the AR4

ensemble, particularly at the low sensitivity end of the

range, and only two of the lower sensitivity models in the

PPE (adumd and adrea) have forcing or feedback values

whose confidence intervals are distinct from the ensemble

mean (Fig. 2; Table 3). We attribute this to the fact that

low sensitivity models reach equilibrium more quickly, and

so have fewer values which the linear regression can be

applied to, resulting in a smaller signal to noise ratio. The

problem is exacerbated in the PPE because the low sensi-

tivity model versions tend to have more interannual vari-

ability (not shown).

The two highest sensitivity models adsea and adrye are

similar in that both of their high sensitivities are due to

their global feedback parameters (Fig. 2; Table 3). In

adsea, low latitude ocean, mid-latitude ocean and high

latitude regions all make comparable contributions

(Fig. 5). The low-latitude contribution is mainly due to the

clear-sky longwave feedback component (not shown).

Stainforth et al. (2005) showed that versions of HadSM3

with low values of the convective entrainment parameter

tend to have high climate sensitivities. Joshi et al. (2010)

found a positive stratospheric water vapour feedback in an

experiment with the convective entrainment set to 0.6,

caused by a buildup of relative humidity below the tropo-

pause which provided a source for increased stratospheric

water vapour. adsea has an entrainment parameter which is

690 M. J. Webb et al.

123

near the lower end of the range explored in our PPE

(Table 6), which suggests a possible contribution from this

feedback mechanism. However, this value is not as low as

in Joshi et al. (2010), because model versions with very

low entrainment values were screened out in the selection

process for members of the PPE, which included an

assessment of present day performance using the index of

Murphy et al. (2004). adsea also has a quite small ice fall

speed parameter, which Rougier et al. (2009) showed has a

tendency to increase the climate sensitivity in HadSM3.

Reduced ice fall speeds might be expected to increase

upper tropospheric humidity and hence affect the water

vapour feedback. Over the mid-latitude oceans, the cloud

component dominates (Fig. 6), mainly due to the longwave

component (not shown). This may also be a consequence of

the reduced ice fall speeds. The high latitude component is

mainly due to a strongly positive clear-sky shortwave

feedback (not shown) which is the strongest in the

ensemble, and is presumably due to a surface albedo

feedback. The value of the ocean ice diffusion parameter in

adsea is at the upper end of the ensemble range, but

Rougier et al. (2009) show that high values of this

parameter generally tend to reduce the climate sensitivity

of HadSM3. Rougier et al. (2009) also showed that large

values of precipitation efficiency and small values of the

precipitation threshold parameters (both of which adsea

has) tend to increase climate sensitivity in HadSM3. So it

seems likely that these contribute also.

The feedback value for adrye can be attributed to the

same regions as adsea. Again, the low-latitude contribu-

tion is mainly due to the clear-sky longwave feedback

component (not shown). adrye has a relatively large

critical relative humidity threshold, which Rougier et al.

(2009) have shown tends to increase the climate

sensitivity of HadSM3. A larger critical relative humidity

would be expected to inhibit the formation of cloud and

subsequent fallout of ice at upper levels, leading to a

moister upper troposphere in the control simulation. This

could affect the water vapour feedback, as discussed

above. Over the mid-latitude oceans, the cloud component

dominates, but is mainly a shortwave effect in this case

(not shown). The high latitude component is due to a

combination of clear-sky shortwave and shortwave cloud

feedback components (not shown). These shortwave cloud

feedback components may well be due to the precipitation

efficiency value of adrye, which is near the upper end of

the range. Negative shortwave cloud feedbacks can occur

at mid-high latitudes in models, for example when low-

level ice clouds change into liquid water clouds with

smaller drops in the warmer climate (Senior and Mitchell

1993). It is possible that a larger precipitation efficiency

for warm clouds might reduce liquid water contents to

such an extent that this negative shortwave cloud feed-

back would become weaker or even positive. The clear-

sky shortwave feedback component may be caused by the

relatively large value of the sea ice albedo at 0�C in

adrye, which is shown by Rougier et al. (2009) to

increase the climate sensitivity in HadSM3.

adsbb has the strongest global forcing, the largest con-

tribution to which originates over the low latitude oceans

(mainly a shortwave cloud effect, not shown). This model

version has one of the largest precipitation efficiencies in

the ensemble, which would be expected to reduce the

liquid water contents of low clouds. It also has the largest

value of the CAPE timescale parameter in the convection

scheme, which clearly has the potential to affect the

response of shallow and deep convection to changes in

stability following a rapid doubling of CO2.

Table 6 Selected parameter values and switches used in the perturbed physics ensemble for high-mid sensitivity versions

adsea adrye adrhl adrya adsbh adsbb adsbd adseb

Entrainment rate 2.37726 4.51062 3.75359 3.61375 3.16226 3.83715 2.42950 2.16414

Ice fall speed 0.54306 0.64807 0.52716 0.99225 0.57054 0.87884 0.65138 0.53499

Flow dependent RHcrit On Off On On Off On Off Off

Critical rel. humidity Off 0.87689 Off Off 0.68958 Off 0.79788 0.67225

Sat. cloud fraction (BL) 0.51262 0.51606 0.50077 0.51981 0.62522 0.56161 0.50408 0.54746

Sat. CF (free trop.) 0.50631 0.50803 0.50038 0.50991 0.56261 0.53080 0.50204 0.52373

Precip. efficiency 3.50e-4 3.08e-4 1.97e-4 2.36e-4 2.50e-4 3.76e-4 9.90e-5 2.44e-4

Land precip. threshold 1.41e-4 3.23e-4 1.11e-4 1.91e-4 1.49e-4 1.67e-4 2.70e-4 1.04e-3

Ocean precip. threshold 3.23e-5 8.07e-5 2.33e-5 4.73e-5 3.47e-5 4.01e-5 6.75e-5 2.60e-4

Sea ice albedo at 0 �C 0.60104 0.64455 0.53872 0.64619 0.58361 0.58108 0.62855 0.63533

CAPE timescale Off 1.28 Off Off Off 2.43 1.39 Off

Ocean ice diffusion 3.73e-4 3.71e-4 3.62e-4 3.53e-4 3.59e-4 3.45e-4 3.54e-4 3.74e-4

Vertical gradient cloud area Off Off Off Off Off Off Off Off

Climate sensitivity, forcing and feedback in climate models 691

123

adumd has the most negative global feedback parameter

in the ensemble, the largest contribution to which arises

over the mid-latitude oceans (due to longwave cloud and

clear-sky components, not shown). This model version has

quite large values of saturated cloud fraction (Table 7), and

Rougier et al. (2009) show that this tends to result in lower

climate sensitivities in HadSM3.

adrea is the same model version analysed in GW08

(their ‘modified HadSM3’ experiment). Its low sensitivity

can be explained by its small global forcing value, which

originates mainly over the low latitude oceans (primarily

due to a negative shortwave cloud component, not shown).

Negative feedbacks in the mid-latitude ocean and high

latitude regions also contribute substantially. The mid-lat-

itude feedback is mainly a shortwave cloud effect, which

coincides with a strong negative shortwave CRE bias in the

control, the largest in the ensemble (not shown). adrea has

values of saturated cloud fraction which are very close the

the upper end of the range, and one of the smallest values

of precipitation efficiency. Both of these factors would be

expected to increase cloud amount and cloud water in the

control simulation, and have been shown by Rougier et al.

(2009) to reduce the climate sensitivity of HadSM3.

6 Cloud responses in stability regimes over low

latitude oceans

In this section we sort cloud components of the feedback

and forcing terms over the low latitude oceans into per-

centile ranges of LTS, following the approach of Wyant

et al. (2009). This is to see the extent to which the model

features described above originate from different stability

regimes of the tropics. If the conclusions of Williams and

Webb (2009) apply to this more diverse set of models, then

we would expect to see the largest differences in the stable

regions, where stratocumulus clouds are frequent.

Composite responses of LTS, EIS (Wood and Brether-

ton (2006) and pressure velocity at 500 hPa are also

examined to see if any relationships are present between

these large scale forcings and the cloud terms. EIS is

included because it is a slightly better predictor of observed

variations in low cloud amount, and because it makes an

allowance for the systematic increase in static stability due

to the warming free troposphere staying close to a moist

adiabat (Wood and Bretherton 2006). We also examine

composites from the control simulations and compare them

with observational estimates of the equivalent quantities

from ERBE, ISCCP FD, ERA40 and MERRA.

The G04 method is applied in the LTS composite

framework as follows. Ocean grid points between 30�N/S in

each monthly mean are sorted by LTS (defined as the dif-

ference between the potential temperature at the surface and

700 hPa). These are then divided into ten bins covering

equal areas. Ten area-weighted averages of the CRE are

then calculated across these bins for each monthly mean in

the control and CO2 integrations. These are annually aver-

aged, after which the G04 method is applied to estimate

forcing and feedback terms. Bin values are regressed

against global temperatures, to ensure that the forcing/

feedback components within the bins sum exactly to the

equivalents using the full domain average. A similar pro-

cedure is used to calculate the rapid responses of the LTS,

EIS and pressure velocity at 500 hPa, and their responses

with increasing global temperature. To highlight potential

relationships of interest we correlate the net cloud responses

with the other variables in each bin, showing correlations

with a magnitude greater than 0.8. This threshold is well

Table 7 Selected parameter values and switches used in the perturbed physics ensemble for mid-low sensitivity versions

adrhj adseo adumf adumd adtlg adrea aduvb

Entrainment rate 2.98215 4.85597 2.78167 4.35483 3.49381 2.91705 2.89934

Ice fall speed 0.50546 1.42575 1.12286 0.94015 1.04130 0.98441 0.93976

Flow dependent RHcrit Off On Off Off Off On Off

Critical rel. humidity 0.82018 Off 0.72846 0.78531 0.71434 Off 0.84077

Sat. cloud fraction (BL) 0.73756 0.59233 0.67577 0.73136 0.67342 0.79552 0.79598

Sat. CF (free trop.) 0.61878 0.54616 0.58789 0.61568 0.58671 0.64776 0.64799

Precip. efficiency 3.29e-4 3.80e-4 1.10e-4 2.35e-4 6.10e-5 6.30e-5 1.63e-4

Land precip. threshold 1.72e-3 6.78e-4 1.95e-4 1.45e-3 2.76e-4 1.40e-3 1.75e-4

Ocean precip. threshold 4.30e-4 1.69e-4 4.85e-5 3.62e-4 6.90e-5 3.50e-4 4.25e-5

Sea ice albedo at 0 �C 0.63406 0.52116 0.52653 0.53246 0.50429 0.59963 0.50666

CAPE timescale 1.40 Off 1.97 1.56 Off 1.02 Off

Ocean ice diffusion 3.59e-4 3.44e-4 3.63e-4 3.72e-4 3.72e-4 3.74e-4 3.67e-4

Vertical gradient cloud area Off Off Off Off Off Off On

692 M. J. Webb et al.

123

above the 95 % confidence limit, and correlations above 0.8

can account for approximately two thirds of the variance.

(CSIRO Mk3 is not included because pressure velocity at

500 hPa is not available.)

6.1 Cloud feedback components

Previous studies (e.g. Bony and Dufresne 2005; Webb

et al. 2006) have found that, in the AR4 generation of

models, areas where low clouds predominate contribute

more to the inter-model spread in cloud feedback than

those dominated by high-top clouds. This is confirmed by

Fig. 7a. Inter-model differences in the net CRE component

of the feedback are smallest at the low end of the stability

range, where high clouds might be expected to dominate,

becoming larger in the mid-stability range, where trade

cumulus is expected, and largest in upper stability range,

where stratocumulus and stratocumulus-trade cumulus

transition clouds might be expected. The PPE shows

qualitatively consistent results, although the differences in

the stable regions are larger (Fig. 8). Previous studies have

also noted a tendency for model differences in net cloud

feedback to be dominated by the contribution from the

shortwave. This is also clear from Fig. 7. In the 60–100 %

stability range, inter model differences in the shortwave are

much larger than the longwave, and are strongly correlated

with the net. This is also true in the PPE for the 80–100 %

LTS range. The differences in the longwave terms become

progressively larger with towards lower stability bins,

becoming comparable with the range in shortwave differ-

ences. The range in shortwave and longwave is larger than

the net, indicating cancellation in longwave and shortwave

terms, presumably due to changes in high clouds.

Medeiros et al. (2008) showed that differences in trop-

ical climate sensitivity in atmosphere only versions of

NCAR CCSM3.0 and GFDL AM2.0 (forced with globally

uniform SST perturbations) are dominated by different

responses in regions of weak subsidence which would be

expected to be dominated by trade cumulus. Figure 7 also

supports this conclusion for the slab versions of those two

models; their responses are more different in the interme-

diate range, where trade cumulus would be expected to

dominate, than in the stable end of the range, where per-

sistent stratocumulus decks would be expected. When all of

the models are considered however it is clear that the dif-

ferences are larger at the strongly stable end of the range

than the intermediate range, as would be expected from

Williams and Webb (2009). This is also consistent with the

finding of Soden and Vecchi (2011) that traditional

stratoculumus regions show the largest inter-model differ-

ences in cloud feedback in coupled models. This does not

necessarily mean however that the clouds in the stable bins

contribute more to the overall differences in feedback

across the low latitude oceans. As pointed out by previous

studies including Soden and Vecchi (2011), trade cumulus

clouds cover a larger fraction of the tropics than stratocu-

mulus clouds.

Here we attempt to quantify the relative contributions

from these two regimes. It is not immediately clear where

the dividing line should be drawn between these regimes.

Medeiros and Stevens (2011) separated clouds in subsi-

dence regimes into a stratocumulus and a cumulus regime

using an LTS threshold of 18.5 K, which was based on the

point at which stratus cloud fraction reaches 50 % in the

Klein–Hartmann relation. They found that applying this

criterion to ERA40 data over the tropical oceans (30�N/S)

predicted stratocumulus 5 % of the time and trade wind

cumulus 30 % of the time, but noted that this classification

might place transitional cloud types like cumulus topped by

stratiform cloud into the trade-wind cumulus regime.

Figures 1 and 2 of Klein and Hartmann (1993) show maps

of annual stratus cloud amount and net CRE from obser-

vations. Examining these indicates that the 30 and 40 %

contours of stratus cloud amount sit between the regions

classically associated with stratocumulus (with values of

net CRE values below -20 W m-2) and trade cumulus

(with net CRE values above -10 W m-2). Maps showing

the frequency of occurrence of the Williams and Webb

(2009) clusters for ISCCP and MODIS (see their Electronic

Supplementary Material) show that this division captures

the dividing line between the trade cumulus cluster and the

sum of the transition/stratocumulus clusters reasonably

well. According to the Klein–Hartmann relation, a 35 %

value of stratus cloud fraction corresponds to an LTS of

15.9 K. For the models and observations examined here,

the average value of the 80th LTS percentile is 15.8 K. For

this reason, we consider the 80th percentile of LTS to

represent a reasonable dividing line between stabilities

favouring trade cumulus and those favouring stratocumulus

and stratocumulus-cumulus transition clouds. The Wil-

liams and Webb (2009) clustering method gives a fre-

quency of occurrence of 5.7 and 10.4 % for stratocumulus

and transition clouds respectively over the 30�N/S oceans,

a total of 16.1 %, compared with a trade cumulus fre-

quency of occurrence of 42 %. This also supports the

choice of the 80th percentile.

Based on this we estimate that differences in net cloud

feedback components in the 30–80 % LTS range contrib-

ute 0.6 K to the range in climate sensitivity in the AR4

ensemble, while those in the 80–100 % range contribute

0.5 K. From this we conclude that the two regimes are

likely to be of comparable overall importance to the cloud

feedback differences over the low latitude oceans in the

AR4 ensemble. In the PPE, inter-model differences in the

80–100 % LTS range do contribute more than those in

the 30–80 % LTS range.

Climate sensitivity, forcing and feedback in climate models 693

123

The net and shortwave cloud feedback components are

positive in the strongly stable LTS range in eight of the

models in the AR4 ensemble, and very weakly negative in

three (Figs. 7a, b). This is in spite of the fact that LTS

increases in all models in all regimes (Fig. 7d). Klein and

Hartmann (1993) showed that larger amounts of stratus

cloud are observed when LTS is highest, for both seasonal

and interannual variations, but this relationship does not

correctly predict the cloud feedbacks seen here in stable

regions.

Net CRE feedback component (W/m2/K)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-2

0

2

4

6

8

Net

CR

E f

eedb

ack

com

pone

nt (

W/m

2 /K

)

GISS-ERCCSM3GFDL 2.0CCCMA-3MRI-2.3.2AECHAM5HadSM3CCCMA-4MIROC3.2HadGEM1IPSL-CM4

(b) SW CRE feedback component (W/m2/K)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-2

0

2

4

6

8

SW C

RE

fee

dbac

k co

mpo

nent

(W

/m2 /

K)

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

GISS-ERCCSM3GFDL 2.0CCCMA-3MRI-2.3.2AECHAM5HadSM3CCCMA-4MIROC3.2HadGEM1IPSL-CM4

-1.0

0.0

0.2

0.4

corr

elat

ion

(c) LW CRE feedback component (W/m2/K)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-2

0

2

4

6

8

LW C

RE

fee

dbac

k co

mpo

nent

(W

/m2 /

K)

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

GISS-ERCCSM3GFDL 2.0CCCMA-3MRI-2.3.2AECHAM5HadSM3CCCMA-4MIROC3.2HadGEM1IPSL-CM4

corr

elat

ion

LTS response with global temperature (K/K)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

0.0

0.2

0.4

0.6

LTS

resp

onse

with

glo

bal t

empe

ratu

re (

K/K

)

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

GISS-ERCCSM3GFDL 2.0CCCMA-3MRI-2.3.2AECHAM5HadSM3CCCMA-4MIROC3.2HadGEM1IPSL-CM4

corr

elat

ion

(e) EIS response with global temperature (K/K)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-0.2

0.0

0.2

0.4

EIS

res

pons

e w

ith g

loba

l tem

pera

ture

(K

/K)

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

GISS-ERCCSM3GFDL 2.0CCCMA-3MRI-2.3.2AECHAM5HadSM3CCCMA-4MIROC3.2HadGEM1IPSL-CM4

corr

elat

ion

(f) w500 response with global temperature (hPa/day/K)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-4

-2

0

2

4

6

8

(hPa

/day

/K)

w50

0 re

spon

se w

ith g

loba

l tem

pera

ture

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

GISS-ERCCSM3GFDL 2.0CCCMA-3MRI-2.3.2AECHAM5HadSM3CCCMA-4MIROC3.2HadGEM1IPSL-CM4

corr

elat

ion

(a) (d)Fig. 7 LTS composites of net,

shortwave and longwave cloud

components of feedback over

the low latitude oceans (30�N/S)

in the AR4 ensemble, and

responses per degree change in

global temperature of LTS, EIS

and 500 hPa vertical pressure

velocity. The dashed black linesshows the ensemble mean.

Diamonds indicate correlations

with the net CRE response

which are greater than 0.8

694 M. J. Webb et al.

123

An interesting question to consider at this point is how

much of the response in a given LTS bin can be attributed

to changes in LTS within that bin (assuming any rela-

tionship between LTS and CRE in the control climate

remains unchanged) and how much is due to a change in

such a relationship, or other factors. Figure 11 shows the

present-day CRE as a function of LTS, from the models

and from two sets of satellite observations and analyses. If

CRE was a pure function of LTS and nothing else, then the

CRE values in the warmer climate would simply move

(a) Net CRE feedback component (W/m2/K)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-2

0

2

4

6

8

Net

CR

E f

eedb

ack

com

pone

nt (

W/m

2 /K

)

aduvbadreaadtlgadumdadumfadseoadrhjadsebadsbdadsbbadsbhadryaadrhladryeadsea

(b) SW CRE feedback component (W/m2/K)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-2

0

2

4

6

8

SW C

RE

fee

dbac

k co

mpo

nent

(W

/m2 /

K)

aduvbadreaadtlgadumdadumfadseoadrhjadsebadsbdadsbbadsbhadryaadrhladryeadsea

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

corr

elat

ion

(c) LW CRE feedback component (W/m2/K)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-2

0

2

4

6

8

LW C

RE

fee

dbac

k co

mpo

nent

(W

/m2 /

K)

aduvbadreaadtlgadumdadumfadseoadrhjadsebadsbdadsbbadsbhadryaadrhladryeadsea

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

corr

elat

ion

(d) LTS response with global temperature (K/K)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

0.0

0.2

0.4

0.6

LTS

resp

onse

with

glo

bal t

empe

ratu

re (

K/K

)

aduvbadreaadtlgadumdadumfadseoadrhjadsebadsbdadsbbadsbhadryaadrhladryeadsea

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

corr

elat

ion

(e) EIS response with global temperature (K/K)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-0.2

0.0

0.2

0.4

EIS

res

pons

e w

ith g

loba

l tem

pera

ture

(K

/K)

aduvbadreaadtlgadumdadumfadseoadrhjadsebadsbdadsbbadsbhadryaadrhladryeadsea

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

corr

elat

ion

(f) w500 response with global temperature (hPa/day/K)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-4

-2

0

2

4

6

8

(hP

a/da

y/K

)

w50

0 re

spon

se w

ith g

loba

l tem

pera

ture

aduvbadreaadtlgadumdadumfadseoadrhjadsebadsbdadsbbadsbhadryaadrhladryeadsea

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

corr

elat

ion

Fig. 8 As previous figure, but

for the PPE

Climate sensitivity, forcing and feedback in climate models 695

123

along the curves in the figure. Figure 11 also shows that the

gradients in the models in the 80–100th percentile range

vary from negative to weakly negative in the net CRE, and

negative to weakly positive in the SW CRE. If this rela-

tionship was unchanged in the warmer climate, the larger

LTS values would translate into mostly negative feedbacks

in the 80–100th percentile range. Since the cloud feedback

components are mostly positive here, this demonstrates that

they cannot be understood as simple responses to stability

increases, and that other factors must contribute.

The change in the EIS looks very similar in character to

that in LTS, but is approximately 0.2 K/K smaller on

average (Fig. 7e). This offset is expected because EIS is

formulated to be less sensitive than LTS to changes in lapse

rates in the free troposphere (Wood and Bretherton 2006).

No strong correlations are seen between the net cloud

feedback components and the LTS or EIS responses here,

making it unlikely that the differences between the cloud

feedbacks are caused primarily by differing stability

responses. This does not mean that stability changes are

irrelevant however. The range of stability responses seen in

the models is substantial, and any idealised studies which

aim to reproduce the range in model feedbacks should

explore this large scale forcing comprehensively.

There are of course a number of potential factors other

than stability and subsidence which could explain the

positive feedbacks seen here. Clement et al. (2009) argue

that a weakening of the circulation in the North East Pacific

could reduce low cloud fraction in the warmer climate in

spite of increasing LTS. Watanabe et al. (2011) argue that

increased surface evaporation driven by the strengthening

hydrological cycle might force clouds to break up follow-

ing the deepening warming mechanism which was pro-

posed by Bretherton and Wyant (1997) to explain the

observed transition between subtropical stratocumulus and

trade cumulus clouds in the current climate. Richter and

Xie (2008) do show evidence of increased surface evapo-

ration in the subtropics in the warmer climate in models,

consistent with this idea. Alternatively, Stevens and

Brenguier (2009) argued that increases in free tropospheric

clouds and/or humidity might encourage stratocumulus

breakup by suppressing longwave radiative cooling at

cloud top. Klein et al. (1995) found that observations of

stratocumulus from weather ship P showed small cloud

fractions on days with relatively moist free tropospheric

soundings. More recently, Brient and Bony (2012) have

argued that changes in the vertical gradient of moist static

energy in a warmer climate increase the amount of dry air

imported into the boundary layer in a new version of the

IPSL model, leading to a reduction in cloud and a positive

feedback.

It is also interesting to note that the strong negative low

cloud feedback diagnosed by Wyant et al. (2009) in stable

areas of the low latitude ocean in the ‘super parametrized’

SP-CAM in a uniform ?2K SST perturbation experiment is

not reproduced by any of the models. This difference may

be explained by Blossey et al. (2009), who conclude that

negative low cloud feedbacks in SP-CAM may be exag-

gerated by under-resolution of trade cumulus boundary

layers.

We see decreases in subsidence in most models in mid-

high stability regions (Fig. 7f), as expected given the

established weakening of the Walker Circulation with

increasing temperatures (Vecchi and Soden 2007). A

strong negative correlation between the 500 mb pressure

velocity and the net cloud feedback component is seen in

the 40–60 % percentile range. This seems to be at least in

part due to the unusual behaviour of MPI ECHAM5 and

NCAR CCSM3.0, which show strengthening subsidence

and negative net cloud feedback components in both of the

40–60 % LTS bins. The negative cloud feedback compo-

nent is mainly due to the longwave in NCAR CCSM3.0,

but due to negative shortwave and longwave components

in MPI ECHAM5. This suggests that strengthening subsi-

dence in the trades may be a factor in explaining negative

feedbacks in some models, in contrast to the positive cloud

feedbacks seen with weakening subsidence in the majority

of models. If this behaviour is present in the models par-

ticipating in CFMIP-2, it might be possible to constrain

these feedbacks via comparisons with Cloudsat data via

COSP—for instance if these cloud changes can be shown

to only occur if there is excessive and/or overly optically

thick cloud in these regions compared to observations.

It is also interesting to note that the models with

increasing subsidence in the mid-stability regions show

stronger ascent in the unstable regions, and that most of the

models in the PPE seem to exhibit this behaviour also. This

may be a consequence of the eastward shift of deep con-

vection in the Pacific noted by Vecchi and Soden (2007)

being stronger in some models than others. The correlation

mentioned above is not present in the PPE, which suggests

that it may be due to structural differences between the

members of the AR4 ensemble (e.g. different convective

parametrizations). Ringer and Ingram (submitted) show that

replacing the convective parametrization in HadSM3 with a

simple adjustment scheme substantially weakens the pattern

in the tropical cloud feedback and circulation change,

making it resemble the multi-model mean response.

6.2 Cloud components of CO2 forcing

In the AR4 ensemble, the spread in the net cloud compo-

nents of the forcing is largest at the stable end of the LTS

range, and this is mainly due to the shortwave components

(Fig. 9). This suggests that different rapid adjustments in

low clouds are a leading order cause of differences in the

696 M. J. Webb et al.

123

cloud components of the CO2 forcing over the low latitude

oceans. The same is true in the PPE (Fig. 10). Differences

in the net cloud components of the forcing in the mid-

stability range are however relatively small in both

ensembles. Opposing changes in shortwave and longwave

components cancel to some extent in the net, suggesting

changes in upper level clouds in some of the models.

The net and shortwave CRE components of the forcing

take a range of positive and negative values in all stability

bins in the AR4 ensemble. A strong anti-correlation is seen

(a) Net CRE forcing component (W/m2)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-10

-5

0

5

Net

CR

E f

orci

ng c

ompo

nent

(W

/m2 )

GISS-ERCCSM3GFDL 2.0CCCMA-3MRI-2.3.2AECHAM5HadSM3CCCMA-4MIROC3.2HadGEM1IPSL-CM4

(b) SW CRE forcing component (W/m2)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-10

-5

0

5

SW C

RE

for

cing

com

pone

nt (

W/m

2 )

GISS-ERCCSM3GFDL 2.0CCCMA-3MRI-2.3.2AECHAM5HadSM3CCCMA-4MIROC3.2HadGEM1IPSL-CM4

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

corr

elat

ion

(c) LW CRE forcing component (W/m2)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-10

-5

0

5

LW C

RE

for

cing

com

pone

nt (

W/m

2 )

GISS-ERCCSM3GFDL 2.0CCCMA-3MRI-2.3.2AECHAM5HadSM3CCCMA-4MIROC3.2HadGEM1IPSL-CM4

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

corr

elat

ion

(d) LTS 2CO2 rapid response (K)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-1.0

-0.5

0.0

0.5

1.0

LTS

2CO

2 rap

id r

espo

nse

(K)

GISS-ERCCSM3GFDL 2.0CCCMA-3MRI-2.3.2AECHAM5HadSM3CCCMA-4MIROC3.2HadGEM1IPSL-CM4

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

corr

elat

ion

(e) EIS 2CO2 rapid response (K)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-1.0

-0.5

0.0

0.5

1.0

EIS

2C

O2 r

apid

res

pons

e (K

)

GISS-ERCCSM3GFDL 2.0CCCMA-3MRI-2.3.2AECHAM5HadSM3CCCMA-4MIROC3.2HadGEM1IPSL-CM4

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

corr

elat

ion

(f) w500 2CO2 rapid response (hPa/day)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-15

-10

-5

0

5

10

15

w50

0 2C

O2 r

apid

res

pons

e (h

Pa/d

ay)

GISS-ERCCSM3GFDL 2.0CCCMA-3MRI-2.3.2AECHAM5HadSM3CCCMA-4MIROC3.2HadGEM1IPSL-CM4

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

corr

elat

ion

Fig. 9 Lower tropospheric

stability (LTS) composites of

net, shortwave and longwave

CRE components of forcing

over the low latitude oceans

(30�N/S) in the AR4 ensemble,

and rapid responses of LTS,

estimated inversion strength

(EIS) and pressure velocity at

500 hPa

Climate sensitivity, forcing and feedback in climate models 697

123

between the EIS responses and the net cloud components in

the most stable bin. This highlights a tendency for models

with the largest positive cloud components (IPSL CM4 and

MIROC 3.2 medres) to show reductions in EIS, and those

with the most negative (HadSM3 and HadGEM1) to show

increases, and is fully consistent with what would be

(a) Net CRE forcing component (W/m2)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-10

-5

0

5

Net

CR

E f

orci

ng c

ompo

nent

(W

/m2 )

aduvbadreaadtlgadumdadumfadseoadrhjadsebadsbdadsbbadsbhadryaadrhladryeadsea

(b) SW CRE forcing component (W/m2)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-10

-5

0

5

SW C

RE

for

cing

com

pone

nt (

W/m

2 )

aduvbadreaadtlgadumdadumfadseoadrhjadsebadsbdadsbbadsbhadryaadrhladryeadsea

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0co

rrel

atio

n

(c) LW CRE forcing component (W/m2)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-10

-5

0

5

LW C

RE

for

cing

com

pone

nt (

W/m

2 )

aduvbadreaadtlgadumdadumfadseoadrhjadsebadsbdadsbbadsbhadryaadrhladryeadsea

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

corr

elat

ion

(d) LTS 2CO2 rapid response (K)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-1.0

-0.5

0.0

0.5

1.0

LTS

2CO

2 r

apid

res

pons

e (K

)

aduvbadreaadtlgadumdadumfadseoadrhjadsebadsbdadsbbadsbhadryaadrhladryeadsea

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

corr

elat

ion

(e) EIS 2CO2 rapid response (K)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-1.0

-0.5

0.0

0.5

1.0

EIS

2C

O2

rap

id r

espo

nse

(K)

aduvbadreaadtlgadumdadumfadseoadrhjadsebadsbdadsbbadsbhadryaadrhladryeadsea

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

corr

elat

ion

(f) w500 2CO2 rapid response (hPa/day)Oceans [30S,30N]

0 20 40 60 80 100

Percentiles of LTS (%)

-15

-10

-5

0

5

10

15

w50

0 2C

O2

rap

id r

espo

nse

(hPa

/day

)

aduvbadreaadtlgadumdadumfadseoadrhjadsebadsbdadsbbadsbhadryaadrhladryeadsea

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0co

rrel

atio

n

Fig. 10 As previous figure, but for the PPE

698 M. J. Webb et al.

123

expected from the observed relationships in Wood and

Bretherton (2006). This suggests that stability changes are

relatively more important for the cloud adjustments than is

the case with the feedbacks.

The longwave components in the AR4 ensemble are

mostly negative, which is in part attributable to the cloud

masking component of the longwave CRE response

(Andrews and Forster 2008). The shortwave components

are positive slightly more often than negative, and modest

positive values are present in most bins for MPI ECHAM5

and CCCMA CGCM3.1, the models with compensating

forcing and feedback components highlighted above. The

relationship between EIS and cloud fraction seen in the

observations cannot explain the tendency of the shortwave

cloud components to be positive more often than negative,

as the EIS increases in most models.

Colman and McAvaney (2011) found that a positive

shortwave cloud adjustment in a version of the Australian

Bureau of Meteorology Research Centre (BMRC) climate

model was due to reductions in low-mid level cloud frac-

tion associated with enhanced heating rates and increased

temperatures from increased CO2, and reductions in rela-

tive humidity. Meanwhile, Wyant et al. (in press) and

Watanabe et al. (2011) have found positive adjustments in

the SP-CAM and MIROC models respectively, coincident

with a shallowing of the boundary layer in subtropical

regions. Wyant et al. (in press) argue that the shallowing of

the boundary layer seen in the SP-CAM is a local effect

caused by a reduction in entrainment, stemming from a

suppression of net longwave cooling in the cloud-topped

boundary layer with increased CO2. Meanwhile, Watanabe

et al. (2011) argue that the boundary layer in MIROC5

shallows because of reduced surface latent heat fluxes in

response to a reduction in the strength of the global

hydrological cycle with increased CO2. We have estimated

the boundary layer depth in the AR4 ensemble using the

difference between the surface pressure and the pressure

level at which the relative humidity drops below 50 %.

This difference reduces in magnitude in the mid-high sta-

bility range in all but a couple of the models examined here

(not shown). This finding is consistent with what would be

expected if boundary layer depth was reducing. However,

this result could equally be explained by a reduction in

relative humidity near top of the boundary layer, as seen in

Colman and McAvaney (2011).

It is also worth noting that the MIROC 3.2 medres is

quite unusual, in that it shows a substantial decrease in LTS

and EIS in all bins, the strongest value being the most

stable bin where the shortwave cloud term is the most

positive. This may be the key to understanding the global

forcing value for MIROC 3.2 medres, which is one of the

largest in the AR4 ensemble. Increasing CO2 would be

expected to warm the lower troposphere in the absence of

any other changes in local diabatic heating terms, leading

to an increase in stability. The decrease in LTS and EIS

suggests that the diabatic heating terms (from the radiation

code or other terms such as the convective heating) must be

acting to reduce temperatures at 700 hPa in response to

CO2 doubling. The MIROC group are currently examining

these terms in a fixed SST experiment with CO2 quadru-

pling and the results will be presented in a later study. A

smaller reduction in EIS is seen in IPSL CM4 in the most

stable bin, coincident with the largest net cloud component

of the CO2 forcing.

The PPE shows results which are similar to the AR4

ensemble in many respects, but it does not capture the

behaviour of MIROC 3.2 medres or IPSL CM4, tending to

favour strong negative shortwave cloud components in the

most stable bins (Fig. 10). Again there is a strong anti-

correlation suggesting a relationship between the adjust-

ments and the EIS at the stable end of the range. The

shortwave components show less of a tendency for positive

values than in the AR4 ensemble, and a slight tendency for

more strongly negative longwave cloud components results

in a tendency for most of the net terms to be negative.

These differences are probably due to the fact that the PPE

is based on HadSM3, which has the strongest negative

shortwave component in the AR4 ensemble, and negative

longwave components in all bins (Fig. 9). The relative

sizes of the spread in unstable, stable and intermediate bins

of stability are quite similar to the AR4 ensemble, however

the spread is larger overall, mainly because of the short-

wave term.

We also note a slight increase in subsidence on average

across the low latitude oceans in both ensembles. This is

expected given the increased transport of heat from land to

ocean with increased CO2, as the tropical atmosphere acts

to minimise zonal temperature gradients in the free tro-

posphere, reducing any rapid land warming due to CO2

increases (Lambert et al. 2011; Wyant et al. in press).

Dong et al. (2009) show evidence of a warming in the

free troposphere spreading out from land regions in a CO2

doubling experiment with the atmosphere component of

HadSM3, and Williams et al. (2008) show evidence of

rapid warming over land in the slab versions of HadSM3

and HadGEM1. Such behaviour might explain the rela-

tively large increase in stability in the stable bins in

HadSM3, some members of the PPE, and to a lesser extent

HadGEM1. All of these models exhibit a dependence of

stomatal conductance on CO2 which reduces evapotrans-

piration and low level cloud when CO2 is increased,

resulting in a positive cloud feedback over land and

enhanced warming (Joshi et al. 2008; Doutriaux-Boucher

et al. 2009). This might also explain why the largest neg-

ative cloud adjustments are seen in stable regions, which

tend to be coastal in the subtropics. It would not explain the

Climate sensitivity, forcing and feedback in climate models 699

123

(a) Net CRE (W/m2)Oceans [30S,30N]

10 15 20 25

LTS (K)

-100

-80

-60

-40

-20

0

Net

CR

E (

W/m

2 )xERBE /ERA40

+ISCCP FD /MERRA

GISS-ERCCSM3GFDL 2.0 CCCMA-3MRI-2.3.2AECHAM5HadSM3CCCMA-4MIROC3.2HadGEM1IPSL-CM4

(b) SW CRE (W/m2)Oceans [30S,30N]

10 15 20 25

LTS (K)

-100

-80

-60

-40

-20

SW C

RE

(W

/m2 )

xERBE /ERA40

+ISCCP FD /MERRA

GISS-ERCCSM3GFDL 2.0 CCCMA-3MRI-2.3.2AECHAM5HadSM3CCCMA-4MIROC3.2HadGEM1IPSL-CM4

(c) LW CRE (W/m2)Oceans [30S,30N]

10 15 20 25

LTS (K)

0

10

20

30

40

50

60

70

LW C

RE

(W

/m2 )

xERBE /ERA40

+ISCCP FD /MERRA

GISS-ERCCSM3GFDL 2.0 CCCMA-3MRI-2.3.2AECHAM5HadSM3CCCMA-4MIROC3.2HadGEM1IPSL-CM4

(d) Net CRE (W/m2)Oceans [30S,30N]

10 15 20 25

LTS (K)

-100

-80

-60

-40

-20

0

Net

CR

E (

W/m

2 )

xERBE /ERA40

+ISCCP FD /MERRA

aduvbadreaadtlgadumdadumfadseoadrhjadsebadsbdadsbbadsbhadryaadrhladryeadsea

(e) SW CRE (W/m2)Oceans [30S,30N]

10 15 20 25

LTS (K)

-100

-80

-60

-40

-20

SW C

RE

(W

/m2 )

xERBE /ERA40

+ISCCP FD /MERRA

aduvbadreaadtlgadumdadumfadseoadrhjadsebadsbdadsbbadsbhadryaadrhladryeadsea

(f) LW CRE (W/m2)Oceans [30S,30N]

10 15 20 25

LTS (K)

0

10

20

30

40

50

60

70

LW C

RE

(W

/m2 )

xERBE /ERA40

+ISCCP FD /MERRA

aduvbadreaadtlgadumdadumfadseoadrhjadsebadsbdadsbbadsbhadryaadrhladryeadsea

Fig. 11 Lower tropospheric

stability (LTS) composites of

net, shortwave and longwave

CRE over the low latitude

oceans (30�N/S) in the AR4

ensemble, PPE and observations

700 M. J. Webb et al.

123

decreases in EIS seen in MIROC 3.2 medres and IPSL

CM4 however. It is also possible that the relationship

between cloud adjustment and EIS is partly a coupled

effect, as local SST’s adjust to changes in surface radiation

caused by rapid cloud adjustments. As pointed out by

GW08, the rapid adjustment to CO2 in a transient experi-

ment diagnosed by regressing on global near-surface tem-

perature may include the effects of changes in local surface

temperatures as well as in the troposphere. A fixed-SST

experiment might therefore be somewhat different because

sea surface temperatures are held fixed everywhere. If local

SST adjustments play an important role in the relationship

we see here, then it will not be present in the fixed

SST experiments being performed for CMIP5/CFMIP-2.

Aquaplanet experiments will be also performed with fixed

SSTs and quadrupled CO2. If cloud adjustments are mainly

driven by land warming, then such experiments will show

no evidence of them.

6.3 Performance compared to observations

We now return to Fig. 11 and consider the present-day

performance of the models in the LTS composite frame-

work. The distribution of the LTS values from the analyses

are captured quite well by most models in the AR4

ensemble and the PPE. NCAR CCSM3.0 has a tendency to

be more slightly more stable than either MERRA or

ERA40, which is indicative of a warm bias at 700 mb.

Equivalent plots with EIS on the x-axis are qualitatively

very similar (not shown).

It is clear that many of the models fail to capture the

variation of the net and shortwave CRE seen across LTS

regimes in the observational estimates. Many of the models

exhibit net CRE values which are too negative in the

intermediate stability range, which presumably reflects the

difficulty that models have in representing the stratocumu-

lus to trade cumulus transition in the subtropics. The PPE

shows larger biases than the AR4 ensemble, particularly at

higher LTS values, which may be the result of model tuning

in the AR4 ensemble. The longwave CRE is generally better

simulated than in the shortwave in both ensembles.

As discussed above, the model feedbacks in the stable

bins are not generally negative as would be the case if the

Klein–Hartmann relationship was maintained under cli-

mate change. This could be due to an inability of the

models to reproduce the Klein–Hartmann relationship even

for the current climate. If so, there would no reason to

expect them to do so for a climate change. Clement et al.

(2009) showed that many coupled models do not capture

the observed relationships between cloud fraction and LTS

on decadal timescales on the subtropical East Pacific,

which does not give us much confidence in the ability of

models to reproduce such relationships for seasonal

variations in within stratus cloud regions as identified by

Klein and Hartmann (1993)

We are not in a position to evaluate the present day Klein–

Hartmann relationship directly in our models because low

level stratus cloud fractions are not available. We can

however assess the ability of the models to reproduce the

negative gradient in net CRE in the 80–100 % percentile

range in our observational spatio-temporal composites in

Fig. 11. This will be due to the combined effect of interan-

nual, seasonal and spatial covariations in CRE and LTS, and

might be consistent with the Klein–Hartmann relation. Klein

and Hartmann (1993) showed that observed stratus cloud

fraction varies by 5.7 % per K increase in LTS. They also

showed that observed net CRE varies by -1.2 W m-2 per

one percent increase in stratus cloud amount. Combining the

two gives a variation of -6.8 W m-2 per degree K increase

in LTS. Our spatio-temporal composites have a gradient in

the 80–100th LTS precentile range of -7.0 W m-2 for

ISCCP FD/MERRA and -6.4 W m-2 for ERBE/ERA40

and so agree very well with what would be expected from the

Klein-Hartmann relation.

Figure 11 shows that most of the models fail to repro-

duce this relationship. HadGEM1 (which performed well in

Clement et al. 2009) is the only model to lie within the

envelope of the observations in the 80–100th LTS per-

centile range. NCAR CCSM3.0 also has a strongly nega-

tive slope, but not as strong as that observed. These two

models produce very different cloud feedbacks however

(positive in HadGEM1 and weakly negative in NCAR

CCSM3.0). This suggests again that other factors other

than LTS must be contributing to differences between

model cloud feedback components. None of the members

of the PPE fall within the envelope of the observed esti-

mates for net or shortwave CRE in the 80–100th LTS

percentile range.

7 Conclusions

We have diagnosed CO2 forcings and feedbacks in Atmo-

sphere/ Ocean Mixed Layer ‘slab’ climate models from

CMIP3/CFMIP1 (the AR4 ensemble) and from a parameter

perturbed ensemble of HadSM3 experiments (the PPE)

using the method of Gregory and Webb (2008). This

diagnoses an effective CO2 forcing, considering the radia-

tive effects of rapid cloud adjustments in response to CO2

forcing as a component of radiative forcing for analysis

purposes. These adjustments operate on short atmosphere/

land response timescales, in contrast to conventional cli-

mate feedbacks, which operate on longer ocean surface

temperature response timescales (Gregory and Webb 2008).

Differences in feedbacks contribute approximately twice

as much to the range in effective climate sensitivity as

Climate sensitivity, forcing and feedback in climate models 701

123

differences in effective CO2 forcings, qualitatively consis-

tent with the findings of earlier studies which did not allow

for the effects of rapid cloud adjustments on radiative

forcing (e.g. Webb et al. 2006; Dufresne and Bony 2008).

However, the inclusion of adjustment effects means that the

forcing differences contribute more than in Webb et al.

(2006).

In the AR4 ensemble, cloud effects are capable of

explaining the full range in climate sensitivity. Cloud

feedback components contribute four times as much as

cloud components of CO2 forcing to this range, relatively

more than indicated by Gregory and Webb (2008). This is

in part due to the inclusion of additional models, for which

the necessary outputs were not available at the time of

Gregory and Webb (2008) and Andrews and Forster

(2008). These include IPSL CM4, which has the highest

sensitivity in the AR4 ensemble, due to a strong positive

cloud feedback component.

Differences in low latitude oceans regions (30�N/S)

contribute more to the range than in mid-latitude oceans

(30–55�N/S), low/mid latitude land (55�N/S) or high lati-

tude ocean/land (55–90�N/S). Contributions from these

other regions are still substantial however, and are required

to account fully for the higher model sensitivities. Exam-

ples include contributions from mid-latitude oceans and

low/mid latitude land in IPSL CM4, and from mid-latitude

oceans and high latitudes in HadGEM1, the two highest

sensitivity models in the AR4 ensemble.

The models with the highest sensitivities are those

which have strong feedbacks operating in two or more

large scale regions. This suggests that the highest sensi-

tivity models are not so much the ones which have the

strongest local feedbacks, but more the ones which happen

to have positive feedbacks over larger areas. This idea is

also supported by the analysis of Webb et al. (2006), which

showed that inter-model differences in feedbacks from

regions dominated by shortwave cloud feedbacks were

almost entirely due to these regions covering larger areas in

the higher sensitivity models, rather than differences in the

strength of the feedbacks within the regions.

Although many studies have (quite justifiably) focused

on low latitude feedbacks, it will be necessary to scrutinise

other regions as well if the causes of the highest model

sensitivities are to be fully understood. Although it is well

established that inter-model differences in cloud responses

explain more of the inter-model spread than non-cloud

forcings and feedbacks, it does not necessarily follow that

the high or low sensitivities of individual models are pri-

marily attributable to cloud effects. Non-cloud feedbacks

contribute substantially to the high sensitivities of some

models. For example, the largest contribution to the high

sensitivity of HadGEM1 is from a high-latitude clear-sky

shortwave feedback, and clear-sky longwave feedbacks

contribute substantially to the highest sensitivity members

of the PPE. This fact is relevant to studies that consider the

impact of cloud feedback on climate sensitivity—e.g.

Clement et al. (2009).

We identify a number of cases where individual models

show unusually strong forcings and feedbacks compared to

other members of their respective ensembles. This should

not in itself give us reason to doubt the credibility of these

models, as such models may include some key physical

mechanism which is present in the real world and not the

other models. We would like to encourage the modelling

groups to investigate these unusual features in more detail,

performing sensitivity experiments to see what aspects of

the model formulations are responsible.

Effective climate sensitivities in the PPE are strongly

correlated with the global net radiation balance, as in

Yokohata et al. (2010). This is mainly due to relationships

between the biases in the present day shortwave CRE

compared to observations and the strength of the shortwave

cloud feedback arising in the Southern mid-latitude ocean

regions.

We do not find any clear relationships between present

day biases and forcings or feedbacks across the AR4

ensemble (a null result, in contrast with the PPE). We think

that even in the absence of such relationships, it is useful to

highlight unusual values in individual models for further

investigation. We do find a few cases where unusually

strong forcings or feedbacks coincide with unusually large

present day biases in the AR4 ensemble. For these cases we

encourage the modelling groups to perform sensitivity tests

to establish whether or not climate forcings and feedbacks

in these regions are sensitive to local present-day biases.

The observational measures we are using here are crude,

and we would not wish to see any models discounted

purely on the basis of this analysis. More detailed diag-

nostics will be available from the CMIP5 models to pro-

mote better evaluation with observations. For example, the

daily cloud regime analysis of Williams and Webb (2009)

should be possible for all models in CMIP5 using cloud

diagnostics produced by the ISCCP simulator (Klein and

Jakob 1999; Webb et al. 2001). The ISCCP simulator, as

well as new simulators for CloudSat (Bodas-Salcedo et al.

2008) and CALIPSO (Chepfer et al. 2008) will be applied

to the CMIP5 models as part of a programme of activities

coordinated under the Cloud Feedback Model Intercom-

parison Project (CFMIP) using the CFMIP Observation

Simulator Package (COSP, Bodas-Salcedo et al. 2011).

Net cloud feedback components across the low latitude

oceans sorted into percentile ranges of LTS show largest

differences in stable regions, mainly due to their shortwave

components. Although smaller, differences in the mid-

stability range are still substantial, and cover a larger area.

The two regimes contribute comparable amounts to the

702 M. J. Webb et al.

123

overall differences in cloud feedback components over the

low latitude oceans in the AR4 ensemble, but the stable

regions dominate in the PPE.

The net and shortwave cloud feedback components

range from weakly negative to substantially positive in the

strongly stable LTS range in both ensembles. This is in

spite of the fact that LTS and EIS increase in almost all

cases; the observed relationships between LTS, EIS and

low cloud would predict mostly negative feedbacks in this

scenario (Klein and Hartmann 1993; Wood and Bretherton

2006). The range of stability responses seen in the models

is substantial however, and we recommend that future

idealised studies that aim to reproduce the range in model

feedbacks in a single column modelling (SCM) framework

should explore a range of LTS of at least 0.2–0.6 K per

degree global warming.

We see decreases in subsidence in most models in mid-

high stability regions, as expected given the established

weakening of the Walker Circulation with increasing tem-

peratures (Vecchi and Soden 2007). A strong negative cor-

relation between the pressure velocity at 500 hPa and the net

cloud feedback is seen here, suggesting that strengthening

subsidence in the trades may be a factor in explaining nega-

tive feedbacks in some models, in contrast to the positive

cloud feedbacks seen with weakening subsidence in the

majority of models. We recommend that future single column

model studies that aim to reproduce the range in model

feedbacks should explore a range of subsidence forcing of up

to ±2 hPa/day per degree global warming at 500 hPa.

Cloud components of CO2 forcing have the largest dif-

ferences in stable regions, and take a range of positive and

negative values, mainly due to their shortwave compo-

nents. A strong anti-correlation is seen between the EIS

responses and the net cloud components in the most stable

bin. This is qualitatively consistent with what would be

expected from the observed relationships in Wood and

Bretherton (2006). The ability of models to reproduce the

observed relationship between EIS and low level cloud

fraction in the present day (both qualitatively and quanti-

tatively) will be very relevant to their ability to their ability

simulate cloud adjustments correctly, and this could be the

basis for a useful model performance metric. We recom-

mend that future single column studies that aim to repro-

duce the range in model cloud adjustments should explore

a range of stability changes which yield a range of at least

±0.5 K in EIS for CO2 doubling.

The shortwave components are positive slightly more

often than negative, and many of the models in the AR4

ensemble show a reduction in the height of the 50 % rel-

ative humidity level, which might be explained by reduc-

tions in the depth of the boundary layer (Wyant et al.,

in press; Watanabe et al., 2011) or reduced relative

humidity (Colman and McAvaney 2011).

The models struggle to reproduce the observed negative

gradient in net CRE with increasing LTS in the stable

regions of the subtropics, which is consistent with the

observed relationship between LTS and low cloud from

Klein and Hartmann (1993). Clearly this area should

remain a priority for model improvement activities for the

foreseeable future.

One of the main limitations of the Gregory et al. (2004)

method applied to slab models subject to CO2 doubling is

the difficulty of dealing with substantial regression uncer-

tainties in CO2 doubling experiments, a problem which is

particularly acute for low sensitivity models. This problem

is addressed in CMIP5 in three ways. Firstly, the slab

model experiments have been replaced by fully coupled

experiments subject to a sudden quadrupling of CO2, which

we have proposed to enable the Gregory and Webb (2008)

method to be applied with improved signal to noise ratios

(although time dependence of feedbacks will be more of an

issue). Second, we proposed an additional ensemble of

eleven CO2 quadrupling experiments of five years in

length, starting from consecutive months, which can be

used to reduce uncertainty in the diagnosis of the CO2

forcing further, as in Doutriaux-Boucher et al. (2009). Low

signal-to-noise estimates of the effective forcing will also

be available from 30 year experiments with fixed SSTs and

quadrupled CO2 diagnosed using the Hansen et al. (2002)

method. The availability of these experiments for multiple

models will allow the first systematic comparison of the

different methods for diagnosing effective CO2 forcing,

and will be the subject of a follow on study (Andrews

et al., in preparation).

Equilibrium 2CO2 change experiments and the resulting

climate sensitivities are to some extent artificial. However,

they do provide a framework in which CO2 forcing and

atmospheric feedbacks can be consistently diagnosed and

compared. These values are useful because they can be

used to predict transient global temperature responses

under more realistic scenarios. CO2 forcings and feedbacks

diagnosed from slab models subject to instantaneous CO2

doubling are routinely used to calibrate simple energy

balance climate models such as MAGIC6 (Meinhausen

et al. 2011), which can be used to explore a range of cli-

mate change scenarios for which full climate models have

not been run. For example, Rogelj et al. (2011) use the

MAGIC6 model to explore emission pathways consistent

with a 2 K global temperature increase, an exercise which

would be prohibitively expensive with full GCMs. We

have shown that cloud adjustments can contribute much as

0.9 K to climate sensitivity differences between models. A

simple model such as MAGIC6 will inevitably have diffi-

culty predicting the transient response of a full climate

model if it has been calibrated with values which incor-

rectly partition forcing and feedback. Hence we think that

Climate sensitivity, forcing and feedback in climate models 703

123

these effects should be in included in estimates of CO2

forcing so that simple models such as MAGIC6 can be

calibrated using the most accurate forcing and feedback

estimates available.

Instantaneous CO2 doubling is also artificial in the sense

that it is not applied so rapidly in the real world. However,

CO2 concentrations are expected to double by the end of

the 21st century in most climate change scenarios.

Applying the forcing rapidly is simply a diagnostic device

which allows the CO2 forcing to be separated from the

feedback which operates on a longer timescale. It is not

necessarily the case that cloud adjustments diagnosed fol-

lowing an abrupt CO2 doubling are good predictors of the

responses that would be seen with more gradual increases

in CO2. However, Good et al. (2011) show that a simple

model based on results from a 2CO2 step experiment per-

forms well when reconstructing global temperature

responses in a range of emission scenarios with HadCM3,

and Good et al. (submitted) shows that this approach works

well when predicting global temperature responses for a

number of CMIP5 models forced with various RCP sce-

narios. This would not be the case if cloud adjustments

diagnosed following step forcings were inconsistent with

those which occur in response to more gradual increases of

CO2.

Separating the effects of forcing and feedback is also a

necessary step on the road to understanding why some

models have high sensitivities and others have low sensi-

tivities. CFMIP-2 will diagnose forcings and feedbacks in

the next generation of models in a hierarchy of atmosphere

only experiments based on AMIP experiments, aquaplanets

and single column models, to which CO2 forcings and sea

surface temperature perturbations will be applied. These

experiments will include additional diagnostics including

high frequency outputs at fixed sites, radiative fluxes

internal to the atmosphere and temperature, humidity and

cloud physics tendency terms. Single column versions of

these models will also be compared with LES (large eddy

simulation) models with equivalent large scale climate

forcings. Given the failure of observationally based sta-

bility relationships to explain the cloud feedbacks seen in

the models, it will be necessary to develop and test alter-

native physical hypotheses. We have discussed a number of

possibilities, and encourage the modelling groups to test

these in the experimental framework proposed by CFMIP-

2. In this way we hope to gain a better understanding of the

mechanisms controlling climate sensitivity, CO2 forcings

and feedbacks in climate models.

Acknowledgments We would like to acknowledge Rob Wood for

providing code to calculate the EIS, and Tim Andrews, Alejandro

Bodas-Salcedo, Ben Booth, Chris Bretherton, Philip Brohan, Leo

Donner, William Ingram, Manoj Joshi, Adrian Lock, Tomoo Ogura,

Mark Ringer, David Sexton, Yoko Tsushima, Keith Williams, Tokuta

Yokohata and the anonymous reviewers for their helpful comments

and suggestions. We acknowledge the modelling 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 and CFMIP multi-model

datasets. Support of these datasets is provided by the Office of Sci-

ence, US Department of Energy. This work was supported by the

Joint DECC/Defra Met Office Hadley Centre Climate Programme

(GA01101).

References

Andrews T, Forster PM (2008) CO2 forcing induces semi-direct

effects with consequences for climate feedback interpretations.

Geophys Res Lett 35:L04802. doi:10.1029/2007GL032273

Blossey PN, Bretherton CS, Wyant MC (2009) Understanding

subtropical low cloud response to a warmer climate in a superpa-

rameterized climate model. part ii: Column modeling with a cloud-

resolving model. J Adv Model Earth Syst 1. doi:10.3894/

JAMES.2009.1.8

Bodas-Salcedo A, Webb MJ, Brooks ME, Ringer MA, Williams KD,

Milton SF, Wilson DR (2008) Evaluating cloud systems in the

Met Office global forecast model using simulated Cloud Sat

radar reflectivities. J Geophys Res 113:D00A13. doi:10.1029/

2007JD009620

Bodas-Salcedo A, Webb MJ, Bony S, Chepfer H, Dufresne JL, Klein S,

Zhang Y, Marchand R, Haynes JM, Pincus R, John VO (2011)

COSP: satellite simulation software for model assessment. Bull Am

Meteorol Soc 92(8):1023–1043. doi:10.1175/2011BAMS2856.1

Boer GJ, Yu B (2003) Climate sensitivity and response. Clim Dyn

20:415–429

Bony S, Dufresne JL (2005) Marine boundary layer clouds at the

heart of cloud feedback uncertainties in climate models.

Geophys Res Lett 32(20):L20806

Bony S, Colman R, Kattsov VM, Allan RP, Bretherton CS, Dufresne

JL, Hall A, Hallegate S, Holland MM, Ingram WJ, Randall DA,

Soden BJ, Tselioudis G, Webb MJ (2006) How well do we

understand and evaluate climate change feedback processes?

J Clim 19(15):3445–3482. doi:10.1175/JCLI3819.1

Bony S, Webb MJ, Bretherton CS, Klein SA, Siebesma AP,

Tselioudis G and Zhang M (2011) CFMIP: Towards a better

evaluation and understanding of clouds and cloud feedbacks in

CMIP5 models. CLIVAR Exchanges 16(56):20–24

Bretherton CS, Wyant MC (1997) Moisture transport, lower-tropo-

spheric stability, and decoupling of cloud-topped boundary

layers. J Atmos Sci 51(1):148–167

Brient F, Bony S (2012) Interpretation of the positive low-cloud

feedback predicted by a climate model under global warming.

Clim Dyn. doi:10.1007/s00382-011-1279-7

Chepfer H, Bony S, Winker D, Chiriaco M, Dufresne JL, Seze G

(2008) Use of CALIPSO lidar observations to evaluate the

cloudiness simulated by a climate model. Geophys Res Lett

35:L15704. doi:10.1029/2008GL034207

Clement AC, Burgman R, Norris JR (2009) Observational and model

evidence for positive low-level cloud feedback. Science

325(5939):460–464. doi:10.1126/science.1171255

Collins M, Booth BBB, Harris GR, Murphy JM, Sexton DMH, Webb MJ

(2006a) Towards quantifying uncertainty in transient climate change.

Clim Dyn 27(2–3):127147. doi:10.1007/s00382-006-0121-0

Collins WD, Bitz CM, Blackmon ML, Bonan GB, Bretherton CS,

Carton JA, Chang P, Doney SC, Hack JJ, Henderson TB, Kiehl

JT, Large WG, McKenna DS, Santer BD, Smith RD (2006b) The

community climate system model version 3 (CCSM3). J Clim

19(11):2122–2143

704 M. J. Webb et al.

123

Collins M, Booth BBB, Bhaskaran B, Harris G, Murphy JM, Sexton

DMH, Webb MJ (2010) A comparison of perturbed physics and

multi-model ensembles: model errors, feedbacks and forcings.

Clim Dyn. doi:10.1007/s00382-010-0808-0

Colman R (2003) A comparison of climate feedbacks in general

circulation models. Clim Dyn 20:865–873

Colman RA, McAvaney BJ (2011) On tropospheric adjustment to

forcing and climate feedbacks. Clim Dyn 36(9–10):1649–1658

Delworth TL, Rosati A, Stouffer RJ, Dixon KW, Dunne J, Findell K,

Ginoux P, Gnanadesikan A, Gordon CT, Griffies SM, Gudgel R,

Harrison MJ, Held IM, Hemler RS, Horowitz LW, Klein SA,

Knutson TR, Lin SJ, Milly PCD, Ramaswamy V, Schwarzkopf

MD, Sirutis JJ, Stern WF, Spelman MJ, Winton M, Wittenberg

AT, Wyman B (2006) GFDL’s CM2 global coupled climate

models—part 1: formulation and simulation characteristics.

J Clim 19(5):643–674

Dong B, Gregory JM, Sutton R (2009) Understanding land-sea

warming contrast in response to increasing greenhouse gases.

Part I: transient adjustment. J Clim 22:3079–3097. doi:10.1175/

2009JCLI2652.1

Doutriaux-Boucher M, Webb MJ, Gregory JM, Boucher O (2009)

Carbon dioxide induced stomatal closure increases radiative

forcing of climate via a rapid reduction in low cloud. Geophys

Res Lett L02703. doi:10.1029/2008GL036273

Dufresne JL, Bony S (2008) An assessment of the primary sources of

spread of global warming estimates from coupled atmosphere-

ocean models. J Clim 21:5135–5144. doi:10.1175/2008JCLI

2239.1

Efron B, Tibshirani RJ (1993) An introduction to the bootstrap,

monographs on statistics and applied probability, vol 57.

Chapman and Hall, New York

Good P, Gregory JM, Lowe JA (2011) A step-response simple climate

model to reconstruct and interpret AOGCM projections. Geo-

phys Res Lett 38:L01703. doi:10.1029/2010GL045208

Good P, Gregory JM, Lowe JA, Andrews T (submitted) Predicting

and understanding CMIP5 representative concentration pathway

projections using the response to abrupt CO2 change. Clim Dyn

Gordon C, Cooper C, Senior CA, Banks H, Gregory JM, Johns TC,

Mitchell JFB, Wood RA (2000) The simulation of SST, sea ice

extents and ocean heat transports in a version of the Hadley

Centre coupled model without flux adjustments. Clim Dyn

16:147–168

Gordon HB, Rotstayn LD, McGregor JL, Dix MR, Kowalczyk EA,

O’Farrell SP, Waterman LJ, Hirst AC, Wilson SG, Collier MA,

Watterson IG, Elliott TI (2002) The CSIRO Mk3 climate

system model. Technical report, CSIRO Atmospheric Research,

Aspendale

Gregory JM, Webb MJ (2008) Tropospheric adjustment induces a

cloud component in CO2 forcing. J Clim 21:58–71

Gregory JM, Ingram WJ, Palmer MA, Jones GS, Stott PA, Thorpe

RB, Lowe JA, Johns TC, Williams KD (2004) A new method for

diagnosing radiative forcing and climate sensitivity. Geophys

Res Lett 31:L03205. doi:10.1029/2003gl018747

Hansen J, Sato M, Nazarenko L, Ruedy R, Lacis A, Koch D, Tegen I,

Hall T, Shindell D, Santer B, Stone P, Novakov T, Thomason L,

Wang R, Wang Y, Jacob D, Hollandsworth-Frith S, Bishop L,

Logan J, Thompson A, Stolarski R, Lean J, Willson R, Levitus S,

Antonov J, Rayner N, Parker D, Christy J (2002) Climate

forcings in Goddard Institute for Space Studies SI2000 simula-

tions. J Geophys Res 107. doi:10.1029/2001JD001143

Hansen J, Sato M, Rudy R, Nazarenko L, Lacis A, Schmidt GA,

Russel G, Aleinov I, Bauer M, Bauer S, Bell N, Cairns B, Canuto

V, Chandler M, Cheng Y, Del Genio A, Faluvegi G, Fleming E,

Friend A, Hall T, Jackman C, Kelley M, Kiang N, Koch D, Lean

J, Lerner J, Lo K, Menon S, Miller R, Romanou A, Shindell D,

Stone P, Sun S, Tausnev N, Thresher D, Wielicki B, Wong T,

Yao M, Zhang S (2005) Efficacy of climate forcings. J Geophys

Res 110:D18104. doi:10.1029/2005JD005776

Harris GR, Sexton DMH, Booth BBB, Collins M, Murphy JM, Webb MJ

(2006) Frequency distributions of transient regional climate change

from perturbed physics ensembles of general circulation model

simulations. Clim Dyn 27(4):357–375. doi:10.1007/s00382-

006-0142-8

Harrison EP, Minnis P, Barkstrom BR, Ramanathan V, Cess RD,

Gibson GG (1990) Seasonal variation of cloud radiative forcing

derived from the earth radiation budget experiment. J Geophys

Res 95:18687–18703

Hasumi H, Emori S (2004) K-1 coupled model (miroc) description.

Technical report, Center for Climate System Resarch, University of

Tokyo, Tokyo, p 34. Available from http://www.ccsr.u-tokyo.ac.jp/

kyosei/hasumi/MIROC/tech-repo.pdf

Hourdin F, Musat I, Bony S, Braconnot P, Codron F, Dufresne JL,

Fairhead L, Filiberti MA, Friedlingstein P, Grandpeix JY,

Krinner G, LeVan P, Li ZX, Lott F (2006) The LMDZ4 general

circulation model: climate performance and sensitivity to

parametrized physics with emphasis on tropical convection.

Clim Dyn 27:787–813. doi:10.1007/s00382-006-0158-0

Johns TC, Durman CF, Banks HT, Roberts MJ, McLaren AJ, Ridley

JK, Senior CA, Williams KD, Jones A, Rickard GJ, Cusack S,

Ingram WJ, Crucifix M, Sexton DMH, Joshi MM, Dong BW,

Spencer H, Hill RSR, Gregory JM, Keen AB, Pardaens AK,

Lowe JA, Bodas-Salcedo A, Stark S, Searl Y (2006) The new

Hadley Centre climate model HadGEM1: evaluation of coupled

simulations. J Clim 19(7):1327–1353

Joshi MM, Gregory JM, Webb MJ, Sexton DMH, Johns TC (2008)

Mechanisms for the land/sea warming contrast exhibited by

simulations of climate change. Clim Dyn 30:455–465. doi:

10.1007/s00382-007-0306-1

Joshi MM, Webb MJ, Maycock AC, Collins M (2010) Stratospheric

water vapour and high climate sensitivity in a version of

HadSM3 climate model. Atmos Chem Phys Discuss 10:7161–

7167

Klein SA, Hartmann DL (1993) The seasonal cycle of low stratiform

clouds. J Clim 6(8):1587–1606

Klein SA, Jakob C (1999) Validation and sensitivities of frontal

clouds simulated by the ECMWF model. Mon Weather Rev

127(10):2514–2531

Klein SA, Hartmann DL, Norris JR (1995) On the relationships

among low-cloud structure, sea-surface temperature and atmo-

spheric circulation in the summertime northeast Pacific. J Clim

8(5):1140–1155

Klocke D, Pincus R, Quaas J (2011) On constraining estimates of

climate sensitivity with present-day observations through model

weighting. J Clim 24(23):6092–6099. doi:10.1175/2011JC

LI4193.1

Lambert FH, Webb MJ, Joshi MM (2011) The relationship between

land-ocean surface temperature contrast and radiative forcing.

J Clim 24:3239–3256

Martin GM, Ringer MA, Pope VD, Jones A, Dearden C, Hinton TJ

(2006) The physical properties of the atmosphere in the new

Hadley Centre Global Environmental Model, (HadGEM1).

Part I: model description and global climatology. J Clim

19:1274–1301. doi:10.1175/JCLI3636.1

Medeiros B, Stevens S (2011) Revealing differences in GCM

representations of low clouds. Clim Dyn 36(1–2):385–399. doi:

10.1007/s00382-009-0694-5

Medeiros B, Stevens B, Held IM, Zhao M, Williamson DL, Olson JG,

Bretherton CS (2008) Aquaplanets, climate sensitivity, and low

clouds. J Clim 21(19):4974–4991. doi:10.1175/2008JCLI1995.1

Meinhausen M, Raper SCB, Wigley TML (2011) Emulating coupled

atmosphere–ocean and carbon cycle models with a simpler

model, MAGICC6. Atmos Chem Phys 11:1417–1456

Climate sensitivity, forcing and feedback in climate models 705

123

Murphy JM, Sexton DMH, Barnett DN, Jones GS, Webb MJ, Collins

M, Stainforth DA (2004) Quantification of modelling uncertain-

ties in a large ensemble of climate change simulations. Nature

430:768–772

Pincus R, Batstone CP, Patrick-Hofmann RJ, Taylor KE, Gleckler PE

(2008) Evaluating the present-day simulation of clouds, precip-

itation and radiation in climate models. J Geophys Res

133(D14209). doi:10.1029/2007JD009334

Pope VD, Gallani ML, Rowntree PR, Stratton RA (2000) The impact

of new physical parametrizations in the Hadley Centre climate

model—HadAM3. Clim Dyn 16:123–146

Randall DA, Wood RA, Bony S, Colman R, Fichefet T, Fyfe J,

Kattsov V, Pitman A, Shukla J, Srinivasan J, Stouffer RJ, Sumi

A, Taylor KE (2007) Climate models and their evaluation. In:

Solomon S, Qin D, Manning M, Chen Z, Marquis MC, Averyt

KB, Tignor M, Miller HL (eds) Climate change 2007: the

physical science basis. Contribution of working group I to the

fourth assessment report of the Intergovernmental Panel on

Climate Change, pp 589–662

Richter I, Xie SP (2008) Muted precipitation increase in global

warming simulations: a surface evaporation perspective. J Geo-

phys Res 113. doi:10.1029/2008JD010561

Rienecker MM, Suarez MJ, Gelaro R, et al (2011) MERRA—

NASA’s modern-era retrospective analysis for research and

applications. J Clim pp 3624–3648. doi:10.1175/JCLI-D-11-

00015.1

Ringer MA, Ingram WJ (submitted) Influence of bounday-layer cloud

and convection on the tropical precipitation response to global

warming. Geophys Res Lett

Roeckner E, Bauml G, Bonaventura L, Brokopf R, Esch M, Giorgetta

M, Hagemann S, Kirchner I, Kornblueh L, Manzini E, Rhodin A,

Schlese U, Schulzweida U, Tompkins A (2003) The atmospheric

general circulation model ECHAM 5. Part I: model description.

Technical rep 349, Max Planck Institute for Meteorology

Rogelj J, Hare W, Lowe J, Van Vuuren DP, Riahi K, Matthews B, Hanaoka

T, Jiang K, Meinshausen M (2011) Emission pathways consistent

with a 2�C global temperature limit. Nat Clim Chang 1:413–418

Rougier J, Sexton DMH, Murphy JM, Stainforth DA (2009)

Analyzing the climate sensitivity of the HadSM3 climate model

using ensembles from different but related experiments. J Clim

22(13):1327–1353

Sanderson BM, Knutti R, Aina T, Christensen C, Faull N, Frame DJ,

Ingram WJ, Piani C, Stainforth DA, Stone DA, Allen MR (2008a)

Constraints on model response to greenhouse gas forcing and the

role of subgrid-scale processes. J Clim 21:2384–2400

Sanderson BM, Piani C, Ingram WJ, Stone DA, Allen MR (2008b)

Towards constraining climate sensitivity by linear analysis of

feedback patterns in thousands of perturbed-physics GCM simu-

lations. Clim Dyn 30:175–190. doi:10.1007/s00382-007-0280-7

Schmidt GA, Ruedy R, Hansen JE, Aleinov I, Bell N, Bauer M, Baue

S, Cairns B, Canut V, Cheng Y, Genio AD, Faluvegi G, Friend

AD, Hall TM, Hu Y, Kelley M, Kiang NY, Koch D, Lacis AA,

Lerner J, Lo KK, Miller RL, Nazarenko L, Oinas V, Perlwitz J,

Perlwitz J, Rind D, Romanou A, Russell GL, Sato M, Shindell

DT, Stone PH, Sun S, Tausnev N, Thresher D, Yao MS (2006)

Present day atmospheric simulations using GISS Model E:

Comparison to in-situ, satellite and reanalysis data. J Clim

19:153–192

Senior CA, Mitchell JFB (1993) Carbon dioxide and climate: the

impact of cloud parameterization. J Clim 6:393–418

Sexton DMH, Murphy J, Collins M, Webb M (2012) Multivariate

probabilistic projections using imperfect climate models. Part I:

outline of methodology. Clim Dyn. doi:10.1007/s00382-011-

1208-9

Shine KP, Cook J, Highwood EJ, Joshi MM (2003) An alternative to

radiative forcing for estimating the relative importance of

climate change mechanisms. Geophys Res Lett 30:2047. doi:

10.1029/2003GL018141

Soden BJ, Held IM (2006) An assessment of climate feedbacks in

coupled ocean-atmosphere models. J Clim 19:3354–3360

Soden BJ, Vecchi GA (2011) The vertical distribution of cloud

feedback in coupled ocean-atmosphere models. Geophys Res

Lett 38:12704. doi:10.1029/2011GL047632

Soden BJ, Broccoli AJ, Hemler RS (2004) On the use of cloud forcing

to estimate cloud feedback. J Clim 17(19):3661–3665. doi:

10.1175/1520-0442(2004)017\3661:OTUOCF[2.0.CO;2. http://

journals.ametsoc.org/doi/full/10.1175/1520-0442%282004%

29017%3C3661:OTUOCF%3E2.0.CO%3B2

Stainforth DA, Aina T, Christensen C, Collins M, Frame DJ,

Kettleborough JA, Knight S, Martin A, Murphy J, Piani C,

Sexton D, Smith LA, Spicer RA, Thorpe AJ, Allen MR (2005)

Uncertainty in predictions of the climate response to rising levels

of greenhouse gases. Nature 433:403–406

Stevens B, Brenguier JL (2009) Cloud controlling factors—low

clouds. In: Clouds in the perturbed climate system

Taylor KE, Crucifix M, Braconnot P, Hewitt CD, Doutriaux C,

Broccoli AJ, Mitchell JFB, Webb MJ (2007) Estimating

shortwave radiative forcing and response in climate models.

J Clim 20:2530–2543

Trenberth KE, Fasullo JT (2010) Simulation of present-day and

twenty-first-century energy budgets of the southern oceans.

J Clim 23(2):440–454. doi:10.1175/2009JCLI3152.1

Uppala SM, Kallberg PW, Simmons AJ, Andrae U, daCosta Bechtold V,

Fiorino M, Gibson JK, Haseler J, Hernandez A, Kelly GA, Li X,

Onogi K, Saarinen S, Sokka N, Allan RP, Andersson E, Arpe K,

Balmaseda MA, Beljaars ACM, van de Berg L, Bidlot J, Bormann

N, Caires S, Chevallier F, Dethof A, Dragosavac M, Fisher M,

Fuentes M, Hagemann S, Holm E, Hoskins BJ, Isaksen L, Janssen

PAEM, Jenne R, McNally AP, Mahfouf JF, Morcrette JJ, Rayner

NA, Saunders RW, Simon P, Sterl A, Trenberth KE, Untch A,

Vasiljevic D, Viterbo P, Woollen J (2005) The ERA-40 re-analysis.

Q J R Meteorol Soc 131:2961–3012. doi:10.1256/qj.04.176

Vecchi GA, Soden BJ (2007) Global warming and the weakening of

the tropical circulation. J Clim 20(17):4316–4340. doi:10.1175/

JCLI4258.1

von Salzen K, McFarlane NA, Lazare M (2005) The role of shallow

convection in the water and energy cycles of the atmosphere.

Clim Dyn 25:671–688

Watanabe M, Shiogama H, Yoshimori M, Ogura T, Yokohata T,

Okamoto H, Emori S, Kimoto M (2011) Fast and slow timescales in

the tropical low-cloud response to increasing CO2 in two climate

models. Clim Dyn. doi:10.1007/s00382-011-1178-y

Webb M, Senior C, Bony S, Morcrette JJ (2001) Combining ERBE

and ISCCP data to assess clouds in the Hadley Centre,

ECMWF and LMD atmospheric climate models. Clim Dyn

17:905–922

Webb MJ, Senior CA, Sexton DMH, Ingram WJ, Williams KD,

Ringer MA, McAvaney BJ, Colman R, Soden BJ, Gudgel R,

Knutson T, Emori S, Ogura T, Tsushima Y, Andronova NG, Li

B, Musat I, Bony S, Taylor KE (2006) On the contribution of

local feedback mechanisms to the range of climate sensitivity in

two GCM ensembles. Clim Dyn 27(1):17–38. doi:

10.1007/s00382-006-0111-2

Wetherald RT, Manabe S (1988) Cloud feedback processes in a

general circulation model. J Atmos Sci 45:1397–1415

Williams KD, Tselioudis G (2007) GCM intercomparison of global

cloud regimes: present-day evaluation and climate change

response. Clim Dyn 29:231–250. doi:10.1007/s00382-007-

0232-2

Williams KD, Webb MJ (2009) A quantitative performance assess-

ment of cloud regimes in climate models. Clim Dyn

33(1):141–157. doi:10.1007/s00382-008-0443-1

706 M. J. Webb et al.

123

Williams KD, Ingram WJ, Gregory JM (2008) Time variation of

effective climate sensitivity in GCMs. J Clim 21(19):5076–5090.

doi:10.1175/2008JCLI2371.1

Wood R, Bretherton CS (2006) On the relationship between

stratiform low cloud cover and lower tropospheric stability.

J Clim 19:6425–6432

Wyant MC, Bretherton CS, Bacmeister JT, Kiehl JT, Held IM, Zhao M,

Klein SA, Soden BJ (2006) A comparison of low-latitude cloud

properties and their response to climate change in three agcms

sorted into regimes using mid-tropospheric vertical velocity. Clim

Dyn 27(2-3):261–279. doi:10.1007/s00382-006-0138-4

Wyant MC, Bretherton CS, Blossey PN (2009) Understanding

subtropical low cloud response to a warmer climate in a

superparameterized climate model. Part I: regime sorting and

physical mechanisms. J Adv Model Earth Syst 1. doi:10.3894/

JAMES.2009.1.7

Wyant MC, Bretherton CS, Blossey PN, Khairoutdinov M (in press)

Fast cloud adjustment to increasing CO2 in a superparameterized

climate model. J Adv Model Earth Syst

Yokohata T, Webb MJ, Collins M, Williams KD, Yoshimori M,

Hargreaves JD, Annan JD (2010) Structural similarities and

differences in climate responses to CO2 increase between two

perturbed physics ensembles. J Clim 23(6):1392–1410. doi:

10.1175/2009JCLI2917.1

Yukimoto S, Noda A, Kitoh A, Hosaka M, Yoshimura H, Uchiyama

T, Shibata K, Arakawa O, Kusunoki S (2006) Present-day

climate and climate sensitivity in the Meteorological Research

Institute GCM Version 2.3 (MRICGCM2.3). J Meteorol Soc Jpn

84:333–363

Zhang M, Bretherton CS (2008) Mechanisms of low cloud climate

feedback in idealized single-column simulations with the

community atmospheric model (CAM3). J Clim 21(18):4859–

4878. doi:10.1175/2008JCLI2237.1

Zhang Y, Rossow WB, Lacis AA (1995) Calculation of surface and

top of atmosphere radiative fluxes from physical quantities based

on ISCCP datasets 1. Method and sensitivity to input data

uncertainties. J Geophys Res 100:1149–1165

Climate sensitivity, forcing and feedback in climate models 707

123