Global-mean radiative feedbacks and forcing in atmosphere-only and coupled atmosphere-ocean climate change experiments

©2014 American Geophysical Union. All rights reserved.

Global-mean radiative feedbacks and forcing in atmosphere-only and

coupled atmosphere-ocean climate change experiments

Mark A. Ringer1, Timothy Andrews and Mark J. Webb

Met Office Hadley Centre, Exeter, U.K.

(22 May 2014)

Submitted to Geophysical Research Letters

_______________________

1Corresponding author: Mark A. Ringer, Met Office Hadley Centre, FitzRoy Road, Exeter,

EX1 3PB, U.K. ([email protected])

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/2014GL060347


Abstract

Analysis of the available CMIP5 models suggests that SST-forced, atmosphere-only global

warming experiments (‘amip4K’, ‘amipFuture’, ‘aqua4K’) are a good guide to the global

cloud feedbacks determined from coupled atmosphere-ocean CO2-forced simulations,

including the inter-model spread. Differences in the total climate feedback parameter between

the experiments arise primarily from differences in the clear-sky feedbacks which can largely

be anticipated from the nature of the experimental design.

The effective CO2 radiative forcing is anti-correlated with the total feedback in the coupled

simulations. This anti-correlation strengthens as the experimental design becomes simpler,

the number of potential degrees of freedom of the system’s response reduces, and the relevant

physical processes can be identified. In the aquaplanet simulations the anti-correlation is

primarily driven by opposing changes in the rapid cloud adjustment to CO2 and the net cloud

response to increased surface warming. Establishing a physical explanation for this behavior

is important future work.


1. Introduction

The climate feedback parameter, , relates the change in global-mean surface temperature,

ST , to the energetic response of the climate system to a radiative forcing. For small

perturbations to an initial equilibrium state this relationship is approximately linear [Gregory

et al., 2004],

SN F T ,

where F is the radiative forcing and N is the change in net downward radiative flux at the

top-of-atmosphere (TOA).

If the system is allowed to return to equilibrium (i.e. 0N ) then /EQT F is the

equilibrium surface warming. For the radiative forcing resulting from a doubling of

atmospheric CO2 concentration EQT is usually referred to as the equilibrium climate

sensitivity.

The feedback parameter provides a useful metric for comparing the responses of different

global climate models to any perturbation to the system. The CMIP5 experimental design

[Taylor et al., 2012] provides several types of simulation from which , F and ST can be

estimated. These include (i) increasing the atmospheric CO2 concentration in fully-coupled

atmosphere-ocean models and (ii) raising the sea-surface temperatures, either uniformly or

with geographical variation, in atmosphere-only models.


The question we address here is whether these estimates of the climate feedback parameter

agree. In particular, does the spread in across the ensemble of atmosphere-only, SST

warming simulations provide a good guide to what we might expect in fully-coupled models

in response to increased atmospheric CO2?

This is an important question because SST-forced experiments have long been used to assess

climate feedbacks in models but it is unclear how the idealized experimental design relates to

more realistic coupled feedbacks. For the first time CMIP5 enables such a comparison.

Here we: (i) compare estimates of the global mean radiative feedbacks in the different CMIP5

experiments; (ii) explore the relationship between feedbacks derived from the ‘simplified’

experiments in CMIP5 (e.g. uniform SST warming) and those derived from the fully-coupled

experiments (abrupt CO2 quadrupling); and (iii) investigate if the spread of the feedbacks

across the ensemble of coupled models is captured by the simplified experiments.

In addition, we also examine the relationship between the cross-ensemble variations in CO2

radiative forcing and feedbacks in the models and its dependence upon the experimental

design.

2. Model simulations and methods

We use data from the following CMIP5 experiments (see Taylor et al., 2012 for the formal

definitions): piControl and abrupt4×CO2 (years 1 – 150); amip, amip4K, and amipFuture

(30-year means, 1979 – 2008); aquaControl and aqua4K (5-year means); amip4×CO2 (30-

year means, 1979 – 2008); aqua4×CO2 (5-year means); sstClim and sstClim4×CO2


(“Hansen” experiment; 30-year means). The models used for the different experiments are

listed in Table S1 of the supporting information.

The radiative feedbacks are estimated as follows. For the abrupt4×CO2 simulations, the

feedbacks are estimated from the slopes of the linear regression of annual, global-mean

changes in top-of-atmosphere fluxes on global-mean changes in surface temperature

[Gregory et al., 2004]; note that the total radiative forcing and (if the stratospherically-

adjusted forcing is known) the rapid tropospheric adjustments can also be estimated as the

intercepts from these regressions. For the amip4K, amipFuture and aqua4K experiments, the

feedbacks are estimated using differences of the long-term mean fluxes and surface

temperatures from the appropriate control simulations. The forcings and adjustments are

estimated in the same manner using the amip4×CO2, aqua4×CO2 and sstClim4×CO2

experiments.

Cloud and non-cloud (clear-sky) feedbacks are separated using the cloud radiative effect

(CRE), i.e. the differences between all-sky and clear-sky TOA fluxes. Limitations of this

method and the possible impacts on our results are discussed in the text.

3 Results

3.1 Radiative feedbacks in the CMIP5 experiments

Figure 1 summarizes the different radiative feedbacks estimated from the aqua4K, amip4K,

amipFuture, and fully-coupled abrupt4×CO2 experiments for the CMIP5 models.


The shortwave clear-sky feedback ( ,sw clr ) is robustly small and positive across the

aquaplanet models. In the amip experiments the sea-ice concentration is fixed (although sea-

ice albedo may depend on temperature), so over ocean this is essentially the SW water vapour

feedback; over land these experiments also include snow retreat. Consequently ,sw clr is larger

than in the aqua simulations but is generally still smaller than in the coupled models because

there is no reduction in sea-ice in response to the warming. In the abrupt4×CO2 experiments

the sea-ice retreat leads to a considerably larger positive feedback. Note the greater spread at

the lower end of range when we use the full ensemble, indicating the importance of the

sample size when assessing the sea-ice feedback. In general the differences between ,sw clr in

the experiments match our expectations given the different experimental designs.

The longwave clear-sky feedback ( ,lw clr ) differs little between aqua and amip experiments,

i.e. it is insensitive to either the presence of the land or the enhanced land warming. The

median, minimum, and maximum values all imply a weaker (negative) feedback in the

abrupt4×CO2 experiments. This could result from differences in the warming patterns but

might also be due to the effect of the increased opacity of the atmosphere due to the elevated

CO2 concentration [e.g. Good et al., 2012], which is not present in SST-forced experiments.

The end result is greater inter-model spread in the abrupt4×CO2 experiments.

The longwave CRE feedback ( ,lw cre ) is small – the median value is close to zero – with a

range of around ±0.5Wm-2

K-1

and this varies little with experimental design. There is a

suggestion that ,lw cre is more likely to be slightly positive in the abrupt4×CO2 experiments

and slightly negative in the amip experiments.


The variation of the shortwave CRE feedback ( ,sw cre ) in the aqua experiments is dominated

by two models with large negative feedback (see also below); this dominates the net CRE

feedback in these models. In general ,sw cre is small and positive in the amip experiments; it is

more negative in the coupled simulations, with a larger inter-model spread.

Some of the differences between the experiments arise due to the so-called ‘cloud masking’

effect, which can lead to difficulties in interpreting changes in CRE [Soden et al., 2004]. In

particular, this will affect comparisons of the SW CRE feedbacks as the amip experiments do

not include reductions in sea-ice extent (although they do include snow retreat over land).

Soden et al. [2008] estimate the clear-sky albedo feedback cloud masking effect as 0.26 Wm-2

K-1

. Adding this offset to ,sw cre derived from the abrupt4×CO2 experiments – assuming that

the dominant contribution results from the sea-ice reduction and varies little between models

– leads to closer agreement with the amip experiments in the absolute value of the feedback

in most, but not all, of the models. This issue is less important for the LW CRE feedback as

the masking effect in this case will be much less dependent on the experimental design:

accounting for cloud masking would mean that the LW cloud feedback is likely to be positive

in all cases. Overall, the impact of cloud masking is to slightly alter our perspective on

comparisons between the absolute values of the feedbacks but not on their spread across the

respective ensembles.

In all of the experiments the median net CRE feedback ( ,net cre ) is close to zero but the spread

is large, especially towards positive feedbacks. Differences in the median total feedback

across the different types of experiment are thus primarily determined by differences in the

clear-sky feedbacks.


3.2 Relationships between feedbacks in the amip and aquaplanet experiments and the

coupled models

We can also examine the relationship between the feedbacks in the different experiments

directly for models where all of the experiments have been performed. Although this reduces

the sample size somewhat (10 – 12 models) it still provides a useful comparison (Figure 2).

Both ,sw clr and ,lw clr in amip and aqua lie to the left of the one-to-one line, indicating

weaker positive and stronger negative feedbacks respectively, compared to the fully-coupled

simulations. As anticipated from Fig. 1, the amip and aqua experiments are distinct for ,sw clr

but not for ,lw clr . Aside from the two aquaplanet models (MPI-ESM-LR/MR) with large

negative shortwave CRE feedback, in aqua most models lie close to the one-to-one line for

both ,sw cre and ,lw cre . The greater spread in the shortwave compared to the longwave CRE

feedback in the coupled experiments is captured by both the amip and aqua simulations.

The net cloud feedback, ,net cre , also lies very close to the one-to-one line. The aqua and amip

experiments thus appear to be a good guide to the global-mean net cloud feedback in the

coupled models, including its spread across the ensemble. This suggests that the global-mean

net cloud feedback in models does not depend on the detailed spatial pattern of the SST

warming, and may not even be greatly influenced by the land-sea contrast in the surface

warming. This differs from previous studies which have suggested that the SST pattern is

important to determining the cloud feedbacks [e.g. Zhu et al., 2007; Dessler, 2013]. However,

it should be noted that these studies usually compare the climate change responses with

present-day interannual variability, ENSO in particular. The contrast between the large


circulation shifts associated with El Niño and La Niña events and the more spatially uniform

feedback patterns associated with the global warming response probably explains a large part

of the difference with our findings [Dessler, 2013].

In addition, feedbacks which are not represented in the aqua and amip experiments may not

have much impact on the global-scale cloud feedback, although differences between the

warming patterns larger than considered here might make manifest such an influence.

This close relationship between the net cloud feedback in the experiments in turn means that

for the total feedback, , the difference between estimates from the coupled and simplified

experiments arises primarily from differences in the clear-sky feedbacks (cf. Fig. 1).

Both Andrews et al. [2012] and Stevens et al. [2013] have drawn attention to deviations from

linearity in the forcing-response relationship derived from the abrupt experiments with

certain models. This means that estimates of both and the effective climate sensitivity

(ECS) will differ depending on whether one uses the early part of the run, the later part, or a

combination of both. This in turn could alter our interpretation of the comparisons with the

simplified experiments.

We have re-calculated the feedbacks in the abrupt experiments separately for the first ten

years after CO2 quadrupling and for year 11 onwards to examine this. The net cloud feedback

for the earlier period is small and negative – as opposed to being small and positive for the

later period – for most models and agrees less well with the amip experiments. Although the

consequent reduction in the total feedback means that estimates of from years 1 – 10 now

appear to agree better with the amip experiments, this is actually a result of compensating the


original disparity due to clear-sky feedbacks (Fig. 1). This suggests that the feedbacks

estimated from the amip experiments are likely to be more relevant to the later part of the

abrupt4×CO2 experiments than to the early part, although clearly more work is necessary to

understand both the nature of the non-linearity and its relationship to the more idealized

simulations.

3.3 Relationship between global-mean feedbacks and forcing

Andrews et al. [2012] and Webb et al. [2013] have noted an anti-correlation between the

radiative forcing due to CO2 and the total climate feedback ( ) in GCMs, which we here

examine in more detail.

For reference, estimates of the 4×CO2 radiative forcing from the different CMIP5

experiments are shown in Fig. S1 (supporting information), together with those from the

previous two IPCC assessments. The median value tends to be around 7 Wm-2

, with an

interquartile range of 6-8 Wm-2

; this does not appear to have changed greatly since the TAR,

even though these earlier assessments did not explicitly account for tropospheric adjustments.

The range is wider in the abrupt experiments compared to both the amip and Hansen

estimates; this results from uncertainties in the linear regression method used to derive the

forcing in the abrupt experiments (possibly including non-linearities not captured by the

method itself). The median CO2 forcing derived from the aqua4×CO2 experiments is larger

than all of the other estimates: this suggests that it results from the lack of rapid land warming

and the increased LW emission to space this induces.


Cross-ensemble correlations between the CO2 forcing and the total and net cloud feedbacks

in the different experiments (Table 1) suggest an anti-correlation that becomes more evident

as the experimental design becomes simpler, driven primarily by the ensemble spread in the

cloud feedback (i.e. the cloud feedback is the dominant contribution to the spread in ).

Figure 4 confirms the strong relationship between the 4×CO2 forcing and the +4K feedbacks

in the aquaplanet experiments. Models with a larger CO2 forcing have a more negative cloud

feedback, the variation of which is dominated by that in the shortwave component (the

longwave component is small in all of the models and varies much less across the ensemble).

This is true for the two models which did not indicate a strong relationship between the cloud

feedback in the aquaplanet and fully-coupled experiments (Fig. 2), i.e. they appear unusual

regarding the relationship between the aquaplanet and coupled versions but not in the context

of the aquaplanet model ensemble itself.

It thus appears that the simplified experimental design not only allows us to explore the

factors driving the inter-model spread in the feedbacks in the coupled models but also

provides a potentially useful framework for understanding the relationship between the CO2

forcing and the feedbacks.

What drives this F relationship and why is it more evident in the aquaplanet models? A

possible reason is that it emerges more clearly due to the reduced degrees of freedom, so that

as the models become more complex the additional feedbacks (and rapid adjustments) this

allows then act to blur the relationship between the forcing and the feedbacks.


Further analysis suggests that the F relationship is primarily driven by that between the

4×CO2 cloud adjustment and the cloud feedback. Neglecting one of the models (FGOALS-

s2) the cross-ensemble correlation between the net CRE adjustments and feedbacks is -0.90,

i.e. stronger negative adjustments are associated with stronger positive feedbacks (Fig. S2,

supporting information). Moreover, the cross-ensemble variations in the net cloud adjustment

and the net cloud feedback make the dominant contributions to the spread in F and

respectively: the linear correlation co-efficient between the net cloud adjustment and F is

0.96, while that between ,net cre and is 0.99.

The one GCM that does not follow this behaviour is FGOALS-s2. Examination of vertical

profiles of relative humidity and cloud amount (not shown) indicates increases in both

quantities in the boundary layer in this model in response to 4×CO2. This is opposite to the

responses which we see in all of the other aquaplanet models, which are consistent with

previous work [e.g. Kamae and Watanabe, 2012]. This likely results from the

parameterization of low-level cloud as a function of lower-tropospheric stability in the

FGOALS model [Qu et al., 2013], with the impact of increasing stability (leading to

increased cloud) dominating the 4×CO2 response. In addition, the boundary layer is much

drier in the FGOALS-s2 control simulation than it is in the other models: the global-mean

relative humidity is 65% at 925 hPa, whereas it ranges between 83-90% in the other ensemble

members.

Taken together, these findings show that the strong forcing-feedback anti-correlation in the

aquaplanet simulations can be explained by identifying the dominant components of the

cross-ensemble spread in each. In all but one of the models considered here this relationship

is dominated by the opposing effects of changes in cloud resulting from these perturbations to


the system. As the experimental design becomes more complex, and the degrees of freedom

of the system to respond to the initial perturbation consequently increase, this relatively

straightforward explanation for the F relationship becomes less evident. It nonetheless

provides a very good starting point for trying to understand the relationship between forcing

and feedbacks due to increased atmospheric CO2.

4. Discussion and conclusions

Our estimates of F and from each model can be combined to determine the effective

climate sensitivity (ECS) from the different experiments (Table 2). As expected, the

differences in lead to lower estimates of ECS (median, 1st and 3

rd quartiles, maximum and

minimum) using the amip experiments compared to the abrupt4×CO2 simulations. The full

range of ECS derived using all of the available coupled models (2.1 – 4.7 K) corresponds to

those given in Andrews et al. [2012] and Forster et al. [2013]. Note that compensation

between the differences in the estimates of F and in the aquaplanet experiments (the anti-

correlation described above) means that the range of ECS in this case is comparable to that

derived from the coupled simulations for models where both experiments are available.

Our analysis of the CMIP5 models demonstrates that the simplified (and computationally less

burdensome) amip and aquaplanet SST warming experiments are a good guide to the global-

mean cloud feedback in the fully-coupled CO2-forced experiments, both its value in any

particular model and its spread across the ensemble. Differences in the total climate feedback

parameter estimated from the experiments arise primarily due to differences in the clear-sky

feedbacks which can largely be anticipated from the nature of the experimental design. The

amip and aquaplanet experiments thus provide an ideal test bed for investigating physical


mechanisms of cloud feedbacks and cloud adjustments, for example via targeted sensitivity

tests [e.g. Webb and Lock, 2013].

The anti-correlation between the effective CO2 radiative forcing and the total feedback in the

fully-coupled simulations becomes stronger as the experimental design becomes simpler and

the number of potential degrees of freedom of the system to respond reduces. As this happens

the relevant physical processes can then be more clearly identified.

We emphasise that, as with all climate model inter-comparison studies, the available

ensembles are small, so that it is important not to over-interpret these results. Nonetheless,

they do provide useful information on the relationships between global radiative feedbacks

and forcing estimated from the different experimental designs. Our findings extend previous

studies showing an anti-correlation between adjusted forcing and net feedback by

demonstrating that this relationship is also found in CMIP5, is stronger in aquaplanet and

AMIP simulations than in coupled models, and is driven by an anti-correlation between cloud

adjustment and cloud feedback. Establishing a physical explanation for this behavior is

important future work.

Acknowledgments. This work was supported by the Joint UK DECC/Defra Met Office Hadley

Centre Climate Programme (GA01101). We thank William Ingram, John Mitchell and Jonathan

Gregory for comments on the original manuscript. We acknowledge the World Climate Research

Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank

the climate modeling groups for producing and making available their model output. For CMIP the

U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides

coordinating support and led development of software infrastructure in partnership with the Global

Organization for Earth System Science Portals.


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Figure 1: Global mean radiative feedbacks (all in Wm-2

K-1

) in the different CMIP5

experiments. “abrupt” refers to the reduced set of 12 models which overlap with amip

experiments, “abrupt_all” refers to the complete ensemble of 24 coupled experiments. These

are box plots with the box showing the inter-quartile range, the horizontal line the median,

and the whiskers indicating the full range (maximum and minimum values). Note that the

range spanned by the y-axis is the same for all of the feedbacks although the absolute values

are different.


Figure 2: Global mean radiative feedbacks (Wm-2

K-1

) compared between aqua/amip

experiments (x-axis) and fully-coupled abrupt 4×CO2 simulations (y-axis). Note that the axes

on the left have the same range, but not necessarily the same absolute values, as the

corresponding plot on the right. The one-to-one line is also shown in all of the plots. Note

that the overlap between abrupt/aqua experiments and the abrupt/amip experiments is not

identical.


Figure 3: Relationship between the global-mean total and cloud feedbacks (Wm-2

K-1

) and

the total 4×CO2 radiative forcing (Wm-2

) in the aquaplanet model ensemble. The solid line is

the linear regression fit to the points and the linear correlation co-efficient is shown in each

case.


Table 1: Cross ensemble correlations between the 4×CO2 radiative forcing and the total and

net cloud feedbacks in the different experiments. Single and double asterisks indicate

correlations significant at the 95% and 99% levels respectively. Experiments are listed in

order of increasing complexity.

λ

(total)

λ

(net_cre)

aqua_4K -0.82** -0.95**

amip_4K -0.65* -0.68*

amip_Future -0.48 -0.53

abrupt -0.43* -0.46*


Table 2: Effective climate sensitivity (K) estimated from the different experiments. “abrupt”

refers to the reduced set of 12 models which overlap with amip experiments, “abrupt_all”

refers to the complete ensemble of 24 coupled experiments.

Experiment Median Interquartile

range

Range

aqua 3.7 3.0 – 4.1 2.6 – 4.2

amip_4K 2.4 2.2 – 2.7 2.1 – 3.0

amip_Future 2.4 2.1 – 2.6 1.9 – 3.2

abrupt 3.2 2.7 – 4.1 2.6 – 4.6

abrupt_all 3.1 2.6 – 3.9 2.1 – 4.7

Documents

Global-mean radiative feedbacks and forcing in atmosphere-only and coupled atmosphere-ocean climate change experiments