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How well do we understand and evaluate climate change
feedback processes?
S. Bony, R Colman, Vm Kattsov, Rp Allan, Cs Bretherton, Jl Dufresne, A
Hall, Stéphane Hallegatte, Mm Holland, W Ingram, et al.
To cite this version:
S. Bony, R Colman, Vm Kattsov, Rp Allan, Cs Bretherton, et al.. How well do we understand and
evaluate climate change feedback processes?. Journal of Climate, American Meteorological Society,
2006, pp.3445. �10.1175/JCLI3819.1�. �hal-00716782�
REVIEW ARTICLE
How Well Do We Understand and Evaluate Climate Change Feedback Processes?
SANDRINE BONY,
a
ROBERT COLMAN,
b
VLADIMIR M. KATTSOV,
c
RICHARD P. ALLAN,
d
CHRISTOPHER S. BRETHERTON,
e
JEAN-LOUIS DUFRESNE,
a
ALEX HALL,
f
STEPHANE HALLEGATTE,
g
MARIKA M. HOLLAND,
h
WILLIAM INGRAM,
i
DAVID A. RANDALL,
j
BRIAN J. SODEN,
k
GEORGE TSELIOUDIS,
l
AND MARK J. WEBB
m
a
Laboratoire de Météorologie Dynamique, IPSL, CNRS, Paris, France
b
Bureau of Meteorology Research Centre, Melbourne, Australia
c
Voeikov Main Geophysical Observatory, St. Petersburg, Russia
d
Environmental Systems Science Centre, University of Reading, Reading, United Kingdom
e
Department of Atmospheric Sciences, University of Washington, Seattle, Washington
f
Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California
g
Centre International de Recherche sur l’Environnement et le Développement, Nogent-sur-Marne, and Centre National de Recherches
Météorologiques, Météo-France, Toulouse, France
h
National Center for Atmospheric Research, Boulder, Colorado
i
Atmospheric, Oceanic and Planetary Physics, Clarendon Laboratory, Oxford, and Hadley Centre for Climate Prediction and
Research, Met Office, Exeter, United Kingdom
j
Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado
k
Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida
l
NASA GISS, and Department of Applied Physics, Columbia University, New York, New York
m
Hadley Centre for Climate Prediction and Research, Met Office, Exeter, United Kingdom
(Manuscript received 4 July 2005, in final form 1 December 2005)
ABSTRACT
Processes in the climate system that can either amplify or dampen the climate response to an external
perturbation are referred to as climate feedbacks. Climate sensitivity estimates depend critically on radia-
tive feedbacks associated with water vapor, lapse rate, clouds, snow, and sea ice, and global estimates of
these feedbacks differ among general circulation models. By reviewing recent observational, numerical, and
theoretical studies, this paper shows that there has been progress since the Third Assessment Report of the
Intergovernmental Panel on Climate Change in (i) the understanding of the physical mechanisms involved
in these feedbacks, (ii) the interpretation of intermodel differences in global estimates of these feedbacks,
and (iii) the development of methodologies of evaluation of these feedbacks (or of some components) using
observations. This suggests that continuing developments in climate feedback research will progressively
help make it possible to constrain the GCMs’ range of climate feedbacks and climate sensitivity through an
ensemble of diagnostics based on physical understanding and observations.
1. Introduction
The global mean surface air temperature change in
response to a doubling of the atmospheric CO
2
concen-
tration, commonly referred to as the climate sensitivity,
plays a central role in climate change studies. Accord-
ing to the Third Assessment Report (TAR) of the In-
tergovernmental Panel on Climate Change (IPCC), the
equilibrium climate sensitivity
1
estimates from general
circulation models (GCMs) used for climate change
projections range from 2° to 5°C (Houghton et al.
2001). This range, which constitutes a major source of
uncertainty for climate stabilization scenarios (Caldeira
et al. 2003), and which could in fact be even larger
Corresponding author address: Sandrine Bony, LMD/IPSL,
Boite 99, 4 Place Jussieu, 75252 Paris CEDEX 05, France.
E-mail: bony@lmd.jussieu.fr
1
“Equilibrium climate sensitivity” refers to the global mean
surface air temperature change experienced by the climate system
after it has attained a new equilibrium in response to a doubling
of the atmospheric carbon dioxide concentration.
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(Murphy et al. 2004; Stainforth et al. 2005), principally
arises from differences in the processes internal to the
climate system that either amplify or dampen the cli-
mate system’s response to the external forcing [(Na-
tional Research Council) NRC (2003)]. These pro-
cesses are referred to as climate feedbacks (see appen-
dix A for a more formal definition of climate
feedbacks).
Every climate variable that responds to a change in
global mean surface temperature through physical or
chemical processes and that directly or indirectly affects
the earth’s radiation budget has the potential to consti-
tute a climate change feedback. In this paper, we focus
on the feedbacks associated with climate variables (i)
that directly affect the top-of-the-atmosphere (TOA)
radiation budget, and (ii) that respond to surface tem-
perature mostly through physical (rather than chemical
or biochemical) processes. We will thus focus on the
radiative feedbacks associated with the interaction of
the earth’s radiation budget with water vapor, clouds,
temperature lapse rate, and surface albedo in snow and
sea ice regions, whose role in GCM estimates of equi-
librium climate sensitivity has been widely established.
On the other hand, we will not consider the feedbacks
associated with the response to temperature of the car-
bon cycle or of aerosols and trace gases, nor those as-
sociated with soil moisture changes or ocean processes,
although these processes might have a substantial im-
pact on the magnitude, the pattern, or the timing of
climate warming (NRC 2003).
Water vapor constitutes a powerful greenhouse gas,
and therefore an increase of water vapor with tempera-
ture will oppose the increase in radiative cooling due to
increasing temperature, and so constitute a positive
feedback. The earth’s cryosphere reflects part of the
incoming shortwave (SW) radiation to space, and
therefore the melting of snow and sea ice with rising
temperature constitutes another positive feedback. The
temperature lapse rate in the troposphere (i.e., the rate
of decrease of atmospheric temperature with height)
affects the atmospheric emission of longwave (LW) ra-
diation to space, and thus the earth’s greenhouse effect
(the stronger the decrease of temperature with height,
the larger the greenhouse effect). Therefore, an atmo-
spheric warming that is larger (smaller) in the upper
troposphere than at low levels produces a negative
(positive) radiative feedback compared to a uniform
temperature change. Clouds strongly modulate the
earth’s radiation budget, and a change in their radiative
effect in response to a global temperature change may
produce a substantial feedback on the earth’s tempera-
ture. But the sign and the magnitude of the global mean
cloud feedback depends on so many factors that it re-
mains very uncertain.
Several approaches have been proposed to diagnose
global radiative feedbacks in GCMs (appendix B), each
of these having its own strengths and weaknesses
(Soden et al. 2004; Stephens 2005). Since the TAR,
some of them have been applied to a wide range of
GCMs, which makes it possible to compare the feed-
backs produced by the different models and then to
better interpret the spread of GCMs’ estimates of cli-
mate sensitivity.
Figure 1 compares the quantitative estimates of glob-
al climate feedbacks (decomposed into water vapor,
lapse rate, surface albedo, and cloud feedback compo-
nents) as diagnosed by Colman (2003a), Soden and
Held (2006), and Winton (2006). The water vapor feed-
back constitutes by far the strongest feedback, with a
multimodel mean and standard deviation of the feed-
back parameter [as estimated by Soden and Held
(2006) for coupled GCMs participating in the IPCC
Fourth Assessment Report (AR4) of the IPCC] of
1.80 0.18 W m
2
K
1
, followed by the lapse rate
feedback (0.84 0.26 W m
2
K
1
), the cloud feed-
back (0.69 0.38 W m
2
K
1
), and the surface albedo
feedback (0.26 0.08 W m
2
K
1
). These results indi-
cate that in GCMs, the water vapor feedback amplifies
the earth’s global mean temperature response (com-
pared to a basic Planck response, see appendix A) by a
FIG. 1. Comparison of GCM climate feedback parameters (in W
m
1
K
1
) for water vapor (WV), cloud (C), surface albedo (A),
lapse rate (LR), and the combined water vapor lapse rate (WV
LR). ALL represents the sum of all feedbacks. Results are
taken from Colman (2003; in blue), Soden and Held (2006, in red),
and Winton (2006, in green). Closed and open symbols from Col-
man (2003) represent calculations determined using the PRP and
the RCM approaches, respectively. Crosses represent the water
vapor feedback computed for each model from Soden and Held
(2006) assuming no change in relative humidity. Vertical bars
depict the estimated uncertainty in the calculation of the feed-
backs from Soden and Held (2006).
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factor of 2 or more, the lapse rate feedback reduces it
by about 20% (the combined water vapor plus lapse
rate feedback amplifies it by 40%–50%),
2
the surface
albedo feedback amplifies it by about 10%, and the
cloud feedback amplifies it by 10%–50% depending on
GCMs. Interestingly, these results do not substantially
differ from those published in the pioneering work of
Hansen et al. (1984).
Although the intermodel spread of feedback strength
is substantial for all the feedbacks, it is the largest for
cloud feedbacks. The comparison also reveals quite a
large range in the strength of water vapor and lapse rate
feedbacks among GCMs. A strong anticorrelation be-
tween the water vapor and lapse rate feedbacks of mod-
els is also seen, consistent with long-held views on the
relationships between the two feedbacks (e.g., Cess
1975). A consequence of this anticorrelation is that the
spread of the combined water vapor–lapse rate feed-
back is roughly half that of the individual water vapor
or lapse rate feedbacks, smaller than that of cloud feed-
backs, but slightly larger than that of the surface albedo
feedback. As suggested by Colman (2003a), Soden and
Held (2006), and Webb et al. (2005, manuscript submit-
ted to Climate Dyn., hereafter WEBB) the range of
climate sensitivity estimates among models thus pri-
marily results from the spread of cloud feedbacks, but
also with a substantial contribution of the combined
water vapor–lapse rate and surface albedo feedbacks.
This spread in climate feedbacks and climate sensitivity
is not a new issue. It is a long-standing problem that is
central to discussions about the uncertainty of climate
change projections. A number of reasons for the slow
progress in this area are proposed.
First, climate feedback studies have long been fo-
cused on the derivation of global estimates of the feed-
backs using diagnostic methods that are not directly
applicable to observations and so do not allow any ob-
servational assessment (see Stephens 2005 for an exten-
sive discussion of these aspects). Indeed, climate feed-
backs are defined as partial derivatives [Eq. (A2)]. Al-
though partial derivatives can be readily computed in
models, it is not possible to compute them rigorously
from observations because we cannot statistically ma-
nipulate the observations in such a way as to insure that
only one variable is changing. Nevertheless, the deriva-
tion and the model-to-model comparison of feedbacks
have played a key role in identifying the main sources
of “uncertainties” (in the sense of intermodel differ-
ences) in climate sensitivity estimates.
Second, the evaluation of climate change feedbacks
raises methodological difficulties because observed
variations of the climate system may not be considered
to be analogs of a global, long-term climate response to
greenhouse gas forcing for example because (i) ob-
served climate variations may not be in equilibrium
with the forcing, (ii) the natural forcings associated with
short-term insolation cycles (diurnal/seasonal) or with
volcanic eruptions operate in the SW domain of the
spectrum while long-term anthropogenic forcings asso-
ciated with well-mixed greenhouse gases operate
mostly in the LW domain, (iii) the geographical struc-
tures of natural and anthropogenic forcings differ, and
(iv) the fluctuations in temperature and in large-scale
atmospheric circulation at short and long time scales
are not comparable. In addition, in nature multiple pro-
cesses are usually operating to change climate, for in-
stance volcanic eruptions, the El Niño–Southern Oscil-
lation (ENSO), and the annual cycle are often present
together, and attributing an observed change to a par-
ticular cause may be problematic. These limitations
make relationships between temperature, water vapor,
and clouds inferred from the current climate not di-
rectly useful to estimate feedback processes at work
under climate change (Hartmann and Michelsen 1993;
Bony et al. 1995; Lau et al. 1996).
Third, the complexity of the climate system and the
innumerable factors potentially involved in the climate
feedbacks have long been emphasized and considered
as an obstacle to the assessment of feedbacks, both in
nature and in models.
Given these difficulties, how may we evaluate the
realism of the climate change feedbacks produced by
GCMs and thereby reduce the uncertainty in climate
sensitivity estimates? We think that a better apprecia-
tion of the physical mechanisms behind the global esti-
mates of climate feedbacks would help us (i) to under-
stand the reasons why climate feedbacks differ or not
among models, (ii) to assess the reliability of the feed-
backs produced by the different models, and (iii) to
guide the development of strategies of model–data
comparison relevant for observationally constraining
some components of the global feedbacks.
With these issues in mind, we present below some
simple conceptual frameworks that may help to guide
our thinking, we review our current understanding of
the main physical mechanisms involved in the different
radiative feedbacks, and we discuss how observations
may be used to constrain them in climate models. Al-
though the cloud, water vapor, lapse rate, and ice feed-
backs all interact with each other (in particular the
cloud–surface albedo feedbacks in snow or sea ice re-
gions, the water vapor–cloud feedbacks, and the water
2
As explained by Hansen et al. (1984) and in appendix A, the
feedback parameters and the feedback gains are additive but not
the feedback factors.
1A
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vapor–lapse rate feedbacks), we will consider them
separately for the sake of simplicity of presentation.
Ordering the feedbacks according to their contribution
to the spread of climate sensitivity estimates among
GCMs (Fig. 1), we will consider in turn cloud feedbacks
(section 2), the combined water vapor–lapse rate feed-
backs (section 3), and cryosphere feedbacks (snow and
sea ice, section 4). For this discussion, we will not at-
tempt an exhaustive review of the literature, but will
focus on major advances that have taken place since the
TAR of the IPCC (Houghton et al. 2001).
2. Cloud feedbacks
Cloud feedbacks have long been identified as the
largest internal source of uncertainty in climate change
predictions, even without considering the interaction
between clouds and aerosols
3
(Cess et al. 1990; Hough-
ton et al. 2001). Recent comparisons of feedbacks pro-
duced by climate models under climate change show
that the current generation of models still exhibits a
large spread in cloud feedbacks, which is larger than for
other feedbacks (Fig. 1). Moreover, for most models
the climate sensitivity estimate still critically depends
on the representation of clouds (e.g., Yao and Del
Genio 2002; Ogura et al. 2005, manuscript submitted to
J. Meteor. Soc. Japan). Defining strategies for evalua-
tion of cloud feedback processes in climate models is
thus of primary importance to better understand the
range of model sensitivity estimates and to make cli-
mate predictions from models more reliable. Progress
has been made during the last few years in our under-
standing of processes involved in these feedbacks, and
in the way these processes may be investigated in mod-
els and in observations.
a. Conceptual representations of the climate system
Much of our understanding of the climate system,
and of climate feedbacks in particular, is due to studies
using simple or conceptual models that capture the es-
sential processes of the climate system in a simplified
way (Pierrehumbert 1995; Miller 1997; Larson et al.
1999; Kelly et al. 1999; Lindzen et al. 2001; Kelly and
Randall 2001). Drawing connections between simple
climate model idealizations and the three-dimensional
climate of nature or climate models would help to bet-
ter understand and assess the climate feedbacks pro-
duced by complex models. As a first step toward that
end, we present below some simple conceptual frame-
works through which climate feedbacks and cloud feed-
backs in particular may be analyzed. This will serve
afterward as a pedagogical basis to synthesize results
from recent observational, theoretical, and modeling
studies.
As is already well known (and illustrated in Fig. 2),
the atmospheric dynamics and thus the large-scale or-
ganization of the atmosphere is a strong function of
latitude. In the Tropics, large-scale overturning circula-
tions prevail. These are associated with narrow cloudy
convective regions and widespread regions of sinking
motion in the midtroposphere (generally associated
with a free troposphere void of clouds and a cloud-free
or cloudy planetary boundary layer). In the extratrop-
ics, the atmosphere is organized in large-scale baro-
clinic disturbances.
The large-scale circulation of the tropical atmosphere
and its connection to cloudiness is shown as a schematic
3
In this paper, we will not discuss the microphysical feedbacks
associated with the interaction between aerosols and clouds. As
Lohmann and Feichter (2005) say: “The cloud feedback problem
has to be solved in order to assess the aerosol indirect forcing
more reliably.”
FIG. 2. Composite of instantaneous infrared imagery from geostationary satellites (at 1200 UTC 29 Mar 2004) showing the contrast
between the large-scale organization of the atmosphere and of the cloudiness in the Tropics and in the extratropics. [From SATMOS
(©METEO-FRANCE and Japan Meteorological Agency).]
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![FIG. 3. Two conceptual representations of the relationship between cloudiness and large-scale atmospheric circulation in the Tropics: (a) structure of the tropical atmosphere, showing the various regimes, approximately as a function of SST (decreasing from left to right) or mean large-scale vertical velocity in the midtroposphere (from mean ascending motions on the left to large-scale sinking motions on the right). [From Emanuel (1994).] (b) Two-box model of the Tropics used by Larson et al. (1999). The warm pool has high convective clouds and the cold pool has boundary layer clouds. Air is rising in the warm pool and sinking across the inversion in the cold pool.](/figures/fig-3-two-conceptual-representations-of-the-relationship-1otpfe48.webp)
![FIG. 6. Composite spatial distributions of 1000-hPa wind (arrows), SST (nearly horizontal lines), 500-hPa pressure vertical velocity (other solid and dashed lines), and ISCCP cloud anomalies (color) centered on locations within the region 30°–50°N, 155°–215°E where advection of the 1000-hPa wind over the SST gradient ( V · SST) is (a) maximum positive during July, (b) maximum positive during January, (c) maximum negative during July, and (d) maximum negative during January. Composites were constructed from local noon data during 1984–2001. The SST contour interval is 2°C with a thick line for the 16°C isotherm. The vertical velocity contour interval is 20 hPa day 1 for July and 40 hPa day 1 for January with negative (upward) contours dashed, positive (downward) contours solid, and no zero contour. Each 2.5° 2.5° grid box in the plot is filled with 25 pixels, and each pixel represents an additional 2% cloud amount or clear-sky frequency beyond the climatological value for the ISCCP category associated with that color (see legend in figure). Only cloud anomalies statistically significant at 95% are shown, and negative cloud anomalies are not plotted. [From Norris and Iacobellis (2005).]](/figures/fig-6-composite-spatial-distributions-of-1000-hpa-wind-sb9yuho6.webp)
![FIG. 14. Interannual variations in (a) surface temperature, (b) column-integrated water vapor, (c) atmospheric normalized greenhouse trapping, and (d) 6.7- m brightness temperature for the SST-forced model (shaded), model with all known forcings (dashed), and observations (solid). Substantial differences between SST only forced experiments and “full forcing” experiments in (c) indicate that the model normalized greenhouse effect is very sensitive to the input of volcanic aerosols and changes in greenhouse gases. [From Allan et al. (2003).]](/figures/fig-14-interannual-variations-in-a-surface-temperature-b-exh2mykh.webp)
![FIG. 4. (a) PDF P of the 500-hPa monthly mean large-scale vertical velocity 500 in the Tropics (30°S–30°N) derived from ERA-40 meteorological reanalyses, and composite of the monthly mean (b) GPCP precipitation and (c) ERBE-derived longwave and shortwave (multiplied by 1) cloud radiative forcing in different circulation regimes defined from ERA-40 500 over 1985–89. Vertical bars show the seasonal standard deviation within each regime. [After Bony et al. (2004).]](/figures/fig-4-a-pdf-p-of-the-500-hpa-monthly-mean-large-scale-3lne5tsm.webp)