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Missing IRIS effect as a possible cause of muted hydrological change and high climate sensitivity in models

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Equilibrium climate sensitivity to a doubling of CO2 falls between 2.0 and 4.6 K in current climate models, and they suggest a weak increase in global mean precipitation. Inferences from the observational record, however, place climate sensitivity near the lower end of this range and indicate that models underestimate some of the changes in the hydrological cycle. These discrepancies raise the possibility that important feedbacks are missing from the models. A controversial hypothesis suggests that the dry and clear regions of the tropical atmosphere expand in a warming climate and thereby allow more infrared radiation to escape to space. This so-called iris effect could constitute a negative feedback that is not included in climate models. We find that inclusion of such an effect in a climate model moves the simulated responses of both temperature and the hydrological cycle to rising atmospheric greenhouse gas concentrations closer to observations. Alternative suggestions for shortcomings of models — such as aerosol cooling, volcanic eruptions or insufficient ocean heat uptake — may explain a slow observed transient warming relative to models, but not the observed enhancement of the hydrological cycle. We propose that, if precipitating convective clouds are more likely to cluster into larger clouds as temperatures rise, this process could constitute a plausible physical mechanism for an iris effect.
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In the tropics, radiative cooling predominantly occurs in dry and
clear subsiding parts of the atmosphere. e radiative cooling
is balanced mainly by latent heat released in precipitating deep
convective clouds (Fig.1). Processes that may change the balance
in favour of dry and clear regions in warmer climates have been
proposed to constitute a possible negative feedback not represented
by climate models1. is potential feedback has been termed the
iris eect, in analogy to the enlargement of the eye’s iris as its pupil
contracts under the inuence of more light (Box 1). It is unclear,
however, whether an iris eect can be directly detected in observed
variations of tropical cloud1,2, precipitation3–5 or radiation elds6–8
that co-vary with sea surface temperature. We approach the ques-
tion dierently, and instead investigate whether the presence of an
iris eect would lead to other physical changes that might be more
readily observed. We suggest that candidate changes are a possibil-
ity of a low-end climate sensitivity despite positive shortwave cloud
feedback, and enhanced precipitation increases with warming to
balance atmospheric cooling associated with an iris eect.
Past debate on the iris eect
Observations of natural variations of upper-level cloud cover with
underlying sea surface temperatures over the warm-pool region in
the western Pacic led to the conceptual idea of an iris eect. Cloud
cover was found to be reduced by about 22% per degree warming1.
Although the magnitude of the reduction is somewhat dependent
on methodology, the sign is robust9,10. A reduced upper-level cloud
cover with warmer surface temperatures could constitute a negative
feedback on climate change, because thin and cold high-level ice
clouds have a net warming eect (Box 1).
Taking a high-cloud reducing eect of a warming sea surface into
account has led to an estimate of the equilibrium climate sensitivity
(ECS) — the expected long-term surface warming associated with
a doubling of atmospheric CO2 — of only about 1K (ref. 1). is is
Missing iris eect as a possible cause of muted
hydrological change and high climate sensitivity
in models
Thorsten Mauritsen* and Bjorn Stevens
Equilibrium climate sensitivity to a doubling of CO2 falls between 2.0 and 4.6K in current climate models, and they suggest
a weak increase in global mean precipitation. Inferences from the observational record, however, place climate sensitivity
near the lower end of this range and indicate that models underestimate some of the changes in the hydrologi cal cycle. These
discrepancies raise the possibility that important feedbacks are missing from the models. A controversial hypothesis suggests
that the dry and clear regions of the tropical atmosphere expand in a warming climate and thereby allow more infrared radiation
to escape to space. This so-called iris eect could constitute a negative feedback that is not included in climate models. We
find that inclusion of such an eect in a climate model moves the simulated responses of both temperature and the hydrological
cycle to rising atmospheric greenhouse gas concentrations closer to observations. Alternative suggestions for shortcomings of
models — such as aerosol cooling, vol canic eruptions or insucient ocean heat uptake — may explain a slow observed transient
warming relative to models, but not the observed enhancement of the hydrological cycle. We propose that, if precipitating
con vective clouds are more likely to cluster into larger clouds as temperatures rise, this process could constitute a plausible
physical mechanism for an iris eect.
well below that of any climate model and also below the 1.5K that
is generally thought to be the lowest possible ECS based on vari-
ous lines of evidence11. But this low ECS-estimate is conditional not
only on the rate of reduction of high-level clouds, but also on cloud
optical properties (Box 1). e cooling implied by the iris eect is
strongest if the thinnest clouds diminish. If it is instead primarily
the thicker anvils that are reduced in a warmer climate, the negative
feedback of the iris eect is weaker12,13.
Detection of an iris eect in observations is not straightforward.
Convection preferentially occurs over the warmest surface tempera-
tures, which makes it challenging to interpret natural variations. If
an analysis focuses on small regions, it may appear as if upper-level
cloud cover increases with temperature, despite an average decrease
at a larger scale. e observed cloud reductions that accompanied
an increase in the surface temperature within convecting regions1
occurred thousands of kilometres away from the location where
the actual convection occurred2, which raised questions about the
causal relationship between these changes. But depending on the
scale at which tropical convection organizes, this clearing in the sur-
roundings can be interpreted as an expansion of the associated dry
and clear regions.
Patterns of local warming can be associated with circulation
changes that one would not expect in a warming climate, and
unravelling whether changes in cloudiness are the cause or the
consequence of the circulation changes is not straightforward14.
Monthly variations of the tropical energy budget
Alternatively, uctuations in the top-of-atmosphere radiation bal-
ance that accompany surface temperature uctuations can be stud-
ied directly, and constitute a potential feedback14,15. An updated
analysis is focused on the tropics (Fig.2) because this region is
central to the debate over the iris eect6–8. When temperatures are
anomalously warm, the surface emits more longwave radiation to
Max Planck Institute for Meteorology, Bundesstrasse 53, 20146 Hamburg, Germany. *e-mail: thorsten.mauritsen@mpimet.mpg.de
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cool o. But the strength of the net temperature restoration depends
on atmospheric feedback mechanisms. In the case of open domains
such as the one analysed here, lateral uxes of energy out of the
domain can change.
Satellite data from the Clouds and the Earths Radiant Energy
System (CERES) instruments show a strong negative longwave
regression close to the Planck feedback in the tropics16, and a
weak positive shortwave regression to yield a net regression of
3.2±1.0Wm2K-1 (Fig.2, Supplementary Table 2). e ensemble
mean of the climate models of the h phase of the Coupled Model
Intercomparison Project (CMIP5) matches the observed relation-
ship between temperature and net shortwave radiation, albeit with
considerable scatter, but systematically exhibits a longwave regres-
sion that is too weak (Fig.2b, Supplementary Table 3). Analysis
of irradiances measured in cloud-free regions reveals that the dis-
crepancy in the longwave radiation is due to both water vapour and
clouds, with the latter dominating. e regression coecients are
sensitive to methodological details, for instance the treatment of vol-
canoes7,15, or the lag (or no lag) in the radiative response relative to
temperature changes8, but the discrepancy between the observations
on the one hand and the models on the other is robust.
e relationship between the slope of the regression of net radia-
tion against temperature and ECS is not strong. Reference8 provides
estimates of ECS from a set of 11 previous-generation CMIP3 mod-
els, as well as the actual ECS based on CO2-doubling experiments.
Omitting one model with innite estimated ECS, a dissatisfying
correlation between estimated and actual ECS of 0.11 is found,
making it dicult to argue that a more negative regression coe-
cient between monthly anomalies of net radiation and temperature
perse implies a smaller ECS. In the analysis of the CMIP5 ensemble
presented here, we obtain stronger correlations of +0.42 and +0.15
between net regression and the inverse ECS for the Atmospheric
Model Intercomparison Project (AMIP) and historical experi-
ments, respectively (Supplementary Table 3). Of the nine models
that match CERES net regression in either experiment, three have
ECS above 3K and six below. Only the two versions of the Beijing
Climate Center (BCC) model match observations in the slope of
the regression between net, longwave and shortwave radiation with
temperature, and only if run with a prescribed evolution of sea sur-
face temperatures (AMIP). If run in coupled mode (historical) the
model is far from matching CERES data.
us, whereas the discrepancy between the model ensemble
and observations is suggestive of missing processes, the analysis of
monthly variability in the tropical radiation budget poses at best
weak constraints on global ECS.
Convective aggregation as a possible mechanism
One objection to the idea of an iris eect is that it is not clear what
the physical mechanism might be. An iris eect could result if
the eciency of precipitation within deep convective cloud tow-
ers increased with warming, leading to less detrainment into their
anvils5,17. is could occur if aggregation of convective clouds into
large clusters is temperature-dependent. Aggregation is due to an
instability of radiative-convective equilibrium, whereby relatively
dry regions cool radiatively, resulting in local subsidence and fur-
ther suppression of convection, ultimately leading to an aggregated
state with localized convective clusters18. e cooling of the dry
and clear regions is expected to increase with warmer temperatures
and hence promote aggregation19. In addition, in a warmer climate
convective clouds may further be invigorated by enhanced latent
heatrelease20.
As larger convective clouds dilute less by lateral mixing they pre-
cipitate more of their water during ascent, and fewer large clusters
can provide the necessary latent heating to sustain atmospheric radi-
ative cooling (Fig.1). Both cloud-resolving simulations21 and obser-
vations22 conrm that outgoing longwave radiation does increase as
a consequence of a drying environment in more aggregated states.
Shortwave absorption also increases, which tends to cancel some of
the eect. All in all, however, we conclude that it is plausible that con-
vective aggregation constitutes a negative longwave feedback on cli-
mate change — and to our understanding, the underlying processes
are not explicitly represented in climate models.
In principle, the problem of convective aggregation lends itself to
ne-scale simulations that explicitly resolve the dynamics of indi-
vidual convective clouds. In small-domain simulations, however,
whether or not convection will aggregate depends critically on reso-
lution, domain-size and initial conditions23. is complicates the
interpretation of possible temperature dependencies. Pioneering
work to simulate convective clouds at the global scale has suggested
a somewhat puzzling combined upper-level reduction in cloud ice
with an increase in cloud cover in response to warming24. But the
model’s feedback is highly sensitive to the representation of physical
processes that remain unresolved. Cloud microphysics, in particular,
represents a challenge to the application of ne-scalesimulations25.
Climate model test with a simple parameterization
Convective processes that could give rise to an iris eect are
crudely represented in most global climate models. Despite some
progress in understanding how convective aggregation could be
enhanced at warmer temperatures, knowledge of how to incorpo-
rate such processes remains primitive. For this reason, we simply
scale the conversion rate from cloud water to rain (Cp) in convec-
tive clouds in the ECHAM6 atmosphere general circulation model
(Supplementary Methods) with local surface temperature, similar to
a previousapproach17:
C
p
(T
s
) = C
o
(1
+
I
e
) (1)
T
s
T
o
where Co=2×104s1 is the default conversion rate in ECHAM6,
Ts is surface temperature, and To is a reference temperature set to
25°C — a value typically found in the tropics. e parameter Ie is
included to control the strength of the iris eect, and is here set
to 0.2, 0.5 and 1.0, corresponding in the most extreme case to a
doubling of the conversion rate per degree warming. Because the
rate at which cloud water is converted to precipitation in convective
clouds is not a directly observable quantity, but is important for the
behaviour of the parameterization, it is frequently used as a tuning
Dry and clear
y
an
d
c
l
M
oist an
d
c
loudy
s
ex
p
an
s
Radiative cooling
Latent heating
Strong OL
RW
eak OLR
ing tropopause
r
op
o
R
is
i
u
Figure 1 | Illustration of the tropical atmospheric circulation.
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parameter, and as such has been varied by almost two orders of
magnitude26. By comparison, the changes introduced through our
simple parameterization, equation (1), are small. With these set-
tings, in particular the choice of To, the present-day mean climate
of ECHAM6-Iris is not appreciably dierent from that of the origi-
nal model (Supplementary Figs 1–5 and Supplementary Table 1).
Moreover, because precipitation outside the tropics is foremost
carried by the large-scale cloud scheme, mid-latitude storms and
circulation are not directly aected much by the modication.
e approach is clearly simplistic, but allows an exploration of
the implications of an iris eect in a simple and controllable way.
Indeed, we nd that whereas ECHAM6 was among the largest out-
liers in its representation of month-to-month tropical co-variabil-
ity in radiation and surface temperature (Fig.2), all three settings
of Ie yield longwave regression coecients in statistical agreement
with thedata.
e tropical atmosphere consists of moist and cloudy regions asso-
ciated with large-scale rising motion, convective storms and pro-
nounced precipitation on the one hand, and dry and clear regions
with subsiding motion on the other hand (Fig.1). e atmos-
pheric circulation maintains an approximate balance between
radiative cooling, which occurs preferentially in the dry and clear
regions, and latent heating from the condensation of water vapour
in precipitating clouds. As a conceptual starting point, convection
occurs in a narrow intertropical convergence zone (ITCZ) near
the Equator and subsidence is predominant in the subtropics,
although the reality is, of course, more complicated.
Shis in the tropical circulation in a warming climate can act
either to amplify or to dampen the temperature change through
feedback mechanisms. Positive and well-understood feedbacks
arise; for example, specic humidity increases in a warmer cli-
mate, and the altitude of convective cloud tops rises. Both these
feedbacks act to reduce the outgoing longwave radiation (OLR),
and thereby amplify surface warming.
e controversial ‘iris hypothesis’ proposes that the frac-
tion of the dry and clear regions could increase with warming1
and exert a negative feedback: a larger extent of the dry and clear
regions would lead to a less cloudy upper troposphere and hence
an increase in OLR. Such an eect could mitigate against climate
change. But a drier upper troposphere would also allow more solar
radiation to be absorbed by the Earth and atmosphere, rather
than reected back to space by the clouds, so that the net eect of
reducing high clouds is not obvious12,13. On balance, the eect is
thought to be negative.
Evidence for an iris eect is found in observations of tropical
variability of upper-level cloud cover, precipitation and the radia-
tion balance co-varying with natural variations of the surface tem-
perature. ese ndings have led to estimates1,8 of the sensitivity of
surface temperature to a doubling of atmospheric CO2 concentra-
tions of only about 1K, much lower than the broadly accepted
range of 1.5–4.5K (ref. 11). e estimate of ECS with an iris eect,
however, depends not only on the rate of reduction of high-level
clouds, but also on the cloud optical properties of the most sensi-
tive clouds. If the thinnest clouds are preferentially removed, the
eect on outgoing longwave radiation is stronger than that on
reectivity, and the iris eect is stronger. On the other hand, if the
reduction in cloud cover aects thicker clouds more strongly, the
loss in reectivity plays a more important role, and the iris eect
is less pronounced.
Notwithstanding its exact strength, the evidence for an iris eect
has been contested, and the lack of a clear physical mechanism has
caused widespread scepticism.
Box 1 | The tropical circulation and the iris eect.
Obs: CERES-EBAF 2.8
CMIP5 historical
CMIP5 AMIP
ECHAM6
Ie = 0.2
Ie = 0.5
Ie = 1.0
Temperature
(K)
Obs
Obs
Zero net regression
Shortwave regression (W m–2 K–1)
Net radiation (W m–2)
Longwave regression (W m
–2
K
–1
)
CERES
net regression
ab
–2.0
–4.0
–6.0
2.04.0
4.0
1.0–1.0
–4.0
6.00.0
Figure 2 | Regression lines calculated from anomalies of top of atmosphere radiation versus surface temperature in the tropics (20°S to 20°N).
a, De-trended monthly mean de-seasonalized anomalies (shown as black dots) of observed net radiation (CERES-EBAF 2.8) against surface temperature
(HadCRUT4) for the full years 2001–2013. The black line shows a linear regression on the data, and orange is the 5–95% confidence interval obtained
from a two-sided t-test. Regressions from models are shown as grey and coloured lines according to the legend and are performed for the period
1995–2005 to avoid the influence of the Pinatubo eruption. b, The relation between the shortwave and longwave contributions to net regression. Error bars
indicate 5–95% confidence intervals on the regression coecients. In the t-test we account for temporal autocorrelation in the surface temperature record
of about 10 months. Longwave Planck feedback (green) of –3.94Wm2K1 and Planck feedback plus water vapour feedback evaluated at constant relative
humidity (pink) of –2.12Wm2K1 for the tropical region, 20° N to 20° S, are obtained from ref.16.
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Climate sensitivity
Equilibrium climate sensitivity is inversely proportional to total
feedback, which in turn can be split into individual additive feed-
back mechanisms (Fig.3). Robust and well-understood positive
feedback mechanisms — such as the increase in absolute humidity
that accompanies a rise in temperature at xed relative humidity;
lowered surface solar reection due to a reduction in surface snow
and ice; and a positive feedback associated with rising convective
anvil clouds in a warming climate — together yield a null-hypoth-
esis ECS around 2.7K (ref. 27). is estimate neglects uncertain
shortwave cloud feedbacks28, and a possible iris eect. If cloud
shortwave feedbacks are positive29–33, ECS would be larger and fall in
the range of 3 to 5K. If one further assumes that a lower ECS is asso-
ciated with a better match between model output and the observed
record34–36, then an additional unrepresented negative feedback,
such as an iris eect, is required.
e standard version of ECHAM6 has an ECS of 2.8K to a dou-
bling of CO2 (Fig.3a), consistent with the null-hypothesis27 plus
a small positive shortwave cloud feedback (Fig.3b). To achieve a
climate sensitivity in line with the estimated range suggested for
an active iris eect1 would require to change the total feedback
of ECHAM6 by more than 1.0Wm2K1 (Fig.3a, dashed lines).
ECHAM6-Iris exhibits a reduction of the water vapour and cloud
longwave feedbacks of together 1.0to 1.8Wm2K1 depending
on Ie, which alone is sucient to produce an ECS between 1.2 and
1.6K (Fig.3, green symbols). us, ECHAM6-Iris exhibits a strong
iris eect, as designed, with a negative longwave cloud feedback
clearly outside the range of present modelling.
e reduction in the positive water vapour feedback is largely
compensated, however, by a weakening of the negative lapse-rate
feedback associated with a less amplied warming of the upper
troposphere; this type of compensation arises also in other models.
Only considering lapse-rate compensation would yield a somewhat
higher ECS of 1.4–1.7K. A weakening of the lapse rate feedback
(Supplementary Figs 7 and 8), although small, is in line with obser-
vations of tropical tropospheric temperature trends suggesting
weaker warming alo than commonly found in models37,38. In
addition to a weaker lapse-rate feedback, ECHAM6-Iris exhibits
an enhanced positive shortwave cloud feedback such that the net
cloud feedback is only slightly reduced in the global mean (Fig.3b,
Supplementary Figs 3–5). Together, these mechanisms compensate
the strong negative feedbacks associated with longwave cloud radia-
tive eects, so that the resulting reduction of ECS (from 2.8K to
2.2–2.5K) is relatively modest (Fig.3a, Supplementary Fig. 6).
Cloud shortwave feedback compensation is the most important
countervailing factor responsible for the moderation of ECS when
including an iris eect. Some shortwave compensation was origi-
nally assumed from the anvil cloud reduction1, but seems to have
been underestimated12,13. We nd a reduction of cloud fraction and
cloud condensate not only in regions of deep convection but virtu-
ally everywhere, associated with a general drying of the atmosphere
(Supplementary Figs 12–15), such that shortwave compensation
dominates in the sub- and extratropics (Supplementary Figs9–11).
Even though deep convection occurs predominantly in the trop-
ics, the enhanced conversion is evident globally. And although the
strength of the shortwave cloud compensation could dier from
what is produced by ECHAM6, some compensation seems inevi-
table. It is hard to imagine an atmosphere with an iris eect that
does not dry in terms of relative humidity. In fact, over-compen-
sation by shortwave cloud feedback was found when including
Equation(1) in the NCAR climate model: the result was a rise in
ECS (A.Gettelman, personal communication). Together, our nd-
ings of a robust lapse-rate compensation and the likelihood of a pos-
itive shortwave cloud feedback suggest that the current consensus
lower bound of 1.5K for the ECS11 may be a conservative choice.
Hydrological sensitivity
Changes to the global mean hydrological cycle are tied to the atmos-
pheric energy budget39,40, whereby increased radiative cooling of the
atmosphere under global warming is predominantly balanced by
latent heating through precipitation (Fig.1). An iris eect is predicted
to increase hydrological sensitivity, because the longwave radiative
Water vapour
Lapse-rate
Water vapour
+ lapse-rate
CMIP5 subset
Cloud feedback:
LW SW Net
Equilibrium climate sensitivity (K)
Total feedback (W m–2 K–1)
Feedback (W m–2 K–1)
Iris eect only
CMIP5 ensemble
ECHAM6
Ie = 0.2
Ie = 0.5
Ie = 1.0
/
/
/
ab
–5.0 –4.0 –3.0–2.0–1.0 0.0 1.0
1.0
2.0
3.0 1.0
–1.0
–2.0
0.0
2.0
3.0
4.0
5.0
Figure 3 | Decomposition of feedback into individual mechanisms that control variations in model equilibrium climate sensitivity. a, ECS is inversely
proportional to the total feedback, as indicated by the solid black line, whereby the forcing is assumed to be 3.7Wm2 for a doubling of CO2. The grey
filled circles are values for the individual CMIP5 climate models estimated by linear extrapolation to equilibrium in the idealized experiment with abruptly
quadrupled CO2. The CMIP5 results are divided by two to be comparable to a doubling of CO2 as applied here. The pale green range is that suggested by
ref.1. Green and yellow symbols are diagnosed estimates of climate sensitivity given only the change in water vapour feedback and longwave cloud feedback
for each value of Ie. b, Decomposition by partial radiative perturbations of the total feedback into components that are due to change in water vapour, lapse-
rate, surface albedo and clouds (see Supplementary Methods). Cloud feedback is further split into longwave (LW) and shortwave (SW) components. Planck
and surface albedo feedback (not shown) do not vary substantially with Ie. Ranges of feedbacks are from a subset of 11 CMIP5 models estimated by the
radiative kernel method28; systematic dierences may be due to dierent methodologies.
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cooling of the atmosphere associated with reduced upper-level cloud-
iness must be balanced by enhanced latent heating by precipitation.
CMIP5 models exhibit a relatively narrow range in the rise of
global mean precipitation of 2.0–3.3%K1 (Fig.4); ECHAM6 has
a mid-range hydrological sensitivity of 2.9% K1. When including
an iris eect, however, hydrological sensitivity is enhanced beyond
the CMIP5 ensemble to around 3.5–4% K1 (Fig.4a). e increase
in the hydrological sensitivity is due mainly to clouds shiing from
warming the atmosphere in ECHAM6 to cooling with an iris eect
(Fig.4b). Also, water vapour heats the atmosphere less in the drier
atmosphere that accompanies an iris eect, and a slight reduction in
surface sensible heat ux contributes to enhanced precipitation, too.
On the other hand, the increase in hydrological sensitivity is damp-
ened by a weakening lapse-rate feedback that leads to less radiative
cooling of the atmosphere. us an iris eect could help to reconcile
studies based on station data41, satellite observations42, ocean sur-
face salinity change43 and reconstructions44 which, notwithstanding
the large uncertainty in the measurements, have raised the question
as to whether models collectively underestimate the rate at which
precipitation increases globally with warming.
In a warming climate, wet regions in the deep tropics and in mid-
and high latitudes are expected to get wetter whereas the dry sub-
tropical regions get drier40. is tendency is also found in ECHAM6
and ECHAM6-Iris (Supplementary Figs 16 and 17). But whereas
ECHAM6 sharpens the intertropical convergence zone (ITCZ),
ECHAM6-Iris splits the ITCZ by drying near the Equator in the
Pacic and wetting o the Equator. Likewise, the subtropical dry-
zones move further poleward (Supplementary Fig. 18). is could
help to reconcile models with observations of greater Hadley cell wid-
ening during recent warming45. Further, if convective aggregation is
enhanced in warmer climates, one might speculate that extreme pre-
cipitation events could increase faster than the increase in available
atmospheric water vapour of about 7% K1 (ref. 20), as larger storms
can converge more water from the surrounding regions.
Challenging alternatives
In the past, the slow observed warming consistent with lower-end
ECS estimates has been explained by greater than anticipated aerosol
cooling46, a failure to incorporate forcing from recent volcanic erup-
tions47, or unobserved heat uptake by the deep ocean48. If anthropo-
genic or volcanic aerosols temporarily cool the Earth more, or heat
ows faster into the deep oceans than expected, then the tempera-
ture response to rising CO2 is merely delayed with little impact on
ECS itself. Such eects are consistent with a weaker rate of transient
warming. None of them, however, can explain the higher observed
hydrological sensitivity, because both CO2 and aerosol forcing heat
the atmosphere by absorbing radiation49, acting to reduce global
mean precipitation in the absence of signicant surface warming.
Furthermore, inferences about deep ocean heat uptake in the past
decade are not able to detect a signicant contribution from the
ocean below the current observing system50.
In contrast, the iris hypothesis predicts a coherent pattern of the
climate response to increasing atmospheric greenhouse gas concen-
trations, which helps to reconcile climate model simulations with
observations in a number of respects. Our simulations with a simple
parameterization of an eect akin to the iris eect combine realis-
tic month-to-month variability of longwave uxes out of the trop-
ics, the possibility of sustaining a low-end ECS34–36 at the same time
as a strong shortwave cloud feedback29–33, and an enhancement of
the hydrological sensitivity41–44, while present-day climate remains
plausible overall. We suggest that apparent discrepancies between
models and observations could be a consequence of the fact that the
current climate models systematically miss the eects of convective
organization and its dependence on temperature.
Methods
Data sources. Surface temperature data are from HadCRUT 4.3.0.0 from http://www.
cru.uea.ac.uk/, satellite radiation data from CERES-EBAF 2.8are from http://ceres.
larc.nasa.gov/, CMIP5 model outputs are obtained from http://esgf-data.dkrz.de/.
Relevant model output is available on request from publications@mpimet.mpg.de.
Code availability. e ECHAM6 atmosphere model is distributed on http://www.
mpimet.mpg.de/. e code changes to introduce an iris eect and to calculate feedback
with PRP (revision 2885) are available on request from publications@mpimet.mpg.de.
Received 23 September 2014; accepted 13 March 2015;
published online 20 April 2015
2% K–1
3% K
–1
4% K
–1
Equilibrium climate sensitivity (K)
Global mean precipitation change (%)
ab
Chang
e in atmospheric heating (W
m–2 K–1)
Water vapour
Lapse-rate
Clouds
Sensible heat flux
–1.2
–0.8
–0.4
0.0
0.4
12
9
6
3
0
3
1.02.0 3.04.0 5.0
ECHAM6
Ie = 0.2
Ie = 0.5
Ie = 1.0
Figure 4 | Analysis of hydrological sensitivity and a separation into the contributions from individual mechanisms. a, Global mean precipitation change
for 32 coupled CMIP5 models in experiments with abruptly quadrupled CO2 are compared with the mixed-layer ocean simulations performed in this
study by the use of linear extrapolation to equilibrium. Hydrological sensitivity is indicated by the dashed lines, although the initial suppression due to
CO2 heating of the atmosphere is slightly model-dependent. The CMIP5 results are divided by two to be comparable to a doubling of CO2 as applied here.
b, Decomposition of the change in the atmospheric energy heating per degree warming after reaching equilibrium with a doubling of CO2 (Supplementary
Methods). For reference, a 1% increase in precipitation corresponds to about 0.8–0.85Wm2 atmospheric heating. The eects of CO2, Planck and surface
albedo change are not substantially dierent among the models.
PERSPECTIVE
NATURE GEOSCIENCE DOI: 10.1038/NGEO2414
© 2015 Macmillan Publishers Limited. All rights reserved
© 2015 Macmillan Publishers Limited. All rights reserved
6 NATURE GEOSCIENCE | ADVANCE ONLINE PUBLICATION | www.nature.com/naturegeoscience
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Acknowledgements
Contributions from S. Bony, P. Forster, Quiang Fu, A. Gettelman, J. Gregory, I. Held,
S.Klein, R. Lindzen, R. Pierrehumbert, S. Po-Chedley, D. Popke, F. Rauser, S. Sherwood
and M. Zelinka were valuable in advancing this study. CERES data were obtained from
the NASA Langley Research Center, HadCRUT4 data are provided by the Met Oce
Hadley Centre and the Climatic Research Unit at the University of East Anglia, and
CMIP5 data from the coupled modelling groups (Supplementary Table 3) coordinated by
the World Climate Research Programme’s Working Group on Coupled Modelling. is
work was supported by the Max-Planck-Gesellscha (MPG) and by funding through
the EUCLIPSE project from the European Union, Seventh Framework Programme
(FP7/2007-2013) under grant agreement no. 244067. Computational resources were
made available by Deutsches Klimarechenzentrum (DKRZ) through support from
Bundesministerium für Bildung und Forschung (BMBF).
Additional information
Supplementary information is available in the online version of the paper. Reprints
and permissions information is available online at www.nature.com/reprints.
Correspondence should be addressed to T.M.
PERSPECTIVE NATURE GEOSCIENCE DOI: 10.1038/NGEO2414
© 2015 Macmillan Publishers Limited. All rights reserved
© 2015 Macmillan Publishers Limited. All rights reserved
... Previous work (17)(18)(19)(20) has proposed somewhat conflicting mechanisms through which PE might impact Effective Climate Sensitivity (ECS) but evidence to support these hypotheses is inconsistent across different global climate models (GCMs; see (11) for a review). Specifically, two studies that imposed an identical increase in PE sensitivity to temperature in different GCMs led to opposing changes in climate sensitivity (18,19). ...
... Previous work (17)(18)(19)(20) has proposed somewhat conflicting mechanisms through which PE might impact Effective Climate Sensitivity (ECS) but evidence to support these hypotheses is inconsistent across different global climate models (GCMs; see (11) for a review). Specifically, two studies that imposed an identical increase in PE sensitivity to temperature in different GCMs led to opposing changes in climate sensitivity (18,19). While simulated cloud responses from imposed PE changes are structurally complex and model dependent, reduction in cloud liquid and ice water path (i.e., the vertically integrated liquid and ice within an atmospheric column) at higher PE is unequivocal (18)(19)(20). ...
... Specifically, two studies that imposed an identical increase in PE sensitivity to temperature in different GCMs led to opposing changes in climate sensitivity (18,19). While simulated cloud responses from imposed PE changes are structurally complex and model dependent, reduction in cloud liquid and ice water path (i.e., the vertically integrated liquid and ice within an atmospheric column) at higher PE is unequivocal (18)(19)(20). ...
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... Further, whether the iris effect occurs in nature has been questioned in some studies [84][85][86] while other studies are supportive of its relevance [87][88][89] . Nevertheless, when an iris effect was imposed in an ad-hoc manner in a climate model, a more La Niñalike SST and precipitation response was simulated 90 . (Supplementary Figs. 6 and 16 in ref. 90 show that an increase in the iris effect results in more La Niña-like SST and precipitation changes). ...
... Nevertheless, when an iris effect was imposed in an ad-hoc manner in a climate model, a more La Niñalike SST and precipitation response was simulated 90 . (Supplementary Figs. 6 and 16 in ref. 90 show that an increase in the iris effect results in more La Niña-like SST and precipitation changes). ...
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... Various aspects of CSA in RCE have already been investigated. This includes the general characteristics in time and space (Wing and Cronin 2016;Yang 2018aYang , 2021Patrizio and Randall 2019;Yanase et al. 2020, hereafter Y20), onset and maintenance mechanisms (Bretherton et al. 2005;Muller and Held 2012;Wing and Emanuel 2014;Haerter 2019), similarities to tropical disturbances such as the Madden-Julian Oscillation and tropical cyclones (Arnold and Randall 2015;Carstens and Wing 2020;Emanuel 2013, 2018;Muller and Romps 2018;Nolan et al. 2007), and implications for the effect on climate sensitivity (Bony et al. 2016;Cronin and Wing 2017;Mauritsen and Stevens 2015;Ohno and Satoh 2018;Wing 2019). These viewpoints complement each other for an understanding of CSA. ...
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... The lumped model does not require referenced and change periods, and only assesses the relative contributions of the two-source influences (Wang, 2014). Many previous studies that have used one or more of the three traditional approaches to address this problem have achieved important progress (e.g., Mauritsen and Stevens, 2015;Nigussie and Altunkaynak, 2016;Oki and Kanae, 2006;Piao et al., 2010;Ramanathan et al., 2001;Thanapakpawin et al., 2007;Wu et al., 2013), especially in the Yellow River Basin (YRB) in China (e.g., Chang et al., 2016;Huang et al., 2015;Li and Wei, 2011;Su et al., 2021;Wang, 2014). ...
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Equilibrium climate sensitivity (ECS) is con-strained based on observed near-surface temperature change, changes in ocean heat content (OHC) and detailed radiative forcing (RF) time series from pre-industrial times to 2010 for all main anthropogenic and natural forcing mechanism. The RF time series are linked to the observations of OHC and temperature change through an energy balance model (EBM) and a stochastic model, using a Bayesian approach to estimate the ECS and other unknown parameters from the data. For the net anthropogenic RF the posterior mean in 2010 is 2.0 Wm −2 , with a 90 % credible interval (C.I.) of 1.3 to 2.8 Wm −2 , excluding present-day total aerosol effects (direct + indirect) stronger than −1.7 Wm −2 . The posterior mean of the ECS is 1.8 • C, with 90 % C.I. ranging from 0.9 to 3.2 • C, which is tighter than most previously published estimates. We find that using three OHC data sets simultane-ously and data for global mean temperature and OHC up to 2010 substantially narrows the range in ECS compared to us-ing less updated data and only one OHC data set. Using only one OHC set and data up to 2000 can produce comparable results as previously published estimates using observations in the 20th century, including the heavy tail in the proba-bility function. The analyses show a significant contribution of internal variability on a multi-decadal scale to the global mean temperature change. If we do not explicitly account for long-term internal variability, the 90 % C.I. is 40 % narrower than in the main analysis and the mean ECS becomes slightly lower, which demonstrates that the uncertainty in ECS may be severely underestimated if the method is too simple. In ad-dition to the uncertainties represented through the estimated probability density functions, there may be uncertainties due to limitations in the treatment of the temporal development in RF and structural uncertainties in the EBM.
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