PUBLICATIONSJournal of Geophysical Research: Atmospheres
RESEARCH ARTICLE
10.1002/2016JD025827
Key Points:• Satellite observations reveal the iris
effect of tropical cirrus clouds• The concentration of cloud systems
very likely contributes to the iris effect• Climate models show that thecirrus-precipitation relationshipstrongly linked to climate sensitivity

Correspondence to:
Y.-S. Choi,
ysc@ewha.ac.kr

Citation:
Choi, Y.-S., W. Kim, S.-W. Yeh, H.
Masunaga, M.-J. Kwon, H.-S. Jo, and L.
Huang (2017), Revisiting the iris effect of
tropical cirrus clouds with TRMM and
A-Train satellite data, J. Geophys. Res.
Atmos., 122, 5917–5931, doi:10.1002/
2016JD025827.
Received 26 AUG 2016
Accepted 27 MAY 2017
Accepted article online 31 MAY 2017
Published online 15 JUN 2017

Revisiting the iris effect of tropical cirrus clouds
with TRMM and A-Train satellite data
Yong-Sang Choi1,2 , WonMoo Kim3 , Sang-Wook Yeh4
Min-Jae Kwon6 , Hyun-Su Jo7, and Lei Huang2,8, Hirohiko Masunaga5,

1

Department of Environmental Science and Engineering, Ewha Womans University, Seoul, South Korea, 2NASA Jet
Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA, 3Climate Prediction Department, APEC
Climate Center, Busan, South Korea, 4Marine Sciences and Convergent Technology, Hanyang University, Seoul, South
Korea, 5Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Japan, 6Department of Atmospheric
Science and Engineering, Ewha Womans University, Seoul, South Korea, 7CSIRO Oceans and Atmosphere, Aspendale,
Victoria, Australia, 8Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles,
California, USA

Abstract Just as the iris of human eye controls the light inﬂux (iris effect), tropical anvil cirrus clouds may
regulate the Earth’s surface warming by controlling outgoing longwave radiation. This study examines thispossible effect with monthly satellite observations such as Tropical Rainfall Measuring Mission (TRMM)
precipitation, Moderate Resolution Imaging Spectroradiometer cirrus fraction, and Clouds and the Earth’s
Radiant Energy System top-of-the-atmosphere radiative ﬂuxes averaged over different tropical domains fromMarch 2000 to October 2014. To conﬁrm that high-level cirrus is relevant to this study, Cloud-Aerosol Lidarwith Orthogonal Polarization high cloud observations were also analyzed from June 2006 to December 2015.
Our analysis revealed that the increase in sea surface temperature in the tropical western Paciﬁc tends toconcentrate convective cloud systems. This concentration effect very likely induces the signiﬁcant reduction ofboth stratiform rain rate and cirrus fraction, without appreciable change in the convective rain rate. This
reduction of stratiform rain rate and cirrus fraction cannot be found over its subregion or the tropical eastern
Paciﬁc, where the concentration effect of anvil cirrus is weak. Consistently, over the tropical western Paciﬁc, thehigher ratio of convective rain rate to total rain rate (i.e., precipitation efﬁciency) signiﬁcantly correlates withwarmer sea surface temperature and lower cirrus fraction. The reduced cirrus eventually increased outgoing
longwave radiation to a greater degree than absorbed solar radiation. Finally, the negative relationshipbetween precipitation efﬁciency and cirrus fraction tends to correspond to a low global equilibrium climate
sensitivity in the models in the Coupled Model Intercomparison Project Phase 5. This suggests that tropicalanvil cirrus clouds exert a negative climate feedback in strong association with precipitation efﬁciency.
Plain Language Summary The Earth may have a cooling mechanism, so called the iris effect, undergreen house gas forcing, as if human eyes control the light inﬂux. This feedback process of the tropical cloud
to increased sea surface temperature is conﬁrmed using various satellite observation. It is found that the
precipitation efﬁciency increases as the temperature increases and the cirrus decreases as the precipitation
efﬁciency increases in the tropical Western Paciﬁc. This negative relationship suggests the presence of thecooling mechanism (negative feedback). Our climate models also tend to show lower warming rate if theobserved negative relationship is properly mimicked, vice versa.
1. Introduction
Clouds are the most powerful radiative substances in the Earth’s atmosphere. They can control global outgoingand incoming energy ﬂuxes. It is known that the climate sensitivity (or the climate scenario for given increasing
greenhouse gases) is directly associated with the clouds. Current climate models show a large diversity in climate sensitivity estimates, especially due to our lack of understanding of clouds [Stocker et al., 2013]. Different
climate sensitivity estimates in current climate models are mainly attributed to highly uncertain cloud feedbacks[Bony et al., 2015; Sherwood et al., 2014; Zelinka and Hartmann, 2010]. However, a limited capacity to observe
clouds veils that clouds play the multiple roles in the climate feedbacks [Choi et al., 2005; Ohring et al., 2005].

©2017. American Geophysical Union.
All Rights Reserved.

CHOI ET AL.

Clouds react to sea surface temperature (SST) change, especially in the tropical warm pool region. High SSTs
over 27°C can typically invigorate deep convective clouds and thereby affect upper tropospheric anvil cirrus

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clouds [Graham and Barnett, 1987; Sud et al., 1999; Kubar et al., 2011; Behrangi et al., 2012]. This cloud reaction inturn changes SST usually within a few days; that is, clouds alter as well as respond to SST [Waliser and Graham,
1993]. The present question is if this tropical cloud feedback process is vitally important in climate sensitivity.
Several different processes of tropical cloud feedbacks to SST have been suggested [Hartmann and Larson,
2002; Horváth and Soden, 2008; Ramanathan and Collins, 1991; Rondanelli and Lindzen, 2008; Wood, 2007].
The suggested cloud feedbacks, though they can be valid individually, cannot account for the observed total
longwave energy reaction to SST in the tropics. Several observational studies clearly show that at least tropical outgoing longwave radiation (OLR) anomalies strongly increase with increasing SST anomalies [Cho et al.,2012; Lindzen and Choi, 2011; Mauritsen and Stevens, 2015] by about 4 to 6 W m2 K1. This is indeed far
above the Planck response to increasing SST, which would not be achieved if any type of clouds increased
with increasing SST. In other words, these OLR analysis results, in fact, entirely imply the possibility of negative
longwave cloud feedbacks.
Lindzen et al. [2001] suggested that a longwave effect of anvil cirrus clouds would act as a negative feedback.
This is the so-called Iris effect, where clouds behave like the iris in the human eye, controlling the incoming
light. It is based on signals from geostationary satellites showing daily radiances where cirrus clouds decrease
with increasing SST. Subsequent studies using other data and methodologies have found supporting results[Rondanelli and Lindzen, 2008, 2010a, 2010b]. A slower hydrological cycle was presented in a radiativeconvective simulation of the iris effect in Rondanelli and Lindzen [2010a]. Likewise, a recent paper by
Mauritsen and Stevens [2015] showed that implementing the iris effect in climate models improves simulationof the global hydrological cycle, and global OLR and lapse rate responses, to changing SST.
In fact, this iris effect was tested in several aspects. First, Hartmann and Michelsen [2002a] argued that thecirrus was from the subtropics, not from the tropical convective system. Their assertion was primarily
grounded by the correlation analysis between cloud amount for “each grid” and cloud-weighted SST
averaged “over the whole domain.” Lindzen et al. [2002] responded that such an analysis is not appropriate
to test the iris effect since it cannot capture clouds popping up in different locations over the tropical western
Paciﬁc and moving to the next grid. Second, a net radiative effect of anvil cirrus clouds was questioned [Fu et al.,
2002; Lin et al., 2002]. Mauritsen and Stevens [2015] also show that the shortwave effect of cirrus clouds still provides a net positive feedback in their model. With respect to this, Choi and Ho [2006] showed that on the contrary, most tropical cirrus clouds have a stronger longwave effect than a shortwave effect in direct observations
from a satellite. Nevertheless, this study will resolve this issue by directly showing both longwave and shortwave radiative ﬂuxes in relation to cirrus fraction. Other criticisms were about discrepancies in the cloudy moist
area between Lindzen et al. [2001] and the analyses by others [Chambers et al., 2002; Lin et al., 2004, 2006; Rappet al., 2005; Su et al., 2008]. The detailed replies to those criticisms can be found in Chou et al. [2002a, 2002b;
Chou and Lindzen, 2002], Chou and Lindzen [2005], and Rondanelli and Lindzen [2010b].
Rondanelli and Lindzen [2010b] pointed out several methodology cautions when using satellite data to study
the iris effect. They include classifying cloudy regions into convective updrafts and anvils, using cloudweighted SST, and truncating and sampling errors of orbital satellite data. By careful consideration of theseearlier points, this study inquires whether the iris effect of tropical anvil cirrus clouds plays a signiﬁcant rolein global climate sensitivity.

2. Data and Methods
We used monthly cloud fraction, radiative ﬂuxes, SSTs, and rainfall rates from different sources. The analysis
period is from March 2000 to October 2014, during which all the following satellite retrievals are available(Terra was launched in March 2000, and the Tropical Rainfall Measuring Mission (TRMM) data were available
until October 2014). First, the cloud fraction is estimated from the Moderate Resolution Imaging
Spectroradiometer (MODIS)/Terra by the National Aeronautics and Space Administration (NASA)(MOD08_M3 version 6 for 1° monthly data and MOD08_D3 version 6 for daily data). The MODIS clouds fromdifferent retrieval methods have different optical depth coverages. We used the MODIS cirrus fraction with
1.38 μm reﬂectivity (SWIR) (greater than optical depth of ~0.1) [Choi et al., 2005] (hereafter, CiF). This
MODIS cirrus fraction covers most of the high clouds above 500 hPa and is approximately twice ofGeostationary Meteorological Satellite-5 (GMS) high clouds determined by 11–14 μm brightness temperatures (<260 K) used in Lindzen et al. [2001] and [Choi et al., 2005]. Moreover, this MODIS cirrus cloud

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fraction covers all high clouds including cumulonimbus and often overlaying low clouds [Choi and Ho, 2006].
Accordingly, there is no upper cutoff of optical depth in the MODIS cirrus cloud fraction.
This MODIS cirrus fraction limitation was overcome using vertical cloud retrievals from the Cloud-Aerosol
Lidar with Orthogonal Polarization (CALIOP), which is a dual wavelength polarization lidar designed toacquire vertical proﬁles of attenuated backscatter from a near nadir-viewing geometry during both day
and night phases [Winker et al., 2007]. It is one member of the A-Train [L’Ecuyer and Jiang, 2010] satellite constellation and has been continuously collecting high-resolution (1/3 km in the horizontal and 30 m in the vertical below 8.3 km, and 1 km in the horizontal and 60 m in the vertical for the higher altitudes of 8.3–20.2 km)
proﬁles of the attenuated backscatter by aerosols and clouds at 532 nm and 1064 nm wavelengths along withpolarized backscatter at 532 nm between 82°N and 82°S [Winker et al., 2007]. The sensor is known to be particularly sensitive to high cirrus clouds. The features identiﬁed by CALIOP are ﬁrst classiﬁed into aerosol andcloud using a cloud-aerosol discrimination (CAD) algorithm [Liu et al., 2009]. The level of conﬁdence in theaerosol-cloud classiﬁcation is reﬂected by a CAD score, with negative values (100–0) for aerosol and positive
values (+100–0) for cloud. In this study, we used the CALIOP Version 3 Level 2 cloud proﬁle data
(CAL_LID_L2_05kmCPro), which are reported at a uniform spatial resolution of 60 m vertically and 5 km horizontally, over a nominal altitude range from 20 km to 0.5 km. By accumulating the pixel data from June
2006 to December 2015, we generated monthly 5° longitude × 4° latitude gridded cloud frequency data toensure there was a sufﬁcient number of samples in each grid box.
The radiative ﬂux at the top of the atmosphere (TOA) is from the Clouds and the Earth’s Radiant Energy
System (CERES)/Terra by NASA (the ES4 Terra-Xtrk Edition 3 product for 2.5° monthly data available athttps://eosweb.larc.nasa.gov/project/ceres/ceres_table) [Wielicki et al., 1996]. We used TOA absorbed shortwave radiation (ASR) and outgoing longwave radiation (OLR). The ASR was calculated by subtracting
reﬂected shortwave radiation from incident shortwave radiation. ASR minus OLR therefore determine thenet energy gain in the Earth-atmosphere system. The uncertainties of the SW and LW radiations at TOA
directly measured by the CERES are ~5% and ~1%, respectively [CERES Science Team, 2013].
The SST is from the National Oceanic and Atmospheric Administration (NOAA) (the optimal interpolated SST
version 2 0.25° data). Relating the aforementioned monthly cloud, rainfall, and radiative ﬂux data necessarily
requires monthly averaging of the daily SSTs. However, monthly averaging is also needed for other reasons. In
all domains of this study (section 3), the land surface temperature was not considered due to its excessively
fast response to many factors. Whether land surface temperature was included or excluded, the usual coefﬁcient of determination between daily cloud and SST becomes very low (r2 = 0.05). In order to resolve thisunclear cloud-SST relationship, in Lindzen et al. [2001], SST was weighted by cloud area (< 260 K) since clouds
are not affected by SST for the whole domain but by SST under cloud layers. However, this cloud weighing canamplify random cloud effects that change SST [Hartmann and Michelsen, 2002b; Cho et al., 2012]. Furthermore,
clouds may not only be affected by SSTs under cloud layers. SST gradients away from clouds can drive local
circulations and cloud development as well. This study is therefore based on “monthly” averaged SST dataover the domains to minimize locally varying SST by cloud forcing. “Daily” SST has more local variation.
Note that the monthly averaged SST also avoids the sampling problem from the Sun-synchronizing satellites.
The rain rate is from the TRMM by NASA and Japan Aerospace Exploration Agency (3A25 version 7 for 0.5°
monthly data). The TRMM rainfall rate has three types: total, convective, and stratiform rainfall rates (inmm h1). The TRMM 3A25 provides monthly mean rainfall from precipitation radar (PR) measurements.
The TRMM PR selectively detects precipitating particles with little sensitivity to smaller, nonprecipitating
cloud droplets and ice crystals. As such, convective and stratiform rain are measured independently of cirrus
clouds from MODIS, which contain a much broader area of high clouds regardless of precipitation beneaththe clouds. The distinction between convective and stratiform rainfall is determined based on the existenceof bright band (BB) and the reﬂectivity change near the BB height [Awaka et al., 2007]. The domain-averaged
convective rain (CR) and stratiform rain (SR) are calculated asPi Rc;i N c;i =Nobs;i cosθi
P
CR ¼
(1a)
i cosθiP
i Rs;i Ns;i =N obs;i cosθi
P
SR ¼
(1b)
i cosθi

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where i is the ith grid of the domain and θ is the latitude. The subscripts c, s, and obs, represent the convective
rain, stratiform rain, and total observed pixels, respectively. R is the rainfall rate (mm h1), and N is the numberof pixels. R should be weighted by N since R per se is only the average over the rainy pixels and does notinclude the other observed pixels in a grid. The CR and SR are hereafter displayed in mm/d (mm d1) since
the area-averaged values in mm h1 are too small. All rainfall data were obtained from TRMM observations.
The constraint of anvil cirrus clouds may be subject to the distribution of the two types of rain; stratiform rain
originates from the anvil clouds, while convective rain from convective cloud cores. Convective rain is muchstronger (more precipitation in a given time) than stratiform rain. Therefore, the overall precipitation efﬁciencyof the convective cloud system is determined by the ratio of the two types of rain [Rondanelli and Lindzen,
2008], convective and stratiform rain. From TRMM data available in this study, the convective rain fractionbased precipitation efﬁciency (PE) is deﬁned as the convective rain rate normalized by the total rain rate:
PE ¼

CR
CR þ SR(2)

By the deﬁnition 2, a higher PE means that more moisture is allotted to convective rain than stratiform rain.
Note that this is different from the traditional concept of precipitation efﬁciency deﬁned in terms of theratio of precipitation to surface moisture convergence [Sui et al., 2007, and references therein], which refers
to the combined effects of dynamics and cloud microphysics in producing rainfall. However, the greater
ratio of CR to total rain in equation 2 is eventually associated with the higher precipitation efﬁciency
deﬁned traditionally.
Finally, we also applied the same methodology to the Coupled Model Intercomparison Project Phase 5(CMIP5) (i.e., twentieth century runs for the same period as in this study). Only 22 models whose outputs
include SST, precipitation ﬂux and convective precipitation ﬂux, CiF, and OLR together are shown in thisstudy. Among these models, only 13 models have documented equilibrium climate sensitivity (ECS)[Sherwood et al., 2014]. The ECS is effective climate sensitivity calculated from abrupt 4 × CO2 experiments
following a standard regression procedure between TOA radiance and surface temperature [Sherwood
et al., 2014]. In these models, the precipitation efﬁciency is deﬁned as the convective precipitation ﬂux(kg m2 s1) divided by the total precipitation ﬂux (kg m2 s1). CiF is substituted for cloud area fractionin the atmospheric layers, 0.1 and 0.2 hybrid sigma pressure levels. Our analyses are based on these top-levelcloud layers in the models since our interest is their strong longwave warming effect and their different behaviors in changing outgoing longwave radiation. However, these cloud fractions in the models cannot bedirectly compared with MODIS CiF since the modeled outputs do not clearly identify the actual total fractionof overlapping multilayer clouds like MODIS.

3. Observed Behaviors of Tropical Cirrus and Precipitation
The key mechanism behind the iris effect lies in the cloud-precipitation relationship wherein precipitation
constrains the cloud system. Mauritsen and Stevens [2015] dealt with cloud-to-precipitation conversion ratein their climate model for identifying the iris effect, but the cloud-precipitation relationship determining
the conversion rate remains largely uncertain in the observations.
Moreover, the choice of the domain requires careful consideration of cirrus cloud behavior. Cloud clusters areaggregated to a warmer SST in TWP [Lindzen et al., 2001; Mauritsen and Stevens, 2015]. So if the domain is toolarge, cloud clusters intrude into the domain from outside. Also, if the domain is too small, it cannot cover (orcan miss) movement of cloud clusters or the entire fraction of cirrus anvils. Therefore, the domain needs tocover an area large enough to track cloud clusters over a distance, but small enough to avoid exterior clouds.
Several choices in the tropics were tested as research domains for this study. A high fraction of tropical cirrus
clouds is widely distributed over the tropical western Paciﬁc [Choi and Ho, 2006] (TWP [120°E–155°W, 30°
S–30°N]) (Figure 1a). Over the TWP, clouds commonly react fast to the SST changes [Graham and Barnett,
1987; Zhang, 1993; Sud et al., 1999; Kubar et al., 2011; Behrangi et al., 2012]. Thereby, TWP has been popularly
chosen to study cloud feedback [Choi and Ho, 2006; Lindzen et al., 2001]. We also conﬁned our analysis to thesubregion of the TWP, the Paciﬁc warm pool (PWP [130°E–170°W, 20°S–20°N]) where the land surface andsubtropical Hadley subsidence are excluded, following previous studies [Horváth and Soden, 2008;
Ramanathan and Collins, 1991; Rondanelli and Lindzen, 2008].

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Figure 1. (a) Correlation between the time series of monthly MODIS cirrus fraction of the entire PWP region and that of the
1° grid boxes over the globe. The TWP, PWP, and TEP regions are indicated as boxes. Only signiﬁcant correlation at 95%
conﬁdence level is shaded in colors. (b) The meridional region (130°E–170°W), and (c) the zonal region (20°S–20°N)
averaged correlations between the time series of CALIOP cloud fraction of all the levels (0–17 km) and that integrated
above 6.5 km (~440 hPa) in the PWP region. The results in Figures 1b and 1c are calculated from CALIOP observations from
June 2006 to December 2015.

Over the tropical eastern Paciﬁc (TEP [155°W–70°W, 30°S–30°N]), clouds do not respond well to the SSTchanges. There is the asymmetry of the near equatorial Paciﬁc, with cool SSTs and subsidence prevalent
year-round in the southeastern Paciﬁc, which is included in the TEP. On the other hand, the TWP is very warmand convectively active. Thereby, it is known that the cirrus fraction over the TWP is much larger than over the
TEP [Choi and Ho, 2006; Kubar et al., 2007]. Likewise, we will see that the warmer PWP region, a subregion of
the TWP, has more cirrus clouds than the TWP in general. Therefore, we selected the TEP to be referenced as acold region far from the TWP. The TEP is better than a smaller eastern Paciﬁc domain for this study to avoid
the effect of a seasonal change in the Intertropical Convergence Zone (ITCZ) and to compare with the resultsfrom the other similar-sized domains, TWP or PWP. We note that all the relationships shown hereafter (ofCiF-CR, CiF-SR, CiF-PE, and CiF-SST) over a part of the ITCZ [110°W–170°W, 4°N–10°N] were found to be theopposite to those of the TWP. Therefore, this shows an important compensating subsidence away fromthe center of anomalous convection when examining the iris effect.
The cloud-precipitation relationship should reﬂect the concentration of several deep convective clouds(DCCs) on the domains. This is directly supported by our spatial correlation analysis clearly showing thatmonthly MODIS cirrus fraction over the PWP, a subregion of the TWP, varies inversely with that over thesurroundings (Figure 1a). Quantitatively, the correlation coefﬁcient (r) between the area-averaged cirrus
fraction over the PWP and that over the surrounding region outside the PWP (but inside the TWP) is0.44. This negative correlation is conﬁned to the tropics within about 30°S to 30°N. It is also broadly distributed over the Indian Ocean. On the other hand, the MODIS cirrus clouds used in Figure 1a include all highclouds with tops above 500 hPa but may not distinguish a single-layer cirrus from cirrus overlaying low-levelclouds or cumulonimbus towers. It is possible to clarify the cloud type issue with CALIOP cloud data that arevertically resolved and sensitive to cirrus clouds. Our additional analysis with CALIOP cloud data (June 2006 to
December 2015) reconﬁrms that this negative correlation is relevant to high cirrus clouds (Figures 1b and 1c).
It shows that the decrease in tropical cirrus clouds outside the PWP, with increasing clouds over the PWP, is

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10.1002/2016JD025827

1

Figure 2. SST against (a) CR in mm d , (b) SR in mm dslope, and r indicates the correlation coefﬁcient.

, and (c) PE over TWP, PWP, and TEP. s indicates the regression

clear particularly between 10 km and 16 km. However, this ﬁgure indicates neither the iris effect of a single
convective cloud nor the self-aggregation of several convective clouds [Muller and Bony, 2015]. Rather, it
may represent a large-scale analogy of the aggregation effect whose mechanism relies more upon thelarge-scale dynamics [Bony et al., 2016]. That is to say, when the ascent anomaly inside the PWP increases,
it forces the descent anomaly in adjacent areas, leading to the reduced convection and cirrus fraction
outside the PWP.
Consequently, many cirrus anvil layers from different convective cores can overlap and are constrained bymoisture converged into the system. In other words, a doubling of tropical convective cloud systems would
not double anvil cirrus fraction and precipitation. A cloud feedback process should involve thisconstraint efﬁciency.
To embrace this constraint efﬁciency, the TWP may be more appropriate than PWP (a subregion of TWP).
Figure 2 shows SST-CR, SST-SR, and SST-PE relationships for each domain. The plots designate the domainaveraged monthly SST and precipitation values by equations 1a, 1b, and 2. First of all, more DCCs were typically expected for higher SST, but the CR is not so dependent on SST regardless of the domain (Figure 2a). Thisis likely because CR also originates from shallow convective clouds under a lower SST. Overall, the SST-SR or
SST-PE relationship becomes stronger with the expansion of the domain from the PWP to the TWP. It implies
that these relations are largely manifested by large-scale dynamical effects such that the area between the
TWP and the PWP borders undergoes atmospheric subsidence in response to the deep convection overthe PWP.
In Figure 2b (top), the SR tends to decrease with increasing SST over the TWP. The decreasing rate of SR per
SST is approximately 0.48 mm d1 K1 over TWP (r = 0.51). The rate and correlation persist for lagged SSTs
from the next month to SR but disappear completely for leading SSTs from 1 month in advance of SR(not shown). Moreover, the PWP SST is weakly related to any type of precipitation, such that colder
SSTs in between the PWP and TWP may be affected by cloud variations [Hartmann and Michelsen, 2002a;

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Su et al., 2008]. In the physical relationship of monthly variables, the atmospheric radiative forcings more
directly affect SST tendency (the difference between the two consecutive months) than SST itself [Choiet al., 2014]. Therefore, the relationship between SST tendency and SR is also discussed here. The relationshipbetween SST tendency and SR in the preceding month turns out to be almost entirely trivial (r = 0.1)
over the TWP, but signiﬁcant (r = 0.4) over the TEP (not shown). This means that the cloud cooling effecton SST is signiﬁcant over the TEP while not over TWP. The latter result with SST tendency is seemingly
contradictory to the former result with SST. This may result from the fact that clouds and SST interact witheach other. The cloud cooling effect on SST more clearly appears on a shorter time scale (hours to days);
however, on a monthly scale, it is rather more clearly observed convective activities responding to the
SSTs warmer than about 27°C. Consequently, the negative SST-SR relationship in the TWP (Figure 2b, top)
can arise from not only the cloud cooling effect on SST (that is dominant outside the PWP) but also the cloud
response to SST (that is dominant inside the PWP).
Over the PWP, the SST-SR and SST-PE relationships are very weak (Figures 2b (middle) and 2c (middle)). Thismeans that the entire cloud/precipitation responding to SST may not be captured over PWP but escapes out
over a larger area, the TWP. The inﬂuence of the large-scale climate variability like El Niño–Southern
Oscillation (ENSO), Madden-Julian Oscillation, and Tropospheric Biennial Oscillation is not likely affecting
the relationships in Figure 2, since SST-SR or SST-PE over the PWP is fairly different from those over the
TWP. This apparent difference in the results between the TWP and PWP indicates how sensitive the results
are to the choice of the domain.
It is known that a convective core occupies a much smaller area than a stratiform cirrus (<1/100) [Choi andHo, 2006]; however, CR originating in the convective core is larger than SR from a cirrus area. This means thatprecipitation cannot just be weighted by cloud fraction but should be scaled by the total mass inﬂux into theconvective cloud system [Choi and Ho, 2006]. This is why we need to look further into PE. Consequently, the
SST-PE relationship appears to be positive over the TWP (r = 0.53), which is more apparent than in the PWP(r = 0.18). The PE/SST was found at a rate about 5% K1 over the TWP and less over the PWP or TEP. This positive SST-PE relation over the TWP is also shown in Rondanelli and Lindzen [2008] (6–12% K1) which is basedon 2° grid monthly variables for the year 2001. Their study reﬂects more spatial variation within the TWP andallows a wider SST range from 16°C to 33°C, while the present study reﬂects more temporal variation of thedomain averages. Over the PWP and TEP, none of the precipitation types were clearly related to SST (Figure 2,
middle and bottom).
We then examined precipitation-CiF relationships for each domain (Figure 3). The MODIS cirrus fraction andTRMM precipitation retrievals used in this analysis are from independent satellite algorithms, so their relationship would not be an artifact. CiF over both the TWP and PWP has a strong positive relationship with CR. The
concentration effect of DCCs on the domains, as shown above, may somewhat differentiate CR-CiF relations
between the TWP and PWP (Figure 3a, top and middle). On the other hand, CiF over both the TWP and PWP
has a clear positive relationship with SR, while such is hardly realized in the TEP (Figure 3b). CiF may not be
constrained by any type of precipitation over the TEP. In accordance with the relationship in Figure 3b, CiF
has a strong negative relation with PE over the TWP and PWP (Figure 3c).
The negative PE-CiF (Figure 3c) is intriguing because this is consistent with the expectation based on the irismechanism [Lindzen et al., 2001]. In the iris mechanism [Lindzen et al., 2001], if more rain falls out from theconvective core of DCCs, there remains less moisture at the upper level cloud core per total mass ﬂux into
the system. The reduced moisture may induce the reduction of stratiform precipitation as well as anvil cloud
fraction. Based on this mechanism, the anvil cirrus fraction is expected to be in a close relationship to SR, but
not so to CR, and therefore inversely proportional to PE. Such a difference between the CR-CiF and SR-CiF
correlations is more distinctive over the TWP than the PWP.
One may suppose that the PE-CiF relationship is always negative as just a mathematical artifact, because thedenominator of equation 2 calculating PE contains SR that was found to increase with increasing CiF(Figure 3b). However, the regression slope (and correlation) of CiF/PE (Figure 3c) can vary greatly with the
CR-SR relationship, which should retain physical meaning. If SR is linearly proportional to CR, PE will be nearly
constant (by equation 2) regardless of the CiF. This case is inconsistent with the negative PE-CiF relationshipover the TWP (Figure 3c, top). Over the TEP, CiF is hardly related to PE (Figure 3c, bottom). Apparently, the negative CiF-PE relationship occurs when SR is inversely proportional to CR or independent of CR (i.e., SR per given

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10.1002/2016JD025827

Figure 3. (a) CR, (b) SR, and (c) PE against CiF over TWP, PWP, and TEP. Note the different scales.

mass inﬂux decreases with increasing convective strength). Our observations show that the latter corresponds
to the actual situation over the TWP (Figure 3c, top).
The above slopes and correlations in Figures 2 and 3 do not change much when only taking thearea-averaged variables for each season. This means that the relationships do not just come from the annual
cycle. The detailed seasonal results are shown in Table 1.
Figure 4 shows the combination of Figures 2 and 3. It implies a mechanism in cloud feedback to SST. Over the
TWP, less CiF seems to be constrained by higher PE in association with warmer SST (Figures 2c (top) and 3c(top), and 4a). Although the constraint of CiF by PE is somewhat evident, the inﬂuence of SST is unclear overthe PWP (Figures 2c (middle) and 3c( middle), and 4b). Over the TEP, these are entirely unclear (Figures 2c(bottom) and 3c (bottom), and 4c). The TEP is widely known as a large-scale subsidence region where SST
is relatively low and uncoupled with convective cloud systems. Thus, the anvil cirrus there is seldom participating in cloud feedback. These results indicate that the connection between cirrus, precipitation, and SST isregion dependent, which may have caused very different conclusions concerning cloud feedback to SST in
previous studies. In summary, the cloud feedback concerning CiF is sound only over TWP.

4. Radiative Effect of Tropical Cirrus and Equilibrium Climate Sensitivity
Quantitatively, the 1% reduction in CiF corresponds to the increase in OLR of ~0.7 W m2 (Figure 5a, top) andthe increase in ASR of ~0.3 W m2 over the TWP (Figure 5b, top). The OLR dominates over ASR in the total
radiative ﬂux at TOA (Figure 5c). ASR ﬂuctuates to a large extent by the annual cycle (clusters in Figure 5b),but the increasing rate of ASR per CiF is cleaner (and somewhat higher) when conﬁning the plots to a

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Table 1. The Regression Slopes and Correlations for the Area-Averaged Variables for DJF (December–February Period),
MAM (March–May Period), JJA (June–August Period), and SON (September–November Period). CR/SST and SR/SST in
1 1
1
1
mm d
K , and PE/SST in K
(Figure 2); CiF/CR, and CiF/SR in % mm
d Figure 3; CiF/PE in %; and OLR/CiF,a2 1
ASR/CiF, and (ASR–OLR)/CiF in W m % (Figure 5)
Domains

TWP (12,024°E–155°W,
30°S–30°N)

Seasonal Relations

Slope

Correlations

Slope

Correlations

Slope

Correlations

–0.07
–0.52
–0.21
–0.05
–0.42
–0.62
–0.37
–0.24
0.05
0.02
0.03
0.03
7.61
11.90
9.17
13.62
10.44
10.65
7.33
9.38
–101.56–75.23–49.57–64.61
0.89
–0.82
–0.91
–0.77
0.60
1.54
–1.29
–1.80
0.29
2.36
–0.38
–1.03

0.10
0.69
0.22
0.07
0.40
0.60
0.27
0.21
0.46
0.24
0.19
0.21
0.32
0.65
0.56
0.61
0.73
0.79
0.66
0.66
0.70
0.53
0.47
0.50
0.89
0.84
0.92
0.84
0.29
0.42
0.35
0.76
0.14
0.60
0.11
0.55

0.04
0.57
0.22
0.10
0.35
0.48
0.51
0.03
0.04
0.00
0.04
0.01
14.88
14.33
10.86
13.54
12.33
11.02
7.67
8.72
150.59130.00
87.13
104.28
1.04
0.88
1.07
0.92
0.66
0.62
0.94
1.07
0.38
1.49
0.13
0.15

0.04
0.47
0.18
0.11
0.18
0.24
0.25
0.02
0.29
0.00
0.26
0.08
0.56
0.64
0.68
0.68
0.85
0.81
0.79
0.75
0.80
0.71
0.69
0.69
0.97
0.94
0.96
0.92
0.48
0.26
0.40
0.70
0.30
0.55
0.06
0.12

0.09
0.06
0.09
0.22
0.14
0.06
0.05
0.23
0.02
0.01
0.01
0.01
6.20
6.52
4.14
3.62
11.45
6.25
8.52
4.23
43.75
16.74
37.72
30.25
0.65
0.77
0.52
0.68
2.49
2.56
0.10
0.71
1.85
1.80
0.42
0.03

0.46
0.10
0.31
0.65
0.36
0.09
0.12
0.58
–0.20
–0.06
0.12
0.26
0.18
0.33
0.23
0.24
0.66
0.37
0.60
0.33
–0.66
–0.24
–0.53
–0.31
–0.72
–0.78
–0.63
–0.75
–0.61
–0.36
–0.02
–0.36
–0.49
–0.26
0.08
0.02

CR/SST

SR/SST

PE/SST

CiF/CR

CiF/SR

CiF/PE

OLR/CiF

ASR/CiF(ASR–OLR)/CiF

a

DJF
MAM
JJA
SON
DJF
MAM
JJA
SON
DJF
MAM
JJA
SON
DJF
MAM
JJA
SON
DJF
MAM
JJA
SON
DJF
MAM
JJA
SON
DJF
MAM
JJA
SON
DJF
MAM
JJA
SON
DJF
MAM
JJA
SON

PWP (130°E–170°W,
20°S–20°N)

TEP (15,524°W–70°W,
30°S–30°N)

Regressions validated to the 95% conﬁdence level were denoted by bold letters.

Figure 4. SST against CiF over (a) TWP, (b) PWP, and (c) TEP.

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Journal of Geophysical Research: Atmospheres

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Figure 5. CiF against (a) OLR, (b) ASR, and (c) net radiative ﬂux (ASR minus OLR).

particular season or month (Table 1). This is compatible with the results of Choi and Ho [2006] based on a
combination of the MODIS MOD08 and CERES ES4 data from March 2000 to June 2005. They
documented that the cloud radiative effect of tropical high clouds (mainly cirrus with optical depth < 9)
was positive overall. Moreover, cirrus clouds can readily warm the troposphere, simulating radiativeconvection equilibrium, even though their thickness is sufﬁcient to induce the negative net radiative
effect at TOA [Liou, 1986]. Based on the direct regression between the 176 monthly variables (OLR (used in
Figure 5a, top) and SST (used in Figure 2, top), the increasing rate of OLR per SST was calculated to be~6.2 W m2 K1 over TWP (r = 0.53). While it is not as much as the direct regression slope, this positive
SST-OLR relationship can also be estimated by a multiplication of the positive SST-PE relationship(Figure 2c, top), negative PE-CiF relationship (Figure 3c, top), and negative CiF-OLR relationship (Figure 5a,top) (3.7 W m2 K1 = 0.05 · 104.80 · 0.71). This is indeed larger than what is expected by Planck
response (~3.3 W m2 K1) plus a radiative damping of the Planck response by the strong water vapor
feedback in the tropics. These results imply the presence of negative cloud feedback through the iris-likebehavior of clouds.
We, therefore, questioned if the tropical anvil cirrus can control the global climate sensitivity. To answer thisquestion, we applied the same methodology to the current climate models in CMIP5. The detailed rates andcorrelations between the variables for the models over the TWP are summarized in Table 2. In our analysis, amajority of models simulate the positive relationship between PE and SST (mean PE/SST = 0.14 K1). Whilethe deﬁnition of PE for these observations may be different from that for the model, the modeled PE/SST
values are spread around the observed value of 0.05 K1. However, it should be noted that the PE-SST
correlations in the models are generally feeble (mean r = 0.24), compared to the observations (r = 0.53).
The change in the domain-averaged OLR per this CiF of the climate models vary centering on the meanvalue of 0.34 (at 0.1 sigma level) or 1.39 (at 0.2 sigma level) W m2%1 (see Table 2). In fact, these values
are respectively higher and lower than the observed value of 0.71 W m2%1 for the TWP.
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Journal of Geophysical Research: Atmospheres
Table 2. The Variables From 22 CMIP5 Models for TWP Include PE/SST in Ka
Kelvin [Sherwood et al., 2014]
PE/SST

1

2, CiF/PE in Percent, OLR/CiF in W m

CiF(0.1σ)/PE

CiF(0.2σ)/PE%110.1002/2016JD025827, and Equilibrium Climate Sensitivity (ECS) in

OLR/CiF(0.1σ)

OLR/CiF(0.2σ)

ModelsSlope

Correlations

Slope

Correlations

Slope

Correlations

Slope

Correlation

Slope

Correlation

ECS

CanESM2
CCSM4
CESM1-BGC
CESM1-CAM5
CMCC-CESM
CMCC-CM
CMCC-CMSCSIRO-Mk3.6.0
Inmcm4
IPSL-CM5A-LR
IPSL-CM5A-MR
IPSL-CM5B-LR
MIROC5
MIROC-ESM
MIROC-ESM-CHEM
MPI-ESM-LR
MPI-ESM-MR
MPI-ESM-P
MRI-CGCM3
MRI-ESM1
NorESM1-M
NorESM1-ME
Mean Value

0.00
2.09
0.04
0.25
0.04
0.06
0.04
0.03
0.02
0.03
0.07
0.02
0.03
–0.01
–0.03
0.01
0.00
0.03
0.08
0.11
0.05
0.04
0.14

0.06
0.16
0.23
0.10
0.34
0.52
0.36
0.35
0.12
0.33
0.50
0.24
0.39
0.07
0.16
0.17
0.00
0.37
0.36
0.46
0.33
0.30
0.24

8.75
43
44
0.00
25
32
28
4.50
24.10
4.75
7.27
1.91
13.4035.92
19.37
19.5019.9025.4014.5015.4046.9036.8015.26

0.05
0.42
0.45
0.03
0.28
0.43
0.37
0.01
0.49
0.05
0.11
0.01
0.13
0.43
0.23
0.14
0.19
0.22
0.61
0.61
0.28
0.21
0.19

0.09
2.27
20
0.1
5.8
1
9
10.1
64.05
10.19
11.07
11.3
2.65
1.392
1.6
4.61
0.4
13.6
3.48
4.71
39.8
39.5
1.97

0.001
0.58
0.61
0.057
0.102
0.06
0.19
0.16
0.482
0.227
0.359
0.18
0.071
0.023
0.02
0.07
0
0.20
0.08
0.12
0.61
0.58
0.08

0.01
0.81
0.72
0.16
0.34
0.45
0.75
0.06
0.71
0.49
0.79
0.32
0.59
0.00
0.16
0.39
0.56
0.54
0.01
0.05
0.70
0.72
0.34

0.01
0.67
0.67
0.19
0.35
0.51
0.63
0.12
0.3
0.49
0.66
0.49
0.52
0.00
0.13
0.45
0.47
0.55
0.00
0.01
0.67
0.67
0.35

1.99
0.29
0.28
0.50
0.1
0.16
0.15
2.1
0.72
1.9
2.26
1.44
2.39
1.45
1.49
1.55
1.37
1.44
2.09
2.13
2.37
2.41
1.39

0.85
0.9
–0.88
–0.27
–0.69
–0.73
–0.86
0.83
0.81
0.88
–0.86
0.91
0.77
0.87
–0.86
0.83
0.85
0.84
0.89
0.89
0.89
0.88
0.82

3.68
2.92
3.99
2.07
4.10
2.59
2.71
4.65
3.60
3.44
3.42
2.59
2.83
3.68
3.3

a

Regressions validated to the 95% conﬁdence level were denoted by bold letters.

A majority of models reproduce the negative relationship between the PE and CiF at the 0.1 sigma level in the
TWP (mean CiF/PE = 15.26%) and at the 0.2 sigma level in the TWP (mean CiF/PE = 1.97%). Although the
majority agrees on the negative PE-CiF relationship, even some of the models show positive relationships.
The PE/CiF ratio indeed varies with models. It is fairly different from the strong clear negative relationshipin the observations (r = 0.67 and 0.71 for the TWP and PWP, respectively). This implies that our current
climate models could be underestimating the negative PE-CiF relationship.
In fact, the simulated PE-CiF relationship over the TWP is signiﬁcantly correlated with the models’ equilibrium climate sensitivity (ECS) (Figure 6). Models that have a stronger negative relationship as in the observations tend to have lower ECS, while the models of highest ECS (> 3.5°C) exhibit a neutral or even positive
PE-CiF relationship. The strong correlation between CiF/PE ratio and ECS (r = 0.63) and between PE-CiF
correlation and ECS (r = 0.80) suggests that a large share of global climate sensitivity is controlled by thetropical convective system and cloud processes. Therefore, tropical anvil cirrus may suppress global
warming via the iris-like mechanism as the precipitation efﬁciency effectively constrains cirrus clouds inconvective cloud systems.

5. Summary and Discussions
Figure 7 summarizes our observational results. The DCCs typically bring upper level anvil cirrus (comprising
the widest area of the DCC), stratiform rain (the dotted line) (comprising the partial area of the DCC), andconvective rain (the solid line) (taking the limited narrow area of the convective core). The cirrus clouds
effectively block outgoing longwave radiation (OLR) from the surface due to their very cold cloud tops.
When the DCCs are concentrated into the PWP (the subregion of the TWP) under anomalously high SST,
the convective rain per the total rain of DCCs increases (relatively more solid lines than the dotted lines)by overlapping cirrus clouds from different convective cores, thereby allowing more OLR out to space andcooling the surface.
Comparison with modeled cirrus clouds needs some discussion. While a direct comparison between theobserved and modeled cirrus cloud fractions may not be warranted, this observed feature in Figure 1 was

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not so comparable with those in themodels. Rather, in most models, highclouds over the PWP and surrounding
areas have often negligible or evenstrong positive correlations (not shown).
This implies that intermodel differencesin cloud-precipitation relationships overthe domains are quite large. This problem is very likely relevant to the current uncertainty in simulating cloud
feedback and climate sensitivity.
It is worth clarifying that this study does
not necessarily contradict most of theprevious studies concerning the changeof total precipitation per se. According
to those previous studies, more moisture can be lifted up with intensiﬁed
convection, moistening the upper
atmosphere via the detrainment process [Sohn et al., 2008]. In this process,all types of rain together can increase
with the cirrus fraction [Del Genio andKovari, 2002]. In large-scale dynamics,
this concept emphasizes the strengthening of the hydrological cycle in a
warmer climate. While the global hydrological cycle is strengthened for a warmer climate because of an increase in
Figure 6. (a) CiF/PE ratio and (b) correlation coefﬁcient between PE andglobal precipitation with warming at a
CiF at the 0.1 sigma level against equilibrium climate sensitivity (ECS) of
1
13 CMIP5 models over the TWP region. At the 0.2 sigma level, such evident rate of about 3% K , the tropical circulation as measured by the convective
relations could not be found. The observed CiF/PE ratio and correlation
coefﬁcient between PE and CiF are 104.80 and 0.67, respectively.
mass ﬂux decreases with the warming.
Thus, there is a weakening of the circulation in the tropical Paciﬁc basin [He and Soden, 2015; Held and Soden, 2006; Tokinaga et al., 2012; Zhou
et al., 2011]. In fact, the strength of the hydrological cycle in the tropical Paciﬁc is strongly associated with

Figure 7. An illustration of the concentration effect of deep convective clouds (DCCs) over the TWP region. Under warmer
SST, OLR emission becomes more efﬁcient due to the shrinkage of cirrus fraction per a DCC (the iris effect).

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a zonal gradient of SST. This seemingly contradictory relation between precipitation and circulation may be
explained by Su et al. [2014, 2017] and Lau and Kim [2015]. They clearly demonstrated that with more complicated Hadley circulation changes under global warming in the deep tropics, ascending motions arestrengthened and narrowed, not weakened.
The large-scale circulations (e.g., Walker and Hadley circulations) are tightly entangled with the convective
activity over the western Paciﬁc region, and they are not separate phenomena. When the convective activity
over the TWP actively alters the strength of the global circulation, the strength of the Walker (and the Hadley)cell would indeed intensify. On the other hand, if any external forcings (e.g., ENSO) cause the Walker circulation, the convective activity over the western Paciﬁc will also be modulated. In either case, the stronger convection in association with the higher SST anomaly will induce more effective precipitation as a convective
rainfall (i.e., more precipitation is allotted to the convective rainfall). This reduces the cirrus per unit mass ﬂux(as measured by precipitation efﬁciency in this study), and eventually results in negative feedback andstrengthens the processes summarized in Figure 7. Such processes in the convectively active region can occasionally expand to other regions, e.g., to the central Paciﬁc as the warm pool expands during El Niño events[Cai et al., 2014] or to the ITCZ following the annual cycle. However, these effects were found to be marginal in
our research, and the higher order characteristics needs to be scrutinized elsewhere.
A notable negative relationship between the precipitation efﬁciency and the cirrus fraction is found in independently retrieved satellite measurements. However, this strong negative relationship is not well represented in our state-of-the-art climate models (Coupled Model Intercomparison Project Phase 5, CMIP5).
Given that cirrus clouds are powerful infrared radiative substances, such a misrepresentation would lead toan overestimation of the ECS in the models. In fact, the atmospheric water vapor increase over years closely
follows the theoretical Clausius-Clapeyron increase of the moisture contents in the atmosphere (~7% pereach degree of warming) [Held and Soden, 2006] in the absence of change in the hydrological cycle. Whenthe precipitation efﬁciency does not constrain the cirrus response, this increase in water vapor as well as
the other type of known cloud responses (e.g., the reduction of low cloud fraction and the rise of cloud
top height) will exert a strong positive feedback accelerating the temperature response to greenhouse gas
forcing. On the other hand, the positive covariance between the SST and precipitation efﬁciency in thetropics, and their negative correspondence to cirrus clouds, would serve as a strong negative feedback
and reduce positive climate feedbacks.
Acknowledgments
This study is supported by“Development of Climate andAtmospheric Environmental
Applications” project, funded by ETRI,
which is a subproject of “Developmentof Geostationary Meteorological
Satellite Ground Segment(NMSC-2017-01)” program funded by
NMSC of KMA. Y.-S. Choi and L. Huang
are supported by the Jet Propulsion
Laboratory, California Institute ofTechnology. W. Kim is supported by
APEC Climate Center. S.-W. Yeh and H.-S.
Jo are supported by the Korea Ministryof Environment (MOE) as “Climate
Change Correspondence Program”. We
thank Richard S. Lindzen for valuablecomments, Roberto Rondanelli and two
anonymous reviewers for reviewing thepaper, and Jung Ok for data display. All
the data used in this study are publicly
open and can be accessed as describedin section 2.

CHOI ET AL.

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