RESEARCH LETTER
10.1029/2022GL097997
Key Points:• A -Train satellite observations areanalyzed to investigate the physicalprocesses considered to account for
the iris effect• The anvil cloud fraction reduces withincreasing upper-tropospheric stability
while unlikely related to precipitation
efficiency• The cloud radiative effects associatedwith the stability iris effect are nearly
neutral when integrated over a diurnal
cycle
Supporting Information:

Supporting Information may be found in
the online version of this article.
Correspondence to:

H. Masunaga,
masunaga@nagoya-u.jp
Citation:

Ito, M., & Masunaga, H. (2022). Processlevel assessment of the iris effect overtropical oceans. Geophysical Research
Letters, 49, e2022GL097997. https://doi.
org/10.1029/2022GL097997
Received 21 JAN 2022
Accepted 1 APR 2022
Corrected 4 JUN 2022
This article was corrected on 4 JUN 2022.
See the end of the full text for details.

© 2022. The Authors.
This is an open access article under
the terms of the Creative Commons
Attribution License, which permits use,
distribution and reproduction in any
medium, provided the original work is
properly cited.

ITO AND MASUNAGA

Process-Level Assessment of the Iris Effect Over Tropical
OceansMasato Ito1 and Hirohiko Masunaga2
1
Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan, 2Institute for Space-Earth Environmental
Research, Nagoya University, Nagoya, Japan

Abstract The iris hypothesis suggests a cloud feedback mechanism that a reduction in the tropical anvil
cloud fraction (CF) in a warmer climate may act to mitigate the warming by enhanced outgoing longwave
radiation. Two different physical processes, one involving precipitation efficiency and the other focusing on
upper-tropospheric stability, have been argued in the literature to be responsible for the iris effect. In thisstudy, A-Train observations and reanalysis data are analyzed to assess these two processes. Major findings are
as follows: (a) the anvil CF changes evidently with upper-tropospheric stability as expected from the stability
iris theory, (b) precipitation efficiency is unlikely to have control on the anvil CF but is related to mid- andlow-level CFs, and (c) the day and nighttime cloud radiative effects are expected to largely cancel out when
integrated over a diurnal cycle, suggesting a neutral cloud feedback.
Plain Language Summary Tropical anvil clouds, or extensive high clouds produced by deep
convection, are known to be a key player in modulating the Earth's radiation budget. The iris hypothesis claims
that anvil clouds have a stabilizing effect on the climate as they shrink and allow more heat to radiate out tospace as the climate warms. The iris hypothesis, however, remains controversial partly because the processes
behind it have not been well validated against observations. We analyze satellite observations and reanalysis
data to test two known theories on the processes explaining the iris effect. The results show that a theory
focusing on the air temperature structure around anvil clouds is likely at work in the tropical atmosphere,
although the anvil's warming and cooling effects would offset each other during the whole day and night.
1. Introduction
Cirrus clouds prevail over tropical oceans, modulating the Earth's energy budget through the reflection of solar
radiation and the absorption and emission of longwave radiation. Of particular interest are anvil cirrus clouds
produced by detrainment from deep convection, which are a key player in the potential interactions between tropical convective dynamics and cloud radiative effects (CREs). There is rich literature on the cloud feedback involving the interactions of convection, clouds, and radiation. Ramanathan and Collins (1991) argued that the shieldingof solar radiation by anvil clouds overwhelm the enhanced greenhouse effect in a moist, convectively active
atmosphere over warm ocean, acting as a natural thermostat regulating sea surface temperature (SST). Other
hypotheses put focus on the longwave effect of anvil clouds. Lindzen et al. (2001) proposed an “iris” mechanismthat anvil clouds shrink and allow more warm radiation to escape out to space as sea surface temperature (SST)
increases, resulting in a negative feedback on sea surface temperature (SST). The thermostat and iris hypotheses,
although very different in mechanism and each challenged by a series of criticisms (see Pierrehumbert (1995) for
discussions on the thermostat hypothesis and Hartmann and Michelsen (2002) and Lindzen and Choi (2021) on
the iris effect), share a common ground in that anvil clouds play a role in stabilizing the climate system.
Another line of research (Hartmann & Larson, 2002; Zelinka & Hartmann, 2010) explored the possibility thatthe longwave effect of anvil clouds could give rise to a positive feedback rather than a stabilizing one. In the
hypotheses known as fixed anvil temperature (FAT) and proportionately higher anvil temperature (PHAT), anvil
cloud temperature remains nearly constant regardless of a surface warming, so that outgoing longwave radiation
cannot efficiently remove an extra heat associated with the warming. The fixed anvil temperature FAT/PHAT
theory is built upon an upper-tropospheric thermodynamic and mass balance consideration, interrelating a chainof processes such as temperature-limited moisture, radiative cooling, static stability, and horizontal divergence/
convergence. Elements of this idea were reorganized into a “stability iris” theory (Bony et al., 2016) in light ofa growing interest in convective self-aggregation (see reviews by Holloway et al., 2017; Wing et al., 2017). The

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stability iris hypothesis predicts that anvil cloud cover should be reduced as the result of an enhanced upper-tropospheric stability in a warmer climate. The stability iris resembles the iris hypothesis as originally devised by
Lindzen et al. (2001), whereas the underlying mechanism is entirely renewed.
Mauritsen and Stevens (2015) shed new light on the iris effect in the context of climate and hydrological sensitivities, demonstrating that climate model simulations are improved if the conversion from cloud water to precipitation is tuned so as to accelerate with a surface warming. Such a temperature dependence of precipitation
efficiency was speculated by Lindzen et al. (2001) as a possible driver of the iris effect, although not supported todate by firm evidence (Sui et al., 2020). Rapp et al. (2005), Lin et al. (2006), and Choi et al. (2017) each evaluatedprecipitation efficiency in different manners and reached mixed conclusions regarding the iris effect. Observational evidence supportive of the stability iris was presented by Saint-Lu et al. (2020), although the consequenceson cloud fraction (CREs) have yet to be examined.
A primary motivation of the present paper is to investigate the physical processes underlying the iris effect, if theiris exists in the real atmosphere. We do not seek observational evidence for the feedback hypothesis itself, given
the difficulty of assessing the long-term regulation of SST with short-term observations. Our focus is restricted
to the process-level linkage of precipitation efficiency or stability with the anvil cloud cover and cloud fraction(CREs).
At the heart of the analysis lie the measurements from the CloudSat radar and the Cloud-Aerosol Lidar andInfrared Pathfinder Satellite Observation (CALIPSO) lidar to sample anvil clouds and their parent convective
clouds at the same time. This approach enables to preclude in-situ cirrus without direct relevance to convective
dynamics, which amount to more than a half of the whole tropical cirrus (Luo & Rossow, 2004). A-Train satellite
measurements and the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) data
are combined to evaluate the ratio of cloud water to precipitation (RCP), introduced as a proxy of precipitation
efficiency, and upper-tropospheric stability (SUT). A composite analysis is then carried out to explore the dependence of anvil cloud properties on RCP and SUT. The net radiative effect is analyzed as well as the longwave effectfor examining to what extent the shortwave component counteracts the longwave iris effect.

2. Data and Method
The Aqua satellite carries six instruments including Advanced Microwave Scanning Radiometer for the Earth
Observing System (AMSR-E), Clouds and the Earth's Radiant Energy System (CERES), and Moderate Resolution Imaging Spectroradiometer (MODIS). The Aqua, CloudSat, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellites, as part of the A-Train constellation, make near-simultaneous
observations. The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), CloudSat,
Clouds and the Earth's Radiant Energy System (CERES), and MODIS (CCCM) Release D1 product integrates
observations from these instruments to yield the vertical structure of clouds and the in-cloud and clear-sky radiative flux estimates (Kato et al., 2011). The CCCM algorithm defines cloud fraction (CF) as the fractional coverageof CloudSat- and CALIPSO-detected clouds within each nadir ∼20-km Clouds and the Earth's Radiant Energy
System (CERES) footprint. The in-cloud radiative flux is computed with the cloud optical properties diagnosed
from the CloudSat, CALIPSO, and MODIS products combined together. The Advanced Microwave ScanningRadiometer for the Earth Observing System (AMSR-E) daily (practically instantaneous) product by Remote
Sensing Systems (RSS) provide precipitation and liquid water path (LWP) estimates (Hilburn & Wentz, 2008;
Wentz & Meissner, 2000). Upper-tropospheric stability and horizontal divergence are quantified using the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) data (Hersbach et al., 2020).
The CCCM cloud and radiative properties are accumulated on a quarter-degree grid to match the Advanced
Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) datasets. Only the portions of AMSR-E and ERA5
data intersected by CloudSat/CALIPSO ground tracks are sampled for the analysis. Deep convection is defined
to occur when cloud fraction (CF) exceeds 0.5 at all levels between 1 and 16.6 km in altitude for a given CCCM
column. The lower limit of 1 km is chosen to be above the lifting condensation level as a proxy of convective
cloud base, which is typically about 500 m above surface over tropical oceans. The upper boundary is set to
be 16.6 km because the cold point tropopause reaches as high as 16–17 km in the Tropics (Gettelman & de F.
Forster, 2002) and tropical deep convection often reaches close to this level (Takahashi & Luo, 2012).
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In case that a sequence of CCCM pixels in row are identified as deep convection, the consecutive pixels are
combined into a single convective event with their midpoint defined to be the geographical center of deep convection. It has been confirmed with sensitivity studies (not shown) that lower or higher cloud fraction (CF) thresholds than 0.5 do not qualitatively alter main conclusions, except that the sample size increases as the CF thresholdis lowered. The cloud properties and atmospheric profiles within ±5° along CloudSat/CALIPSO tracks aroundeach convection center are recorded for the composite analysis described next.
CloudSat and CALIPSO see only a limited portion of extensive cirrus clouds on an instantaneous basis. Nevertheless, “undersampled” convective events eventually constitute a large number of unbiased samples when accumulated over a period of time, given that satellite tracks homogeneously cover the globe and randomly intersectwith cloud systems. The composite cloud structure shown in this work is thus considered to be a robust statisticalrepresentation of cirrus clouds in their entirety.
Composite analysis is carried out to look into the statistical characteristics of deep convection and the associatedcloud properties and atmospheric states. Satellite measurements sampled around the deep convection centers areaveraged together into a two-dimensional composite space comprised of along-track distance from the convection center and altitude. The compositing method is similar to the technique devised by Igel et al. (2014) but
is substantially simplified. The primary interest of Igel et al. (2014) lay in the anvil cloud structure and how itchanges with SST beneath, while the present work is focused more on the resulting radiative effects in reference
to RCP and SUT (precise definitions are given later). To this end, composite samples are broken down by quartilesof RCP and of SUT, with outliers (i.e., below the first quartile minus 1.5 × IQR or above the third quartile plus
1.5 × IQR, where IQR is an abbreviation of the interquartile range) removed from the samples.
Precipitation efficiency is conventionally defined by the ratio of precipitation to either the large-scale moisture
influx or the condensation rate (Sui et al., 2007). The variables appearing in the denominator, however, are difficult to evaluate from satellite observations alone. As a workaround, precipitation efficiency can be substituted
by a combination of cloud water mass (or cloud size) and precipitation, both of which are available from satellite
measurements (e.g., Lau & Wu, 2003; Rapp et al., 2005). In the present work, the ratio of precipitation P tocolumn-integrated cloud water,𝑃𝑃,
𝑅𝑅CP ≡
(1)
LWP + IWP

is employed as a proxy of precipitation efficiency in the current analysis. Here liquid water path (LWP) and
precipitation are obtained from the AMSR-E product, while ice water path (IWP) is estimated as the vertical integral of CCCM cloud ice water content. This compromise is necessitated by the technical limitations in satellite
measurement capabilities that (1) microwave radiometry is insensitive to ice clouds and (2) CloudSat radar and
CALIPSO lidar echoes are often heavily attenuated before reaching down to the liquid layer inside deep convective clouds. For every deep convection sample, P, liquid water path (LWP), and ice water path (IWP) are each
averaged within ±2° about the convection center before combining into RCP by Equation 1.
There is inherent difficulty in quantifying the accuracy of satellite-measured liquid water path (LWP) and ice
water path (IWP) because ground truths are unavailable for these variables. The absolute values of LWP and IWP
estimates are in general associated with large uncertainties. The absolute values, however, do not come into play
in interpretations of the current results because relative magnitude is all that matters when RCP is discussed. As
such, AMSR-E LWP and CloudSat IWP offer a useful qualitative measure.
The iris effect as proposed by Lindzen et al. (2001) is built upon the assumption on changes in the precipitation
efficiency of convective clouds (Lindzen & Choi, 2021, and references therein). The current choice of the CF
threshold of 0.5 at all levels from 1 to 16.6 km (see above) likely favors the occurrence of deep convection, but itis worthwhile to test this presumption by running another run with non-convective (or stratiform) clouds explicitly excluded from the analysis. To this end, an ancillary composite analysis is carried out in which the same CF
threshold as above is augmented with an additional constraint that all the CloudSat footprints falling within ±2°(the domain employed for computing RCP) around the convection center are required to be identified as “deepconvection” according to the CloudSat 2B-CLDCLASS cloud mask (Wang & Sassen, 2001).
Upper-tropospheric stability (Bony et al., 2016) is quantified as,

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𝑇𝑇 𝜕𝜕𝜕𝜕
𝑅𝑅𝑅𝑅
𝜕𝜕𝜕𝜕
=
−
at 200 hPa,
𝑆𝑆UT ≡ −(2)𝜃𝜃 𝜕𝜕𝜕𝜕
𝑐𝑐𝑝𝑝 𝑝𝑝𝜕𝜕𝜕𝜕

where R and cp are the gas constant and specific heat of dry air, respectively, θ is potential temperature, T is air
temperature, and p is pressure. Throughout this study, SUT is defined only where AMSR-E precipitation is zeroand ERA5 pressure velocity (ω) is positive so that SUT is related to the radiatively driven subsidence as formulated
in the stability iris hypothesis.
The study period spans 4 years from 1 January 2007 to 31 December 2010. Since AMSR-E precipitation and
LWP are available only over oceans, at least 90% of CCCM pixels within ±5° (the study domain in composite
space) about each convection center are required to be over ocean. Anvil clouds lingering around the convection
centers may be extended to over land. The target region consists of all longitudes bound in latitude between 20°S
and 20°N. This choice of latitudes is intended to ensure that the analysis is limited to the tropics by factoring out
unwanted influences of the pronounced regional gradient from tropics to subtropics. Additional analyses withlatitudinal boundaries raised to 30° did not essentially alter the results in any case. Indeed, the geographicaldistribution of the convection centers is consistently trapped within the deep Tropics regardless of SUT (Figure
S1 in Supporting Information S1) and of RCP (Figure S2 in Supporting Information S1), implying that there islittle regional bias between high and low values of SUT and RCP. This is because the present compositing method
is conditioned on the occurrence of deep convection and, as a result, different values of SUT and RCP arise fromvariability inside the deep Tropics where convection can develop deep.
The total sample size is 3,770 (or 2,210 when screened by the deep convection mask) for the RCP composite and3,641 for the SUT composite, of which roughly two thirds are from descending (1:30) tracks and the other one thirdfrom ascending (13:30) overpasses.

3. Results
3.1. Composite Cloud Fraction
The composite vertical structure of CF for different stability environments is plotted in Figure 1. The four
panels in the top row represent very unstable (SUT < S1), moderately unstable (S1 < SUT < S2), moderately stable(S2 < SUT < S3), and very stable (S3 < SUT) upper tropospheres, where S1 = 27.4 mK hPa −1, S2 = 34.3 mK hPa −1,
and S3 = 42.0 mK hPa −1 are the first, second, and third quartiles of SUT, respectively. All the four cases share acommon feature that anvil clouds are spread extensively above 200 hPa from the deep convection towering at
the center, except that the anvil cloud extent narrows systematically with increasing SUT. The dependence on
SUT is clearly evidenced by Figure 1e, where the anvil CF at the level of maximum convergence is shown. Hereconvergence profiles were sampled from rain-free subsidence areas (i.e., conditioned on P = 0 and ω > 0 as
was for SUT) and then averaged together at each level in composite space. The composite convergence profiles(Figure 1f) have a maximum near the tropopause with its magnitude diminishing with increasing SUT. The interrelationship among the anvil CF, SUT, and upper-tropospheric convergence derived from Figure 1 is in line withthe expectations of the stability iris theory (Bony et al., 2016).
The dependence of CF on RCP is shown in Figure 2. The three quartiles of RCP are R1 = 0.619 hr −1, R2 = 0.942 hr −1,
and R3 = 1.30 hr −1. CF exhibits a slight but consistent enhancement with increasing RCP at levels lower than∼400 hPa as evident in Figure 2e. In contrast, upper-tropospheric CF hardly varies across the whole range of RCP.
It follows that an enhancement in precipitation efficiency is unlikely to have appreciable consequences on thehorizontal extent of anvil clouds, although leading to a modest increase of CF in the mid and lower tropospheres.
Figure 2f shows an additional composite plot with the “deep convection” mask applied (see Section 2). It isevident that the composite CF profiles are hardly sensitive to whether or not the convection centers are explicitly
screened for convective clouds. In the remainder of this paper, therefore, the results with the deep convection
mask are not shown to avoid redundancy.
3.2. Cloud Radiative Effects
Next examined are the impacts of SUP and RCP on the CREs associated with tropical deep convection. The net CRE
at the top of the atmosphere (TOA) is,
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Figure 1. (a) Composite cloud fraction (CF) for SUT < S1, (b) CF for S1 < SUT < S2, (c) CF for S2 < SUT < S3, and (d) CF for S3 < SUT, where S1–S3 are the first to third
quartiles of SUT. (e) CF at the level of maximum convergence for different stabilities. (f) Convergence averaged horizontally at each level over rain-free subsidence areas
within ±5° about the convection center.

Figure 2. (a) Composite cloud fraction (CF) for RCP < R1, (b) CF for R1 < RCP < R2, (c) CF for R2 < RCP < R3, and (d) CF for R3 < RCP, where R1–R3 are the first to
third quartiles of RCP. (e) CF for different precipitation efficiencies, averaged horizontally at each level over ±5° about the convection center. (f) As (e) but from anancillary analysis with the “deep convection” mask applied.

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Figure 3. Top row (a–d): scatter plots between the anvil cloud fraction (CF) and the net cloud radiative effects (CRE) at top of the atmosphere sorted by SUT for (a)descending tracks (midnight), (b) ascending tracks (afternoon), (c) descending and ascending tracks combined, and (d) daily mean (diurnally corrected) estimates (seetext for details). Points represent different values of SUT as indicated by color scale on the right. The point size is proportional to the sample size for each bin having afixed width of ΔSUT = 5 mK hPa −1. The mean μ, standard deviation σ, and linear regression coefficients are shown in each panel. Bottom row (e-h): As the top row but
sorted by RCP with a bin width of ΔRCP = 0.05 hr −1.
clr
all
clr
all
CRE = 𝐹𝐹SW
− 𝐹𝐹SW+ 𝐹𝐹LW− 𝐹𝐹LW,(3)where F is upwelling radiative flux at top of the atmosphere (TOA), SW and LW denote shortwave and longwave,“clr” and “all” stand for clear and all skies, respectively. Every term on the rhs is available from the CCCM product, in which the clear-sky component is a synthetic estimate computed with all clouds taken out. CRE is sorted
by SUT and RCP as done for the composite analysis in the previous section, except that this time the convection-anvil samples are broken down into finer bins of ΔSUT = 5 mK hPa −1 and ΔRCP = 0.05 hr −1 instead of quartiles.
This is for demonstration purposes: it has been confirmed that the conclusions remain unchanged with quartile
bins (i.e., just four points in each scatter plot).
Figures 3a–3d show the scatter plots of the statistical relationship between the anvil CF and the net CRE at topof the atmosphere (TOA). The midnight CRE from descending satellite tracks (Figure 3a), consisting exclusivelyof longwave cloud warming, is tightly coupled with the anvil CF. An enhancement of CRE is accounted for by
an increase of the anvil CF, which accompanies a reduction of upper-tropospheric stability (from warm to cold
colors) as we have seen in Figure 1. Afternoon observations of the net CRE (Figure 3b), by contrast, imply astriking shortwave effect largely overwhelming the longwave effect. When the morning and afternoon measurements are averaged together, the resulting CRE tends to fall on the negative side (i.e., have a potential of coolingthe planet) with its magnitude weakly increasing with the anvil CF (Figure 3c). Note that since the size of deep
convection samples is larger for midnight (descending) tracks than for afternoon (ascending) tracks, Figure 3b
would be even more dominated by shortwave cooling if the ascending and descending tracks are individually
averaged first and then combined together. This day-night combined CRE, however, is not an adequate measureof the daily mean CRE because the daytime equatorial crossing time of 13:30 is nearly the peak hour of incoming
solar radiation. To remedy this, a simplistic diurnal correction is applied assuming a semi-sinusoidal diurnal
cycle of solar irradiation,
18 [
( )]
𝜋𝜋𝜋𝜋
𝜋𝜋𝜋𝜋
−cos
𝑑𝑑𝑑𝑑 = 2𝐼𝐼0 ,𝐹𝐹 SW = 0
(4)
12 ∫6
12

where
𝐴𝐴
𝐹𝐹 SW is the daily mean shortwave flux and t is local time in hour (e.g., Shinoda & Hendon, 1998). The
obs
amplitude𝐴𝐴
𝐴𝐴𝐴𝐴0 is eliminated with a given A-Train measurement at t = 13.5𝐴𝐴hr or 𝐴𝐴SW
= −𝜋𝜋𝜋𝜋0 cos(13.5𝜋𝜋∕12) as
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obs
2𝐹𝐹SW
obs
(5)
≈ 0.638𝐹𝐹SW.
𝐹𝐹 SW = −𝜋𝜋 cos(13.5𝜋𝜋∕12)Equation 5 relies on a number of naive assumptions (e.g., cloud properties do not vary with local time) and is only
intended to offer an approximate (yet useful) correction factor. The diurnal variation of cloud properties (e.g.,
Chepfer et al., 2019) may modify the corrected CRE to a certain extent. Figure 3d shows the daily mean net CRE
with the above correction applied. Interestingly, the diurnally corrected shortwave CRE almost precisely offsets
the longwave CRE for almost all anvil CFs.
The scatter plots broken down by RCP are presented in Figures 3e–3h. The correlation of the anvil CF with the
net CRE is recognizable when midnight and afternoon observations are separated, whereas no longer visuallydiscernible in the daily mean (Figure 3h). Unlike the SUT cases, color gradient is primarily vertical across the lineof zero daily-mean CRE, implying that a low (high) RCP is likely to accompany a positive (negative) net CRE forreasons unrelated to anvil clouds. This correlation is presumably ascribed to variability in the mid-to low-levelCFs as was found in Figure 2e.

4. Conclusion and Discussion
A-Train satellite observations and ERA5 datasets were analyzed to investigate the physical processes considered
to be crucial for the iris effect, namely the cloud-water to precipitation ratio RCP (a substitute for precipitation
efficiency) and upper-tropospheric stability SUT. Composite analysis was carried out to illustrate the spatial structure of deep convective clouds and the accompanying anvil clouds, and to examine how CREs vary with the anvil
CF. The composite samples are separated by SUT and RCP in search of evidence for the possible roles of theseparameters behind the iris effect. The present analysis is focused on anvil clouds accompanying rather extreme
convection reaching higher than 16 km. The cloud-top height of deep convection was chosen in this way to facilitate comparison with upper-tropospheric stability at the fixed level of 200 hPa. Anvil clouds formed at somewhat
lower levels will be investigated in future work.
A main conclusion is, in line with a previous study (Saint-Lu et al., 2020), that SUT is clearly linked with the anvil
CF as hypothesized by the stability iris theory. To the contrary, the anvil CF is invariant over the whole range of
RCP, although the mid-to low-level CFs modestly but systematically increase with RCP. A possible explanation
for this coherent variability in mid- and low-level CFs may be given in light of the mesoscale organization of
convective systems. Sui et al. (2020) argued that precipitation efficiency rises as the degree of convective organization increases (i.e., the stratiform rain fraction becomes higher). Choi et al. (2017) underscored the key roles ofstratiform rain clouds in the context of the iris effect. Changes in the mid-to low-level CFs with RCP are presumably relevant to the observed modulation in CREs, although in a way at odds with what Lindzen et al. (2001)
hypothesized with precipitation efficiency.
Choi et al. (2017) found that a higher precipitation efficiency (the ratio of convective rain to the total rain in theirdefinition) is associated with a lower cirrus fraction over the tropical west Pacific, not necessarily in line with thepresent findings. The disagreement is presumably attributable to multiple reasons, including the differences inthe definition of precipitation efficiency and in analysis strategies. The analysis of Choi et al. (2017) was basedon monthly-mean regional statistics of satellite observations, while the current conclusions are derived from thecomposite statistics constructed with event-by-event CF profiles. Another possible source of uncertainty liesin the definition of convective clouds. The Tropical Rainfall Measuring Mission Precipitation Radar data wereadopted by Choi et al. (2017) to separate convective and stratiform rains, while the CloudSat cloud mask is usedin this study.
The current work does not seek evidence for the climatic consequences of the processes investigated here, butnevertheless it would be beneficial to briefly discuss the implications for cloud feedback. The longwave CRE
at midnight infers a negative feedback just as predicted by the stability iris hypothesis, while the noontime CRE
suggests a weak positive feedback in which the shortwave heating owing to a shrinkage of anvil clouds slightly
outruns the longwave effect. These competing effects are estimated to largely cancel each other out when averaged over a diurnal cycle. The stability iris effect is suggested to be nearly radiatively neutral in a climatologicalcontext, although away from neutrality on subdaily time scales.
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The shortwave and longwave CREs of tropical cirrus have been long known to have a tendency of offsetting eachother (e.g., Hartmann et al., 2001; Ramanathan et al., 1989). The near cancellation of CRE is rarely achieved
on a cloud-by-cloud basis but results from an ensemble of clouds with different altitudes and optical depths
averaged together (Hartmann & Berry, 2017). The dynamical, microphysical, and radiative properties of anvil
clouds are intertwined with the convective life cycle (Gasparini et al., 2019; Masunaga & Bony, 2018; Takahashi
et al., 2017), and so would be the transient CRE imbalances on a subdaily time scale. These short-term processes
remain a missing piece of puzzle to draw the whole picture of the anvil cloud feedback.

Data Availability Statement
The CCCM Release D1 product is available from https://doi.org/10.5067/AQUA/CERES/CCCM-FM3-MODISCAL-CS_L2.RELD1 (NASA/LARC/SD/ASDC, 2011). The Remote Sensing Systems Advanced Microwave
Scanning Radiometer for the Earth Observing System data may be downloaded from https://www.remss.com/
missions/amsr/. The European Centre for Medium-Range Weather Forecasts Reanalysis version 5 data set isavailable at https://doi.org/10.24381/cds.bd0915c6 (Hersbach et al., 2018).

Acknowledgments
H. Masunaga is supported by Japan Society for the Promotion of Science (JSPS)
Grants-in-Aid for Scientific Research(KAKENHI) 19H01966 and by Nagoya
University Research Fund.

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Erratum
In the originally published version of this article, delineated author contributions were included accidentally. This
contribution list has been removed, and the present version may be considered the authoritative version of record.

ITO AND MASUNAGA

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