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Radiative Invigoration of Tropical Convection by Preceding Cirrus Clouds
HIROHIKO MASUNAGA
Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, JapanSANDRINE BONY
LMD/IPSL, CNRS, Sorbonne Universities, University Pierre and Marie Curie, Paris, France(Manuscript received 23 November 2017, in final form 12 February 2018)
ABSTRACTThis work seeks evidence for convective–radiative interactions in satellite measurements, with a focus on
the variability over the life cycle of tropical convection in search of the underlying processes at a fundamental
level of the convective dynamics. To this end, the vertical profiles of cloud cover and radiative heating fromthe CloudSat–CALIPSO products are sorted into a composite time series around the hour of convective
occurrence identified by the TRMM PR. The findings are summarized as follows. Cirrus cloud cover begins toincrease, accompanied by a notable reduction of longwave cooling, in moist atmospheres even 1–2 days before
deep convection is invigorated. In contrast, longwave cooling stays efficient and clouds remain shallow where
the ambient air is very dry. To separate the radiative effects by the preceding cirrus clouds on convection fromthe direct effects of moisture, the observations with enhanced cirrus cover are isolated from those with
suppressed cirrus under a moisture environment being nearly equal. It is found that rain rate is distinctly
higher if the upper troposphere is cloudier regardless of moisture, suggesting that the cirrus radiative effects
may be linked with the subsequent growth of convection. A possible mechanism to support this observational
implication is discussed using a simple conceptual model. The model suggests that the preceding cirrus clouds
could radiatively promote the moistening with the aid of the congestus-mode dynamics within a short periodof time (about 2 days) as observed.

1. Introduction
The latent heat released by vapor condensation is theprimary heat source being balanced against the net radiative cooling in the global atmospheric energy budget(Trenberth et al. 2009; L’Ecuyer et al. 2015), underscoring the central importance of moist convection andradiative processes, and in particular the mechanism
underlying their coupling. Perturbations to the cloudand moisture fields brought by moist convection radiatively modify its ambient thermodynamics, which has
the potential to promote or suppress the convective
activity in return. Such a radiative–convective feedback
is arguably at work in aspects of the global or tropicalclimate (e.g., Wetherald and Manabe 1988; Randall

et al. 1989; Ramanathan and Collins 1991; Tompkins
and Craig 1998; Raymond 2000; Lindzen et al. 2001;
Hartmann and Larson 2002; Stephens et al. 2008; Bonyet al. 2016) and in its internal variability such as theintraseasonal oscillations (e.g., Hu and Randall 1994;
Raymond 2001; Lee et al. 2001; Sobel and Gildor 2003;
Lin et al. 2004; Bony and Emanuel 2005; Kim et al.
2015). Whereas specific details of the mechanism varyfrom one study to another, a radiative perturbation exerted by extensive anvil clouds is in general thought ofas a critical ingredient of radiative–convective feedbacks. Water vapor and shallow clouds can be additional
key players (Nilsson and Emanuel 1999; Fermepin andBony 2014) and have drawn growing interest in thecontext of convective self-aggregation (Bretherton et al.
2005; Muller and Held 2012; Wing and Emanuel 2014;
Coppin and Bony 2015).

Denotes content that is immediately available upon publication as open access.

Corresponding author: Hirohiko Masunaga, masunaga@nagoya-u.jp
DOI: 10.1175/JAS-D-17-0355.1
Ó 2018 American Meteorological Society

This article is licensed under a Creative Commons
Attribution 4.0 license (http://creativecommons.
org/licenses/by/4.0/).

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By contrast, much less attention has been paid to therole of radiation in exploration for the dynamics of individual convective systems. The radiative processes areconventionally considered not to substantially alter the
structure and evolution of vigorous convective systems(Houze 2004) except perhaps as a driver of the diurnal
modulation (Gray and Jacobson 1977; Xu and Randall
1995; Chen and Houze 1997), although some existing
work reported discernible impacts by radiation (Webster
and Stephens 1980; Houze 1982; Tao et al. 1996). One
might rule out radiation by arguing that latent heating indeep convective clouds reaches 10–100 K day21 or evenmore and hence overwhelms radiative cooling, which istypically 1–10 K day21 in magnitude on an instantaneousbasis. The radiative effects, however, prevail widely andpersistently as opposed to the localized, sporadic natureof latent heating. The averaging over a large domain candilute the predominance of latent heating over radiative
cooling to the extent that, for instance, radiation canoffset 20% of net condensation in the large-scale temperature budget (Tao et al. 2004; Li et al. 2013). The
imbalance between the two would be further mediatedwhen integrated over time, explaining partly why aradiative–convective feedback is identified always in
the climatological or intraseasonal context in the literature above. The widely accepted perception that radiative processes vary slowly, however, is challenged by
the observations showing a significant day-to-day variability in radiative heating (Ciesielski et al. 2003), raising
the question of whether radiation can influence tropicalmeteorology on short time scales as well. Recalling thatthe lifetime of individual convective systems does not far
exceed 0.5–1 day even for a long-lived organized system(Chen and Houze 1997), it is unclear yet whether asalient radiative–convective feedback emerges only as
the result of low-pass spatial–temporal filtering, or issomehow intrinsic to a more fundamental level of theconvective dynamics as well. This study is motivated to
explore this latter possibility, which appears to havebeen practically ignored in a large body of past work.
In the current analysis, the evolution of the cloud structure
and the associated radiative field over the passage of tropicaldisturbances are investigated using satellite observations. To
put discrete satellite snapshots into the evolutionary context,
the CloudSat and CALIPSO measurements are sorted
into a statistical time series on a subdaily to a multiple-day
time scale, using the TRMM PR detection of precipitation
as an anchor for constructing the composite time sequence.
A more thorough description of the data and analysis
scheme is provided in section 2. Section 3 presents theanalysis results, followed by an attempt to discuss the implications based on a simple conceptual model in section 4.
The findings are concluded in section 5.

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2. Data and method
The satellite datasets and analysis method are described in this section.

a. Satellite data
The CloudSat and CALIPSO satellites, launched in
April 2006, fly in formation as a part of the A-Train constellation and have sun-synchronous overpasses fixed
approximately at 0130 and 1330 local time (LT) at
any location that the satellites fly over. The CloudSat
carries a W-band (94 GHz) cloud-profiling radar capable of measuring a broad spectrum of clouds from cumulonimbus to cirrus, with the caveat that optically thincirrus clouds that cover 30% of the tropics are left undetected (Haladay and Stephens 2009). The CALIOP, adual-wavelength lidar aboard the CALIPSO, complements CloudSat observations owing to its high sensitivity
to thin cirrus clouds at the expense of severe attenuation
within modest to thick cloud layers. This work adopts themerged CloudSat and CALIPSO (2B-GEOPROF-lidar)
Release 4 (R04) product (Mace et al. 2009) to analyze thevertical structure of cloud cover. The vertical profiles of radiative heating rate QR are obtained also from the combined radar–lidar product or 2B-FLXHR-lidar R04
dataset (L’Ecuyer et al. 2008; Henderson et al. 2013).
This product provides the estimates of longwave (LW)and shortwave (SW) QR by applying radiative transfer
calculations to the observed cloud properties and the collocated ECMWF analysis of air temperature and humidity.
The occurrence of precipitation is determined by the
TRMM PR whenever the radar echo exceeds 19 dBZ
across three or more consecutive range bins at any
height in the column. This number is chosen to beslightly higher than the nominal value of the minimum
detectable level or 17 dBZ (Kummerow et al. 1998)
because the PR sensitivity has been slightly reduced
since the TRMM orbital boost in August 2001. The
TRMM 2A25 version 6 product is analyzed here, while
the PR calibration has been quite stable and is virtually
unaffected by the product version in use. Column water
vapor (CWV) estimates from the TRMM TMI are obtained from the Remote Sensing Systems (RSS) version
7 daily (i.e., instantaneous) product. The PR precipitation and TMI CWV are by design sampled on asimultaneous basis. In addition, the Aqua AMSR-E estimates of SST, CWV, and precipitation are employedfrom the RSS version 7 daily product. The AMSR-E
instrument, as a member of the A-Train constellation,
provides synchronized observations with CloudSat and
CALIPSO measurements. To avoid confusion, CWV
obtained from the TMI is hereinafter denoted by CWV0,
as it serves as the value at the origin on the composite

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time axis (see section 2b). CWV without any subscript
refers to the AMSR-E estimates.
The present analysis is targeted on the 3.5-yr period
from 1 July 2006 to 31 December 2009 over global
tropical oceans bound between 208S and 208N with allislands masked out.

b. Analysis method
Low-Earth-orbiting (LEO) satellites such as the
CloudSat and CALIPSO fly over a given location only
sporadically, so the observations from those satellites do
not constitute a continuous time sequence delineating
the evolution of individual cloud systems. A series ofprevious work initiated by Masunaga (2012, 2013)devised a method to statistically reconstruct the temporal evolution of convective systems from intermittent
LEO satellite observations. The current analysis strategy is based on this methodology as outlined below.
First, CloudSat–CALIPSO snapshots are collected every time when a precipitation event is spotted by the
TRMM PR some hours before or after at a same location. The CloudSat–CALIPSO measurements within
the distance of 100 km from the PR-detected precipitation are included in the analysis so that the resulting statistics represent the large-scale mean fieldwith a horizontal scale of O(100) km. Each snapshot isthen projected onto a time axis with respect to thetemporal difference between the CloudSat–CALIPSO
and TRMM observations, with the TRMM being always
assigned onto time zero. For example, a column of
CloudSat–CALIPSO measurements is time stamped
as 23 h if observed earlier by 3 h than the TRMM
measurements. The time difference between the satellites varies from one orbit to another, as the local time ofsun-asynchronous TRMM overpasses drifts over time.
Once this procedure is completed for all the data to beanalyzed, the CloudSat–CALIPSO snapshots are averaged within each hourly bin along the time axis to yield
the composite time series of cloud properties. Although
the composite base point (t 5 0) is not designed to single
out a specific stage of convective evolution, it turns outthat the life cycle of convection naturally emerges itself
in the composite time series. The analysis results arepresented in the following section.

3. Composite time seriesa. The vertical structure of cloud cover
The general aspects of the composite cloud properties
are first examined. Throughout this work, the satellite
measurements are analyzed separately for dry and moist
atmospheres since the magnitude and vertical structure1329

of radiative heating vary substantially with the moistureenvironment. This is done by stratifying the composite timeseries by the CWV measured at time zero (CWV0). The
ambient humidity is classified here by three CWV classes:dry (CWV0 # 35 mm), moderate (35 , CWV0 # 50 mm),and moist (CWV0 . 50 mm). The composite time series iscomposed of ;2 3 104 CloudSat–CALIPSO samples at
each hour for the dry class, ;7 3 104 for the moderate,and ;4 3 105 for the moist.
Figure 1 shows the composite time series of CloudSat–
CALIPSO cloud cover for the three moisture classes.
The contrast is striking across different moisture environments. The dry case (Fig. 1, top panel) is virtuallycloud-free above the boundary layer (or the layer between 0 and 2 km). Shallow clouds confined in theboundary layer prevail at all times, with a clear sign ofdiurnal cycle that is aliased into the time series. Cloud
cover somewhat enhances above 2 km, particularly near
the tropopause in the moderately humid atmosphere(middle). The moist environment (bottom) allows vigorous cloud systems to develop around time zero as
signified by a rapid growth of cloud cover to beyond 0.4
in the whole thickness of the troposphere. Given that thecomposite field is defined for a large-scale (;100 km)
domain, a cloud cover exceeding 0.4 suggests a substantial population of cumulonimbus towers and/or
stratiform cloud anvils and hence the occurrence of organized convective systems.
The moist case also exhibits a few other notable features. Cirrus clouds prevail even during a convectively
inactive period away from time zero. Cirrus cloud coverstays persistently near 0.4 and is further enhanced to
beyond 0.5 during 1–2 days prior and subsequent to t 5 0.
The boundary layer (less than ;2 km in height) is initially
not as cloudy as the upper troposphere, but is increasingly populated with shallow clouds over hours prior
to the peak convection in parallel with the cirrus enhancement. On the other hand, midtropospheric cloudiness does not rise until t 5 212 h and decays quickly aftert 5 12 h. Interestingly, midtropospheric cloudiness isslightly larger above ;5 km than immediately below,
resulting in a modest vertical discontinuity in between.
Similar cloudiness profiles have been observed during
past field campaigns over tropical ocean (Zuidema 1998;
Yasunaga et al. 2006).
One would expect that cirrus cloud cover increases
during and after the vigorous convection accompaniedby extensive anvil cloud decks. Somewhat surprising, on
the other hand, is the fact that cirrus cloud cover begins
to flourish as early as t 5 224 h, long before the convection intensifies into the penetrating cloud tower. The
cirrus increase prior to the convection is a robust signal
beyond the statistical error inherent in the composite

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FIG. 1. The composite time series of CloudSat–CALIPSO cloud cover, color-shaded asindicated on the right and contoured every 0.05, for the (top) dry (CWV0 # 35 mm), (middle)
moderate (35 , CWV0 # 50 mm), and (bottom) moist (CWV0 . 50 mm) cases.

method, with the leading cirrus being even greater incloud cover than the trailing cirrus (see appendix).
The possible radiative effects of those preceding
cirrus clouds in various moisture environments are
examined next.

b. Radiative heating and moisture environment
The radiative heating profiles are plotted in Fig. 2. To
first assess the LW effects separately from the SW, only
the descending or midnight (0130 LT) section of
CloudSat–CALIPSO orbits are included in the composite statistics shown here, setting aside the shortwave
effects until section 3d. As will become clearer later, thecloud radiative effects (CREs) on the atmosphere (i.e.,the difference in the all-sky flux minus clear-sky flux
between the top and bottom of the atmosphere) are amajor factor responsible for striking features seen in
Fig. 2. When the atmosphere stays dry (Fig. 2, top
panel), a strong radiative cooling of ;23 K day21
dominates the lower troposphere immediately above
2 km, ascribable to LW emissions from shallow cloud
tops (Fig. 1, top). In contrast, the LW cooling is largely
reduced below 4 km in the moist environment (Fig. 2,
bottom), owing to the downward LW radiation fromclouds above, primarily from cirrus clouds, remotely
influencing the lower troposphere. A band of suppressed
cooling at 4 km arises presumably because the verticalminimum in cloud cover at this height (see Fig. 1, bottom) locally stifles the LW emission. Another band of
muted cooling just above the surface is likely brought
about by downward emissions from shallow cloud basein addition to the far-reaching cirrus LW effect. As cloud
cover drastically increases at all heights during the peak
convection, the cloud greenhouse effect grows so strongthat QR approaches 0 K day21 below 2 km around t 5 0.
To further look into the dependence of LW cooling on
the ambient moisture, the composite evolutionary tracks
are projected onto the hQRi–CWV plane, where hQRi
denotes column radiative heating integrated from thesurface to the top of the atmosphere (Fig. 3). The five
trajectories represent different moisture environmentscomposed of the three cases presented in Figs. 1 and 2
and two additional analysis runs carried out with the intermediate ranks of moderately dry (30 , CWV0 # 40 mm)
and moderately moist (40 , CWV0 # 55 mm). The clearsky hQRi is plotted in grayscale in addition to the all-sky
hQRi in color. The all-sky hQRi appears to systematicallyincrease with the ambient moisture over a broad range ofAPRIL 2018

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FIG. 2. As in Fig. 1, but for radiative heating rate QR (K day21) with descending (0130 LT)
CloudSat–CALIPSO orbits only. The contour interval is 0.25 K day21.

CWV, while the clear-sky hQRi is significantly less sensitive
to CWV. It follows that clouds, rather than water vapor,largely dominate the dependence of LW cooling on CWV,
suggestive of a tight coupling between the cloud properties(coverage, thickness, top height, etc.) and the ambientmoisture. The cloud radiative effect, or the all-sky hQRi
minus clear-sky hQRi, is found to be 110–20 W m22 for
CWVs of 40–45 mm and can be as large as 160 W m22
when CWV is greater than 55 mm, indicating that the cloud
greenhouse effect strengthens to a remarkable degree as theenvironment moistens.
Figure 3 also shows that the cloud LW effect is neutral
around a CWV of 35 mm and is even slightly negative forlower CWV values. The LW radiation by shallow-cloud
tops efficiently escapes to space when the free troposphere is very dry, as depicted by a sharp cooling peakaround 2 km (Fig. 2, top), so an additional low-cloud
cover enhances the LW emission out of the atmosphere.

Jensen and Del Genio 2006), so it is no surprise thatconvection develops deeper in a moister atmosphere(Fig. 1). The suppression of convection in a dry environment may be ascribed to the direct effects of midtropospheric dry air including a buoyancy loss due to thec. Radiative impacts on convection

FIG. 3. The composite trajectory projected on the hQRi–CWV
plane, constructed with descending (0130 LT) CloudSat–CALIPSO
orbits only. Different trajectories are obtained for the five moisture
setups as indicated for the all-sky hQRi in color and the clear-sky hQRi
in gray. The color and gray scales designate the composite time (h) as
shown on the right.

The sensitivity of tropical convection to freetropospheric moisture has been well known from observations (e.g., Numaguti et al. 1995; Yoneyama andFujitani 1995; Brown and Zhang 1997; Sherwood 1999;

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FIG. 4. The composite time series of (top) cirrus cloud cover and (bottom) its standard
deviation at an altitude of 13 km for different moisture classes as indicated on the right. Bothascending and descending sections of CloudSat–CALIPSO orbits are included here because
this cloud cover serves as the reference common to the midnight and afternoon composites.

entrainment and dry downdrafts into the subcloud layer.
From the moist static energy (MSE) perspectives, on theother hand, column radiative heating is among thecentral players controlling the large-scale circulation
involving moist convection (detailed discussion will begiven in section 4a). It is difficult to separate the direct
moisture effects from the radiative effects in the resultspresented so far.
Figure 4 shows the composite evolution of cloud coverat 13 km, a level selected to correspond roughly to thevertical maximum of cirrus cloud cover (Fig. 1, top
panel). Repeating the same analysis with the mean
upper-tropospheric cloud cover averaged between 10
and 15 km instead of the single layer at 13 km does notnoticeably affect the results to follow (not shown). The
standard deviation of cloud cover within the hourly
samples (bottom) shows that the CloudSat–CALIPSO
samples have considerable statistical spread aroundeach composite curve, so the cloud radiative effect may
actually be quite variable from one measurement toanother even within a same CWV category. To partition
the composite curves by hQRi with CWV being fixed asmuch as possible, another set of composite analysis runs
are repeated but this time are broken down into two
subsamples with high and low cirrus cloudiness, depending on whether cloud cover at 13 km is higher orlower than the original unconditional composite plottedin Fig. 4. In this way, the radiative effects by cirrus clouds
are attempted to be isolated under a similar moistureenvironment. Note that the partitioning of cirrus
cloudiness is applied to all CloudSat–CALIPSO

measurements at each hour along the composite timeaxis. As such, the strategy here is to delineate a contrast
between two extreme cases having persistently high or
low cirrus cloud cover, which are somewhat unrealistic
but useful for testing the underlying physics. The implications for realistic applications will be discussed laterin section 4.
Figure 5 illustrates the cirrus-rich and cirrus-poor cases separated as described above, together with the‘‘control’’ case from Fig. 3 for comparison. As expected,
LW cooling is suppressed (i.e., hQRi is less negative) in
the cirrus-rich composites compared to the control,
while enhanced in the cirrus-poor cases. The differencein hQRi increases with increasing CWV. Two pairs ofcontrasting cases are chosen to assess the possible rolesof the cirrus radiative effects in different moisture environments: cases H1–L1 and H2–L2, as indicated inFig. 5, that each span a common CWV range of approximately 45–50 mm and 50–55 mm, respectively.
Here the prefixes H and L denote the cirrus-rich (highcirrus cloudiness) cases and cirrus-poor (low cirrus
cloudiness) cases, respectively. It is noted that the H and
L cases in each pair belong to different CWV categories
despite that they actually have a similar CWV range.
This is because the H-case composite finds itself near thehigher end of each specified CWV range while the
L composite from the same CWV category ends up nearits lower end, owing to the fact that cirrus clouds generally prefer as moist an environment as possible. CWVs
lower than 45 mm are not examined because considerable statistical noise due to the rare occurrence of cirrus

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drying effect of the elevated rainfall, while the increase in
rainfall is attributed to something other than moisture.

d. The shortwave effects

FIG. 5. As in Fig. 3, but with the cirrus-rich and cirrus-poor cases,
designated by crosses and filled circles, respectively, both in grayscale. The cases labeled as H1, H2, L1, and L2 are selected forFig. 6. The control analysis (color), identical to the all-sky cases inFig. 3, is shown again for comparison (the clear-sky plots areomitted). Arrows are for use later in section 4.

clouds in a dry environment conceals the signal, if itexists, for any systematic evolution.
The composite time series of CWV, SST, and precipitation are presented in Fig. 6 for the selected cases.
CWV by design stays close between each pair of cases
L1–H1 and L2–H2 (top), except that the cirrus-rich casehas a slightly but consistently lower CWV than its cirruspoor counterpart during 612 h around time zero. In SST(middle), cases L1 and H2 are virtually identical whilecases L2 and H2 are marginally apart by 0.58C or less. It
is nonetheless found that the disagreement in CWV andSST stays small within each pair compared to across thedifferent pairs, rendering confidence in the aforementioned premise that the L1–H1 and L2–H2 pairs each
represent a practically identical thermodynamic environment except radiation.
In spite of the similarity in CWV and SST, the composite evolution of precipitation (bottom) follows distinctly separate paths depending on cirrus cloud cover.
The cirrus-rich case therefore gives rise to a precipitation
roughly 50% heavier than the cirrus-poor case. This
precipitation enhancement is significant in that it
distinctly exceeds the standard error of precipitation(2.9 3 1024 mm h21) estimated for the same composite
analysis as done in the present analysis (Masunaga 2013).
The precipitation peak is slightly earlier than t 5 0 likely
because, as discussed by Masunaga and L’Ecuyer (2014),
the time zero is statistically dominated by horizontallyextensive events while intensive rainfall is often brought byspatially confined deep convection that typically precedes
stratiform anvils. It is difficult to attribute this precipitation
excess to a direct moisture effect, recalling that CWV isalike or even smaller for the cirrus-rich cases during thehours of intensified rainfall (Fig. 6, top panel). Rather,
the reduction of CWV is more likely a consequence of the

The radiative heating profiles shown so far are confined to LW cooling by using only the midnight sectionof CloudSat–CALIPSO orbits. The results from the afternoon (1330 LT) orbits are outlined here to illustrate
the net radiative heating of the atmosphere with the SW
effects included in addition to the LW.
Column radiative heating as a function of CWV for theafternoon orbits is plotted in Fig. 7. Additional analysis
runs with cirrus-rich and cirrus-poor cases are performedin the same manner as done above for the midnight orbits.
It is evident that the inclusion of the SW contribution
does not qualitatively alter any fundamental characteristics seen earlier (Fig. 5). Major updates are found inquantitative aspects. SW heating, increasing with CWV,
partially offsets or more than cancels LW cooling. The
breakdown of the SW effects (not shown) indicates thatthe water vapor absorption accounts for an increaseby ;40 W m22 while the cloud absorption explains anincrease of about 20 W m22 for the entire CWV range
studied. It is noted that, since the afternoon overpasses
are sampled at an hour of near-peak insolation, the relative strength of the SW effects to their LW counterpart isexaggerated in comparison with the daily mean.
Cirrus-rich (H1 and H2) and cirrus-poor (L1 and L2)
composites are constructed from the afternoon samples
on the basis of Fig. 7 in the same manner as done for the
midnight samples. The evolution of CWV, SST, andprecipitation for H1–L1 and H2–L2 cases is shown in
Fig. 8 to compare with Fig. 6 above. All major featuresare surprisingly common to the midnight and afternoon
hours, that is, precipitation is systematically heavier
when cirrus cloud cover is substantially larger and so is
hQRi, despite that CWV and SST are virtually identicalor even indicative of less rainfall.
In the following section, we will discuss a possible
physical mechanism that explains the QR control on
tropical rainfall using a simplistic conceptual model.

4. A conceptual modela. Steady-state energy balance
A fundamental role of radiation in tropical convective
dynamics may be glimpsed in the framework originatedby Neelin and Held (1987) crudely summarized (withmodifications) as follows. The MSE budget in a steadysingle-column atmosphere is expressed as
hv›p hi 5 FT 1 hQR i ,

(1)

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FIG. 6. The composite time series of (top) CWV (mm), (middle) SST (8C), and (bottom) surface precipitation (mm h21) for cases L1 (blue dashed), H1 (blue solid), L2 (red dashed), and H2(red solid) (see Fig. 5 for the notation). Precipitation for cases L2 and H2 is vertically offset
by 10.2 mm h21 as labeled on the right axis for visual clarity. Only the descending section of
CloudSat–CALIPSO orbits is included.

where hi denotes the vertical integral over the wholeatmospheric column, h is MSE, FT designates the surface turbulent fluxes collectively, and v is verticalpressure velocity. Given the amplitude of vertical velocity v^ separated from the structure function fv(p) asv( p) [ v
^ fv (p), (1) provides a measure of large-scalevertical motion in response to the diabatic forcing(FT 1 hQRi),
v^5

FT 1 hQR i.
h fv ›p hi(2)Precipitation is approximately scaled by v
^ in the tropicalatmosphere, as proved by omitting temperature fluctuations and the sensible heat flux in the dry static energy(DSE) budget,
LP ’ v^ h fv ›p si ,(3)

where P is precipitation, L is the specific latent heat ofvaporization, and s is DSE. Equations (2) and (3) are
combined into
LP ’ (FT 1 hQR i)

h fv ›p si
h fv ›p hi.

(4)Equations (2) and (4) claim that the large-scale circulation and precipitation are governed by the diabatic
forcing in a manner sensitive to vertical atmosphericprofiles and the specific form of fv. This may provide acrude explanation for the precipitation excess underovercast sky as shown by Figs. 5 and 7.
It should be noted that the demonstrations in Figs. 5
and 7 are rather artificial in that each case is forced tohave a larger or smaller cloud cover than usual at all
times. These composite plots are each an idealized representation of perpetually high or low cirrus cloudiness,consistent conceptually with the steady-state thought

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Dhqi
5 2h1 hv›p qi1 2 h2 hv›p qi2
Dt’ 2h1 hv1 ›p qbg i 2 h2 hv2 ›p qbg i ,FIG. 7. As in Fig. 5, but for ascending (1330 LT) CloudSat–CALIPSO
orbits only.

experiment as above. The real question, however, iswhether a gradual increase over time in cirrus cloud cover
could impose any radiative impact on the subsequent
growth of convection. The observations in this work are notuseful for testing this hypothesis, because the composite
time series is a collection of originally fragmentary snapshots and thus any information such as anomalous cloudiness at an early hour does not propagate through laterhours. Some theoretical thinking is necessary to complement this deficiency.
For this purpose, some premises behind (2) and (3)need to be refined. It has been implicitly assumed that agiven, fixed form of fv applies universally. In reality, fv
is by no means invariant but should be given flexibility
to account for the transition from shallow updraft todeep ascent. This can be conveniently dealt with by
expanding the vertical structure into a series of orthogonal modes, among which the first two baroclinic modes
are known to suffice to capture the essential characteristics of tropical convective dynamics (e.g., Mapes andHouze 1995). The second baroclinic mode withlower-tropospheric ascent, or the ‘‘congestus mode’’(Khouider and Majda 2006), is of particular interest here
as a main driver of the dynamics prior to the deep convective burst (Masunaga and L’Ecuyer 2014). The vertical MSE advection associated with the second mode ispositive, that is, the second mode imports MSE into the
air column, and is therefore no longer able to consume
the energy input by the diabatic forcing in a steadybalance, necessitating the time-dependent formulation.
These requisites are incorporated in a conceptual model
discussed next, which is based loosely on the simple
model devised by Masunaga and Sumi (2017).

b. Cirrus as a radiative trigger of convection
Masunaga and Sumi (2017) proposed a moisturestorage closure characterized by a pair of nondimensional parameters h1 and h2 defined as

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(5)

where q is the vapor mixing ratio and D/Dt [ ›/›t 1 V  =is the Lagrangian derivative operator with respect tohorizontal motion. The subscripts 1, 2, and ‘‘bg’’ denotethe first and second baroclinic modes and the timeindependent background field, respectively. The second
equality above holds when the vertical structure of q
remains practically preserved during the evolution,leaving the vertical motion solely responsible for thevariability of vertical MSE advection. This is in general ajustifiable assumption because the vapor mixing ratio
anomaly is small in amplitude (less than 1–2 g kg21)
against the background (cf., Fig. 3 of Masunaga 2013),
perhaps with the exception of extreme dry air intrusions.
The parameters h1 and h2 are the portion of the moisture accumulated by vertical advection that is left
behind in the air, and hence loosely measure the inefficiency of rainfall production.
The h parameters have multiple physical interpretations. The reevaporation of condensates is one of thegoverning factors for h, and the portion of moisture
convergence that is not directly fed into cumulus convection is another. As such, h1 and h2 summarize a rangeof the cumulus effects unresolved on a large scale just as
conventional cumulus parameterizations do. Chikira(2014) proposed the moistening efficiency (or a in his
notation), defined by the ratio of the vertical gradient ofmoisture to that of DSE, as a convenient tool for diagnosing the moist processes. The a parameter may
seem similar in concept to h1 and h2, but the difference
lies in that a is a practical measure constructed withreference solely to large-scale sounding rather thanserves as a closure assumption.
The congestus mode dominates early stages of theevolution before the first mode sets in (Mapes et al.
2006), so in practice h2 is of prime importance forstudying the effects of cirrus clouds preceding the peak
convection. Masunaga and Sumi (2017) found that h2
is initially close to unity and then decreases as precipitation picks up, that is, the moisture accumulation
owing to the second mode is barely convertible into
precipitation in the beginning, although this inefficiency
is somewhat alleviated later in the evolution.
The basic equation for the present model is thetime-dependent, column-integrated MSE budget
equation,
Dhhi
5 2hv›p hi 1 FT 1 hQR i .
Dt

(6)

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VOLUME 75

FIG. 8. As in Fig. 6, but for ascending (1330 LT) CloudSat–CALIPSO orbits only.

By separating the background field out in each variable
as h 5 hbg 1 h0 , where the prime designates the anomaly,(6) is updated as
Dhh0 i5 2hv0 ›p hbg i 1 FT0 1 hQ0R i ,Dt(7)

where the vertical MSE advection is again assumed toarise only from v0 . The background terms are required
to satisfy the steady-state MSE balance, (1), on theirown and have been subtracted to yield (7). Since v0 is
composed of the two baroclinic modes (v1 and v2) in thecurrent formulation, (7) may be further modified into

is a version of the normalized gross moist stability (GMS;
Raymond et al. 2007) that is here defined individually forthe first (i 5 1) and second (i 5 2) baroclinic modes. A
useful aspect of the mode-specific GMS is that g i is timeinvariant, because the amplitude of vi is precisely canceled out in (9). The variability in the surface turbulent
flux is outside the present scope in order to focus on theroles of radiative cooling, so hereinafter FT0 5 0.
Combining (8) with the moisture storage closure [(5)]in the absence of the first mode (i.e., v1 5 0), one finds

11


g2
Dhq0 i5 hQ0R i .
L
Dt
h2

(10)

Dhq0 i5 g1 Lhv1 ›p qbg i 1 g2 Lhv2 ›p qbg i 1 FT0 1 hQ0R i ,Dt(8)Recalling the systematic dependence of hQRi on CWV(Fig. 3), the radiative cooling anomaly is now parameterized explicitly with the CWV anomaly as

where the temperature contribution to the MSE tendency has been omitted and

hQ0R i 5 aR Lhq0 i ,Lhvi ›p hbg i

gi [ 2
Lhvi ›p qbg i

(9)

(11)

where aR is a prescribed coupling constant (s21).
Equations (10) and (11) together lead to a concise
prognostic equation to predict the CWV anomaly as

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MASUNAGA AND BONY

aR
Dhq0 i05g hq i .
Dt
11 2
h2

1337

(12)

For comparison, another equation of the same form isderived for the first mode by assuming v2 5 0 asaR
Dhq0 i05
g1 hq i .
Dt
11
h1

(13)

Equations (12) and (13) may be interpreted to be an
asymptotic realization of the moisture evolution before
the deep convective onset (i.e., v1 5 0) and during thepeak convective activity (v2 5 0), respectively. The
formal solution of the CWV anomaly for a Lagrangian
air column is readily found to be
hq0 i 5 hq0 i0 exp(t R t), where t R [



1
g
,
11
aR
h

(14)

where hq0 i0 is the initial condition of hq0 i. The subscriptof g and h is now omitted so (14) represents the generalized solution for the two idealized setups individually.
The solution (14) claims that the CWV anomaly grows
or decays with time at the e-folding rate of t R. Figure 9shows the growth rate of hq0 i (or the inverse of t R) as afunction of h for aR 5 4 3 1027, 1 3 1026, and 2 3 1026 s21.
These three numbers, indicated by arrows in Fig. 5, arechosen to cover a realistic range of the observed sensitivity of hQRi to CWV. For each aR, t 21
R (h) is depicted
for g2 5 20.5 and g1 5 0.2 correspondingly to (12) and(13). These values of g1 and g2 were determined fromin situ soundings over the tropical Indian Ocean(Masunaga and Sumi 2017). The above choice of g1
and g2 may be considered to be representative of
tropical atmospheres in general.
The most outstanding feature in Fig. 9 is that thegrowth rate for the second mode has a sharp rise as
h approaches 0.5 from above, as expected from (14),where t 21
R is diverged to infinity for h / 2g. Thisbehavior has a close relevance to the instability induced by negative GMS as discussed extensively byRaymond et al. (2009). Such an instability occurs wheng 1 h diminishes to below zero in the current formulation, with the growth rate beginning to climb dramatically as h decreases to the vicinity of 2g or 0.5. Figure 9shows that t R for the second mode reaches about 50 h at
h ’ 0.8 for aR 5 2 3 1026 s21 and 100 h or longer for thesmaller values of aR. Given that h2 does not descend far
below 0.8 in reality (Masunaga and Sumi 2017), thesevalues set the upper limit to the realistic growth rate inpractice for each setup of aR.

FIG. 9. The growth rate of CWV anomaly t21
R plotted against the
moisture storage ratio h. Solid and dashed curves show the second
baroclinic mode (g 5 20.5) and the first mode (g 5 0.2), respectively, for aR 5 4 3 1027, 1 3 1026, and 2 3 1026 s21 as designated in the key. Arrows indicate the direction of the temporal
evolution prior to the peak convection (see text for details).

The growth rate is in theory allowed to rise limitlessly
if h decreases to 0.5, but this is unlikely to occur in thereal atmosphere. The assumption of v1 5 0 is ruled out
as the evolution proceeds further and the second mode
gives way to the first mode. Once the first mode exceeds
the second in magnitude, the growth rate follows that of
the first mode (dashed lines in Fig. 9), implying only avery slow growth of CWV anomaly (t R  100 h) regardless of h and aR.
The contrasting behaviors between the first and second modes may be understood as follows. A moist
anomaly introduced by vertical moisture advection istranslated into an increase of hQRi through (11), which
in turn feeds back on the initial perturbation according
to the MSE conservation law [(8)]. When the second
mode dominates, the air column imports MSE andmoisture at the same time (i.e., g being negative) andhence is capable to dynamically amplify the low-levelascent fed by the radiative heating anomaly. On thecontrary, vertical MSE advection is negative and thus
offsets, rather than amplifies, the hQRi forcing for a deep
ascent associated with the first mode. The competition
between the export of MSE and column radiative
heating is central of the theory developed by Raymondand Zeng (2000), who indeed found a growth over a timescale of a few 100 h. This time scale is far too long in lightof the life time of convective systems, and is instead
relevant to the weekly to intraseasonal variabilities.
Recall that the growth time is as short as 2 days when the
dynamics is driven by the congestus mode, offering a

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VOLUME 75

FIG. 10. (top) The composite time series of the CWV anomaly for the five moisture classes
as indicated. The anomaly is calculated with respect to the background level defined as the
temporal mean of the first and last 12 h combined. Three dashed lines show theoretical curves
with tR 5 40, 50, and 100 h, from top to bottom, as a reference. (bottom) The composite timeseries of precipitation.

potential mechanism triggering the growth of individual
convective systems through the swift accumulation ofmoisture.
It is expected that the growth time depends on theambient moisture, recalling that aR monotonically increases with CWV (Fig. 5). To confirm this, the observed
time series of the CWV anomaly is plotted in Fig. 10.
The anomaly is computed against the background level
defined as the temporal mean of the first and last 12 hcombined together for each moisture class. The CWVanomaly increases over time toward time zero at a rate
varying with the ambient moisture. The theoreticalprediction given by (14) is also depicted as a reference for t R 5 40, 50, and 100 h, where the equationis slightly modified to exp[(t 1 72 h)/t R] 2 1 so itmatches the present format. A very humid atmosphere
experiences a notable moistening prior to the peak
convection, consistent with an elevated growth rate as
predicted (t R ; 40–50 h) when aR is large. On the other
hand, the CWV anomaly stays neutral or even slightly
decreases during the same hours in dry atmospheres (thedry and moderately dry curves in Fig. 10). The muted
moistening in a dry environment is explained by thedependence of tR on aR as depicted by Fig. 9.
The slight drying might be ascribed to the negative
CRE as seen in Fig. 3. A negative CRE implies a negative aR if cloudiness is positively correlated with CWV,in which case a moist anomaly would decay with time
according to (14). Although not explicitly included in

the present model, the shallow-cumulus mode, representing shallow updraft just above the boundary layer, isarguably the dominant vertical mode in dry atmospheres
and may act to maintain the dry state in a stable manner(Masunaga 2014).
The simple theory and the analysis results above
confirm the possibility that the congestus mode could
give rise to a significant moistening when coupled with aradiative heating anomaly brought by cirrus clouds. This
could be a trigger of the subsequent pickup of precipitation. In Fig. 10 (bottom panel), the sharp rise of
precipitation is striking in a humid environment while
minimal when the ambient air is dry, echoing the CWV
dependence of cloud cover profiles presented in Fig. 1. It
confirms that radiation can be a key factor for intensifying convection and that the cirrus clouds leading
the onset of convection are a major source of the radiative perturbation.

5. Summary and discussion
Satellite observations are exploited in search of possible evidence for radiative–convective interactions withfocus on the hourly to daily variability intrinsic to the life
time of convective systems over tropical oceans. To thisend, CloudSat–CALIPSO snapshots of the vertical
cloud structure are projected onto a composite timeseries centered around the moments of convective occurrence identified by the TRMM PR. Such a composite

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MASUNAGA AND BONY

technique has been demonstrated to be useful for illustrating the systematic change of large-scale atmosphericstate associated with the development and dissipation of
convective systems (e.g., Masunaga 2012). The composite time series are broken down by CWV at time
zero in order to test different radiative environments
individually. It is confirmed that clouds are boundbelow ;2 km when the ambient air is very dry while
vertical developing convection is present in a moist
atmosphere. Cirrus clouds are prevailing in the moist
environment, accompanied by a modest enhancement
in cloud cover before and after the peak convection
with a slight temporal asymmetry in which the leading
cirrus slightly exceeds the trailing cirrus.
The preceding cirrus clouds could in theory havemultiple origins. Cirrus clouds detrained from an existing convective system nearby may be transported into
the target area before the parent system eventuallymarches in, as reported by past observational work (e.g.,
Yamada et al. 2010). The present composite technique
by design applies to the Eulerian frame, and it is difficult
to assess to what extent the intruding cirrus may bealiased into the time series. Another possible origin is
the in situ formation of cirrus clouds, which accounts formore than a half of tropical cirrus clouds (Luo andRossow 2004). Johnson et al. (2015) found evidence forcirrus formation in response to the Kelvin-wave induced
cool anomalies prior to the MJO convective burst anddiscussed its radiative impact on the local energy budget.
Their analysis may suggest the prior development of
cirrus may influence the convection on a broad range oftime scales. The third potential source of the preceding
cirrus is the detrainment from scattered deep convection
prevailing over tropical oceans. Isolated cumuli themselves may be barely discernible in cloud cover shown by
Fig. 1, but the cirrus clouds they produce could begin tofill in a large-scale domain at some point in the evolution. While it is unable to single out where the leading
cirrus comes from, this study finds that cirrus clouds
have physical consequences on the subsequent evolution of convection irrespective of their origins.
Composite analysis runs with descending (0130 LT)
A-Train orbits show that LW cooling is heavily suppressed by developing clouds in a humid environment,
while very efficient owing to the absence of cirrus clouds
when the ambient air is dry. The CRE is even negativein a very dry environment, where the LW emissions
from low clouds are hardly absorbed as they escape tospace and thus an increase in low cloud cover would
enhance the LW cooling. The composite trajectories
appear to be lined up practically on a unique, continuous
curve across a broad range of CWV when projected
onto the hQRi–CWV plane. This may imply a universal

1339

relationship between CRE and CWV and hence that thecloud optical properties and cloud-top-height distribution may be somehow tightly coupled with CWV.
The composite statistics, however, exhibit significant
spread: the ‘‘universal’’ curve is split when the composite samples are divided into those with a high cirrus
cloudiness and the others with a low cirrus cloudiness on
the basis of the cloudiness at 13 km in each hour along
the time axis. Two pairs of these cirrus-rich and cirruspoor composites each under a similar CWV range are
selected as a test bed for isolating the radiative effects
from the direct moisture effects on convection. Precipitation is found to be distinctly heavier when cirrus
cloud cover is larger despite that the moisture environment is virtually identical, suggestive of a link between
the radiative perturbation by cirrus clouds and theconvective intensity. Another set of analysis runs withascending A-Train orbits (i.e., the SW effects included)
arrives at the same conclusion in a qualitative sense.
The physical basis behind this radiative–convective
linkage may be understood to the zeroth-order approximation in terms of the MSE budget. If the variability in the surface turbulent flux is small, a fluctuationin column radiative heating should be balanced against
the MSE export associated with large-scale ascent (andtherefore precipitation). The sensible and latent heat
fluxes abruptly increase by 15–20 and 30–70 W m21, respectively, around the peak convection (Yokoi et al.
2014), which could momentarily reinforce the effect ofhQRi on the MSE budget during a limited period of time(a few hours) with enhanced gustiness. The large-scale
motion is often considered in the relevant literature(Neelin and Held 1987; Raymond and Zeng 2000) to
be a deep ascent represented by the first baroclinic
mode. The present simple model describing the evolution of the CWV anomaly yields a growth time much
longer than 100 h when the large-scale motion is governed by the first baroclinic mode. This does not contradict the fact that a radiative–convective feedback
takes a weekly or intraseasonal time scale to occur inexisting studies. The present analysis sheds light on theimportance of the second baroclinic (or congestus)
mode, which gives rise to a rapid coupling of cirrusinduced radiative effects with convective dynamics. Thismechanism has the potential to help improve our understanding of the link between cirrus clouds and atmospheric circulation, which remains a key problem inclimate simulations (Bony et al. 2015).
Although beyond the current single-column framework,
it may be argued that the horizontal radiative gradient between cloudy and cloud-free domains possibly plays anadditional role in driving the overturning circulation. Recent studies revealed that a radiatively driven subsidence

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JOURNAL OF THE ATMOSPHERIC SCIENCES

owing to longwave emissions by low clouds is a crucial
process for the convective self-aggregation to make progress (e.g., Holloway et al. 2017). Such a subsidence induces ashallow circulation across moist and dry regions (Coppin
and Bony 2015), which has the potential to strengthen thecongestus-mode dynamics discussed in this work.
The radiative–convective interactions are importantfor MJO not only during the convectively active phase inwhich the first mode governs the local dynamics, but
play a distinct role in weakly precipitating periods (Kimet al. 2015). This is in line with the present finding thatthe cirrus radiative effects are of particular importancewhen the congestus (or the second baroclinic) mode isdominant. The congestus mode could amplify the CWVanomaly at a growth time as short as ;50 h in moist
atmospheres, because shallow circulation having a negative GMS (Back and Bretherton 2006) is prone bynature to grow on its own. The congestus mode is a fastworking agent of radiative–convective interactions,which arguably helps invigorate the convective activity
within a period of a few days when assisted radiatively
by the preceding cirrus clouds.
Acknowledgments. The authors are grateful to JeanYves Grandpeix, Dave Thompson, Dave Randall, Dick
Johnson, Johnny Luo, Claudia Stubenrauch, Caroline
Muller, Chris Kummerow, and three anonymous reviewers
for their comments, which helped enrich aspects of the
discussions presented in the paper. This work is supported
by Japan Society for the Promotion of Science (JSPS)
Grants-in-Aid for Scientific Research (KAKENHI) Fundfor the Promotion of Joint International Research Development (15KK0157) and the European Research Council(Grant Agreement 694768). The CloudSat–CALIPSO data
products were provided by the CloudSat Data Processing Center (http://www.CloudSat.cira.colostate.edu),

VOLUME 75

the TRMM PR dataset by the Japan Aerospace Exploration Agency (JAXA; https://www.gportal.jaxa.jp/gp/top.
html), and the TRMM TMI and AMSR-E products by
Remote Sensing Systems (http://www.remss.com).

APPENDIX
Temporal Asymmetry of Cirrus Cloud CoverThe central interest of this work is the cirrus increase
1–2 days prior to deep convection, especially in thecontext of its potential radiative impacts on the subsequent development of convection. One might argue
that the leading cirrus could be a statistical artifact due
to sampling noise, with the possibility in mind that the
TRMM may detect precipitation at later stages of theconvective life cycle when anvil clouds have alreadygrown extensively. With the assumption that such stochastic spread over time is random by nature, the signalsof preceding cirrus are tested for robustness by decomposing cloud cover fc into the temporally symmetric andasymmetric components, fsym and fasym, respectively, asf (t 1 t) 1 fc (t0 2 t)
fsym (t) 5 c 0
2f (t 1 t) 2 fc (t0 2 t)fasym (t) 5 c 0
2

and
for

t $ 0,

where t0 refers to the hour of the peak precipitation(roughly around 0 h). Figure A1 shows each of the twocomponents for the moist case obtained from the bottom
panel of Fig. 1. While a positive asymmetry (i.e., more
cloudiness after the peak than before) above 4 km implies
stratiform precipitation dominating the mature phase of
the convective life cycle, a negative asymmetry takes over fort . 12 h above 12 km, suggesting that the preceding cirrus
should embody a real entity beyond a sampling artifact.

FIG. A1. The temporally symmetric (contour) and asymmetric (color shaded) components ofCloudSat–CALIPSO cloud cover about the time of peak precipitation (see text for definitions)for the moist case. The contour interval is 0.05.

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