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Variability in the Characteristics of Precipitation Systems in the Tropical Pacific.
Part II: Implications for Atmospheric Heating
TRISTAN S. L’ECUYER, HIROHIKO MASUNAGA, AND CHRISTIAN D. KUMMEROW
Colorado State University, Fort Collins, Colorado(Manuscript received 4 April 2005, in final form 14 September 2005)
ABSTRACTThis paper explores changes in the principal components of observed energy budgets across the tropical
Pacific in response to the strong 1998 El Niño event. Multisensor observations from the Tropical Rainfall
Measuring Mission (TRMM) Microwave Imager (TMI), Visible and Infrared Scanner (VIRS), and precipitation radar (PR) instruments aboard TRMM are used to quantify changes in radiative and latentheating in the east and west Pacific in response to the different phases of the El Niño–Southern Oscillation.
In periods of normal east–west SST gradients there is substantial heating in the west Pacific and cooling in
the east, implying strong eastward atmospheric energy transport. During the active phase of the El Niño,
both the east and west Pacific tend toward local radiative–convective equilibrium resulting in their temporary energetic decoupling. It is further demonstrated that the response of these regions to ENSO-induced
SST variability is directly related to changes in the characteristics of clouds and precipitation in each region.
Through quantitative analysis of the radiative and latent heating properties of shallow, midlevel, and deep
precipitation events and an equivalent set of nonprecipitating cloud systems, times of reduced atmosphericheating are found to be associated with a shift toward shallow and midlevel precipitation systems andassociated low-level cloudiness. The precipitation from such systems is typically less intense, and they do nottrap outgoing longwave radiation as efficiently as their deeper counterparts, resulting in reduced radiativeand latent heating of the atmosphere. The results also suggest that the net effect of precipitating systems on
top-of-the-atmosphere (TOA) fluxes and the efficiency with which they heat the atmosphere and cool thesurface exhibit strong dependence on their surroundings. The sensitivity of cloud radiative impacts to theatmospheric and surface properties they act to modify implies the existence of strong feedbacks whose
representation may pose a significant challenge to the climate modeling community.

1. Introduction
Globally, atmospheric radiative cooling through theemission of thermal radiation to space and the surface
is balanced by the absorption of solar radiation and thetransfer of energy from the surface to the atmospherein the form of latent and sensible heating. This radiative–convective equilibrium (RCE), however, seldom
occurs locally because of the highly variable nature of
the frequency, structure, and radiative properties ofclouds and precipitation. This variability leads to substantial regional imbalances in atmospheric heating thatdrive large-scale circulation patterns. Numerous studies
have sought to characterize the impacts of cloud radia-

Corresponding author address: Dr. Tristan L’Ecuyer, Department of Atmospheric Science, Colorado State University, Fort
Collins, CO 80549.
E-mail: tristan@atmos.colostate.edu© 2006 American Meteorological Society

tive heating and latent heating from precipitation onscales ranging from those of individual mesoscale convective systems (e.g., Houze 1982, 1989; Mapes andHouze 1995), intraseasonal oscillations (e.g., Lau andPeng 1987; Lee et al. 2001), and the strength of the
Hadley circulation (e.g., Slingo and Slingo 1988, 1991)
and Walker circulation (e.g., Hartmann et al. 1984;
Sherwood et al. 1994). The common conclusion to be
drawn from these studies is the importance of accurately representing the processes that govern the microphysical properties of clouds and precipitation in order to reliably model their impact on atmospheric radiative heating.
A key obstacle to properly modeling the influence ofclouds in climate is the problem of understanding andultimately representing critical feedback processes that
occur as a result of the strong interdependence between
radiation, clouds, precipitation, and the environments
in which they reside (Stephens 2005). It has been con-

15 APRIL 2006

L’ECUYER ET AL.

jectured, for example, that increases in highly reflective
cirrus clouds in response to increasing sea surface temperature (SSTs) act to limit temperatures in the ocean
mixed layer to less than 305 K (Ramanathan and Collins 1991). Alternatively, Lindzen et al. (2001) proposethat clouds act as an iris, opening and closing to regulate the amount of longwave (LW) radiation emitted tospace in response to increases or decreases in SST.
They conjecture that the area of moist cloudy regions
decreases with increasing SST in such a way that theirability to trap longwave radiation decreases more rapidly than their ability to reflect solar radiation, thereby
modulating SSTs. Both studies employ simple models
to quantify the impacts of the proposed feedbacks that
invariably invoke a number of assumptions that significantly influence interpretation of their results. Consequently, these studies have been attacked on the basisof the appropriateness of these assumptions such as themagnitude of assumed atmospheric heat transports orprescribed cloud optical properties (Pierrehumbert
1995; Lin et al. 2002; Chambers et al. 2002; Hartmann
and Michelsen 2002). The recent study by Rapp et al.
(2005), for example, found no systematic variation inconvective cloud area normalized by precipitation withincreasing SST.
Recent studies using both (relatively) simple models(e.g., Bretherton and Sobel 2002; Sobel and Gildor
2003) and more complex general circulation models(GCMs) (e.g., Lee et al. 2001) have also demonstrated
the sensitivity of models to parameterizations of theradiative impacts of clouds associated with convection.
The strength of the modeled Walker circulation, thepropagation speed of tropical intraseasonal oscillations,and the ability of clouds to suppress hot spots in theseexamples are all highly sensitive to the modeled
strength of cloud–radiation feedbacks regardless of specific details that govern how they impact the model.
Despite the implications of these studies, progress
toward understanding cloud feedback processes in theatmosphere has been slow, as evidenced by the fact that
the Intergovernmental Panel on Climate Change(IPCC) has concluded that they represent a primarysource of uncertainty in predicting the impacts of climate change in each of their last three assessments.
Given the variability in the magnitude of cloud heatingin current GCMs owing to, for example, their sensitivity
to fall speed parameterization (Jakob 2002), the challenge of quantifying relationships between the energetic properties of precipitation systems and the processes by which they form falls to the observational
community. Numerous studies have been conducted
over the past three decades focusing on determining theimpact of regional variations in cloudiness on the1389

earth’s radiation budget (e.g., Vonder Haar and Suomi
1971; Gray 1973; Hartmann and Short 1980; Arking
1991; Stephens and Greenwald 1991a,b; Hartmann etal. 1992; Del Genio and Kovari 2002, to name but afew). Building on the results of these works, this study
and a companion paper by Masunaga et al. (2005, hereafter, Part I) make use of recent data from the TropicalRainfall Measuring Mission satellite (TRMM) to explore the changes in the character of clouds and precipitation in response to SST variability in the Pacific
Ocean. Unlike many previous studies, however, we further explore the manner by which these changes in turnimpact the energetic properties of their surroundings.
Our primary focus is to quantify the coupled responseof clouds, precipitation, and atmospheric energetics inthree regions in the tropical Pacific to the different
phases of the El Niño–Southern Oscillation (ENSO).
Rather than seeking to discover a grand cloud feedback
mechanism like those proposed by Ramanathan and
Collins (1991) or Lindzen et al. (2001), however, ourgoal is to provide insights into the important processes
that warrant further study in order to accurately represent them in a complete theory of global climatechange.
The results indicate that the radiative heating fromprecipitating cloud systems varies systematically with
SST, precipitable water (W, and surface wind speed
anomalies associated with the strong 1998 El Niño
event. By virtue of changes in the relative frequenciesof occurrence of different cloud systems, the west Pacific transforms from a region of intense atmosphericheating in the presence of normal SST gradients acrossthe Pacific to conditions of approximate local RCE during the El Niño event. Conversely, a substantial increase in the occurrence of deep convection modifies
the east Pacific, normally a region of strong radiative
cooling, into a heating region. As a result, the implied
eastward transport of energy is temporarily halted inagreement with the observed reduced strength of theWalker circulation. While the influence of ENSO on
the strength of the Walker circulation has been known
for some time, the analysis establishes the connection
between variations in atmospheric heating in the Pacificto changes in the relative frequency of occurrence ofdifferent precipitating cloud systems in response to
ENSO-induced SST variability.
Unlike many prior studies that are based on regional
datasets or model-derived fields, the analysis conductedhere makes use of an extensive set of satellite-derived
products that offer spatial coverage afforded by models
but avoid the need to parameterize important physicalprocesses. The observationally based conclusions regarding relationships between radiation and hydrology

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that emerge from this study therefore provide an independent test of the combined effects of the parameterizations that lie at the root of numerical weather prediction (NWP) and climate models. The results demonstrate the need for models to explicitly represent
differences in the radiative properties of distinct cloudsystems and support the use of observations to provide
guidance concerning the response of cloud distributions
to particular modes of climate variability.
Section 2 describes the algorithm and relevant
datasets used to estimate radiative and latent heat
fluxes from TRMM observations. The overall energy
budgets of the east and west tropical Pacific are explored in section 3, and their properties under conditions characteristic of a strong El Niño event are compared to those representative of the climatologicalmean in the region. Section 4 quantifies the energetic
properties of each storm type identified in Part I as well
as a number of nonprecipitating pixel classes and decomposes the regional energy budgets into components
owing to each pixel type. Finally, the implications ofchanges in the regionally averaged radiative heating
and cooling efficiencies of precipitating systems formodeling the impacts of clouds in global climate change
will be addressed in section 5.

2. Analysis approach
The analysis that follows derives from a combinationof the storm type classification methodology outlined inPart I and the technique for estimating components of
the atmospheric energy budget and hydrologic cyclefrom satellite observations developed in L’Ecuyer andStephens (2003, hereafter LS03). The salient features ofthese techniques will be described here, but the interested reader is encouraged to read those manuscripts
for further details.

a. Pixel classification
Traditional approaches to classifying clouds and rainfall systems have focused on either measurements ofinfrared brightness temperature (TB) that are representative of cloud-top height or vertical structure information obtained from radar observations that is typicallymost sensitive to precipitation. Part I demonstrated theutility of combining these observations to develop aclassification that simultaneously captures properties ofthe precipitation and associated cloud fields to provide
a more complete representation of the microphysicalproperties of the precipitation system as a whole. In thisstudy, we follow the procedure outlined in Part I toclassify precipitation systems based on their 10.8-␮m TB

VOLUME 19

TABLE 1. Definition of storm categories. Adapted from Part I.

Storm categoryRadarecho-top height

Infrared
TB

Shallow
Cumulus congestus
Deep stratiform
Deep convective⬍4 km
4–6 km
4–6 km⬎6 km⬎260 K⬎245 K⬍245 K⬍245 K

measured by the Visible and Infrared Scanner (VIRS)
and their echo-top height characterized by the precipitation radar (PR) both of which fly aboard TRMM.
Histograms of these parameters created by carefully
matching VIRS and PR observations suggest that mostof the precipitating cloud systems observed by TRMM
can be grouped into the four precipitation classes summarized in Table 1. Storms with echo tops less than 4
km and TBs warmer than 260 K are appropriately classified as “shallow” precipitation. At the other end of
the spectrum, “deep convective” precipitation eventsare defined as those having cloud tops colder than 245
K and echo tops in excess of 6 km. Between these extremes two additional storm categories are defined
both with echo tops near the freezing level between 4
and 6 km. The “deep stratiform” category exhibits
cloud tops colder than 245 K while the “cumulus congestus” category has cloud tops warmer than thisthreshold. Some justification for the distinction between these two storm types is provided in Part I by thefact that the deep stratiform category is dominated bystratiform precipitation while the cumulus congestus
category is composed primarily of convective rainfall(as defined by the TRMM PR-based 2A23 product described below).
Since the primary objective of this paper is to examine the radiative properties of the various cloud systems
observed in the Tropics, it is also important to identify
clouds in nonprecipitating regions so that their contribution to the energetic properties of the tropical atmosphere can also be identified. To this end, two additional components are added to the classification described above, an infrared high cloud mask again using
VIRS and a low cloud mask derived from TRMM Microwave Imager (TMI) observations, both of which aredescribed in greater detail below.

b. The Hydrologic Cycle and Earth Radiation
Budget algorithm
Corresponding radiative flux and heating rate profiles are estimated following the technique outlined in
LS03. The approach, known as the Hydrologic Cycle
and Earth Radiation Budget (HERB) algorithm, syn-

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thesizes complementary information from the sensors
aboard TRMM, to initialize broadband radiative transfer calculations for deriving the spatial and temporal
distribution of radiative heating in the tropical atmosphere. Distinct retrievals of high-cloud optical properties using VIRS (Cooper et al. 2003), liquid clouds from
TMI (Greenwald et al. 1993), and precipitation fromthe TMI-based Goddard Profiling Algorithm (GPROF;
Kummerow et al. 2000) are first combined to obtain acomposite view of the three-dimensional structure ofclouds and precipitation in the atmosphere. Surface
rainfall estimates from GPROF are used to discriminate between raining and nonraining pixels. In all pixels
with nonzero surface rainfall rates, liquid and ice hydrometeor profiles from GPROF are adopted, and collocated 10.8-␮m radiances from VIRS are used to constrain cloud-top height. VIRS and TMI observations
are then used to establish the presence and propertiesof liquid and ice clouds in all other pixels. Any pixel forwhich the VIRS 10.8-␮m TB falls more than 5 K below
the climatological mean clear-sky value is considered tocontain a high cloud. Low-lying liquid clouds, on theother hand, are identified using the liquid water path
retrieval algorithm described by Greenwald et al.
(1993). Any pixel with LWP exceeding 0.05 kg m⫺2 isconsidered to contain a low cloud. While neither ofthese masks are devoid of false detections or missed
clouds, they provide adequate qualitative information
to categorize the data on the time and spatial scales ofinterest to this study. Based on the detailed error analysis of LS03, uncertainties introduced through missed
and/or false detections resulting from these procedures
are expected to be ⬃5 and 10 W m⫺2 in longwave andshortwave (SW) fluxes, respectively.
These cloud and precipitation fields are supplemented with profiles of temperature and humidity from
European Centre for Medium-Range Weather Forecasts (ECMWF) analyses, ocean surface properties
from Remote Sensing Systems (RSSs), and a crude
model of the global distribution of aerosol optical properties from the Global Aerosol Climatology Project(GACP). The data are subsequently mapped onto a
uniform 0.25° grid to facilitate footprint matching andused as input to a broadband radiative transfer model
to compute upwelling and downwelling LW and SW
radiative flux profiles for each pixel. The model, known
as BUGSrad, is founded on the ␦-two-stream approximation to the radiative transfer equation (e.g.,
Stephens et al. 2001). It is applied over 6 SW and 12 LW
spectral bands and accounts for the effects of scattering,absorption, and emission from all four hydrometeor
classes retrieved by GPROF using the parameterizationof cloud optical properties introduced in Stephens et al.

1391(1990). Computed flux profiles are used to derive principal components of the radiation budget and theirbreakdown into clear-sky and cloudy components. In
the case of precipitating pixels, an estimate of columnintegrated latent heating (LH) is also made using LH ⫽␳lL␷R, where ␳l is the density of liquid water, L␷ is thelatent heat of vaporization, and R is the retrieved surface rainfall rate. This relationship is only justified on
spatial and temporal scales that encompass the complete life cycle of precipitating cloud systems, a good
assumption for the large regions and monthly averages
considered here (see LS03 for more details).
LS03 perform an extensive analysis of the uncertainties in all components of the algorithm using a combination of sensitivity studies and detailed comparisonswith observed top-of-the-atmosphere (TOA) fluxes.
Direct propagation of the uncertainties in all input parameters through the algorithm demonstrate that thetechnique provides monthly mean estimates of oceanic
LW fluxes at 1° resolution to an accuracy of 10 W m⫺2.
Corresponding SW flux estimates are found to be accurate to 25 W m⫺2. After careful examination of thesources of these errors, LS03 conclude that uncertainties in LW fluxes are dominated by the lack of ice cloudvertical placement information in the VIRS observations as well as errors in the vertical distribution ofwater vapor, particularly in the upper troposphere. Uncertainties in SW fluxes, on the other hand, are dominated by errors in the detection of low clouds andpoorly constrained liquid cloud microphysical properties. These estimates are consistent with the comparisons of outgoing LW and SW radiative flux estimates at
1.0° resolution to observations from the Clouds and theEarth’s Radiant Energy System (CERES) instrument(Wielicki et al. 1996). Root-mean-square differences inmonthly means outgoing longwave radiation (OLR)
and outgoing shortwave radiation (OSR) are 8 and 21
W m⫺2, respectively, while corresponding differences ininstantaneous fluxes are 30 and 77 W m⫺2.

c. Datasets
Together, the HERB products and the classification
scheme described above make it possible to probe therole of various precipitating cloud systems in defining
regional energy budgets in the Tropics. This study combines four distinct TRMM products covering themonths of February 1998 during the strong 1998 El
Niño event and February 2000 that corresponded to
weak La Niña conditions. The 1B01 VIRS calibrated
radiance product supplies the 10.8-␮m TB information
to constrain cloud-top height while storm echo-top
height is obtained from the PR 2A23 dataset. HERB’s
low-cloud retrievals make use of vertically and horizon-

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FIG. 1. Mean atmospheric and surface properties in the tropical Pacific for the month ofFebruary 1998. The TWP, TCP, and TEP regions defined for the analysis are highlighted bywhite rectangles. For comparison purposes, the smaller TWP region defined by Cess et al.
(2001) is also highlighted.

tally polarized TBs from the TMI 1B11 product, andliquid and ice water content profiles for precipitating
systems are obtained from the TMI-based 2A12 TMI
profiling product. Detailed descriptions and access toeach of these datasets is provided by the Goddard Distributed Active Archive Center (DAAC; http://lake.
nascom.nasa.gov/data/dataset/TRMM/).
Three oceanic regions between 10°N and 10°S are
analyzed: the tropical west Pacific (TWP: 105°–150°E),
central Pacific (TCP: 180°⫺150°W), and east Pacific(TEP: 120°⫺90°W). Selected input fields to the HERB
algorithm are illustrated in Figs. 1 and 2 to highlight thedifferences between these regions in February 1998 and2000. Also shown is the west Pacific region analyzed by
Cess et al. (2001, hereafter CESS01) for comparison.
The top panels illustrate the eastward migration of
warmest SSTs during the strong El Niño event thattook place in 1998 (Fig. 1) relative to more climatologically “normal” conditions in 2000 (Fig. 2). The remaining panels compare daily mean precipitation and cloud15 APRIL 2006

L’ECUYER ET AL.

1393

FIG. 2. Same as in Fig. 1, but for the month of February 2000.

fractions for the two months illustrating a distinct eastward shift of intense precipitation and high clouds associated with the changing SST. These figures suggestthat there is a significant response in the distribution ofhigh clouds and precipitation in response to the change
in SST gradient associated with El Niño. Based on thisobservation, it is logical to ask how the energy balancein the east and west Pacific regions are modified by thechanging character of local clouds and precipitation
and, in turn, how such changes feedback on other properties of the regions through changes in the circulation
between them.

3. Regional energy budgets
To provide a foundation for analyzing the impacts ofvariations in the character of precipitating cloud systems on local atmospheric energetics, it is instructive tofirst examine the overall energy budget in each region.
Figure 3 illustrates the principal components of the atmospheric radiation budget for the TWP in February
1998 along with corresponding error estimates based on
the analysis of LS03. It is important to note that theuncertainties quoted here provide an upper bound on
the uncertainties in each flux since no reduction of ran-

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FIG. 3. Principal components of the regional energy budget of the TWP for the month ofFebruary 1998. All fluxes have been normalized by the mean solar insolation at TOA in theregion for the month of February (433 W m⫺2). The flux labels adopted here are defined inTable 1.

dom noise through temporal or spatial averaging has
been accounted for. Direct comparisons with CERESTOA fluxes suggest that the estimates are likely a factor of 2 more accurate than indicated. The labels assigned to the various fluxes are defined in Table 2,
where they are further decomposed into fractions owing to raining pixels, nonprecipitating cloudy pixels, andclear pixels.
The imbalance between incoming solar radiation andoutgoing SW and LW fluxes results in a net surplus ofradiation into the region of 95 W m⫺2. Perhaps somewhat surprisingly, Table 2 indicates that the west Pacific, generally considered a source of atmosphericheating, is actually found to exhibit a small net coolingof ⬃5 W m⫺2 when one compares the radiative coolingof ⫺126 W m⫺2 to the inferred latent heat release of 28⫻ 4.33 ⫽ 121 W m⫺2. This is likely accounted for by thetransfer of energy from the ocean to the atmosphere in
the form of sensible heat, but since this contribution istypically an order of magnitude smaller than that due tolatent heating, we can conclude that the TWP very
nearly exhibits local atmospheric energy balance during
the 1998 El Niño event.
Equivalent results for the month of February 2000
are summarized in Table 3, for comparison. Again theregion experiences a net surplus of 100 W m⫺2 of energy but now, during this period of more climatologi-

cally normal SST gradients across the Pacific, the TWP
experiences a net atmospheric heating of ⬃70 W m⫺2.
Tables 2 and 3 also indicate that the fraction of pixels
containing either clouds or precipitation is significantly
suppressed during the 1998 El Niño to 34% from almost 50% in the normal year. Evidence of the impact of
this change can be seen in the contributions of cloudy
and precipitating pixels to components of the energy
budget in the region. In the normal year, for example,
these pixels accounted for 67% of the solar radiationreflected to space while they accounted for only 50% in
February 1998. Similarly, 41% of the outgoing longwave radiation occurs in cloudy and precipitating skiesin the normal year while these conditions only accountfor 27% of the OLR during the 1998 El Niño.
Also summarized in Tables 2 and 3 are the cloud
impacts on SW and LW fluxes at TOA defined as thedifference in OSR and OLR between the all-sky conditions that are observed and an equivalent pixel withall clouds and precipitation removed. On average
clouds reflect 30% more SW radiation back to space
and trap almost 50% more LW radiation in the normal
year consistent with the significant reduction in the frequency of occurrence of clouds and rainfall during the
El Niño event. Ultimately, the reduced cloudinessand precipitation in 1998 result in increased OLR of 21
W m⫺2, increased SW absorption of ⬃12 W m⫺2, and a

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L’ECUYER ET AL.

TABLE 2. Decomposition of the February 1998 energy budget of the TWP into three hydrologic categories: precipitating pixels (rain),nonprecipitating cloudy pixels (cloud), and clear-sky pixels (clear). The fraction of pixels that fall into each category are given in the
top row. In subsequent rows, fluxes in the first column are expressed as a percentage of the mean incoming solar radiation at the TOA,
433 W m⫺2, while remaining columns provide the percentage of the total attributed to each pixel type. The second group of rows
summarize the impacts of clouds on SW and LW fluxes at TOA and SFC in W m⫺2 and their breakdown into precipitating andnonprecipitating cloud types. The last three rows summarize the net radiation budgets at the TOA, within the atmosphere, and at thesurface in W m⫺2, respectively.
Flux

Interpretation

Total

Rain

Cloud

Clear

f
↓
FSW,TOA↑FSW,TOA⌬FSW↓
FSW,SFC↑
FSW,SFC↑
FLW,SFC↓
FLW,SFC⌬FLW↑
FLW,TOA
LH
CSW,TOA
CLW,TOACSW,SFC
CLW,SFC
F NET
TOA
F NET
ATM
F NET
SFC

Pixel fractions
Incoming solar
Outgoing SW
SW absorption
Downwelling SW at SFC
Upwelling SW at SFC
Surface emission
Downwelling LW at SFC
LW divergence
Outgoing LW
Latent heat
SW cloud impact TOA
LW cloud impact TOA
SW cloud impact SFC
LW cloud impact SFC↑↑↓
FSW,TOA⫺FSW,TOA⫺FLW,TOA⌬FSW ⫹ ⌬FLW↓↓↑↑
FSW,SFC⫹FLW,SFC⫺FSW,SFC⫺FLW,SFC

100
100
20
19
68
7
107
97
⫺48
58
28
⫺24
23
⫺26
5

14
16
22
18
13
13
14
14
13
12
100
⫺9
6
⫺11
2

20
18
32
19
14
14
20
21
14
15
0
⫺15
17
⫺15
3

66
66
46
63
73
73
66
65
73
73
0
0
0
0
0

94
⫺125
219

decrease of 44 W m⫺2 in the latent heat release in theatmosphere. This is partially compensated by a small(⬃16 W m⫺2) reduction in solar reflection to space, but
overall the TWP atmosphere experiences both suppressed radiative and latent heating in response to thechange in cloud cover associated with the 1998 El Niño.
The opposite behavior is observed in the central and

TABLE 3. Same as in Table 2, but for the month of February
2000.
Flux

Total

Rain

Cloud

Clear

f
↓
FSW,TOA↑FSW,TOA⌬FSW↓
FSW,SFC↑
FSW,SFC↑
FLW,SFC↓
FLW,SFC⌬FLW↑
FLW,TOA
LH
CSW,TOA
CLW,TOACSW,SFC
CLW,SFC
F NET
TOA
F NET
ATM
F NET
SFC

100
100
24
22
61
6
107
98
⫺44
53
38
⫺32
34
⫺29
5

21
22
29
25
17
17
20
21
21
20
100
⫺13
10
⫺14
3

28
28
38
25
24
24
28
28
20
21
0
⫺19
24
⫺15
2

51
50
33
50
59
59
52
51
59
59
0
0
0
0
0

101
⫺97
198

east Pacific as suggested by Table 4, which summarizes
the observed energy budget in the TEP region for both
February 1998 and 2000. During the normal year,
clouds and precipitation constitute less than 25% of theobserved pixels while they make up 43% of the scenes
observed in 1998. As a result, while the region as awhole experiences a net surplus of TOA radiation inboth months, the atmosphere loses 67 W m⫺2 undernormal SST conditions while gaining 84 W m⫺2 during
the 1998 El Niño. This is a direct consequence of enhanced radiative and latent heating associated with theincreased clouds and precipitation in the region in direct contrast to the El Niño response observed in the
TWP.
Similar behavior is observed in the TCP (not shown)indicating that in February 2000, and presumably otherperiods with more or less normal SST gradients between the east and west Pacific, there is a net eastward
transport of energy associated with the Walker circulation. This finding is not new, but it is reassuring that it
agrees with the findings of, for example, CornejoGarrido and Stone (1977), who suggest that the Walker
circulation is primarily driven by zonal variations incondensation. The results from February 1998 support
the fact that ENSO exerts a strong influence on thestrength of this circulation by significantly reducing (ifnot completely eliminating) the required transport ofenergy to establish equilibrium across the Pacific.

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TABLE 4. Same as in Tables 2 and 3, but comparing the properties of the east Pacific in 1998 and 2000.
1998

2000

Flux

Total

Rain

Cloud

Clear

Total

Rain

Cloud

Clear

f
↓
FSW,TOA↑FSW,TOA⌬FSW↓
FSW,SFC↑
FSW,SFC↑
FLW,SFC↓
FLW,SFC⌬FLW↑
FLW,TOA
LH
CSW,TOA
CLW,TOACSW,SFC
CLW,SFC
F NET
TOA
F NET
ATM
NET
F SFC

100
100
20
20
67
7
107
98
⫺47
56
47
⫺35
39
⫺38
7

21
20
26
23
17
17
21
21
20
18
100
⫺14
12
⫺16
3

22
20
33
20
15
15
22
23
17
17
0
⫺21
27
⫺22
4

57
60
41
57
68
68
57
56
63
65
0
0
0
0
0

100
100
21
22
65
7
104
92
⫺49
61
13
⫺16
6
⫺16
4

12
12
13
14
11
11
12
13
12
12
100
⫺3
2
⫺3
1

12
11
25
11
6
6
12
13
12
11
0
⫺13
4
⫺13
3

76
77
62
75
83
83
76
74
76
77
0
0
0
0
0

104
⫺117
221

79
⫺118
197

It is worth noting that the ratio of cloud impacts on
TOA SW to LW fluxes in the TWP region during the1998 El Niño is not as large as the value of 1.25 presented in CESS01 although it is significantly larger thanthat found in February 2000. The source of this discrepancy can be traced to the fact that we define the TWP
region as the area bounded by 10°S–10°N, 105°–150°E
while CESS01 focus on the smaller region bounded by
5°S–5°N, 140°–165°E centered farther to the east. Table
5 summarizes cloud impacts on TOA and surface LWand SW fluxes for February 1998 and 2000 over theregion defined by CESS01. Over this region N ⫽ CSW/
CLW varies from 1.05 in normal conditions to 1.25 during the 1998 El Niño in excellent agreement with thedifferences between normal conditions and those during the 1998 El Niño reported by CESS01. From Fig. 1,
there is a greater likelihood of encountering areas of
low clouds in 1998 within the region defined by CESS01
while our box captures a substantial amount of convection south of 5°S, likely accounting for the observed

TABLE 5. Comparison of cloud impacts on SW and LW radiative fluxes at TOA in February 1998 and 2000 for the region
defined by CESS01. Corresponding quantities for the TWP region
defined in the current study are summarized for reference. All
fluxes are in W m⫺2.
Year

Region

CSW,TOA

CLW,TOA

N

1998
2000
1998
2000

Cess et al. TWP
Cess et al. TWP
Current study TWP
Current study TWP

⫺17
⫺14
⫺24
⫺32

14
13
23
34

1.24
1.05
1.05
0.94

differences. This demonstrates an important fact thatwill be discussed further in the sections that follow,
namely that the parameters defining the net influenceof clouds on local energy balance are strong functionsof the region and time period analyzed. From the modeling perspective, this suggests that one should exercise
caution when employing empirically derived relationships from regional field experiments to derive parameterizations of key physical processes in the atmospheresuch as the relationship between cloud LW heating andlatent heating.

4. The role of different cloud systems
It is clear that the differences in the regional energy
budgets associated with different phases of ENSO canbe traced to changes in the distribution of clouds andprecipitation in response to variability in the SST gradient between the west and east Pacific. However themechanism that produces the observed differences goesbeyond a simple increase or decrease in precipitationand cloudiness requiring also a fundamental change inthe character of the precipitation systems as suggested
in Part I. To quantify the effects of such changes, flux
estimates from the HERB algorithm have been used tocharacterize the radiative and latent heating propertiesof each of the storm types defined in Part I.

a. The radiative properties of each cloud type
From the analysis of correlation scale length in Part I
it emerged that, in a mean sense, each of the defined15 APRIL 2006

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L’ECUYER ET AL.

FIG. 4. Column-integrated atmospheric (a) LW radiative cooling, (b) SW radiative heating, (c) net radiative
cooling, and (d) latent heating as a function of radial distance from a pixel of the type designated by the legendat the top.

storm types exhibits a unique spatial extent in terms ofboth rainfall and associated cloudiness. Since systems
with larger horizontal scales can be expected to exhibit
larger radii of influence on local energy balance, it isanticipated that different storm types will have significantly different impacts on their environment. Figure 4
compares column-integrated LW, SW, and net atmospheric radiative heating as well as atmospheric latentheating for each of the four storm categories defined inTable 1. Also shown is the mean value of these parameters for all data points within concentric circles aroundeach type of pixel with radii ranging from 25 to 250 km
calculated in a manner loosely based on the analysis ofcorrelation scale lengths defined in Part I. The expectedvalue of the mean value of a parameter X within adistance di of the reference pixel xref follows from Eq.
(8) in Part I:

具X典 ⫽

1
Ar

冕

Ar

X共d, xref兲 dx ref ⬇

1
Nref

Nref

兺 X共d , x 兲, 共1兲irefwhere Nref is the number of pixels of the given type thatfall within the area of interest, Ar, andX共di, xref兲 ⫽兺X 共d 兲i

ntot共di兲共2兲

is the mean value of X computed from the ntot pixels
within a distance di of each individual reference pixel.
The sum in Eq. (1) runs over all pixels of each cloudtype observed during February 1998 and 2000 and theresults provide insights into the homogeneity of the environments where each type of pixel is typically found.
The properties of all oceanic pixels between 25°S and
25°N (solid black curve) and an equivalent set of pixels
from which all condensate has been artificially removedby ignoring clouds and precipitation in the radiativetransfer calculations (solid gray curve, denoted“cleared sky”) have been included for reference.
Precipitating cloud systems tend to reduce atmospheric radiative cooling by increasing the absorptionof SW radiation and decreasing the emission of LW
radiation. The lone exception is the shallow category,for which a slight increase in atmospheric LW cooling
due to enhanced emission to the surface almost exactly
balances the increase in SW heating resulting in a more-

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FIG. 5. Same as in Fig. 4, but for nonprecipitating cloud systems.

or-less neutral impact on total column radiative cooling. The impact of the clouds associated with each of
the other three precipitation categories on atmospheric
SW heating is substantially less than their LW impacts,
but all precipitating pixels give rise to a small increasein the absorption of solar radiation. The magnitude ofthis effect increases with increasing cloud height anddecreases monotonically with radial distance fromstorm center. The LW heating from the two deep categories also decreases with increasing radius resulting
from the fact that the optical depth of the cirrus anvil
associated with deep convection decays with increasing
distance from the precipitation core coupled with thefact that all other precipitation and cloud types exhibit
significantly more cooling than the deepest events.
Figure 5 presents analogous results for nonprecipitating pixels separated according to whether they contain both high and low clouds, either type of cloud independently, or no clouds at all. The range of atmospheric SW absorption in nonprecipitating pixels is lessthan 0.1 K day⫺2 bounded by clear-sky pixels at 0.65
and those containing both high and low clouds at 0.75 Kday⫺1. Differences in column-integrated radiative cooling between the cloud types is, therefore, dominated bytheir influence on LW emission to the surface and to
space. High-cloud pixels trap the greatest amount of
LW radiation and their influence falls off with increasing radius in a manner similar to the deep convective
and deep stratiform classes for the reasons noted
above. The cloud-free scenes cool at a nearly uniform
rate of ⬃1.8 K day⫺1, exhibiting only a slight warmingwith increasing radius, which indicates that the clear
areas are generally the largest in spatial extent. Low
clouds exhibit the strongest atmospheric LW cooling byvirtue of their warm tops and enhanced emission to thesurface.
When combined with SW heating, the net atmospheric radiative cooling of the nonprecipitating cloudtypes spans a range very similar to that of the precipitating systems varying by a factor of 4 between thehigh-cloud-only and low-cloud-only categories. Thereis, of course, no latent heating in the immediate area of
the nonprecipitating pixels. As the averaging radius isincreased, however, some latent heating becomes evident particularly in the areas surrounding high clouds,
indicating that they are often associated with precipitating cores, as one might expect.
In addition to atmospheric heating, there are a vari-15 APRIL 2006

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L’ECUYER ET AL.

FIG. 6. Mean radiative impacts of each cloud category as a function of radial distance from the central pixel. (a),(b) The ratio of the cloud impact on TOA LW radiation to that on TOA LW radiation for precipitating andnonprecipitating cloud systems, respectively. (c), (d) Cloud impacts on broadband albedo and greenhouse parameter (see text), respectively.

ety of other measures of the impact of clouds on theirenvironment. CESS01, for example, analyze the ratio ofcloud impacts on TOA SW to LW fluxes, N, which has
the benefit that values greater than (less than) unity are
indicative of a net cooling (heating) effect in the atmosphere. This ratio is presented for each storm and cloudtype in the upper panels of Fig. 6, respectively. Clearly
N is a strong function of the type of cloud being observed. Shallower cloud systems such as the low-cloudand shallow-precipitation category exhibit substantiallymore SW cooling than LW heating by virtue of their
low cloud tops. Deep systems, on the other hand, trap
an amount of LW radiation that is approximately equal
to the SW radiation they reflect. As a result, N is remarkably close to unity for the deep convective anddeep stratiform storm categories and actually less than
one for the less reflective nonprecipitating high-cloud
category. It is interesting to note that for all other categories cloud SW radiative effects are of the same magnitude or larger than their LW counterparts. Whencoupled with the fact that the cloud impact on atmospheric SW heating is generally small (Figs. 4 and 5),
this demonstrates that cloud SW cooling is generally
felt at the surface rather than in the atmosphere, while
the converse holds for LW heating.
Two other useful diagnostics of cloud impacts areprovided by their impact on the albedo, CA ⫽ Aallsky ⫺Aclear, and the greenhouse parameter defined as theratio of the flux emitted from the earth’s surface to thatemitted to space,
G⫽

␴T s4
␴T 4a

,

共3兲

where Ts is the surface temperature and Ta is theequivalent blackbody temperature of the atmosphere(Stephens and Greenwald 1991a,b). The radial variability in these parameters for each storm and cloud type
are presented in the lower panels of Fig. 6. Since CA
and CG ⫽ Gallsky ⫺ Gclear represent the deviation from

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clear-sky conditions, these parameters provide a measure of the magnitude of the cloud impact on SW and
LW fluxes while the upper panels compared their relative strengths. The deepest cloud systems are clearly
the brightest and exert the strongest influence on theatmospheric greenhouse effect resulting from the factthat they reside at very cold temperatures above a majority of the atmospheric water vapor. The impacts ofother cloud systems decrease monotonically with cloud
top height consistent with the increasing strength ofwater vapor absorption and emission with increasing
depth in the atmosphere. As a result, low clouds andshallow precipitation exert a factor of 2 smaller impact
on albedo than the deep systems and have a minimal
effect on the LW greenhouse effect.
This result has important consequences for the magnitude of net heating/cooling averaged over a particularregion and time period. The mean ratio of cloud impacts, N, will, of course, derive from the collective effects of all clouds found within that region. It is important to realize, however, that the impact of any given
cloud type on N is weighted by the magnitude of its
impact on SW and LW fluxes at TOA. As a result, deep
convective and high-cloud systems will always dominate N in a region where they occur with any regularity
by virtue of the fact that they are typically the brightest
and coldest clouds. Thus the near cancellation of LW
and SW cloud radiative effects in the Tropics observedby Kiehl (1994) is a direct result of the high frequencyof occurrence of high-/deep-cloud systems that generally reside in the region, but it does not necessarily
preclude the existence of low and midlevel clouds thatcan play an important role in determining the distribution of lower-tropospheric moisture within the region.

VOLUME 19

FIG. 7. Mean LW radiative cooling, SW radiative heating, andlatent heating properties of each of the storm and cloud types
analyzed. Note that the latent heating due to deep convective
pixels has been reduced by 15 K day⫺1 to facilitate plotting.

(RFO) can significantly impact the energetic propertiesof a region. This is demonstrated in Fig. 8 where themean heating properties of each storm category isweighted by their frequency of occurrence in the TWP
in February 1998. Figure 9 provides similar information
for February 2000. By breaking the energy budget
down in this way, the connection between the reduced
atmospheric heating observed during the 1998 El Niño
and the reduction in deep convective and stratiform
pixels becomes readily apparent. As noted in Part I,
there is a distinct reduction in the amount of deep con-b. Implications for regional energy budgets
The mean energetic properties of each individual
precipitating and nonprecipitating cloud category considered in this study are summarized in Fig. 7 with associated error bars that once again represent an upperbound on the uncertainties in heating and cooling estimates. By far the largest source of variability in theheating effects of each category arises from the latent
heat release. On average pixels falling into the deep
convective category release 3 times as much latent heatin the atmosphere than any other type. Furthermore, asa result of their latent heat release, all precipitating
pixels heat the atmosphere while all nonprecipitating
pixels cool it.
Given the substantial differences in the radiative andlatent heating properties of the different precipitating
and nonprecipitating cloud systems, it is easy to see howvariability in their relative frequency of occurrence

FIG. 8. Net radiative heating/cooling and latent heating in thewest Pacific during February 1998 broken down by storm andcloud type. The numbers in parentheses represent the total fraction of pixels that fall into each category and the heating bars on
the far right represent the cumulative heating due to all types.

15 APRIL 2006

L’ECUYER ET AL.

1401

FIG. 9. Same as in Fig. 8, but for the month of February 2000.

FIG. 11. Same as in Fig. 10, but for the month of February 2000.

vective and stratiform rainfall and high clouds in February 1998 dramatically reducing the latent heat releasein the atmosphere. Simultaneously, the number ofcloud-free pixels increases from 51%–67%, substantially increasing the LW radiative cooling in the region.
The net result is that the west Pacific goes from being
characterized by the generation of intense atmosphericheating implying substantial energy transport out of theregion in normal years to exhibiting conditions that approach local RCE during the 1998 El Niño event.
The breakdown of the energy budget in the TEP in
February 1998 and 2000 is presented in Figs. 10 and 11,
showing precisely the opposite trend. During normal
years, rainfall in the region is dominated by shallow
precipitation systems while a majority of nonprecipitating systems contains low clouds or is cloud free. This
results in LW radiative cooling that exceeds SW andlatent heating by more than 50%. The eastward propagation of warm SSTs associated with the 1998 El Niño,
however, creates conditions more favorable to the generation of deep convection, resulting in surplus heatingin the region. Thus the variations in the distribution ofclouds and precipitation that are driven by changes in
the SST gradient across the Pacific, shift the ratio ofradiative cooling to latent heating in the atmospheretoward local equilibrium in both the east and west Pacific, eliminating the required energy transport to reestablish balance. In normal years, heating preferentiallyoccurs in the west Pacific while cooling occurs in theeast, resulting in strong eastward energy transport by
the Walker circulation.
It is worth noting that the region of the TWP analyzed in CESS01 (not shown) exhibits the same generalfeatures as those in Figs. 8 and 9. This suggests that theobserved change in the net influence of clouds on TOA
radiative fluxes that occurred during the 1998 El Niño
are tied to a fundamental shift in the character of precipitation systems from the deep convective events typical of normal years to less intense midlevel and shallow
systems that reflect more solar radiation than trap LW
radiation. Interestingly, in both February 1998 and
2000, 13% of the pixels in the TWP fall into the low andmidlevel precipitation and cloud categories. The fraction of deep systems in these regions, however, decreases from 36% in the normal year to 14% during the1998 El Niño. This indicates that the deviation of thecloud effect ratio from 1.0 noted in CESS01 is a directconsequence of the reduction of high-/deep-cloud systems that ordinarily dominate the net radiative effects
of clouds in the region. The results further indicate that
this shift in cloud type does more than just modify TOA
radiative fluxes—it completely changes the character of

FIG. 10. Same as in Fig. 8, but for the east Pacific.

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JOURNAL OF CLIMATEVOLUME 19

TABLE 6. Linear cloud surface cooling and atmospheric heating efficiencies (Bretherton and Sobel 2002) and their relationship to
local environmental parameters in the three study regions during February 1998 and 2000. HCF and PF are expressed in percent andare mutually exclusive (i.e., both cloud categories include only nonprecipitating pixels).
Year

RegionSST (K)W (kg m⫺2)

Va␷ (m s⫺1)

HCF

PF

rC

rH

1998
2000
1998
2000
1998
2000

TWP
TWP
TCP
TCP
TEP
TEP

301.8
302.4
301.7
299.6
301.9
298.9

47.8
53.4
50.4
36.8
50.4
41.6

5.91
3.75
7.76
6.21
5.78
3.91

16.62
34.90
32.36
4.90
24.21
4.49

11.92
19.77
17.72
8.93
19.75
12.27

0.206
0.176
0.164
0.259
0.145
0.305

0.160
0.168
0.152
0.035
0.125
0.047

the TWP from a source of atmospheric heating to aregion in which an approximate balance between theradiative cooling and latent heat released in precipitation result in a local thermal equilibrium that temporarily isolates it (energetically) from the rest of theTropics.

5. Discussion
The results from Part I and the previous sections
offer quantitative evidence that the distribution andcharacter of precipitating cloud systems found in thetropical Pacific exhibit significant responses to the SST
anomalies associated with El Niño. The resulting
changes in clouds and precipitation in turn influence
the thermal and hydrologic properties of each region as
evidenced by the changes in latent and cloud radiative
heating of the atmosphere, solar heating at the surface,
and transport of water vapor to upper levels by the
implied convective air motions associated with the observed deep systems. Together these observations imply the existence of strong feedbacks owing to the interdependencies of SST, water vapor, clouds, precipitation, and radiation that must be modeled in order toaccurately represent the coupled response of thesequantities in a climate change scenario. In this section,
we explore some additional diagnostics that may provide insights into the strengths of these feedbacks andexamine their relationship to their environment.
Table 6 summarizes a number of properties of thethree analysis regions in February 1998 and 2000. The
signature of El Niño is clearly evident in the dramatic
warming of SSTs in the TEP and TCP during Februaryof 1998. Also evident are increases in both Wand meansurface wind speed during this period indicative of theincreased evaporation from the surface due to thewarmer SSTs and the stronger winds found in the inflow and outflow regions associated with deep convection. There is also evidence of increased amounts ofhigh cloud associated with precipitation over warmer
SSTs. This is most notable for the TCP and TEP where

the ratio of high cloud area to precipitation area is considerably less than one during the normal year but exceeds unity above the warmer SSTs associated with the1998 El Niño. While less dramatic, a similar trend canbe seen in the TWP where the ratio of high cloud fraction (HCF) to precipitating cloud fraction (PF) decreases during the El Niño year. These results demonstrate that changes in the Walker circulation in response to El Niño lead to more extensive areas of highclouds for a given precipitation area in regions ofwarmer SST. This is not to say that the precipitation
efficiency decreases with increasing SST, however,since the comparison relates cloud area to precipitation
area rather than rainfall intensity. In fact, the results ofRapp et al. (2005) suggest that the ratio of high-cloudarea to surface rain amount is invariant to changes in
SST for all but the warmest clouds, which show a decrease with increasing SST.
In light of feedback studies like that of Lindzen et al.
(2001), there is considerable value in examining quantities related to precipitation efficiency. Rather than
repeating the analysis of Rapp et al. (2005), however,
we examine two parameters that relate the radiative
impact clouds to surface rainfall in the last two columnsof Table 6. These quantities are analogous to the cloud
feedback parameter adopted by Bretherton and Sobel(2002) to represent cloud radiative feedbacks in theirmodel of the Walker circulation. Motivated by the needto provide their model with a simple, yet plausible, representation of the impact of precipitation on radiativeflux divergence in the atmosphere, they assume thatclouds reduce the clear-sky radiative cooling by anamount proportional to precipitation, P, via
R ⫽ R0 ⫺ rP,

共4兲

where R0 represents cooling in the absence of clouds.
Bretherton and Sobel (2002) assume a value of r ⫽ 0.2
and note that its uncertainties may be as large as 50%
because of constraints imposed the datasets used to define it. Sobel and Gildor (2003) adopt a similar parameterization in their coupled ocean–atmosphere model15 APRIL 2006

L’ECUYER ET AL.

used to explore the role of clouds in modulating SST
hot spots. They further assume that cloud-induced SW
cooling of the ocean surface is governed by an identical
relationship, S ⫽ Ŝ ⫺ rP, noting the approximate cancellation in SW and LW cloud radiative effects at TOA
in regions dominated by high clouds as justification.
While these simple relationships were born frompractical modeling considerations, the linear radiative
feedback parameter, r, provides a useful measure of theextent to which precipitating systems radiatively heat
the atmosphere and cool the surface that also varies in
response to El Niño. However, since we are not motivated by the need for simplicity, we define distinct values of r corresponding to both the cloud SW cooling
effect at the surface,rC ⫽ ⫺1.0 ⫻↓↓兲allsky ⫺ 共FSW,SFC
兲clrsky
共FSW,SFC,
LH

共5兲

and the radiative heating in the atmosphere,rH ⫽共⌬FLW ⫹ ⌬FSW兲allsky ⫺ 共⌬FLW ⫹ ⌬FSW兲clrsky
,
LH

共6兲
↑
↓
↑
where ⌬FLW ⫽ F LW,SFC⫺F LW,SFC⫺F LW,TOAand ⌬FSW↓↑↓↑⫽ F SW,TOA⫹F SW,SFC⫺F SW,SFC⫺F SW,TOA are the LW
and SW atmospheric flux divergence, respectively,
while LH denotes the latent heating equivalent of surface precipitation. Here, rH and rC can be viewed asmeasuring the efficiency with which a storm system radiatively heats the atmosphere and cools the surface
per unit rainfall rate or, equivalently, latent heat releasein the atmosphere. Since the net radiative impact of acloud system depends, in part, on its spatial extent andtotal mass, these quantities are closely related to themore traditional precipitation efficiency defined as theratio of precipitation to total condensate. They may,
however, provide a closer link to the true physical processes by which clouds feed back on their environment
since they represent a direct measure of a storm’s rolein establishing the local gradients in atmospheric heating that ultimately drive changes in stability.
Unlike the model of Sobel and Gildor (2003), whererC and rH are assumed equal and held fixed independent of the changing SST in their column model, Table
6 shows that the heating and cooling efficiencies of precipitating systems vary significantly between El Niño
and normal conditions. Furthermore, the efficiency of astorm system to cool the surface and its ability to warm
the atmosphere seem to respond to ENSO in opposite
ways. In the TEP, for example, rH increases with theonset of El Niño resulting from a shift toward deeper,colder cloud systems with larger high-cloud anvils,1403

while rC decreases possibly because of an increase in
the precipitation efficiency of shallow, warm rain systems consistent with the findings of Lau and Wu (2003)
and Rapp et al. (2005). As a result, storms in the TCP
and TEP cool the surface much more efficiently thanthey heat the atmosphere under normal conditions,while rC and rH are nearly equal over the much warmer
SSTs during the 1998 El Niño event. In the TWP, both
efficiencies are equal with a value of ⬃0.17 under normal conditions but the slight cooling in the TWP leads
to a shift toward greater surface cooling than atmosphere heating during the El Niño.
It is interesting to note that these trends in rH and rC
manifest themselves on time and space scales otherthan that of the El Niño. Figures 12a and 12c present
scatterplots of 20-day mean values of rH and rC, respectively, as functions of SST for 10° pixels in the TEP.
While there is significant variability from pixel to pixel,rC exhibits a distinct decrease with increasing SST in
the statistical sense. Conversely, rH generally increases
with SST, particularly over cooler waters approaching a
constant value for SST ⬎ 301.5. These results suggestthat in the TEP both the ability of a precipitating cloudsystem to heat the atmosphere and cool the surface
vary with SST in a systematic way. It is important tonote, however, that the satellite observations themselves cannot establish the mechanism governing theobserved dependences of rC and rH on SST. The analyses of Fu et al. (1990), Lau et al. (1997), and Bony et al.
(1997), for example, suggest that the observed changes
in radiative efficiency are likely manifestations of variations in large-scale ascent that may occur in response tochanges in local SST gradients. In fact, Figs. 12b and12d demonstrate that trends in the TWP are substantially smaller than those in the TEP, suggesting thatfactors other than SST govern variations in rC and especially rH at SSTs warmer than 302 K; this is consistent
with the findings of Bony et al. (1997), who demonstrate that large-scale vertical motion is largely decoupled from SST above 302.5 K in reanalysis datasets.
The explicit representation of precipitation intensity inthe radiative efficiencies now allow us to conclude thatthe changes in OLR and shortwave cloud forcing with
SST reported by Bony et al. (1997) and Lau et al.
(1997), respectively, are indeed traced to systematicchanges in precipitation efficiency. Thus with the exception of regions with the warmest waters, SST provides a useful proxy for the physical processes that govern the ability of a storm system to radiatively heat theatmosphere and cool the surface. The impact of thesedependencies on the local radiation balance of the TEP
and TWP during an El Niño event is clearly evident
from the preceding analysis, but further investigation is

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FIG. 12. SST vs rC: (a) in the TEP and (b) in the TWP. (c), (d) Equivalent results for SST vs rH for each region.
Each data point represents a 20-day average at a resolution of 10° ⫻ 10°. The solid lines and error bars represent
the mean and standard deviation of each parameter in the indicated SST bin.

required to assess their implications for cloud–radiation
feedbacks on global scales.
The sensitivity of cloud radiative efficiencies to theproperties they act to modify introduces a new level ofcomplexity in modeling cloud feedbacks. Numerous
competing processes are at work that collectively determine the regional responses to the forcing that has
been introduced. Some of these processes reinforce one
another, like the excess latent heat release in the eastand central Pacific, the weakening of the Walker circulation, and even the role of wind-induced enhanced
evaporation from the ocean’s surface that may play arole in maintaining available moisture to sustain convection in these regions. On the other hand, cloud radiative heating at upper levels coupled with SW coolingat the surface has a stabilizing effect on the environment, while decreased cloudiness in the TWP allows
more solar radiation to reach the surface acting to reestablish the temperature gradients that are present under normal conditions. The challenge of correctly representing the relative magnitudes of all of these effects
puts an enormous burden on model physics. Throughsatellite-based studies of the combined response of atmospheric energetics and hydrology, however, constraints can be levied against model parameterizations
that should ultimately drive them toward the best possible representation of the climate system.
It is important to note that feedback processes themselves have not been studied in this paper. To do so
requires an in-depth analysis of the evolution of precipitation systems in the region and detailed study ofthe particular interactions between their constituent
elements. Instead, the results presented here are intended to demonstrate the complexity of the relationships that underlie the response of regional energy budgets to large-scale forcings like SST variability associated with ENSO. In addition, they highlight thebenefits of combining multisensor satellite observations
to gain a deeper understanding of critical feedback processes in the climate system. As our ability to represent
the physical processes that govern the generation ofclouds and precipitation advances, numerical models
should become capable of capturing regional and temporal variability in the magnitudes of cloud radiative15 APRIL 2006

L’ECUYER ET AL.

effects at TOA and the surface, their ratio, and theatmospheric heating and surface cooling efficiencies described above. This study and its companion paper
demonstrate that progress toward this goal requiresthat models be able to explicitly represent the distributions of distinct precipitating and nonprecipitating
cloud types and account for their evolution in response
to climate forcings like ENSO. Further study ofdatasets like these will ultimately allow us to isolate
dominant feedback mechanisms and establish their impact on the environment so that parameterizations may
be developed to test and eventually constrain parameterizations of cloud radiative effects in climate models.
Acknowledgments. This research was supported by
NOAA’s office of Global Program Grant
NA17RJ1228#15, NASA’s Precipitation Program
Grants NAG5-13694 and NAG6-13637, and NASA Research Grant NNG04GB97G. The authors thank W.
Berg and A. Rapp for helpful comments and discussions. The TRMM datasets (1B01, 1B11, 2A12, and
2A23) were provided by the National Aeronautics andSpace Administration (NASA) Goddard Space Flight
Center (GSFC). The SST, CWV, and surface windspeed was obtained from the Remote Sensing Systems(RSS) at http://www.ssmi.com/, sponsored by NASA’s
Earth Science Information Partnerships (ESIP): a federation of information sites for Earth Science; and by
NASA’s TRMM Science Team.
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