RESEARCH LETTER
10.1029/2023GL107113
Key Points:• LNB is lower during El Niño due to agreater UT stability, but convective
outflow levels remain consistent across
ENSO cycles• The intensity of convective cores
during El Niño has to be stronger thanthat during La Niña
• Our finding necessitates a modification
to FAT and the proportionately higher
anvil temperature (PHAT)

Investigating Convective Processes Underlying ENSO: New
Insights Into the Fixed Anvil Temperature Hypothesis
Hanii Takahashi1
Akira T. Noda5, Zhengzhao Johnny Luo2

, Hirohiko Masunaga3, Rachel Storer1,4

, and

1

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA, 2Department of Earth andAtmospheric Sciences, the City College of New York of City University of New York, New York, NY, USA, 3Institute forSpace‐Earth Environmental Research, Nagoya University, Nagoya, Japan, 4Joint Institute for Regional Earth System
Science and Engineering, University of California, Los Angeles, CA, USA, 5Japan Agency for Marine‐Earth Science andTechnology, Yokohama, Japan

Abstract Interannual variations provide insight into the sensitivity of convective processes. Thus, CloudSat
Correspondence to:
H. Takahashi,
Hanii.Takahashi@jpl.nasa.gov

Citation:
Takahashi, H., Luo, Z. J., Masunaga, H.,
Storer, R., & Noda, A. T. (2024).
Investigating convective processes
underlying ENSO: New insights into the
fixed anvil temperature hypothesis.
Geophysical Research Letters, 51,
e2023GL107113. https://doi.org/10.1029/
2023GL107113
Received 1 NOV 2023
Accepted 4 MAR 2024
Author Contributions:Conceptualization: Hanii Takahashi,
Zhengzhao Johnny Luo,
Hirohiko Masunaga, Akira T. Noda
Formal analysis: Zhengzhao Johnny Luo
Investigation: Hanii Takahashi,
Zhengzhao Johnny Luo,
Hirohiko Masunaga, Rachel Storer
Methodology: Hanii Takahashi,
Zhengzhao Johnny Luo,
Hirohiko Masunaga
Supervision: Hanii Takahashi, Zhengzhao
Johnny Luo
Validation: Hanii Takahashi, Zhengzhao
Johnny Luo, Hirohiko Masunaga,
Rachel Storer
Visualization: Hanii Takahashi© 2024 Jet Propulsion Laboratory,
California Institute of Technology and The
Authors. Government sponsorship
acknowledged.
This is an open access article under theterms of the Creative Commons
Attribution‐NonCommercial‐NoDerivs
License, which permits use anddistribution in any medium, provided theoriginal work is properly cited, the use isnon‐commercial and no modifications or
adaptations are made.

TAKAHASHI ET AL.

and ERA5 are used to explore the relationship among convective cores, outflows and environmental conditions
during El Niño‐Southern Oscillation (ENSO) cycles. Results reveal greater upper‐tropospheric stability during
El Niño, resulting in a lower level of neutral buoyancy compared to La Niña. However, outflow levels remain
relatively consistent across ENSO cycles. This suggests that, despite less favorable conditions for deep
convection during El Niño, stronger convective intensity is required to achieve outflow levels comparable tothose in La Niña. Indeed, our results suggest that convection observed during El Niño tends to have broader
cores and lower entrainment rates, translating to greater intensity compared to La Niña. These findings
emphasize the importance of considering both large‐scale and convective‐scale processes, providing an updateto the fixed anvil temperature (FAT) and the proportionately higher anvil temperature (PHAT) hypotheses as
originally proposed.

Plain Language Summary Examining year‐to‐year variations provides unique insights into
understanding how storms may change in a warmer climate. We use CloudSat and ERA5 reanalysis to examine
variations in convective outflow (i.e., an airflow pushed out of storms), environmental factors, and cloudproperties during ENSO cycles. Comparing El Niño to La Niña, we find that the atmosphere higher up in the
troposphere tends to be more stable, which usually slows down the development of storms, during El Niño.
However, the heights where convective outflow occurs do not change much during El Niño and La Niña events.
This happens because during El Niño, the upward movement of air in storms is more powerful. This stronger
upward movement offsets the stabilizing effect of the upper troposphere, so the overall outflow from the storms
stays about the same. Our study aligns with the main idea of the FAT hypothesis, which postulates that in a
warming climate, the temperature of the anvils (i.e., wide, flat clouds crawling out of the storm top) stays
relatively constant primarily due to a thermodynamic constraint. However, our results show that convective‐
scale processes with dynamic control play a key role as well, providing an update to the FAT as originallyproposed.

1. Introduction
One of the primary effects of tropical deep convection on climate arises through cirrus anvils developed from deep
convective outflows. These cirrus anvils cause a considerable radiative warming in the upper troposphere (UT)and play an important role in radiative‐convective feedbacks (e.g., Stephens, Van Den Heever, & Pakula, 2008).
Therefore, knowing where and how convective outflows occur in relation to the environment is vital for understanding the present and future climate.
The source of convective outflows is vertically transported mass by convective updrafts that is eventuallyexported horizontally. For vertical transport, the primary center of action is the convective core, and one of the key
processes that affects the convective core is entrainment. Entrainment dilutes the strength of convective updraft
cores and reduces convective buoyancy, which in turn weakens the intensity of vertical transport and lowers theconvective outflow levels (Houze, 2014). It has been believed that the effect of entrainment dilution is smaller forlarger convective cores (i.e., the larger the core, the smaller the entrainment rate) since they are better protectedfrom the environment and thus are less diluted by entrainment (e.g., Simpson & Wiggert, 1969). Thus,
1 of 10

Writing – original draft:
Hanii Takahashi, Zhengzhao Johnny Luo,
Hirohiko Masunaga, Akira T. Noda
Writing – review & editing:Hanii Takahashi, Zhengzhao Johnny Luo,
Hirohiko Masunaga, Rachel Storer, Akira
T. Noda10.1029/2023GL107113

entrainment rate, convective core characteristics, and the relationship between them is among the crucial subjects
to explore.
CloudSat Cloud Profiling Radar (CPR) can reveal the internal vertical structure of deep convection including both
embedded convective cores and attached anvil outflows. The anvil outflows are tightly linked conceptually to thelevel of neutral buoyancy (LNB), where convection stops ascending and starts to detrain convective mass horizontally. Based on CloudSat CPR, a series of our previous works (Takahashi et al., 2017, 2021, 2023; Takahashi& Luo, 2012, 2014) have developed methods to estimate the intensity of convective cores, the degree ofentrainment dilution, and convective outflow levels, including the maximum convective detrainment levels,
through the concept of LNB. These studies also examined the entrainment‐core relation on the basis of derived
convective properties.
Our recent studies (Takahashi et al., 2017, 2021) have suggested that compared to oceanic deep convection,
continental deep convection tends to have less diluted and wider convective cores that are associated with stronger
convective updrafts and higher convective outflows. Interestingly, our result indicates a negative correlation
between convective entrainment rate (λ) and size of the convective core (R). This inverse λ‐R relation has been
adopted as an important assumption in cumulus cloud modeling since the 1960s (e.g., Simpson & Wiggert, 1969):
9 K
Simpson et al. (1965) formulated it as λ = M1 dM
dz = 32 R , where M is the mass in the rising convective tower and K
is the entrainment coefficient. Based on the laboratory results (Turner, 1962, 1963), K was estimated to be 0.71,
which led to the well‐known equation of λ = Rα with α = 0.2. However, the inverse λ‐R relation had never beensystematically explored based on satellite observations. Takahashi et al. (2021) is the first, to our knowledge, tosupport the inverse correlation based on CloudSat observations, to show that the inverse λ‐R relation cannot
simply scale with a fixed value of α, and to confirm that convective cores are wider and entrainment rates arelarger over land than ocean, mainly due to deeper planetary boundary layers over land than ocean (Takahashi
et al., 2023).
In this study, we expand our analysis to investigate the inverse λ‐R relation focusing on the El Niño/La Niña
contrast. It is known that caution needs to be taken when we interpret an El Niño/La Niña comparison in reference
to long‐term climate change. Nevertheless, studying interannual variations will provide unique insights into how
convection may change in a warmer climate. Deepening our knowledge of where convective outflows occur andhow they vary under natural climate variability has important implications for better understanding current andfuture climate.
Hartmann and Larson (2002) addressed these aspects, proposing a hypothesis that the temperature at theconvective outflow level is not sensitive to the surface temperature and will stay mostly constant during climatechanges. This is known as the fixed anvil temperature (FAT) hypothesis. The FAT hypothesis can be explained bya simple argument based on a consideration of the large‐scale energy and mass balance. This is because theconvective outflow is related to clear‐sky subsidence (conservation of mass) that is controlled by clear‐sky
radiative cooling rate (energy balance), and also because the clear‐sky radiative cooling rate depends primarily
on water vapor and hence is a strong function of temperature (the Clausius–Clapeyron relation). Therefore, one
should expect the convective outflow level to be concentrated at the temperature where water vapor concentrations and the associated radiative cooling decrease rapidly with height. It follows that temperature of the
maximum convective outflow should remain largely unchanged as climate warms. The FAT hypothesis was laterslightly modified by Zelinka and Hartmann (2010) as the Proportionately Higher Anvil Temperature (PHAT),
which took into account a slight change of static stability, but the general idea remains similar.
The FAT and PHAT hypotheses have been supported by cloud‐resolving model simulations (e.g., Kuang &Hartmann, 2007) and evaluated by observations in the context of El Niño‐Southern Oscillation (ENSO) timescales (Li et al., 2012; Xu et al., 2007; Zelinka & Hartmann, 2011). Xu et al. (2007) showed that the FAT hypothesis is generally valid for cloud top temperature (CTT) with horizontally large clouds (e.g., diameters greaterthan 300 km) using the Tropical Rainfall Measuring Mission (TRMM)/Clouds and the Earth's Radiation Energy
System (CERES) Single Scanner Footprint data (Wielicki et al., 1996). Using A‐Train observations (Stephenset al., 2002; L’Ecuyer & Jiang, 2010), Zelinka and Hartmann (2011) examined variability in tropical high clouds
and demonstrated that the cloud top heights change approximately isothermally in accordance with the FAT/PHAT hypothesis and the changes can be well explained by upper‐tropospheric convergence computed from themass and energy budget of the clear sky. Similar findings were obtained in Li et al. (2012), although they pointed

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out there are regional variations. One caveat in the above‐mentioned observational studies is that they all focusedon CTT, whereas the FAT hypothesis applies to the maximum mass outflow of tropical deep convection. Analysis
of ground‐based and airborne radar observations showed that the maximum mass outflow of deep convection isusually a few kilometers lower than the top of cirrus anvils (Mullendore et al., 2009; Takahashi et al., 2017;
Takahashi & Luo, 2012). In this study, we also use observations from CloudSat CPR to examine the variation ofdeep convective outflow to further test the FAT hypothesis.
This is a follow on to a series of recent publications that exploits the CloudSat vertical profiling view of deep
convection to elucidate the relationships between convective outflows and a number of factors that influence
convective outflow levels, including characteristics of convection itself (e.g., entrainment‐core relation) andthermodynamic structure of the atmosphere (e.g., LNB), as well as large‐scale energy and mass balance (e.g.,large‐scale convergence/divergence, and the FAT or PHAT hypothesis). In this study, we cast these relationships into the context of natural climate variability by contrasting changes associated with different
phases of the ENSO cycle. First, we investigate the variation of entrainment rate (λ) and the size convective
cores (R) during El Niño and La Niña, to reveal if the inverse λ‐R relation changes with the ENSO phases.
This investigation shows what controls the amount of entrainment dilution and helps to deepen our understanding of convective outflow mechanisms. Second, we explore how the altitude and temperature atconvective outflows change between El Niño and La Niña. We use ERA5 reanalysis to examine the ENSO
variation of altitudes and temperatures where peak divergence occurs in order to discuss the results in light of
the FAT/PHAT hypothesis. The rest of the paper is organized as follows. Section 2 describes the data andanalysis methods used in this study. Results are presented in Section 3 and Section 4 summarizes and interprets the findings.

2. Data and Methodology
2.1. CloudSat Observations
CloudSat (http://cloudsat.cira.colostate.edu) is a polar‐orbiting satellite having the equator crossing time of∼1:30 a.m./p.m in local solar time. CloudSat carries a 94‐GHz (Cloud Profiling Radar) CPR whose footprint isaround 1.7 km along track and 1.3 km across track with the vertical resolution of 480 m (oversampled to 240 m).
The CloudSat CPR is sensitive to both cloud‐size and precipitation‐size particles. Two CloudSat products, 2B‐
GEOPROF and ECMWF‐AUX, are used in this study. The former provides radar reflectivity and cloud mask,
and the latter provides temperature and moisture profiles from the European Center for Medium‐Range Weather
Forecast (ECMWF) operational analysis interpolated in space and time to the CloudSat track. Stephens, Vane,et al. (2008) summarizes an overview of the CloudSat mission.
2.2. AIRS Observations
In this study, we use the Atmospheric Infrared Sounder (AIRS) data to evaluate moist static energy (MSE)
profiles in Sections 3.2 and 3.3. AIRS onboard the Aqua satellite employs a cross‐track scanning sounder thatutilizes infrared radiances to retrieve temperature and moisture profiles. At nadir, AIRS achieves a horizontal
resolution of roughly 13.5 km. The accuracy of AIRS temperature measurements is approximately 1 K over non‐polar ocean surfaces from the surface up to 300 hPa, as well as over land from 2 km to 300 hPa. Furthermore, theaccuracy ranges from 1 to 2 K over non‐polar land surfaces from the surface to 2 km and in polar regions (Tianet al., 2013). Across the tropics, AIRS temperature sampling biases span from 1.5 to 0.75 K at 850 hPa and±0.25 K at 500 and 300 hPa (as depicted in Figure 2 and Figure S1 in Hearty et al., 2014). A comprehensive
analysis of the AIRS data is available in Chahine et al. (2006).
2.3. ERA5 Reanalysis
The ECMWF Reanalysis version 5 (ERA5) provides hourly, quarter‐degree gridded data of atmospheric variables(Hersbach et al., 2020). The ERA5 horizontal winds matched in space and time to the CloudSat tracks are
employed to calculate the vertical profile of horizontal divergence from 1,000 hPa to 50 hPa. Similar to Ito and
Masunaga (2022), the divergence profiles averaged over a rain‐free area within ±5° around the center ofconvective cores are estimated for each selected anvil cloud object described next.

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2.4. Selection of Anvil Cloud Objects, Convective Core, and Convective Outflows
Since El Niño warming is generally maximized in boreal winter, we focus our analysis on December, January andFebruary (DJF). Based on the Niño 3.4 index (5°N–5°S, 120°W–170°W), we select El Niño (2006–2007DJF,
2009–2010DJF) and La Niña (2007–2008DJF, 2010–2011DJF) months over the whole tropics (30°N–30°S). A
total of 595 and 486 single‐core anvil cloud objects are selected during El Niño and La Niña, respectively, over thewhole tropics based on 5 years (2006–2011) of CloudSat data similarly to Takahashi et al. (2017, 2021, 2023). As
in these previous studies, we focus on the single‐core cases in our analysis to facilitate a straightforward interpretation of the relationship between convective core properties and the ambient clear‐sky environment.
The selection method of convective cores and convective outflows is as follows. First, we define deep convective
cloud objects based on 2B‐GEOPROF data when cloud mask ≥20 (, which corresponds to reflectivity ‐ 30 dBZ)
and there is a continuous CPR echo from cloud top (≥10 km) to within 2 km above the surface. When an area of
echo top height (ETH) of 10 dBZ ≥10 km is observed in a deep convective cloud, which is our definition of deep
convective cores (DCCs), then we search on both sides of the cloud object for attached anvils. Note that ETH of
10 dBZ is the highest altitude that a 10 dBZ radar echo reaches, and thus the radius (R) of DCCs is defined as
size of DCC. Anvils should have a cloud mask ≥20 and cloud base ≥5 km, and the horizontal extension of the anvil
2should be ≥20 km to ensure that the anvil is well developed. Deep convective cloud objects with convective coresand these attached anvils are called anvil cloud objects.
These well‐developed anvil outflows are tightly linked to the concept of level of neutral buoyancy calculated using
the ambient sounding (LNBsounding), where convection stops ascending and starts to detrain mass horizontally. For
each selected anvil cloud object, we estimate two forms of convective outflow levels based on satellite observations,or LNBobservation: LNBCTH and LNBmaxMass. These terms follow the terminology of Takahashi and Luo (2012) as well
as Takahashi et al. (2017, 2021, 2023). A schematic showing LNBsounding, LNBCTH and LNBmaxMass can be found in
Takahashi et al. (2017, Figure 1). LNBCTH is the cloud top height (CTH) of the anvils and represents the highest
detrainment level. LNBmaxMass is the height where the maximum radar reflectivity within the anvil column isobserved, and is most relevant to convective mass transport since it has been well correlated with the maximum
mass detrainment level (Mullendore et al., 2009). Note that LNBCTH and LNBmaxMass are calculated profile byprofile first and then averaged only over the “fresh” anvils. Here “fresh” anvils refer to the anvil clouds within thefirst 20 km in distance from the convective core. This approach is taken to reduce the impact of bias caused byparticle sedimentation, which tends to prolong the anvil lifetime beyond the control of parent convective core.
2.5. Measurement of Entrainment Dilution
LNBsounding is estimated based on a parcel theory valid for the ideal case of an undiluted convective core in theabsence of entrainment. However, in reality, convection interacts with the environment and loses buoyancy due toentrainment dilution, which is the case of LNBobservation. Therefore, as Takahashi et al. (2017) suggested, thedifference between LNBsounding and LNBobservation can be interpreted as a measure of the magnitude of theentrainment effects: the greater the entrainment dilution, the larger the height difference. We estimate LNBsounding
based on the moist static energy (MSE = CpT + gz + Lvq, where Cp, g, z, Lv, and q are the specific heat ofcondensation, the gravitational constant, height, the latent heat of vapourization, and specific humidity,
respectively) using temperature and moisture profiles from ECMWF operational analysis interpolated in space
and time to the CloudSat track. LNBsounding is the level where MSE is equal to the maximum MSE between thesurface and 925 hPa (Liu & Zipser, 2005).
The difference between LNBsounding and LNBobservation can be cast into a simple framework of a one‐dimensional
entraining plume model:∂MSEp= λ(MSEe∂z

MSEp ),(1)

where subscripts p and e refer to properties of the in‐cloud air parcel and of the environment, respectively.
Following a similar approach to Luo et al. (2010), we calculate the moist static energy (MSE) of an air parcel forvarious values of λ. To do this, we integrate Equation 1 over height using collocated ECMWF profiles of temperature and humidity from the planetary boundary layer (PBL). The ECMWF sounding represents the ambient

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Figure 1. The difference between convective cloud objects over El Niño and La Niña, as a function of convective core radius size (x axis) and entrainment rate relative to(a) LNBCTH or (b) LNBmaxMass (y axis). The black contour lines show values of (a) αCTH or (b) αmaxMass.

environment surrounding the air parcel. We start with λ set at 1% per kilometer and increase it by an increment of
1% per kilometer at each step. The iterative process continues until we find a value of λ that satisfies the conditionof neutral buoyancy for the air parcel at the specified outflow level (the calculated MSEp is almost equal to thesaturated MSE at LNB). In this way, we can estimate bulk entrainment rates, λCTH and λmaxMass (unit: %/km)
relative to LNBCTH and LNBmaxMass, respectively. Detailed information of selection of convective cloud objects,
estimation of LNBCTH and LNBmaxMass as well as λCTH and λmaxMass can be found Takahashi and Luo (2012) and
Takahashi et al. (2017, 2021, 2023).

3. Result3.1. The Entrainment‐Core Relation
The differences between El Niño and La Niña in the joint probability distribution of λ and R are shown in
Figure 1, with black contour lines of α (i.e., λ = Rα ) with respect to LNBCTH and LNBmaxMass following Takahashi

Figure 2. Box‐scatter diagrams for (a) altitudes (top panels) and (b) temperature (bottom panels) of LNBsounding and LNBobservation (LNBCTH and LNBmaxMass) during El
Niño (red) and La Niña (blue) whose bottom and top of the boxes show, respectively, the 25 and 75 percentiles, the stars show the mean, and the central lines show themedian, together with (c) the divergence profiles during El Niño (red) and La Niña (blue).

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et al. (2021). Results clearly show that convection in El Niño and La Niña events are clustered into different
domains in the λ‐R space, and together they accentuate the inverse λ‐R relationship. Convective clouds during El
Niño tend to have smaller λ and larger R compared to La Niña, which seems to suggest that convective cores are
more intense and better protected during El Niño than La Niña. This implies that vertical mass transport byconvective cores is more efficient (i.e., not as much diluted by the ambient air mass) during El Niño than La
Niña.
3.2. Variation of Convective Outflows
LNBsounding and LNBobservation, the latter of which is represented by LNBCTH and LNBmaxMass collectively, arecompared between El Niño (red) and La Niña (blue) in terms of altitude in Figure 2a (top panels) and temperaturein Figure 2b (bottom panels). It is interesting that LNBsounding is higher during La Niña than in El Niño, while theopposite is true for LNBobservation. In terms of median, LNBsounding during La Niña is 0.43 km higher than thatduring El Niño, whereas LNBCTH and LNBmaxMass during El Niño are 0.28 and 0.12 km higher than those during
La Niña, respectively. The Student’s t‐test confirms that the differences in LNBsounding and LNBCTH are statistically significant at the 0.95 confidence level, while there are little differences in LNBmaxMass.
To test the FAT or PHAT hypothesis, temperatures at LNBsounding and LNBobservation are examined in Figure 2b(bottom panels). LNBsounding has a significant temperature difference between El Niño and La Niña: the medianvalue of LNB sounding is 4.63 K warmer during El Niño than during La Niña. However, the temperature difference
between El Niño and La Niña is very small in LNBobservation, especially for LNBmaxMass: the differences in medianvalues for LNBCTH and LNBmaxMass are 0.92 and 0.21 K, respectively.
If one converts height differences (0.28 km for LNBCTH and 0.12 km for LNBmaxMass) to temperatures assuming amoist adiabatic lapse rate (which is close to 9.8 K/km at 12 km where water vapor is scarce), then the temperature
difference for LNBCTH and LNBmaxMass would be 2.8 and 1.2 K, respectively. However, the actual temperature
differences for these levels are only 0.92 and 0.21 K, respectively. The Student’s t‐test also indicates that thetemperature difference in LNBsounding is statistically significant at the 0.95 confidence level, but that is not significant in LNBobservation. Hence, the temperature at the convective outflow levels (LNBCTH and LNBmaxMass) iseven less variable across different phases of the ENSO cycle than expected from the small difference in altitude.
This seems to echo the FAT/PHAT hypothesis, although the ENSO cycle is not a surrogate for climate change.
The most intriguing part is that the FAT hypothesis is more evident when looking at LNBmaxMass (i.e., the smallest
temperature difference) than at LNBCTH; this is consistent with the idea that LNBmaxMass is most relevant toconvective mass outflow and convective transport.
Originally, the FAT/PHAT hypotheses were built upon the assumption that the anvil height is predominantly
determined by temperature through divergence directly below the tropical tropopause layer. However, how the
dynamical field (divergence) strongly constrains the anvil height has not been verified at a process level.
Therefore, it is of great interest to explicitly examine the relationship between LNBobservation and the height where
maximum wind divergence occurs. Figure 2c summarizes the wind divergence profiles during El Niño and La
Niña, which shows that the peak of divergence occurs around 12.5 km and 220 K across the ENSO phases.
Interestingly, the peak of wind divergence occurs in between LNBCTH and LNBmaxMass. Since the FAT hypothesis
assumes that the maximum divergence determines the height of convective detrainment, it makes sense that theheight of LNBobservation and the height of maximum wind divergence are closely related, and their variations arealso similar. Our results confirm that the difference in height between LNBobservation and maximum winddivergence remains relatively constant across the ENSO phases.
LNBsounding, on the other hand, shows a different behavior compared to LNBobservation. To understand why
LNBsounding is higher during La Niña than El Niño, Figure 3a shows MSE (solid line) and MSE* (saturated moist
static energy, dotted line) during El Niño (red line) and La Niña (blue line) over the anvil cloud objects. The
vertical arrows demonstrate the corresponding pseudoadiabatic pathways based on parcel theory. As expected,
MSE is higher during El Niño than La Niña at all levels. The difference is smaller near the surface (the differenceis ∼716 J/kg at 0.2 km) than in the upper troposphere or UT (the difference is ∼1,215 J/kg at 12 km). In thewarmer ENSO phase, the MSE in the upper troposphere changes more rapidly with height than MSE in the lower
troposphere, such that the MSE curve in the UT is more “squashed” during El Niño than in La Niña. This leads toan LNBsounding that is higher during La Niña than during El Niño. So why is the MSE difference near the surface
small and the MSE difference in the UT large?
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Figure 3. (a) MSE (solid line) and MSE* (dotted line) during El Niño (red line) and La Niña (blue line), together with thethermodynamic pathways based on parcel theory during El Niño (red arrow) and La Niña (blue arrow) based on ECMWF‐
AUX. (b) The illustration of relative importance of ΔMSE due to the temperature change (ΔT) and due to the specifichumidity change (Δq) based on ECMWF‐AUX. (c) MSE over the whole tropics during El Niño (red line) and La Niña (blueline) based on AIRS. (d) The divergence changes during El Niño and La Niña based on ERA5.

3.3. A Synthesis
To answer this question, we decompose the change of MSE (i.e., MSE difference between El Niño and La Niña)
over the anvil cloud objects into two terms, the change of temperature (T) and the change of specific humidity (q):∆MSE = Cp ∆T + Lv ∆q,
where∆MSE = MSEELMSELA ,

∆T = TEL

TLA ,∆q = qEL

qLA ,and the notation of EL and LA refer to the El Niño and La Niña, respectively. To understand what controls theMSE change (ΔMSE = MSEEL MSELA), Figure 3b illustrates the relative contribution of ΔMSE due to thetemperature change (ΔT ) and due to the specific humidity change (Δq). It shows that ΔMSE near the surface and
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near the UT are mostly controlled by Δq and ΔT, respectively. The fact that the temperature difference near the
UT is larger than that near the surface leads to an enhanced stability in the UT during El Niño. This is a key cause
for the difference in LNBsounding between El Niño and La Niña.
We also examine MSE over the whole tropics during El Niño and La Niña, including non‐cloudy regions, basedon AIRS in Figure 3c. It is confirmed that even if we include non‐cloudy regions, the tendency toward smaller
ΔMSE near the surface and larger ΔMSE in the UT is observed. This UT stabilization effect supports the above
conclusion from ERA‐based MSE data. One consequence of the UT stabilization is that an undiluted air parcel
ascending from the surface will lose buoyancy at a lower altitude. This effect alone should favor convective
outflows at lower altitudes during El Niño.
Why, then, do convective outflows end up occurring at roughly the same level during El Niño and La Niña? Our
analysis indicates that this is made possible by stronger convective intensity during El Niño than La Niña: theinverse λ‐R relation (Section 3.1) illustrates that deep convection has a smaller entrainment effect and larger
convective cores during El Niño than La Niña, both pointing to stronger convective intensity during El Niño than
La Niña. Hence, although the environment is less favorable for convection to grow deeper during El Niño,
stronger convective intensity compensates for the disadvantage, resulting in the same outflow levels as during La
Niña. Moreover, as illustrated in Figure 3d, convergence is stronger in the lower levels and divergence is larger inthe upper levels during El Niño compared to La Niña. This provides additional evidence for a stronger convective
intensity during El Niño than during La Niña.

4. Summary and Interpretation
This study takes full advantage of CloudSat vertical profiling of deep convection to elucidate the relationships
between convective outflow and important factors that affect it, including environmental factors (e.g., LNB, large‐
scale convergence/divergence, and the FAT or PHAT hypothesis), and convective cloud properties (e.g.,
entrainment dilution and convective cores). To achieve this, we analyze and compare convective outflows basedon the parcel theory (LNBsounding) and those based on the CloudSat observations (LNBobservation), along withinvestigating the relationship between entrainment and convective core, specifically during El Niño and La Niña
events. Four key findings from this study are summarized as follows:••••

The altitude of LNBobservation experiences a slight elevation during El Niño, while the temperature at
LNBobservation remains insensitive to ENSO cycles, which is most evident for LNBmaxMass.
LNBobservation and the level corresponding to maximum wind divergence are closely related to each other, andtheir association remains constant across different phases of the ENSO cycle.
Upper‐tropospheric stabilization leads to a lower LNBsounding during El Niño than La Niña.
The contrast between El Niño and La Niña in the entrainment‐core diagram suggests that convective cores are
more intense and less diluted during El Niño than La Niña.

The first and second findings indicate that the altitudes of convective outflow levels undergo some changes acrossover different phases of the ENSO cycle, while the temperatures of these levels remain relatively constant. This islargely consistent with what the FAT hypothesis predicts, although the underlying mechanism differs from theoriginal theory. Unlike previous studies that relied on cloud tops, our study uses convective outflow levels derived
from CloudSat CPR profiles of cirrus anvils, providing a more direct observation of mass outflow. As a result, theFAT hypothesis is most evident in the results based on LNBmaxMass, which is most relevant to the maximum
convective detrainment level.
The third finding reveals upper‐tropospheric (UT) stabilization during El Niño compared to La Niña, which, on its
own, would suppress the vertical development of deep convection, seemingly contradicting the first and second
findings. The fourth finding provides a reconciliation, that is, convection becomes more intense during El Niño,
offsetting the less favorable thermodynamic environment. Both smaller entrainment rate and larger convective
core size during El Niño point to an increase in convective intensity. The reduced entrainment rate during thewarm phase of the ENSO could be linked to a moister mid‐troposphere that diminishes the impact of entrainment
dilution, enabling deeper convection development (e.g., Henderson et al., 2018; Jensen & Del Genio, 2006). It’s
important to point out that while the original FAT hypothesis predicts a near‐constant anvil temperature due totemperature control of radiative cooling (via the Clausius–Clapeyron relationship) and large‐scale wind divergence, our study takes a step further. We identify detailed convective‐scale processes and mechanisms that
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contribute to achieving this outcome. This finding advances the FAT and PHAT hypotheses. An implication of
the current findings is that the height of cirrus anvils is not always directly tied to the environmental sounding as
originally assumed by the FAT theory (see Seeley et al., 2019 for another example).
Our results show that convective processes undergo changes during El Niño and La Niña, leading to nearly
constant convective outflow levels. Although our analysis is based on a fixed sampling time (around 1:30 a.m./p.m.
local time), potentially introducing certain biases, it contributes to advancing our knowledge of the relationships
between convective outflows and influencing factors. This not only enhances understanding of the mechanisms
responsible for convection‐climate feedback, but also has the potential to improve the representation of convection
in global models by incorporating the observed relationships into the parameterization schemes.
Finally, it is also worth mentioning uncertainties in R, λ, and LNBobservation associated with our analysis, which are
documented in Takahashi et al. (2021). The main sources of uncertainties in R derive from potential bias insampling geometry since CloudSat CPR does not always profile through the center of the cores, and our estimation of R could be systematically underestimated by approximately 21%. Additionally, the variation of
LNBobservation leads to ±2.00%/km uncertainties in λ. As these biases and uncertainties are consistent during
ENSO cycles, we do not expect this issue to impact our comparison between El Niño and La Niña.

Data Availability Statement
The data of A‐Train observations can be found from the CloudSat Data Processing Center at www.cloudsat.cira.
colostate.edu. More specifically, detailed information on 2B‐GEOPROF and ECMWF‐AUX data can be foundfrom the CloudSat Data Processing Center at https://www.cloudsat.cira.colostate.edu/data‐products/2b‐geoprof
products/2b‐geoprof, and https://www.cloudsat.cira.colostate.edu/data‐products/ecmwf‐aux, respectively. The
instructions for how to access 2B‐GEOPROF and ECMWF‐AUX are summarized at https://www.cloudsat.cira.
colostate.edu/order/sftp‐access.
Acknowledgments
The research was carried out at the Jet
Propulsion Laboratory, California Instituteof Technology, under a contract with the
National Aeronautics and Space
Administration (NASA). This study was
supported by NASA Grant
80NSSC20K0090 and part of this work
was performed at the Jet Propulsion
Laboratory, California Institute ofTechnology, under a contract with the
National Aeronautics and Space
Administration (80NM0018D0004,
Project/Task is 105357/
967701.02.01.03.05) as well as funding
support from INCUS project under Grant
80LARC22DA011 (Project/Task is107931/04.01.A01, and 108177/04.01.
A01). ZJL would like to acknowledge
funding support from NASA Grants
80NSSC18K1600 and 80NSSC23K0116
awarded to CUNY. ATN was supported by
the Advanced Studies of Climate Change
projection (SENTAN)
JPMXD0722680395 from the Japanese
Ministry of Education, Culture, Sports,
Science and Technology.

TAKAHASHI ET AL.

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