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Influence of convection on the water isotopic
composition of the tropical tropopause layer
and tropical stratosphere
D. S. Sayres,
1
L. Pfister,
2
T. F. Hanisco,
1,3
E. J. Moyer,
1,4
J. B. Smith,
1
J. M. St. Clair,
1,5
A. S. O’Brien,
1
M. F. Witinski,
1
M. Legg,
6
and J. G. Anderson
1
Received 31 August 2009; revised 26 April 2010; accepted 28 April 2010; published 25 September 2010.
[1] We present the first in situ measurements of HDO across the tropical tropopause,
obtained by the integrated cavity output spectroscopy (ICOS) and Hoxotope water isotope
instruments during the Costa Rica Aura Validation Experiment (CR‐AVE) and Tropical
Composition, Cloud and Climate Coupling (TC4) aircraft campaigns out of Costa Rica
in winter and summer, respectively. We use these data to explore the role convection plays
in delivering water to the tropical tropopause layer (TTL) and stratosphere. We find that
isotopic ratios within the TTL are inconsistent with gradual ascent and dehydration by
in‐situ cirrus formation and suggest that convective ice lofting and evaporation play a strong
role throughout the TTL. We use a convective influence model and a simple parameterized
model of dehydration along back trajectories to demonstrate that the convective injection
of isotopically heavy water can account for the predominant isotopic profile in the
TTL. Air parcels with significantly enhanced water vapor and isotopic composition can be
linked via trajectory analysis to specific convective events in the Western Tropical Pacific,
Southern Pacific Ocean, and South America. Using a simple model of dehydration
and hydration along trajectories we show that convection during the summertime TC4
campaign moistened the upper part of the TTL by as much as 2.0 ppmv water vapor. The
results suggest that deep convection is significant for the moisture budget of the tropical
near‐tropopause region and must be included to fully model the dynamics and chemistry
of the TTL and lower stratosphere.
Citation: Sayres, D. S., L. Pfister, T. F. Hanisco, E. J. Moyer, J. B. Smith, J. M. St. Clair, A. S. O’Brien, M. F. Witinski,
M. Legg, and J. G. Anderson (2010), Influence of convection on the water isotopic composition of the tropical tropopause layer
and tropical stratosphere, J. Geophys. Res., 115, D00J20, doi:10.1029/2009JD013100.
1. Introduction
[2] Water vapor and ice exert a controlling influence on
the radiative and dynamical balance of the upper tropo-
sphere and lower stratosphere (UT/LS) and are key con-
stituents in determining this region’s response to climate
forcing [Smith et al., 2001; Fasullo and Sun, 2001;
Minschwaner and Dessler, 2004]. The concentration of
water vapor in the stratosphere also impacts the dosage of
UV radiation reaching the surface through water’s control
of heterogeneous stratospheric ozone depletion [Dvortsov
and Solomon, 2001; Kirk‐Davidoff et al., 1999]. In the
UT/LS water vapor concentrations are central to the for-
mation, evolution, and lifetime of cirrus that not only play a
critical role in the radiative balance in the UT/LS but also in
the dehydration of air ascending through the tropical tro-
popause layer (TTL). Changes in water vapor concentrations
and the cirrus associated therewith control the radiative
imbalance that amplifies climate forcing by carbon dioxide
and methane release at the surface and therefore quantifying
the mechanisms that control water vapor in the TTL are key
to predicting future changes in the climate system.
[
3] Quantifying the importance of convection in trans-
porting boundary layer air to the TTL (for the purposes of
this paper defined as the region between 360 K and 380 K
theta surfaces) and lowermost stratosphere is pivotal for
understanding the mechanisms that control the stratospheric
water vapor budget and accordingly that of other trace gases
and particulates. Due to this importance much emphasis has
been placed on understanding the mechanisms that control
the water vapor budget of the TTL and UT/LS. In general,
water vapor in the TTL is removed by in‐situ condensation
and cirrus formation on cooling during ascent or advection
1
School o f Engineering a nd Applied Scie nces, Harvard University,
Cambridge, Massachusetts, USA.
2
NASA Ames Research Center, Moffett Field, California, USA.
3
Now at NASA Goddard Space Flight Center, Greenbelt, Maryland,
USA.
4
Now at Department of Geophysical Sciences, University of Chicago,
Chicago, Illinois, USA.
5
Now at Geology and Planetary Sciences Division, California Institute
of Technology, Pasadena, California, USA.
6
BAERI, Sonoma, California, USA.
Copyright 2010 by the American Geophysical Union.
0148‐0227/10/2009JD013100
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115, D00J20, doi:10.1029/2009JD013100, 2010
D00J20 1of13
through local cold regions [Holton et al., 1995; Holton and
Gettelman, 2001; Fueglistaler et al., 2005]. Convection can
however provide additional sources of water via evaporation
of convective ice in undersaturated TTL air [Fu et al., 2006;
Hanisco et al., 2007; Dessler et al., 2007]. Distinguishing
the importance of the relevant mechanisms will allow
models to better simulate how water vapor pathways linking
the troposphere and stratosphere will change with increased
climate forcing by carbon dioxide and methane. Many
modeling studies that attempt to reproduce the observed
water vapor mixing ratio of the TTL have suggested that
convective ice lofting and evaporation may be unimportant
to the region’s water budget, and that mixing ratios of water
vapor in air crossing the tropical tropopause can be well
explained simply by the minimum temperature experienced
by those air parcels [Fueglistaler et al., 2004, 2005; Gulstad
and Isaksen, 2007; Cau et al., 2007]. However, attempts to
simultaneously model HDO mixing ratios find that con-
vection is necessary to accurately reproduce observed pro-
files of both H
2
O and HDO [Dessler et al., 2007; Bony
et al., 2008]. Because water vapor isotopic composition is
altered by all processes involving condensation or evapo-
ration, the ratio of water vapor isotopologues (HDO/H
2
Oor
H
2
18
O/H
2
O) can act as a tracer of an air parcel’s convective
history [Pollock et al. , 198 0; Moyer et al., 1996; Keith,
2000]. Therefore, adding HDO to models constrains the
amount of convection allowable and necessary in the model.
Any model that attempts to explain the water vapor mixing
ratio must also explain the water vapor isotopologue ratio
which is usually written as the ratio of the heavier isotope
(e.g. HDO or H
2
18
O) to the more abundant lighter isotope
(H
2
O) referenced to a standard. In the case of water the
reference is the ratio in Vienna Standard Mean Ocean Water
(R
VSMOW
)[Craig, 1961a]. Deviations from the standard, d,
are reported in permil ( ‰) where for the HDO/H
2
O ratio
dD = 1000(HDO/H
2
O/R
VSMOW
− 1). Values of dD ≈−80‰
are found close to the boundary layer and more negative
values (e.g. dD=−600‰) are found in highly dehydrated air
masses near the tropopause.
[
4] Measurements of dD from canisters and remote
observations have reported enriched values of HDO compared
to what would be expected from simple thermally controlled
dehydration mechanisms [Moyer et al., 1996; Johnson et al.,
2001; Kuang et al., 2003; Ehhalt et al., 2005]. To try to
better model the observed dD ratio [Dessler et al., 2007],
hereafter Dessler07, used the Fueglistaler et al. [2005] tra-
jectory model and added a climatological representation of
convective ice flux to demonstrate that addition of water
from evaporating convective ice was indeed a plausible
explanation for isotopic enhancements observed by remote
sensing instruments. Dessler07 were able to reproduce the
dD profile from remote observations with only a small
perturbation to the water vapor mixing ratio produced from
the Fueglistaler et al. [2005] model.
[
5] In this paper we present the first in situ tropical
measurements of HDO obtained during both winter and
summer. The high spatial and temporal resolution of the in
situ data offer a more detailed test case than did the rela-
tively coarse remote sensing data used by Dessler07. The in
situ data are tied directly to diabatic back trajectories from
the point of the measurement to identify sources of recent
convective influence and to quantify the effect of convection
on the dD ratio and the water vapor mixing ratio. We then
apply our own convective influence scheme, which uses
real‐time convection observations as opposed to the clima-
tology employed by Dessler07, to evaluate whether a mea-
surable difference in dD and water vapor mixing ratio is
observed between data recently influenced by convection.
Finally, we simulate the motion of air parcels along trajec-
tories, tracking both water vapor and HDO in order to test if
our model reproduces the profiles from the in situ data.
2. Measurements
[6] Isotopologue ratios were measured in situ aboard
NASA’sWB‐57 high‐altitude research aircraft during the
Costa Rice Aura Validation Experiment (CR‐AVE) in
January and February, 2006 and the Tropical Composition,
Cloud and Climate Coupling (TC4) campaign in August,
2007, both based out of Alajuela, Costa Rica, at 9.9° North
latitude. Measurements of H
2
O, HDO, and H
2
18
O were
obtained during these campaigns using the Harvard ICOS
isotope instrument [Sayres et al., 2009]. We focus here
primarily on HDO which, although less abundant than
H
2
18
O, experiences stronger fractionation on condensation,
giving the isotope ratio observations more robustness
against any instrument systematics. For TC4, H
2
O and HDO
measurements were also obtained by the total water Hox-
otope instrument [St. Clair et al., 2008]. Water vapor mixing
ratios are reported using the Harvard Lyman‐a hygrometer
[Weinstock et al., 1994], which has a long heritage on the
WB‐57 aircraft.
[
7] Data reported here are screened for both potential
contamination and potential instrument systematics. To
preclude inclusion of any ICOS data subject to contamina-
tion from water desorbing off the instrument walls, we
report only ICOS data where ICOS water vapor is less than
0.5 ppmv greater than that reported by Harvard Lyman‐a.
An additional potential source of measurement uncertainty
in the ICOS data is optical fringing and other artifacts in the
baseline power curve, which can produce measurement
biases that manifest themselves as offsets in measured dD.
While fitting routines developed for ICOS (as described by
Sayres et al. [2009]) mitigate some potential sources of bias,
residual offsets on the order of 50‰ to 100‰ are still
occasionally present. Periods of high potential bias are
however readily identified and for this work we have
removed all data with potential biases greater than the short
term 1‐s measurement precision. Quality‐controlled ICOS
data during CR‐AVE in the driest, most signal‐limited
conditions (H
2
O < 10 ppmv) show a maximum uncertainty
in dDof17‰ (30 sec., 1‐s). (In wetter air, signal to noise is
higher and therefore isotopic ratio uncertainty lower). For
the TC4 mission, a laser change in the ICOS instrument
resulted in increased bias uncertainty in low‐signal condi-
tions. We therefore show here ICOS data from TC4 only for
wetter conditions (H
2
O > 10 ppmv) and use Hoxotope data
for dry conditions or for flights when ICOS did not report
data. The base Hoxotope precision is 50‰ (30 sec., 1 s).
Although these uncertainties exceed those of laboratory‐
based mass spectrometers, they represent the most sensitive
in situ water isotope measurements made in these condi-
tions, and are comparable with the performance of remote
SAYRES ET AL.: CONVECTIVE INFLUENCE D00J20D00J20
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sensing instruments while providing far higher spatial and
temporal resolution.
[
8] In order to restrict our analysis to true tropical air
masses we show here only data from tropical flight seg-
ments out of Alajuela, Costa Rica in which the WB‐57
aircraft made vertical transects through the tropopause while
in the tropics, i.e. at latitudes below 10° North. Flights with
segments meeting both the geographic and data quality
criteria in the wintertime (CR‐AVE) campaign occurred on
January 30, and February 1, 2, and 7, 2006 and in the
summertime (TC4) campaign on August 6, 8, and 9, 2007
(Hoxotope) and August 8 and 9, 2007 (ICOS). We include
water vapor data from the Lyman‐ a instrument for all these
flight legs.
3. Mean Tropical dD Profiles
[9] The isotopic composition of water vapor in the trop-
ical atmosphere shows a sharp distinction in behavior
between the bulk of the troposphere and the TTL (Figure 1).
Below the TTL, both water vapor and dD fall off with
altitude much as expected in pure Rayleigh distillation,
where preferential removal of heavier condensate leaves the
residual vapor progressively lighter [Jouzel et al., 1985;
Ehhalt et al., 2005]. Within the TTL water vapor con-
centrations continue to decrease to the tropopause. How-
ever, starting below the base of the TTL ( = 340–360 K)
isotopic composition decreases more slowly than expected
if temperature alone were the controlling mechanism and
in the TTL dD remains roughly constant. This trend is
persistent in both summertime and wintertime observations,
but some seasonal difference is evident. The lower part
of the TTL is isotopically lighter and drier in the winter-
time CR‐AVE data (Figures 1a and 1b) with a mean dD
of −650‰ and minimum water vapor mixing ratio of
2.5 ppmv. The upper part of the TTL, above 370 K, is
characterized by an increase in dD to isotopically heavier
air with a mean of −500‰. This shift is correlated with
the slight increase in the water vapor measurements above
the tropopause, though the shift in dD starts below the tro-
popause. In the stratosphere proper, the dD measurements
are invariant within the precision limits of the data, while the
water vapor mixing ratio increases linearly. During the
wintertime there is little variability in the profiles between
flights.
[
10] In the summertime TC4 data (Figures 1c and 1d),
with nearby ITCZ convection, the TTL is isotopically
heavier with a mean dDof−550‰ and its composition is
continuous with the stratosphere proper. The variability is
greater in the summertime with noticeable differences
between flights. Within the TTL there are three distinct
water profiles (Figure 1d). The August 6th profile, plotted in
blue, shows typical water vapor mixing ratios for the sum-
mertime of about 7 to 8 ppmv. The August 9th profile,
plotted in red, is the wettest with mixing ratios around
10 ppmv. The August 8th profile, plotted in green, shows a
Figure 1. Profiles of (left) dD and (right) water vapor mixing ratio versus potential temperature for
flights during (a, b) CR‐AVE and (c, d) TC4. For CR‐AVE profiles made during the flights of January
30th (blue), February 1st (green), February 2nd(red), and February 7th (cyan) are shown. For TC4 profiles
made during the flights of August 6th (blue), August 8th (green), and August 9th (red) are shown. For
Figures 1a and 1c the shaded region represents the range of values from a Rayleigh distillation model.
The Rayleigh curve plotted he re is based on minimum and maximum temperature profiles during each
campaign and bounded on the left by an ideal curve where vapor condenses at 100% relative humidity
and the condensate is immediately removed and on the right by a curve that includes the effect of
80% condensate retention as the air parcel rises. Retention is important in the liquid phase where
re‐equilibration can occur. The shift in the Rayleigh cu rve du e to condensati on under supersaturated
conditions are shown as dashed and dot‐dashed black line s for relative humidity of 120% and
150%, respectively.
SAYRES ET AL.: CONVECTIVE INFLUENCE D00J20D00J20
3of13
sharp dip in the middle of the TTL with water vapor mixing
ratios as low as 5 ppmv. The dD measurements show similar
differences (Figure 1c). The profiles from August 6th and
9th have mean dD values of −550‰ and − 450‰, respec-
tively. The dD values from August 8th are isotopically
depleted compared to the other flights with a mean of
−600‰. Above the TTL there are two distinct profiles. The
profile from August 6th is the drier of the two with a min-
imum at 410 K of 5.5 ppmv. The second profile was sam-
pled on both August 8th and 9th with a mean at 410 K of
6 ppmv. Similar to the TTL, the dD profiles are isotopically
depleted for the drier profile and enriched for the moister
profile.
[
11] As noted in the introduction, observations of isoto-
pically heavy water vapor within the TTL are incompatible
with simple dehydration during gradual ascent, which would
produce significantly more isotopic depletion along with
dehydration. In the bulk of the troposphere, from the lowest
observations to a of 345 K in the summertime (Figure 1c)
and 355 K in the wintertime (Figure 1a), observed water
isotopic composition is roughly consistent with a Rayleigh
distillation model. Water vapor concentrations fall by over
two orders of magnitude and isotopic composition drops to
approximately −500‰ and −600‰, in the summertime and
wintertime, respectively. Within the TTL, however, observed
near‐constant isotopic composition cannot be explained by a
simple Rayleigh distillation model. Modeled Rayleigh cal-
culations are shown for comparison in Figures 1a and 1c
(gray shaded region; see caption for model parameters).
Gradual ascent and pure Rayleigh distillation within the
TTL would further reduce vapor isotopic composition to a
dDof−900‰ as opposed to the observed dD values
between −400‰ and −650‰. In the stratosphere proper, we
would expect no further change save a slight increase due to
methane oxidation as air ages. To within the precision of the
data, dD is indeed invariant above the tropopause; the air-
craft flights do not sample high enough altitudes or old
enough air ages for methane oxidation to be significant.
[
12] Thus far we have evaluated the water vapor and dD
measurements separately even though when in equilibrium
they follow a very tight relationship that can be seen by
plotting the logarithm of the water vapor mixing ratio versus
dD (Figure 2). As shown in Figure 1 the data are isotopically
heavy compared to a simple Rayleigh model (dark‐gray
shaded region). The difference between the Rayleigh model
and the data is slightly different in Figures 1 and 2 due to the
difference in using potential temperature versus water vapor
as the altitude proxy. The Rayleigh relationship modeled
here assumes that the air parcels sampled followed a single
Rayleigh distillation curve given by the average temperature
profile measured by the WB‐57 out of Costa Rica. Even if
this temperature profile is representative of the tropics, ice
evaporation in the free troposphere or lower part of the TTL
would have the effect of shifting the Rayleigh curve. As an
example, if the Rayleigh curve is shifted by 200‰ or 350‰
below the TTL, the subsequent relationship between water
vapor and dD would follow the two lighter‐gray shaded
regions shown in Figure 2. The CR‐ AVE data and most
of the TC4 data greater than 10 ppmv water vapor fall on
these shifted curves indicating that convection at or below
the base of the TTL is important for setting the dD value
at the base of the TTL. Below 10 ppmv water vapor most
of the TC4 data are consistent with the shifted Rayleigh
curves, while the CR‐AVE data show an additional shift to
isotopically heavier values. The enrichment at the base of
the TTL may be due to convective ice evaporation in the
mid‐troposphere, more likely in summertime. In the winter-
time that enrichment may have taken place in a different part
of the tropics or perhaps even reflect an influx of midlatitude
air [Hanisco et al., 2007; James and Legras, 2009].
[
13] One other possible mechanism that would shift the
Rayleigh curve is ice formation under supersaturated con-
ditions. When ice forms under these conditions, kinetic
effects between the isotopologues dominate over the ther-
modynamics with the result that dD is shifted to less
depleted values as shown in Figure 1 by the dashed black
lines representing condensation at 120% and 150% relative
humidity over ice.
[
14] Whether condensation at high supersaturation is a
major factor in determining the dD ratio can be evaluated by
looking at the relationship between dD and d
18
O. If con-
densation follows a Rayleigh process (i.e. is in thermody-
namic equilibrium) then the slope of the dDtod
18
O
relationship follows the well known meteoric water line
(MWL) (Figure 3, thick black curved line) [Craig, 1961b].
As the level of supersaturation increases, the heavier H
2
18
O
isotopologue is less depleted relative to HDO resulting in
the slope between dD and d
18
O becoming shallower as
shown by the dashed lines in Figure 3. Data falling below
the MWL can occur from mixing between parcels with
d ratios at different points along the MWL. Data from
CR‐AVE and TC4 are plotted in blue and cyan respectively,
and while some data indicate condensation under supersat-
urated conditions, most lie either on the MWL or below in
the mixed region. We therefore conclude that condensation
at high supersaturation is not a major factor controlling the
shift in dD away from the Rayleigh curve and leaves con-
vective ice lofting and subsequent evaporation as the sole
possible mechanism for the observed enhancements in dD.
This is in agreement with th e conclusions of Keith [200 0]
and Johnson et al. [2001].
[
15] To better quantify the effect of convection within the
TTL on dD and water vapor mixing ratio, we conduct two
Figure 2. The dD versus water vapor w ith data from
CR‐AVE and TC4 shown in blue and cyan, respectively. The
shaded regions represent Rayleigh curves (as in Figure 1)
with the lighter‐gray curves shifted by 200 ‰ and 350‰
from the dark‐gray curve below the base of the TTL.
SAYRES ET AL.: CONVECTIVE INFLUENCE D00J20D00J20
4of13
studies. First, we use a back‐trajectory model and maps of
past convection to qualitatively examine whether isotopi-
cally heavy and moister air can be attributed to specific
convectively active regions. Second, we model isotopic
evolution along those trajectories to quantitatively verify
that addition of convective ice can produce the observed
enhancements and quantify the amount of water vapor added
by convection.
4. Back Trajectories and Convective
Influence Model
[16] The high spatial and temporal resolution of the in situ
data allow for the use of back trajectory analysis to deter-
mine which sampled air parcels have been influenced by
recent convective events and evaluate whether convective
influence is indeed correlated with isotopic enhancement.
We use for this purpose an analysis framework similar to
that of Pfister et al. [2001] and briefly documented by
Pfister et al. [2010]. Diabatic back ‐trajectories are per-
formed along the flight tracks of the WB‐57 aircraft using
the GSFC trajectory model [Schoeberl and Sparling, 1995]
driven by the GEOS‐4 analysis [Bloom et al., 2005] and
radiative heating rates. For the TC4 calculations, we use the
mean July clear‐sky radiative heating rates from Rosenfield
[1991]. For CR‐AVE, we use mean winter clear‐sky radi-
ative heating rates calculated by Yang et al. [2010]. For each
aircraft point, a cluster of 20 day trajectories are calculated
in order to minimize errors from the back‐trajectory analysis
and also to allow for a gradient in convective influence, as
the convective systems in the TTL are narrow and scarce.
Each cluster has 15 points at 3 altitudes; 0.5 km above the
aircraft level, at the aircraft level, and 0.5 km below the
aircraft level. At each level there are 5 points along a line
perpendicular to the aircraft flight track, each separated by
0.3 degrees. The trajectories are run along theta surfaces,
with the parcels moving across theta surfaces as indicated by
the GEOS‐4 heating rates. To calculate convective influ-
ence, the trajectories are run through a time varying field of
satellite brightness temperature, using global geostationary,
8 km resolution, 3 hourly satellite imagery. Convective
influence is defined as occurrences along the trajectories
where the satellite brightness temperature is less than or
equal to the trajectory temperature. Convective encounters
are allowed even if the trajectory is as much as 0.25 degrees
distant from the cold temperature. While using the cluster of
trajectories and allowing for some distance between the tra-
jectory and the cold temperature convection the model may
still miss some convective events either because of the tem-
poral resolution of the model or because ice crystals may
reach above the cloud top as defined by the cold temperature.
Figure 3. The d
18
OversusdDwithdatafromCR‐AVE
and TC4 shown in blue and cyan, respectively. Thick black
line represents the meteoric water line with the dashed lines
showing the effect of supersaturation on the relationship
between d
18
O and dD. Points below the meteoric water line
result from mixing of air parcels with different d values.
Figure 4. Back‐trajectories for flights during the CR‐AVE
mission. The trajectories start 20 days before the WB‐57
sampled the air parcels and end at the point the trajectories
intersect the WB‐57 flight track, which is shown in black
(near 0°– 10°N and 80°W). Shown are all trajectories that
end above the 355 K isentrope and are colored coded by
the potential temperature along the trajectory as given by
the colorbar to the right of each plot. Also shown are points
along the trajectory where the air was influenced by convec-
tion, plotted as black squares also color coded by potential
temperature.
SAYRES ET AL.: CONVECTIVE INFLUENCE D00J20D00J20
5of13
[17] The CR‐AVE trajectories (Figure 4) show a clear
separation in the origin of the air near the tropopause. Air
from the free troposphere and throughout the TTL up to
370 K (colored blue and cyan) mostly originates in the
Western Tropical Pacific (WTP) or has circumnavigated the
tropics once during the previous 20 days. Some trajectories
on February 7th (Figure 4d) also originate from the Southern
Pacific Ocean. Air above 370 K (colored green, yellow, and
red) has spent most of the previous 20 days over the
Caribbean and South America. A few trajectories also fol-
low the tropospheric air that originates from the Southern
Pacific and Asia (mostly from the flight of February 7th; see
Figure 4d). During CR‐AVE deep convection (above =
380 K) occurs over South America (Figures 4a, 4b, and 4c),
the Southern Pacific Ocean (Figure 4d), and the west coast
of Africa (Figures 4c and 4d). The altitude of the shift in
origin of air and the observations of deep convection cor-
respond to the shift to isotopically heavier and moister air
shown in Figures 1a and 1b. While the air parcels sampled
below 370 K were also influenced by convection, this
mostly occurred over the WTP at the start of the trajectories.
By the time the air was sampled during CR‐AVE, most of
the water that would have been added by convection may
have been removed by desiccation, though the dD values
still retain some signature of convective influence. Though
convection was not directly measured above the base of the
TTL during TC4 or CR‐AVE, there is clear observational
evidence of convection penetrating above 400 K in the
tropics. Kelly et al. [1993] and Pfister et al. [1993] noted
hydration associated with convection up to 410 K in
northern Australia. More recently, Corti et al. [2008] have
noted hydration up to 420 K, both in Australia and South
America.
[
18] The tropical trajectories from TC4 (Figures 5a and 5b)
originate either from the Asian monsoon region and move
westward or from the Southern Pacific Ocean and move
eastward. Most of the trajectories have been influenced by
convection either over Asia, Africa, or South America. On
August 9th (Figure 5c) the WB‐57 sampled sub‐tropical
air originating from the North American monsoon (mid‐
latitudes). The profiles from August 9th (Figures 1c and 1d,
red trace) are heavier and moister, consistent with air from
mid‐latitudes. The convective influence model also shows
significant convection over North America (Figure 5c). The
trajectories from August 6th show a higher percentage were
influenced by convection as compared with August 8th,
with many trajectories influenced recently over South
America. This correlates with the difference in water vapor
mixing ratio measured on August 6th and 8th (Figure 1d,
blue and green traces). Whether the water vapor and dD
differences measured during TC4 can be quantitatively
attributed to convection and to what extent this convection
permanently alters the water vapor mixing ratio will be
further explored in the next few sections.
5. Model Description
[19] To help establish whether evaporating convective ice
can indeed explain the observed non‐Rayleigh isotopic
profiles in the tropical TTL, we have added a simulation of
isotopic evolution to the back‐trajectories and convective
influence model described in section 4. We use NCEP
reanalysis temperatures [Kalnay et al., 1996], provided by
the NOAA/OAR/ESRL PSD (Boulder, Colorado; http://
www.esrl.noaa.gov/psd/), to provide initial water vapor and
HDO concentrations for the start of each trajectory assuming
that the initial water vapor mixing ratio is equal to the sat-
uration mixing ratio. The initial dD is calculated assuming a
value of −75‰ in the boundary layer and follows a Rayleigh
profile to the altitude at the start of each trajectory. Since
this assumption will only be valid in the free troposphere,
we restrict our model to trajectories that start below or at
the base of the TTL. In order to allow for a representation
of mixing in the atmosphere, we run the model along each
of the five trajectories at the aircraft altitude from the
cluster of trajectories for each aircraft point. As an exam-
ple, the top map in Figure 6 shows one set of five tra-
jectories. The trajectories follow almost the same path,
however one was influenced by convection over Asia, two
over Africa (the two squares in Figure 6 overlap), and the
last two were not influenced by convection during the pre-
vious 20 day period. As the trajectory is run forward, if the
air parcel cools the relative humidity is kept equal to 100%
and it is assumed that any removal of water by condensa-
tion follows Rayleigh distillation and this provides the
resultant dD. If the temperature increases and the air
becomes undersaturated then the concentration of H
2
O and
the dD value are left constant unless there is convection.
If there is convective influence the model hydrates the air
to saturation with evaporated ice that has a dDof−100‰
Figure 5. Same as in Figure 4 but for flights during the
TC4 mission.
SAYRES ET AL.: CONVECTIVE INFLUENCE D00J20D00J20
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(see ice data highlighted by Figure 1 and Hanisco et al.
[2007]). After convection, the air parcel continues along the
trajectory as before.
[
20] Figure 6b shows the modeled evolution of water
vapor mixing ratio and dD for each of the five trajectories.
The trajectory that encountered convection over Asia (pur-
ple trace) is nearly saturated at the time of possible con-
vective influence and therefore very little effect on water
vapor or dD is observed. All five trajectories reach their
minimum temperature and therefore their minimum water
vapor mixing ratio and dD on July 24th. After that two of
the trajectories (red and green traces) encounter convection
over Africa with both water vapor and dD increasing
sharply. By the end of the trajectories on August 8th the
water vapor mixing ratios have decreased to nearly the same
value as if they had never encounter convection (H
2
O=
5 ppmv), but the dD values retain the signature of convection
(dD=−600‰). The mean water vapor mixing ratio and dD
for this set of trajectories is show as a black dot. Once the
model has been run for all trajectories it is the mean from each
set of cluster trajectories that is compared to the in situ data
(Figures 6c and 6d).
6. Sensitivity Analysis
[21] Before comparing the in situ data with the model for
all the flights we first evaluate the sensitivity of our model to
assumptions about the relative humidity at which water
vapor condenses and the initial dD value of the parcel. To
quantify the effect of supersaturation on our model results
we vary the relative humidity at which condensation occurs
between 100% and 180% relative humidity with respect to
ice. Once this threshold is reached, water vapor is dehy-
drated to 100% relative humidity. Shown in Figure 7 are the
mean fractional differences between the model results and
the in situ data for both water vapor mixing ratio and dD,
with a value of zero being perfect agreement. We show the
results from three sample flights. Only the water vapor data
are used as a metric for determining the best saturation value
to use in the model. The dD data are shown for reference as
to the effect of higher saturations on dD. August 8th and
February 7th are representative of most of the flights from
TC4 and CR‐AVE. August 6th does not behave in the same
manner as the other days and we show it to illustrate the
variability observed in the data. On August 8th and February
7th the model results, using a relative humidity of 100% for
Figure 7. Sensitivity of water vapor mixing ratio and dD
from the model to the relative humidity at which condensa-
tion occurs for select days. Shown are the fractional differ-
ences between the model and the in situ data using a relative
humidity required for condensation of 100% (blue), 120%
(cyan), 140% (green), 160% (magenta), and 180% (red).
Also shown is a temperature dependent relative humidity
curve as described in the text (dashed black line). Negative
values mean the model results are lower than the in situ data
and positive values mean the model results are higher than
the in situ data.
Figure 6. Example of model simulation. (a) The 5 cluster
trajectories at the aircraft level color coded by their potential
temperature (note the trajectories fall nearly on top of each
other). The locations of convective influence are shown as
black squares. (b) Temperature, potential temperature, water
vapor mixing ratio, and dD of each of the five trajectories
plotted versus time along the trajectories (note that the red
and green traces and the purple, cyan, and blue traces have
nearly the same values). The black points at the end of the
trajectories show the mean wa ter vapor mixing ratio and
dD of the cluster. (c, d) A sample of the results from the
model f or all trajector ies on a parti cular day. The model
results are shown as colored dots with the in situ data plotted
as black crosses.
SAYRES ET AL.: CONVECTIVE INFLUENCE D00J20D00J20
7of13
the condensation point (blue traces in Figure 7), agree well
with the in situ data below a of 370 K. Above 370 K
higher saturation levels (up to 160–180%, magenta and red
traces) are needed to produce better agreement between the
data and model. This is not surprising as observations of
high supersaturation are common at cold temperatures
[Jensen and Pfister, 2005; Jensen et al., 2005]. In order to
better simulate this dependence on temperature, we use a
relative humidity of 100% for temperatures greater than
195 K. For temperatures below 195 K we increase the relative
humidity allowed as temperature decreases using a linear
interpolation with a relative humidity of 100%, 130%,
180%, and 200% at temperatures of 195K, 190K, 185K, and
180K (based on archived data from tropical missions:
CWVCS, Pre‐AVE, AVE, CR‐AVE, and TC4). This scheme
produces overall better agreement in water vapor for both
CR‐AVE and TC4 throughout the TTL and this is the
scheme used for the model results presented in sections 7
and 8. August 6th stands out as in order to achieve agree-
ment between the model and the in situ data a relative
humidity of 160% is needed at all temperatures, though in
several other days higher supersaturation at the coldest
temperatures would produce better results (for example on
August 8th above of 375 K the red trace gives better
agreement than either the blue or dashed‐black traces).
Whether this is due to the simplicity of our model or real
variability in the atmosphere is unclear.
[
22] The dD results from the model are also effected by
relative humidity as condensation under supersaturated
conditions produces less fractionation. dD is also sensitive
to the initial assumptions. If we assume that the dD at the
start of each trajectory is set by Rayleigh distillation up to
that point, then in general the model underestimates the dD
value, especially above a of 370 K. However, we know
that convection can occur lower down in the troposphere
(i.e. before the start of the trajectory) and the signature of
that convection will remain. This is in agreement with the
observations from CR‐AVE and TC4 that show that data
below the TTL is shifted from a pure Rayleigh curve (see
Figure 2). We perform a sensitivity study allowing the initial
value of dD to be increased from its pure Rayleigh value by
between 0 and 400‰ (Figure 8). The results of this study
show a high level of variability from day to day and with
altitude, likely reflecting the high variability of convection
in the tropics. To limit the number of parameters in the
model we choose a single dD shift using the in situ data to
set the shift in dD. For CR‐AVE we add 200‰ to the start of
each trajectory and for TC4 we add 300‰ to the start of
each trajectory.
[
23] Due to the long duration of these trajectories (20 days)
and that the minimum temperature encountered by the air
parcels occurs within those 20 days we find that the model is
insensitive to assumptions about the initial water vapor
mixing ratio (i.e. that the parcel starts saturated). The other
parameter that the model is sensitive to is the d D of the ice.
We base the dD of ice in the model on the limited number of
observations available. However, it is likely that this value is
variable and dependent on a variety of parameters including
the amount of air entrained along the convective turret, the
location of the convection, and the amount of mixing
between the convection and ambient air. Since we do not
have sufficient observations to map the variability we fix the
dD of ice at 100‰.
7. Model Results From TC4
[24] We use the three flights from the TC4 mission to test
whether the temperature histories experienced by the sam-
pled air parcels during the previous 20 days along with the
representation of convective influence can accurately
reproduce the observed water vapor mixing ratio and dD
values. We limit our analysis to the trajectories ending (i.e.
the altitude at which the WB‐57 sampled the air parcels) in
the TTL region with between 360 and 380 K. We pick the
TTL region both because of the flight to flight variation
observed in this region during TC4 and because the use of a
Rayleigh model to provide the initial dD values at the start
of the trajectory is only reasonable for trajectories that
originate below the TTL.
[
25] With the assumptions and parameters laid out in
sections 5 and 6 the model reproduces the in situ water
vapor mixing ratio and dD profiles observed during TC4
(Figure 9). There are several features worth noting. The
model correctly reproducing the observed variability in the
water vapor profiles during TC4 and also the atmospheric
variability within each profile. As shown in Figure 1, three
different air masses were sampled with different water vapor
mixing ratios and the model quantitatively reproduces those
differences. On August 6th and 8th (Figures 9b and 9d)
when the in situ data form a very tight profile (i.e. little
spread in the data at each altitude) the model results are also
tightly correlated. On August 9th (Figure 9f) than the water
vapor mixing ratios indicating that the effect of convection
is more complicated than the effect of temperature. The
agreement tends to be worse in the lower part of the TTL,
Figure 8. Sensitivity of dD from the model to the initial dD
at the start of the trajectory. Shown are the fractional differ-
ences between the model and the in situ data for a dD shift
of +0‰ (blue), +100‰ (cyan), +200‰ (green), +300‰
(magenta), and +400‰ (red).
SAYRES ET AL.: CONVECTIVE INFLUENCE D00J20D00J20
8of13
with the model having dD values less depleted than the in
situ data (Figures 9c and 9e). Better agreement at the top of
the TTL is achieved by the model, with the best agreement
occurring on August 6th (Figure 9a). On all three days a few
trajectories do not intersect convection (blue dots). While
the effect on the water vapor mixing ratio is apparently
small, the effect on the dD value is larger with the trajec-
tories that encountered no convection having dD values 100
to 200‰ lower (i.e. more depleted).
[
26] While the results presented in Figure 9 clearly show
that using the past 20 day temperature history along with a
model of convective influence can accurately reproduce the
water vapor and dD profiles observed during TC4 in the
TTL, it does not demonstrate whether convection was a
necessary component and whether the effect of convection
was to increase the water vapor mixing ratio or simply alter
the ratio of isotopologues. To quantitatively evaluate the
effect of convection on the water vapor mixing ratio, we
turn off convection in our model and look at the water vapor
profile when only temperature is used to determine it
(Figure 10). Note that we still include the dD offset due to
convection prior to the start of the trajectories. As expected
with no additional input to enrich dD, the modeled dD
profiles fall off with altitude to a minimum of around
−800‰ on both August 8th and 9th (Figures 10c and 10e).
The model results from August 6th are less affected by the
lack of convection, possibly indicating that convection older
than 20 days was more important in setting the dD for these
data. The water vapor results through most of the TTL are
mostly invariant regardless of convective influence, but in
the upper part of the TTL (above 375 K) the water vapor
profiles that include convection (Figure 9) are noticeably
wetter than those that do not include convection (Figure 10).
This is most obvious on August 6th (compare Figures 9b
and 10b).
[
27] To quantify the importance of including recent con-
vection in reproducing the in situ data we plot the fractional
difference between the model results and the in situ data for
both the model with convection and the model without
convection for water vapor (Figures 11a, 11d, and 11g) and
for dD (Figures 11b, 11e, and 11h). As qualitatively dis-
cussed by comparing Figures 9 and 10 the addition of
convection makes a larger difference for dD than for water
vapor. On August 8th and 9th the model with convection
(red trace) is in closer agreement with the in situ dD data,
having a fractional difference near 0, than the model without
convection (blue trace). On August 6th, as noted previously,
the addition of convection makes little difference for the
level of agreement with the in situ data. The opposite is true
with respect to water vapor. With the exception of a few
data points on August 6th (Figure 11a), the agreement
between either model and the in situ data is the same.
However, even though the mean fractional change is small
there is still a net addition of water to the TTL from con-
vection. We evaluate this addition by taking the difference
between the modeled water vapor mixing ratios with and
without convective influence (Figures 11c, 11f, and 11i).
The difference corresponds to the amount of water vapor
added by convection for each trajectory. At the base of the
TTL all three days show some enhancements of on
average between 0.2 and 1 ppmv. On August 9th (Figure 11i)
the enhancements are contained below a of 365 K. On
both August 6th and 8th convection added on average
between 1 and 2 ppmv of water vapor to the top of the TTL
( > 375 K).
Figure 9. Comparison of model results and in situ data
from TC4. Calculated and measured (a, c, e) dD and (b, d,
f) water vapor are plotted versus potential temperature.
Model results are plotted in blue and red for trajectories that
were influenced by convection and those that were not influ-
enced by convection in the past 20 days. The measured mix-
ing ratios and dD are shown as black crosses.
Figure 10. Same as in Figure 9 except model does not
include the effect of convection. Only the temperature along
each trajectory is used to determine the final mixing ratio
and dD.
SAYRES ET AL.: CONVECTIVE INFLUENCE D00J20D00J20
9of13
[28] The convective influence model shows that nearly all
the trajectories during TC4 encountered convection in the
previous 20 days. However, convection occurring at the
beginning of the trajectories had little effect of the final
water vapor mixing ratio as the air parcels went through their
minimum temperature after the convection. Even so, 60% of
the trajectories in the TTL and 55% of the trajectories above
375 K still retained some amount of water vapor added by
convection at the end of the 20 day trajectories.
8. Model Results From CR‐AVE
[29] We performed similar comparisons for the data from
CR‐AVE the results of which are shown in Figures 12, 13,
and 14. As in TC4 we find that most of the trajectories were
influenced by convection in the past 20 days, though a
higher proportion saw no convection (blue points in
Figure 12). These points have dD values of around −800‰,
more depleted than any of the in situ data. In addition, on
February 1st and 2nd (Figures 12d and 12f) the model
under‐predicts the amount of water vapor, which may
indicate either missed convection or higher supersaturation
both of which are supported by the modeled dD being
depleted as compared to the in situ data. Qualitatively the
model does show an increase in dD above 370 K as in the in
situ data. On February 1st and 2nd when the trend to more
enriched dD is most pronounced in the in situ data the model
shows enriched values while water vapor is still decreasing.
This is compared to January 30th and February 7th when the
model shows more depleted values as does the in situ data.
However, quantitatively the model under‐predicts the
increase in dD. In general the agreement during CR‐AVE
between the model results and the in situ data is not as
robust as for the TC4 data. Comparing the model results
with and without convection (Figures 12 and 13) we see the
same differences as in TC4. The dD results only agree with
the in situ data if convection is included in the model. The
water vapor shows little difference and as the convection in
CR‐AVE is almost entirely at the beginning of the trajec-
tories this is not surprising. Quantitatively comparing the
water added by convection we find on average 0.5 ppmv of
water added at the base of the TTL, with the exception of
January 30 (Figure 14c). Through out the rest of the TTL
there is almost no net addition of water vapor with the ad-
ditions being between 0 and 0.2 ppmv. In total 30% of the
trajectories in the TTL had some positive, though small,
addition of water due to convection during the previous
20 days.
9. Conclusions
[30] We have presented the first in situ measurements of
dD in the tropics during summertime and wintertime. This
Figure 11. Fractional differen ce between in situ data and
model results including convective influence (red line) and
not including convective influence (blue dashed‐line) for
(a, d, g) water vapor and (b, e, h) dD. (c, f, i) Also shown
is the absol ute differ ence in water vapor mixing ratio
between model results that include convective in fluence
(as in Figure 9) and model results that do not include con-
vective influence (as in Figure 10). Differences for each
trajectory from TC4 are plotted versus potential temperature
as black crosses. The mean addition of water vapor binned
every 2 K in potential temperature are shown as gray bars.
Figure 12. Comparison of model results and in situ data
from CR‐AVE. Calculated and measured (a, c, e, g) dD
and (b, d, f, h) water vapor are plotted versus potential tem-
perature. Model results are plotted in blue and red for trajec-
tories that were influenced by convection and those that
were not influenced b y convection in the past 20 days.
The measured mixing ratios and d Dareshownasblack
crosses.
SAYRES ET AL.: CONVECTIVE INFLUENCE D00J20D00J20
10 of 13
represents a unique data set to test models of dehydration
and convective influence in the TTL. As expected, in situ
profiles of dD measured in the tropics during both sum-
mertime and wintertime show enrichment compared to if
water vapor mixing ratio was controlled solely by minimum
temperature. The wintertime measurements have a minimum
dDof−650‰ at the base of the TTL and are then constant
up to 370 K. At the top of the TTL and through the lower
tropical stratosphere there is an increase in dDto−500‰
accompanied by a small increase in water vapor mixing
ratio. Back trajectory models show this enhancement linked
to deep convection over South America and the Southern
Pacific Ocean.
[
31] The summertime data show enriched air starting at
the base of the TTL and a uniform dD value throughout the
TTL and stratosphere with flight to flight variations ranging
from −450‰ to −600‰ with instrumental uncertainties
(30 sec., 1 s) of ±17‰ and ±50‰ for CR‐AVE and TC4,
respectively. Water vapor data show similar variations in
mixing ratio with the wetter profiles corresponding the
isotopically heavier data. In both TC4 and CR‐AVE the
range of dD values is small with measurements neither
showing very enhanced (dD>−200‰) nor very depleted
(dD<−800‰) air parcels. While this is in agreement
with observations from remote sensing instruments, those
instruments average over large spatial areas, which would
lead to the profiles representing the average dD value. Even
with the high spatial and temporal in situ data the TTL
region is fairly homogeneous with all data showing enrich-
ment due to convection.
[
32] We use a simple scheme of convective ice evapora-
tion to model the effect of convection on trajectories in the
TTL. Dessler07 used this scheme with climatological data to
show that by averaging over many trajectories, convection
can be used to explain the enhancements in dD observed by
remote sensing instruments. Here we have applied that idea
to a very detailed case study using the data from TC4 and
CR‐AVE to demonstrate that minimum temperatures along
with a representation of evaporated ice can qualitatively and
quantitatively reproduce the in situ data.
[
33] The model shows that almost all trajectories both in
winter and summer encountered convection in the previous
20 days. 60% of the summer and 30% of the winter tra-
jectories having a net positive addition of water vapor due to
convection. During the wintertime the convection mostly
occurred early in the trajectories and therefore the net
change in water vapor was on average less than 0.2 ppmv.
For the summertime data more recent convection produced
Figure 14. Fractional difference between in situ data and
model results including convective influence (red line) and
not including convective influence (blue dashed‐li ne) for
(a,d,g,j)watervaporand(b,e,h,k)dD.(c,f,i,l)Also
shown is the absolute difference in water vapor mixing ratio
between model results that include convective influence (as
in Figure 12) and model results that do not include con-
vective influence (as in Figure 13). Differences for each
trajectory from CR‐AVE are plotted versus potential tem-
perature as black crosses. The mean addition of water vapor
binned every 2 K in potential temperature is shown as gray
bars.
Figure 13. Same as in Figure 9 except model does not
include the effect of convection. Only the temperature along
each trajectory is used to determine the final mixing ratio
and dD.
SAYRES ET AL.: CONVECTIVE INFLUENCE D00J20D00J20
11 of 13
enhancements of up to 2 ppmv at the top of the TTL
( > 375 K).
[
34] In winter convection occurred mainly over the
Western Tropical Pacific and South America with some
convection also over Africa and the Southern Pacific Ocean.
Most convection was below 365 K, with the exception of
the convection over South America that reached potential
temperatures above 390 K. This deep convection is collo-
cated with the air sampled above 370 K and explains the
shift in dD values at this altitude. Summertime convection
occurred over Asia, Africa, and South America in the tropics
and air from the North American monsoon was also sampled
with convection occurring over North America.
[
35] The data and model results presented here demon-
strate that water vapor mixing ratio is mainly controlled by
minimum temperature history and the d D values are mainly
set by the history of convective encounters. This is in
agreement with the conclusions of Dessler07 and others who
show that addition of convective ice can explain dD with
only a small perturbation to water vapor. Here we have
quantified that addition of water vapor and show that,
though small, convection still has a positive influence on the
water vapor mixing ratio. During the wintertime the addition
on average is less than 0.2 ppmv throughout most of the
TTL and up to 0.5 ppmv at the base of the TTL. During the
summertime the addition of water vapor by convection is
variable with some days showing almost no increase in
water vapor by convection. On other days convection is
more significant with mean additions of 1 to 2 ppmv at the
top of the TTL which accounts for between 20 and 30% of
the water vapor measured.
[
36] Measurements of dD can be used to effectively
constrain the amount of convective ice that needs to be
added to models and are necessary to include in models to
accurately predict the amount of water vapor in the TTL and
entering the tropical stratosphere. The data presented here
were limited to near Costa Rica. To further quantify the
amount of water vapor added by convection a more detailed
sampling of the tropics needs to occur with measurements
especially in the other convectively active regions. In
addition, sampling of convective outflow and measurements
of the dD of ice at various altitudes is necessary to under-
stand the variability in the dD of ice which would allow for
better constraints on the model.
[
37] Acknowledgments. The authors wish to thank the WB‐57 pilots
and crew for their hard work and dedication without which these measure-
ments would not be possible. We also wish to thank A.E. Dessler for his
comments and suggestions on this manuscript. Support from NASA grants
NNG05G056G and NNG05GJ81G is gratefully acknowledged.
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