Introduction
The tropical tropopause layer (TTL) is a key region in the atmosphere that
controls the transport of tropospheric air into the stratosphere
. Situated above the level of main
convective outflow , the TTL is
penetrated by deep convection which becomes increasingly rare with altitude
. Outside of convective towers, the vertical motion
is weak. The TTL region encompasses the level of zero radiative heating
(LZRH) which marks the transition from negative to positive radiative heating
values, thus creating a barrier for the large-scale transport of air parcels
into the stratosphere . Quantifying the transport paths
across the TTL and their spatial and temporal variability is important for a
better understanding of the chemical composition of the air entering the
stratosphere and the relation with source regions
.
In this work, we focus on how parcels detrained from the convective clouds
are transported across the TTL and reach the tropopause, defined as the
380 K surface, upon the combined effect of heating rate fluctuations and
vertical distribution of convective sources.
Although the distribution of tropical convection is well known, the location
and intensity of convective sources in the TTL are still debated. The
questions addressed in this work are whether the intensity is linked only to
the altitude of cloud tops
, what the role of cloud
heating is in favoring the crossing of the LZRH ,
what the role of horizontal transport and in-mixing from extratropical
latitudes is
, and what the special role of continental convection is during
the Asian monsoon in particular above the Tibetan Plateau
.
We address the whole range of these questions by performing for the first
time a comprehensive set of forward and backward trajectories in the TTL
between convective sources and the tropopause. We do not account for the
transport from the boundary layer to convective tops, which is assumed to be
fast and local.
In Sect. 2, we describe the data and the methods used in this work to
retrieve trajectories connecting the clouds and the 380 K potential
temperature surface. Section 3 discusses the distribution of convective
sources and transit times in the TTL. Section 4 discusses the sensitivity of
our results to uncertainties in the data and methods. Section 5 takes a
further step by calculating the mass flux across the 380 K surface
and the contributions of convective sources. Section 6 offers a summary and
outlook.
Lagrangian trajectories and convective sources
Lagrangian trajectories of air parcels are calculated within the TTL between
the time of detrainment from convective sources and the crossing of the
380 K potential temperature surface, taken as the lower boundary of the
stratospheric overworld .
Determination of the altitude of deep convective clouds
A prerequisite of this study is a characterization of cloud tops that is
global in both space and time. We use the CLAUS data set , which
provides global 3-hourly maps of brightness temperature at 30 km
resolution, combined with ERA-Interim data to determine the
pressure of the top of the convective clouds. Since we are only interested by
air parcels which are able to reach the LZRH and above, we consider only cold
pixels with brightness temperatures less than 230 K. In the deep
tropics between 15∘ S and 15∘ N, this temperature
corresponds to a pressure of about 240 hPa, which is below the main
detrainment level near 200 hPa and well below the all-sky LZRH, which
is usually located above 155 hPa. As the mean detrainment levels and
the LZRH are even higher over continents and in particular over Asia during
monsoon season, this is a conservative choice.
This method is, however, limited by the inability to distinguish overlaying
cirrus from convective tops and is known to underestimate the altitude of the
deep convective clouds by about 1 km
. We correct the altitude provided by CLAUS
by an upward shift of 1 km. This approximation is consistent with
more recent comparisons of cloud top height determined from active sounders
. The sensitivity to this correction is
described in Sect. . Another limitation is that the
brightness temperature can be colder than the environment and is sometimes
colder than the cold point tropopause. This is possible under fast adiabatic
cooling within active convective towers and is
found to occur for less than 2 % of the cold pixels for each month in the
20∘ S–40∘ N band. In such cases, we follow
and consider that the parcels rise adiabatically from
an altitude of about 40 hPa below the cold point tropopause.
Three-dimensional Lagrangian trajectories
We compute forward and backward diabatic three-dimensional trajectories in
the TTL using TRACZILLA, which is a modified version of FLEXPART
. The horizontal part of the motion is
calculated using the 3-hourly wind fields of ERA-Interim ,
combining analyses at 00:00, 06:00, 12:00 and 18:00 UTC, 3 h forecasts at 03:00 and 15:00 UTC and 9 h forecasts
at 09:00 and 21:00 UTC. The vertical (cross-isentropic) displacement is
calculated using the 3-hourly average all-sky radiative heating rates of
ERA-Interim (including the radiative effect of the clouds but excluding the
latent heating) which are archived at 01:30, 04:30, 07:30, 10:30, 13:30,
16:30, 19:30 and 22:30 UTC.
The forward calculations are meant to estimate the impact and efficiency of
convective sources while the backward calculations estimate the contribution
of sources to the air composition at the 380 K surface. The two
calculations provide, however, very consistent results as shown below. The
calculations are performed over the whole period 2005–2008 and are discussed
below in terms of monthly statistics.
Distribution of the geographical regions within the tropics and the
subtropics. In color: Africa (Af), Intertropical Atlantic (ITA), South Asian
Pacific (SAP), Asian mainland (AML), North Asian Pacific Ocean (NAPO),
Central America (CAm), South America (SAm), Tibetan Plateau (Tibet),
Indian Ocean (IO), North Central Pacific (NCP) and Southeast Pacific (SEP).
Tibet is defined as the region in Asia above 3500 m.
Backward simulation
In the backward calculation, parcels are launched from the 380 K
potential temperature surface, every 2 days, between 40∘ S and
40∘ N, on a regular grid of 0.5
∘ in latitude and
longitude. We retain the first encounter of a parcel with the top of a cold
cloud within the previous 3 months as in , except
our criterion is based on pressure rather than temperature. A parcel
encounters a cloud when its pressure is larger than the pressure of the
corrected top as described above. The comparison is performed 3-hourly along
the trajectory of the parcel with the CLAUS pixel containing the parcel at
that time. It is clear that we may miss some cloud encounters in this way
because the parcels can sometimes travel by 300 km or more over 3 h,
that is 10 CLAUS pixel sizes, under strong wind conditions. The sensitivity
to such effect is tested in Sect.
Forward simulation
In the forward calculation, parcels are launched from 3-hourly CLAUS maps,
with one parcel at the center of each cold pixel (brightness temperature
≤ 230K) at the corrected altitude of the cloud top, for
locations between 20∘ S and 40∘ N. Trajectories are
integrated for 3 months and we retain the first crossing of the
380 K surface between 40∘ S and 40∘ N when it
occurs. Parcels encountering other clouds along their path, i.e., those which are
found within cloudy pixels with cloud tops higher than their altitude, are
discarded. On the average, between 16 % (January) and 24 % (August) of
the launched parcels are eliminated for this reason. Such parcels are mostly
on a descending path and keeping them instead of discarding them only
marginally affects our results for parcels crossing the 380 K
surface.
Source distribution
We focus our study on different geographical regions as defined by the color
boxes on Fig. . The boundaries are chosen to highlight the
regions where convection is intense in the tropics and subtropics and to
separate land and oceanic contributions. The South Asian Pacific (SAP) region
corresponds to the warm pool. The Asian monsoon region is divided into the
continental Asian mainland (AML), the North Asian Pacific Ocean (NAPO), which
includes the Bay of Bengal, the Sea of China and the Philippine Sea, and the Tibetan Plateau
(Tibet), defined as the region in Asia above 3500 m. America is
divided into South America (SAm) and Central America (CAm). There is a single
region for Africa (Af).
Annual cycle
The black curve in Fig. a shows that on average
86 % of the backward parcels reach a cloud top within 3 months. The
time axis for this curve is the launch time. The parcels which do not reach a
cloud are to a vast majority initialized in the subtropics and ascend
backward in the deep Brewer–Dobson circulation of the extratropics. The
proportion of parcels reaching a cloud varies very little over the mean
annual cycle, with a maximum in April (88.7 %) and a minimum in July
(84.7 %).
The other curves in the same panel show the contributions of each region
among the trajectories reaching a cloud within the ensemble of all regions in
Fig. . In order to facilitate the comparison with forward
calculations, the time axis for these curves is that of the intersection of
each parcel with a cloud, grouped by months.
The main feature between November and April is the dominance of the SAP
region, which alone accounts for a maximum of 68.4 % of all sources in
January. The next boreal winter contributors are Af and SAm with a maximum
contribution of 19.4 % for Africa in April.
From June to September, NAPO is the leading source with a maximum share of
35.3 % in July followed by AML, which peaks at 19.8 % in July. Together
these two regions represent from 45 to 55 % of all sources from June to
September. The following contributors are SAP, CAm, North Central Pacific
(NCP) and Af. Tibet is a tiny overall contributor (with a maximum share of
2.5 % in July).
(a) Color lines: source distribution from backward
calculations, calculated as the proportion of backward trajectories reaching
a convective top within a given region among all trajectories reaching a
convective top. The sum of all the contributions is 100 %. Black line:
proportion of backward trajectories reaching a convective top.
(b) Source distribution from forward calculations, calculated as the
proportion of forward trajectories reaching the 380 K isentropic
surface from a given region among all the trajectories reaching this surface.
(c) Efficiency of transport from convective tops, calculated as the
proportion of parcels released in a given region that reach the 380 K
isentropic surface in the forward calculations. On the right of (a)
and (b): source distribution for the inside of the Asian monsoon
anticyclone averaged over JJA. The statistics are obtained for the period
2005–2008. The time axis refers to the instant grouped by months where a
parcel leaves (forward) or meets (backward) a cloud except for the black curve
in panel (a) for which the time axis is that of the launch of the
parcels on the 380 K surface. Curves are plotted for each region,
with the color code of Fig. .
The forward estimate of the source distribution (see
Fig. b) is calculated from the trajectories
launched at cloud top level which have reached the 380 K surface. The
quantity shown is the monthly ratio of the number of parcels from a given
region to the total number originating from the ensemble of regions in
Fig. . The time associated with each trajectory is that of its
launch, grouped into monthly bins. There is a striking agreement between the
forward and backward distribution of sources in
Fig. a and b. In
spite of some slight quantitative changes in the proportions, the general
pattern and the ordering of sources is almost identical throughout the whole
year. The largest change is for Tibet, which displays a forward contribution
of 7.6 % in July and August (3 times its backward contribution) but
still remains a minor overall contributor.
Vertical source distribution of the parcels in boreal winter (DJF)
and boreal summer (JJA) for forward and backward calculations as a function of
potential temperature of the source. The vertical axis counts the number
of parcels calculated as daily averages summing events at the eight CLAUS sampling times
(then averaged over DJF and JJA and over 2005–2008)
and are given in K-1day-1.
Curves are plotted for each region,
with the color code of Fig. . The arrows on the upper axes
indicate the corresponding mean LZRH levels over the season and 2005–2008.
The similarity is not totally unexpected as forward and backward calculations
are solving dual equations for the Green function of the advection–diffusion
equation . Here diffusion has no role because
we consider averages over regions and durations much larger than the
diffusion scale and diffusion time defined in . It is,
however, surprising that a somewhat coarse discretization (only one parcel per
cold 30km×30km pixel in the forward case and one
parcel on a half-degree grid every 2 days in the backward case) which
under-samples the flow quite drastically is able to reach good agreement
between forward and backward calculations. The sampling is made at a higher
resolution than the ERA-Interim winds but the transit time between the cloud
top and the 380 K surface is long enough (as shown below) to make the
trajectories numerically irreversible due to the chaotic aspects of
transport. Our results show that the sampling is good enough to reestablish
the reversibility in the statistical sense as predicted by the Green function
formalism for a continuous sampling .
Figure c shows the proportion of forward
trajectories released in a given region from high convective tops that reach
the 380 K surface within 3 months. As most of the other trajectories
have returned back in the lower troposphere, this proportion conveys the
efficiency of each region at converting convective air into stratospheric
air. Tibet displays a singular behavior by reaching an efficiency of
84.5 % in July, which indicates that this proportion of air detrained at the
top of clouds reaches the 380 K surface. During boreal summer, after
Tibet, AML conveys up to 53.5 % of parcels to the stratosphere, while NAPO
is less efficient at about 32.5 % but still the largest contributor due to
its size and the frequency of high convective clouds within its domain.
The other regions exhibit efficiencies lower than 30 % with SAP lying just
above SAm, CAm and Af. However, when the Asian land convection ramps down,
the efficiency of SAP combines with the intensity of convection in this
region to let it dominate the transfers over half of the year.
The high efficiency above Tibet is consistent with previous studies that found
a confinement of Tibetan air within the monsoon anticyclone
which persists above Asia during boreal
summer, trapping tracer compounds that recirculate inside
. The contribution to the inside of the summer
Asian monsoon anticyclone (AMA) has been further estimated by calculating
sources over the restricted portion of the 380 K surface confined
between 20 and 40∘ N, 25 and 125∘ E, and with potential
vorticity smaller than 4×106 m2 s-1 kg-1 K. It is
found that 87 % of the AMA parcels originate from Asia in the forward
calculation (see Fig. b) among which 19.5 %
from the Tibetan Plateau. In the backward calculations (see
Fig. a) the proportions are redistributed among
Asian continental convection between AML and Tibet (47.5 and
19.5 % in forward and 54.3 and 8.8 % in backward, respectively, the sum varying only
from 67 to 63.1 %). The NAPO contribution is unchanged.
Characteristic numbers of the vertical distribution of sources for
the contributing regions during boreal winter (DJF) and boreal summer (JJA). All the
quantities but the last column are potential temperatures in units of K.
The modal peak is based on
the discretized mean histogram shown in
Fig. . The mean, median and standard
deviation are calculated from a cubic spline interpolation over the
340–380 K interval. For each region and each season the upper line
refers to backward calculations and the lower line to forward calculations as
indicated in the modal peak column. Regions with low contribution have
been masked.
Season
Region
LZRH
Modal peak
Mean
Median
SD
% above LZRH
DJF
Af
353.4
355 (B)
358.1
356.8
6.3
75.8
355 (F)
359.5
357.9
6.8
82.5
SAP
352.6
355 (B)
356.1
355.1
5.2
76.3
355 (F)
358.7
357.3
5.9
89.5
SAm
353.6
353 (B)
357.8
356.2
6.8
69.2
355 (F)
358.7
357.4
6.4
77.1
JJA
Af
355.7
361 (B)
362.7
362
7.2
83
359 (F)
363.1
362.2
7.2
83.7
SAP
352.1
351 (B)
354.5
353.3
5.3
62
353 (F)
356
354.9
5.5
75.3
AML
359
361 (B)
363.1
362.5
5.7
75.4
363 (F)
364.9
364.2
5.7
85.9
NAPO
352.6
357 (B)
358.5
357.7
6.2
83
359 (F)
360.5
359.6
6.5
90.7
Tibet
367.3
363 (B)
365.4
364.7
5.2
31.4
367 (F)
367.9
367.4
4.5
50.8
NCP
351.3
351 (B)
353.8
352.4
5.5
62
353 (F)
355.7
354.5
5.6
80
CAm
350.7
353 (B)
357.4
355.5
7.4
82.5
353 (F)
358
356.3
6.8
90.3
Characteristic numbers of the distribution of transit times for the
contributing regions during boreal winter (DJF) and boreal summer (JJA). All the quantities
are in units of days. The modal peak is based on the discretized mean histogram
shown in Fig. . The mean, median and
standard deviation are calculated from a cubic spline interpolation over the
0–90-dayinterval. For each region and each season the upper line
refers to backward calculations and the lower line to forward calculations as
indicated in the modal peak column. Regions with low contribution have
been masked.
Season
Region
Modal peak
Mean
Median
SD
DJF
Af
26 (B)
36.9
33.6
19.4
26 (F)
27.5
25.7
14.6
SAP
22 (B)
34.2
30.8
19.0
18 (F)
23.4
20.6
13.3
SAm
30 (B)
36.7
33.7
19.5
26 (F)
28.2
26.5
14.4
JJA
Af
30 (B)
35.4
32.7
20.0
26 (F)
29.5
27.8
16.9
SAP
22 (B)
38.0
33.8
19.7
22 (F)
30.9
27.6
15.3
AML
26 (B)
32.5
29.4
19.7
18 (F)
21.3
19.2
13.7
NAPO
30 (B)
38.7
35.7
19.6
26 (F)
28.4
26.6
15.8
Tibet
14 (B)
29.5
24.3
19.6
10 (F)
15.1
13
9.8
NCP
22 (B)
37.2
33.3
19.2
22 (F)
31.4
28.5
15.3
CAm
30 (B)
37.9
35.2
19.5
30 (F)
32.2
30.2
15.9
Vertical distribution of sources
We investigate now the vertical distribution of sources within each region
and its relation to the LZRH. During boreal winter (see
Fig. a
and b) forward and backward calculations
predict that the distribution of cloud top sources in the dominating SAP
region peaks at 355 K. This is located above the all-sky LZRH at
352.6 K during that season. A proportion of 89.5 % of the sources
is then located above the LZRH in the forward calculation and 76.3 % in
the backward calculation. The other contributing regions (Af, SAm, NAPO and
NCP) also all exhibit a modal peak at 355 K in the forward
distribution. The backward distribution exhibits a shift towards small
potential temperature which also affects the mean and the median of the
distribution (see Table ).
As in , a more complex pattern emerges during boreal summer
with several competing regions (see Fig. c
and d). Both forward and backward calculations produce similar distributions
of sources with the main differences being a stronger contribution of Asian
land regions (AML and Tibet) in the forward distribution. SAP, CAm and NCP
regions are grouped in the forward calculation with a modal peak at
353 K which is below the boreal winter peak at 355 K in SAP.
A similar pattern is found in the backward calculation. The AML and Tibetan
modal peaks are above 360 K, reaching 367 K for Tibet. The
African modal peak is also near 360 K with a fairly flat distribution,
and NAPO modal peak is intermediate near the common boreal winter peak at
355 K. The backward shift in the sources towards small potential
temperature is smaller than during boreal winter in SAP. Most of the sources
are again located above the LZRH, up to 90 % for the forward trajectories
from NAPO and CAm (see Table ). The only exception is Tibet, for
which the LZRH, much higher than in other regions at 367.3 K, is
located above the modal peak and just at the median in forward calculations.
Nevertheless, Tibet was found as the region with highest efficiency during
boreal summer, which might be explained by the very high level of the LZRH
and sources in this region. The separation of NAPO and Asian land sources
explains the double peak pattern shown in Fig. 8 of .
Transit time
The differences between forward and backward calculations are mostly seen in
the transit time distribution shown in
Fig. . The peak of the distribution is
always shifted to smaller values in the forward calculations and the tail is
also decaying much faster. As a result, the ratio backward/forward for the
median and the mean is of the order of 1.5 in SAP during boreal winter and in
AML and NAPO during boreal summer. It is lower but always larger than 1.1 in
the other regions with the exception of Tibet with a near 2 factor (see
Table ). In the forward calculation, the mean transit time over
contributing oceanic regions (SAP, NCP and CAm) is of the order of 30 days
during boreal summer, which is larger than the boreal winter value of 23 days
in SAP. On the contrary, Asian land regions during boreal summer exhibit
shorter transit times (21 days for AML and 15 days for Tibet) than their
boreal winter counterparts (Af and SAm) near 28 days. Transit times from Af
show very little change between boreal winter and boreal summer.
Distribution of the transit times in boreal winter (DJF) and boreal summer (JJA)
for forward and backward calculations. The vertical axis is a
probability density function in day-1. Curves are plotted
according to the color code of Fig. but only for the
active convective regions during each season. In the upper part of each panel,
the triangles and the crosses indicate, respectively, the median and the mean for each curve.
Same as Fig. when the +1 km
altitude correction of the cloud tops is not applied.
The backward distribution is biased by the fact that sampled backward
trajectories can easily miss a cloud by passing a pixel away and then wander
away in another region. The effect is the largest for small regions like
Tibet or regions ventilated by intense large-scale circulation such as Asia
during boreal summer. The backward trajectories can also, at least in
principle, get trapped into unstable trajectories oscillating about the LZRH
from which trajectories diverge in forward time and to which they therefore
converge in backward time, although this has seldom been observed. As it
appears, estimates of transit times are more sensitive to sampling effects
than the source distribution.
Sensitivity studies
In this section, we study the sensitivity of the results presented in
Sect. to changes in data and the design
of our calculations.
Sensitivity to the cloud top offset
As the estimate of cloud top and the +1 km correction are subject to
uncertainty, we have redone the analysis without the +1 km
correction. Since the LZRH is the same, the direct effect is to reduce the
proportion of forward trajectories reaching the 380 K surface (see
Table ). The ratio is about 45 % for both boreal summer and
boreal winter except for continental Asia during boreal summer, when it is
smaller. Tibet is the most sensitive region with a ratio of 22 %.
Figure a and c show the change in the
vertical distribution of sources for forward calculations. Besides the
overall reduction, it is visible that the modal peaks are unmoved except for
NAPO and Tibet during boreal summer when they move, respectively, from 358
to 355 K and from 367 to 362 K. The vertical distribution of
sources is made narrower by reducing the tail of the distribution towards the
upper end of the interval as the highest clouds are shifted down.
The proportion of backward trajectories reaching a cloud within 3 months is
now 85 % during DJF and 82.7 % during JJA, which is less than but close to the
value when the offset is applied (87 and 85.2 %, respectively) and hence with
much less variations than the forward efficiency. The modal peaks are
slightly shifted to lower values (with larger shift for NAPO and Tibet) and
the narrowing is well pronounced with almost no sources within the range
[365, 370 K] except over continental Asia during boreal summer.
The lower end cutoff value of sources (at about 345 K for maritime
convection and 350 K for AML) is preserved in both forward and
backward calculations except for Tibet during boreal summer. This indicates that
outside Tibet the cutoff is determined by the transport properties across the
LZRH which are unchanged by offsetting the cloud top or changing the
threshold brightness temperature, providing it is warm enough. Over Tibet,
where the modal source peak is below the LZRH and where a 1 km displacement
is a larger jump in θ than at other location, the lower cutoff is
shifted downward and the number of sources is, proportionally, more reduced
than in the other regions.
In accordance with these limited changes in the sources, the transit time
distribution for all regions but Tibet is weakly affected for both forward
and backward calculation (see Fig. ). The
change is localized in the small time contribution in agreement with the
narrowing of the source distribution, which reduces the proportion of short
transit paths. Tibet is an exception with a shift by about a factor of 2 of
the forward and backward transit times towards larger values. Tibet differs
from the other regions by having a very high LZRH and a distribution of
sources laying mainly under this level. This effect is amplified when the
cloud top correction is canceled with almost no contribution left above the
LZRH, inducing a significant shift in the transit time distribution.
Sensitivity to increase of the size of cloud pixels
In this section we address the sensitivity to the density of cloud
observation and the effect of missing cloud encounters in backward
calculations. We modify the encounter criterion by enlarging the pixel size
by a factor of 3 in both latitude and longitude, retaining the smallest top
pressure among the nine CLAUS pixels surrounding the parcel at a given time.
This modification enlarges high clouds and has largest effect in regions
where convective systems are small and sparse.
Figure (first and second rows) compares the
distributions of backward sources in boreal winter and boreal summer 2005
with and without enlarging the pixel size. The total number of trajectories
meeting a cloud is again quite insensitive, increasing by 3 % during boreal
winter and 1.5 % during boreal summer. The distribution of sources is,
however, modified by shifting the distribution to higher potential
temperatures and widening the profile. Hence, the effect is qualitatively
opposite to the lowering of the top of clouds done in
Sect. .
Same as Fig. when the +1 km
altitude correction of the cloud tops is not applied.
Sensitivity to the daily cycle of the heating rates
Cloud radiative forcing and the resulting heating rates are a priori
sensitive to the daily cycle of convective activity in the tropics. We test
here the sensitivity to the daily cycle of heating rates by replacing the
3-hourly sampling by a time moving average. This average at time t is
performed as a discretization of
X̃(t)=12τ∫t-τt+τX(t′)1+cosπt-t′τdt′,
where τ is 30 days.
Figure (third row) shows that the
distributions of sources are only weakly affected even at levels below the
LZRH. This result is in agreement with , who did a similar
test with MERRA reanalysis. However, the maps (not shown) of the smoothed
heating rates still contain a large amount of spatial variability. This
suggests that the horizontal motion that samples this variability is more
important than daily fluctuations of the heating rates to cross the LZRH.
Comparison of the vertical source distribution, for ERA-Interim, for boreal winter (DJF)
and boreal summer (JJA) between the standard backward calculation for 2005 (upper two panels),
when the CLAUS pixel size is enlarged by a factor of 3 as described in the text (middle two panels)
and when the 3-hourly sampling of daily cycle of heating rates is replaced by
a time moving average as described in the text (lower two panels).
Sensitivity to the reanalysis
One main source of uncertainty is the error in the reanalysis wind and
heating rates. It has been shown that the heating rates differ quite
significantly among reanalyses and that the horizontal
wind may contain large errors in tropical regions poorly covered by
radio-soundings . It is therefore important to assess
how our results are sensitive to a change of the reanalysis. As an extensive
comparison among all available reanalyses at the time of this writing would
have consumed a lot of resources, we limit the comparison to two reanalyses,
JRA-55 and MERRA . JRA-55 has
higher horizontal resolution than the ERA-Interim (spherical T319 truncature
instead of T255) and the same number of levels. Winds and heating rates are
available every 6 h at model resolution. MERRA has about the same horizontal
resolution as ERA-Interim but, because the heating rates are only available in
this format, we use winds and heating rates on a 1.25∘ horizontal
grid and a reduced set of vertical pressure levels every 3 h.
Backward calculations have been performed for 2005 using the same setup as
for ERA-Interim. Figure shows that in the
three cases, SAP dominates during boreal winter and NAPO is the largest
contributor during boreal summer. There are, however, significant
differences. The relative contributions of NAPO is largest in JRA-55 and the
distributions are narrower in MERRA. The main difference is in the vertical
location. JRA-55 sources are slightly shifted upward by 2 to 3 K with
respect to ERA-Interim. MERRA sources are even more shifted by up to
12 K for oceanic sources (SAP, NAPO, CAm) during boreal summer. The
shift is smaller for SAP in boreal winter (+5 K) and for AML and
Tibet in boreal summer (+4 K). The total proportion of backward
trajectories meeting a cloud remains, however, close to that of the
ERA-Interim (80.7 % for JRA-55 and 74.4 % against 85.4 % for
ERA-Interim in 2005).
Vertical source distribution for boreal winter (DJF) and boreal summer (JJA)
calculated for 2005 with JRA-55 and MERRA reanalyses using the same setup as for
ERA-Interim. Upper two panels, for ERA-Interim, are the same as those shown
on the two upper panels of Fig. .
Mean heating rate profiles, as a function of potential temperature,
for the four reanalyses: ERA-Interim (blue), MERRA (red),
MERRA2 (dashed red) and JRA-55 (black), in K-1day-1.
The upper panel is for January and the lower panel is
for July, averaged over 2005–2008. Curves are plotted for each region
defined on Fig. . The region “l20”
represents the 20∘ S–20∘ N band.
In order to interpret these results, Fig.
compares the mean profiles of all sky heating rates among the reanalyses for
each region in January and July. In addition to the three reanalyses, we show
also the curve for MERRA2 . It is clear that all the curves
are always close within non-convective regions (AML, Cam, NCP,
SEP and Tibet during
boreal winter; SAm and SEP during boreal summer) where heating rates are
calculated from clear sky radiative transfer. In these regions, however, ERA-Interim often displays larger heating rates than the two others above
370 K without affecting the LZRH.
Over convective regions, where additional cooling or heating is provided by
clouds, there is a clear separation between JRA-55/ERA-Interim and
MERRA/MERRA2. ERA-Interim still displays larger heating rates than JRA-55 and
this shifts down its LZRH by a few K. In the Asian region during boreal
summer and in SAP during boreal winter, ERA-Interim cools less than
JRA-55 below 340 K but this does not affect the LZRH. This is
consistent with the shift observed in the source distribution.
MERRA/MERRA2 exhibit a very special pattern over convective regions with
reduced heating near 355 K with respect to the two other reanalyses
and a strong heating near 345 K, resulting in a characteristic “S”
pattern. As a result, the LZRH is pushed upward and multiple LZRH occur over
NAPO and CAm during boreal summer. In all cases, MERRA2 is very close to
MERRA except over Tibet during boreal summer, where MERRA does not differ
very much from ERA-Interim and JRA-55 while MERRA2 does quite unexpectedly.
Mass flux across the 380 K surface and regional distribution
In this section, we take a further step by determining the mass flux across
the 380 K surface and the contribution of each convective region.
Method and validation
The instantaneous diabatic mass flux M across the 380 K surface,
over a specific domain Γ of the sphere, can be estimated from all sky
radiative heating rates as
Mdiab=∫∫Γσ380Kdθdt380Kds,
with σ380K=-1g∂p∂θ at θ=380K, obtained from the temperature and
pressure profile of ERA-Interim, where g=9.81ms-2. For
practical purposes, the integration is replaced by a weighted sum over the
gridded surface. From these instantaneous fluxes, one can define monthly
averages by replacing dθdt380K in Eq. () by its monthly average
dθdt380K. The monthly upward
Mdiab↑ and downward Mdiab↓(ϕ)
fluxes can then defined be integrating separately on the subdomains where
dθdt380K is, respectively, positive and negative. It
is known that upward and backward fluxes are ill-defined when the time
interval goes to 0 . This is no longer the case after
time smoothing which removes the noise, but the flux then may depend on the
applied time smoothing interval.
Another, more traditional, method is based on the residual mean meridional
circulation v∗‾,w∗‾, with
v∗‾=v‾-1p∂∂zpv′θ′‾θz‾,w∗‾=w‾+1acosϕ∂∂ϕcosϕv′θ′‾θz‾,
as defined by in log pressure coordinates
z=Hlog(p0/p), where the overbar indicates a zonal average. The kinematic
mass flux is then given by
Mkine=∫∫p380KgHw∗‾-1a∂z∂ϕ380Kv∗‾+1g∂p∂t380Kds,
where p380K is the pressure at the 380 K surface,
a=6371km and H=7km. Monthly upward and downward fluxes,
Mkine↑ and Mkine↓, can be separated
according to the sign of the monthly average term under the integral
Eq. (). Gridded summation is applied in the same way
as for Mdiab.
Annual variations of the monthly upward mass flux at the 380 K
surface, calculated from the ERA-Interim data. Mdiab↑
and Mdiab,corr↑: diabatic mass flux calculated from the
radiative heating rates of ERA-Interim with and without the mass
conservation correction of the radiative heating rates, respectively. Mkine↑:
kinematic mass flux calculated from the residual mean meridional circulation.
All quantities are calculated and averaged over the years 2005 to 2008.
Figure compares the monthly average upward
fluxes calculated from Eq. () and
Eq. (). Here the diabatic flux is calculated from
3-hourly data and then averaged for each month over the period 2005–2008.
The upward flux is calculated as indicated above for each month and then
averaged over the 4 years. The kinematic mass flux is calculated in the
same way from monthly averages of the residual circulation and pressure at
380 K. The contribution of the pressure variation term in
Eq. () is then 2 orders of magnitude smaller than
that of the residual velocity and can be neglected.
The two estimates display a similar modulation with a minimum during boreal
summer and agree with other estimates from ERA-Interim .
There is, however, a shift between Mdiab↑ and
Mkine↑ which increases with the size of the latitude
band. The mean difference is close to 25 % in the three latitude bands. It
is notable that the upward flux and the total flux are identical in the
20∘ S–20∘ N band because monthly mean diabatic heating
rates on the 380 K surface within this domain are positive throughout
the year. It is also notable that there is little change in the upward flux
as the domain is expanded to 30∘ S–30∘ N and
40∘ S–40∘ N because most of the upward motion occurs
within the 20∘ S–20∘ N band.
Uncorrected upward diabatic mass flux on the 380 K
surface. Mdiab↑ (red) versus the Lagrangian diabatic
mass flux Mback↑ on the same surface calculated with
delays Δt=24h (green) and Δt=48h (blue).
The black curve shows Mconv↑, which is the part of
Mback↑ with Δt=48h that originates
from backward trajectories encountering a cloud within the previous 3 months.
The discrepancy between diabatic and kinematic fluxes can be reduced by
correcting the diabatic mass flux to satisfy global mass conservation. There
is no physical mean of ensuring such mass conservation when calculating
heating rates from radiative transfer. Actually, the mean
total mass flux across the 380 K provided by Eq. ()
and ERA-Interim data is 7.9×109kgs-1 over 2005–2008.
Applying a uniform compensating correction to the heating rates over the
sphere defines a new mass flux Mdiab,corr↑ which reduces
the discrepancy with Mkine↑ for all latitudes and all
times. However, as this uniform correction is entirely ad hoc in the absence
of any information about the spatial and temporal distribution of the errors,
we refrain from applying it in the subsequent analysis.
The definition used for the diabatic flux is consistent with what follows.
However, a more appropriate definition to compare with the kinematic fluxes
would be to take a zonal average before applying the sign criterion defining
the upward flux. The resulting upward flux (not shown) is indeed larger than
Mdiab↑ but the difference in the
40∘ S–40∘ N band is of the order of 1 to 2 %. This shift
is negligible and cannot explain the shift between kinematic and diabatic
fluxes.
Notice that the total mass flux across an isentropic surface does not need to
vanish instantaneously unlike across an isobaric surface under the
hydrostatic approximation. However, the small part of the pressure variation
term in the monthly flux indicates that the mass balance must be satisfied
over monthly averages in the same proportion, i.e., within about 1 %.
This is in agreement with the stratospheric overworld mass variations shown
by .
Having compared the diabatic mass flux from heating rates to the kinematic
flux, we now check that the diabatic mass flux can also be retrieved from the
Lagrangian trajectories. Here the mass flux is calculated from the
displacement of backward Lagrangian trajectories launched at 380 K.
The heating rate is estimated as Δθ/Δt, where Δθ is the variation of the potential temperature along the trajectory
during an interval Δt after the launch. Here the interval Δt
is always a multiple of 24 h to ensure that the averages are taken
over an integer number of daily cycles. The density σ is calculated
from the ERA-Interim at the location and time of the launch. Actually, due to
combined horizontal and vertical motion, and the inclination of potential
temperature surfaces with respect to isobars, pressure can temporarily
decrease for a descending parcel with increasing potential temperature. As a
general rule, pressure undergoes much larger fluctuations than potential
temperature along a Lagrangian trajectory. The mass flux over a domain
Γ can then be calculated as a sum over all parcels belonging to this
domain:
Mback=∑i∈ΓσiΔθiΔt,δsi,
where δsi is the surface of the 0.5×0.5∘
element associated with the parcel. The monthly upward flux
Mback↑(ϕ) is calculated by averaging over each month
the individual parcel flux at each location over the grid, selecting all grid
points where the monthly flux is positive and then averaging in longitude.
Figure compares the temporal evolution of
Mdiab↑ and Mback↑ over the years 2005
to 2008. It is clear that the Lagrangian estimates with the delays Δt=24 or 48h provide an accurate estimate of the upward flux at
all latitudes.
Ratio of transport efficiency between forward calculations without
cloud top offset and with a +1 km cloud top offset. The ratio is averaged
over DJF and JJA for the 2005–2008 period and all regions. The values for
weakly contributing regions, excluded from Tables 1 and 2, are based on a
small number of events and are italicized.
Af
ITA
SAP
AML
NAPO
CAm
SAm
Tibet
IO
NCP
SEP
DJF
0.45
0.38
0.45
0.33
0.37
0.42
0.46
0.62
0.41
0.43
0.34
JJA
0.49
0.43
0.46
0.36
0.42
0.46
0.45
0.22
0.34
0.46
0.34
Distribution of annual mass flux, averaged over 2005–2008, for all
the regions (in %).
Af
ITA
SAP
AML
NAPO
CAm
SAm
Tibet
IO
NCP
SEP
10.8
2.4
39.2
8
18
7.5
6
0.8
1.2
5.9
0.2
Regional distribution of the upward mass flux
Based on these premises we calculate how the monthly upward flux is
distributed among regions in the following way. First all the grid points on
the 380 K surface within the domain 40∘ S–40∘ N
where the monthly average flux is positive are selected for each month. The contributions of parcels originating from each source region
are then
quantified. Then the mass flux of each region is calculated as the sum of
mass fluxes at the selected grid points from which backward trajectories link
to this region as the source. The sum of all these regional contributions
constitutes the convective upward flux at the 380 K surface
Mconv↑. In this procedure, a trajectory is labeled with
the time of its launch on the 380 K surface (unlike the calculations
leading to Fig. where the trajectories are
labeled according to their arrival or departure from clouds).
Figure shows that Mconv↑
basically explains the seasonal variations of Mback↑. The
difference between these two estimates has a mean of
4.8×109kgs-1 and a standard deviation of
9×108kgs-1 in the 40∘ S–40∘ N band.
As a result, the ratio Mconv↑/Mback↑
varies between 78.7 % in boreal winter and 80.5 % in boreal summer (80 %
on the average). The non-convective contribution is mostly accounted for by
parcels which are mixed into the TTL from the extratropics
.
The mean annual cycle is shown in Fig. . For each
region, it exhibits a maximum and a minimum which lag by about 1 month with
respect to the corresponding source curve
Fig. a, where the time axis is that of the
intersection with convection. This is particularly clear for SAP with a
maximum in February and a minimum in September that determines those of the
total convective flux. The delay is consistent with the distribution of
transit time among the main sources (see Table 2). It is only for AML that
the lag is not clear, but this is also a region with short transit times. The
larger boreal winter to boreal summer modulation of
Mconv↑ than the total source curve in
Fig. a suggests that this modulation is mostly
due to variations of transport properties within the TTL rather than to a
modulation of the properties of convective sources.
Mean annual cycles of monthly upward mass fluxes through the
380 K isentropic surface within the 40∘ S–40∘ N
band attributed to each source region. Fluxes are based on backward trajectories
during 2005–2008. The black curve shows Mconv↑.
Time is defined relative to back trajectory launch at 380 K,
rather than the convective source.
Figure shows the same hierarchy among source
regions as Fig. a with enhanced domination of
the SAP contribution over the year which accounts for 39 % of the total
Mconv↑ flux while NAPO accounts for 18 % (see Table 4).
If one adds NCP, CAm which is mostly oceanic, and the small contribution from
the Atlantic, Indian Ocean and South East Pacific, we see that the
contribution from oceanic regions (which include some large islands) is
74.5 %, a proportion in line with the fractional surface area of the oceans
in the region 20∘ S–20∘ N.
We stress that the flux Mconv↑ is the mass flux crossing
the 380 K surface that originates from the region of convective outflow
within the TTL but is not necessarily purely made of air processed by
convection. This air mass contains convective air detrained from the clouds
in the vicinity but is also mixed with environmental air which may have been
transported from distant and earlier convective sources and partly originates
from in-mixing or extratropical lower stratosphere transported into the
tropics . Therefore Mconv↑ must be
taken as an upper bound of the flux of convectively processed air.
Summary and outlook
We have shown that a consistent vertical distribution of convective sources
of stratospheric air over the tropical regions is obtained from backward and
forward diabatic trajectories in the TTL.
The seasonal cycle of sources is binary with a domination of the single SAP from November to April and a more complicated
pattern dominated by the regions of the Asian monsoon from June to September.
The distribution of sources among regions is qualitatively robust to
uncertainties in the method and the data but the quantitative distribution is
somewhat sensitive to the representation of cloud tops and the reanalysis
used to drive the trajectories. Generally, increasing the weight of highest
clouds shifts the distribution of sources towards higher altitude and wider
vertical dispersion, but it does not change the proportion of backward
trajectories meeting a cloud.
There is a pronounced seasonal cycle of the monthly average upward mass flux
across the 380 K surface, with a maximum in February and a minimum in
September, which is shifted by about month to the seasonal cycle of sources,
due to the mean time of transit of parcels across the TTL.
The forward transit times average 1 month with a significant
standard deviation of about 15 days. Transit times are shorter from
convection over continental Asia during boreal summer (3 weeks from
AML and 2 weeks for Tibet). The backward transit times are
longer. The discrepancy between forward and backward transit times is most
pronounced for transport from convection over Tibet.
One of the main motivations of this study was to study how air parcels
detrained from clouds find their way across the LZRH. Our results, however,
show that the sources are mostly (80 %) located slightly above the LZRH,
but not by much. This implies that only the small percentage of convective
events penetrating high enough in the TTL is relevant as stratospheric
source. The proximity of sources to the LZRH provides evidence that the
vertical flow separation at this level is an important factor in determining
the distribution of the sources. This will be further demonstrated in a
companion paper.
The high contribution of the tallest clouds raise a concern about the lack of
representation of small-scale convective features in the CLAUS data set and in
the ERA-Interim heating rates. In particular, CLAUS captures very well the
large anvils that form at the top of convective systems but misses the short
and elusive overshooting events over these anvils. The velocities and
radiative heating rates from reanalyses also miss the cross-isentropic mixing
provided by such events or represent them with very crude parameterizations.
The comparison between three modern reanalyses show that the heating rates
can differ substantially in the TTL (see ) with
significant consequences on its distributions of sources. While JRA-55
remains fairly close to the ERA-Interim, MERRA shifts the sources by up to
12 K in the vertical location in potential temperature space. The
discrepancy is even larger with the calculations of
who uses heating rates based on the
observed distribution of clouds . The main differences are
during boreal summer season. find a proportion of
backward trajectories reaching clouds falling to 15 % in boreal summer from
60 % in boreal winter. We find instead a maximum a maximum in April
(88.7 %) and a minimum in July (84.7 %) with ERA-Interim. The second
important difference is in the fact that continental sources in Asia prevail
over oceanic sources during boreal summer in
.
perform radiative calculations using clouds retrieved from
the CALIPSO based on their own processing of the L1 data combined with
monthly averaged ISCCP data for opaque clouds. More recently,
have studied the radiative effect of clouds in the TTL
in the Asian monsoon region using a combined flux and heating rate product of
the CLOUDSAT and CALIPSO missions (2B-FLXHR-LIDAR). Both studies conclude
that the average cloud radiative heating is positive in the TTL. This agrees,
qualitatively, with the calculations of ERA-Interim but contradicts MERRA,
which finds a net cooling (see Supplement Figs. S1 and S2 for ERA-Interim,
Figs. S3 and S4 for MERRA). However, find that the
heating is reinforced over active convective regions of the Asian monsoon
while find cooling in these regions above 15 km,
in particular over the Bay of Bengal. Such differences might explain the
discrepancies between our finding and those of . The net
cloud radiative heating in the TTL is the result of the opposite tendencies
of convection, which is mainly cooling, and thick and thin cirrus clouds which
are mainly warming.
It is actually very difficult to compare the calculated heating rates with
observations. However, the integrated cloud radiative effect (CRE) of the
three reanalyses considered here has been evaluated against CERES
observations by who found that “spatial correlation of CREs
and TOA upward radiation fluxes in ERA-Interim is the best among the three
reanalyses” in spite of some discrepancies in the global mean CRE.
also note that all three reanalyses have difficulties
reproducing boreal summer CREs over East Asia, a conclusion also supported by
based on a study of radiation budgets in AMIP-5 models.
Therefore we are led to conclude that the heating rates and the radiative
effect of clouds in the Asian monsoon region are still a puzzle that requires
further investigations.
Our study corroborates the special role of the Tibetan Plateau in providing
air to the AMA. We find that 87 % of the AMA
air originates from continental Asia, which agrees very well with the finding
of (90 %), but our results differ from these authors by
giving a much stronger weight to the Asian continental regions outside Tibet
(AML). We find, however, that this proportion is sensitive to the
representation of cloud tops and that it varies a lot within the literature:
and , find like , a
prevalent role of the Tibetan Plateau, while and
meet our conclusions. finds
that during mid-summer 2012 the AMA is fed mostly from continental sources
over North India and the Tibetan Plateau and stress the role of the south
boundary of the AMA as a transport barrier. Nevertheless, Tibet is
characterized by a very high efficiency at carrying air from the top of the
clouds to the 380 K surface and short transit times. This provides a
hint that Tibet is a sensitive area for the increase of air pollution as boundary
layer compounds processed by convection can be carried efficiently and
rapidly to the stratosphere. It remains that Tibet is an overall small
contributor to the global transport into the stratosphere because most of the
air entering the stratosphere during boreal summer is not processed inside
the AMA but is transported around, separating the location of convection from
the entry point into the stratosphere
.
Our results can be compared with those of and
. use kinematic trajectories from the
boundary layer focusing on the boreal summer season while
analyze impulse tracers transported by a general circulation model and
provides a whole year analysis. Although both use a different set of regions
within the tropics, our results regarding the regional distribution of
sources and their seasonal variations are basically consistent with these two
studies. There are discrepancies, however, in the transit timescales.
show a distribution of transit times from the boundary
layer to the stratosphere which has a strong modal peak over Asia at about 3
days while, contrarily, show a distribution which
peaks at about 2 months. Our results lay somewhere between, but such a
discrepancy highlights an issue that needs to be resolved. Transit times are an
important factor in understanding and modeling the behavior of many
short-lived chemical species in the TTL and the lower stratosphere.
In spite of displaying the highest cloud tops , the
continental convection above Af and SAm is a
weaker provider than the oceanic convection of SAP and NAPO. However, our conclusions do not account for the effect of
small-scale overshoots which are more commonly observed over these regions
than over the oceans due to the larger available
convective energy over land.