We constructed a database of cross-tropopause convection
in the Asian monsoon region for the months of May through October of 2017
using overshooting tops (OTs), deep convective features that penetrate the
local cirrus anvil layer and the local tropopause, with Meteosat-8
geostationary satellite detections. The database of 40 918 OTs represents a
hemispheric record of convection covering the study domain from
10∘ S to 55∘ N and from 40 to 115∘ E. With
this database, we analyzed the geographic, monthly, and altitude
distribution of this convection and compared it to the convective
distributions represented by satellite observations of outgoing longwave
radiation (OLR) and precipitation. We find that cross-tropopause convection
is most active during the months of May through August (with daily averages
of these months above 300 OTs per day) and declines through September and
October. Most of this convection occurs within Northern India and Southern India, the
Bay of Bengal, and the Indian Ocean regions, which together account for
75.1 % of all OTs. We further identify distinct, differing seasonal trends
within the study subregions. For the Northern India, Southern India, and Bay of
Bengal regions, the distribution of OTs follows the development of the Asian
monsoon, with its north–south movement across the study period. This work
demonstrates that when evaluating the effects of convection on lower
stratospheric composition over the Asian monsoon region, it is important to
consider the impact of cross-tropopause convection specifically, as well as
the contributions from both land-based and oceanic regions due to the
significant geographic and monthly variation in convective activity.
National Aeronautics and Space AdministrationNNX15AF60GNational Science Foundation1203583Introduction
Deep convection in the Asian monsoon region provides an important pathway
for the transport of boundary layer air to the stratosphere (e.g., Brunamonti et al., 2018; Khaykin et al., 2022; Lelieveld et al., 2018; Park
et al., 2007; Randel et al., 2010; Santee et al., 2017). Which areas within
the Asian monsoon region, however, contribute the most to this convective
transport remain uncertain. Moreover, while the current understanding of
convective transport considers deep convection that reaches the upper
troposphere, less attention has been paid to the role of cross-tropopause
convection (Legras and Bucci, 2020; Tissier and Legras, 2016). To
investigate the potential impacts of cross-tropopause convection on
convective transport and the composition of the lower stratosphere, we
construct a seasonal database of “overshooting tops” (OTs), deep
convective updrafts that penetrate both the cirrus anvil layer and the local
tropopause, in the Asian monsoon region from geostationary satellite
infrared imagery for the period of May through October of 2017. With this
database we identify the primary source regions of cross-tropopause
convection as well as the seasonal and altitude trends of this deep
convection. Understanding the convective transport within the Asian monsoon
is critical to understanding the transport of both radiatively and
chemically active species from the boundary layer to the lower stratosphere
(Brioude et al., 2010; Claxton et al., 2019; Pisso et al., 2010; Tegtmeier
et al., 2020a). In particular, the timing and location of convection
determine which species in what quantities are transported upwards from the
source region boundary layer to the lower stratosphere.
Deep convection associated with the Asian monsoon has been shown to
influence the composition of the lower stratosphere. Convective transport of
boundary layer air to the upper troposphere–lower stratosphere (UTLS) over
the Asian monsoon impacts the concentrations of many chemical species that
are both radiatively and chemically significant. Observations of this
convective influence range from large-scale satellite studies to small-scale
in situ measurements and cover a wide range of chemical perturbations of
tropospheric origin. To begin with, satellite observations have shown water
vapor and carbon monoxide maxima as well as ozone minima concurrent with the
Asian monsoon anticyclone that develops in the UTLS region (Luo et al.,
2018; Park et al., 2007; Randel et al., 2010; Santee et al., 2017).
Modeling studies, including chemistry–climate models (e.g., Pan et al.,
2016; Wu et al., 2020) and Lagrangian-trajectory models (e.g., Legras and
Bucci, 2020; Tissier and Legras, 2016), have demonstrated the convective
source of these water vapor, carbon monoxide, and ozone perturbations.
Further, recent in situ measurements have also verified their convective
origins (Bucci et al., 2020; Johansson et al., 2020; von Hobe et al., 2021).
In addition, deep convection has been shown to transport anthropogenic
pollutants such as peroxyacetyl nitrate and acetylene (Johansson et al.,
2020) and other non-methane hydrocarbons (Baker et al., 2011). Both
satellite and in situ measurements have also indicated that deep convection
is the primary source of the aerosols in the UTLS in the Asian monsoon
region (e.g., Bian et al., 2020; Brunamonti et al., 2018; Hanumathu et al.,
2020; Höpfner et al., 2019; Vernier et al., 2015, 2018).
Furthermore, this convection may transport very short lived species (VSLS),
including ozone-depleting substances (ODSs), to the UTLS (Adcock et al.,
2021; Fiehn et al., 2018; Hossaini et al., 2016; Tegtmeier et al., 2020a).
Trajectory studies have identified radiatively driven uplift across the
tropopause within the Asian monsoon anticyclone (AMA) as the primary pathway
for convective influenced air to enter the lower stratosphere (e.g., Legras
and Bucci, 2020; Vogel et al., 2019; Yan et al., 2019). Rapid convective
transport deposits boundary layer air in the upper troposphere above the
level of zero radiative heating (LZRH) and subsequent heating results in
slow ascent within the AMA (Bergman et al., 2013; Chen et al., 2012; Tissier
and Legras, 2016). The rate of the slow ascent in these trajectory studies
depends on the reanalysis used to evaluate the wind fields (Tegtmeier et
al., 2020b); for example, an ascent of approximately 1.0–1.5 K per day has
been shown using ERA-Interim (e.g., Legras and Bucci, 2020). The AMA, a
large anticyclonic circulation in the UTLS with a variable spatial extent
that spans from northeastern Africa to eastern Asia (e.g., Pan et al., 2016; Randel
and Park, 2006; Vogel et al., 2015), acts as a transport barrier to confine
air detrained by convection (Ploeger et al., 2015; Poshyvailo et al., 2018).
This results in a “spiralling ascent” over months within the AMA across
the tropopause (Legras and Bucci, 2020; Vogel et al., 2019).
The impact of tropospheric air convectively transported into the lower
stratosphere, however, is not confined to the AMA. Both eastward and
westward eddy shedding by the AMA has the potential to transport pollutants
and boundary layer air from the Asian monsoon region into the midlatitudes
and global stratosphere (Fadnavis et al., 2018; Fujiwara et al., 2021; Garny
and Randel, 2013; Popovic and Plumb, 2001; Vogel et al., 2014). In situ
measurements of stratospheric air with perturbed chemical compositions
(Lelieveld et al., 2018; Müller et al., 2016; Rolf et al., 2018) and
lidar observations of enhanced stratospheric aerosols (Khaykin et al., 2017)
sourced back to the AMA have been observed in the midlatitudes of the
Northern Hemisphere.
The location and timing of deep convection influence the kind and amount of
tropospheric source gases and aerosol transported into the AMA. Due to the
heterogeneity of surface emissions within the Asian monsoon region, the
chemical composition of convectively transported boundary layer air will
vary significantly depending on the source region. Furthermore, the location
of the initial convective transport determines the pathway of entry into the
lower stratosphere and therefore which regions are impacted by subsequent
transport within the large-scale circulation. For example, several studies
investigating the ozone-depleting potentials (ODPs) of various chemical
species have found that the influence of source regions and seasons alone
results in significantly different ODPs for the same emissions (Brioude et
al., 2010; Claxton et al., 2019; Pisso et al., 2010; Tegtmeier et al.,
2020a). Therefore, understanding the distribution of deep convection within
the Asian monsoon region is necessary to predict its net effect on the
composition of the UTLS, both locally and globally.
Prior work on convective influence within the Asian monsoon region has
focused primarily on convective transport from the boundary layer to upper
troposphere followed by diabatic heating and ascent into the lower
stratosphere. Yet, the relative importance of various source regions remains
unclear. For example, modeling studies (Bergman et al., 2013; Heath and
Fuelberg, 2014; Pan et al., 2016; Wu et al., 2020) and satellite
observations (Fu et al., 2006) have found that deep convection over the
Tibetan Plateau is a key contributor. In contrast, additional studies have
identified the Bay of Bengal and the western Pacific as critical source regions
using model simulations (Chen et al., 2012; James et al., 2008) and
satellite observations (Devasthale and Fueglistaler, 2010). Other studies
that combine trajectory analysis with satellite observations of deep
convective cloud tops have suggested that while the Tibetan Plateau may
present a uniquely efficient pathway into the UTLS, it is a minority
contributor compared to other source regions including the Asian mainland
(Legras and Bucci, 2020; Tissier and Legras, 2016). Moreover, these
convective source regions are found to have distinct tropopause-crossing
regions (Chen et al., 2012). Much of this uncertainty likely arises from the
difficulty in accounting for, or resolving, the small temporal and
spatial scales necessary to represent convection. In particular, in
modeling studies differences in reanalyses used and parameterizations of
convection will further complicate the representation of convective
transport.
Previous studies investigating the global distribution and frequency of
extreme convection using non-Sun-synchronous satellite observations have
shown the Asian monsoon region to be of importance (Liu and Liu, 2016; Liu
et al., 2020; Zipser et al., 2006). This work also found that extreme
convection was more likely to occur over land than over ocean and that the
midlatitudes had frequencies comparable to the tropics. Importantly, when
considering extreme convection that reached or surpassed the tropopause, the
Asian monsoon region remained an area of significant activity (Liu and Liu,
2016; Liu et al., 2020).
The research produced by the StratoClim project, which examined deep
convection within the Asian monsoon region during the same time period as
the present study (2017), provides important context. Aircraft in situ
measurements of water vapor (Khaykin et al., 2022; Lee et al., 2019), CO
(Bucci et al., 2020; Lee et al., 2021; von Hobe et al., 2021), ozone
(Johansson et al., 2020; von Hobe et al., 2021), and N2O (von Hobe et al.,
2021), as well as HNO3, peroxyacetyl nitrate, ethylene, and formic acid
(Johansson et al., 2020), show clear evidence of convective influence on the
UTLS comprising up to 100 % of the air (Bucci et al., 2020). While some
studies indicate that transport of convectively influenced air across the
tropopause primarily occurs via slow ascent (von Hobe et al., 2021), others
demonstrate that cross-tropopause convection directly impacts the LS
(Khaykin et al., 2022; Lee et al., 2019, 2021). As von Hobe et
al. (2021) discuss, these results are not necessarily contradictory but
indicate the complexities of the microphysics during convective transport.
Modeling studies of transport pathways into the UTLS show that convection
that surpasses the LZRH will ultimately influence the stratosphere due to
slow ascent across the tropopause (Legras and Bucci, 2020; Nützel et
al., 2019; Vogel et al., 2019; Yan et al., 2019). Convection that directly
crosses the tropopause, however, has a greater probability of influencing
the LS of the Northern Hemisphere than ascending through the tropical pipe
compared to convectively influenced air that enters the stratosphere via
slow ascent (Yan et al., 2019).
The present analysis builds on prior studies of the convective transport of
boundary layer air into the lower stratosphere and of the frequency of
intense convection over the Asian monsoon region by focusing specifically on
cross-tropopause convection across a broad geographic region for a time
period that covers the entire Asian monsoon. Cross-tropopause convection is
focused on as a special class of extreme convection that directly reaches
the lower stratosphere bypassing slow-ascent across the tropopause and
directly connecting the convective source region and the location of entry
into the lower stratosphere. We accomplish this by exploiting convective
features (OTs), which represent both the finest-scale and deepest
convection, to assess convective transport directly into the lower
stratosphere within this specific region. Using OTs identified with
geostationary satellite infrared imagery (Bedka and Khlopenkov, 2016), we
construct a database of cross-tropopause convection and describe the
geographic, monthly, and altitude distribution of this convection over the
Asian monsoon region from May through October of 2017, a period selected to
overlap with the StratoClim project. We also compare this distribution of
cross-tropopause convection to the distribution of overall convection as
represented by satellite observations of outgoing longwave radiation (OLR)
and precipitation for the same period and region. This work is complementary
to the previously discussed studies which largely focused on tropospheric
convection that reaches altitudes above the LZRH before slowly ascending
into the stratosphere.
The boundaries of the study area and the subdivisions of the
areas used for the regional analysis.
Data and methodsOT database
The OT database, derived from Meteosat-8 data (European Organisation for the Exploitation of Meteorological Satellites, 2017), represents a hemispheric
record of convection covering the study domain from 10∘ S to
55∘ N and from 40 to 115∘ E (see Fig. 1) for the time
period of 1 May through 31 October 2017. The OTs are algorithmically
identified using multispectral imagery from Meteosat-8 with a horizontal
resolution of approximately 4 km (Bedka and Khlopenkov, 2016; Yost et al.,
2018). Updrafts that overshoot their anvil altitude but do not reach the
tropopause are not included in this study. Only OTs with detection
probability ratings (as determined by the temperature differences between
the OT and the anvil, the tropopause as identified with the WMO lapse-rate
definition and the MERRA-2 reanalysis data (Global Modeling and Assimilation Office, 2015), and the local level of neutral
buoyancy) greater than or equal to 0.9 were used in this analysis. This
threshold detected ∼50 % of human-identified OTs randomly
sampled throughout the world (Bedka and Khlopenkov, 2016). At this rating,
the false detection rate was determined to be ∼10 %, and
these errant detections were typically found in close proximity to actual OT
regions, so inclusion of these samples does not adversely affect the
results. The Bedka and Khlopenkov (2016) OT identification method is a
conservative methodology that underpredicts OTs allowing for high confidence
in the OTs that are detected. The potential temperature of each OT was
derived from the OT IR temperature and pressure derived from the OT height
and MERRA-2 (Gelaro et al., 2017) reanalysis, using the method of Griffin et
al. (2016).
The OT database does not represent a complete budget of cross-tropopause
convection occurring in the study region and over the time period
considered. This is because the Meteosat-8 OT data were acquired every 15 min, while the average lifetime of an OT can be as short as several
minutes (Bedka and Khlopenkov, 2016). Consequently, the OT dataset used in
this study represents a small percentage of the total number of OTs that
occurred and should not be used to estimate total convective outflow. OTs
detected in this study consist of multiple pixels identified from the
Meteosat-8 multispectral imagery. The OT detection algorithm first
identifies local brightness temperature minima embedded within likely anvil
clouds. Then, after filtering the minima to identify possible OTs, the
algorithm characterizes the mean temperature of the anvil immediately
surrounding each possible OT. Pixels surrounding the OT minima that are
colder than the anvil are assumed to comprise OT regions. Here, a pixel
refers to the minimum spatial unit resulting from the Meteosat-8 horizontal
resolution (approximately 4 km).
We report OTs as distinct events rather than by pixel count because the
temporal resolution (15 min) is insufficient to accurately track the
temporal evolution of the spatial extent of an OT. The OT database, however,
is still valid for analyzing intraseasonal variability, evolution, and
distribution because the consistent and frequent sampling, the long time
period under study, and the large region of interest retain the major
features of OT frequency, depth, and geographic distribution. This OT
detection methodology has been used in prior studies of the geographic and
seasonal distributions of cross-tropopause convection (Clapp et al., 2019, 2021).
Study regions
To identify regional differences in geographic, monthly, and vertical
distribution of OTs, the study domain was subdivided into 12 regions. These
regions (see Fig. 1) were selected to capture geographically distinct
areas of cross-tropopause convection and to allow for comparison to prior
work (e.g., Bergman et al., 2013; Fu et al., 2006; Heath and Fuelberg, 2014)
that examined sources of convective influence on the lower stratosphere in
the Asian monsoon region. Further, many of these regions have been shown to
have differences in convective characteristics such as convective initiation
mechanisms and seasonal dependences (Bhat and Kumar, 2015; Romatschke and
Houze, 2011; Saikranthi et al., 2018; Virts and Houze, 2016). The region of
a given OT was determined by the average latitude and longitude of the
pixels constituting that OT.
The Tibetan Plateau region was included because prior studies have
identified it as a significant source region of convective influence on the
UTLS. It has a unique convective environment due to its topography and is
centrally located within the Asian monsoon anticyclone (e.g., Bergman et al.,
2013; Heath and Fuelberg, 2014). The southern edge of the region, between
70 and 95∘ E, which separates it from the Northern India
region, is defined by the 3 km topographic height, taken from the MERRA-2
reanalysis. Similar boundaries have been used previously (e.g., Fu et al.,
2006; Heath and Fuelberg, 2014).
The Northern India and Southern India regions were included to capture the primary areas
of land-based Asian monsoon convection. The separate regions are necessary
to distinguish the differences in seasonality, such as monsoon onset
(Kajikawa et al., 2012; Walker and Bordoni, 2016), and convective
character, such as the importance of orography to convective initiation in
Northern India (Romatschke and Houze, 2011). This distinction has also been
used in prior work (e.g., Fu et al., 2006; Heath and Fuelberg, 2014). Northern
India was also identified as a potential source region of convectively
transported NH3 observed during StratoClim (Höpfner et al., 2019).
The Bay of Bengal, Arabian Sea, and Indian Ocean regions were defined to
account for oceanic monsoon convection. The Indian Ocean region captures and
separates the influence of the intertropical convergence zone (ITCZ), seen
in the OT distribution (Fig. 2a), from the other two oceanic regions (Bay
of Bengal and Arabian Sea). The Bay of Bengal region has also been
previously studied as a region of significant convective influence (e.g., Devasthale and Fueglistaler, 2010).
The geographic distribution of convection over the Asian monsoon
region during the study period (1 May through 31 October 2017).
Cross-tropopause convection is shown in panel (a). The colorbar shows OT
frequency, evaluated in 1∘× 1∘ bins, within the
satellite observation window. Also shown is the average Montgomery potential
stream function at 400 K for the months of JJA (dashed black contours from
3.59×105 m2 to 3.63×105 m2 s-2 by
0.01×105 m2 s-2) to illustrate the location of the Asian
monsoon anticyclone. The minimum and average daily OLR values are shown in
panels (b) and (c), respectively. The minimum and average daily precipitation
are shown in panels (d) and (e), respectively.
The Southeast Asia, Eastern China, and western Pacific regions were included to
distinguish the eastern portion of the Asian monsoon from the subcontinental
convection, which is expected to have different intraseasonal variability
and large-scale drivers (Wang et al., 2001; Wei et al., 2015; Yihui and
Chan, 2005). The Arabian Peninsula region was included to investigate the
climatology of the high density of OTs observed over the southwestern Arabian
Peninsula and Ethiopia (Fig. 2a). The remaining areas within the study
region, in which few OTs are observed, are covered by the northern
latitudes, and Africa regions.
Outgoing longwave radiation and precipitation data
The OLR data are taken from the NOAA Interpolated Outgoing Longwave Radiation
climatology, which has a global coverage and a resolution of 2.5∘× 2.5∘ (Liebmann and Smith, 1996). The precipitation data are from
the Global Precipitation Climatology Project (GPCP) Climate Data Record
(CDR) (Adler et al., 2020), which has global coverage and a resolution of 1∘× 1∘ (Huffman et al., 2001).
Results
We begin with an analysis of the geographic distribution of all OTs observed
across the entire study region and time period from 1 May through 31 October 2017. The distribution of cross-tropopause convection is compared with other
convective indicators: OLR as a proxy for deep convection and daily
precipitation as an indicator of general tropospheric convection. The
results of this analysis are then used to identify specific regions of
interest for cross-tropopause convection within the larger context of
monsoon convection.
Geographic and seasonal distribution of cross-tropopause convection
Figure 2a shows the geographic locations of all OTs across the study region
for the entire study period, May through October of 2017. OTs are binned
into a latitude–longitude grid of 1∘× 1∘ resolution.
Areas of significant cross-tropopause convection include Northern India (in
particular the northwestern coast and northern coast of the Bay of Bengal)
and the Bay of Bengal. The Indian Ocean shows a high volume of dispersed
cross-tropopause convection located in an east–west band around the Equator.
In contrast, the southern edge of the Arabian Peninsula has a smaller number
of OTs that are highly concentrated enough to show number densities
comparable to the largest source regions (174 in the Arabian Peninsula
region compared to 154 and 183 in the Northern India and Bay of Bengal regions,
respectively). Intense convective events over the Arabian Peninsula have
been previously observed (Liu and Liu, 2016; Liu et al., 2020; Zipser et
al., 2006). The spatial distribution of OTs over Asia (Fig. 2a) shows the
importance of both land-based and oceanic convection with a high frequency of
cross-tropopause convection occurring in both environments.
Another significant feature is the absence of cross-tropopause convection
occurring over the Tibetan Plateau. In contrast, a large quantity of OTs is
observed over the southern slope of the plateau. Local topography defines a
distinct boundary north of which few OTs occur. This is consistent with the
conclusions of Tissier and Legras (2016) and Legras and Bucci (2020) in
which the Tibetan Plateau is not a numerically significant contributor to
convective transport, but it is found to be particularly efficient. This
efficiency results from the high proportion of convective events over the
Tibetan Plateau that reach the minimum level in the UT above which the slow
uplift that occurs across the entire AMA will transport convectively
influenced air into the LS. The lack of OT activity over the Tibetan Plateau
confirms that convection in this region influences the LS through the slow
uplifting process, not through cross-tropopause convection. The agreement
with Legras and Bucci (2020) is particularly relevant as their study also
covered the 2017 Asian summer monsoon. Similarly, the distributions observed
in the global studies of extreme convection also exhibit a ridge of
increased frequency along the southern slope but few convective events over
the Tibetan Plateau itself (Liu and Liu, 2016; Liu et al., 2020; Zipser et
al., 2006). Further, Devasthale and Fueglistaler (2010) find that while
there is a significant cloud fraction over the Tibetan Plateau during July
and August at 200 and 150 hPa, at 100 hPa there is almost none. Our
climatology of cross-tropopause convection confirms that convection rarely
transports boundary layer air directly into the lower stratosphere over the
Tibetan Plateau.
Figure 2b and c show the OLR minimum and average daily value for the
entire study period. The minimum OLR spatial distribution, as a proxy for
the “deepest” convection, matches the cross-tropopause convective
distribution better than the average OLR. In particular, Northern India, the
Bay of Bengal and the Indian Ocean are regions of significant convective
activity as shown by OLR. The cross-tropopause convection activity over the
Arabian Peninsula visible in the OT distribution, however, is not shown to
the same degree in the OLR data. The greatest disagreement between the OT
distribution and the average daily OLR is present over the Tibetan Plateau,
which has OLR values comparable to the other major convective regions, but
very few OTs. This is likely a consequence of the unique structure of the
Tibetan Plateau. The high altitude of the region results in particularly low
OLR values for tropospheric convection. This could also be a consequence of
frequent convection within the Tibetan Plateau region that, however, does
not cross the local tropopause as often as convection in the regions marked
by high OT counts.
Figure 2d and e show the GPCP maximum daily precipitation and average
daily precipitation for the entire study period for each grid box
(1∘× 1∘). When comparing the geographic distribution
of cross-tropopause convection to the distribution of maximum daily
precipitation, there is agreement over the Bay of Bengal and Northern India,
which are dominant regions of convection. For example, the northwestern coast
of the Bay of Bengal and over northwestern India have “hotspots” of both high
maximum daily precipitation and high densities of OTs. Further, the southern
slope of the Tibetan Plateau also forms a ridge of high precipitation
values, northward of which there is less activity, similar to the sharp
transition in OT frequency. The Indian Ocean also shows a band of
precipitation at the Equator, similar to the band of cross-tropopause
convection, although of lesser magnitude. The large maximum precipitation
values in central India, eastern China, and Southeast Asia, however, do not
correspond to OT activity, indicating that convection in this region does
not reach the LS. The high concentration of convective activity visible in
the OT distribution over the Arabian Peninsula is also not present in the
precipitation data. This is a potential consequence of the convective events
over the Arabian Peninsula being small, and therefore not detected by the
relatively coarse GPCP and OLR observations.
The seasonal development of cross-tropopause convection within the
study region. Panel (a) shows the time series of the total daily number
(left axis) and percent (right axis) of OTs in black as well as the rolling
8 d average in grey (dashed). Panel (b) shows the time series of the daily
absolute number (left axis) and percent (right axis) of OTs observed within
each region, as indicated by color and line style. Panel (c) shows the
cumulative total number (left axis) and percent (right axis) of OTs within
each region, as indicated by color and line style, as a function of time.
Panel (d) shows the daily-normalized fractional contribution of each region,
as indicated by color and line style, as a function of time.
Figure 3 displays the seasonal development of cross-tropopause convection.
As seen in Fig. 3a, during the active period of May through August, the
daily number of OTs fluctuates largely between 200–400 OTs per day. As the
monsoon season declines through September and October so does the frequency
of cross-tropopause convection. For example, during the active months of May
through August, on average 22.9 % of the total OTs occur per month. In
contrast, during the months of September and October 13.4 % and 6.3 % of
total OTs occur, respectively. From Fig. 3b, however, it is clear that
different regions dominate cross-tropopause convection at different times.
For example, the Northern India region is the most important source region
during June and July, while the Bay of Bengal and Indian Ocean regions are
more significant in May. As the OT database is not a complete budget of
cross-tropopause convection, the values presented here quantify the
contributions of different regions relative to each other, not their total
convective output.
Despite different source regions having time periods of particular
significance, the Northern India region has the highest daily number of OTs
observed (see the late June and early July peaks in Fig. 3b). This
indicates that although June and July have similar total amounts of
cross-tropopause convection to May, these months are uniquely dominated by a
single source region. This is emphasized in Fig. 3d, which shows that
during June and July, the largest daily fractional contribution values are
from Northern India (0.59 on 25 June and 0.64 on 14 July). This daily
fractional contribution is only surpassed in October by the Bay of Bengal
region. There are a lot fewer total cross-tropopause events during this
period, however, and this peak is likely caused by specific meteorological
conditions, a tropical depression within the Bay of Bengal.
From Fig. 3c, the total impact of each region is visualized across these
varying time periods of relative importance. Northern India has the greatest
total amount of cross-tropopause convection (11 844 OTs, 29.0 %). The
Indian Ocean and Bay of Bengal regions are the next most significant
contributors with 7861 OTs (19.2 %) and 6212 OTs (15.2 %), respectively,
followed by the Southern India region with 4781 OTs (11.7 %). Together the
four largest contributors account for 75.1 % of all OTs in the study area.
Of the remaining OTs within the study area, most occur within the Southeast
Asia region (2556 OTs, 6.3 %), the Arabian Sea region (2321 OTs, 5.7 %),
and the Arabian Peninsula region (2021 OTs, 4.9 %). The northern latitudes
region has 1919 OTs (4.7 %); however, given the large size of the region
this is geographically dispersed. The Tibetan Plateau and the western Pacific
regions have little cross-tropopause convection with 628 OTs (1.5 %) and
582 OTs (1.4 %), respectively. The remaining regions of Eastern China and
Africa have 99 OTs and 94 OTs, respectively, less than 1.0 % combined. The
different contributions to cross-tropopause convection of each region are
summarized in Table 1.
Summary of the regional distribution of cross-tropopause
convection including the total number of OTs observed throughout the entire
study period, the percentage of total OTs, and the area-normalized percent
contribution for each region.
The vertical distribution of all OTs within the study region.
Panels (a) and (b) show the distributions of the average and maximum
potential temperature of each OT binned by 1 K, respectively. Panels (c) and
(d) show the distributions of the average and maximum tropopause relative
potential temperature of each OT binned by 1 K, respectively. Within each
bin, color indicates the fractional contribution of each month. For example,
the 12 K average tropopause relative potential temperature bin is comprised
of 315 May OTs, 344 June OTs, 376 July OTs, 227 August OTs, 141 September
OTs, and 79 October OTs. The mean, standard deviation, and number of OTs for
each distribution is indicated in the upper-right corner of each panel.
Figure 4 shows frequency distributions for both the average and maximum
potential temperature of OTs (Fig. 4a and b) and the average and maximum
tropopause relative potential temperature (Fig. 4c and d) over the study
region for the entire study period. Here the average potential temperature
of an OT refers to the average potential temperature of all the pixels that
constitute that OT. The maximum potential temperature of an OT refers to the
highest potential temperature reached by the pixels that constitute that OT.
Tropopause relative values are calculated for each pixel using the local
tropopause, before reporting the average and maximum of these values for all
pixels within a given OT. As not all pixels of a given OT cross the
tropopause, a small population of OTs have an average negative tropopause
relative height. The color subdivisions indicate the proportions of
cross-tropopause convection occurring during each month within each bin. As
the full range of the distribution is broad, all OTs with potential
temperature values outside of the plotted range are included in the
lowermost and uppermost bins. The distributions of the high-value tails are
shown in Figs. S1 and S2 in the Supplement.
The average OT reached an average potential temperature of approximately 381 K and a maximum potential temperature of 388 K. In tropopause relative
potential temperature this corresponds to +7.7 and +14.5 K,
respectively. In both potential temperature and tropopause relative
potential temperature, the distributions shift higher from May through
August, before descending again through September and October. This is most
pronounced in the maximum potential temperature distribution, with the
greatest number of OTs falling in the 386 K bin in May, rising to 389 K in
August, before falling to 382 K in October. As this shift is present in
tropopause relative potential temperature as well as potential temperature,
it is indicative of convection reaching further into the stratosphere
because the seasonal vertical motion of the tropopause is accounted for in
the vertical coordinate. For further information on the tropopause during
the study, Fig. S3 shows the temporal evolution of the daily
average of the local tropopause potential temperatures associated with each
OT.
Both distributions exhibit long tails. This is seen in both directions in
potential temperature space, with the lower values being most frequent in
May. This is likely due to the climatologically lower tropopause earlier in
the season. When normalized to the tropopause, however, the tail of the OT
vertical distribution is largely at higher altitudes. This is visible in the
large number of OTs within the highest bins (204 and 285 OTs for average and
maximum tropopause relative potential temperature, respectively), which
capture all OTs that reach a height above 40 K above the tropopause. These
infrequent, extreme convective events account for approximately 0.6 % of
all OTs and occur in every month, although October has only one such OT (see
Figs. S1 and S2). Ultimately, 83.8 % of the 40 918 OTs that
occurred during the study period reached 380 K, with 9.7 % reaching above
400 K, well into the lower stratosphere.
Cross-tropopause convection source regions
The intraseasonal distributions of cross-tropopause convection vary from
region to region as shown in Fig. 3a. In this section, we examine the
individual distributions of the largest source regions.
The potential temperature distribution of OTs within the Southern
India (a, b) and Northern India (c, d) regions.
Panels (a) and (b) show the distributions of the average and maximum
potential temperature of OTs occurring within the Southern India region binned
by 1 K, respectively. Panels (c) and (d) show the distributions of the
average and maximum potential temperature of OTs occurring within the Northern
India region binned by 1 K, respectively. Within each bin, color indicates
the fractional contribution of each month, as in Fig. 4. The mean,
standard deviation, and number of OTs for each distribution are indicated in
the upper-right corner.
Figure 5 shows the frequency distributions for both the average and maximum
potential temperature of OTs over the Southern India region (Fig. 5a and b)
and over the Northern India region (Fig. 5c and d). Cross-tropopause
convection in the Northern India region is more numerous (11 844 OTs compared
to 4781 OTs) and reaches higher potential temperatures (392 K average
maximum potential temperature compared to 389 K) than that over Southern India.
This difference in potential temperature distribution, however, is primarily
due to the latitude dependence of tropopause height. In tropopause relative
coordinates, the average maximum potential temperatures are 392 and 389 K
for the Northern India region and Southern India region, respectively.
The seasonality of the two regions also reveals a key difference. The Southern
India region has the most cross-tropopause convection occurring in May (1380
OTs), June (1011 OTs), and September (1127 OTs), with few OTs appearing in
July (152 OTs) and August (521 OTs). In contrast, over Northern India the
majority of cross-tropopause convection occurs in July (4269 OTs), followed
by June (3215 OTs) and August (2321 OTs), with relatively little in May (911
OTs) and September (1008 OTs). The distribution of cross-tropopause
convection exhibits a northward shift from May through August, followed by a
southward return in September.
The geographic distribution of monthly convection as shown by OTs
(left column), daily average OLR (middle column), and daily average
precipitation (right column).
This northward migration is consistent with the expected geographic
evolution of the Asian monsoon (Abhik et al., 2013; Ganai et al., 2019;
Goswami, 2011; Kajikawa et al., 2012; Romatschke et al., 2010; Sikka and
Gadgil, 1980). Figure 6 shows the monthly geographic distribution of OTs
(Fig. 6, left column), average daily OLR (Fig. 6, middle column), and
average daily precipitation (Fig. 6, right column). In both OLR and
precipitation, a northward shift is visible in the geographic position of
extreme values over land on the subcontinent from May through August,
followed by a southward retreat in September and dissipation in October. In
addition to the large-scale similarity in latitudinal intraseasonality, the
regions of most frequent cross-tropopause convection are co-located with OLR
and precipitation values indicative of significant convective activity. For
example, the high density of OTs in northwestern India during July corresponds
to a region of low OLR and high precipitation. However, there are regions of
low OLR and high precipitation that do not have corresponding OT activity,
such as off the southwestern coast of India during June. This is expected
because cross-tropopause convection is a unique subset of extreme convection
and not all convection, as represented by OLR or precipitation, is expected
to result in OT activity. The agreement among multiple convective metrics
suggests that the geographic and seasonal trends observed in OTs associated
with cross-tropopause convection follow the development of the Asian
monsoon.
As Fig. 5, but for the potential temperature distributions of OTs
over the Bay of Bengal region (a, b) and the Indian Ocean
region (c, d).
Figure 7 shows the frequency distributions for both the average and maximum
potential temperature of OTs over the Bay of Bengal region (Fig. 7a and
b) and over the Indian Ocean region (Fig. 7c and d). Both regions have
similar vertical distributions to the total OT distribution (Fig. 4) in
terms of the average (380 and 386 K for the Bay of Bengal, and 381 and
387 K for the Indian Ocean). The most significant contrast between these
regions is the differences in the seasonality of cross-tropopause
convection.
The Bay of Bengal has significant OT occurrence across all months, with the
most activity occurring earlier in May and June (1574 and 1984 OTs,
respectively). As shown in Fig. 6, the monthly geographic distribution of
the Bay of Bengal OTs matches the spatial distributions of low OLR and high
precipitation. However, while the spatial distribution is replicated, the
relative importance of each month in terms of number of OTs, is not
distinguishable in either OLR or precipitation for the Bay of Bengal region.
This is most apparent in the months of July and August (915 and 572 OTs,
respectively), which exhibit the greatest areal extent of low OLR and high
precipitation, but have fewer cross-tropopause convective events than May
and June. This is likely due to the lower tropopause in the earlier months
as shown in Fig. 7a and b in which the lowermost potential temperature
bins have a greater proportion of May and June contributions. Therefore,
while convection over the Bay of Bengal may have a maximum in July and
August, this convection is less likely to cross the tropopause.
The most notable feature of the distribution of cross-tropopause convection
in the Indian Ocean region is the relative lack of OTs in the month of June
(482 OTs, 6.1 %), compared to the other summer months with the next least
convectively active month, August, having 1379 OTs (17.5 %). Figure 6
suggests that this pause in cross-tropopause convection reflects a lack of
convection more generally. Both the OLR and precipitation indices show that
the Indian Ocean region has much less convective activity in June. The band
of OTs clustered around the Equator in July and August is consistent with
the position of the southern band of the Indian Ocean's “double” ITCZ
during the summer months (Berry and Reeder, 2014; Hu et al., 2007; Walsier
and Gautier, 1993).
As Fig. 5, but for the potential temperature distributions of OTs
over the Arabian Peninsula region (a, b) and the Arabian Sea
region (c, d).
Figure 8 shows frequency distributions for both the average and maximum
potential temperature of OTs over the Arabian Peninsula region (Fig. 8a
and b) and over the Arabian Sea region (Fig. 8c and d). Though these
regions are minority contributors to cross-tropopause convection over the
entire study region (4.9 % and 5.7 % for the Arabian Peninsula and Arabian
Sea regions, respectively), they exhibit high geographic concentrations of
OTs comparable to the more convectively active regions. The Arabian
Peninsula and Arabian Sea regions have maximum OT densities of 174 and 138
OTs per 1∘× 1∘ grid cell, respectively, compared to 154
OTs per 1∘× 1∘ grid cell for the Northern India region.
Further, while the vertical distribution of the cross-tropopause convection
in these regions is similar to the overall distribution across the entire
study region, they have distinct seasonal features.
The geographic distribution of monthly convection over eastern Africa
and the southwestern Arabian Peninsula as shown by OTs (first column), daily
average OLR (second column), and daily average precipitation (third column).
Additionally, the specific humidity and wind vectors at 850 hPa from MERRA-2
are shown (fourth column).
The cross-tropopause convection in the Arabian Peninsula region occurs
almost exclusively in May and August (which together contribute 1459 OTs,
72.2 %). June, July, and September only contribute 41 (2.0 %), 164
(8.1 %), and 347 OTs (17.2 %), respectively. The OTs in this region are
co-located with the Asir Mountains, which run parallel to the Red Sea coast,
suggesting that the cross-tropopause convection is likely orographic in
origin (Abdullah and Al-Mazroui, 1998; Chakraborty et al., 2006; Segele
and Lamb, 2005). Figure 9 shows the monthly geographic distribution of OTs
(Fig. 9, first column), average daily OLR (Fig. 9, second column),
average daily precipitation (Fig. 9, third column), and average daily
specific humidity at 850 hPa (Fig. 9, fourth column). Although OLR and
precipitation show similar local maxima in the same region of the southeastern
coast of the Red Sea, they do not reproduce the relative intensity evident
in the OTs. The specific humidity at 850 hPa, however, shows more intense
local maxima along this coastline (Fig. 9p), further indicating orographic
lifting of this moist air as a likely source of cross-tropopause convection
in this region.
The OT distribution over the Red Sea region is in part decoupled from larger
scale patterns evident in OLR and precipitation. The different distributions
agree in that the most convectively intense month according to the OLR and
precipitation distribution occurs in August. However, both OLR and
precipitation indicate more intense convection is occurring over Ethiopia
than over the southeastern coast of the Red Sea, and there is substantial
convection during June and July, when there is little evidence for
cross-tropopause convection in the OTs. This indicates that the southeastern
coast of the Red Sea may be uniquely conducive to cross-tropopause
convection, possibly due to orographic initiation. As the study region does
not extend further into Africa and the study period is limited to 2017, this
conclusion is preliminary. Nonetheless, global studies of extreme convection
also indicate more convective activity over the Arabian Peninsula than over
Ethiopia (Liu and Liu, 2016; Liu et al., 2020). Further research into the
circumstances that result in this tight geographic concentration of
cross-tropopause convection is warranted.
The Arabian Sea region has most of its cross-tropopause convection confined
to the months of May and June, as seen in Fig. 8c and d. During
August–October combined, only 404 OTs (17.4 %) occur. The seasonal
dependence of OTs is also reflected in other measures of convection in this
region. As before, Fig. 6 shows the monthly geographic distribution of OTs
(Fig. 6, left column), average daily OLR (Fig. 6, middle column), and
average daily precipitation (Fig. 6, right column) of this region.
Precipitation in particular has maxima co-located with the regions of most
OT activity during May and June. OLR has a similar geographic distribution
but does not resolve the fine-scale variability across the region. For
example, the “point” maximum in June is present in the OLR distribution
but is not as intense relative to the overall region. The June
cross-tropopause convection is so geographically concentrated that it is
likely sourced from a single large storm system that passed through the
Arabian Sea during the first week of June. The region is in its tropical
cyclone season at this time (Deshpande et al., 2021; Evan et al., 2011).
Figure S4 shows the passage of the large storm system that is
the likely source of the cross-tropopause convection in the Arabian Sea in
visible reflectance near 0.6 µm data from the gridded International
Satellite Cloud Climatology Project B1 data (Knapp et al., 2011). We note
that this geographic “point” source of convective influence over the
Arabian Sea is also visible in a study by Legras and Bucci (2020), of the
2017 Asian monsoon.
Summary and discussion
We construct a database of cross-tropopause convection in the Asian monsoon
region for the months of May through October of 2017 using Meteosat-8
geostationary satellite detections of OTs. We analyze 40 918 individual OTs
to identify geographic regions of high-frequency and high-density
cross-tropopause convection and to characterize their vertical distributions
and intraseasonal variability. Additionally, through comparison with OLR and
precipitation observations we place the cross-tropopause database in the
context of tropospheric convection generally.
We find that cross-tropopause convection is active in the Asian monsoon
region during the months of May through August (with daily averages of these
months above 300 OTs per day) and declines through September and October. Most
of the OTs occur over South Asia with Northern India contributing 29.0 % of
all OTs, Southern India contributing 11.7 % of all OTs, and the Bay of Bengal
contributing 15.2 % of all OTs. Together with the Indian Ocean region
(19.2 %), the most cross-tropopause convection occurs in these regions,
and they cumulatively account for 75.1 % of all OTs. While the Arabian
Peninsula and Arabian Sea regions are smaller sources of cross-tropopause
convection (4.9 % and 5.7 %, respectively), they exhibit geographic OT
densities comparable to the major source regions. The majority of
cross-tropopause convection within the entire study region reaches a maximum
height above 380 K (83.8 %), with an average OT maximum height of 387 K
corresponding to 14.5 K above the tropopause.
We further identify distinct intraseasonal behavior within these subregions.
The four regions of most cross-tropopause convection (Northern India, Indian
Ocean, Bay of Bengal, and Southern India) correspond to the areas of
significant general tropospheric convection identified by the average OLR
and precipitation. Within these regions, the geographic distribution of
cross-tropopause convection most closely matches the distribution of intense
convection as shown by the minimum OLR and maximum daily precipitation. For
the Northern India, Southern India, and Bay of Bengal regions, this results in the
distribution of OTs following the north–south movement of the development of
the Asian Monsoon across the study period. These findings are largely in
agreement with Vogel et al. (2015), which examined the intraseasonal
variability of convective source regions within the Asian monsoon region
during 2012 with small differences likely arising from differences in time
period considered and in the spatial regions considered.
Within the Arabian Peninsula and Arabian Sea regions, the seasonal and
geographic distributions are controlled by the sources of cross-tropopause
convection unique to these regions. In the Arabian Peninsula, OTs are
primarily observed in May and August, corresponding to a confluence of
low-level moisture along the western slope of the Asir Mountains indicating
a likely orographic source of convection. In the Arabian Sea, most OTs occur
in June, with a “hotspot” corresponding to a single large storm system.
Whether these behaviors in cross-tropopause convection observed in 2017 are
recurring features should be explored in future research.
Our examination of cross-tropopause convection is complementary to the large
body of research that has examined the effects of deep convection that
reaches the upper troposphere and subsequently enters the lower stratosphere
through diabatic ascent. There has been disagreement in prior work
regarding whether oceanic or land-based convective source regions for the
Asian monsoon anticyclone are more important. For example, many studies have
found that convection over the Tibetan Plateau is the primary contributor
(Bergman et al., 2013; Fu et al., 2006; Heath and Fuelberg, 2014; Pan et
al., 2016; Wu et al., 2020). In contrast, other studies have shown that the
Bay of Bengal or the western Pacific are the majority contributors (Chen et
al., 2012; Devasthale and Fueglistaler, 2010; James et al., 2008). Here,
differences between the reanalyses used in modeling studies of Asian
monsoon anticyclone convective source regions (e.g., Bergman et al., 2013;
and Chen et al., 2012) contribute to the discrepancies amongst their
conclusions. We find that both oceanic and land-based regions contribute
significant amounts, though with different seasonal distributions.
Further, the highest frequency of land-based cross-tropopause convection is
centered on Northern India, not the Tibetan Plateau. This is a significant
difference from studies that find the Tibetan Plateau to be the primary
conduit by which tropospheric convection reaches the lower stratosphere
(Bergman et al., 2013; Pan et al., 2016). Our analysis of cross-tropopause
convection most agrees with the studies that conclude that the Tibetan
Plateau is a region favorable to diabatic ascent but not itself a major
convective source (Legras and Bucci, 2020; Tissier and Legras, 2016).
Moreover, Tissier and Legras (2016) also show both oceanic and land-based
cross-tropopause convective sources within the Asian monsoon region in
agreement with our spatial distribution.
Within the context of the StratoClim project, our results indicate that
cross-tropopause convection is not uncommon, in agreement with the in situ
observations of elevated water vapor in the LS attributed to
cross-tropopause convection (Khaykin et al., 2022; Lee et al., 2019). We
also find that the attribution of convective influence in the LS to
land-based convection over India during the StratoClim research flights
(Bucci et al., 2020; Khaykin et al., 2022) matches the most convectively
active region, as indicated by OT distribution, during that time period of
late July and early August.
As this analysis only covers May through October of 2017, it is unable to
assess any interannual variability that may alter the seasonal and
geographic characteristics identified here. Furthermore, the geographic
boundaries of the OT dataset from the Meteosat coverage do not include two
areas that merit further investigation. First, is the convective “hotspot”
in the western Pacific Ocean, at approximately 135∘ E, that has
been identified as a potential key region for convective influence on the
UTLS (e.g., Chen et al., 2012). Second, along the western boundary of our study
region, there is indication of additional cross-tropopause convective over
Africa, and in particular the African Monsoon region may be an area of
significant OT activity. Expanding this research to analyze additional years
would allow for an assessment of interannual variability and improve the
identification of region-specific properties. Further, additional work to
normalize geostationary satellite observations across platforms would
facilitate assessments of the global impact of cross-tropopause convection.
Overall, this analysis demonstrates the importance of cross-tropopause
convection when considering the influence of convection on the lower
stratosphere within the Asian Monsoon region. Not only does the timescale
of the transport mechanism differ from the diabatic ascent pathway,
resulting in potentially different chemical species reaching the lower
stratosphere, but the geographic distribution of cross-tropopause convection
also indicates a need to account for regions of convective activity beyond those
previously identified as primary sources of convective influence via
diabatic ascent (e.g., northwestern India in Fig. 2). Further, the
distribution of cross-tropopause convection shows that both oceanic and
land-based regions must be considered for a complete accounting of
convective influence on the Asian monsoon anticyclone. Our work also
demonstrates that it is critical to consider multi-month timescales given
the large intraseasonal differences in the contributions of each source
region in agreement with Vogel et al. (2015). Confining an analysis to a
single month would overemphasize certain source regions while missing
others. For example, considering only August would show that Northern India has
more convective activity than the Bay of Bengal, though the inverse is
apparent in May. Given the importance of timing and location when assessing
the potential transport of pollution into the lower stratosphere in the
Asian monsoon region, in addition to its importance in determining
subsequent transport pathways into the global stratosphere, a perspective on
convective impacts on the lower stratosphere that includes cross-tropopause
convection and covers the entire spatiotemporal region of convective
activity is critical.
Data availability
Meteosat-8 multispectral imagery is available here: https://navigator.eumetsat.int/product/EO:EUM:DAT:MSG:HRSEVIRI-IODC (European Organisation for the Exploitation of Meteorological Satellites, 2017).
MERRA-2 reanalysis data are available here: 10.5067/QBZ6MG944HW0 (Global Modeling and Assimilation Office, 2015).
NOAA OLR data are available here: https://psl.noaa.gov/data/gridded/data.olrcdr.interp.html (NOAA Physical Sciences Laboratory, 1996).
GPCP Climate Data Record v. 1.3 is available here: 10.5065/ZGJD-9B02 (Adler et al., 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-23-3279-2023-supplement.
Author contributions
CEC designed the study and performed the analysis. KMB developed the OT
detection algorithm and applied it to the Asian monsoon region. JBS provided
scientific analysis and assisted with figure development. JGA supervised the
study. CEC wrote the manuscript; all co-authors edited and revised the
manuscript.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We would like to thank the members of the Anderson group for their support.
Financial support
This research has been supported by the National Aeronautics and Space Administration (grant no. NNX15AF60G) and the National Science Foundation (NSF) Arctic Observing Network (AON) Program (NSF award 1203583) (Collaborative
Research: Multi-Regional Scale Aircraft Observations of Methane
and Carbon Dioxide Isotopic Fluxes in the Arctic).
Review statement
This paper was edited by Martina Krämer and reviewed by two anonymous referees.
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