Balloon-borne measurements of cryogenic frost-point hygrometer (CFH) water vapor, ozone and
temperature and water vapor lidar measurements from the Maïdo Observatory on Réunion Island in the southwest Indian Ocean (SWIO) were
used to study tropical cyclones' influence on tropical tropopause layer (TTL) composition. The balloon
launches were specifically planned using a Lagrangian model and Meteosat-7
infrared images to sample the convective outflow from tropical storm (TS)
Corentin on 25 January 2016 and tropical cyclone (TC) Enawo on 3 March
2017.
Comparing the CFH profile to Aura's Microwave Limb Sounder's (MLS) monthly climatologies, water vapor anomalies
were identified. Positive anomalies of water vapor and temperature, and
negative anomalies of ozone between 12 and 15 km in altitude (247 to 121 hPa),
originated from convectively active regions of TS Corentin and TC Enawo 1 d before the planned balloon launches according to the Lagrangian
trajectories.
Near the tropopause region, air masses on 25 January 2016 were anomalously
dry around 100 hPa and were traced back to TS Corentin's active convective
region where cirrus clouds and deep convective clouds may have dried the
layer. An anomalously wet layer around 68 hPa was traced back to the southeast Indian Ocean where a monthly water vapor anomaly of 0.5 ppmv was observed. In
contrast, no water vapor anomaly was found near or above the tropopause
region on 3 March 2017 over Maïdo as the tropopause region was not
downwind of TC Enawo. This study compares and contrasts the impact of two
tropical cyclones on the humidification of the TTL over the SWIO. It also
demonstrates the need for accurate balloon-borne measurements of water
vapor, ozone and aerosols in regions where TTL in situ observations are sparse.
Introduction
Deep convection plays an important role in delivering water and other
chemical constituents to the tropical tropopause layer (TTL; ∼ 14–19 km altitude; Fueglistaler et al., 2009) and lower stratosphere
regions. Two important pathways for trace gas transport from the surface to
the tropical stratosphere are (i) deep convective injection directly into the
stratosphere (Danielsen, 1982; Dessler and Sherwood, 2003) and (ii) convective
detrainment into the TTL followed by a slow ascent into the stratosphere
(Holton and Gettelman, 2001). Moist boundary-layer air is transported to the
upper troposphere by deep convection with the main outflow region at about
13 km (Folkins and Martin, 2005). However, very deep convection may overshoot
the 18 km level into the stratosphere, injecting water vapor and ice
crystals directly (Corti et al., 2008; Khaykin et al., 2013; Avery et al.,
2017). Studies based on Eulerian cloud resolving models have shown that
those overshoots can moisten the lower stratosphere due to the evaporation of
ice crystals (Dauhut et al., 2015; Frey et al., 2015). However, convection
can also cool the cold point tropopause (CPT) (Kuang and Bretherton, 2004),
which can enhance dehydration via in situ formation of cirrus clouds. In
fact, the net impact of deep convection on TTL humidity (e.g., moistening
versus dehydration) depends on the initial pre-convection TTL relative
humidity with respect to ice (RHi) conditions and size of the ice crystals
formed in the convective updrafts (Jensen et al., 2007; Ueyama et al.,
2018). In sub-saturated TTL air, condensed ice is not removed quickly enough
to produce net dehydration. Recent studies based on Lagrangian models
(Schoeberl et al., 2014; Ueyama et al., 2015) that include convection and
cirrus clouds microphysics show that convection impacts TTL cirrus clouds
and water vapor near the tropical tropopause by 10 %–30 % (∼1 ppmv). Therefore, they concluded that convection is significant for the
moisture budget of the TTL and must be included to fully model the dynamics
and chemistry of the TTL and lower stratosphere.
As the exact role of convection in hydrating/dehydrating the stratosphere is
still under debate, additional accurate TTL observations and modeling work
are still needed to quantify the overall impact of convection on TTL
composition and climate. At the moment, a realistic representation of deep
convection and its effects remains a challenge for most global-scale climate
models and numerical weather prediction models (NWP).
Our understanding of how deep convection controls TTL humidity and
composition to a large extent results from experiments in South America,
the western Pacific and Southeast Asia (e.g., Toon et al., 2010; Jensen et
al., 2017; Brunamonti et al., 2018). The role of the Indian Ocean (IO) in
the global climate system is less understood than that of the Pacific Ocean,
which has been more intensively observed and studied.
The tropical Indian Ocean has seen an unprecedented rise in heat content and
is now home to 70 % of the global ocean heat gain in the upper 700 m of
the ocean during the past decade (Lee et al., 2015). Liu and Zipser (2015)
showed using radar observations from the Global Precipitation Measurement
(GPM) satellite that deep convection deeper than 15 km (Fig. 1 in Liu and
Zipser, 2015) can occur over the south IO with dozens of systems reaching
above 17 km. These systems are likely tropical cyclones over the southwest Indian Ocean (SWIO) or
thunderstorms that are often observed over Madagascar during austral summer
(Roca et al., 2002; Bovalo et al., 2012).
Tropical cyclones are unique among tropical and subtropical convective
systems in that they persist for many days and hydrate a deep layer of the
surrounding upper troposphere (Ray and Rosenlof, 2007). Ray and Rosenlof (2007) used measurements from the Atmospheric Infrared Sounder (AIRS) to assess the impact of tropical cyclones
in the Atlantic and Pacific basins on the amount of water vapor in the
tropical upper troposphere (UT). They showed that tropical cyclones can hydrate a deep layer of
the surrounding upper troposphere by ∼ 30–50 ppmv or more
within 500 km of the eye compared to the surrounding average water vapor
mixing ratios. In addition, a modeling study by Allison et al. (2018) for
tropical cyclone (TC) Ingrid (2013) in the Gulf of Mexico indicated overshooting convection
within the cyclone and associated strong vertical motions that transported
large quantities of vapor and ice to the lower stratosphere.
Using 11-year Tropical Rainfall Measuring Mission (TRMM) precipitation satellite observations, Tao and Jiang (2013) identified overshooting tops in tropical cyclones (above 14 km) and
showed that the south IO is the second basin after the northwest Pacific in
terms of total number of overshooting tops (cf. Table 2 of Tao and Jiang,
2013). Even though convection occurs predominantly over land in the tropics,
overshooting convection in tropical cyclones contributes ∼15 % of the total convection reaching the tropopause (Romps and Kuang,
2009).
The location of Réunion Island (21∘ S, 55∘ E) is thus ideal to
study tropical cyclone's effects on TTL composition. Réunion Island was
formally designated as a Regional Specialized Meteorological Centre (RSMC) –
tropical cyclones for the southwest Indian Ocean (0–40∘ S, 30–100∘ E) by the World Meteorological Organization (WMO) in
1993. The RSMC Réunion Island is responsible for the monitoring of all
the tropical systems occurring over its area of responsibility. The SWIO is
the third most active tropical cyclone basin with an average of 9.3 tropical
storms with maximum sustained winds ≥63 km h-1 forming each year
(Neumann, 1993). In the SWIO basin, a storm system is called a tropical
cyclone when wind speeds exceed 118 km h-1.
We take advantage of the position of Réunion Island in the SWIO to study
tropical cyclones' influence on TTL composition (water vapor and ozone) during
austral summers 2016 and 2017. Austral summer (November–March) is the ideal time
to sample convective outflow from tropical cyclones or mesoscale convective
systems forming near Madagascar.
The present work is organized as follows. Section 2 has a description of the
data used in this study. Section 3 presents the model used to infer the
convective origin of the measurements. Section 4 presents the water
vapor/ozone distributions over Réunion Island during the two storm
events and thermodynamics of the troposphere and TTL. Section 5 discusses
the convective influence on the measurements as inferred from an analysis of
Lagrangian trajectories. The results are discussed in Sect. 6.
Section 7 contains a summary of our study.
DataBalloon data
Balloon-borne measurements of water vapor and temperature in coordination
with ground-based instrumentation (lidars) started in 2014 at the Maïdo Observatory (21.08∘ S, 55.38∘ E) within the framework of
the Global Climate Observing System (GCOS) Reference Upper-Air Network
(GRUAN) (Bodeker et al., 2016). The balloon sonde payload consists
of the cryogenic frost-point hygrometer (CFH) and the InterMet iMet-1-RSB
radiosonde for data transmission. The iMet-1-RSB radiosonde provides
measurements of pressure, temperature, relative humidity (RH) and wind data
(speed and direction from which zonal and meridional winds are derived). The
CFH was developed to provide highly accurate water vapor measurements in the
TTL and stratosphere where the water vapor mixing ratios are extremely low
(∼2 ppmv). CFH mixing ratio measurement uncertainty ranges
from 5 % in the tropical lower troposphere to less than 10 % in the
stratosphere (Vömel et al., 2007); a recent study shows that the
uncertainty in the stratosphere can be as low as 2 %–3 % (Vömel et al.,
2016). However, water vapor measurements in the stratosphere by the CFH can
be contaminated by sublimation of water from an icy intake or from the
balloon and payload at a pressure lower than 20 hPa (Jorge et al., 2020). The iMet-1-RSB has a temperature measurement uncertainty of 0.3 ∘C, or
5 % in RH, with an altitude independent bias of 0.5±0.2∘C (Hurst et al., 2011). As for the vertical coordinate, we use the
geopotential height calculated from the iMet-1-RSB measurements of pressure,
temperature and RH. Hurst et al. (2011) reported altitude-dependent
differences of -0.1 to -0.2 km above 20 km between the geopotential
altitudes derived from the Vaisala RS92 and InterMet iMet-1-RSB sondes. The
CFH and iMet-1-RSB measurements have high vertical resolution (5–10 m) and
are binned in altitude intervals of 200 m to reduce measurement noise. Here
we present CFH measurements (water vapor mixing ratio and relative humidity
with respect to ice, RHice) from two soundings performed in austral
summers 2016 and 2017, when deep convection was active near Réunion
Island (TS Corentin and TC Enawo; cf. Fig. 1). During austral
summer, balloon launch planning is optimized using a Lagrangian forecasting
tool. The 5 d backward Lagrangian trajectories initialized from the location
of the Maïdo Observatory at different altitudes (9.5, 12.5, 15.5 and 18 km) are run twice a day and superimposed on current geostationary infrared
satellite images to identify ongoing convection over the SWIO
(http://geosur.univ-reunion.fr/foot, last access: 7 September 2020). This allows the identification of air
masses with a convective origin that can be measured at the observatory,
thereby maximizing local resources by only measuring when convectively
influenced air masses will be sampled.
In addition to CFH measurements at the observatory, weekly Network for the
Detection of Atmospheric Composition Change (NDACC) and Southern Hemisphere
ADditional OZonesondes (SHADOZ) ozonesondes (Thompson et al., 2003; Witte et
al., 2017) are launched from the airport (Roland Garros; 21.06∘ S,
55.48∘ E), located on the north side of the island (the flying
distance between the Maïdo Observatory and the airport is
∼20 km). The ozonesonde is flown with a Meteomodem M10
radiosonde that provides meteorological variables such as temperature,
pressure, relative humidity and winds. In this study, the NDACC/SHADOZ ozone
and temperature measurements are reported in 200 m altitude bins.
Water vapor lidar data
A Raman water vapor lidar emitting at 355 nm has been operating at the
Maïdo Observatory since April 2013 (Baray et al., 2013; Keckhut et al.,
2015; Vérèmes et al., 2019). Laser pulses are generated by two
Quanta-Ray Nd:Yag lasers, the geometry for transmitter and receiver is
coaxial, and the backscattered signal is collected by a Newtonian telescope
with a primary mirror of 1200 mm diameter. Raman shifted wavelengths of 387 nm (N2) and 407 nm (H2O) are used to retrieve the water vapor
mixing ratio. Depending on the scientific investigations, specific filter
points and integration times can be chosen. The raw vertical resolution is
15 m. Data are smoothed with a low-pass filter using a Blackman window.
Based on the number of points used for this filter to vertically average the
data, the vertical resolutions are 100–200 m in the lowest layers, 500 m in
the middle troposphere, 600 m in the upper troposphere and 700–750 m in the
lower stratosphere. In order to convert the backscattered radiation profiles
into water vapor mixing ratio profiles, the calibration coefficient is
calculated from water vapor column ancillary data: GNSS (Global Navigation
Satellite System) IWV (Integrated Water Vapor). The description of the
calibration method and the total uncertainty budget can be found in
Vérèmes et al. (2019).
At the Maïdo Observatory, the lidar provides four to eight water vapor
profiles per month. The calibrated lidar water vapor database extends from
November 2013 to December 2017. The time slot of routine operations is
around 19:00 to 01:00 (+1) local time, but there are intensive periods of
observation during field campaigns that allow a longer measuring span. The
Raman lidar water vapor observations were validated during the Maïdo
ObservatoRy Gaz and Aerosols NDACC Experiment (MORGANE) intercomparison
exercise in May 2015 (Vérèmes et al., 2019). During the MORGANE
campaign, CFH radiosonde and Raman lidar profiles showed mean differences
smaller than 9 % up to 22 km a.s.l (above sea level).
Here we used the Raman lidar measurements for 2 nights when the CFH sondes
were launched at the observatory (25 January 2016 and 3 March 2017). The
lidar water vapor profiles correspond to an integration time of 239 and
184 min for the nights of 25 January 2016 and 3 March 2017, respectively. The
lidar water vapor profiles are interpolated to the same 200 m vertical grid
used for the CFH data and are shown up to 14.5 km. The mean lidar
uncertainties for the troposphere below this level are 10.5 % and 8.7 %
for 25 January 2016 and 3 March 2017, respectively.
Satellite data
The brightness temperatures of the infrared (IR) channel at 10.8 µm of
the geostationary weather satellite Meteosat-7 have been used to provide the
regional characteristics of deep convection over the Indian Ocean. The
satellite centered at 57.5∘ E provided images for the Indian Ocean
from December 2005 to March 2017.
Aura's Microwave Limb Sounder (MLS) v4.2 water vapor and ozone data were
included in the study to compare with the in situ measurements and to
evaluate the spatial extent of the convective air masses measured at the
observatory. In particular we have used water vapor from the Stratospheric
Water and OzOne Satellite Homogenized (SWOOSH) dataset (Davis et al.,
2016). The SWOOSH dataset contains monthly mean stratospheric water vapor
and ozone profiles from several satellite instruments for the period 1984 to
present. The data are available on a 3D (latitude/longitude/pressure) grid.
The SWOOSH input data for the period August 2004 to the present day correspond
to measurements from the Aura MLS satellite. The MLS water vapor data are available on a pressure grid with 31 levels between 316 and 1 hPa (e.g., the vertical resolution is ranging from 1.3 to 3.6 km between 316 and 1 hPa).
The estimated accuracy for MLS water vapor decreases from 20 % at 216 hPa to 4 % at 1 hPa and is ∼10 % in the TTL region (150–70 hPa).
Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) has been making
backscatter measurements at 532 and 1064 nm since June 2006. We use the
total attenuated backscatter coefficients β532′ available from
the CALIPSO V4.10 level 1 lidar data products. Following Vaughan et al. (2004), the attenuated scattering ratio SR532 (Eq. 3 in Vaughan
et al., 2004) profiles are computed as the ratio of β532′ (corrected for molecular attenuation and ozone absorption) to the molecular
backscatter coefficient βm. βm is calculated using
the number density of molecules from the GEOS-5 global model of the NASA
Global Modeling and Assimilation Office (GMAO) and the Rayleigh scattering
cross section. More details are given in the CALIOP Algorithm Theoretical
Basis Documents (ATBDs; cf. Eqs. 4.13a and 4.14).
Model
The origin of air masses measured at the Maïdo Observatory were
assessed using the FLEXible PARTicle (FLEXPART) Lagrangian particle
dispersion model (Stohl et al., 2005). FLEXPART is a transport model that
can be run in either forward or backward mode in time. FLEXPART was driven
by using ECMWF analysis (at 00:00 and 12:00 UTC) and their hourly forecast fields
from the operational European Centre for Medium-Range Weather Forecasts's Integrated Forecast System (ECMWF-IFS). In March 2016, ECMWF introduced a
new model cycle of the IFS into operations with a grid spacing of 9 km,
roughly doubling the previous grid spacing of 16 km used since January 2010.
The ECMWF model has 137 vertical model levels with a top at 0.01 hPa since
June 2013. To compute the FLEXPART trajectories, the ECMWF meteorological
fields were retrieved at 0.50 and 0.15∘ and on full
model levels from the Meteorological Archival and Retrieval System (MARS)
server at ECMWF. The 0.50∘ fields were used to drive the FLEXPART
model over a large domain configured as a tropical channel; i.e., the domain
is global in the zonal direction but bounded in the meridional direction (at
latitudes ±50∘). Furthermore, higher-resolution domains
can be nested into a mother domain in a FLEXPART simulation. Thus, to have a
better representation of convective transport associated with mesoscale
convective systems or tropical cyclones with a horizontal dimension on the
order of a couple of hundred kilometers over the SWIO, we included a nest
domain covering the SWIO region (40∘ S–10∘ N, 20–80∘ E). If a particle resides in the
high-resolution nest, the ECMWF meteorological data at 0.15∘ from
this nest are interpolated linearly to the particle position. If not, the
0.50∘×0.50∘ ECMWF meteorological data from the mother domain are
used to compute the trajectories. Retrieving high-resolution ECMWF fields
from the MARS server for FLEXPART consists of several steps.
Retrieve the meteorological model data output from ECMWF (horizontal winds,
temperature, humidity, surface fields).
Compute total and convective precipitation rates and sensible and latent heat
fluxes from the surface.
Calculate the vertical velocity from the continuity equation.
Therefore, the ECMWF high-resolution vertical velocity field already
contains a convective mass flux component from the Tiedtke scheme used in
ECMWF. The convective scheme used in the ECMWF-IFS, originally described in
Tiedtke (1989), has evolved over time. Changes made include a modified
entrainment formulation leading to an improved representation of the tropical
variability of convection (Bechtold et al., 2008) and a modified convective available potential energy (CAPE) closure leading to a significantly improved diurnal cycle of convection (Bechtold et
al., 2014). Particles are transported both by the resolved winds and
parameterized sub-grid motions, including a vertical deep convection scheme.
FLEXPART uses the convective parameterization by Emanuel and
Zivkovic-Rothman (1999) to simulate the vertical displacement of particles
due to convection. The results from model runs with and without a cumulus
scheme in FLEXPART have been compared to assess whether convective mass
fluxes could be resolved in the higher-resolution nest domain. The results
of FLEXPART runs with and without a cumulus scheme look fairly similar (not
shown), and thus here we will present only the model results with the cumulus
scheme turned off.
To determine the transport history of air masses sampled by balloon
launches, a so-called retroplume was calculated consisting of 10 000 back
trajectory particles released from each 1 km layer of balloon launches used
in this study and advected backwards in time. The initial positions of the
50 000 particles were distributed randomly within 19 vertical layers
(corresponding to the MLS pressure levels between 316 and 10 hPa) with a
depth of 1 km and 0.10∘× 0.10∘ latitude–longitude bins
centered on the balloon location. The dispersion of a retroplume backwards in
time indicates the likely source regions of the air masses sampled by the in
situ instruments.
Figure 1 shows the best tracks (i.e., a smoothed representation of the
tropical cyclone's location over its lifetime; red line in each panel of
Fig. 1) of tropical storm (TS) Corentin and tropical cyclone (TC) Enawo.
The best track represents the best guess of the location of the tropical
cyclone center every 6 h. TS Corentin started to form on 19 January 2016 east of 70∘ E. The Meteosat-7 IR brightness temperatures on
19 January 2016 at 11:00 UTC indicate a vast clockwise circulation with some
organization (not shown), indicative of tropical cyclone formation in the
Southern Hemisphere. The strengthening of the northerly monsoon flow favored the deepening of
the system in the subsequent days. Corentin became a moderate tropical storm
(10 min maximum sustained wind speeds of 65 km h-1) on 21 January 2016 at 00:00 UTC, and at that time the TS center was located at 14.93∘ S,
75.63∘ E, ∼2200 km to the northeast of the island.
TS Corentin continued to intensify on 22 January while moving towards the
south (see best track in Fig. 1). TS Corentin reached its peak intensity
on 23 January at 00:00 UTC with 10 min maximum sustained wind speeds of 110 km h-1 and the pressure at the center was 975 hPa. On 23 January 2016,
convection was strong around 10∘ S in the Mozambique Channel and
near TS Corentin, especially in the northern part of the system. On 24 January, Corentin had weakened into a moderate tropical storm. On 25 January at
18:00 UTC (time of the balloon launch at the Maïdo Observatory), the storm
was located about 2500 km southeast of Réunion Island, near
26.03∘ S, 79.19∘ E (Fig. 1).
Infrared (10.8 µm) brightness temperature (K)
observed by Meteosat-7 at the time of the CFH launch for 25 January 2016
at 18:00 UTC (a) and 3 March 2017 at 18:00 UTC (b). The red lines correspond to the
best tracks of TS Corentin (19–31 January 2016) and TC Enawo
(2–11 March 2017). The orange squares indicate the positions of the TC
centers (defined as the minimum pressure in the Météo-France best
track data) at the time of the satellite observation. The brown stars
indicate the position of the Maïdo Observatory on Réunion Island
(21.08∘ S, 55.38∘ E). The yellow lines correspond to CALIPSO orbit
tracks on 25 January 2016 at 21:06 UTC and 3 March 2017 at 21:41 UTC. Arrows
on the maps represent the wind field at 150 hPa from the ECMWF analyses at
18:00 UTC. The white contours indicate ECMWF geopotential heights at 150 hPa.
The Madden–Julian Oscillation (MJO) was active at the end of February and
during the first week of March 2017 with a signal centered over Africa and
the Indian Ocean. A monsoon trough was well defined all over the basin along
9∘ S. On 28 February 2017 at 10:00 UTC, a zone of disturbed weather
formed around 6.5∘ S, 70.2∘ E (not shown) with the
building of a clockwise rotating movement inside the cloud pattern. Favored by
the MJO active phase and the arrival of an equatorial Rossby wave, Enawo
initially formed as a tropical disturbance on 2 March with 10 min maximum
sustained wind speeds of ∼40 km h-1. Enawo intensified to a
moderate tropical storm at 06:00 UTC on 3 March. At the time of the balloon
launch at the observatory (∼ 3 March, 18:00 UTC), Enawo was a
tropical storm located near 13∘ S, 56.42∘ E, about 900 km north–northwest of Réunion
Island (Fig. 1). It strengthened into a severe tropical storm cyclone on 5 March at 00:00 UTC and became a category 1 tropical cyclone at 12:00 UTC. TC Enawo
continued to intensify while moving toward Madagascar. It became a category 4 tropical cyclone on 6 March at 18:00 UTC, with 10 min maximum sustained
winds of 194 km h-1. Enawo reached its peak intensity at 06:00 UTC on 7 March,
with 10 min maximum sustained winds of 204 km h-1 and the central pressure
at 932 hPa. TC Enawo reached Madagascar's northeastern coast on 7 March at
around 09:30 UTC and was the third strongest tropical cyclone on record to
strike the island. After 8 March, TC Enawo gradually weakened to a tropical
storm while moving southward over Madagascar.
The two balloon launches at the observatory on 25 January 2016 and 3 March 2017 were specifically planned using FLEXPART Lagrangian trajectories and
Meteosat-7 infrared images. The goal was to sample the convective outflow
from TS Corentin and TC Enawo, as well as convection north of Madagascar on
24 January 2016.
Maps of convective cloud cover (gray shading) computed
using 3-hourly data of Meteosat-7 infrared brightness temperature at 5 km
resolution for 22–25 January 2016 (a) and 28 February–3 March 2017 (b). The red dots indicate pixels with the coldest tops (≤190 K)
that capture the deepest part of convection. The dashed circle indicates a
range ring of 1000 km around the Maïdo Observatory (blue star).
To assess the potential effects of deep convection in the upper troposphere
and near the tropopause, we looked at the distribution of deep convective
clouds in the days preceding the soundings. The location of deep convective
clouds can be assessed by using maps of Meteosat-7 infrared brightness
temperature. Figure 2 shows convective cloud coverage for the 3 d period
preceding the sonde launch date at the Maïdo Observatory. Convective
cloud coverage was estimated using 3-hourly Meteosat-7 infrared brightness
temperatures at 5 km resolution. A threshold of 230 K is used to detect deep
convective clouds in the Meteosat-7 brightness temperature data (i.e., pixels
with brightness temperatures less than 230 K correspond to convective
clouds). This threshold has been previously used to identify convection on
geostationary satellite infrared images (e.g., Tissier et al., 2016). This
temperature corresponds to a height of about 11 km in the NDACC/SHADOZ
climatological mean summertime profile of temperature. Prior to 25 January 2016, the main deep convective activity was located ∼1500 km
north of the island between 50 and 70∘ E and around tropical storm
Corentin. From 28 February to 3 March 2017, convective clouds were located
∼500 km north of the island and correspond to the
intensifying tropical cyclone Enawo. The coldest cloud tops (≤190 K)
that correspond to the deepest convection are indicated by red dots in Fig. 8.
Monthly mean water vapor distributions
Figure 3 show MLS water vapor volume mixing ratios at 215 and 100 hPa
averaged over January 2016 and March 2017. These values were computed by
averaging the SWOOSH monthly mean water vapor concentrations gridded on a
regular pressure/latitude/longitude (a resolution of 5∘× 20∘) grid.
MLS water vapor mixing ratios (ppmv) gridded in the SWOOSH
dataset at 215 hPa for January 2016 (a) and for March 2017 (b). The gray lines correspond to the best tracks of TS Corentin (19–31 January 2016) and TC Enawo (2–11 March 2017). (c, d) Same as (a) and (b) but for 100 hPa.
When comparing the water vapor mixing ratio at 215 hPa in January 2016 to
the one observed in March 2017, one can see that the upper troposphere over
the SWIO was much moister in January 2016 than in March 2017 with three
distinct regions of enhanced water vapor over central Africa, the Indian
Ocean and the Maritime Continent. The mean water vapor mixing ratio at 215 hPa over the SWIO in January 2016 is greater by ∼23 ppmv
compared to March 2017. Interannual variability modes such as the
El Niño–Southern Oscillation (ENSO) can affect the TTL temperature and
thus water vapor distribution. The NOAA Climate Prediction Center Ocean
Niño Index (ONI), which is based on sea surface temperature anomalies in the Niño 3.4
region, was equal to +2.5 K in January 2016 versus +0.1 K in March 2017
(http://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php, last access: 7 September 2020). January 2016 corresponded
to strong El Niño conditions (one of the strongest El Niño events
since 1950 according to the ONI index), while March 2017 was associated with
neutral ENSO conditions. The water vapor mixing ratios at 215 hPa for
January 2016 are in agreement with MLS December–March (DJFM) climatological values of water
vapor at 215 hPa for El Niño conditions (not shown). Overall, during El
Niño conditions, water vapor mixing ratios at 215 hPa are enhanced over
the SWIO west of 80∘ E. Ho et al. (2006) have studied the
variations of TC activity in the southern Indian Ocean in relation to ENSO
effects. During El Niño periods, TC genesis was shifted westward,
enhancing the formation west of 75∘ E and reducing it east of
75∘ E. Therefore, on January 2016, the peak of water vapor west of
80∘ E at 215 hPa may be related to an increase in convection
associated with strong El Niño conditions.
The quasi-biennial oscillation (QBO) also affects TTL temperatures and
humidity (e.g., Zhou et al., 2001; Yuan et al., 2014; Davis et al., 2013).
Following Davis et al. (2013), we defined a QBO index as the zonal mean
(10∘ S–10∘ N) of the difference in the ERA-Interim zonal
wind at 70 and 100 hPa. A positive QBO index (u70hPa–u100hPa>0) corresponds to westerly shear conditions and the warm
phase of the QBO (Baldwin et al., 2001). A negative QBO index corresponds to
easterly shear conditions and the cold phase of the QBO (CPT temperatures
are cooler during the easterly shear phase of the QBO). The mean January 2016 water vapor mixing ratio at 100 hPa over the SWIO is 4.2 ppmv versus
3.7 ppmv in March 2017 compared to the climatological values of 3.51 ppmv
for January and 3.44 ppmv from March. The difference of 0.50 ppmv between the
two periods cannot be explained by the phase of the QBO as both months
corresponded to QBO westerly shear conditions (2.33 m s-1 for January 2016 and
4.79 m s-1 for March 2017). However, the higher water vapor mixing ratio at
100 hPa in January 2016 could be related to strong El Niño conditions as
Avery et al. (2017) have reported large lower stratospheric (82 hPa) water
vapor anomalies (∼+0.9 ppmv) associated with the strong
2015/2016 El Niño. The highest SWOOSH water vapor mixing ratio anomalies
of ∼+1 ppmv were observed over the Indian Ocean in December 2015 (not shown). In January 2016, the anomalies over the SWIO had eased to
0.7 ppmv (not shown).
ObservationsWater vapor/ozone profiles
Figure 4 shows two CFH water vapor mixing ratio profiles (black lines)
taken at the Maïdo Observatory on 25 January 2016 at 17:50 UTC and 3 March 2017 at 18:00 UTC. The lidar water vapor profiles for those 2 nights
are also displayed in green. The red and purple lines correspond to
NDACC/SHADOZ ozonesonde balloon profiles launched from Roland Garros airport on 18 January 2016 (purple line), 4 February 2016 (red line) and 3 March 2017 (purple line
in panel b). The ozonesonde data correspond to daytime measurements
(balloon launches at ∼ 11:00 UTC), while the CFH water vapor data
correspond to nighttime measurements in order to coincide with water vapor
lidar measurements at the Maïdo Observatory. Overall good agreement is
seen between the lidar and CFH water vapor profiles over the whole
troposphere. Note that the CFH water vapor profiles were not used to
calibrate the lidar water vapor profiles as explained in Sect. 2.2.
Vertical profiles of (a) CFH and lidar water vapor
profiles (ppmv) on 25 January 2016 (black and green line, respectively) and
NDACC/SHADOZ ozone profiles on 18 January 2016 (purple line) and 4 February 2016 (red line); (b) CFH and lidar water vapor profiles (black and green line
respectively) and NDACC/SHADOZ ozone profile (purple line) on 3 March 2017.
The location of the cold point tropopause is indicated by the dashed green
line. Also shown in each plot is the 1998–2017 climatological mean ozone
profile (blue line) for DJFM and the ± 1 standard deviation of the
climatology corresponds to the dashed blue line The most important layers in
the water vapor/ozone profiles are shaded and named.
The altitude range of 2–12 km on 25 January 2016 is moister by ∼50 % than the same altitude range on 3 March 2017 (mean water vapor mixing
ratio of 5076 and 4375 ppmv between 2 and 12 km on 25 January 2016 for
the CFH and lidar, respectively, versus 3335 and 3398 ppmv on 3 March 2017 for the CFH and lidar, respectively). The austral summer season, with
warmer temperatures and greater cloudiness, reaches its peak in
January/February, and this could explain in part the higher humidity observed
in January than March. In addition, January 2016 corresponded to a strong El
Niño period, and this could lead to higher tropospheric moistening
associated with ENSO (Tian et al., 2019). On 3 March 2017, a moist layer was
observed between ∼12 and 16 km in both CFH and lidar water
vapor profiles with corresponding low ozone values (Fig. 4b). On 25 January 2016, two local moist layers around 10 and 15 km associated with low
ozone were observed. The lidar smoothes out the peak of water vapor at 10 km
observed on 25 January 2016, but this could be due to the longer integration
time used for that night (239 min). The CFH water vapor mixing ratio
profiles have a minimum of 2.5 ppmv at 17.10 km (94 hPa) and 2.70 ppmv at
18.10 km (77.1 hPa) on 25 January 2016 and 3 March 2017, respectively.
Also shown is the climatological mean ozone profile for DJFM 1998–2017 (blue
lines in Fig. 4). Anomalously low mixing ratios approaching surface values
are seen in the upper troposphere for both the 4 February 2016 (red line in
Fig. 4a) and 3 March 2017 (purple line in Fig. 4b) ozone sonde flights. In the
upper troposphere, the climatological mean ozone mixing ratios range from
about 60 ppbv at 10 km to 100 ppbv at 15 km. There is a steep gradient above
17 km, indicating the transition from the troposphere to the stratosphere. On 3 March 2017, ozone mixing ratios between 10 and 15 km are ∼45 ppbv below the climatological values (mean value of 25.10 ppbv for the 10–15 km layer on 3 March 2017 versus 70.1 ppbv for the climatological ozone
profile).
Between 18 January and 4 February 2016, ozone mixing ratios in the upper
troposphere decreased by ∼30 ppbv and are 38 ppbv below the
climatological values on 4 February 2016. Tropical storm Corentin reached
its peak intensity on 23 January 2016 at 00:00 UTC, and its center was located
1735 km east of Réunion Island. These low ozone mixing ratios in the
upper troposphere on 4 February 2016 were observed after the storm had had its
major influence on the UT ozone, transporting air with surface ozone values
upward via strong convection and mixing out into the larger environment. In
comparison, the 18 January 2016 ozone profile was not influenced by TS
Corentin. The lower ozone values on 3 March 2017 compared to those observed
on 4 February 2016 could be explained by the fact that TC Enawo was closer
to the island (∼902 km north of the island), was still
intensifying and was a stronger system than TS Corentin. Above
∼17 km, the ozone profiles in January/February 2016 and March 2017 are more similar to the climatological mean ozone profile, suggesting
that deep convection did influence the upper troposphere but not the lower
stratosphere. We will later show using FLEXPART that the moist and low ozone
layers in Fig. 4 are associated with the convective outflow of a mesoscale
convective system north of Madagascar on 23 January 2016.
Relative humidity and temperature profiles
Figure 5 shows the CFH profiles of RHice, computed using the
Goff–Gratch equation (Goff and Gratch, 1946) for water vapor pressure, on 25 January 2016 and 3 March 2017, as well as collocated CALIOP nighttime
backscatter measurements. The CALIOP measurements shown in Fig. 5 include
only those within ±5∘ latitude and ±10∘
longitude of the Maïdo Observatory. The CALIOP measurements on 25 January 2016 correspond to a CALIPSO overpass east of the island around 4 h after the balloon launch, and the mean longitude difference between the
CALIPSO overpass and the Maïdo Observatory is 2.4∘ for Fig. 5a and b. On 3 March 2017, the CALIPSO overpass was west of the island and also
4 h after the balloon launch. The mean longitude difference between the
CALIPSO overpass and the Maïdo Observatory is 5.3∘. The
latitude–height cross section of CALIOP SR532 in Fig. 5 corresponds
to measurements with a 60 m vertical resolution. The horizontal interval of
the CALIOP data along its orbit is 330 m; for this study we use a 9-point
running average to reduce noise.
(a, c) Vertical profiles of temperature and
relative humidity with respect to ice (black and blue line, respectively)
measured on 25 January 2016 at 17:52 UTC and 3 March 2017 at 18:00 UTC. The
dashed green line corresponds to the cold point tropopause. The NDACC/SHADOZ
climatological mean summertime (DJFM) profile of temperature (red line), the
± 1 standard deviation (red shading) and temperature anomaly
(magenta line) are also shown. (b, d) Latitude–altitude
distribution of CALIOP backscattering ratio at 532 nm along CALIOP track
near Réunion Island on 25 January 2016 (b) and 3 March 2017 (d). The mean longitude difference between the CFH profile and
the CALIOP track is 2.4∘ on 25 January 2016 and 5.3∘ on
3 March 2017. The red curve on each CALIOP plot corresponds to the
tropopause height provided by the GEOS-5 global model data available in the
CALIPSO level 1 data files. The latitude of the Maïdo Observatory is
indicated by the black star on each CALIOP plot.
Figure 5a and b show significant structure in the RHice profile
measured on 25 January 2016. Higher values of RHice (>40 %) between 13 and 15 km coincide with higher values of CALIOP
SR532 between 12 and 15 km. The RHice reaches its maximum value at
the cold point altitude (17.3 km). The CALIOP SR532 indicates a cirrus
cloud between ∼12 and 15 km north of the island. The cirrus
layer extends from ∼16.2 to 20∘ S
corresponding to a horizontal scale of ∼ 400 km. Meteosat-7
infrared brightness temperature at 21:30 UTC, so ∼10 min
before the CALIPSO overpass at 21:39 UTC in Fig. 5a and b, indicates a
large area of deep convection near 15∘ S and extending from
∼50 to 75∘ E (not shown). The monsoon
trough was located between 17∘ S, 50∘ E and 14∘ S, 70∘ E on 25 January 2016, which promoted deep convection, and
convective activity was also observed in the southeastern quadrant of TS
Corentin. The cirrus cloud observed below 15 km in Fig. 5a and b was most
likely from convective detrainment north of Réunion Island. The
RHice profile on 25 January indicates intertwined layers of dry air
(RHice less than 40 %) at 7, 9, 12 and 16 km and less dry air
(RHice∼50 %) at 8, 11, 15 and 17 km. While convection
north of Réunion Island around 15∘ S and TS Corentin had mixed
the troposphere over the southwest Indian Ocean, no cirrus clouds were
directly observed on 25 January 2016 above the Maïdo Observatory. The
layers of RHice∼50 % at 15 and 17 km may be due to
convective detrainment. The cirrus cloud below 15 km detected by CALIPSO
north of the island on 25 January indicates that deep convection detrained
ice and water vapor in the upper troposphere north of the island. There was
a northerly wind between 10 and 17 km on 25 January 2016 with a peak around 25 m s-1 at 15 km (cf. Fig. 1). Moist air detrained by deep
convection north of Réunion near 15∘ S may have been
transported to Réunion Island in ∼6 h, and during that
time the moist air mass could have mixed with drier air, thereby explaining
the layers of RHice∼50 % at 15 and 17 km in Fig. 5.
The origin of these layers has also been determined using the FLEXPART
Lagrangian model, and the results are presented in the next section.
On 3 March 2017, a layer close to saturation (RHice>80 %) can be observed between 12 and 16 km (Fig. 5c) with
RHice up to ∼100 % at 12.5 and 14 km below the
cold point altitude (16.1 km). The altitude range 12–15.5 km corresponds to
cloudy air, and a cirrus cloud can be seen in the CALIOP measurements of
SR532 between ∼13 and 15 km extending from
18.4 to 21.2∘ S (Fig. 6d). Above
Réunion Island, the cirrus is ∼1.5 km thick and the
maximum thickness of ∼3 km is observed north of the island at
20.5∘ S. A second cirrus cloud can also be observed below 15 km
north of 17.4∘ S.
The CPT height is 16.10 km on 3 March 2017, while it is 1.2 km higher on 25 January 2016 (Fig. 5). The CPT temperature was 192.64 K on 25 January 2016
and 194.58 K on 3 March 2017. On 3 March 2017, the layer between 16 and 18 km was almost isothermal with a mean temperature of 195 K, while the
tropopause was sharper on 25 January 2016.
Lagrangian analysis
The convective origin of air masses sampled in the upper troposphere and
near the tropopause during the passage of TS Corentin and TC Enawo is
evaluated using the FLEXPART Lagrangian model. Figure 6 presents the origins
of air masses sampled within layer L1 (12.1–13.1 km, ∼178 hPa)
and layer L2 (16.3–17.3 km, ∼100 hPa), which are altitudes that
correspond to RHi peaks in Fig. 5 on 25 January 2016 above the Maïdo
Observatory. The origins and pathways of these air masses were examined by
computing 10 d FLEXPART back trajectories. In Fig. 6, the origins of air
masses measured in the upper troposphere (layer L1) and near the tropopause
(layer L2) are shown for 2 d and 3 d prior to the launch. The
position of each air mass is depicted by 10 000 dots color coded by their
altitude and is overlaid over Meteosat-7 infrared images valid at the time
of the back trajectories. For example, trajectories that were originally in
the lower troposphere (below 5 km) and middle troposphere (between 5 and 10 km) 2–3 d before are indicated by orange and brown dots,
respectively. In other words, these air masses were transported from the
troposphere to the upper troposphere/tropopause region in 2 or 3 d before being sampled by the CFH instrument on 25 January 2016 around 18:30 UTC above the Maïdo Observatory. The air mass fractions for different
altitude ranges are also indicated in Fig. 6c and d. Variations in
the air mass fractions over time (e.g., from the lower troposphere below 5 km) can be interpreted in terms of changes in the vertical transport due to
convection over the SWIO.
Backward trajectories calculated with the FLEXPART model
for the CFH flight on 25 January 2016. In (a) and (b), backward
trajectories were initialized at 178 hPa (layer L1) on 25 January 2016. The
particle positions 2 d before (on 23 January 2016 at 20:00 UTC; a) and 3 d before (on 22 January 2016 at 20:00 UTC; b) are
shown with respect to the Meteosat-7 cloud distribution at those times. The
altitude range of the particles (e.g., 0–5 km) and the percent of particles in
that altitude range are indicated according to a color code shown in the
bottom of each panel. (c, d) Same as (a) and (b) but for backward
trajectories initialized at 100 hPa (layer L2) on 25 January 2016.
The ability of FLEXPART to represent isolated deep convective cells is
limited due to the 0.15∘× 0.15∘ spatial resolution of the ECMWF
operational fields. At that resolution, isolated deep convective cells are
not fully resolved in the ECMWF vertical wind field, and their updraft
intensity and the altitude of the level of neutral buoyancy could be
underestimated. However, the vertical transport of convective cells
organized at mesoscale such as convection in tropical cyclones that cover
several degrees in latitude and longitude is better resolved by the
0.15∘× 0.15∘ ECMWF meteorological fields. Recent improvements of
the ECMWF IFS model have enhanced its forecasting skills of tropical
cyclones (Magnusson et al., 2019). Hence, the FLEXPART back trajectories
driven by the ECMWF operational wind field give a qualitative sense of
convective origins of vertical layers measured at Maïdo in relation to
tropical cyclones.
According to FLEXPART, layer L1 measured above the Maïdo Observatory on
25 January 2016 ∼ 18:30 UTC has two different origins. At 2 d before (Fig. 6a), 48 % of this air mass was below 10 km
(with ∼31 % below 5 km) and ∼1000 km
northeast of Réunion Island in a region with convective clouds with cold
brightness temperatures less than 220 K (∼12 km). Therefore,
we can infer that the majority of layer L1 was lifted by convection
associated with TS Corentin 2 d prior to the launch. These trajectories
are rather spread in the lower troposphere, suggesting that they experienced
turbulent mixing and changes in wind direction in the lower troposphere. The
rest of the trajectories are located higher in altitude, in the 10–15 and
15–17 km altitude ranges. They are also located above convective clouds but
are less scattered than the trajectories in the lower troposphere,
suggesting that these trajectories were less mixed with the surrounding
upper troposphere.
At 3 d before (Fig. 6b), 66 % of layer L1 originated from
the lower and middle troposphere (41 % within the 0–5 km layer, 25 %
within the 5–10 km layer) over the northeastern convective region of TS
Corentin and 32 % from the upper troposphere (within 10–15 km) above TS
Corentin. The upper tropospheric branch had a counterclockwise rotation with
an origin near TS Corentin, in agreement with the upper divergence
associated with TS Corentin. Hence, most of the air mass was located either
in the lower troposphere or near the top of convective clouds 3 d
before.
Layer L2 measured at Maïdo on 25 January 2016 stayed in the upper
troposphere and near the tropopause 2 d before reaching Réunion
Island (Fig. 6c). The trajectories followed a counterclockwise
rotation associated with Corentin's dynamics and were located
∼250 km north of the center of TS Corentin. Only 3 % of
trajectories that originated in the lower troposphere were found. On 22 January at 17:00 UTC (3 d before the launch), the trajectories were
located east of the center of Corentin (Fig. 6d). About 8 %
of the trajectories were below 10 km (6.4 % below 5 km). Note that TS
Corentin reached its peak intensity on 23 January 2016 at 06:00 UTC (pressure
at the center of 975 hPa, 10 min maximum sustained winds of 110 km h-1).
Hence, according to FLEXPART back trajectories and the Meteosat-7 infrared
images, the origin of layer L2 was traced back to the active convective
regions of TS Corentin and its upper divergence dynamics, but a small
fraction originated from the lower troposphere. However, due to the
0.15∘ spatial resolution of the ECMWF winds used to drive
FLEXPART, the vertical updrafts of the deepest convective clouds that may
reach the tropopause region/lower stratosphere may not be well represented
in FLEXPART.
Backward trajectories calculated with the FLEXPART model
for the CFH flight on 3 March 2017. In (a) and (b), backward trajectories
were initialized at 178 hPa (layer L4) on 3 March 2017. The particle
positions 2 d before (on 1 March 2017 at 20:00 UTC; b) and 3 d before (on 28 February 2017 at 20:00 UTC; a) are shown with
respect to the Meteosat-7 cloud distribution at those times. The altitude
range of the particles (e.g., 0–5 km) and the percent of particles in that
altitude range are indicated according to a color code shown in the bottom
of each panel. (c, d) Same as (a) and (b) but for backward
trajectories initialized at 100 hPa (layer L5) on 3 March 2017.
Figure 7 is similar to Fig. 6 but for back trajectories associated with the
launch on 3 March 2017. Most of layer L4 measured on 3 March 2017 at
18:42 UTC was lifted by convection 800 km north of the island 2–3 d before (Fig. 7a and b). At 2 d before (Fig. 7a), the
back trajectories indicate that a large fraction (69 %) of layer L4 is from
the lower troposphere (below 10 km) over a convective region associated with
TC Enawo. At 3 d before reaching Réunion Island (Fig. 7b), the trajectories were dispersed in the lower troposphere around the
forming storm as Enawo was in the early stage of its formation at that time
(tropical depression).
The FLEXPART back trajectories for layer L5 measured above the Maïdo
Observatory on 3 March 2017 at 18:52 UTC stayed in the upper troposphere 2–3 d before the launch (Fig. 7c and d). The trajectories were
confined to the same latitude band east and west of Réunion Island in a
clear sky region away from convective clouds. It shows that air masses near
the tropopause above Réunion Island on 3 March 2017 were most likely not
affected by Enawo at this stage of its development as Enawo was still
intensifying.
In a nutshell, the FLEXPART back trajectories clearly identify a convective
origin for layers L1 and L4 sampled on 25 January 2016 and 3 March 2017
associated with TS Corentin and TC Enawo. The convective
transport from the lower troposphere to the upper troposphere occurred
roughly 2 d before each launch. As for the tropopause region over
Réunion Island on 25 January 2016, FLEXPART back trajectories suggest
that the air masses were embedded in TS Corentin's upper divergence dynamics
over a region where convection was active. Deep convective clouds within TS
Corentin may have reached the tropopause region (layer L2) on 23 January 2016 when the storm was at its peak intensity and may have influenced the
water vapor content near the tropopause. On 3 March 2017, the tropopause
region measured by the CFH sounding was not affected by deep convection
associated with Enawo according to the model, at least not at the time of
the observation. At that time, TC Enawo was still intensifying, and the
deepest convective cloud developed later after 4 March 2017.
DiscussionCFH and MLS comparisons
The CFH measurements analyzed in this study are compared to coincident MLS
profiles. The match criteria used are ±18 h, ±500 km
north–south distance (around ±5∘ latitude) and ±1000 km
east–west distance (around ±10∘ longitude). The same match
criteria are used in Davis et al. (2016). In addition, FLEXPART
back trajectories initialized at each MLS pressure level are used to isolate
the MLS profiles that were originating from TS Corentin and TC Enawo. Five and
three matched MLS profiles are found for 25 January 2016 and 3 March 2017,
respectively. On 25 January 2016, distances between the Maïdo
Observatory and the matched MLS profiles ranged from 259 to 494 km with a
mean distance of 346 km. The mean time difference for all matched profiles
is 3.7 h. On 3 March 2017, the three matched MLS profiles are closer to the
Maïdo Observatory with a mean distance of 281 km and are east of the
island. However, a larger mean time difference of 16.4 h is observed for the
matched MLS profiles.
(a, b) High-resolution (black line) and
convolved (blue line) CFH water vapor profiles and closest-matched MLS
profiles (thin red line) on 25 January 2016 (five profiles) and 3 March 2017 (three profiles). The mean MLS profile for each date corresponds to the thick
magenta line. The location of the cold point tropopause is indicated by the
dashed green line. Important water vapor features are shaded and named.
(c) Mean percent difference between the convolved CFH water vapor
profile and MLS coincident profiles on 25 January 2016 (red line) and 3 March 2017 (blue line). The horizontal bars indicate twice the standard
error of the mean percent difference. Markers for each pressure level on 3 March 2017 are slightly offset in pressure for clarity. Corresponding
altitude values for MLS pressure levels are also shown in each plot.
To compare the high-resolution CFH water vapor profile to the MLS satellite
data, we smooth the high-resolution sonde measurements to match the
resolution of the satellite profiles using the MLS vertical averaging
kernels, following the procedure described in Read et al. (2007) and Davis
et al. (2016). The procedure for applying the MLS averaging kernels to a CFH
profile requires an a priori profile as input; this is the same a priori
profile used in the MLS retrieval. Figure 8 shows the matched MLS profiles
and the CFH profiles convolved with the MLS averaging kernels. The matched
MLS profiles on both dates illustrate how water vapor is more variable in
the upper troposphere (between 316 and ∼147 hPa) compared to
above it. The lower part of the tropopause layer from 147 hPa to the cold point
tropopause (dashed green line in Fig. 8) is a transition region where
water vapor mixing ratios become lower but could still be influenced by deep
convective outflow. The application of the averaging kernel to the CFH
profiles smoothes the fine-scale structures observed in the CFH profiles in Fig. 4 but still captures the deep layers of moist air in the upper
troposphere between 261 and 147 hPa. To facilitate the comparison of CFH and
MLS water vapor profiles in the upper troposphere and stratosphere where
water vapor mixing ratios decrease by 3 orders of magnitude, we compute a
mean percent difference of the MLS collocated profiles to the CFH and MLS
data (i.e., percent difference = (MLS - CFH)/((CFH + MLS)/2) × 100). The
same definition is used in Davis et al. (2016) and ensures that the
distribution of percent difference at each pressure level is not skewed
toward positive values larger than 100 % (since water vapor values are
constrained to be positive). In addition, this facilitates the comparison with
the study of Davis et al. (2016) that established a comparison between the
2004–2015 MLS water vapor data record and both routine monitoring and field
campaign frost-point hygrometer balloon soundings at various stations around
the world.
Several factors could explain why a dry bias exists between the mean MLS
profile and CFH convolved profile on 3 March 2017. First, the 3 km deep wet
layer observed in March 2017 in the CFH profile will not be well captured by
MLS with a 2–3 km vertical resolution in the upper troposphere. In addition,
the CFH launch on 3 March 2017 at 18:00 UTC was planned using FLEXPART
Lagrangian trajectory analysis and satellite images in the days prior to the
launch to sample the convective detrainment of TC Enawo. Therefore, the
planning of the CFH launch on 3 March 2017 was optimal to sample moist air
from convective detrainment, and an average of three MLS coincident profiles over
a larger region/time window could be an underestimate of the storm-related
moistening. It is also known that the stirring of air masses due to tropical
cyclones generates a rather inhomogeneous atmospheric composition up to the
TTL (Cairo et al., 2008, and references therein). It is possible that the CFH
on 3 March 2017 sampled a fresher tropospheric filament with higher humidity
than the three MLS profiles.
On 25 January 2016, the mean MLS water vapor profile agrees well with the
convolved CFH profile over the entire lower tropical stratosphere within
layer L3. The mean percent difference is +7±10 % (+0.3 ppmv)
and lies within the previously published uncertainty of both instruments
(Hurst et al., 2014; Vömel et al., 2007; Davis et al., 2016; Yan et
al., 2016).
On 3 March 2017, larger differences of +18 % (∼ 0.6 ppmv)
are observed in the lower stratosphere between 121 and 32 hPa. It is not
clear why there are larger differences in the stratosphere on 3 March 2017.
Both CFH instruments launched on 25 January 2016 and 3 March 2017 were
prepared by the same operator and calibrated using the same recommended
procedure. During these two flights, the CFH data streams were transmitted
to receiving equipment on the ground through the InterMet radiosonde. From
an instrumental standpoint, there is nothing that might explain a CFH dry
bias on 3 March 2017 compared to 25 January 2016. Unfortunately, the CFH
sondes are not recovered on the island after each flight as they land in the
ocean, and thus it was not possible to examine in more details the instrument
after the flight on 3 March 2017. To our knowledge, the CFH instrument on
that night measured as well as it could in the stratosphere. Even though
the CFH instrument launched on 3 March 2017 had a dry bias of 1 ppmv in the
stratosphere, such a bias does not affect the results in this paper found for
TC Enawo.
Overall, the MLS mean profile agrees within the uncertainty range with the CFH
profile on 25 January 2016. On 3 March 2017, the MLS mean profile is drier
than the CFH in the upper troposphere probably due to a lack of vertical
resolution in MLS and inhomogeneity in the atmospheric composition.
Temperature anomaly
The hypothesis of a potential influence of convection on the CFH water vapor
profile is further tested by analyzing the profile of the temperature anomaly. A
seasonal mean (December–March) temperature profile is computed for the
period 1997–2017 using the NDACC/SHADOZ dataset. The weekly NDACC/SHADOZ
launch is performed at the airport in the north part of the island (Roland Garros,
20 m a.s.l.). The flying distance between the Maïdo Observatory and the
airport is ∼20 km, so while boundary-layer temperature values
will differ for the two sites, free troposphere and TTL temperature
distributions can be compared as they are less influenced by topography. The
seasonal mean CPT height is 17.31 km for the period December–March with a
mean CPT temperature of 193.90 K (Table 1). The tropical tropopause is
higher and colder during austral summer as a response to large-scale
upwelling in the tropical stratosphere (Yulaeva et al., 1994) and convection
(Highwood and Hoskins, 1998). The iMet radiosonde temperature profiles are
then compared to the seasonal mean NDACC/SHADOZ temperature profile. The
upper panels in Fig. 5 show temperature profiles from NDACC/SHADOZ and the
iMet radiosonde. The black line shows the NDACC/SHADOZ seasonal mean
temperature profile, while the red line corresponds to the iMet temperature
profile observed at the Maïdo Observatory.
CPT properties (temperature and height) from the
radiosonde launches on 25 January 2016 and 3 March 2017 and NDACC/SHADOZ seasonal
mean (December–March) CPT properties (for the period 1997–2017).
ObservationsCPT T (K)CPT altitude (km)Mean SHADOZ Dec–Mar (1997–2017)200193.90 (±2.26)17.31 (±0.71)Profile on 25 Jan 20161192.6417.30Profile on 3 Mar 20171194.5816.10
A large positive temperature anomaly is observed on 25 January 2016 over a
broad tropospheric region from 2 to 16 km (mean amplitude of +2.5 K) with
a peak warming of +4.6 K at 10 km (magenta line in Fig. 5). On 3 March 2017, a warm temperature anomaly is mostly observed between 6 and 14 km
(mean amplitude of +1.1 K) with a peak value of +3.1 K near 12 km. The
stronger warming of the troposphere observed in January 2016 may be due to
the strong 2015/2016 El Niño. The connection between interannual
variations in tropical tropospheric temperature and ENSO is well established
(e.g., Yulaeva and Wallace, 1994; Soden, 2000). Using 13 years of temperature
data from the tropospheric channel of the microwave sounding unit (MSU-2),
Yulaeva and Wallace (1994) showed that a tropospheric warming occurs almost
uniformly over the tropics and that the magnitude of the warming is around
0.5–1 ∘C for strong El Niño years. Chiang and Sobel (2002)
updated the analysis of Yulaeva and Wallace to include the response to the
strong 1997/1998 El Niño (ONI of +2.2 K in December–February 1998) and indicated an MSU-2
temperature anomaly of ∼1.2 K in January 1998 (cf. Fig. 1
of Chiang and Sobel, 2002). Note that the MSU-2 temperature data used in
these studies provide a measure of the mean temperature of the 1000–200 mb
layer (corresponding to the surface to ∼11 km using a scale
height of 7 km). Thus, part of the strong tropospheric warming (especially
in the lower part of the troposphere) observed in January 2016 may be due to
the strong 2015/2016 El Niño (ONI of +2.5 K in December–February 2016). Assuming a
tropospheric warming of ∼1 K in response to a strong El
Niño, the magnitude of the upper tropospheric warming observed on 25 January 2016 (mean amplitude of 3.4 K between 10 and 14 km) becomes more
similar to the one observed on 3 March 2017 (mean amplitude of 1.9 K between
10 and 14 km) if the effect of the 2015/2016 El Niño is removed.
Figure 5 indicates cold temperature anomalies within 16–19 km above the
tropospheric warm anomalies on 25 January 2016. The mean amplitude of the
16–19 km temperature anomaly is -1.6 K with a maximum cooling of -3.6 K at
18 km. A similar feature is observed on 3 March 2017 with a cooling between
14 and 17 km, a mean amplitude of -2 K, and maximum cooling of -4.5 K at
15.1 km. The upper tropospheric warming and near tropopause cooling observed
on both dates is consistent with a temperature response to deep convection
(e.g., Sherwood et al., 2003; Holloway and Neelin, 2007; Paulik and Birner,
2012). The cooling around the tropopause can be explained by either
radiative cooling by cirrus clouds over the regions of deep convection
(Hartmann et al., 2001) or diabatic cooling through convective detrainment
(Sherwood et al., 2003; Kuang and Bretherton, 2004). CPT properties can also
be modified by convectively driven waves (Zhou and Holton, 2002; Randel et
al., 2003).
Paulik and Birner (2012) investigated the deep convective temperature signal
based on SHADOZ ozone and temperature data. Low ozone concentrations in the
upper troposphere are indicative of convective transport from the boundary
layer. They looked at temperature anomalies corresponding to low ozone
anomalies between 12 and 18 km, thus temperature anomalies influenced by
deep convection. A strong warming was observed near the level of main
convective outflow at ∼12 km, and cooling was more pronounced
above ∼15 km and near the CPT at ∼17 km. Thus,
the upper tropospheric warm temperature anomalies, as well as cold
temperature above 15 km and near the tropopause in Fig. 5, are consistent
with a deep convective temperature signal. Paulik and Birner's study also
showed that the amplitude of the temperature anomalies increases as
convection strengthens with a warming of ∼2 K in the upper
troposphere and a cooling of around -3 K near 16 km (cf. Fig. 5 of Paulik
and Birner, 2012). Using CloudSat observations of deep convective clouds and
constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) GPS temperature profiles, they showed that the deep convective
temperature signal (i.e., anomalously warm upper troposphere and an
anomalously cold upper TTL) was only present for deep convective clouds
above 15 km. Although the magnitude of the temperature anomalies decreases
with increasing distance from convection, they observed a deep convective
temperature signal during December–February ∼3500 km away from the
convective event. Within 1000 km of the deepest convection (deep convective
clouds above 17 km), the convective temperature anomaly exceeds 0.75 K in
the upper troposphere and ranges from -1 to -2.0 K near 16 km. In our
case, the deepest convective clouds with cloud tops colder than 190 K were
1000 km away from the island on 22–25 January 2016 and were closer to the
island at ∼500 km on 28 February–3 March 2017 (Fig. 2).
Although deep convective clouds observed on 22–25 January 2016 and 28 February–3 March 2017 were not in the immediate vicinity, relatively
fast-moving gravity waves caused by deep convection could spread the deep
convective temperature signals over large regions in short amounts of time
(Holloway and Neelin, 2007). The temperature anomalies in Fig. 5 are much
larger than those reported by Paulik and Birner for temperature profiles
around the time (±6 h) and location of deep convection (within
1000 km). However, we are studying deep convective temperature anomalies
associated with two individual events, while their deep convective
temperature signal was estimated using 4 years of COSMIC data. Therefore,
their estimates correspond to an average deep convective temperature signal;
such a signal is likely larger when considering larger/more organized
convective events such as tropical storms.
Hence, the temperature anomalies derived from the 25 January 2016 and 3 March 2017 profiles are consistent with a deep convective outflow in the upper
troposphere.
Water vapor anomaly
To further assess the impact of TS Corentin and TC Enawo on the upper troposphere–lower stratosphere (UTLS) water
vapor content, we compare the convolved CFH profiles to a monthly
climatological MLS water vapor profile as there are no long-term
stratospheric water vapor measurements on Réunion Island. For each year
between 2004 and 2017, MLS water vapor profiles within ±5∘
latitude and ±10∘ longitude of Réunion Island and over
a period of 15 d surrounding the launch date, i.e., 10 January–9 February
for 25 January 2016 and 16 February–18 March for 3 March 2017, are used
to define a monthly climatological water vapor profile. We also computed a
non-convective monthly climatological MLS water vapor profile by excluding
MLS water vapor profiles with coincident low upper-tropospheric ozone
(probably affected by convection; Paulik and Birner, 2012). The
non-convective and monthly climatological MLS water vapor profile (using all
profiles) looks very similar (not shown). Thus, the climatological MLS water
profile using all profiles is used for comparison with the water vapor
measurements on 25 January 2016 and 3 March 2017.
(a, b) Convolved CFH water vapor profiles (blue
line), mean of closest-matched MLS profiles (magenta) and monthly mean
climatological MLS water vapor profile for Réunion Island (black line;
see text for definition of the MLS climatological profile) on 25 January 2016 (a) and 3 March 2017 (b). The horizontal bars in
black correspond to the ± 1 standard deviation range. (c, d) Relative difference between the convolved CFH water vapor profile and the
MLS climatological profile for Réunion Island (blue line) and the mean
of closest-matched MLS profiles and the MLS climatological profile (magenta
line). The convective fraction computed with FLEXPART back trajectories and
Meteosat-7 infrared brightness temperature is shown in red. Corresponding
altitude values for MLS pressure levels are also shown in each plot.
The monthly climatological MLS water vapor profiles and CFH convolved
profiles are shown in Fig. 9. Both monthly climatological water vapor
profiles have comparable minimum water vapor mixing ratios at 83 hPa (3.5±0.6 ppmv and 3.3±0.5 ppmv for the January and March
climatologies, respectively). In the upper troposphere (316–178 hPa), the
climatologies have mean values of 277.6±269.2 ppmv and 266.1±253.2 ppmv for January and March, respectively. High variability in the UT is
consistent, with deep convection being more active during austral summer.
Higher UT water vapor content in January relative to March is in agreement
with the fact that the austral summer season reaches its peak in
January/February. Both January and March climatologies have comparable TTL
(147–68 hPa) water vapor content (5.3±1.8 ppmv and 5.1±1.7 ppmv for January and March, respectively). The climatological mean
stratospheric (56–22 hPa) value is 4.2±1.3 ppmv for both months.
Relative water vapor differences are defined with respect to the monthly
climatological profile (i.e., relative difference = (CFH - MLS
climatology)/MLS climatology × 100) and are displayed in Fig. 9c and d. In addition to the CFH convolved profile, we also compared the
mean of MLS coincident profiles to the MLS monthly climatological profile
for 25 January 2016 and 3 March 2017.
On 25 January 2016, the mean of MLS coincident profiles and the CFH
convolved profile shows a peak of ∼30 % or 7.7 ppmv in the
relative difference with the MLS climatology in layer L1, but the pressure
level of this peak differs in the two profiles with a peak at 178 hPa for
the CFH convolved profile and 147 hPa for the mean of coincident MLS
profiles.
To further evaluate the portion of the profiles that were influenced by
convection, we calculated a convective fraction profile. For each pressure
level depicted in Fig. 9, 50 000 FLEXPART back trajectories were
initialized. A back trajectory was tagged as convectively influenced when the
IR brightness temperature observed by Meteosat-7 falls below 230 K over the previous 7 d and
if the altitude of the back trajectory falls below 5 km, indicating a lower
tropospheric origin. Hence, the convective fraction profile represents the
percentage of trajectories for each pressure level that was considered convective following those criteria. The convective fraction profile
reaches a maximum of 60 % at 147 hPa and confirms that layer L1 and the
bottom part of layer L2 are convective. The FLEXPART back trajectories from
Fig. 6 and the values of the convective fraction profile confirm that the
positive water vapor anomalies observed in layer L1 are associated with the
convective outflow of TS Corentin.
On 3 March 2017, the hydration of the upper troposphere in layer L4 (between
215 and 121 hPa) is much more pronounced in the CFH convolved profile with a
peak value of ∼180 % or 45 ppmv at 178 hPa. For the mean
of MLS coincident profiles, the moistening is not as large with a relative
difference of 36 % or 8.7 ppmv at 178 hPa. The convective fraction profile
had values of 60 % at 178 and 147 hPa, confirming that layer L4 was
influenced by convection.
Ray and Rosenlof (2007) used measurements from AIRS to assess the impact of
tropical cyclones in the Atlantic and Pacific basins on the amount of water
vapor in the tropical UT. They showed that tropical cyclones can hydrate a
deep layer of the surrounding upper troposphere by ∼ 30–50 ppmv or more within 500 km of the eye compared to the surrounding average
water vapor mixing ratios (cf. Fig. 3 of Ray and Rosenlof, 2007). They
also looked at the evolution of UT water vapor changes as a function of the
storm intensity as measured by the peak wind speed (cf. Fig. 5 of Ray and
Rosenlof, 2007). In both the Atlantic and western Pacific basins, the
average water vapor at 223 hPa around the storm center steadily increased
from 4 to 5 d prior to peak cyclone intensity to 2 d following peak
cyclone intensity. The average water vapor enhancement in the two ocean
basins was from 5 to 20 ppmv with an increase as high as 30–40 ppmv for some
cyclones in the western Pacific. The CFH launch on 3 March 2017 at 18:00 UTC
occurred 3.5 d before Enawo reached its peak intensity on 7 March at 06:00 UTC (pressure at the center of 932 hPa, 10 min maximum sustained winds
of 204 km h-1), and the storm center was ∼700 km away
from the island. Thus, deep convection associated with TC Enawo may have
caused the strong increase in UT water vapor observed on 3 March 2017.
Ongoing work with MLS data to apply the methodology of Ray and Rosenlof (2007) to assess hydration of the UTLS by tropical cyclones for the
2004–2017 cyclone seasons in the southwest Indian Ocean is under way. This
will be the focus of a future study, but preliminary results indicate water
vapor differences of 35 % to 48 % at between 178 and 261 hPa for
category 2 to 4 hurricanes on the Saffir–Simpson scale. Ray and Rosenlof (2007) indicated that tropical cyclones hydrate a deep layer of the UT in
the vicinity of the cyclones by up to 50 % above monthly mean water vapor
mixing ratios. Therefore, our estimate of UT water vapor increases of 20 % to
100 % using CFH and MLS data for TS Corentin (category 1 hurricane at its
peak intensity) and TC Enawo (category 4 hurricane at its peak intensity)
are in broad agreement with our estimates based on the 2004–2017 MLS data
and the study of Ray and Rosenlof (2007).
At 100 hPa (within layer L2), both MLS and CFH data are 20 % (-0.7 ppmv)
below the climatological monthly mean values on 25 January 2016. This would
be consistent with the near tropopause cooling observed in Fig. 5 and the
presence of deep convection around Réunion Island. In addition, TTL
cirrus clouds were observed north of the island on both dates (Fig. 5).
Convectively generated or in situ cirrus clouds in the TTL can dehydrate the
tropopause region. Jensen et al. (1996) showed that ice clouds formed by
large-scale vertical motions can result in the depletion of the water vapor mixing
ratio by about 0.4 ppmv. Chae et al. (2011) investigated temperature and
water vapor changes due to clouds in the TTL using MLS, CALIPSO and CloudSat
datasets. They noted that generally clouds humidify the environment near 16 km (∼100 hPa) or lower but dehydrate the TTL above 16 km.
On 25 January 2016, CFH and MLS data are 11 % (+0.4 ppmv) and 18 %
(+0.7 ppmv) moister than the climatological values at 68 hPa (within layer
L3) above the tropopause. Observational and modeling studies have indicated
that overshooting convection can moisten the lower stratosphere by injecting
water vapor or ice crystals directly above the overshooting clouds (e.g.,
Danielsen, 1993; Corti et al., 2008; Dauhut et al., 2015; Frey et al., 2015;
Allison et al., 2018). In our case, the observation on 25 January 2016 was
not made close to the deepest convective clouds that were ∼1000 km north of the island (Fig. 2) but was downwind of TS Corentin, as
shown by the FLEXPART analysis (Fig. 6). However, FLEXPART
back trajectories indicate that the air masses at 68 hPa (layer L3) originate
from the southeast Indian Ocean in the 20–30∘ S
latitude band, where the MLS water vapor anomaly for January 2016 is around
0.5 ppmv most likely due to the impact of the 2016 strong El Niño event.
Hence, the positive anomaly against the climatological value can also be
explained by horizontal advection from the southeast Indian Ocean toward
Réunion Island.
It is difficult to conclude whether TC Enawo had a direct impact on water
vapor in the lower stratosphere by using only the CFH observation on 3 March 2017. The FLEXPART analysis indicated that the CFH sounding did not sample
the lower stratosphere downwind of Enawo.
Ongoing work with the mesoscale model Meso-NH at a 2 km resolution for TC
Enawo for the period 2–7 March 2017 indicates that deep convective clouds
within 500 km of the cyclone's eye can inject ice crystals and moisten the
lower stratosphere, resulting in an average anomaly of ∼2 ppmv
within 500 km of the tropical cyclone's eye. The strongest humidification in
the lower stratosphere (17–19 km, ∼ 88–66 hPa) was found after
4 March when the storm stalled over the ocean (while intensifying) and after
6 March when it reached its peak intensity. Thus, the CFH observation on 3 March 2017 was made before TC Enawo had influenced the lower stratosphere
above 100 hPa. This is further confirmed by the fact CALIOP did not have a
lower stratospheric signal in Fig. 6.
Tropical cyclones are unique among tropical convective systems in that they
persist for many days and thus could affect the UTLS more than other
mesoscale convective systems. Clouds in tropical cyclones often reach to and
sometimes beyond the tropopause (e.g., Romps and Kuang, 2009). Allison et al. (2018) have investigated the vertical transport of water vapor by the 2013
tropical cyclone Ingrid in the North Atlantic. Results of their
high-resolution numerical simulations indicated that hydration occurred
between 17.5 and 21 km (83 to 56 hPa) due to the injection of ice crystals.
As the exact role of deep convection, and tropical cyclones in particular,
in hydrating the lower stratosphere is still under debate, additional TTL
observations of water vapor and modeling work are needed to quantify the
overall impact of convection on TTL and MLS water vapor. High-resolution (2 km) numerical simulations of TC Enawo for the period 2–7 March 2017 are
underway to gain a closer look at the effect of TC convection on TTL
temperature and water vapor. This work will be the subject of a subsequent
study.
Summary
Two balloon launches from the Maïdo Observatory were specifically planned
using the FLEXPART Lagrangian model and Meteosat-7 infrared images to sample
the convective outflow from tropical storm Corentin on 25 January 2016 and
tropical cyclone Enawo on 3 March 2017. Balloon-borne measurements of CFH
water vapor, ozone, and iMet temperature and water vapor lidar measurements
showed that both storms humidified the TTL, with RHice values exceeding
50 % for TS Corentin and 90 % for TC Enawo in the upper troposphere.
Comparing the two CFH profiles to the climatological monthly mean MLS water
vapor profiles, positive anomalies of water vapor were identified with peak
values of 7.7 ppmv for TS Corentin and 45 ppmv for TC Enawo at 17 hPa.
According to the FLEXPART back trajectories and Meteosat-7 infrared images,
those air masses originated from convectively active regions of TS Corentin
and TC Enawo and were lifted from the lower troposphere to the upper
troposphere around 1 d before the planned balloon launches. In addition,
the CALIOP satellite measurements indicated cirrus clouds north of
Réunion Island for the same altitude range for both storms.
According to the CFH profile on 25 January 2016 and MLS climatology, air
masses measured near the tropopause were anomalously dry around 100 hPa and
anomalously wet around 68 hPa in the lower stratosphere. FLEXPART
back trajectories were used to find the origin of these layers, which could be
traced back to TS Corentin upper-tropospheric divergent flow and active
convective regions. Deep convective clouds and cirrus clouds may have
dehydrated the region around 100 hPa. According to FLEXPART back trajectories,
the positive anomaly at 68 hPa can be explained by a horizontal transport
from the southeast Indian Ocean. The southeast Indian Ocean had a positive
water vapor anomaly of ∼0.5 ppmv in January 2016 most likely
due to the strong 2016 El Niño event (Avery et al., 2017).
On the contrary, no water vapor anomaly was found near or above the
tropopause on 3 March 2017 as the tropopause region was not downwind of TC
Enawo. According to FLEXPART back trajectories, those air masses stayed away
from the upper-tropospheric dynamics of TC Enawo and its convective active
regions. Hence, the tropopause region on 3 March 2017 was not affected by
Enawo, at least not at the time of the balloon launch and at this stage of
Enawo's development.
This study showed the impact of two tropical cyclones on the humidification
of the TTL. It also demonstrates the need to develop balloon borne high
precision observations in regions where TTL in situ observations are sparse,
such as the tropics and the SWIO in particular. High-resolution accurate
observations of water vapor are needed to document the impact of tropical
cyclones and deep convection in general on the TTL. The impact of tropical
cyclones on the TTL water vapor budget will be analyzed in a more
quantitative way using MLS data and tropical cyclone best tracks from 2004
to 2017 in a subsequent paper. In addition, the impact of deep convection
and overshooting clouds within TC Enawo on the water vapor budget of the TTL
will be analyzed using a high-resolution (2 km) mesoscale simulation of TC
Enawo.
Data availability
MLS water vapor data used in this study are available at 10.5067/Aura/MLS/DATA2009 (Lambert et al., 2015) and CALIPSO L1B lidar data are available at
https://opendap.larc.nasa.gov/opendap/CALIPSO/LID_L1-Standard-V4-10/contents.html (Trepte, 2020). The NDACC/SHADOZ ozone
measurements for Réunion Island are available at https://tropo.gsfc.nasa.gov/shadoz/Reunion.html (Posny, 2020). The SWOOSH dataset is
available at https://data.nodc.noaa.gov/cgi-bin/iso?id=gov.noaa.ncdc:C00958 (Davis, 2020). The CFH and lidar water vapor data are available
from the authors (Stephanie Evan, Valentin Duflot, Philippe Keckhut) upon request. The FLEXPART Lagrangian
trajectories can be requested from the corresponding author Stephanie Evan
(stephanie.evan@univ-reunion.fr).
Author contributions
All authors contributed to the paper. SE wrote the paper with contributions from JB, KR, SMD, DH, FP, JMM, VD, GP, HV, PK and JPC. SE, JB, FP,
JMM, DH, JPC, VD, GP and HV performed the CFH, ozone and lidar measurements. HV
processed the CFH data. SE and JB performed the FLEXPART simulations. SMD
provided the SWOOSH dataset. All authors revised the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank the Aura science team for the MLS data (https://mls.jpl.nasa.gov/, last access: 7 September 2020) and the CALIPSO science team for the L1B lidar
data (https://opendap.larc.nasa.gov/opendap/CALIPSO/LID_L1-Standard-V4-10/contents.html, last access: 7 September 2020).
OPAR (Observatoire de Physique de l'Atmosphère à La Réunion,
including Maïdo Observatory) is part of OSU-R (Observatoire des
Sciences de l'Univers à La Réunion) which is being funded by
Université de la Réunion, CNRS-INSU, Météo-France and the
French research infrastructure ACTRIS-France (Aerosols, Clouds and Trace
Gases Research Infrastructure). OPAR's water vapor lidar and ozone
radiosounding belong to the international network NDACC (Network for the
Detection of Atmospheric Composition Change). This work was supported by the
French LEFE CNRS-INSU Program (VAPEURDO).
Stephanie Evan thanks Susanne Koerner (DWD/GRUAN Leadcentre, Germany) for her
training on the CFH instrument.
Review statement
This paper was edited by Federico Fierli and reviewed by two anonymous referees.
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