Introduction
Ozone and water vapor are two potent greenhouse gases in the atmosphere. Over
the tropical regions, stratospheric ozone depletion is not a critical problem
but tropospheric ozone is a serious issue. The absorption of solar radiation
by ozone in the Hartley, Huggins, and Chappuis bands is the major reason for
the heating of the middle and upper stratosphere. Apart from this, the two
strong bands at 9.6 and 15 µm in the infrared region cool the upper
atmosphere and cause the greenhouse effect in the lower atmosphere (Wang et
al., 1980). A large number of ozone and temperature observations are
available at different stations all over the globe (compared to water vapor,
methane, nitrous oxide, etc.). These observations show that the industrial
revolution has changed the ozone precursors in the troposphere through
anthropogenic sources, and thereby the radiative forcing
(∼ 0.40 ± 0.20 W m-2) has increased significantly due to
ozone (IPCC, 2013). Detection and estimation of long-term changes in the
atmospheric constituents and parameters by different statistical methods will
show the natural and anthropogenic effects on the climate change. Only
long-term trend observations can give reliable evidence of the current state
of the atmosphere and the effect on climate and ecosystems. They are
essential for the numerical simulations/climate modeling to predict the
future state of the atmosphere.
(a) Map showing the Indian subcontinent with Trivandrum,
Gadanki, and New Delhi locations. (b) Comparison of rocketsonde ozone
profiles (black) observed over Trivandrum during 1980–1981 with ozonesonde
profiles (blue) observed over Gadanki during 2010–2014.
In India from 1971 onwards, every fortnight ozonesonde launchings have been
conducted by the India Meteorological Department (IMD) from three stations
(Mani and Sreedharan, 1973) namely Trivandrum (8.4∘ N,
76.92∘ E), Pune (18.53∘ N, 73.85∘ E), and New Delhi
(28.6∘ N, 77.2∘ E). Rocketsonde ozone observations from
Trivandrum were conducted during 1980–1981 to study the day to night changes
of ozone at different levels in the tropical stratosphere and the lower
mesosphere. Figure 1a shows the location of Thumba/Trivandrum along with New
Delhi. Long-term trends in tropospheric ozone over the Indian region have
been studied by Saraf and Beig (2004) using the ozonesonde observations from
the three IMD stations for a period of 30 years, from 1971 to 2001. They
reported no statistically significant trend over Trivandrum but a significant
positive trend throughout the troposphere over New Delhi and in the upper
troposphere over Pune. Fadnavis et al. (2013) also reported a positive trend
(0.5–2 % decade-1) in ozone in the upper troposphere over Pune and
New Delhi. The increasing trend in ozone is attributed to the increase in the
NOx concentration in the upper troposphere. They also found a positive
trend in ozone between 100 and 30 hPa and thereafter a negative trend up to
10 hPa.
Regular launching of ozonesonde every fortnight from National atmospheric
Research Laboratory (NARL), Gadanki (13.5∘ N, 79.2∘ E), has been conducted from 2011 onwards (Akhil Raj et al., 2015), and a special campaign
(consisting of seven launchings) was conducted during the 2010 annular eclipse
(Ratnam et al., 2011). A very good comparison between Gadanki ozonesonde and Sounding of the Atmosphere using Broadband Emission Radiometry (SABER), Microwave Limb Sounder (MLS), and ERA-Interim has been found above 20 km altitude (Akhil Raj et
al., 2015). To visualize the changes in ozone vertical distribution in the
last 3 decades, we compared Thumba rocketsonde ozone profiles (Subbaraya
et al., 1985) with ozonesonde profiles observed from Gadanki, which is shown
in Fig. 1b. There were 19 rocketsonde launchings during 1980–1984. Though
most of the rocket measurements start from 16 km, only ozone concentration data
corresponding to 20 km and above are considered in the present study due
to large variation from one measurement to the other in the 16–20 km range. For
altitudes below 20 km ozonesonde measurements from IMD, Trivandrum
(8.5∘ N, 76.9∘ E) observations are used. In the initial
analysis, comparison of 1980s observations with those of the recent decade
shows a significant increase in the ozone concentration at the ozone peak
altitude together with the rise in the altitude of ozone minima in the upper
troposphere. Our primary objective is to investigate the trends in the
stratospheric ozone over India and understand the regional dependence on the
trends. Along with ozone trends we also estimated the water vapor and
temperature trends.
Stratospheric water vapor cools the stratosphere and causes warming of
the surface through the greenhouse effect (de Forster and Shine, 1999). The process of
stratospheric feedback increases stratospheric water vapor, which leads to
additional warming (Dessler et al., 2013). Since solar insolation at the
tropical regions is higher than at midlatitudes and polar latitudes, this feedback
would have more effect on the tropical climate. Though the concentration of
water vapor is low in the upper troposphere and the stratosphere, it can
significantly influence the climate (Held and Soden, 2000). It is well known
that the tropopause temperature controls the seasonal cycle of water vapor
entering the lower stratosphere (Mote et al., 1996). The in situ production
of water vapor by the oxidation of methane (CH4) contributes to the
observed water vapor in the stratosphere apart from the transport from the
upper troposphere.
Global mean cooling of the stratosphere is observed, and evidence points towards
anthropogenic activities as having an impact on climate (Kishore et al., 2014,
and references there in). We have estimated the long-term trends in
temperature along with ozone and water vapor. Several trace gases have strong
absorption bands in the infrared (IR) region (5 to 20 µm), which
contributes to the greenhouse effect by enhancing the opacity of the
atmosphere. Stratospheric temperature perturbation by the IR cooling due to
the increase in trace gases alters the middle stratospheric chemistry via
temperature-dependent reaction rates (Ramanathan et al., 1985, and
references there in). Ozone is one of the kinds of chemicals (gases) that
have temperature-dependent reactions; hence this will lead to the perturbation
of stratospheric ozone. New stratosphere-resolving general circulation
models and chemistry-climate models have predicted the strengthening
of the Brewer–Dobson circulation (BDC) in response to climate change induced by
greenhouse gases (Butchart, 2014; Butchart and Scaife, 2001). The BDC has an
important role in determining many aspects, such as the thermodynamic balance of
the stratosphere, the transport of ozone and other chemical species, and the entry of
water vapor into the stratosphere. The strengthening of the BDC also causes
the transport of tropical lower stratospheric ozone to midlatitudes and
polewards. For the present study we considered 23 years of ozone mixing ratio
data by combining observations from the Microwave Limb Sounder (MLS)
(1993–1999), the Halogen Occultation Experiment (HALOE) (1993–2005)
on board the Upper Atmosphere Research Satellite (UARS), and the Earth Observing System (EOS)
Microwave Limb Sounder (MLS) on board the Aura spacecraft (2004–2015). HALOE
(1993–2005) and SABER (2002–2015) temperature data are used for the
estimation of temperature trends. Along with this, UARS HALOE (1993–2005)
and MLS (2004–2015) on-board Aura water vapor data are also used for
estimating the trends in the water vapor mixing ratio. To investigate the
regional dependence of the trends, we considered two locations within India,
namely Trivandrum, a tropical station, and New Delhi, a subtropical station.
Database and methods
HALOE and MLS on board UARS
HALOE is a solar occultation instrument on board the UARS satellite that takes
observations during limb viewing conditions (Russell et al., 1993) and
gives 15 sunrise and sunset measurements per day. HALOE uses transmittance
solar infrared radiations in the 2.45 to 10.04 µm range and
measures O3, HCL, CH4, NO, NO2, H2O, aerosol extinction,
and temperature versus pressure from 80∘ S to 80∘ N.
However the 57∘ inclination limits the majority of the observations to
the higher latitudes, and the number of observations in the tropics and
subtropics is lower when compared to higher latitudes. The altitude range of
measurement is from 15 km to 60–130 km with a vertical resolution of
∼ 2 km or less depending on the channel. Ozone and water vapor
profiles are retrieved from 9.852 and 6.605 µm transmission
wavelengths, respectively. Temperature is retrieved from the atmospheric
transmission measurements of the 2.80 µm CO2 band. To avoid the
influence of the Mount Pinatubo eruption in the observations, we used ozone, water
vapor, and temperature observations from 1993 to 2005. Since the overpass of
the satellite is not expected every day over a given location, we considered
a 10∘ × 20∘ (lat × long) grid to make sure
a reasonable number of observations is available over Trivandrum and New Delhi
to generate statistics.
Merging procedure illustrated for temperature at 21.5 hPa over
Trivandrum. (a) Monthly mean source data during overlapping period
(January 2002–November 2005) for HALOE and SABER at 21.5 hPa over
Trivandrum. (b) The merged product (black) resulting from the
adjustment of source data to the mean reference indicated by black dashed
line (mean of co-located HALOE and SABER data) along with source data. Blue
and red dashed lines show the mean of HALOE and SABER during the overlapping
period. (c) Final merged temperature by applying additive offset
value to the source data during 1993–2015.
Ozone observations from MLS on board UARS during the time period of 1993 to 1999
are also used for the present study. The MLS retrieves ozone profiles from
the calibrated microwave radiances in two separate bands, at frequencies near
205 and 183 GHz. A detailed description of UARS MLS ozone and other data
products are available elsewhere (Froidevaux et al., 1996; Livesey, 2003).
The MLS instrument measures in the microwave emission spectrum near 63, 205, and 183 GHz
by three different radiometers. The instrument performs Earth's atmosphere
limb scan from around 1 to 90 km tangent point altitude every 65.536 s, and
this is known as one MLS major frame, which consists of 32 MLS minor frames. Due to
the failure of the 183 GHz radiometer in mid-April 1993, we could not use the
ozone and water vapor information from this channel. For the present study we
made use of 205 GHz channel data from 1993 to 1999.
SABER on board TIMED
The SABER is one of the instruments on NASA's TIMED satellite launched on
7 December 2001. The TIMED satellite is at 625 km orbit with an orbital
inclination 74.1∘, and its period is ∼ 97 min. SABER scans the
horizon with a 10-channel broadband limb scanning radiometer ranging from 1.2
to 17 µm wavelength. The ground station will provide approximately
2 km altitude resolution profiles of temperature, O3, H2O, and
CO2 along with other data products from the observed vertical horizon
emission profile (Russell III et al., 1999). Temperature is retrieved from
the atmospheric 15 µm CO2 limb emission (García-Comas et
al., 2008) and ozone on a daily basis in the middle and upper atmosphere from
the 9.6 µm channel (Rong et al., 2009). For the present study we
used version V2.0 temperature data obtained during the period from 2002 to
2015.
MLS on board EOS/Aura
Apart from the UARS HALOE, MLS (UMLS), and SABER, we have also utilized the
data sets from the Earth Observing System (EOS) Microwave Limb Sounder (MLS) on
board the Aura spacecraft (AMLS). AMLS is a second-generation follow-on
experiment to the successful MLS instrument on UARS. AMLS measures several
atmospheric species, cloud ice, temperature, and geopotential height. The
instrument uses heterodyne limb radiometers to make simultaneous and
continuous observations during day and night. The instrument observes the
thermal emission from the atmospheric limb in broad spectral regions
centered near 118, 190, 240, and 640 GHz and 2.5 THz (Waters et al., 2006).
The instrument performs an atmospheric limb scan and radiometric calibration
for all bands every 25 s. With a latitude coverage of
82∘ N–82∘ S for each orbit, AMLS retrieves profiles every
165 km along the suborbital track. In the present work we used version 3
ozone and water vapor data and version 4 nitrous oxide (N2O) data over
Trivandrum and New Delhi from 2004 to 2015.
(a) Systematic error calculated from monthly mean
temperature observations from HALOE and SABER over Trivandrum at 21.5 hPa
with source data. (b) Contour of the temperature over
Trivandrum from 1993 to 2015 after removing the bias.
Merging of satellite data
As different instruments on board the above-mentioned satellites are used
and the time periods are not the same, they need to be suitably merged to
obtain meaningful long-term trends. In order to accommodate the lower vertical
resolution profiles from UMLS we have interpolated higher vertical
resolution data into a standard pressure grid and the pressure grid as
p(i)=1000×10-i/6(hPa),
with i varying from 0 to a product-dependent top. Monthly mean temperature,
ozone mixing ratio, and water vapor mixing ratio are used to produce merged
data. We used a 10∘ × 20∘ grid to construct monthly
mean data. We followed the method of Froidevaux et al. (2015) for merging the
satellite data in the present work. Merging is done by computing average
relative biases between the source data sets during the overlapping periods
and then applying additive offsets to each source of data to adjust them to a
common reference to remove relative biases. Figure 2 illustrates the merging
procedure of temperature at 21.5 hPa over Trivandrum. HALOE and SABER
temperature data are used for producing merged temperature from 1993 to 2015.
Figure 2a shows the source data from HALOE (blue) and SABER (red) during the
overlapping period from January 2002 (SABER data start) through November 2005
(HALOE data end). Figure 2b illustrates the merged product (black) during the
overlapping period resulting from the adjustment of HALOE and SABER to the
mean reference (black dashed line), the average of both time series when
both values exist. We first obtained the merged data via
m1(i)=(1/2)(y2(i)+y2(i)),
and with this we have calculated the offset for y1(i) and y2(i) as
(1/(2n12))∑(y1(i)-y2(i)),
where n12 is the number of overlapping data points between the two time
series. Since the number of collocating points is much lower over the tropics, we
followed another method instead of directly averaging the data points. The
offset is calculated using Eq. (3), where the gap of y2(i) (using HALOE
data) is replaced with the mean of overlapping data to improve the offset value.
This calculated offset is applied to the data sets and the average is calculated to
obtain the merged data during the overlapping period. Figure 2c shows the merged
data for the full-time period (1993 to 2015). The water vapor merged data product
is made up of HALOE and AMLS observations following the same method (as
temperature).
We used ozone data from UMLS (1993–1999), HALOE (1993–2005), and AMLS
(2004–2015) for producing merged data sets over Trivandrum and New Delhi.
Though the basic method is essentially the same, we have followed a slightly
different procedure for merging this data product since there is no
overlapping period between the three data sets. We considered
UMLS as the standard data product and calculated the offset for HALOE ozone as
described above. This offset is applied to the data sets (i.e., HALOE*)
used to calculate the offset for AMLS, and the three data sets are finally
merged to obtain a long period of data.
(a) Zonally averaged (70–80∘ E) ozone mixing
ratio over the Indian subcontinent obtained from AMLS during 2004–2015.
(b) Time averaged ozone mixing ratio contour at 30 km obtained from
AMLS during 2004–2015.
We have calculated systematic error since the standard deviation (SD) between the
data sets could not be calculated due to the lack of data points. Usually the
systematic error may not be symmetric to the merged data, especially when one
of the sources of data is significantly biased compared to the other (Froidevaux et al.,
2015). Figure 3a shows the result of systematic error calculation at
21.5 hPa over Trivandrum along with source data. The lower limit of error is
determined by HALOE data and the upper limit by SABER data. Systematic error
is estimated by calculating the variance via
σu2=1nu-1(∑knyk-1σyk2+∑knykY‾k2+nuUref2-2Uref∑knykY‾k),
where k represents the given instrument (source), nyk represents the
total number of data from a given source (instrument), σyk2
represents the variance of source data, the adjusted time series mean is
taken as Y‾k, and Uref2 is the merged value
(which is not necessarily the average value U‾). In this
paper we used Uref2=1nu∑knyky‾k since we are merging emission-type versus occultation-type
instruments. Systematic error is due to bias or drift in the measurement
system that affects measurements' accuracy (Latifovic et al., 2012). Figure 3b
shows the merged temperature over Trivandrum from 1993 to 2015 after removing
the bias.
Regression analysis
The mean profiles of ozone, temperature, and water vapor are composed of
mainly semi-annual oscillation (SAO), annual oscillation (AO) quasi-biennial
oscillation (QBO), El Niño–Southern Oscillation (ENSO), and an 11-year solar
cycle. It is necessary to remove all these short-term and long-term
periodicities in order to estimate the long-term trends. For this purpose we
applied regression analysis to the monthly mean profiles of ozone,
temperature, and water vapor at each altitude. The general expression of the regression model equation can be written as follows (Randel and Cobb, 1994):
T(t,z)=α(z)+β(z)t+γ1(z)QBO1(t)+γ2(z)QBO2(t)+δ(z)Solar(t)+ε(z)ENSO(t)+resid(t).
The coefficients, α, β, γ1, γ2, δ,
and ε are calculated using the following harmonic expression
(Kishore et al., 2014):
αz=Ao+∑i=13[Ai×cosωit+Bi×sinωit],
where ωi=2πi/12.
Composite monthly mean contours of bias removed (a) ozone
mixing ratio contour constructed using UMLS (1993–199), HALOE (2000–2001),
and AMLS (2002–2015), (b) water vapor mixing ratio constructed
using HALOE (1993–2004) and AMLS (2005–2015), and (c) temperature
constructed using HALOE (1993–2001) and SABER (2002–2015) observations over
Trivandrum.
If S is the sum of squares of residuals, N is the length of data, and M
is the total number of regression constants (N>M), then the error in the
coefficients is given by
σ=SN-M(XTX)-1,
where X is the input data matrix. Singapore (1∘ N,
104∘ E) monthly mean QBO zonal wind (m s-1) at 30 hPa is used
as a QBO1 proxy (QBO1(t)) and QBO zonal wind at 50 hPa is used as
a second QBO2 proxy (QBO2(t)); these data are available at
http://www.geo.fu-berlin.de/met/ag/strat/produkte/qbo. We used Ottawa
monthly mean F10.7 cm solar radio flux indices as a proxy (solar(t)) for
solar activity. These data may be downloaded from the following website:
ftp://ftp.geolab.nrcan.gc.ca/data/solar_flux/monthly_averages/maver.txt.
We used the Southern Oscillation Index (SOI), which is calculated from the
monthly mean sea level pressure (MSLP) at Tahiti (18∘ S,
150∘ W) minus MSLP at Darwin (13∘ S, 131∘ E) as a
proxy for the El Niño–Southern Oscillation (ENSO(t)). These data are
publicly available on the following website:
http://www.cpc.ncep.noaa.gov/data/indices/soi.
Results and discussion
Global distribution of ozone: composite mean
While comparing ozone profiles from two different stations, which are
separated by ∼ 5∘ as shown in Fig. 1b, any differences that may
arise due to latitudinal variation need to be considered. In order to examine
this aspect, in Fig. 4a we plotted the zonal (70–80∘ E) composite mean
of ozone mixing ratio for the period of 2004 to 2015 obtained from AMLS. The
dotted lines in the figure mark the locations of Trivandrum, Gadanki, and New
Delhi. The ozone concentrations at the ozone peak altitude of Trivandrum and
Gadanki are nearly the same, which means that the difference which we observed
in Fig. 1b is not due to the difference in latitudes of the two stations but
can be the combined result of change in chemistry and dynamics. We have
checked the same with ozone number density. Figure 4b shows the mean ozone
mixing ratio distribution over the globe at 30 km (ozone peak altitude at
Trivandrum and Gadanki). Trivandrum, Gadanki, and New Delhi were represented
in the figure as blue dots. From this figure it is clear that the ozone
mixing ratio over Trivandrum and Gadanki at 30 km is higher than that of New
Delhi at 30 km. There must be no significant difference between Trivandrum
and Gadanki ozone mixing ratio in the stratosphere but we can expect
a difference between Trivandrum and New Delhi. Hence it will be interesting to
investigate the regional differences in ozone distribution over the Indian
subcontinent.
LS periodograms obtained from various IMFs for (a) ozone,
(b) temperature, and (c) water vapor over Trivandrum at
6.8 hPa. The dotted line shows the 99 % confidence level.
Climatology of ozone, water vapor, and temperature
Figure 5a, b, and c show the climatology of the monthly mean ozone mixing
ratio (OMR), water vapor mixing ratio, and temperature, respectively, over
Trivandrum in the 20 to 50 km attitude range, calculated from merged data sets. An OMR
maximum is observed between 30 and 37 km. For the same time period we calculated water
vapor mixing ratio using UARS HALOE and AMLS observations. In the lower
stratosphere (∼ 20–23 km), an enhancement of water vapor is found
during the wintertime, and later it started to decrease. Initially the
decreasing of water vapor with altitude is observed; however, water vapor
concentration increases with altitude above the middle stratosphere. Water vapor
shows a SAO in the upper stratosphere, with a maximum in pre-monsoon and
post-monsoon periods, whereas in the lower stratosphere it shows more of an AO, with
a maximum during the summer. Temperature shows an SAO, with a peak occurring in
pre-monsoon and post-monsoon periods, which is more prominently seen in the upper
stratosphere than the lower stratosphere. Heating in the upper stratosphere is
observed over Trivandrum during January–April and September–November,
which is seen in Fig. 5c.
Altitude profile of the response of the (a) ozone mixing
ratio obtained using combined measurements of UMLS, HALOE, and AMLS,
(b) temperature from HALOE and SABER, and (c) water vapor
mixing ratio from HALOE and AMLS over Trivandrum (blue) and New Delhi (red)
to the QBO, ENSO, and the solar cycle. The solid line represents 30 hPa QBO wind
(QBO1) and the dashed line represents 50 hPa QBO wind (QBO2).
Intrinsic mode function (IMF) analysis
Before proceeding to the regression analysis to investigate the long-term
trends in ozone, temperature, and water vapor, we examined the data for
the presence of any dominant periodicities. For this analysis, we used
the empirical mode decomposition (EMD) method. This method is in contrast to the
other methods and works in the temporal domain directly rather than in the
corresponding frequency domain (Huang and Wu, 2008). The EMD method breaks down the nonlinear oscillation patterns
naturally into a number of characteristic intrinsic mode function (IMF) components (Zhen-Shan and Xian, 2007). This
technique is derived from the simple assumption that the IMF is a function that
satisfies the following two conditions. (1) In the whole data set, the number of
extreme points and the number of zero crossings are either equal or differ at
most by 1. (2) At any point, the mean value of the envelopes defined by
local maxima and local minima is zero (Huang et al., 1998). Once the extrema
are identified, we connect the maxima and minima by using cubic spline
interpolation to form upper and lower envelopes. Their mean (m1) is
subtracted from the original data (x(t)), which can be represented as
h1. However, h1 is still not a stationary oscillation pattern.
Hence we replaced the original data with h1 and repeated the process and
calculated h2. This process is repeated until the mean value of the
envelope becomes zero or close to zero, with the SD < 0.2.
Through this process we get the first IMF component, c1. We removed the first
IMF component from the original time series as follows.
r1=x(t)-c1
Since r1 still contains information about the long periods, we repeated the
entire process by replacing r1 with the original data.
r2=r1-c2
The above process is repeated n times, and all the possible IMFs
and the residue are calculated. The original time series can be represented as
xt=∑i=1nci+rn,
where ci is the possible IMF and rn is the residue.
The monthly ozone mixing ratio perturbation was broken down into seven IMFs using
the EMD method, and the amplitude spectrum of the ozone mixing ratio
perturbation is calculated using Lomb–Scargle (LS) analysis and is shown in
Fig. 6a. The same analysis is carried out for the temperature and water vapor
mixing ratio and is shown in Fig. 6b and c, respectively. In the figure
we have shown the analysis at 6.8 hPa for ozone, temperature, and water
vapor. In each periodogram, the dashed line indicates the 99 % confidence
level. From this figure it is clear that the dominant peaks are located near
the SAO, AO, QBO, ENSO, and the solar cycle. The uppermost panel in the Fig. 6 is
the Lomb–Scargle periodogram of original data. The first and second IMFs represent the SAO and AO
(from top). The QBO period shown in the third IMF ranges from 23 to 30 months.
The fourth IMF shows the ENSO periods, which range from 52 to 64 months. The
solar cycle is shown in the fifth IMF, which has a period of approximately
11 years (132 months). The solar cycle spectrum is broader and ranges from
a 10-year to a 12-year period.
Long-term trends
The LS periodogram analysis presented in the previous section revealed the
presence of dominant periodicities in stratospheric ozone, temperature, and
water vapor. In some years monthly averaged data points are missing in the
middle of the time series. Since the trend analysis is strongly dependent on the
points near the beginning and end of the data sets, midpoints do not
contribute much (Saraf and Beig, 2004). With this confidence we proceeded
with linear trend analysis.
Ozone, temperature, and water vapor response to the QBO, ENSO, and
solar cycle
Figure 7a shows the ozone response to the QBO, ENSO, and 11-year solar cycle
derived from the 23 years of merged satellite observations for both
Trivandrum and New Delhi along with SD. We have used two
orthogonal QBO wind (30 and 50 hPa) components for the present study. The
solid line in Fig. 7a shows the ozone response to 30 hPa QBO wind (QBO1) and
the dashed line shows 50 hPa QBO wind (QBO2). The ozone responses to QBO over
Trivandrum and New Delhi are quite different. The QBO1 response over
Trivandrum shows a double peak structure in the lower and middle
stratosphere, with a maximum response around 24 km
(0.056 ± 0.016 ppmv/QBO) and a minimum around 30 km
(-0.025 ± 0.025 ppmv/QBO). Ozone response to QBO1 over New
Delhi is negative in the lower stratosphere and maximum is around 32 km
(-0.059 ± 0.036 ppmv/QBO). The ozone response to the QBO1 and
QBO2 (dashed line) are quite opposite because the QBO30 and
QBO50 are predominantly out of phase. The ozone response to ENSO is larger
over Trivandrum than New Delhi. The ENSO response is negative over both of the
stations. The ENSO effect on ozone in the lower and upper stratosphere is
negligible when comparing over New Delhi and Trivandrum. The ozone response to
the solar cycle is positive in the lower stratosphere over both of the stations.
However this decreases with increasing altitude and becomes negative above
25 km. The solar flux coefficient shows similar characteristics over both the
stations, with a positive peak around 35–40 km. The larger response of solar
cycle is found in the middle stratosphere over both stations, with peaks around
37 km (0.23 ± 0.15 ppmv/100 sfu) and 35 km
(0.19 ± 0.15 ppmv/sfu) over Trivandrum and New Delhi, respectively.
Figure 7b shows the temperature response to QBO, ENSO, and the 11-year solar cycle
derived from the multi-satellite observations. The temperature response to the
QBO1 and QBO2 over Trivandrum and New Delhi shows an opposite
structure. The QBO1 response to temperature over Trivandrum is larger in the
lower and middle stratosphere. A positive response is found in the lower and
upper stratosphere, whereas a negative response is higher in the middle
stratosphere. The QBO1 effect on ozone over New Delhi is larger near the middle
stratosphere, and a positive maximum is found around 32 km
(0.24 ± 0.22 K/QBO). The temperature response to QBO2 over Trivandrum
and New Delhi shows a mirror image kind of structure. The ozone response to
QBO2 is larger than to QBO1. The temperature response to ENSO over
Trivandrum and New Delhi is found to be similar in the middle and upper
stratosphere. The effect of ENSO on Trivandrum temperature is negative up to
32 km. New Delhi shows an opposite result, being negative below 24 km and
becoming positive through the middle stratosphere to the upper stratosphere. The temperature
response to ENSO over Trivandrum and New Delhi shows similar characteristics
above the middle stratosphere. The temperature response to the solar cycle is
negative or low in the lower stratosphere, and its magnitude increases with
altitude and attains a maximum value of 0.97 ± 79 and
0.92 ± 1.0 K/100 F10.7 over Trivandrum and New Delhi, respectively,
around 30 to 32 km. Further, with increasing altitude, the magnitude of solar
response decreased and it became positive over both the stations beyond
38 km.
Vertical variation of trend observed in the (a) ozone
mixing ratio (in % decade-1), (b) temperature (in K decade-1), and (c) water vapor mixing ratio
(in % decade-1) obtained from multi-satellite observations over
Trivandrum (blue) and New Delhi (red) during 1993–2015.
Figure 7c shows the water vapor response to QBO, ENSO, and the solar cycle over
Trivandrum and New Delhi. The water vapor response to QBO1 over Trivandrum
and New Delhi is negative in the lower and upper stratosphere. It shows a
positive peak in the middle stratosphere, with a maximum
0.04 ± 0.02 and 0.03 ± 0.02 ppmv/QBO around 30 and
32 km over Trivandrum and New Delhi, respectively. The ENSO coefficient of water
vapor shows similar characteristics over Trivandrum and New Delhi in the
lower stratosphere. The water vapor response to the ENSO becomes negative in the
upper stratosphere of New Delhi, but not over Trivandrum. The solar flux
coefficient over Trivandrum is positive in the lower stratosphere and it
becomes negative in the middle and upper stratosphere. In the case of New Delhi,
the water vapor response to the solar flux is different from Trivandrum. It
shows a positive peak in the middle stratosphere, and the magnitude decreased
with altitude and became negative in the upper stratosphere. The larger
SD in the ENSO and solar cycle coefficients in Fig. 7 may be
due to the larger variability of ENSO (52–60 months) and the solar cycle
(10–12 years) than QBO (23–30 months).
Long-term trends in ozone, temperature, and water
vapor
Figure 8a shows the altitude profile of ozone trends per decade (in
percentage) with 2 SD obtained over Trivandrum and New
Delhi during 1993 to 2015. Ozone shows a significant decreasing trend in the
lower stratosphere over both stations. The decreasing trend is higher in the
lower stratosphere, and its magnitude decreases with altitude. Sioris et
al. (2014) also reported a significant decreasing trend in ozone in the lower
stratosphere (18.5–24.5 km) during 1984 to 2012 from merged satellite data
of SAGE II and OSIRIS over a latitude bin 7.5∘ N–7.5∘ S.
In our analysis the ozone trend becomes positive over Trivandrum and New
Delhi between 22.5 and 30 km, and the positive trend is higher over
Trivandrum than New Delhi. The positive trend in ozone is significant over
Trivandrum and it is only significant around 27 km over New Delhi. The trend
reverses to negative around 30 km over both the stations, and it remains
negative over New Delhi. The upper stratospheric ozone trend over Trivandrum
is positive and it is significant in the higher altitudes. In general, the
decreasing trend in ozone in the middle and upper stratosphere over New Delhi
is statistically significant. Similarly, the decreasing trend in ozone over
Trivandrum is also significant around 35 km. The statistically significant
positive trend in the upper stratospheric ozone over Trivandrum is the major
difference we found between Trivandrum and New Delhi. The trend reported by
Harris et al. (2015) using revised multiple data sets shows similar results over
the tropics (20∘ N–20∘ S) during 1998–2012 (their Fig. 6). To
compare the trend with these results, we have estimated the ozone trend over
Trivandrum and New Delhi during 1998–2012, shown by Fig. A1 in Appendix A.
We found a good agreement with the ozone trend from Harris et al. (2015), estimated
using GOZCARDS data (their Fig. 6) over the tropics. A maximum increasing trend
(2.62 ± 1.35 % decade-1) is observed around 27 km, and a maximum decreasing trend (-2.94 ± 1.13 % decade-1) in
ozone is found around 35 km. The decreasing trend in ozone over the tropics in
the middle stratosphere also exactly matches with the reported
observations during this period. The increasing and decreasing ozone trends
over Trivandrum are statically significant during this time period. In the case
of New Delhi, an extratropical station, in general, statistically
significant decreasing trend in the stratospheric ozone is found. Gebhardt et
al. (2014) also observed a positive trend between 20 and 30 km and a negative
trend between altitudes of 30 and 40 km using SCIAMACHY limb measurement for
the time period 2002–2012 for the zonal mean in the complete tropical
latitudes (20∘ N–20∘ S). The current analysis shows that
there is an increase in the ozone concentration around 23–30 km; this is
consistent with that observed in Fig. 1b. Ozone trend estimation by varying
the period is also carried out over both the stations, which is shown by
Fig. A2 in Appendix A. The lower stratospheric (above 24 km) increasing
trend in ozone over both stations is consistent, and similarly, the
decreasing ozone trend below 24 km is also found to be consistent over
Trivandrum than New Delhi. The trend estimation from 2000, 2001, and 2002 to
2015 over New Delhi shows an increasing trend in ozone from ∼ 21 km
upwards. A middle stratospheric ozone decreasing trend over the tropics is also
found during all these periods. The major difference between Trivandrum and
New Delhi is in the upper stratospheric ozone trend. An increasing trend in
upper stratospheric ozone is found during these years over Trivandrum, and a
decreasing trend is found over New Delhi.
Figure 8b shows the temperature trend over Trivandrum and New Delhi during
1993–2015. UARS HALOE and SABER data are merged together to make a 23-year
time series. Though the two stations are separated by ∼ 20∘, we
observed similar features in the temperature trend. The trend is almost
identical below 25 km; above that the cooling trend is higher over
Trivandrum than New Delhi. The maximum cooling trend is observed in the
middle stratosphere around 35 to 40 km over both stations. The cooling trend
is maximum around 37 km over Trivandrum
(1.71 ± 0.49 K decade-1) and New Delhi
(1.15 ± 0.55 K decade-1). These results are consistent with the
recent results reported by Kishore et al. (2014) using Gadanki lidar data
(for the period 1998–2011), where they found strong cooling near 38 km
(∼ 1.83 ± 1.1 K decade-1) and 56 km
(∼ 2 K decade-1). The simultaneous satellite trend analysis over
Gadanki showed a cooling trend near stratopause (50 km) altitude, which we have
seen over Trivandrum and New Delhi. The cooling trend is statistically
significant in all the altitudes over Trivandrum. In the case of New Delhi, the
cooling trend is not significant in the upper stratosphere and is around 30 km
in the middle stratosphere.
Monthly averaged N2O (red) and O3 ratios at
(a) 31.2 km and (b) 25.2 km over Trivandrum obtained from
AMLS measurements.
Water vapor trends in the stratosphere are shown in Fig. 8c. UARS HALOE and
AMLS observations are used to obtain 23 years of time series in the
stratosphere from 1993 to 2015. Water vapor shows a non-significant decreasing
trend in the lower stratosphere over Trivandrum and New Delhi. The increasing
trend is found above 24 km over both the stations; however, the trend is only
significant in the upper stratosphere. In the upper and middle
stratosphere, the water vapor trend is higher over New Delhi.
We now discuss the observed trend in ozone in the light of the trends in
temperature and water vapor. It is reported that tropospheric ozone shows, in
general, an increasing long-term trend due to anthropogenic sources (IPCC,
2013). A decrease in ozone concentration is expected to result in a decrease
in temperature and vice versa as ozone is the major heat source in the
stratosphere. The observed trends in ozone and temperature, in general, are
in accordance with this. It may be noted that temperature has a negative
feedback effect on ozone due to temperature dependence of chemical reaction
rates of ozone. Thus, the direct relations between ozone and temperature will
be tempered by this negative feedback between the two. Ozone above 10 hPa is
mostly under the control of photochemical processes, and below this altitude,
both the transport and chemistry control the ozone concentration (Butchart,
2014). Hence, the transport effect is at a maximum in the lower stratosphere
and the temperature effect is at a maximum in the upper and middle
stratosphere. Previous studies on stratospheric ozone have shown tropical
upwelling to be the major reason for the reduction in lower tropical
stratospheric ozone (Oman et al., 2010). Therefore, the decreasing ozone
trend in the lower stratosphere is mainly due to the strengthening of the
Brewer–Dobson Circulation (BDC). Rosenfield et al. (2002) attributed the
reduction in the tropical lower stratospheric ozone to the combined effect of
increased upwelling and the decreased production of ozone to the “reverse
self-healing”, i.e., the reduction in the production of lower stratospheric
ozone as a result of increasing upper and middle stratospheric ozone, which
thereby causes less ultraviolet radiation to penetrate to the lower
stratosphere. The decreasing ozone concentration may be the prime reason for
the cooling of the lower stratosphere over the tropics.
Here the question is regarding the cause for the observed positive ozone
trends around 23 to 30 km over Trivandrum and New Delhi. It is known that
the catalytic reactions involving ClOx, NOy, and HOx play a
major role in the destruction of ozone in the stratosphere. These are mainly
of anthropogenic origin in the troposphere. ClOx can be effective in the
ozone destruction in the lower stratosphere. NOy and HOx can be
effective in the upper stratosphere (> 30 km). The observed negative
trends in ozone in the altitudes above 30 km can be attributed to an
expected positive trend in NOy and HOx due to anthropogenic
sources. The observed positive trend in water vapor, which yields HOx on
photo dissociation, is consistent with this conclusion. The reduction in the
decreasing trend in ozone in the lower stratosphere (25–30 km) can be
caused by a reduction in ClOx. Note that ClOx is also of
anthropogenic origin, and its reduction is reported over the tropics (Connor et
al., 2013; Jones et al., 2011). Actions taken under the Montreal Protocol
have led to a decrease in ozone-depleting substances (ODSs). Equivalent
effective stratospheric chlorine has declined to 10–15 % from the
peak value in the last 10 to 15 years (WMO, 2014). The atmospheric abundance of
ODSs will continue to decrease in the coming decades; however, the increase in
N2O will become the primary source of nitrogen oxides in the
stratosphere and will become more important in future ozone depletion (WMO,
2014). The increasing trend in the tropical upper stratospheric ozone is
attributed to the slowing down of chemical loss cycles due to the cooling of
the stratosphere (Rosenfield et al., 2002). The cooling of the stratosphere has
direct and indirect effects on ozone loss. The direct effect is that it will
slow down the ozone recombination reaction O + O3 → 2 O2.
The indirect effect of stratospheric cooling is the decrease in production of
NOy per N2O molecules and the increase of the loss rate of NOy
(Rosenfield and Douglass, 1998).
Chemistry of nitrous oxide
As mentioned earlier, the tropical mid-stratosphere ozone is more sensitive
to NOy (NO + NO2) changes. Photo-chemical reactions of
N2O are the main source of NOy in the tropical mid-stratosphere.
The concentration of N2O decreases with the photo-dissociation and
produces NOy, and this will undergo catalytic ozone destruction at these
altitudes. The dissociation of N2O is connected with a coupled
chemical–dynamical effect. The observed trend is also strongly dependent on
the vertical transport rate. At altitudes at which the transport is slow,
more N2O will dissociate and form NOy. Nedoluha et
al. (2015), using HALOE observations during 1992 to 2005, reported that the
NO + NO2 is generally increasing, and this increase showed the
ozone loss to be ∼ 10 hPa over the tropics.
Monthly mean AMLS O3 and N2O are presented in Fig. 9 from 2005 to
2015 at two different altitudes, 31.2 and 25.2 km over Trivandrum, to show
the altitude-dependent chemistry of O3, N2O, and NO. A strong
positive correlation (R=0.83) between O3 and N2O is clearly
visible in the middle stratosphere (at altitude 31.2 km), and at 25.2 km we
observed a well pronounced negative correlation (R=-0.73) between O3
and N2O. Nedoluha et al. (2015) also observed a similar positive
correlation between O3 and N2O at 10 hPa between
5∘ S and 5∘ N. The N2O loss and production of NOy
reactions can be found in Brasseur and Solomon (2005) and Olsen et
al. (2001).
The photolysis of N2O is not strong below 10 hPa (∼ below 30 km), odd nitrogen loss
by reactions is higher, and the rapid increase of O(1D)
concentration above the middle stratosphere (∼ 40 km) (Brasseur and
Solomon, 2005) limited the ozone loss by odd nitrogen compounds to the middle
stratosphere. However, correlation or anti-correlation between N2O and
O3 does not necessarily provide evidence for a chemical control of
NOy on O3, as in particular in the lower stratosphere, transport will
have a strong effect on both N2O and O3. Nitrous oxide
concentration decrease over the equator and the tropics is less rapid compared to
higher latitudes. This is probably because of the rate of transport
associated with the rising branch of the BDC at these latitudes. Hence, a much
higher concentration of N2O is reaching the tropical and equatorial
middle and upper stratosphere, as does the photo-dissociation of N2O
by reacting with odd oxygen (O(1D)).
Summary and conclusions
Using multi-satellite observations, we constructed merged ozone, temperature,
and water vapor data during the time period 1993–2015. Using these merged data
products, covering more than 2 decades, long-term trends in ozone, water
vapor, and temperature over the Indian region are investigated. The contributions
of various long-period oscillations to the observed trends like SAO, AO, QBO, ENSO, and the solar cycle
in the stratosphere over Trivandrum and New Delhi are also investigated. The main conclusions drawn from the current
study are summarized in the following.
Ozone shows a significant decreasing trend in the lower stratosphere (20 to
24 km) during 1993–2015. The strengthening of the BDC is the major cause for this
decreasing ozone trend in the tropical lower stratosphere. The trend becomes
positive above 24 km over Trivandrum and New Delhi. The decreasing trend is
around 5 % decade-1 around 20 km over both the stations.
A decreasing trend in ozone is observed in the middle stratosphere over both
the stations. The decreasing trend is higher over Trivandrum when compared to
that observed over New Delhi. The negative trend in the ozone over Trivandrum
reaches a maximum of 0.69 ± 0.50 % decade-1 around 34 km.
This decreasing trend in ozone is mainly due to the odd nitrogen reactions.
However, the production of NOy from N2O decreases as an
indirect effect of stratospheric cooling.
Temperature shows a cooling trend with a maximum around 37 km over Trivandrum
(1.71 ± 0.49 K decade-1) and New Delhi
(1.15 ± 0.55 K decade-1). These results are consistent with
those reported using Gadanki lidar observations. Lower stratospheric cooling is
the result of a reduction in ozone due to the strengthening of tropical upwelling.
The water vapor trend is positive in the middle and upper stratosphere over both
the stations; this may be due to the increase in methane concentration in
the stratosphere.
The QBO response to ozone shows more regional dependence than that of water vapor
and temperature. The solar response of the ozone and temperature over both
stations shows similar features in the stratosphere except at higher
altitudes (40–50 km).
The current study on stratospheric ozone trends shows that ozone concentration
is decreasing in the middle and lower stratosphere at a statistically
significant rate. The trend analysis is highly dependent on the starting and
ending years. This has been shown in Figs. A1 and A2. The maximum ozone
decreasing trend in the middle stratosphere is found during 1998–2012 over
the tropics. This is consistent with earlier results. The middle and lower
stratospheric ozone loss will be an important issue over the tropics in the future
along with the increasing trend in the upper tropospheric ozone. This reduction
in lower stratospheric ozone by transport will increase the ultraviolet
index over the tropics. The effect of the QBO on ozone, temperature, and water vapor
has to be investigated in the future. The subtropical barrier plays an important
role in the horizontal mixing of trace gases between the tropical and
subtropical stratosphere. Detailed analyses of trace gases' distribution and
the modulation of the subtropical barrier by QBO are required to understand
the role of the subtropical barrier in the observed trend.