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Journal ArticleDOI

Improved ozone DIAL retrievals in the upper troposphere and lower stratosphere using an optimal estimation method

20 Feb 2019-Applied Optics (Optical Society of America)-Vol. 58, Iss: 6, pp 1374-1385

TL;DR: A first-principle optimal estimation method to retrieve ozone density profiles using simultaneously tropospheric and stratospheric differential absorption lidar (DIAL) measurements shows a significant improvement in the overlapping region, where the optimal estimation methods can retrieve a single ozone profile consistent with the measurements from both lidars.
Abstract: We have implemented a first-principle optimal estimation method to retrieve ozone density profiles using simultaneously tropospheric and stratospheric differential absorption lidar (DIAL) measurements. Our retrieval extends from 2.5 km to about 42 km in altitude, and in the upper troposphere and the lower stratosphere (UTLS) it shows a significant improvement in the overlapping region, where the optimal estimation method (OEM) can retrieve a single ozone profile consistent with the measurements from both lidars. Here stratospheric and tropospheric measurements from the Observatoire de Haute Provence are used, and the OEM retrievals in the UTLS region compared with coincident ozonesonde measurements. The retrieved ozone profiles have a small statistical uncertainty in the UTLS region relative to individual determinations of ozone from each lidar, and the maximum statistical uncertainty does not exceed a maximum of 7%.
Topics: Stratosphere (58%), Troposphere (52%)

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Improved ozone DIAL retrievals in the upper
troposphere and lower stratosphere using an optimal
estimation method
Ghazal Farhani, Robert J. Sica, Sophie Godin-Beekmann, Gérard Ancellet,
Alexander Haefele
To cite this version:
Ghazal Farhani, Robert J. Sica, Sophie Godin-Beekmann, Gérard Ancellet, Alexander Haefele.
Improved ozone DIAL retrievals in the upper troposphere and lower stratosphere using an opti-
mal estimation method. Applied optics, Optical Society of America, 2019, 58 (6), pp.1374-1385.
�10.1364/AO.58.001374�. �insu-02183026�

Research Article Applied Optics 1
Improved ozone DIAL retrievals in the upper
troposphere and lower stratosphere using an optimal
estimation method
GHAZAL FARHANI
1,*
, ROBERT J. SICA
1
, SOPHIE GODIN-BEEKMANN
2
, GÈRARD ANCELLET
2
, AND
ALEXANDER HAEFELE
3
1
Department of Physics and Astronomy, The University of Western Ontario, 1151 Richmond St., London, ON, N6A 3K7
2
Observatoire de Versailles Saint-Quentin-en-Yvelines, Guyancourt, France 78280
3
Federal Office of Meteorology and Climatology MeteoSwiss, Payerne, Switzerland 1530
*
Corresponding author: gfarhani@uwo.ca
Compiled October 31, 2018
We have implemented a first-principle Optimal Estimation Method to retrieve ozone density profiles us-
ing simultaneously tropospheric and stratospheric Differential Absorption Lidar (DIAL) measurements.
Our retrieval extends from 2.5 km to about 42 km in altitude, and in the upper troposphere and the lower
stratosphere (UTLS) it shows a significant improvement in the overlapping region, where the OEM can
retrieve a single ozone profile consistent with the measurements from both lidars. Here stratospheric
and the tropospheric measurements from the Observatoire de Haute Provence are used, and the OEM
retrievals in the UTLS region compared with coincident ozonesonde measurements. The retrieved ozone
profile have a small statistical uncertainty in the UTLS region relative to individual determinations of
ozone from each lidar, and the maximum statistical uncertainty does not exceed a maximum of 7%. © 2018
Optical Society of America
http://dx.doi.org/10.1364/ao.XX.XXXXXX
1. INTRODUCTION
The upper troposphere and lower stratosphere (UTLS) extends from about 6 km to 25 km in height and plays
a significant role in the atmospheric climate system. In this region of the atmosphere, even small changes
in temperature and in the distribution and concentration of greenhouse gases can result in large changes in
atmospheric radiative forcing, which can trigger climate change [1, 2].
Ozone in the upper troposphere acts as the third largest greenhouse gas contributing to the radiative
forcing of climate change [
1
,
3
]. The ozone distribution in the UTLS is the result of transport mechanisms and
photochemical reactions. Because of stratospheric tropospheric exchange, large spatial and temporal variability
can be observed in the UTLS [4].
In many studies on the UTLS ozone, satellite-borne instruments are used. In limb-viewing instruments,
the elevation angle of the line-of-sight varies during the measurements. As a result, limb sounders can
provide good vertical resolution (about 2 km to 4 km). However, at lower altitudes (lower troposphere), the
atmosphere becomes nearly opaque, and the limb-viewing instruments have difficulties measuring trace
gases. Nadir-viewing instruments can provide measurements in the lower troposphere, but their vertical
resolution is limited (about 6 km to 7 km). Occultation instruments use the Sun or other stars as the source
of radiation, and they can obtain measurements with higher vertical resolution (about 1 km to 2 km). Solar
occultation instruments are restricted by the number of sunsets and sunrises they encounter in one orbit, while

Research Article Applied Optics 2
stellar occultation instruments are limited by the weakness of the stellar source compared to the Sun. The
combination of measurements from different geometrical-based satellite instruments has been used to measure
ozone density.
In the UTLS, large biases (differences) in ozone measurements are reported between instruments. Although
the difference between the data sets is more significant in the tropics and high latitudes (about
±
30%),
significant bias exists at mid-latitudes (about
±
10%) [
5
]. Therefore, a continued detailed intercomparison
between satellite instruments, as well as between satellites and other instruments, is needed, including both
airborne measurements and ground-based measurements.
Differential Absorption Lidar (DIAL) systems provide ozone measurements with high vertical and temporal
resolutions. For example, observatories such as the Canadian Network for the Detection of Atmospheric
Change (CANDAC) Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Maïdo ob-
servatory in Reunion Island, the Observatoire de Haute Provence (OHP) in France, and the NASA Table
Mountain Observatory (TMO) in the United States are equipped with both tropospheric and stratospheric
lidars. At the Eureka observatory, the tropospheric lidar system makes measurements from 0.5 km to about
8 km in altitude and the stratospheric lidar system operates from about 4 km to 35 km [
6
,
7
]. At the Maïdo
observatory, the tropopspheric DIAL makes measurements from 6 km to 16 km, and the stratospheric DIAL
operates in the 13 km to 38 km region [
8
]. At the OHP observatory, the tropospheric DIAL system operates
from 2.5 km to about 14.5 km, and the stratospheric DIAL operates from about 10 km to 45 km [
9
,
10
]. At the
TMO, the tropospheric DIAL system obtains measurements from 3 km to 18 km, and the stratospheric DIAL
system from 10 km to 40 km [
11
,
12
]. Although these systems can produce satisfactory ozone profiles in their
overlapping region (from tropospheric lidar to stratospheric lidar), the uncertainty of merging is not well
defined. Providing a single ozone profile with a full uncertainty budget using both sets of measurements can
significantly improve our measurements of ozone in the UTLS [1315].
Here we apply the Optimal Estimation Method (OEM) to tropospheric and stratospheric DIAL measure-
ments. Measurements from these two systems are simultaneously used by the retrieval to obtain a single
ozone profile. Using the OEM there is no need to “merge” or “glue” level 0 profiles. Moreover, the input
measurements can be in different units with different measurement grids (for example a mix of analog and
digital measurements). Additionally, a full uncertainty budget, including both the systematic and statistical
uncertainties, is calculated for each individual profile. The OEM also provides averaging kernels of the re-
trievals, which allows comparison of the profiles with other measurements which can account for differences in
vertical resolution, such as when compared to space-based measurements. Other atmospheric and systematic
parameters such as air density, the dead time of the system, and the background counts can be retrieved along
with ozone profiles. The application of OEMs to aerosol lidar measurements, Rayleigh scatter temperatures,
and Raman scatter water vapour retrievals has been studied and discussed in detail [
16
18
]. In addition, we
have recently demonstrated an OEM for DIAL stratospheric ozone retrievals [
19
], which we will now expand
to include measurements from tropospheric ozone DIAL systems.
In this paper, focusing on the UTLS region, we show a first principle OEM to retrieve a single ozone
profile by using both tropospheric and stratospheric DIAL measurements directly from the raw (level 0)
measurements using the lidar equation as the forward model. In Section 2, pre-processing steps prior to
applying the traditional DIAL algorithm, as well as the OEM, are discussed. Moreover, the state vectors and
the
b
parameter quantities are defined and a brief overview of the lidar’s specifications is given. In Section 3
results of the OEM retrieval, using both tropospheric and stratospheric lidar measurements, are discussed
in detail. In this Section, we also show our results of comparison between the ozonesonde profiles and our
retrievals. Section 4 is the summary of the paper, and in Section 5 we discuss our future plans. Details of how
to apply our method to a standalone tropospheric DIAL are given in the Appendix.
2. METHODOLOGY
In the DIAL system, two wavelengths are simultaneously transmitted to the atmosphere. One of the emitted
wavelengths is strongly absorbed by the constituent of interest (called the “on-line” wavelength) and the other
is weakly absorbed (called the “off-line” wavelength). For ozone measurements, selecting a wavelength pair

Research Article Applied Optics 3
depends on the altitude range of the measurements. For most studies, the ultraviolet (UV) spectrum is the
most efficient spectral region. A pair of wavelengths with strong UV absorption is needed to detect the small
amount of ozone which resides in the troposphere. However, for stratospheric ozone measurements, choosing
a laser that can reach higher altitudes in the stratosphere is the main concern [11, 20, 21].
The traditional analysis method for ozone number density uses the derivative of the ratio between the
“on-line” and “off-line” channels to calculate the ozone number density
n
o
3
(z)
. A detailed discussion on the
tropospheric and stratospheric ozone retrievals can be found in [
10
,
22
25
]. In the traditional analysis, some
corrections are applied to the raw lidar measurements, for example background counts should be removed.
In many systems this requires including the effects of signal-induced-noise (SIN). Any corrections due to
nonlinearity of the counting system (because of saturation) should also be applied to the raw counts. Finally,
the signals from different channels need to be merged to form a single measurement profile. This “corrected”
count profile is then used to calculate the ozone number density profiles. With the OEM, a forward model
encapsulates the geophysical properties and instrumental characteristics of the system, and our OEM retrieval
uses the raw (level 0) measurements from all available channels. A comprehensive explanation of the OEM
can be found in [26]; a brief description of the OEM follows below.
In the OEM a forward model is defined as the relation between the measurements vector
y
= (
y
1
, y
2
, ..., y
n
),
and the state vector x = (x
1
, x
2
, ..., x
n
). The forward model is:
y = F(x, b) + e (1)
where
b
are the forward model parameters, which are assumed to be known, and
e
is the measurement noise.
We use the lidar equation as the forward model, where the raw counts are the measurements. The lidar
equation for unsaturated counts, N
true
, is:
N
obs
(z, λ
i
) =
C(λ
i
)O(z)
z
2
β(λ
i
, z) exp[2
Z
0
[σ
O
3
(λ, T(z))n
O
3
(z) + α(λ, z) +
e
σ
e
(λ)n
e
(z)]dz] + N
b
(z, λ
i
) (2)
where
N
obs
(
z, λ
i
) is the number of backscattered photons.
C
(
λ
i
) is the lidar constant, which contains the area
of the receiving telescope, the total efficiency of the lidar system, and energy of the scattered photon. The
geometrical overlap is
O
(
z
), and
β
(
λ
i
, z
) are the atmospheric backscattering coefficients which includes both
molecular and aerosol terms. The first term inside the integral corresponds to ozone absorption in which
σ
O
3
(
T
(
z
)
, λ
i
) is the ozone absorption cross section, which depends on atmospheric temperature, and
n
O
3
(
z
) is
the ozone number density. The second term of the integral,
α
(
λ, z
) contains the extinction coefficient which
is the sum of the extinction due to molecules and particles, and the last term
e
σ
e
(
λ
)
n
e
(
z
) is the extinction
by other absorbers. For ozone studies, the most common interfering gases are
SO
2
,
NO
2
and O
2
. The effect
of O
2
is only considered when the selected “on-line” laser wavelength is shorter than 294 nm [
12
,
27
]. In the
case of heavy volcanic eruption,
SO
2
and
NO
2
can significantly affect ozone retrievals [
28
]. However, in most
cases, for both stratospheric and tropospheric ozone studies the effect of these gases in final ozone retrievals is
negligible. Thus, the last term of integration is typically neglected [10].
The background counts are written as
N
b
(
z
). In the presence of SIN, the background is fitted to an exponential
function of the form:
N
b
(z) = a exp(bz) + c (3)
where
a, b,
and
c
are coefficients of the fit, which in the traditional method are determined analytically, but are
retrieved in our OEM retrieval using the analytic values as a priori coefficients [29].
When the intensity of the backscattered signal is high, the counting system can be affected by saturation.
This saturation can result in an observed count rate which is less than the true count rate. For a paralyzable
detector, true counts are related to the observed counts N
obs
as follows:
N
obs
= N
true
exp(κN
true
) (4)
and, for non-paralyzable detectors, the following equation can be used:
N
obs
=
N
true
1 + κN
true
(5)

Research Article Applied Optics 4
where
κ
is the dead time of the detecting system. For the OEM retrieval the value of the dead time for each
channel is retrieved.
A. Implementing the OEM for the OHP lidars
Knowing the measurements vector and its covariance matrix
S
e
, and using an a priori profile and its associated
covariance matrix S
a
, the OEM calculates an optimal a posteriori state by minimizing a cost function:
Cost = (y Kx)
T
S
1
y
(y Kx) + (x
b
x)
T
S
1
a
(x
b
x) (6)
As our forward model is nonlinear, an iterative numerical method is used. For our problem the Levenberg-
Marquardt iteration is a suitable numerical method. Then, the optimized state vector x is given as:
x
i+1
= x
i
+ [(1 + γ
i
)S
1
a
+ K
T
i
S
y
K
T
i
]
1
(
[K
T
i
S
1
y
(y F(x
i
, b)] S
1
a
(x
i
x
a
)
)
(7)
where
i
is the iteration term,
x
a
is the a priori profile, and
K
=
dF
dx
is the linearisation term for our nonlinear
forward model, called the Jacobian matrix. Finally,
γ
i
is a damping factor for the iteration, which is chosen at
each step to minimize the cost function. As suggested by [
30
] if the value of the cost function increases in a
step,
γ
i
will increase by a factor of 10, and if the value of the cost function decreases in a step,
γ
i
will decrease
by a factor of 2. The iteration stops when the cost function decreases to a value much smaller than the number
of measurements. There are other criteria which result in ceasing the iteration. Further details can be found in
[26].
To understand how measurements and a priori profiles contribute in the final retrievals, an averaging kernel
can be used. The relation between the retrieved state and the true state is described by the averaging kernel of
the retrieval. The averaging kernel is calculated as:
A =
d
b
x
dx
= [K
T
S
1
y
K + S
1
a
]
1
K
T
S
1
y
K (8)
The retrieved quantity (
b
x) can be written as follows:
b
x = (I A)x
a
+ Ax + e
r
(9)
where
e
r
is the retrieval uncertainty and
I
is a unity matrix. A perfect retrieval, in the sense all the information
comes from the measurement with no effect from the a priori state, has averaging kernels equal to one, where
the first term of the above equation becomes zero. The width of the averaging kernel gives the resolution of
the retrieval at each height, here defined as the Full Width Half Maximum (FWHM) of each averaging kernel.
In order to find the state vector (from Eq. 7) the following quantities should be known: the measurements
and their covariances, the a priori profiles, the a priori profile’s covariance, and the model (
b
) parameters. The
b
parameters are quantities in the forward model that are not being retrieved, because they are either well-known
or retrieving them is not possible. The uncertainty associated with the retrieval due to the
b
parameters is
calculated after the last iteration of the solution. The forward model and the Jacobians (
K
) for each of the state
vectors are calculated, and the Qpack package is used to perform the retrieval. Details of the Qpack software
are given in [31].
Here we retrieve the ozone density profile, relative air density, dead time values, and background counts.
Overlap functions, ozone cross sections, and Rayleigh scattering cross sections are considered as
b
parameters
in the forward model. Below, we discuss our choices of a priori profiles and
b
parameter values. The covariance
matrices associated with the measurements and a priori profiles are discussed as well, and these values are
summarized in Table 1.
In photon counting mode, when the signal is linear, the measurements statistical uncertainty follows a
Poisson distribution, and the number of counts at each altitude represents the measurement’s variance at
that height. There is no correlation between the digital counts in different layers of the atmosphere, so
the off-diagonal elements of the measurement’s covariance matrix are zero. However, for the OHP lidars,
analog measurements do not follow Poisson distributions. Calculating the measurement variance for each

Figures (14)
Citations
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Robert J. Sica1, Alexander Haefele2Institutions (2)
19 Dec 2014
Abstract: The measurement of temperature in the middle atmosphere with Rayleigh-scatter lidars is an important technique for assessing atmospheric change. Current retrieval schemes for this temperature have several shortcomings, which can be overcome by using an optimal estimation method (OEM). Forward models are presented that completely characterize the measurement and allow the simultaneous retrieval of temperature, dead time, and background. The method allows a full uncertainty budget to be obtained on a per profile basis that includes, in addition to the statistical uncertainties, the smoothing error and uncertainties due to Rayleigh extinction, ozone absorption, lidar constant, nonlinearity in the counting system, variation of the Rayleigh-scatter cross section with altitude, pressure, acceleration due to gravity, and the variation of mean molecular mass with altitude. The vertical resolution of the temperature profile is found at each height, and a quantitative determination is made of the maximum height to which the retrieval is valid. A single temperature profile can be retrieved from measurements with multiple channels that cover different height ranges, vertical resolutions, and even different detection methods. The OEM employed is shown to give robust estimates of temperature, which are consistent with previous methods, while requiring minimal computational time. This demonstrated success of lidar temperature retrievals using an OEM opens new possibilities in atmospheric science for measurement integration between active and passive remote sensing instruments.

36 citations


Journal ArticleDOI
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7 citations


01 Jan 2019
Abstract: Water vapor is the most dominant greenhouse gas in Earth’s atmosphere. It is highly variable and its variations strongly depend on changes in temperature. Atmospheric water vapor can be expressed as relative humidity (RH), the ratio of the partial pressure of water vapor in the mixture to the equilibrium vapor pressure of water over a flat surface of pure water at a given temperature. Liquid water can exist as super-cooled water for temperatures between 0◦C to −38◦C. Thus, RH can be measured either relative to water (RHw) or to ice (RHi). RHi measurements are important in the upper tropospheric region, where the temperature is always less than 0◦C, to study ice supersaturation (ISS) and its relation to the formation of cirrus clouds. I present three studies all using a mathematical scheme called the optimal estimation method (OEM). The OEM is an inverse method that determines the most probable state consistent with the measurements and a priori knowledge. These studies use parts of a large set of existing measurements from the Raman Lidar for Meteorological Observations (RALMO) instrument located at the meteorological observatory in Payerne, Switzerland. I first develop an OEM retrieval for temperature using RALMO’s two pure rotational Raman (PRR) channel measurements. Retrieved temperatures show excellent agreement with coincident balloon-borne radiosonde measurements. A second OEM scheme is introduced to retrieve RHw directly from RALMO measurements of back-scatter due to water vapor and nitrogen. I validate the OEM retrievals developed for temperature and RHw. I then combine the OEM-retrieved temperature and RHw with data from the European Centre for MediumRange Weather Forecasts Re-analysis (ERA5) to compute a new and improved temperature and relative humidity product. The retrieval is enhanced by assimilating it with the ERA5 data. The quality of the RHw retrievals from the assimilated OEM scheme greatly improves over retrievals which use less accurate a priori information. Thirdly, I retrieve RHi to detect ISS layers. I find the frequency of ISS layers in the free troposphere over Payerne to be about 27% using 82.5 hours of measurements.

4 citations


Journal ArticleDOI
Abstract: . A two-part inter-comparison campaign was conducted at L'Observatoire de Haute Provence (OHP) for the validation of lidar ozone and temperature profiles using the mobile NASA Stratospheric Ozone Lidar (NASA STROZ), satellite overpasses from the Microwave Limb Sounder (MLS), the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER), meteorological radiosondes launched from Nimes, and locally launched ozonesondes. All the data were submitted and compared blind , before the group could see results from the other instruments. There was good agreement between all ozone measurements between 20 and 40 km with differences of generally less than 5 % throughout this region. Below 20 km SABER and MLS measured significantly more ozone than the lidars or ozone sondes. Temperatures for all lidars were in good agreement between 30 and 60 km with differences on the order of ±1 to 3 K. Below 30 km, the OHP lidar operating at 532 nm has a significant cool bias due to contamination by aerosols. Systematic, altitude varying bias up to ±5 K compared to the lidars was found for MLS at many altitudes. SABER temperature profiles are generally closer to the lidar profiles, with up 3 K negative bias near 50 km. Uncertainty estimates for ozone and temperature appear to be realistic for nearly all systems. However, it does seem that the very low estimated uncertainties of lidars between 30 and 50 km, between 0.1 and 1 K, are not achieved during LidAr VAlidation NDacc Experiment (LAVANDE). These estimates might have to be increased to 1 to 2 K.

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Cites methods from "Improved ozone DIAL retrievals in t..."

  • ...Improvement of the lidar data processing and removal of this potential bias will be investigated in future work involving optimal estimation techniques (Farhani et al., 2019)....

    [...]

  • ...Improvement of the lidar data processing and removal of this potential bias will be investigated in future work involving optimal estimation techniques (Farhani et al., 2019). Future tropospheric ozone lidar campaigns for NDACC lidars would be required to assess the new technique and fully characterise any residual biases. MLS and SABER ozone profiles agree with the profiles produced by lidars and ECCs from about 20 to above 40 km. Below 20 km, both sets of satellite profiles deviate significantly from the lidars and the ECCs. Above 40 km, ozone measurement uncertainties become large for the lidars, and differences increase while their significance goes down. The assessment of the uncertainty budget for ozone concentration profiles for each instrument showed that the reported measurement uncertainties for both LiO3S and NASA STROZ are well characterised and realistic. The reported measurement uncertainty estimates for ECCs from Tarasick et al. (2016) appear too optimistic for the sondes launched during LAVANDE....

    [...]


Journal ArticleDOI
Abstract: The solution of the lidar equation is an ill-posed problem that requires nonlinear methods to retrieve the atmospheric aerosol optical and microphysical properties Particularly, in the last decades, the most applied solution for the elastic lidars is through the well known Klett-Fernald-Sasano algorithm for retrieving the backscatter coefficient To solve this inversion problem, we propose to apply the optimal estimation method to a Vaisala CL51 ceilometer range corrected signals for retrieving under two different frameworks, the particle backscatter coefficient or the ratio and the lidar constant The optimal estimation is a Bayesian inversion fed by a set of a priori information In this work, to obtain the suitable prior, we have tested two approaches that involved measurements and synthetic data The first data set was obtained from previous inversions using the classical Klett-Fernald-Sasano method, and the second one by using Mie simulations fed by aerosol properties from OPAC database The optimal estimation method used for elastic lidar inversion presents two main advantages compared to the classic approaches On one hand, there is no need for Rayleigh zone determination and on the other hand, the uncertainty of the retrieved products is directly estimated, therefore the quality of the results is highly dependant on the prior selection To evaluate the performance of the model, low and high aerosol accumulations scenarios were considered, finding that the backscatter coefficient was oscillating between 5 and 7 (kmsr)−1 in the first 3 km agl with uncertainties lower than 27 % at degraded spatial resolutions Additionally, constant and height-dependent priors were tested reaching relative errors in percentage up to 5 % between them Besides, relative errors were also analyzed for the prior covariance matrices estimated either from synthetic lidar data and Klett's retrievals, where the errors are lower than 2 % by using one instead of the another However, scale factors were applied to the synthetic prior covariance matrices to reach the convergence The results at retrieving the particle backscattering were compared to those ones estimated from Klett's inversion, considering Klett inversion as the reference For the extreme scenario of inversions, considering aerosol accumulations at different layers, the bias between the optimized profiles was lower than −05 (kmsr)−1 in the first 05 km and 05 (kmsr)−1 above 15 km Here, we also shown a two-parameter optimization for the lidar constant and lidar ratio, applied to 39 aerosol inversions, finding relative errors lower than 1 % and 23 % , respectively, considering those ones from Klett inversion as the reference

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2,257 citations


Journal ArticleDOI
Abstract: In the past, studies of stratosphere-troposphere exchange of mass and chemical species have mainly emphasized the synoptic- and small-scale mechanisms of exchange This review, however, includes also the global-scale aspects of exchange, such as the transport across an isentropic surface (potential temperature about 380 K) that in the tropics lies just above the tropopause, near the 100-hPa pressure level Such a surface divides the stratosphere into an “overworld” and an extratropical “lowermost stratosphere” that for transport purposes need to be sharply distinguished This approach places stratosphere-troposphere exchange in the framework of the general circulation and helps to clarify the roles of the different mechanisms involved and the interplay between large and small scales The role of waves and eddies in the extratropical overworld is emphasized There, wave-induced forces drive a kind of global-scale extratropical “fluid-dynamical suction pump,” which withdraws air upward and poleward from the tropical lower stratosphere and pushes it poleward and downward into the extratropical troposphere The resulting global-scale circulation drives the stratosphere away from radiative equilibrium conditions Wave-induced forces may be considered to exert a nonlocal control, mainly downward in the extratropics but reaching laterally into the tropics, over the transport of mass across lower stratospheric isentropic surfaces This mass transport is for many purposes a useful measure of global-scale stratosphere-troposphere exchange, especially on seasonal or longer timescales Because the strongest wave-induced forces occur in the northern hemisphere winter season, the exchange rate is also a maximum at that season The global exchange rate is not determined by details of near-tropopause phenomena such as penetrative cumulus convection or small-scale mixing associated with upper level fronts and cyclones These smaller-scale processes must be considered, however, in order to understand the finer details of exchange Moist convection appears to play an important role in the tropics in accounting for the extreme dehydration of air entering the stratosphere Stratospheric air finds its way back into the troposphere through a vast variety of irreversible eddy exchange phenomena, including tropopause folding and the formation of so-called tropical upper tropospheric troughs and consequent irreversible exchange General circulation models are able to simulate the mean global-scale mass exchange and its seasonal cycle but are not able to properly resolve the tropical dehydration process Two-dimensional (height-latitude) models commonly used for assessment of human impact on the ozone layer include representation of stratosphere-troposphere exchange that is adequate to allow reasonable simulation of photochemical processes occurring in the overworld However, for assessing changes in the lowermost stratosphere, the strong longitudinal asymmetries in stratosphere-troposphere exchange render current two-dimensional models inadequate Either current transport parameterizations must be improved, or else, more likely, such changes can be adequately assessed only by three-dimensional models

2,181 citations


Journal ArticleDOI
Jennifer A. Logan1Institutions (1)
Abstract: In the present analysis of tropospheric ozone data, attention is given to spatial and temporal variations. Two modes of seasonal behavior are noted for surface ozone at mid-latitudes: a broad summer maximum within a few hundred km of industrial/urban areas in Europe and the U.S., and a minimum in summer or autumn in sparcely populated regions that are remote from industrial activity. These and limited historical data indicate that summertime concentrations of ozone near the surface in the rural areas of Europe and the U.S. may have increased between 20 and 100 percent since the 1940s. It is suggested that the summer maximum in ozone and other observed trends are due to photochemical production associated with anthropogenic emissions of NO(x), hydrocarbons, and CO from fossil fuel combustion.

967 citations


Reference EntryDOI
Keith P. Shine1Institutions (1)
Abstract: Our current understanding of mechanisms that are, or may be, acting to cause climate change over the past century is briefly reviewed, with an emphasis on those due to human activity. The paper discusses the general level of confidence in these estimates and areas of remaining uncertainty. The effects of increases in the so-called well-mixed greenhouse gases, and in particular carbon dioxide, appear to be the dominant mechanism. However, there are considerable uncertainties in our estimates of many other forcing mechanisms; those associated with the so-called indirect aerosol forcing (whereby changes in aerosols can impact on cloud properties) may be the most serious, as its climatic effect may be of a similar size as, but opposite sign to, that due to carbon dioxide. The possible role of volcanic eruptions as a natural climate change mechanism is also highlighted.

606 citations


Journal ArticleDOI
Abstract: [1] This paper provides a review of stratosphere-troposphere exchange (STE), with a focus on processes in the extratropics. It also addresses the relevance of STE for tropospheric chemistry, particularly its influence on the oxidative capacity of the troposphere. After summarizing the current state of knowledge, the objectives of the project Influence of Stratosphere-Troposphere Exchange in a Changing Climate on Atmospheric Transport and Oxidation Capacity (STACCATO), recently funded by the European Union, are outlined. Several papers in this Journal of Geophysical Research– Atmospheres special section present the results of this project, of which this paper gives an overview. STACCATO developed a new concept of STE in the extratropics, explored the capacities of different types of methods and models to diagnose STE, and identified their various strengths and shortcomings. Extensive measurements were made in central Europe, including the first monitoring over an extended period of time of beryllium-10 ( 10 Be), to provide a suitable database for case studies of stratospheric intrusions and for model validation. Photochemical models were used to examine the impact of STE on tropospheric ozone and the oxidizing capacity of the troposphere. Studies of the present interannual variability of STE and projections into the future were made using reanalysis data and climate models. INDEX TERMS: 0341 Atmospheric Composition and Structure: Middle atmosphere—constituent transport and chemistry (3334); 0368 Atmospheric Composition and Structure: Troposphere—constituent transport and chemistry; 3362 Meteorology and Atmospheric Dynamics: Stratosphere/troposphere interactions; KEYWORDS: Brewer-Dobson circulation, trajectories, Lagrangian model, reanalysis, tropopause

396 citations