ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-12511-2018Space–time variability in UTLS chemical distribution in the Asian summer
monsoon viewed by limb and nadir satellite sensorsSpace–time variability in UTLS chemical distributionLuoJialiPanLaura L.HonomichlShawn B.BergmanJohn W.RandelWilliam J.https://orcid.org/0000-0002-5999-7162FrancisGeneClerbauxCathyGeorgeMayahttps://orcid.org/0000-0001-8897-7964LiuXiongTianWenshouKey Laboratory of Semi-Arid Climate Change and College of Atmospheric Sciences, Lanzhou University, Lanzhou, ChinaNational Center for Atmospheric Research, Boulder, Colorado, USABay Area Environmental Research Institute, Sonoma, California, USALATMOS/IPSL,UPMCUniversité Paris 06 Sorbonne Universités, UVSQ, CNRS, Paris, FranceHarvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts, USALaura Pan (liwen@ucar.edu)29August20181816125111253025March201723June20171July201816July2018This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/18/12511/2018/acp-18-12511-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/12511/2018/acp-18-12511-2018.pdf
The Asian summer monsoon (ASM) creates a hemispheric-scale signature in trace-gas distributions in the upper troposphere and lower stratosphere (UTLS).
Data from satellite retrievals are the best source of information for
characterizing these large-scale signatures. Measurements from the Microwave
Limb Sounder (MLS), a limb-viewing satellite sensor, have been the most
widely used retrieval products for these types of studies. This work explores
the information for the ASM influence on UTLS chemical distribution from two
nadir-viewing sensors, the Infrared Atmospheric Sounding Interferometer
(IASI) and the Ozone Monitoring Instrument (OMI), together with the MLS.
Day-to-day changes in carbon monoxide (CO) and ozone (O3) tracer
distributions in response to dynamical variability are examined to assess
how well the data from different sensors provide useful information for
studying the impact of sub-seasonal-scale dynamics on chemical fields. Our
results, using June–August 2008 data, show that although the MLS
provides relatively sparse horizontal sampling on daily timescales,
interpolated daily CO distributions show a high degree of dynamical
consistency with the synoptic-scale structure of and variability in the
anticyclone. Our analysis also shows that the IASI CO retrieval has
sufficient sensitivity to produce upper tropospheric (UT) CO with
variabilities independent from the lower to middle tropospheric CO. The
consistency of IASI CO field with the synoptic-scale anticyclone dynamical
variability demonstrates that the IASI UT CO product is a physically
meaningful dataset. Furthermore, IASI CO vertical cross sections combined
with the daily maps provide the first observational evidence for a model
analyses-based hypothesis on the preferred ASM vertical transport location
and the subsequent horizontal redistribution via east–west eddy shedding.
Similarly, the OMI O3 profile product is shown to be capable of
distinguishing the tropospheric-dominated air mass in the anticyclone from
the stratospheric-dominated background on a daily timescale, providing
consistent and complementary information to the MLS. These results not only
highlight the complementary information between nadir and limb sensors but
also demonstrate the value of “process-based” retrieval evaluation for
characterizing satellite data information content.
Introduction
As a prominent atmospheric circulation feature in the upper troposphere and
lower stratosphere (UTLS) during boreal summer, the Asian summer monsoon
(ASM) anticyclone's large-scale dynamical behavior has been investigated
widely in recent years (e.g., Hoskins and Rodwell, 1995; Highwood and
Hoskins, 1998; Zhang et al., 2002; Liu et al., 2007; Wu et al., 2015). The
ASM anticyclone is bounded by the westerly jet to the north and easterly jet to
the south, and this circulation is linked to enhanced air confinement (e.g.,
Dunkerton, 1995; Randel and Park, 2006; Garny and Randel, 2016; Fan et
al., 2017). Due to the influences of deep convection and air confinement,
seasonal-mean chemical composition within the anticyclone near the
tropopause displays distinctly surface-like characteristics; boundary layer
and tropospheric tracers, such as CO, H2O, HCN, and a large set of
hydrocarbons, are significantly enhanced, while O3 as a stratospheric
tracer is significantly decreased (Park et al., 2004; Li et al., 2005;
Randel and Park, 2006; Park et al., 2007; Randel et al., 2010; Vernier et
al., 2011; Garny and Randel, 2013).
Although the ASM anticyclone is a strong and steady feature of seasonal-scale circulations in the UTLS, it undergoes variations on sub-seasonal
timescales. These include 10–20-day east–west migrations and the associated
eddy shedding (Hsu and Plumb, 2000; Popovic and Plumb, 2001; Zhang et
al., 2002; Garny and Randel, 2013). Previous studies have shown that the
monsoon circulation has active–break cycles that are linked to oscillations
of deep convection with timescales of 10–20 and 30–60 days (e.g.,
Krishnamurti and Bhalme, 1976; Krishnamurti and Ardanuy, 1980; Annamalai and
Slingo, 2001; Randel and Park, 2006). Zhang et al. (2002) found that
the center of the anticyclone shows bimodality in its longitude location
that they classify in terms of the Tibetan mode (centered at about
90∘ E) and the Iranian mode (centered at about 60∘ E),
although the degree of bimodality appears dependent on the meteorological
dataset (Nützel et al., 2016). A number of recent studies have shown
that sub-seasonal-scale dynamical processes in the ASM region may play a
significant role in UTLS transport of trace gases (Yan et al., 2011;
Garny and Randel, 2013; Pan et al., 2016; Vogel et al., 2016). It is evident
that diagnosing intra-seasonal variability in chemical tracers in the UTLS
and their interactions with dynamical fields is important for a more
complete understanding of the ASM anticyclone's chemical impact.
Satellite observations provide an essential source of information in ASM
UTLS-related studies. The Aura Microwave
Limb Sounder (MLS) and the Atmospheric Chemistry Experiment Fourier
Transform Spectrometer (ACE-FTS), in particular, are two widely used
datasets from limb-viewing sensors for this purpose (e.g., Park et al., 2007; Randel et al., 2010).
These limb sounders offer relatively high vertical resolution but have
limited horizontal sampling on daily timescales. Nadir-viewing instruments,
however, offer better horizontal sampling and daily coverage but
have limited vertical resolution and are primarily used to study column
abundances.
This study aims to examine the representation of sub-seasonal chemical
variability in the ASM UTLS from limb- and nadir-viewing sensors. Two
specific nadir datasets we explore are CO from the Infrared Atmospheric
Sounding Interferometer (IASI) and O3 from the Ozone Monitoring
Instrument (OMI). These two datasets will be examined together with MLS CO
and O3 data. CO is a pollution tracer and is also an
effective tracer of transport in the troposphere and lower stratosphere (LS)
(e.g., Bowman, 2006), with a photochemical lifetime of ∼2
months in the troposphere (Xiao et al., 2007). O3 is an effective
transport tracer in the UTLS because of the large gradient in its mixing
ratio across the tropopause and its long lifetime relative to transport timescales in the UTLS region. In the UTLS, O3 mostly serves as a
stratospheric tracer, although it also has tropospheric pollution sources.
Short-term variations in O3 in the UTLS are largely linked to synoptic-scale disturbances in the tropopause region (e.g., Shapiro, 1980). These
satellite datasets are examined with meteorological analyses from the Global
Forecast System (GFS) to address the following questions. (1) Do these
nadir-viewing instruments, designed primarily for retrieving trace-gas
column abundance, have sufficient information to show the ASM
dynamically driven trace-gas distributions and variability at UTLS levels?
(2) Are the data from nadir sensors consistent with the limb-viewing data on
sub-seasonal scales with respect to dynamical variability in tracers in the
ASM region? (3) What can we learn from the complementary information from
limb- and nadir-viewing instruments?
Although the IASI CO and OMI O3 are compared with MLS CO and O3,
respectively, it is not the goal of this work to evaluate the quantitative
agreement between the nadir and limb data. The difference in viewing
geometries makes limb-viewing and nadir-viewing datasets fundamentally
different quantities since the air masses they are sensing represent very
different volume and spatial extent (described in Sect. 2). The goal of
these comparisons, therefore, is to evaluate whether data from the two types
of sensors provide a consistent picture of the ASM dynamical impact on the
UTLS tracer distributions and variability. We characterize this type of
analysis as process-based retrieval evaluation. This analysis provides a perspective of whether the high-density horizontal sampling from the nadir sensors supplements information
from the limb-viewing sensors, despite the relatively coarse vertical
resolution, in the region of strong synoptic-scale horizontal dynamical
variability. This work is therefore not a validation study, but rather, it aims
to complement previous validation studies of the MLS CO and O3, the
IASI CO, and the OMI O3 profile products (Livesey et al., 2008; George et
al., 2009; Liu et al., 2010a, b; Kroon et al., 2011; De Wachter et al.,
2012; Bak et al., 2013; Safieddine et al., 2016; Barret et al., 2016; Huang
et al., 2017).
Although both CO and O3 are examined in this work, the focus is on
their relationship with the ASM dynamical structure. No attempt is included
here to use a tracer–tracer relationship as an additional diagnostic for UTLS
transport. In the CO analysis, we focus on the upper troposphere (UT)
variability associated with convective pumping and the horizontal
redistribution by the dynamics of the anticyclone. In the O3 analysis,
we focus on the tropopause level and the sensitivity of data to the
tropopause structure. Overall, we give more focus on the CO analysis.
Data descriptionSatellite data
For limb-viewing observations, we use MLS version 4, level 2, 215 hPa CO,
147 hPa CO, and 100 hPa O3 data. MLS is a forward-looking sounder onboard the Aura satellite launched in July 2004 (Waters et al., 2006).
Accurate data descriptions of CO and O3, including uncertainties, are
given in Livesey et al. (2017). Briefly, the vertical resolution of CO
retrievals at 147 hPa (215 hPa) is 5.1 km (5.4 km) and the single-profile
precision is ∼16 ppbv (19 ppbv). The systematic uncertainty
for 147 hPa CO is the root sum square (RSS) of 26 ppbv and 30 %. For 215 hPa CO it is 30 ppbv and 30 %. The O3 retrieval has a vertical
resolution of 3 km at 100 hPa. The single-profile precision is estimated to
be ∼30 ppbv. The systematic uncertainty for 100 hPa O3
is estimated to be 5ppbv+7 %
(Livesey et al., 2017). As a limb
sounder, MLS's field of view produces a horizontal resolution of
∼6 km across the track and ∼300 km, 570 km,
and 590 km along the track for 100 hPa O3, 147 hPa, and 215 hPa CO,
respectively, MLS has a relatively low daily sampling density
(∼240 limb scans per orbit with ∼3500 profiles
during both day and night). In order to make the daily output easier to
interpret, daily maps are made by interpolating the output onto a regular
grid.
Nadir-viewing observations of CO are obtained from IASI (level 2 data)
aboard EUMETSAT's Metop satellite. IASI measures the thermal infrared
(TIR) spectrum emitted by the Earth–atmosphere system with twice daily
near-global coverage (with 4 simultaneous pixels of 12 km diameter every 50 km), but limited vertical resolution (Clerbaux et al., 2009). The
tropospheric CO product is derived from the spectra using the FORLI
retrieval algorithm, which uses a single a priori profile and covariance matrix
(Hurtmans et al., 2012; George et al., 2015). The IASI CO
level 2 retrieval product is provided as mixing ratios in 19 1 km layers
from surface to 19 km altitude. The retrieval information content analysis,
however, shows 0.8 to 2.4 (1.5 to 2.0 at mid-latitudes) independent pieces
of information (or degrees of freedom for signal (DOFS); George et al., 2009). How well this information content allows IASI CO retrieval to capture
UT variability at midlatitudes and tropical latitudes is one
of the foci of this study. Our analysis will complement previous validation
studies, including in situ measurements from the “Measurements of ozone,
water vapor, carbon monoxide and nitrogen oxides by Airbus in-service
aircraft” (MOZAIC) project (correlations ∼0.7; De Wachter
et al., 2012), and satellite observations from the Measurements Of Pollution
In The Troposphere (MOPITT) instrument (George et al., 2015). This
work also aims to complement previous IASI data analyses, which show the
data reproduce monthly mean large-scale features in the UTLS over the ASM
region comparable to model results from GEOS-Chem (a chemical transport
model coupled to meteorological analysis from the Goddard Earth Observing
System GEOS-5; Barret et al., 2016).
Nadir-viewing observations of O3 are obtained from OMI, an O3
sounder aboard the Aura satellite that provides daily global coverage in a 13km×24km footprint (Levelt et al., 2006). OMI O3 products
include retrievals of both total O3 columns and vertical profiles. In
this study, we use the O3 profile product by
Liu et al. (2010b) and Huang et al. (2017).
O3 profiles are retrieved at 24 vertical layers covering the surface to
∼60 km using the optimal estimation technique constrained by
a monthly and zonal mean O3 profile climatology (McPeters et al.,
2007). The OMI profile retrievals have 6.0–7.0 degrees of freedom (5.0–6.7
in the stratosphere; Liu et al., 2005, 2010b, a). The distribution of the information content is sufficient to resolve
the UTLS transition region in part owing to the large O3 gradient
across the tropopause, as demonstrated by a number of previous works
(Pittman et al., 2009; Liu et al., 2010a, b; Bak et al.,
2013). For vertical distribution of the averaging kernels and information
content, see Liu et al. (2010b). In this work, we
use a level 3 product gridded to 1∘ longitude × 1∘
latitude horizontal resolution. Only the layer 18 product is used, which is
a layer centered approximately at the 100 hPa level, with retrieval
information from a broad layer of approximately 10 km (Liu et al., 2010b).
In the ASM anticyclone region, this layer is contributed to more from the UT
inside the anticyclone and from the LS outside. OMI has known cross-track-dependent biases (Liu et al., 2010a, b). Thus the data
points from view zenith angles (VZAs) greater than 58∘ are not used
in the mapping process.
To highlight the horizontal sampling density and vertical sensitivity
differences between the limb-viewing and nadir-viewing sensors, Fig. 1
shows the geolocations of all IASI and MLS profiles and the relevant
averaging kernels for the study domain (0–180∘ E, 10∘ S–60∘ N) in a single day (1 August 2008). Both daytime and
nighttime samplings are included. It is apparent from Fig. 1a that IASI has
a much denser horizontal coverage than the MLS. Note that both datasets have
data gaps: while the MLS orbit tracks are separated by ∼20∘ longitude, the IASI retrieval also has a significant data gap each day
due to the cloud coverage (no IASI products are available if the cloud
fraction in the pixel exceeds 25 %). The comparison of O3 data
sampling densities between OMI and MLS is not shown but it is conceptually
similar to that shown in Fig. 1a. This disparity of sampling density and
its implications for representing synoptic-scale variability motivates this
work of exploring the utility of nadir-viewing data in characterizing
chemical distributions in the UTLS on daily to sub-seasonal timescales.
(a) Retrieval geolocations for IASI CO (gray crosses) and MLS CO
(red dots) on 1 August 2008 for the study domain (0–180∘ E, 10∘ S–60∘ N). Both day and night
observations are included. (b) IASI averaging kernels for 19 retrieval
layers from the surface to 19 km, labeled by the layer-center altitudes, and
the standard MLS averaging kernels for the UTLS products (215, 147, and 100 hPa). The IASI curves are the averages of all profiles from the study domain
on 1 August 2008. (c) Distribution of degrees of freedom for signal
(DOFS) for all IASI profiles on 1 August 2008 within (0,
40∘ N) latitude and (40–150∘ E) longitude.
Figure 1b shows the vertical information distribution for both IASI and MLS
UT CO retrievals. The IASI averaging kernels for the CO product in 19 layers
are shown, which are the average of all individual profiles included in Fig. 1a. Note that the altitude labels for these layers are referring to the
centers of the 1 km layer as part of the product identification and they are
not intended for representing independent information from each layer. The
physical information for these layer products is contributed from a broad
layer, as indicated by the averaging kernels (Fig. 1b). To provide a
perspective of retrieval information content in the study region, we show
the distribution of DOFS for IASI profiles in Fig. 1c. The distribution
shows that the majority of the profiles are estimated to have DOFS close to
2, which supports that IASI CO retrievals should have sufficient information
for independent variability in the upper and the middle troposphere (MT). In this
study, we aim to evaluate the upper tropospheric CO variability using the
IASI CO product. The most relevant retrieval product layers are 12–16 km.
Averaging kernels for these layers are highlighted in Fig. 1b.
MLS vertical sensitivity is shown by the standard CO averaging kernels for
the 215, 147, and 100 hPa products (Livesey et al., 2017). Although we
focus on the 147 hPa product in this analysis, 215 and 100 hPa averaging
kernels are included in the figure to contrast the sensitivity distributions
in the two instruments. The figure provides a perspective that IASI
retrieval information content is optimized for the MT. The
UT information is much weaker, maximized over a range of UT layers, and is
not sharply peaked at a particular retrieval layer. In contrast, MLS
information for 147 hPa CO shows a strong maximum near 14 km
(∼150 hPa). The figure also indicates that both the nadir- and
the limb-viewing sensors are expected to have “smoothing errors” in the
retrieval.
A similar figure for OMI is not shown since for ozone analysis we are not
focusing on independent information between the UT and lower troposphere to MT, rather we focus on stratospheric versus tropospheric influence
in ozone distribution near the tropopause level (∼100 hPa
pressure level) and expect the contrast between the air mass inside and
outside the anticyclone to be dominated by the tropopause structure of the
region. For more complete averaging kernel discussions, see the MLS data
quality document (Livesey et al., 2017) and the work of George et al. (2009)
for IASI data and Liu et al. (2010b) for OMI data.
Meteorological analysis data
We use wind fields, geopotential height (GPH), tropopause height, and
potential temperature (derived from temperature and pressure) from the GFS
operational analysis (a product of the National Centers for Environmental
Prediction, NCEP) to diagnose the dynamical variability in the ASM
anticyclone. These 6-hourly data have a horizontal resolution of
1∘ on 26 pressure levels (from 1000 to 10 hPa) (National
Centers for Environmental Prediction/National Weather Service/NOAA/U.S.
Department of Commerce, 2000). Having pressure and height for the tropopause
in the product, and the determination of these levels using the native GFS
grid, is a major strength of the product that motivated our choice (Pan and
Munchak, 2011).
Processing daily maps
Figure 1a highlights the sampling gaps from both MLS and IASI for mapping
daily CO distributions. Careful data interpolation and smoothing to fill
data gaps are essential steps for producing daily maps from the available
retrievals on each given day. In general, the daily representation from MLS
data requires interpolation to increase the density in coverage, while the
IASI (and OMI) data densities are reduced by binned averages. We have
explored three interpolation algorithms, cosine smoothing, natural
neighbor, and inverse distance, for mapping data. All three methods are
similar, conceptually, in filling an empty cell with weighted mean of nearby
observations, but the weightings are determined differently. After
experimenting with various grid sizes and mapping methods, we choose to use
5∘×5∘ longitudes and latitudes for mapping MLS data
and 3∘×2∘ for the IASI data. The results shown in this
paper are mapped using the natural neighbor method (Watson, 1992)
followed by a Gaussian smoothing. We find that these steps produce daily
maps with a good balance between representing the synoptic-scale variability
and the information from the data in localized structures.
(a) MLS 147 hPa CO mixing ratio at retrieval geolocations on
1 August 2008, (b) the interpolated map of MLS CO, and (c) the interpolated
map of IASI CO in the upper troposphere (UT) layer on 1 August 2008. The
selected geopotential height (GPH) contours (white) and horizontal winds
(black arrows) at 150 hPa are superimposed. The MLS CO map is made with
5∘×5∘ longitude and latitude grids. The IASI CO map is
made using 3∘×2∘ grids. Both are interpolated using the
natural neighbor algorithm (Watson, 1992).
Figure 2 provides an example using 1 August 2008 data, in which maps of
retrieved MLS 147 hPa CO (Fig. 2a), interpolated MLS 147 hPa CO (Fig. 2b), and IASI
CO in the UT layer (Fig. 2c) are shown. The IASI UT layer CO mixing ratio is
produced by interpolating the layer product to 150 hPa. Although the
interpolation aimed to find the CO mixing ratio approximation for 150 hPa,
it is clear from the averaging kernel that the retrieval represents a layer.
The retrieval information for this layer is represented by the averaging
kernels for 12.5–15.5 km layers as highlighted in Fig. 1b. To emphasize this
limitation in vertical resolution, we refer to this layer as the UT layer in
the rest of the paper. Note we have used different color scales for MLS and
IASI CO and the rationale for this is given in the next section. The dynamical fields of
150 hPa GPH and the horizontal wind are superimposed for identifying the
location and structure of the ASM anticyclone. Comparison of the MLS data on
the orbital tracks (Fig. 2a) and the interpolated map (Fig. 2b) provides a
useful perspective that the mapping procedure we choose highlights the large-scale dynamical consistency of the CO and the flow pattern instead of the
fine-scale structure. Comparison of MLS (Fig. 2b) and IASI (Fig. 2c) CO maps
provides additional perspective that although both datasets show CO
enhancement in the region of the ASM anticyclone, the appearance and detail
of the enhancement are quite different. These differences are contributed to by
several factors. For example, the missing data in IASI (due to cloud
contamination) and the larger grid size in MLS may both contribute to the
difference in the spatial pattern of the enhancement between 90–120∘ E and 20–30∘ N. Similarly, the
filamentary structure in IASI CO near 150∘ E and 30∘ N,
although hinted at in the MLS orbital data, is represented differently in the
MLS CO map. Additionally, IASI signal-to-noise ratio is likely degraded over
the region of elevated terrain, which will be discussed in later examples.
The two datasets are also obtained at slightly different sampling times, as the MLS and the IASI cross the Equator at
around 01:30 and 09:30 local time, respectively. These factors need to be kept in mind when
interpreting the details.
Comparisons of MLS and IASI CO
Although the focus of this study is to characterize chemical tracers'
space–time variability and dynamical consistency, we make quantitative
comparisons between the MLS and IASI CO data in this section. The
comparisons focus on the consistency between the two datasets in
representing CO UT variability in the study domain and their representation
of the well-demonstrated large-scale spatial pattern associated with the ASM
anticyclone on the seasonal scale. Vertical ranges of the data were chosen
to optimize the overlap of information from nadir- and limb-viewing
instruments with the vertical extent of the anticyclone. Based on the
analyses of the dynamical fields and trajectory calculations, the maximum
chemical confinement in the anticyclone in the vertical range is between
200–100 hPa or 12–16 km (Randel and Park, 2006), although elevated
levels of tropospheric tracers in the ASM anticyclone are evident at up to 68 hPa in the MLS data (Park et al., 2007). Moreover, the strongest closed
circulation of the anticyclone occurs at ∼14–15 km, above the
main convective outflow level (∼12 km) (Park et al.,
2008). Based on this structure and the vertical information content of IASI
CO retrieval (George et al., 2009), we choose to use the
IASI UT layer CO mixing ratio and the 147 hPa MLS CO retrieval product. The
June–July–August (JJA) season of a single year of 2008 is examined.
Figure 3 shows a scatter plot of IASI CO in the UT layer versus the MLS 147 hPa CO level 2 product. Each point represents a co-located daily average in a
10∘×6∘ longitude–latitude bin in the study domain for all days in the
JJA 2008 period. The scatter plot shows that variations in CO in the two
datasets are generally consistent and correlated (r=0.8), although the
IASI CO shows a smaller range of variability than MLS (indicated by the
slope of the linear fit, 0.55). The smaller variability in IASI CO is likely
contributed to by a weaker detection sensitivity in the UT and
the use of a single a priori profile in CO retrieval (George et al.,
2015).
Scatter plot of IASI UT layer CO versus MLS 147 hPa CO for June,
July, and August (JJA) 2008. Each data point represents a daily average of
CO level 2 data from IASI and MLS in the same 10∘×6∘
longitude–latitude box in the study domain. The red line shows a linear fit.
Correlation and slope for the linear fit are given in the upper left corner
of the panel.
JJA 2008 seasonal average CO mixing ratio for (a) MLS 147 hPa,
(b) the MLS 147 and 215 hPa average, and (c) the IASI UT layer. Superimposed white
contours are the 14.3 and 14.2 km GPH (from GFS analysis) at 150 hPa. Note
that the color scales for IASI and MLS CO are different. Both MLS and IASI
are 2∘×2∘ longitude–latitude binned averages.
Figure 4 shows JJA seasonal averages for (a) MLS 147 hPa CO, (b) the MLS CO
average of the 147 and 215 hPa products, and (c) IASI UT layer CO for 2008. Note
that different ranges are used in the MLS and IASI color bar to adjust for the
smaller range of variability in IASI CO as indicated in Fig. 3. Selected GPH
contours and wind vectors at 150 hPa for the same period are shown on all
three maps to indicate the seasonal mean location of the anticyclone. The
chemical signature of the ASM anticyclone is evident for all three seasonal
averages. The clear chemical signature indicates that, despite the
relatively weak UT sensitivity, the IASI data are capable of showing the
impact of the ASM circulation on UT CO. Spatially, the MLS 147 hPa and IASI
UT layer CO mixing ratio fields show noticeable differences in their
horizontal locations. The IASI enhancement pattern shows an overall eastward
shift relative to the MLS. There is a pattern of strong enhancement between
120 and 150∘ E in IASI CO seasonal average that is not
clearly present in the MLS 147 hPa seasonal average. In view of the broad
vertical structure in IASI averaging kernels for the UT layer, we also
constructed a seasonal average layer using both MLS 147 and 215 hPa
products (Fig. 4b), which have a region of CO enhancement very comparable to
the IASI UT CO pattern. This result shows that IASI UT layer CO is
consistent with MLS CO from the combined 147 and 215 hPa product on
seasonal timescales. The comparison also suggests that the region's UT CO
enhancement has an east–west tilted vertical structure. The dynamical
factors that contribute to the tilted chemical structure likely involve the
vertical range of the anticyclone confinement and the altitudinal
distributions of the easterly and westerly jets.
In addition to the weaker enhancement and the location offset, CO
enhancements over the Tibetan and Iranian plateaus are largely missing in
the IASI average. This is likely a result of weakened signal-to-noise ratio
in the nadir sensor retrieval due to the higher surface elevation. We
re-visit this issue in later sections using daily examples.
Overall, these comparisons provide quantitative and qualitative
characterizations on the seasonal and the ASM regional scale variabilities
represented in the IASI CO product relative to the MLS data, which have been
widely used to investigate chemical tracer distributions and transport in
this region (e.g., Park et al., 2007; Santee et al., 2017). We now proceed
to analyze the sub-seasonal-scale variability.
Daily maps of MLS CO mixing ratio at 147 hPa (color shading) on
(a) 16 July, (b) 18 July, and (c) 22 July 2008. Dynamical fields of GPH (white
contours) and horizontal winds (black arrows) are superimposed. Maps are
interpolated using the natural neighbor algorithm (Watson, 1992) to 5∘×5∘ longitude–latitude grids. The location of the Tibetan Plateau
(using 3 km elevation) is also shown in the maps (thick gray).
Sub-seasonal variability in ASM UT CO from MLS and IASI dataUT CO variability from MLS data
We begin our examination of daily maps in Fig. 5, which shows mixing ratios
of MLS CO at 147 hPa and the dynamical fields (winds and GPH) at 150 hPa for
selected days. During the time period, the dynamical evolution of the
anticyclone, as indicated by the selected GPH contours, shows different
phases of the east–west oscillation (Pan et al., 2016). In this sequence,
the anticyclone was initially in the Tibetan mode (16 July, when the maximum
of the anticyclone as represented by the GPH was located near the southern
edge of the Tibetan Plateau). In subsequent days the anticyclone elongates,
and the center migrates westward toward the Iranian mode (18 July). As the
anticyclone further elongates, the center eventually splits, and the
anticyclone forms a double center (22 July), with the two maxima located
around 30 and 80∘ E and a hinted at third center near
135∘ E as indicated by the wind field. During this time period,
the center of maximum CO enhancement also migrated westward from south of the
Tibetan Plateau (16 July) to around 60∘ E (18 July) and
30∘ E (22 July). Additional effect of the anticyclone elongation
is the appearance of an additional CO maximum east of the Tibetan Plateau,
which eventually migrates eastward to the western Pacific near southern Japan
(see Fig. 7a, 26 July), similar to the configuration of 16 July (Fig. 5a).
The 16–26 July time period therefore provided an example of ASM dynamical
and chemical variations in a cycle of a 10–20-day east–west oscillation. We
refer to this additional CO maximum and associated anticyclonic circulation
over the region of the western Pacific near Japan as the western Pacific mode,
which is likely related to a system locally referred to as the Bonin high
(Enomoto et al., 2003).
To quantify the correlation of CO enhancement with the dynamics of the
anticyclone east–west oscillation, we compare the anomaly fields of the GPH
and CO through the season in Hovmöller diagrams (Fig. 6). The
Hovmöller diagrams are constructed by first calculating daily mean GPH
and CO in the latitude band of 10–40∘ N and
0–220∘ E. Note we have extended the longitude range
further east in this calculation to include a larger background. This is
because the study region is dominated by three highs. Including the region
outside the highs is necessary for identifying the highs as positive
anomalies. The anomaly is derived for each 5∘ longitude bin by
subtracting the daily mean. The mean correlation of the spatial
(longitudinal) variability between the CO and GPH anomalies for the
3-month period is 0.92. Note that the Hovmöller diagram shown in
Fig. 6 is constructed using the interpolated CO field. If using the
retrieved CO data only, this correlation is significantly weaker due to the
sparse sampling of MLS data. As a comparison, a similar analysis using a
global model shows a correlation of ∼0.7 (Pan et al., 2016).
Hovmöller diagrams of the 150 hPa GPH and 147 hPa MLS CO
anomalies for JJA 2008. The anomalies are calculated with respect to daily
means over the latitude band 10–40∘ N and longitude
range 0–220∘ E, in 5∘ longitude bins. The
dashed line in each panel indicates the location of the mean (zero anomaly)
of the opposite field. The Pearson's correlation of the two fields for the
3-month period is 0.92.
Note that this analysis is very similar to a previous work of ASM dynamical
and chemical variability in the context of eddy shedding. Using low-PV air
(PV ≤0.3 PVU)
as the dynamical tracer, the correlation analyses between daily PV and MLS
CO data at 370 K during one season (May–September) resulted in a spatial
correlation of ∼0.5 (Garny and Randel, 2013).
Figure 6 demonstrates that the UT CO distribution is closely linked to the
UT dynamical variability in the anticyclone. The dynamics of
this east–west oscillation phenomenon is the focus of a number of works
(e.g., Hsu and Plumb, 2000; Popovic and Plumb, 2001; Liu et al., 2007) in
which
convective pumping of low-PV air to the UT followed by eddy
shedding creates the transient behavior of the anticyclone. The persistent
low PV at the tropopause level occurs around 90∘ E (Popovic and
Plumb, 2001; Garny and Randel, 2013), which is considered the center of the
Tibetan Plateau mode (Zhang et al., 2002). The low-PV air propagates both
westwards and eastwards. A model analysis using CO as a tracer further
concludes that the vertical transport of boundary layer air predominantly
occurs near the southern flank of the Tibetan Plateau, and the enhanced CO
over the entire anticyclone is a result of transient mixing and anticyclone
confinement (Pan et al., 2016). In Fig. 6 both GPH and MLS CO show stronger
westward propagation in 10–20 periods and relatively smaller eastward
propagation.
Overall, Figs. 5 and 6 show that, despite the limited horizontal sampling,
MLS data provide enough information to successfully capture the day-to-day
co-variability in CO with the dynamical fields with the help of careful
mapping procedures.
CO variability associated with ASM dynamics from IASI data
analyses
(a) MLS 147 hPa CO, (b) IASI UT layer CO, and (c) IASI middle
troposphere (MT) layer CO for a selected day (26 July 2008). Dynamical
fields of GPH (white contours) and horizontal winds (black arrows) for the
corresponding levels (150 hPa for a and b, 500 hPa for c) are superimposed.
Elevated terrain is indicated by gray shadings for the 500 hPa map in (c).
The location of the Tibetan Plateau (using 3 km elevation) is also shown in
the maps (thick gray line). The dashed white line in (b) marks the location
of the cross section shown in Fig. 9d.
We begin the IASI data discussion by evaluating the information content in
the IASI UT CO retrieval. Although in the literature the term “retrieval
information content” almost always refers to the DOFS calculated using
forward and retrieval models, we propose an alternative way of demonstrating
the information content in this work through the analyses of dynamical
consistency. This type of evaluation may bring new insight into the
retrieval since it evaluates the result of the retrieval, which may vary
depending on how the sensitivity represented by the DOFS is used.
Figure 7b–c show an example of daily IASI CO maps in two layers: the UT
layer and the MT layer. The MT layer is derived from
interpolating the IASI layer product to 500 hPa. The two layers are chosen
to examine the dependency of the retrieval between the UT
and MT CO. Dynamically, these two layers are associated with
distinct flow patterns, which should have clear signatures in the CO
distribution. Comparing the CO fields between these two layers and with the
flow patterns, shown by the 150 and 500 hPa GPH and winds, provides an
effective test of whether the retrieval sensitivity is sufficient to resolve
independent UT and lower troposphere to MT CO variability. This result
complements the retrieval information content calculated from DOFS, as shown
in Fig. 1c. As a reference, we have also included the MLS CO map for the
same day using the 147 hPa product (Fig. 7a).
Same as Fig. 5, but for the IASI UT layer CO mixing ratio. The maps
are interpolated to 3∘×2∘ longitude–latitude grids. The
dashed white lines mark the location of the cross sections shown in Fig. 9.
This chosen day (26 July 2008) follows the sequence of days from Fig. 5 for
MLS and Fig. 8 for IASI. Dynamically, the UT anticyclone is
in a “tri-center” phase of the east–west oscillation, following the
elongation shown in Fig. 5. There are three anticyclonic centers: the
strongest one over the Tibetan Plateau (∼90∘ E) and
the second near the border of Iran and Iraq (∼50∘ E), both indicated by the maxima of GPH; the third center is
over the western Pacific near 140∘ E, with the closed circulation
indicated by the wind arrows. The IASI UT CO mixing ratio map shows a high
degree of consistency with the flow pattern at the 150 hPa level, and the
distribution in this layer does not appear to be correlated with the MT CO
mixing ratio map. This example demonstrates the capability of the IASI retrieval
to produce CO distribution in the ASM UT independent from the
lower to MT CO.
Figure 7a and b provide another case comparison between the maps based on
MLS and IASI (note the different color scales), adding to the case in Fig. 2. Although the two maps visually show different areas of hot spots, the
overall patterns of CO enhancement are very comparable if using the area
greater than ∼85 ppbv in the MLS map and that of greater than
∼65 ppbv in the IASI map. Over the Tibetan Plateau, the IASI
CO map shows decreased enhancement in the high GPH center, consistent with
degraded signal-to-noise ratio due to the high terrain (marked by gray
shading in Fig. 7c), while over the western Pacific, the IASI CO enhancement
is more intense. This comparison provides a single-day example and
complements the information in Fig. 4 and the associated discussions.
Figure 8 shows the IASI UT CO mixing ratio maps during the same period as
MLS maps in Fig. 5. The overall CO enhancement patterns are very comparable
to the MLS data if comparing the area of 65 ppbv or greater with MLS values
of
85 ppbv or greater. The IASI maps, however, show additional finer-scale
structures, consistent with the flow pattern. Similar to the previous example,
all three maps show the weakening of CO enhancement over the region of high
elevation over both the Tibetan and Iranian plateaus. In all three cases,
the IASI maps show much stronger CO enhancement over or around the western
Pacific high.
Note that physically there is no reason to expect a perfect correlation
between the CO maximum and the GPH maximum since the dynamical field and
the CO mixing ratios are controlled by different processes (Garny and
Randel, 2013). A significant correlation in the UT reflects the strong
influence of the anticyclone dynamics on the air mass and persistent
boundary layer emission and convective pumping. The interesting differences
between the MLS and IASI UT CO enhancements over the western Pacific, again,
suggest that the IASI UT retrievals have a broad vertical sensitivity, as
shown by the averaging kernels (Fig. 1b).
In addition to UT horizontal variability, IASI data provide opportunities to
investigate vertical structure of CO in the monsoon region. One of the
significant conclusions from a model study (Pan et al., 2016) is that the
UT CO enhancement over the Iranian Plateau is not formed by
the vertical transport from the local boundary layer. Rather, it is produced
by the westward shedding from the UT over the region
associated with the Tibetan mode. A similar hypothesis can be made for the
western Pacific enhancement. We examine the IASI CO cross sections to search
for observational evidence for verifying these hypotheses. Four examples are
shown in Fig. 9. These four pressure–latitude cross sections are selected to
examine the vertical structure in the centers of the Tibetan, Iranian, and
western Pacific modes. The locations of the cross sections are marked on the
maps in Figs. 7 and 8.
Selected latitude–height cross sections of the IASI CO retrieval.
The retrieval a priori profile is shown as the leftmost column in each
panel (marked as “A” on x axis). The days and the location of the cross
sections are selected to highlight the different vertical structures of the
three modes of the anticyclone: (a) 90∘ E on 16 July (Tibetan
mode), (b) 135∘ E on 18 July (western Pacific mode),
(c) 50∘ E on 22 July (Iranian mode), and (d) 135∘ E on 27 July
(western Pacific mode). The corresponding maps are given in Figs. 7 and 8. A number of dynamical fields are overlaid, including zonal winds (black
contours, solid (dashed) for westerly (easterly)), meridional wind (pink
arrows), potential temperatures (thin black dashed lines), and the
tropopause height (white dots).
The cross section in Fig. 9a is at the center of the Tibetan mode (see Fig. 8a for map). The CO enhancement in this case extends from the surface to
near 14 km, with a vertical structure consistent with the flow field; i.e.,
the vertical structure of the enhancement is collocated with the region of
strong vertical winds over northern India and the southern flank of the
Tibetan Plateau. Dynamically, this is identified as the ascending branch of
the monsoon Hadley cell (Wang, 2006). For more discussion on the
climatological flow structure in the meridional plane, see analyses in
Zhang et al. (2002). This example also shows that in this region,
the plateau is taking away approximately half of the atmosphere,
consequently degrading the nadir sensor's signal-to-noise ratio for
retrieval, leading to a weakened CO enhancement over the plateau at higher
altitude. This factor likely contributed to the difference between MLS 147 hPa and IASI UT CO data-based maps over the plateau (see Figs. 5a and 8a).
The cross sections in Fig. 9b and d are two examples of the CO enhancement
over the western Pacific high. In both cases, the enhanced layers are shown
in the UT. Similarly, Fig. 9c shows an example of an enhanced
UT CO layer near the southern edge of the Iranian plateau. In all three
cases (Fig. 9b–d) the wind fields indicate a change of circulation from
strong vertical motion in the lower troposphere to MT to the horizontal-flow-dominated UT. Overall, the cross sections support the
hypothesis that the UT CO enhancement over the middle east and the western
Pacific are not a result of local vertical transport but are produced by UT
redistribution via westward and eastward eddy shedding.
Figure 9 not only provides observational evidence supporting the model-based
hypothesis on transport structure, it also provides evidence supporting the
ability of the IASI retrieval to resolve independent variability in the
UT CO. Note that in each cross section, we have also
included the retrieval a priori profile as the leftmost column. Since the IASI
retrieval uses a single a priori profile, the leftmost columns in each of the four
panels are identical. The UT variability shown in each cross section is not
only dynamically consistent but also independent from the lower troposphere to MT and the a priori profile. The effective use of information content in
the IASI retrieval is powerfully demonstrated in these cross sections,
complementing and much more enlightening than the averaging kernels shown in
Fig. 1b.
Similar to Fig. 6, we show Hovmöller diagrams of daily anomaly fields
for 150 hPa GPH and IASI UT CO mixing ratio for JJA 2008 (Fig. 10) to
quantify the correlation in sub-seasonal variability. As expected, the
weakened retrieval signal over the plateaus produced a nonphysical structure
around the 100∘ E longitude segment. On both the eastern and western
edges, the CO anomaly shows a tendency of eastward shift relative to the GPH
anomaly, a feature that is consistent with the discussion in Fig. 4. The
overall correlation is 0.69.
Same as Fig. 6 but for IASI UT layer CO mixing ratio anomaly.
The Pearson's correlation of the two fields for the 3-month period is 0.69.
UTLS O3 analysis using MLS and OMI data
We now turn our attention to the UTLS O3 from MLS and OMI. While CO is
a boundary layer pollution tracer, O3 in the UTLS region is foremost a
transport tracer highlighting the influence of the stratosphere, although
its distribution can also be affected by photochemical production. Here, the
influence of monsoon convection on the UT O3 distribution is somewhat
complicated since the polluted air masses tend to have enhanced precursors
for ozone production. For these reasons, we focus on analyzing ozone
variability at the UTLS level using 100 hPa MLS data and the OMI layer 18
product. The large-scale O3 distribution at the 100 hPa level over the
ASM region reflects the tropospheric influence on the air mass inside the
anticyclone in contrast to the stratospheric influence outside. The
structure of the bulging tropopause in the monsoon region (Bian et
al., 2012; Pan et al., 2016) has a significant influence on the O3
distribution at the 100 hPa level. Lower O3 mixing ratios are expected
inside the anticyclone in the layer near 100 hPa since the tropopause is at
a lower pressure inside the anticyclone than it is outside in this region.
Previous work analyzing MLS 100 hPa CO and O3 led to a similar
conclusion (Park et al., 2007, Fig. 9). We aim to examine how well the
data from MLS, which has relatively sparse horizontal sampling but better
vertical resolution, and OMI, which has high-density coverage horizontally
but with coarse vertical resolution, represent the correlation between the
ozone field and the sub-seasonal-scale dynamical variability in the
tropopause in the ASM region.
JJA seasonal average O3 mixing ratio for the (a) MLS 100 hPa
product and (b) OMI layer 18 product for 2008. Superimposed white contours
are the 16.7 km GPH at 100 hPa and magenta contours are the 100 and 105 hPa
tropopause pressure, i.e., the intersection of the tropopause with the 100
and 105 hPa pressure surfaces. Both MLS and OMI are 2∘×2∘ longitude–latitude binned averages.
Comparison of 100 hPa MLS and OMI O3 data on seasonal-scale
variability
Similar to the CO analysis, we first compare the two O3 datasets on
seasonal timescales. Figure 11 shows 100 hPa MLS and OMI average O3
for JJA 2008. Also included in the figure are seasonal averages of a few
selected dynamical fields for the same time period. The 100 hPa wind field
is included to show the anticyclonic flow associated with the ASM. The
location of the anticyclone is marked by the 16.7 km GPH contour and the
contours of the tropopause intersection with the 100 and 105 hPa pressure
surfaces. The contours of the tropopause pressure and the GPH show a small
south–north offset. The 100 hPa O3 gradient change is well aligned with
the tropopause contours, supporting the concept of ASM creating a
tropospheric “bubble” in the otherwise stratospheric background at this
level. Both the MLS- and OMI-based seasonal means show low O3 in the area
of higher tropopause as expected. MLS O3 shows a band of high O3
near the southern edge of the anticyclone. This is a well-known dynamical
structure associated with the mixing of high-latitude stratospheric air
driven by the anticyclonic flow (e.g., Konopka et al., 2010). This band of
high O3 appears weaker on the OMI map. The average of the finer
structure with spatial variability and the limitation of the coarse vertical
resolution in detecting a shallow layer may both contribute to the weaker
seasonal appearance.
To evaluate the consistency in representing variability in daily data,
Fig. 12 shows a scatter plot of OMI versus MLS daily grid point average
O3 near the 100 hPa in the study region over the JJA 2008 period. The
grid point average is calculated daily in each co-located 10∘×6∘
longitude–latitude box through the study domain. This figure is similar to
the CO scatter plot in Fig. 3, but the correlation between the OMI and MLS
O3 is much better with both the slope (0.94) and the correlation
coefficient (0.96) near unity.
Same as Fig. 3 but for the OMI layer 18 O3 mixing ratio versus
the MLS O3 mixing ratio at 100 hPa for JJA 2008. The red line shows a
linear fit. Correlation and slope for the linear fit are given in the upper
left corner of the panel.
Figures 11 and 12 characterize the good overall agreement between OMI and
MLS O3 on seasonal and ASM regional scales. We now proceed to examine
the daily and sub-seasonal variability represented by the two datasets.
Representation of sub-seasonal-scale variability from MLS and OMI
O3
Daily maps of MLS O3 mixing ratio at 100 hPa and OMI
O3 mixing ratio in layer 18 (color shading) for 18 July (a, b) and 22
July (d, e) 2008. Tropopause pressure maps for the same selected two days
are shown in (c, f). Dynamical fields of GPH (white contours), horizontal winds
(gray arrows), and 105 hPa tropopause pressure contour (pink) are
superimposed. MLS maps are interpolated using the natural neighbor method on
5∘×5∘ longitude–latitude grids while OMI maps are
interpolated on 1∘×1∘ longitude–latitude grids. The
tropopause pressure is from the GFS product. A Gaussian smoothing is applied
to all maps. The location of the Tibetan Plateau (using 3 km elevation) is
also shown in the maps.
Figure 13 shows maps of MLS 100 hPa O3, OMI layer 18 O3, and the
tropopause pressure for two selected days in July 2008. Dynamical fields of
the GPH and horizontal wind are superimposed on the O3 maps. The 105 hPa tropopause contour is included in all maps. Both sets of O3 maps
exhibit the characteristic low O3 mixing ratios inside the anticyclone.
Here the 105 hPa tropopause contour appears to correlate well with the
O3 and wind field gradients. Note that the tropopause pressure here is
from the GFS final analysis product, which is based on the World Meteorological Organization thermal
tropopause definition. Since this quantity is derived from the vertical
gradient and is not analyzed on the pressure surface, its intersection with
the pressure surface can appear noisy. Gaussian smoothing is applied to the
1∘×1∘ tropopause data on all maps.
Longitudinal-time (Hovmöller) diagrams for (a) tropopause
pressure, (b) MLS 100 hPa O3 mixing ratio, and (c) OMI layer 18
O3 mixing ratio for the JJA season 2008. The Hovmöller diagram is
constructed using the daily average over 15–35∘ N. MLS
data have been averaged over 5∘ longitude bins, and OMI data have
been
averaged in 1∘ longitude bins. The 105 hPa zonal average
tropopause pressure is shown by the dashed line on all three fields.
Gaussian smoothing is applied to all three datasets.
For the two selected days, the dynamical structures of the anticyclone are in
two different phases as discussed in relation to Figs. 5 and 8. The ASM
influence at the tropopause level shows a wider longitudinal range on the
18 July (approximately 20–130∘ E), and it is
westwardly migrated on the 22 July (approximately 10–110∘ E) and with a double-centered structure. The OMI O3
map on 18 July shows a close correspondence with the longitudinal range of
the tropopause pressure, while the MLS map shows a westward shift of the low-O3 area. The difference in horizontal sampling density is likely a
contributor. On 22 July, both MLS and OMI O3 gradients are well
co-located with the anticyclone boundary as indicated by the 105 hPa
tropopause contour. The MLS O3 structure shows a more well-defined
double-centered structure. The OMI map shows a smaller O3 depression over
the Tibetan Plateau. We speculate that surface elevation may have
contributed to the structure in OMI O3, similar to the IASI CO
discussion. The high ozone band on the southern side of the anticyclone
shows a large difference between MLS and OMI, with MLS having a much wider
structure. Both the coarser horizontal sampling of MLS and the coarser
vertical resolution of OMI for resolving this shallow layer may contribute
to this difference.
The Hovmöller diagrams in Fig. 14 examine sub-seasonal variations and
the relationship between the tropopause pressure and 100 hPa O3 field
during the JJA season of 2008. All three fields in the figure are dominated by
the persistent location of the anticyclone as indicated by the lower
tropopause pressure and of O3 mixing ratios between 30 and
100∘ E. All three Hovmöller diagrams exhibit westward
propagation in 10–20-day timescales. The correlation in the variability
along the longitudinal dimension is 0.90 between the tropopause pressure and
MLS O3 and 0.76 between the tropopause pressure and OMI O3. In
both cases, the interpolated fields are used to calculate the correlations.
The strong correlation between the tropopause structure and O3 supports
the conceptual model that the higher tropopause over the ASM forms a region
of tropospheric bubble above the mean level of tropical tropopause for
the season. This structure enables a unique transport pathway for air masses
in the bubble to enter the LS via horizontal eddy
shedding, bypassing the equatorial tropical tropopause (e.g., Garny and
Randel, 2016; Ploeger et al., 2017).
While the two O3 datasets provide generally consistent large-scale
ozone structure, there are visible differences between MLS and OMI in
small-scale structures. Potential impacts of clouds on retrievals at 100 hPa
are discussed in a recent OMI validation study (Huang et al., 2017). The
weaker O3 depression near 90∘ E is likely contributed to by the
impact of surface elevation on the OMI retrieval. A better understanding of
the small-scale structures can benefit from validation studies using
airborne measurements targeting the ASM UTLS structure.
Conclusions and discussions
We have examined space–time variability in chemical tracers in the UTLS
associated with the ASM represented by nadir-viewing (IASI and OMI)
satellite instruments in comparison with a widely used limb-viewing (MLS)
dataset. Using CO (a boundary layer pollution tracer) and O3 (a
stratospheric tracer), we focus on the strengths and limitations of these
data for representing the distribution of and variability in UTLS chemical
tracers in the region of the dynamically variable ASM anticyclone. We
explore whether the much denser horizontal samplings of the nadir sensors
provide information complementary to the higher vertical resolution limb
data for the tracer daily distribution in response to synoptic-scale
variability.
Our CO analysis shows that, despite a relatively coarse horizontal sampling
on daily timescales, the interpolated MLS 147 hPa daily CO field exhibits a high
degree of correlation with the dynamical variability on synoptic scales
(Figs. 5 and 6). The spatial correlation between the CO anomaly and the GPH
anomaly at 150 hPa for the ASM region is 0.92 for the 2008 JJA season
studied. The same correlation for IASI CO is much weaker (r=0.69) (Fig. 10), largely due to the missing UT enhancement over the elevated surface of
the Tibetan Plateau. There is also an eastward shift in the CO positive anomaly
pattern relative to the GPH. A comparison between IASI and the MLS CO
seasonal averages leads to an insight that IASI UT CO includes contributions
from a broad layer, comparable to the range of the combined 147 and 215 hPa MLS product, which is consistent with the broad vertical structure shown
in IASI averaging kernels.
Quantitatively, IASI UT CO shows a variability consistent with the MLS 147 hPa product over the ASM season and region, although IASI CO has a smaller
range of variability and misses the enhancement over the plateaus, likely
due to the regions' elevated surface, which reduces the nadir-viewing
sensor's signal (Figs. 3 and 4). On daily to weekly timescales, IASI's data
resolve finer structures in CO distribution owing to its higher horizontal
sampling density. The most important complementary information is provided
by IASI vertical cross sections (Fig. 9), which provide information
identifying the region of upward transport. Selected examples provided the first
observation evidence supporting the model-based hypothesis that the
large-scale UT enhancement over the ASM is a combined result of vertical pumping
and horizontal re-distribution at the UTLS level via eddy shedding (Pan et al.,
2016).
In the O3 analysis, nadir sensor data from OMI show a good agreement
with MLS O3 near the 100 hPa level when averaged seasonally and when
compared using a 10∘×6∘ longitude–latitude grid point daily average (Figs. 11
and 12). The dynamical consistency of OMI O3 mixing ratios in the layer
18 product (centered near 100 hPa) on seasonal and sub-seasonal timescales
demonstrates the sufficient information for the nadir-viewing datasets to
contribute to the ASM dynamically driven UTLS O3 variability. Both MLS
and OMI O3 variability in the region exhibit good correlations with the
tropopause pressure, supporting the conceptual model that the ASM creates a
tropospheric bubble above the season's average tropopause in the tropics
(Pan et al., 2016).
The CO maps from different layers (Fig. 7) and selected cross sections
(Fig. 9) both provide strong evidence that IASI has sufficient information
content to discriminate UT CO variability from that in the
lower troposphere to MT. This result is consistent with and
complementary to the model estimates of retrieval information content, which
show that the DOFS for the interested region is approximately 2 (Fig. 1c).
The overall dynamical consistency found in IASI CO maps and cross sections
demonstrates the value of IASI CO data for ASM transport studies. The OMI
O3 product in the layer near 100 hPa also shows a high degree of
correlation with the MLS product, and dynamical consistency with the
variability in the tropopause. Results of this study therefore demonstrate
the approach of process-based retrieval information content evaluation.
This type of evaluation is different from traditional validation studies,
in which the goals are focused on retrieval accuracies and precisions and
often involve quantitative comparisons with independent and better trusted
data. This type of evaluation also complements the traditional information
content analyses based on forward and inverse model calculations and gives
additional physical meaning to information content from data application in
process studies.
Overall, our analysis demonstrates the value of high horizontal sampling
density from the nadir-viewing sensors in capturing the dynamical
variability in UTLS tracer distributions. Although the retrieval has fewer
degrees of freedom for each profile, the large number of profiles retrieved
daily at finer footprints produce valuable information regarding horizontal
dynamical variability. The results of this analysis not only demonstrated
the significant role of ASM sub-seasonal-scale dynamics in UTLS chemical
distributions but also bring new insight into the dynamics of the ASM through
the differences of these two types of sensors.
NCEP FNL analyses (National Centers for Environmental
Prediction/National Weather Service/NOAA/U.S. Department of Commerce, 2000)
are available at the NCAR Research Data Archive as NCEP FNL Operational Model
Global Tropospheric Analyses and is updated daily continuing from July 1999
by Computational and Information Systems Laboratory. The data are available
at 10.5065/D6M043C6. The MLS data are available from the NASA archive:
https://acdisc.gesdisc.eosdis.nasa.gov/data/Aura_MLS_Level2/ (last
access 10 May 2018). IASI CO profile data are delivered by Eumetcast since
2016 in near real time
(https://www.eumetsat.int/website/home/Data/DataDelivery/EUMETCast/index.html,
last access: 27 August 2018). The JJA 2008 profile data used in this study
are available from ftp://ftp.acom.ucar.edu/user/liwen/IASI (Francis,
2015). OMI O3 profile data (OMPROFOZ) (Yang et al., 2014) are
available at
https://avdc.gsfc.nasa.gov/index.php?site=1389025893&id=74.
LP designed the study as part of JL's PhD research. JL conducted the research analysis,
made the figures, and wrote the paper. SH contributed to data analysis and writing of the paper. GF, CC, and MG contributed the IASI
CO data information for the special analysis. XL provided OMI O3 data and the information content discussion. JB, WR, CC, XL, and WT
discussed the results and all authors contributed to the writing of the paper.
The authors declare that they have no conflict of
interest.
Acknowledgements
This work is part of Jiali Luo's PhD research, funded by the
National Science Foundation of China (41705021, 41630421, and 41575038). The
work was in part conducted at the National Center for Atmospheric Research,
operated by the University Corporation for Atmospheric Research under
sponsorship of the United States National Science Foundation. The IASI
mission is a joint mission of EUMETSAT and the Centre National d'Etudes
Spatiales (CNES, France). We thank the ULB team (Daniel Hurtmans, Pierre Coheur) for the development of the FORLI-CO retrieval algorithm and
Mijeong Park for helpful discussions. We also thank the three anonymous reviewers for
their helpful comments and suggestions.
Edited by: Federico Fierli
Reviewed by: three anonymous referees
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