A three-dimensional gridded climatology of carbon monoxide (CO) has been
developed by trajectory mapping of global MOZAIC-IAGOS in situ measurements
from commercial aircraft data. CO measurements made during aircraft ascent
and descent, comprising nearly 41 200 profiles at 148 airports worldwide
from December 2001 to December 2012, are used. Forward and backward
trajectories are calculated from meteorological reanalysis data in order to
map the CO measurements to other locations and so to fill in the spatial
domain. This domain-filling technique employs 15 800 000 calculated
trajectories to map otherwise sparse MOZAIC-IAGOS data into a quasi-global
field. The resulting trajectory-mapped CO data set is archived monthly from
2001 to 2012 on a grid of 5
The mapping product has been carefully evaluated, firstly by comparing maps constructed using only forward trajectories and using only backward trajectories. The two methods show similar global CO distribution patterns. The magnitude of their differences is most commonly 10 % or less and found to be less than 30 % for almost all cases. Secondly, the method has been validated by comparing profiles for individual airports with those produced by the mapping method when data from that site are excluded. While there are larger differences below 2 km, the two methods agree very well between 2 and 10 km with the magnitude of biases within 20 %. Finally, the mapping product is compared with global MOZAIC-IAGOS cruise-level data, which were not included in the trajectory-mapped data set, and with independent data from the NOAA aircraft flask sampling program. The trajectory-mapped MOZAIC-IAGOS CO values show generally good agreement with both independent data sets.
Maps are also compared with version 6 data from the Measurements Of
Pollution In The Troposphere (MOPITT) satellite instrument. Both data sets
clearly show major regional CO sources such as biomass burning in Central
and southern Africa and anthropogenic emissions in eastern China. While the
maps show similar features and patterns, and relative biases are small in
the lowermost troposphere, we find differences of
The data set shows the seasonal CO cycle over different latitude bands and altitude ranges as well as long-term trends over different latitude bands. We observe a decline in CO over the northern hemispheric extratropics and the tropics consistent with that reported by previous studies using other data sources.
We anticipate use of the trajectory-mapped MOZAIC-IAGOS CO data set as an a priori climatology for satellite retrieval and for air quality model validation and initialization.
Atmospheric carbon monoxide (CO) is an important global air pollutant and
trace gas. Due to its relatively long lifetime of 1–4 months (Hubler et al.,
1992; Law and Pyle, 1993), it is an ideal tracer for long-range atmospheric
transport (Logan et al., 1981; Lelieveld et al., 2001; Shindell et al.,
2006). Moreover, in the tropics, it is an important tracer of upward
transport during convective events (e.g., Pommrich et al., 2014).
Consequently, it has been employed to facilitate interpretations of chemical
measurements (Jaffe et al., 1996; Parrish et al., 1991, 1998; Wang et al.,
1996, 1997) and in validating chemical transport models (Carmichael et al.,
2003; Liu et al., 2003; Tan et al., 2004; Wang et al., 2004). The main
sources of atmospheric CO are relatively well understood (Galanter et al.,
2000; Granier et al., 2011; Holloway et al., 2000); however, the magnitude
of individual sources and their seasonal variability, especially of biomass
burning, are not well quantified. Stein et al. (2014) also reported that
models are also generally biased low due to either an underestimation of CO
sources or an overestimation of its sinks. There are differences in the
emission densities of anthropogenic and natural sources, despite the fact
that the anthropogenic and natural sources are of similar magnitude on a
global scale (Granier et al., 2011; Logan et al., 1981). The anthropogenic
sources are primarily associated with large industrial centers or major
biomass burning regions while the natural sources, such as oxidation of
methane (CH
CO plays a vital role in the chemistry of the atmosphere. This significance
mainly comes from the influence of CO on the concentrations and
distributions of the atmospheric oxidants, ozone (O
In areas with sufficient NO
Global atmospheric chemistry models require accurate CO concentrations on a global scale in order to define spatial and temporal variations of atmospheric oxidants and CO. For this reason measurements of CO are made by different kinds of remote sensing and in situ instruments, in ground-based networks, aircraft programmes, and from space (Novelli et al., 1994, 1998; Rinsland and Levine, 1985; Zander et al., 1989; Brook et al., 2014; Reichle Jr. et al., 1990, 1999; Worden et al., 2013; Petzold et al., 2015). Long-range atmospheric transport redistributes CO widely due to its relatively long lifetime. Typical tropospheric background CO levels range between 50 and 120 ppbv (WHO, 2000). Mixing ratios much higher than 250 ppb have been observed in the upper troposphere over Asia (Nédélec et al., 2005) or over the Pacific (Clark et al., 2015) in biomass burning plumes. CO values as high as 1800 ppbv have been reported over Beijing (Zbinden et al., 2013)
Early studies of ground-based observations showed increasing trends in
global CO before 1980 (Khalil and Rasmussen, 1988; Rinsland and Levine,
1985; Zander et al., 1989), followed by a modest decline in the 1990s
(Novelli et al., 1994, 2003; Khalil and Rasmussen, 1994). More recently
satellite observations have shown that the decline has continued: Worden et
al. (2013) report a global trend from 2000 to 2011 of
The objective of this paper is to present a three-dimensional (i.e., latitude, longitude, altitude) gridded climatology of carbon monoxide that has been developed by trajectory mapping of global MOZAIC-IAGOS CO data from 2001 to 2012. We employ a domain-filling technique, using approximately 15 800 000 calculated trajectories to map otherwise sparse MOZAIC-IAGOS CO data into a global field.
This is a technique that has been used successfully with tropospheric and
stratospheric ozonesonde data (G. Liu et al., 2013; J. Liu et al., 2013). Stohl
et al. (2001) used trajectory statistics to extend one year of MOZAIC
O
CO measurements were made by an improved version of a commercial model 48CTL
CO Analyzer from Thermo Environmental Instruments employing the gas filter
correlation technique. The model 48CTL is based on the principle that CO
absorbs infrared radiation at a wavelength of 4.67
The airports visited by aircraft equipped with MOZAIC-IAGOS instrumentation
are shown in Fig. 1. Further details are available at
Airports visited by MOZAIC-IAGOS aircraft from 2001 to 2012. The color bar indicates the number of profiles available from each airport. The squares show the locations of the selected airports used for the validation in this study.
The sampled data from these airports are unevenly distributed spatially, and also temporally because the frequency of visits to airports by aircraft that take part in MOZAIC-IAGOS varies considerably depending on commercial airlines' operational constraints. Thus at Frankfurt, Germany, we find 12 324 CO profiles while from Dammam, Saudi Arabia, we have only 2 during the period 2001–2012. The trajectory-mapping method is valuable for filling the sparse and variable spatial domain.
MOPITT is a nadir-viewing gas correlation radiometer which provides global
atmospheric profiles of CO volume mixing ratio (VMR) and CO total column
values using near-infrared radiation (NIR) at 2.3
MOPITT was launched in 1999 into sun-synchronous polar orbit with a 10:30
local time (LT) northward or southward equatorial crossover time. The
instrument field of view is
For each CO profile of the MOZAIC-IAGOS data set presented here, the mean CO
VMR was calculated for 1 km intervals from sea level up to 12 km (the
maximum altitude of the aircraft). Cruise data were not used. The HYSPLIT model version 4.9
(Draxler and Hess, 1998; Draxler, 1999) was employed to calculate
trajectories for each level of each profile. The exact location of the
aircraft was used to start the trajectories. HYSPLIT, publicly accessible at
This mapping implicitly assumes that CO chemistry may be neglected over a
timescale of 4 days. Except near major sources, this assumption should be
valid, as the lifetime of CO is much longer. However, trajectories have
significant errors over such timescales. Stohl (1998), in a comprehensive
review, quotes typical errors of about 100–200 km day
The trajectory mapping greatly spreads out the in situ CO information along
the trajectory paths, increasing the spatial domain to include much of the
globe. Two different vertical coordinate systems were utilized for the
binning, and hence the maps were generated for elevations above sea level
and above ground level. Data are available publicly at
Figure 2 shows typical standard errors of the mapping product and the number of samples per grid cell, for typical monthly, annual, and decadal maps at 4.5 km altitude above sea level. Similar figures for other levels are included with the climatology on the FTP site. As can be seen, the largest number of samples per grid cell and the lowest standard errors are found over North America and Europe as there are more frequent MOZAIC-IAGOS aircraft flights in this region. Higher standard errors are found at NH high latitudes and much of the Southern Hemisphere (SH), where airports visits by MOZAIC-IAGOS-equipped aircraft are much fewer. The standard error is computed using all data points found inside a grid cell. This is probably biased low, since some grid cells may contain more than one value from a particular trajectory. This bias is likely not more than a factor of 2, based on typical trajectory lengths. These maps present a visual interpretation that distinguishes regions where the CO climatology is “statistically robust” (for example, North America and Europe) from those regions where the uncertainty is larger. The average number of samples is approximately 20, 90, and 140 per grid cell for the monthly, annual, and decadal maps, and this number does not vary greatly among layers. The average standard error is generally between 3 and 4 % of the mean at 4.5 km for all three averaging periods. The monthly mean shows the highest error and the lowest number of samples per grid cell.
The standard error of the mean (left panels) and number of samples
(right panels) for monthly (July 2012), annual (2005), and seasonal (DJF
2001–2012) means at 4.5 km altitude above sea level. The month and year
shown are chosen as typical; other months and years show similar patterns.
The data are binned on a 5
When comparing the MOPITT retrievals with in situ data, it is necessary to
take into account the sensitivity of the retrievals to the true profiles.
The method used by MOPITT to retrieve tropospheric CO profiles follows that
of Rodgers (2000). In order to perform the most meaningful and accurate
comparison, the in situ data to be compared must be transformed using the
averaging kernel matrix,
The vertical coordinate of the MOZAIC-IAGOS climatology profile is in kilometers above sea level, while the MOPITT a priori profile and averaging kernels are on pressure levels in hPa. Therefore, before applying the MOPITT averaging kernels the climatology data were interpolated using NCEP global pressure profiles that vary as a function of time (month) and latitude, to the 10 vertical pressure grid levels (1000, 900, 800, 700, 600, 500, 400, 300, 200, and 100 hPa) used by MOPITT. The interpolated profile was then convolved with the a priori profile and the averaging kernels following Eq. (2) (Emmons et al., 2004). For the atmospheric residual above the maximum MOZAIC-IAGOS profile altitude, the MOPITT a priori profiles were used.
In order to compare with these transformed CO profiles, the MOPITT CO
profiles, averaging kernels, and a priori profiles were mapped down from the
original horizontal resolution of 1
CO total column amounts are retrieved from the MOPITT observations in
addition to the profile retrievals. The retrieved CO total column
The column operator simply converts the mixing ratio for each retrieval
level to a partial column amount. Using the hydrostatic relation, the
operator
Equation (5) is expressed in molecules cm
Examples of comparisons of monthly means of the trajectory-mapped
MOZAIC-IAGOS CO profiles, with the corresponding MOPITT averaging kernels, a
priori and retrievals. The left panels of each subplot show the original
trajectory-mapped MOZAIC-IAGOS climatology profile (green, i.e. unsmoothed),
the a priori profile, the transformed trajectory-mapped MOZAIC-IAGOS
climatology profile (red, i.e. smoothed), and the MOPITT-retrieved CO
profile. The right panel show the mean averaging kernels, for different
pressure levels, obtained by averaging all daytime averaging kernels in the
5
Validation of the trajectory-mapped MOZAIC-IAGOS CO data set product has been performed by (1) comparing maps constructed using only forward trajectories against those constructed using only backward trajectories; (2) comparing profiles for individual airports against those produced by the mapping method when data from that site are excluded; (3) comparing with global MOZAIC-IAGOS cruise-level data, which were not included in the trajectory-mapped data set; and (4) comparing with independent data from the NOAA aircraft flask sampling program.
Examples of the global distribution, 2001–2012, of
trajectory-mapped MOZAIC-IAGOS CO (ppbv) produced using only backward and
only forward trajectories at 7.5 km a.s.l. Panels correspond to different
seasons:
As a first step in validation of the trajectory-mapped climatology, Figs. 4
and S1 in the Supplement assess the differences between the CO mapping produced using only
backward and only forward trajectories for different seasons using the 7.5 km
level as an example. If chemistry (i.e. local sources or sinks) were a significant source of error then one would expect to see differences between
these maps. In fact, the CO distribution patterns are very similar (Fig. 4).
Differences are most commonly 10 % or less and found to be less than
30 % for almost all cases. They are typically less than 10 % at northern
midlatitudes and less than 20 % in the tropics between
A good test of an interpolation model is to examine how it performs in areas where no data are available. Figure 5 compares the trajectory-mapped climatology profiles at three airport sites (Frankfurt, Germany; Houston, USA; and Tokyo, Japan) with the average of the MOZAIC-IAGOS data from each of these sites for May 2001–2012. Houston and Tokyo are not as well sampled as Frankfurt (Fig. 1). The climatology profiles for each location were produced by excluding data from that location, but using all other MOZAIC-IAGOS data.
Generally, the profiles from the two methods agree very well and the agreement is especially good in the free troposphere, at altitudes between 2 and 10 km. The magnitude of the differences for most altitudes is well under 20 %.
Comparisons of trajectory-mapped MOZAIC-IAGOS CO climatology and MOZAIC-IAGOS profiles at three sites. The climatology profiles for each location were produced by excluding data from that location but using all other MOZAIC-IAGOS data. The horizontal error bar half-length is twice the standard error of the mean (equivalent to 95 % confidence limits on the averages when the number of data points is large).
Figure 6 shows seasonally averaged differences, using this method, for a number of airports with different characteristics. The airport stations that have been selected in this validation study represent tropical and NH midlatitude locations that are subject to different meteorological and CO source conditions. Agreement is generally good in the free troposphere. There are larger differences below 2 km where trajectories have larger errors predominantly due to complex dispersion and turbulence in the planetary boundary layer (Stohl and Seibert, 1998). The largest differences are seen where other sources of data are distant. The smallest overall bias is seen at Frankfurt, even though the exclusion of Frankfurt data removes nearly one-third of the total number of profiles. Apparently data from nearby airports such as Munich (Germany) and Brussels (Belgium) map accurately to the Frankfurt location. The consistency of these validation tests suggests that the trajectory-mapped data set provides a reliable picture of the tropospheric CO distribution.
Seasonal mean relative biases [2(Clim-MOZAIC)
We can also compare the trajectory-mapped profile data and MOZAIC-IAGOS in situ global CO data at cruise altitudes between 8 and 12 km. The right panels of Fig. 7 show the global seasonal mean (December–February, March–May, June–August, and September–November) distribution of CO in the upper troposphere (within 60 hPa below the tropopause) for the period from 2003 to 2011. Elevated CO levels in the upper troposphere are generally seen over the areas where there is strong biomass burning (Central Africa, southern Africa, and South America). High CO emissions are observed over eastern China in MAM primarily due to a rise in coal use (Boden et al., 2009; Gregg et al., 2008; Tie et al., 2006) and an increasing number of vehicles (Cai and Xie, 2007).
The left panels of Fig. 7 show the trajectory-mapped climatology 2001–2012 at altitudes between 7 and 9 km above sea level. The trajectory-mapping yields more data over the oceans and NH high latitudes. However, both figures show high CO values in spring in both hemispheres and elevated CO levels over regions where there are strong sources. Comparable CO values are noticeable from the figures over the Northern Atlantic Ocean, although the trajectory-mapped data appear high over high-elevation areas like Greenland and the Himalayas. This may be due to overcorrection of trajectories for terrain differences. Overall, the qualitative agreement between the trajectory-mapped CO and MOZAIC-IAGOS in situ CO cruise data appears very good, even in remote areas.
Global distribution of seasonal mean trajectory-mapped MOZAIC-IAGOS CO between 7 and 9 km altitudes above sea level for the period from 2001 to 2012. Left: MOZAIC-IAGOS trajectory. Right: MOZAIC-IAGOS cruise altitude.
The vertical in situ CO profiles acquired through NOAA's flask sampling program have been extensively utilized previously for validation of CO measurements of MOPITT (Emmons et al., 2004, 2009; Deeter et al., 2010, 2013). Typically 12–15 flask samples are utilized to derive an in situ profile and a single flask is used to sample air at a unique altitude, providing in situ measurements from near the ground up to about 300–350 hPa. The flasks are shipped to the Global Monitoring Division of NOAA's Earth System Research Laboratory (ESRL) for trace gas analysis. Details on procedures of sample collection are found in Novelli et al. (1992), Lang et al. (1992), and Conway et al. (1994).
Figure 8 shows comparisons between NOAA in situ data and the
trajectory-mapped MOZAIC-IAGOS CO climatology for altitude ranges of 0–2,
2–4, 4–6, and 6–8 km. The comparison uses all available flask data (1940
profiles for the period from 2001 to 2012). NOAA CO data points are matched
with the corresponding grid cell (5
CO mixing ratio comparison between trajectory-derived and NOAA flask
data for the period from 2001 to 2012, for four altitude ranges. Bias is
calculated as the mean of the differences in percent,
[2(NOAA-Clim)
This section is devoted to comparing the trajectory-mapped MOZAIC-IAGOS CO data set with the extensively validated product from the MOPITT instrument onboard the NASA Terra satellite, which has been operating continuously since March 2000 (Drummond and Mand, 1996; Edwards et al., 1999). Global comparison is made for both CO profiles and CO total column for different time periods.
As described in Sect. 2.4, in order to make a rigorous comparison with
MOPITT data, the climatology profiles are first transformed using the
corresponding MOPITT a priori profiles and averaging kernels via Eq. (2).
Figure 3 shows examples of retrieved CO profiles (
When comparing MOPITT CO retrievals and the trajectory-mapped CO profile it is useful to keep in mind the shapes and magnitudes of the averaging kernels. For example, the generally broad and weak averaging kernels for the 100 and 1000 mbar levels indicate that a significant fraction of the information in the retrieval is from the a priori profile and from other altitudes. Figure 3 also cautions that the transformed trajectory-mapped MOZAIC-IAGOS CO is closer to both the MOPITT CO retrievals and a priori profiles when there is less information from the measurement. In the lower troposphere the MOPITT CO retrieval profile is positively biased (Deeter et al., 2014), whereas the bias is negative in the upper troposphere. In Fig. 3, we have used only the dayside retrievals from MOPITT as the dayside retrievals have the maximum information content (Deeter et al., 2004).
Figure 9 shows comparisons between MOPITT retrievals and the MOZAIC-IAGOS climatology for global CO data at pressure levels 900, 700, 500, and 300 hPa. The biases and correlations between MOPITT CO VMR and the CO climatology (after applying the averaging kernels and the a priori profiles) are indicated in each plot. There are clearly two distinct clusters of dots in Fig. 9a and b, and the high CO VMR values seen here are from the tropics, with a very small number from the NH extratropics. Recent work by Ding et al. (2015) shows the association of enhanced CO in the free troposphere with the uplifting of CO from biomass burning and anthropogenic sources.
MOPITT and trajectory-mapped MOZAIC-IAGOS CO climatology mixing ratios are well correlated with correlation coefficients of 0.7 or higher for daytime data over both land and ocean. However, Fig. 9 also reveals significant biases between MOPITT retrievals and the trajectory-mapped MOZAIC-IAGOS CO climatology (geometric) altitudes above the 700 hPa pressure level. Although in Fig. 9 we have chosen to show biases for winter 2001–2012, the same analysis for other seasons yields similar results.
Comparison results for DJF (December, January, February) 2001–2012.
MOPITT CO retrievals at 900, 700, 500, and 300 hPa are plotted against
trajectory-mapped MOZAIC-IAGOS CO climatology profiles that have been
transformed using the MOPITT averaging kernels and a priori data. The red
line is the
These large differences are surprising, since Deeter et al. (2014), who also
use the MOPITT L3 V6 product and NOAA flask data (among other sources),
report biases varying from
Same as Fig. 9 but MOPITT CO retrievals are plotted against MOZAIC-IAGOS CO in situ profiles that have been transformed using the MOPITT averaging kernels and a priori data. The in situ profiles are monthly means from 2001 to 2012 (Frankfurt, Germany). Outliers (CO mixing ratios more than 1.5 standard deviations from the mean at each pressure level) have been removed, which improves the correlation coefficient at 300 hPa but makes no significant change in other derived parameters.
A global comparison between the trajectory-mapped MOZAIC-IAGOS climatology and MOPITT at 600 hPa is displayed in Fig. 11. As can be seen, both data sets capture major features of the CO distribution, particularly anthropogenically polluted (i.e., northeastern China) and biomass burning (i.e., West Africa, Central Africa, southern Africa, and Central America) regions. The CO-rich air in the lower troposphere over West Africa, where biomass burning fires are active, is convectively lifted to the upper troposphere where it disperses over the African tropics towards the east coast of South America (Edwards et al., 2003). Over southern Africa and Southeast Asia, where there are strong sources, and in general at 600 hPa, higher CO VMRs are found by the MOZAIC-IAGOS mapping than by MOPITT.
Global distribution of the seasonal mean trajectory-mapped
MOZAIC-IAGOS CO climatology (left panels), after transformation with the
MOPITT a priori profiles and averaging kernels matrix, and MOPITT CO
retrievals (right panels). CO mixing ratio (ppbv) as a function of latitude
and longitude at 800
Figure S2 shows global maps of percentage differences between MOPITT and the
transformed trajectory-mapped MOZAIC-IAGOS CO climatology at 800 and 600 hPa
pressure levels for DJF and SON 2001–2012. Differences are generally less
than
In the same manner as we have done for the retrieved CO profiles, the
retrievals of CO total column
We have calculated the global total CO columns for both the MOZAIC-IAGOS CO climatology (using the MOPITT a priori and averaging kernels by applying Eq. 6) and for MOPITT CO retrievals and compared different regions of the globe and different time intervals from 2001 to 2012. For most regions the MOPITT CO total columns are 10–20 % lower than the trajectory-mapped MOZAIC-IAGOS CO climatology total columns, with larger differences in high CO source regions. The SH shows a distinct latitude gradient, which is not evident in the NH. This is likely related to the existence of major CO sources in the NH and the absence of large sources of emission in the SH. Figure 12 shows global total column CO for four seasons. It is clear that MOPITT and the climatology are similarly able to capture the CO spatial variability. In NH fall, elevated total column CO is seen over South America, Southeast Asia, and West Africa, which is due primarily to agricultural biomass burning in the regions. High total column CO is seen in all seasons over eastern China, which is one of the major emission regions in the world. NH total columns are much higher than those in the SH, and CO is somewhat more abundant in the NH winter, which is expected due to the lower amounts of OH that are present in the troposphere in that season. Difference plots for the CO maps shown Fig. 12 are shown in Fig. S3.
Global total column CO from the transformed trajectory-mapped
MOZAIC-IAGOS climatology and MOPITT data for December–February, March–May,
June–August, and September–November 2001–2012. Data are averaged in
5
Figure 13 shows scatter plots of retrieved MOPITT CO total columns against the transformed trajectory-mapped MOZAIC-IAGOS climatology for the same periods shown in Fig. 12. Correlations are strong except in SON 2001–2012, and average biases are 12–16 %, with the trajectory MOZAIC-IAGOS higher. The high bias might be in part associated with the airport effect; however, the averaging kernels (Fig. 3) are not very sensitive to CO in the boundary layer.
Global MOPITT CO column retrievals versus transformed
trajectory-mapped MOZAIC-IAGOS CO climatology column for four seasons. The
bias is calculated as the difference for each grid cell,
[2(MOPITT-Clim)
Figure 14 shows the monthly mean CO VMR between 4 and 8 km altitude above sea level for 2001–2012. The climatology is able to capture the CO spatial variability fairly well: the NH concentrations are much higher, and the biomass burning peaks are clearly visible for the NH winter and spring seasons. The climatology shows more abundant CO in the NH during these seasons. This is due primarily to lower OH levels during the cold season which permits a longer lifetime for CO, although there also appears to be an additional source in eastern Asia. Enhanced CO concentration is observed in the tropical regions where wildfire burning is typical during January–April, like West Africa and a large part of Central Africa (Sauvage et al., 2005, 2007). At southern midlatitudes between South America, southern Africa, and Australia, we observe high CO from September to November, during the agricultural burning season. Although Fig. 14 shows a 12-year global map, the strong enhanced CO over these regions (West Africa, South America, and Southeast Asia) is clearly observable as an annual feature with significant interannual variability.
Global monthly mean CO distribution from the trajectory-mapped
MOZAIC-IAGOS CO VMR as a function of latitude and longitude for
January–December 2001–2012 and altitudes between 4 and 8 km a.s.l. The data
are averaged with a bin size of 5
Zonally averaged monthly variation of CO for the latitude bands
45
As can be seen from Fig. 15, CO shows distinct seasonal cycles in both
hemispheres. In the NH extratropics (Fig. 15c), maximum CO VMR is observed
in February–April following a steady increase during fall and winter. This
is followed by a rapid decrease to the lowest CO levels in July–September.
The decline in summer shows the typical seasonal pattern of CO in the NH,
driven by OH increase during this time (Yurganov et al., 2008; Novelli et
al., 1998). In the SH extratropics (Fig. 15d), CO levels peak in
September–October. This is consistent with previous studies by Novelli et
al. (1998). In the SH, the annual CO maximum is earlier at lower altitudes.
Rinsland et al. (2002) suggested that this phenomenon is associated with the
vertical and horizontal CO dispersion away from the biomass burning region
in the tropics. Moreover, CO shows greater seasonal variability,
particularly at higher altitudes, in the SH than in the NH. The seasonal CO
cycle in the tropics (Fig. 15b) and for latitude band 45
Figure 16 shows zonal mean latitude–time cross-section plots of CO VMR at 2.5, 4.5, 6.5, 8.5, 10.5, and 12.5 km altitudes for the period 2001–2012. The latitude–time cross-section shows the seasonal cycle of zonal mean CO for different altitudes, as seen in the previous figures, and also the variation of the interhemispheric CO VMR gradient throughout the year. The strongest interhemispheric gradient occurs in March, at low altitude, and the smallest gradients are seen in northern summer. The gradient in NH spring reverses at higher altitudes and in NH fall, when it is especially strong, at higher altitudes. Plot 14e and f also clearly show the weak seasonal cycle in the NH upper troposphere compared to that in the SH.
Seasonal variation of zonal monthly mean trajectory-mapped
MOZAIC-IAGOS CO climatology at 2.5, 4.5, 6.5, 8.5, 10.5, and 12.5 km
altitudes for the period 2001–2012. The zonal mean data are averaged in
5
Figure 17 illustrates the variation of CO with altitude for the seasons in which we observe maximum CO levels in both the SH and NH (i.e., MAM and SON). The greatest CO VMRs are found at lower altitudes in both hemispheres, although CO declines with altitude faster in the NH than the SH. This results in a decrease in the strength of the interhemispheric gradient (SH to NH) with altitude. This result is consistent with Edwards et al. (2006), who suggested that in the absence of continued CO input from the source regions (i.e., biomass burning in southern Africa and South America), the aged CO is gradually distributed vertically throughout the troposphere in the SH. In fact, in regions where there is deep convection this leads to an enhanced CO concentration in the upper troposphere, as can be seen on the right-hand side of Fig. 17 and in Fig. 18. Moreover, Liu et al. (2006) showed large horizontal CO gradients in association with vertical and horizontal transport of air with different chemical signatures of origin.
Zonal CO mean vertical profiles for February, April, July, and September,
averaged for 2001–2012 over the latitude bands 23.5–66.5
Global distribution of seasonal (the NH spring and fall) mean
trajectory-mapped MOZAIC-IAGOS CO climatology as a function of latitude and
longitude for altitudes 1.5, 3.5, 5.5, 7.5, and 9.5 km a.s.l. The left and
right columns show average CO VMRs for March–April–May and
September–October–November, 2001–2012. The data are averaged with a bin
size of 5
Monthly mean profiles of CO from the trajectory-mapped MOZAIC-IAGOS
CO climatology for February, April, July, and September, averaged for
2001–2012. The different colors represent CO mean VMR for the latitude bands
23.5–66.5
The smoothed time series of the NH extratropical zonal mean CO VMR at 900, 700, 500, and 300 hPa for the trajectory-mapped MOZAIC-IAGOS data set 2001–2012 is shown in Fig. 19. For purposes of comparison we also show data from MOPITT and from the mapped MOZAIC-IAGOS data set transformed with the MOPITT averaging kernels. Gaps in the figure occur whenever one data source is missing. The gaps in June–July 2001 and August–September 2009 were due to a cooler failure of the MOPITT instrument. MOZAIC-IAGOS began CO measurement in December 2001 and there were only partial data available in 2010 and 2011. The observations show an annual late winter or springtime peak in the NH extratropical zonal CO loading each year, in conjunction with low wintertime OH levels. The same interannual cycle of CO is captured by both trajectory-mapped MOZAIC-IAGOS (transformed and untransformed) and MOPITT. They appear to track short-term changes equally well. However, while all show a modest decline in the lower troposphere until about 2008–2009 (after which CO VMR seems to level off), in accordance with the trends found by Worden et al. (2013), in the upper troposphere MOPITT shows a modest increase. It also shows a significant bias with respect to the trajectory-mapped MOZAIC-IAGOS data that decreases with time. Although the untransformed trajectory-mapped MOZAIC-IAGOS CO values show a significant difference against the transformed data in the lower troposphere, they seem to agree well at higher levels. The untransformed trajectory-mapped MOZAIC-IAGOS data show higher CO levels than MOPITT CO retrievals at all levels.
Zonally averaged time series of monthly mean CO VMR, at individual
levels and total column, as retrieved by MOPITT and from the
trajectory-mapped MOZAIC-IAGOS CO climatology (untransformed, and transformed
using MOPITT's averaging kernels) for the latitude band
23.5–66.5
Laken and Shahbaz (2014) found increasing CO trends over widespread regions of South America, Mexico, Central Africa, Greenland, the eastern Antarctic, and the entire region of India and China from MOPITT data. The SH extratropics also show time series similar to those in Fig. 19, but the negative trend is not as clear as that in the NH due to limited data. The annual springtime peak in the SH zonal CO loading is visible in all of the time series. This is predominantly associated with dry season biomass burning emissions in South America, southern Africa, Southeast Asia, and northwestern Australia. In later months, the CO resulting from these emissions is generally destroyed by more active photochemistry during the SH summer. At these times, the retrieved zonal CO falls to background levels (around 40–50 ppbv), which are representative of the remote ocean regions where CO production by methane oxidation is the dominant source (Edwards et al., 2006). We looked at the time series of the zonal monthly mean of CO VMR for the tropics. The biases between the MOPITT retrievals and the trajectory-mapped MOZAIC-IAGOS in general show the same features as for the extratropics, while the seasonal patterns combine those of the NH and SH seen in Fig. 19.
In Fig. S4, we display the monthly mean time series for Frankfurt from December 2001 to December 2012. These also show significant biases, declining with time, between MOPITT and the transformed MOZAIC-IAGOS in situ above 700 hPa, in good agreement with the result shown in Fig. 19. Furthermore, MOPITT shows a modest increase in CO levels in the upper troposphere while MOZAIC-IAGOS in situ (transformed and untransformed) shows a modest decline, consistent with Petetin et al. (2015), who report a similar decrease over Frankfurt. The MOPITT and MOZAIC-IAGOS (transformed and untransformed) CO values for Frankfurt show the same seasonal patterns as the NH extratropics (Fig. 19). This comparison suggests that a prominent bias, declining with time, exists between MOZAIC-IAGOS and MOPITT L3 V6 TIR/NIR products.
We have presented a three-dimensional (i.e., latitude, longitude, altitude)
gridded climatology of CO developed by trajectory mapping of global
MOZAIC-IAGOS data. This quasi-global climatology data set offers a complement
to global satellite measurements, at significantly higher vertical
resolution, that facilitates visualization and comparison of different years
and seasons and offers insight into the global variation and trends of CO.
Even though the MOZAIC-IAGOS aircraft data are unevenly distributed both in
time and space across the globe, the trajectory-mapped data set is uniformly
distributed on a 5
The trajectory-mapped CO data set has been validated by comparing maps
constructed using only forward trajectories and using only backward
trajectories. The two methods show similar global CO distribution patterns.
Differences are most commonly 10 % or less and found to be less than
30 % for almost all cases. They are typically less than 10 % at northern
midlatitudes and less than 20 % in the tropics between
The trajectory-mapped CO data set has also been extensively compared with MOPITT retrievals. Between 700 and 300 hPa, a prominent bias, declining with time, exists between MOZAIC-IAGOS and MOPITT L3 V6 TIR/NIR products.
This study demonstrates one aspect of the value of the MOZAIC-IAGOS
continuous, long-term, global, vertically resolved in situ measurements.
Such routine commercial aircraft observations provide valuable information
on atmospheric composition that can improve our understanding of global and
regional air quality and the potential impact of greenhouse gases on climate
change. The unique 3-D CO climatology data set presented here has the
potential to be used for time series and trend analysis and provides a
quasi-global view of CO changes and transport as well as interannual
variability. It will also be useful as model initial fields and background
and boundary fields. It will be especially useful as an improved a priori
climatology for satellite data retrieval. The global picture it presents is
also expected to be valuable for comparison and validation of model results.
The data are publicly available at
The trajectory-mapped MOZAIC-IAGOS CO climatology data set is publicly available at
The authors acknowledge the strong support of the European
Commission, Airbus, and the airlines (Lufthansa, Air France, Austrian, Air
Namibia, Cathay Pacific, Iberia, and China Airlines so far) that carry the
MOZAIC or IAGOS equipment and perform the maintenance since 1994. MOZAIC is
presently funded by INSU-CNRS (France), Météo-France, Université
Paul Sabatier (Toulouse, France), and Research Center Jülich (FZJ,
Jülich, Germany). IAGOS has been additionally funded by the EU projects
IAGOS-DS and IAGOS-ERI. The MOZAIC-IAGOS database is supported by ETHER
(CNES and INSU-CNRS). Data are also available via the Ether web site