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 µm. For 30 s integration time (the response time of the instrument) the precision achieved is 5 ppb (noise) or 5 % (calibration) CO, with minimum detection limit of 10 ppb. The analyzer samples at a horizontal resolution of about 7 km (since the maximum cruise speed of the Airbus A340 aircraft is nearly 250 m s-1) and the vertical resolution during ascents and descents is nearly 300 m. Nédélec et al. (2003 for MOZAIC, 2015 for IAGOS) give detailed descriptions of the CO analyzer, measurement technique, instrument validation, and quality testing.

The airports visited by aircraft equipped with MOZAIC-IAGOS instrumentation are shown in Fig. 1. Further details are available at http://www.iagos.fr.

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 µm and thermal-infrared radiation (TIR) at 4.7 µm (Drummond and Mand, 1996). CO columns and profiles are retrieved from the infrared emission channels (4.6 µm) for all cloud-free scenes. The MOPITT measurement technique relies on a temperature gradient within the atmosphere, leading to a retrieval dependence on surface temperature, and little sensitivity to CO in the boundary layer. The retrieval uses a priori profiles that vary geographically and temporally. MOPITT-derived CO VMR profiles reflect the vertical sensitivity of the measurement as defined by the retrieval averaging kernel (e.g. Fig. 3) and a priori profile. In this study, we have used Level 3, Version 6 monthly CO mixing ratio profile data, reported on 10 pressure levels, as well as CO total column. Nighttime CO observations of MOPITT have not been validated and appear subject to larger bias (Heald et al., 2004). Hence, we use the daytime data for comparison. MOPITT data are publicly available at the NASA Langley Research Center Atmospheric Science Data Center (https://eosweb.larc.nasa.gov/project/mopitt/mopitt_table).

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 22×22 km2. Cross-track scanning with a 612 km swath provides near complete coverage of the surface of the Earth approximately every 3 days. MOPITT retrievals have gone through intensive validation against in situ measurements from aircraft on a regular basis since the start of the mission (Worden et al., 2010; Deeter et al., 2012, 2013, 2014; Emmons et al., 2004, 2007, 2009; Jacob et al., 2003). MOPITT CO retrievals have also been validated by comparing to ground-based and TES (Tropospheric Emission Spectrometer) satellite measurements (Jacob et al., 2003; Luo et al., 2007). Deeter et al. (2014) employ the MOPITT L3 V6 product and show biases to vary from -5.2 % at 400 hPa to 8.9 % at the surface. Previous studies used earlier versions of the MOPITT product.

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 http://ready.arl.noaa.gov/HYSPLIT.php, uses the reanalysis meteorological wind fields from NCEP/NCAR (Kalnay et al., 1996) as an input to describe the transport of CO in the atmosphere. The reanalysis data are available from 1948 until the present. Both forward and backward trajectories for 4 days at 6 h intervals (32 positions for each level) were calculated for 41 200 CO profiles, and the mean CO mixing ratios from each level (i.e., tropospheric and lower stratospheric air masses) of each profile were assigned to the corresponding trajectory positions along the forward and backward paths. Trajectories only move upward and downward with the meteorological vertical velocity fields since the HYSPLIT kinematic trajectory model employs vertical motions supplied with the NCEP reanalysis meteorological data set. Numerous studies show that the choice of vertical wind velocity has significant impact on the transport of tracers (e.g., Schoeberl et al., 2003; Ploeger et al., 2010, 2012). Kinematic models show excessive dispersion for tracers with strong gradients (e.g., O3 in the vicinity of the tropopause), particularly for trajectories of 7 days or more. Here trajectories were limited to a maximum of 4 days in length. Moreover, unlike O3, CO does not have a strong vertical gradient in the upper troposphere. Trajectories that reach the ground continue at the surface where trajectory robustness is more uncertain. Trajectories that reach the top height of the model (20 000 m above sea level) terminate. Although HYSPLIT is capable of generating a trajectory every hour (i.e., 24 trajectories per day), the typical maximum frequency of CO measurements is around two profiles per day (with the exception of Frankfurt, where we can get up to six profiles per day). In this version, no attempt was made to identify individual CO sources; however, the climatology could in principle be refined by excluding back trajectories from sources identified via emission inventories. We note, however, that if major anthropogenic sources were a significant source of error, we would see differences between the CO mapping produced using only backward and only forward trajectories (see Sect. 3.1).

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-1 in the troposphere. This can be combined with an estimate of the correlation length in the troposphere to yield an estimate for the information value of a mapped measurement. Liu et al. (2009) find that O3 measurements in the troposphere correlate with an exponential dependence of approximately e-rrRR1.5, where r is distance and R is a correlation length of 500–1000 km in the troposphere and 1000–2000 km in the stratosphere. As the CO lifetime is even longer than the ozone lifetime, the correlation length for CO should be at least as large. Therefore, the trajectory-mapped data were binned at intervals of 5∘ latitude and 5∘ longitude, at every 1 km altitude, and averaged with a weighting, w, assigned according to the formula: w=e-150t/R1.5, where R is the correlation length (taken as 700 km in the troposphere and 1500 km in the stratosphere), and t is the age of the trajectory in days.

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 ftp://es-ee.tor.ec.gc.ca/pub/ftpdt/MOZAIC_output_CO/. In this work, we present global CO maps generated for elevations above sea level. Global maps of monthly, annual, seasonal, and decadal means are presented, for each altitude, from 2001 to 2012.

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.

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, A, and a priori profile, xa, as shown by Eq. (2). A “retrieved” comparison profile, xret, is calculated by using the in situ profile, x, as the “true” profile in Eq. (2), which is interpolated to the lower resolution of MOPITT. As described by Emmons et al. (2004), the in situ profile (x) is transformed with averaging kernel matrix (A) and the a priori CO profile (xa) to get a profile (xret), the appropriate quantity to compare with the MOPITT retrievals: xret=xa+Ax-xa+ε=Ax+I-Axa+ε, where I is the identity matrix and ε is the retrieval error due to random errors in the measurement and systematic errors in the forward model (e.g., the error in the atmospheric temperature retrieval). xret, x, and xa are expressed in terms of the logarithm of the VMR. The averaging kernels provide the relative weighting between the true and a priori profiles and reflect the sensitivity of the retrieval to the measurement (Worden et al., 2013). They are very sensitive to the surface temperature and will be different for each point on the globe. The matrix A describes the sensitivity of the retrieved CO log(VMR) profile to perturbations applied at each level of the “true” log(VMR) profile. The quantity xret, the transformed in situ profile, represents the result of applying a linear transformation to the in situ profile in the same way that the remote sensing retrieval process is believed to transform the true profile. Thus, xret can be directly compared against the MOPITT-retrieved CO profile in a manner that is not affected by varying vertical resolution or a priori dependence. The vertical resolution of the retrieved profile is described by the shapes of the averaging kernels. Figure 3 shows that the kernels are broad except at pressure levels between 400 and 300 hPa and exhibit a large degree of overlap. The overlap of the averaging kernels peaking in the boundary layer and those at the top of the atmosphere indicates a significant correlation for the retrieved values at these levels. Typical full-width at half maximum (FWHM) of these curves is approximately 5–8 km. The retrieved CO values at both top and bottom are also influenced by CO at mid-levels and by the a priori CO profile at all pressure levels. The averaging kernels also describe the relative contributions, to the CO VMR retrieved at a given level, of the true and a priori (via I-A) CO profiles at all pressure levels (Eq. 2). Where the area under the averaging kernel is smaller, the a priori information in the retrieved CO profile is relatively larger. MOPITT CO averaging kernels exhibit variability from month to month, season to season, as well as nighttime to daytime, depending on the atmospheric temperature profile, surface pressure, and the CO profile itself.

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∘ × 1∘ in latitude and longitude to a reduced 5∘ × 5∘ grid. Two examples of comparisons of trajectory-mapped MOZAIC-IAGOS CO profiles with individual (reduced) 5∘ × 5∘ MOPITT CO profiles are shown in Fig. 3. The application of the averaging kernels to the MOZAIC-IAGOS CO profile results in a vertical transformation which can shift mixing ratios significantly at some levels. The averaging kernel, for example, identified as “1000” (i.e., surface) shows how changes to the true CO mixing ratio at all 10 retrieval levels would each contribute to a change in the retrieved value at the surface at 1000 mbar. The original trajectory-mapped MOZAIC-IAGOS climatology profile is quite different from the transformed climatology profile and the departures of the transformed CO mixing ratio from the true mixing ratios can be as large as 60 ppb at some pressure levels.

CO total column amounts are retrieved from the MOPITT observations in addition to the profile retrievals. The retrieved CO total column cret (a scalar) is related to the retrieved profile xret (a vector) through the linear relation cret=tTxret, where T indicates the transpose operation and t is the total column vectors (Emmons et al., 2004; Deeter, 2002). The CO total column averaging kernel can be calculated from the profile averaging kernels by a=tTA.

The column operator simply converts the mixing ratio for each retrieval level to a partial column amount. Using the hydrostatic relation, the operator t is expressed as t=2.120×1013Δp.

Equation (5) is expressed in molecules cm-2 ppbv-1 and Δp is the vector of the thicknesses of the retrieval pressure levels (in hPa) as discussed in the MOPITT Version 5 User's Guide (Deeter, 2011).

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 ±30∘ latitude, except in the Pacific and Atlantic oceans where they can be as large as 30 %. Differences (Fig. S1) also show no distinct pattern, except for some clustering in areas where the trajectories are longest and therefore least reliable. As differences between the two distributions are comparable with the uncertainties of the mean value estimates and not systematic, it is reasonable to combine forward and backward mapped values to produce an averaged CO map.

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 %.

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.

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.

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∘ × 5∘ × 1 km) of the monthly climatology, for the same year and month. If the monthly CO value for a particular grid cell is missing, the seasonal mean (if it exists) of the trajectory-mapped CO climatology (2001–2012) is used for the comparison. Above 2 km agreement is fairly good, considering that the comparison is between point measurements and monthly averages over a large volume. The positive bias below 2 km is probably due to the effect of urban sources of CO since airports are located close to cities. In general, MOZAIC-IAGOS CO measurements at takeoff and landing are above background. This “airport effect” decreases rapidly as can be from the figure for higher altitudes. This decrease is because the aircraft not only ascends above the boundary layer but also samples over 150–400 km in distance as the aircraft ascends to, or descends from, cruise altitude.

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 (xret), together with the original climatology (x) and the a priori profiles (xa).

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.

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 -5.2 % at 400 hPa to 8.9 % at the surface. These results are not dissimilar to our comparison in Fig. 8 and would suggest a difference of about 5 % between MOPITT and the trajectory-mapped climatology, with the climatology being higher primarily due to the airport effect. Although the validation data sets are not identical (owing primarily to incomplete global coverage of the MOZAIC-IAGOS product), the relative bias of 22 % at 500 hPa seems excessive. In order to eliminate the possibility that trajectory errors might be contributing to this bias, we have also compared MOZAIC-IAGOS in situ CO profiles against MOPITT retrievals. As an example in Fig. 10, we display the comparison between MOZAIC-IAGOS in situ CO profiles at Frankfurt (Germany) and MOPITT CO retrievals, which have been regridded to 5∘ resolution, over Frankfurt from MOPITT overpasses. The MOZAIC-IAGOS in situ aircraft CO values have been transformed using the MOPITT averaging kernels and a priori data, for the period from December 2001 to December 2012. MOPITT and MOZAIC-IAGOS are again strongly correlated, and biases at 500 and 300 hPa are large and in fact very similar in magnitude to those with respect to the trajectory-mapped MOZAIC-IAGOS CO data set. This implies that the differences at 500 and 300 hPa are not a result of the trajectory mapping.

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.

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 ±20 % at 800 hPa, with a negligible overall bias, but larger at 600 hPa, with MOPITT on average 10–20 % lower. Generally, the comparisons of the CO profiles of the transformed trajectory-mapped MOZAIC-IAGOS and MOPITT for both grid cells as well as zonal mean for different latitude bands show a consistent, significant bias: MOPITT is lower from about 700 to 300 hPa but shows a negligible bias in the lowermost troposphere. Above 300 hPa, they seem to agree better, although this may be partly due to the fact that the retrieved CO values in this region are highly influenced by the MOPITT a priori data for both cases.

In the same manner as we have done for the retrieved CO profiles, the retrievals of CO total column cret may be compared against total column values derived from in situ profiles x. Utilizing Eq. (2), the retrievals of the total CO column cret found in Eq. (3) can be rewritten alternatively as cret=ca+ax-xa, where ca=tTxa is the a priori total column value corresponding to the a priori profile xa, a is the CO total column averaging kernel, and x is the in situ profile.

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.

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.

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.

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.

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.