The European Research Infrastructure IAGOS http://www.iagos.org, last access: 7 May 2021 provides in situ measurements aboard several commercial aircraft. The observations used hereafter have been performed in the framework of the ongoing IAGOS-Core programme that followed the MOZAIC programme . Ozone (CO) measurements started in August 1994 (December 2001), based on an UV (IR) absorption technology, with an accuracy of 2 ppb (5 ppb), a precision of 2 % (5 %) and a time resolution of 4 s (30 s). Further information about the instruments can be found in and for O3 and in for CO. present a more recent evaluation of both ozone and CO instruments in the frame of IAGOS. The IAGOS observations (referring to the IAGOS-Core database hereafter) frequently sample the whole troposphere near airports, measuring vertical profiles during ascent and descent phases, and the UTLS during the cruise phases, mostly in the northern midlatitudes where most of the flight observations are gathered. In these latitudes, a recent analysis of O3 and CO climatologies and trends based on almost two decades of IAGOS cruise measurements has been performed in . In addition to global climatologies, the same analysis also focused on eight well-sampled regions in the UT and the LS separately. In order to generate results comparable with the latter, this study focuses on the same time period (1994–2013) and, where relevant, on the same regions.

The CCMI project is a common initiative from the IGAC and SPARC programmes. CCMI phase 1 gathers a community of 18 CCMs and two CTMs, whose description is given in the review of . A series of experiments has been designed to model tropospheric and stratospheric air compositions for past, present and future climates. For each experiment, a common protocol is recommended to all participating models. Amongst the CCMI simulations, the REF-C1SD reference experiment aims at modelling as realistically as possible the day-to-day tropospheric and stratospheric compositions in a recent climate, using SD. For this purpose, as described in , the simulations are driven by (or nudged towards) dynamical reanalysis data sets (typically ERA-Interim or MERRA) and extending from 1980 to 2010. For this long-term simulation, the 3-D output fields of species concentrations are archived as monthly means.

The MOCAGE model (, ; , ) is an offline global CTM. The chemical scheme is composed by the coupling of the RACM Regional Atmospheric Chemistry Mechanism; and the REPROBUS REactive Processing Ruling the Ozone BUdget in the Stratosphere; schemes, corresponding to tropospheric and stratospheric chemistry, respectively. The MOCAGE REF-C1SD simulation is run using a global domain at a 2∘×2∘ horizontal resolution, and 47 vertical levels, in hybrid σ–pressure levels, distributed from the surface up to ∼5 hPa. The simulation is driven by the meteorological fields from the ERA-Interim reanalysis. The biomass burning and anthropogenic emissions come from the Global Fire Emissions Database version 2 (GFEDv2) and MACC/CityZEN EU project (MACCity) inventories, respectively. The latter is characterized by a 10-year resolution, and a linear interpolation is applied to derive yearly emissions. The period spreads from August 1994 to December 2013, consistent with . The first 14 years come from the MOCAGE REF-C1SD simulation originally produced for the CCMI project. For the years out of the period covered by the experiment, the MOCAGE REF-C1SD run has been extended to 31 December 2013 using the same code and inputs as in the original MOCAGE CCMI REF-C1SD simulation.

At a given point where IAGOS measured a mixing ratio Cobs(X) for species X, the algorithm presented here locates its position on the model grid defined by its longitude, latitude and hybrid σ–pressure coordinates. More precisely, we locate the model grid point which is the closest west and south of, and below (in altitude), the observation point and which corresponds to the ith, jth and kth grid point coordinates, respectively. As shown in Fig. c, a normalized scalar is then computed for each dimension (coefficients α, β, γ), increasing linearly with the distance between the measurement point and the (i,j,k) grid point. Note that the γ vertical coefficient is derived from log-pressure coordinates. Finally, a resulting 3-D weight is computed for each of the eight closest cells. By noting the variable indexes I, J and K belonging to the ensembles {i,i+1}, {j,j+1} and {k,k+1}, respectively, we define the functions fI, gJ and hK, whose values depend on α, β and γ, respectively, such as 1fI(α)=1-αifI=iαifI=i+1gJ(β)=1-βifJ=jβifJ=j+1hK(γ)=1-γifK=kγifK=k+1. The resulting weight of each of the grid points surrounding the measurement location is thus defined as the following product: 2WI,J,K(α,β,γ)=fI(α)⋅gJ(β)⋅hK(γ). In this way, as illustrated in Fig. d, for a given cell (I,J,K) amongst the eight closest ones, this weight decreases when the distance increases between the measurement point and the model grid point. Note that since the simulation outputs are monthly averages, we use the monthly mean surface pressure for determining the hybrid σ–pressure levels on the 47 vertical grid levels for a given model longitude/latitude. Although the surface pressure can show an important intra-monthly variability, we calculated that a 30 hPa change at surface would cause a variation weaker than 2 hPa on a given vertical grid level in the UTLS. Although caution is needed while treating low-altitude measurements, the monthly resolution on the surface pressure field thus has a negligible impact on the distribution of the IAGOS data from the cruise altitudes onto the model vertical grid.

The weighting coefficients defined above correspond to a single observation data point. To obtain monthly averages from the whole observation data set, the last step consists of summing up all the values measured in the vicinity of the (i,j,k) grid points for each month. Thus, for a given grid point (i,j,k), we define n as the index for the measurement performed in its vicinity during the considered month, and the corresponding mixing ratio for the species X is denoted Cobs,n(X), and N the total number of measurements performed in its vicinity. The monthly value of the X mixing ratio at (i,j,k) is then derived with the equation 3Xi,j,k=∑n=1NWi,j,k,n(α,β,γ)Cobs,n(X)∑n=1NWi,j,k,n(α,β,γ), where the denominator is equivalent to the amount of weighted measurement points performed in the (i,j,k) grid cell during the chosen month. Hereafter, we refer to it as Neq. In the end, this method yields monthly fields of IAGOS O3 and CO mixing ratios (or any other variable measured by IAGOS, e.g. water vapour) projected on the MOCAGE grid points where IAGOS data are available. This data set is named IAGOS-DM hereafter, the suffix DM referring to the distribution on the model grid. With this method, the cruise observation data are distributed onto the MOCAGE vertical levels spanning from level 28 up to level 22 and corresponding to the ∼360–175 hPa interval. Note that the measurement points on the MOCAGE vertical levels below level 28 (∼360 hPa) are considered as corresponding to ascent or descent phases of the flights. These measurements are not processed since they are only available in small areas close to airports. Levels 27 and 28 generally correspond to these phases too but include cruise measurements above elevated lands, since hybrid σ–pressure levels tend to follow land elevation. In order to compare the observations and the model at the same locations and months, we apply a mask on the MOCAGE REF-C1SD simulation outputs that allows us to account only for the IAGOS-DM sampled grid points. The subsequent data set is named MOCAGE-M, the letter “M” referring to the mask. Thus, IAGOS-DM and MOCAGE-M data sets are spatially consistent and can be used to make grid-point-to-grid-point comparisons on climatological timescales, as long as we assume the gridded IAGOS data to be representative of the measurement period. The latter point has been tested on a 5-year subsample using the simulation daily outputs instead of monthly outputs. It consisted of comparing MOCAGE-M to a test product derived by calculating monthly averages from the daily outputs and applying a mask based on the IAGOS daily sampling. The results from this test are briefly presented in Sect. .

In order to test the advantages of the linear interpolation involving weighting factors, we also derive another product from the IAGOS database using a simplified method, i.e. by solely averaging the measurement points into the grid cells where they are located. This control product is named IAGOS-DM-noW hereafter, the -noW suffix standing for “no weighting”. Since it changes the spatial sampling distribution, a new subsequent mask has to be applied to the MOCAGE grid to be consistent with the IAGOS-DM-noW product, called MOCAGE-M-noW hereafter.

A second part of this assessment targets the behaviour of the model in the UT and the LS separately. The diagnostics we use for this purpose are adapted from , based on , who used the PV fields from the ECMWF operational analysis to derive the tropopause pressures. In contrast to the latter studies, we define the tropopause layer with the monthly averaged PV fields from ERA-Interim, as used in the MOCAGE REF-C1SD simulation. A given grid point is considered as belonging to the UT if its monthly PV is lower than 2 potential vorticity units (PVU) and to the LS if the PV is greater than 3 PVU. The cells at which PV ranges between 2 and 3 PVU are considered as belonging to the transition zone separating the two layers and are not selected. In order to enhance the distinction between the UT and the transition zone, the first model level below the 2 PVU threshold is also filtered out from the UT. The 2 PVU threshold is derived from a log-pressure interpolation between the grid points. We also filter out the grid boxes where this PV classification is not consistent with the mean observed O3 mixing ratio, i.e. where the monthly O3 level reaches 140 ppb in the UT and where it goes under 60 ppb in the LS. It avoids an additional bias based on errors in the dynamical field leading to unrealistic UT and LS attribution. These thresholds on O3 mixing ratio were chosen according to the O3 seasonal cycles shown in Fig. 3.7 in , where the upper boundary linked to the interannual standard deviation in the UT is less than 100 ppb and where the lower boundary in the LS is greater than 100 ppb. We estimated that a supplementary 40 ppb interval would limit an exaggerated filtering of grid cell monthly values.

As in , we focus our analysis on the seasonal cycles for eight regions in the northern midlatitudes that are well sampled by IAGOS. Their coordinates and their corresponding sampling are detailed in Table 1 in . Because of the 2∘×2∘ horizontal grid resolution in the simulation, we applied a 1∘ eastward or northward shift on the odd-coordinated edges. The subsequent regions defined in this paper are shown in Fig. . For each of them, the monthly means are calculated by averaging the gridded monthly means separately in the UT and the LS. The latter values were defined as described in Sect.  and . In , the regional monthly means with less than 300 data were filtered out. Here, due to the loss of data caused by the monthly resolution, we lowered this minimum threshold to 150 in order to keep taking the less-sampled regions into account, such as western North America and Siberia. Still, we kept the criterion from which required at least 7 d between the first and last measurements in the considered month and region, avoiding the averages to be representative of transient meteorological conditions only. Following the same study, the computation of the seasonal cycles is based on the years exhibiting 7 available months or more, distributed over three seasons at least. This criterion avoids biases linked to the interseasonal differences in the sampling, thus ensuring a good representativeness of the whole year. It is important to note that the sampling threshold mentioned in this paragraph concerns each monthly average within a regional time series, contrasting with the sampling threshold we use for the (multi-)decadal average on each grid cell in the horizontal climatologies.

A first step in the assessment of the methodology consists of testing the monthly representativeness of the IAGOS-DM mean values, in order to evaluate the temporal consistency between IAGOS-DM and MOCAGE-M. For this purpose, as mentioned at the end of Sect. , we compared MOCAGE-M to a test product derived by calculating monthly averages from the simulation daily outputs, after applying a mask based on the IAGOS daily sampling. For this test, the chosen period spreads from 2003 to 2007 inclusive, an uninterrupted measurement period for both ozone and CO. Concerning the mean 3-D distributions, a mean normalized difference between the two products has been found below 1.7 % for each season and each species. In absolute values, 10 % of the yearly mean biases are greater than 6.0 % (4.1 %) for ozone (CO), and 1 % are greater than 13.1 % (10.3 %). Seasonal mean biases are characterized by a 90th percentile generally lower than 10 %, and a 99th percentile from 14.7 % up to 22.8 % for ozone and from 13.0 % up to 18.1 % for CO. The maximum values correspond to winter and spring. Concerning the seasonal cycles, the relative difference between the two MOCAGE products was found to be almost systematically below 5 %, and amongst all the regions, its ozone values seldom reach past 10 %, with a maximum value at 15.2 %. In conclusion of this comparison, the similar results obtained between MOCAGE-M and the test product suggested that in most cases, the IAGOS-DM monthly means could be considered as representative of the month.

Figures 3 and 4 show the yearly mean climatologies, respectively, for O3 and CO, and the model relative biases. The latter are defined as the model bias normalized to the average between the two data sets and are provided in percentages in these figures. Level 22 is seldom reached by the IAGOS measurements, and levels 27 and 28 are sampled only in the vicinity of airports. Thus, only levels 26 up to 23 are represented in these figures. Additionally, the seasonal mean climatologies are available in Appendix .

In Fig. , IAGOS-DM and MOCAGE-M show similar geographical structures. In the tropics and subtropics, the O3 amounts are close, with consistent poleward gradients. Both have maxima located above northeastern Canada. The O3 mixing ratio in the northern midlatitudes is underestimated in the model for levels 24–26, and close to the observations for level 23. The seasonal climatologies in Figs. – show that this feature is representative of spring and fall, whereas ozone tends to be underestimated (overestimated) in all vertical grid levels in summer (winter). Note that the discontinuity over Greenland is due to its topography causing a steep elevation of the vertical grid levels.

In Fig. , CO also shows a good correlation between the two data sets, notably with the same maxima and minima locations. But the CO mixing ratio is generally overestimated by the model, especially over East Asia and India. In the northern midlatitudes, the seasonal climatologies in Figs. – generally show an overestimation in winter and spring and a less-visible underestimation in summer and fall.

Figure  proposes a synthesis for the comparison between the yearly climatologies over the whole period. The same figures can also be found for each season in Appendix . The linear regression parameters indicated in the graphs show a strong geographical correlation, its coefficient spreading from 0.73 up to 0.97. The correlation is better for O3 (≥0.92) and at higher levels for both species. Consequently, the geographical distributions in O3 and CO are well reproduced in the simulation. Their stronger correlations at higher levels suggest a remarkably good consistency of the modelled stratospheric composition with the observations, showing its ability to simulate stratospheric chemistry and transport. The same feature is visible with the regression fit, showing a lower bias for O3, and at highest levels. With respect to the 1:1 line, levels 25 and 26 are characterized by an overestimation of the lower part of the O3 distribution (<120 ppb) and by an underestimation of the higher part, more pronounced during boreal summer according to Fig. . A possible reason is that the summertime tropopause altitude in these regions can be overestimated by the model, or that the vertical stability is underestimated. These biases have been largely improved with the most recent version of MOCAGE used to run CCMI phase 2 simulations. Concerning CO, the highest values (generally >100 ppb) correspond to the strongly emitting and convective regions: South Asia, East Asia and tropical Africa. Figure  allows us to identify the high mixing ratios close to the 1:1 line at tropical African points, whereas the high mixing ratios with a positive bias were associated with both South and East Asian areas. The latter can be due to an overestimation of convection in this region and/or an overestimation in the inventory for Asian emissions. On the contrary, CO above tropical Africa shows good results, indicating a realistic combination between convection and emissions.

The method proposed in this paper to evaluate MOCAGE REF-C1SD against IAGOS data in the UTLS aims at being applied to other chemistry–climate simulations, like the REF-C1SD simulations from other models. Since IAGOS is mapped onto the model vertical grid, the latter differing from one model to another, we also plotted a synthetic regression in Fig. , where all the points at all levels have been gathered into a single scatterplot. This summarized model performance concerning mean spatial distributions includes the final products of our evaluation methodology for climatologies. From the whole ensemble of ∼13000 (∼12500) sampled grid points for O3 (CO), the correlation shows a good agreement between the simulation and the observations, especially for O3 (r=0.95). Its regression fit is dominated by an overestimation for lower values (<100 ppb) and an underestimation for higher values, especially between 200 and 300 ppb. Above 350 ppb, the balance between overestimated and underestimated O3 values tends to be more balanced.

Table  gives a synthesis of the biases and associated deviations, for the assessment of MOCAGE-M versus IAGOS-DM. The yearly MNMB equals -0.012 for O3 and 0.049 for CO, demonstrating a very good estimation of these two species in the UTLS on a hemispheric scale, especially for O3. More precisely, it shows a balance between positive and negative normalized biases. The yearly fractional gross error (FGE), corresponding to the averaged normalized bias absolute value is also low, with 0.150 and 0.112 for O3 and CO, respectively. The seasonal patterns show that metrics linked with CO biases (MNMB and FGE) generally yield values closer to 0, compared to O3. The O3 seasonal behaviour is characterized by a balance between opposite seasons: the most positive (negative) bias takes place in winter (summer) and is equal to 0.144 (-0.169), whereas the less negative (positive) bias takes place in spring (fall) and is equal to -0.033 (0.027). CO mixing ratio is slightly overestimated in winter and spring similarly (MNMB=0.098), with lower biases during summer (-0.011) and fall (0.024). Nevertheless, all MNMB and FGE are very low, showing good skills from the MOCAGE REF-C1SD simulation.

Table  compares the assessment of MOCAGE-M versus IAGOS-DM with the assessment of MOCAGE-M-noW versus IAGOS-DM-noW versions. This comparison is based on the IAGOS-DM-noW sampling level. In Table , the comparison between the two methods shows a better agreement between the model and the observations when we apply the interpolation with the weighting factors. The O3 correlation with the “noW” products decreased to 0.84–0.92 compared to the 0.90–0.95 derived from our method, and the CO correlation dropped from 0.72–0.81 down to 0.63–0.72. The MNMB and the FGE show better scores for the “noW” products in each case, except the O3 MNMB in DJF. The general improvement of normalized biases, normalized errors and spatial correlations, compared to a simplified gridding method, suggests that the use of a weighting function in our methodology can significantly enhance the model assessment.

In this section, we attempt to evaluate the simulation in the UT and the LS separately, focusing on the seasonal cycles. For this, we sort both data sets between the two layers as explained in Sect. . As a first step, before comparing the simulation to the observations, we analyse the impact of the mapping method for IAGOS onto the MOCAGE grid on a monthly basis. For this purpose, two versions of the IAGOS data set are used. Hereafter, IAGOS-HR refers to the high-resolved IAGOS data synthesized in , where every single measurement was categorized as belonging to the UT (PTP+15hPa<P<PTP+75hPa), the tropopause transition layer or the LS (P<PTP-15hPa), and where regional monthly means were derived by averaging all the concentrations measured above the defined region. In contrast, IAGOS-DM refers to the new product presented in this paper, i.e. the IAGOS data distributed onto the model's grid, then assigned to either the UT or the LS based on the monthly averaged PV at each model grid point. Note that IAGOS-HR seasonal cycles were computed on the original regions' coordinates, but the changes induced by the 1∘ difference in some of the regions are expected to be negligible, based on the geographical sensitivity tests mentioned in .

The comparison between the two IAGOS products in the matter of seasonal cycles is proposed in Figs.  and , respectively, for O3 and CO. They are shown with their corresponding interannual variability (IAV), defined as a year-to-year standard deviation. For complementary information, a more exhaustive representation is proposed in Figs.  and in the Appendix, showing the results with each region in a distinct panel. In Fig. , both IAGOS versions show a summertime O3 maximum in the UT and a springtime maximum in the LS. A lessened contrast between the UT and the LS is observed in IAGOS-DM. In the UT, the O3 volume mixing ratio and its interannual variability are higher in IAGOS-DM than in IAGOS-HR for the winter and fall seasons (∼60±20 compared to ∼50±10 ppb), whereas they are similar in spring and summer. In this layer, the most important differences between the two versions thus take place during lower-ozone seasons.

In the LS, the O3 amounts are lower in IAGOS-DM (∼110–375 ppb) than in IAGOS-HR (∼150–450 ppb) during the whole year. There are two main reasons that explain the lower O3 amounts in the LS and the higher amounts in the UT in IAGOS-DM compared to IAGOS-HR. The first is the projection of IAGOS observations with a very fine vertical resolution onto the MOCAGE vertical grid with a ∼800 m vertical resolution. Second, the use of a monthly PV cannot provide the description of the day-to-day variations of the tropopause altitude, whereas the latter can be important to sort the data points between the two layers. In other words, the effect of time averaging leads to a loss of tropopause sharpness, thus resulting in a misclassification of a non-negligible part of the individual measurements. For a given layer, it introduces a bias due to unexpected mixing with another layer. Figure  also makes it possible to compare the behaviour of each region. In the LS, the differences between northern and southern regions shown in IAGOS-HR are generally also visible in IAGOS-DM. The regional behaviours discussed in , i.e. the low summertime O3 mixing ratio in the northwestern North American UT and in the Middle Eastern LS remain visible in IAGOS-DM, although the last one is substantially less pronounced. We also note high ozone values in November in the Siberian UT seen by IAGOS-DM only. It is linked to a strong positive anomaly in November 1997 due to an upper-layer air mass that could not be differentiated to the UT, and weakly balanced by the average with too few other years. In Fig. , the CO seasonal cycles in the UT are consistent between IAGOS-HR and IAGOS-DM, with a generally low difference, a common springtime maximum and a consistent inter-regional variability: a higher CO level in the two regions on the Pacific coast (northwestern North America and northeastern Asia), higher summertime amounts in northeastern Asia, and lower CO levels in one of the two southernmost regions (the Middle East). Note that the monthly resolution of both PV and filtering leads to a lessened sampling in the UT in IAGOS-DM. In the North Atlantic region where aircraft trajectories describe a narrow altitude range, the resulting seasonal cycles were incomplete, so we chose to exclude them from both figures. We applied the same treatment to CO in the UT above the western Mediterranean Basin and Siberia, where the level of sampling during winter and spring (not shown) is insufficient to provide complete seasonal cycles. In the LS, the CO mixing ratio is always higher in IAGOS-DM (∼50–95 ppb) than in IAGOS-HR (from ∼40 up to 65 ppb). In IAGOS-HR, a seasonal cycle is noticeable only in the Middle East and northeastern Asia, whereas it is the case for almost every region in IAGOS-DM. The influence of the troposphere is increased in IAGOS-DM, with a high peak in May for the western Mediterranean Basin, in June–July for northeastern Asia and in July for Siberia, likely related to the effects of boreal biomass burning in the latter. Thus, mapping the observations onto the model grid significantly changes the CO seasonal cycles in the LS.

As for O3, the reason why the CO amounts in IAGOS-DM are higher in the LS and lower in the UT comes from the coarse vertical resolution in the MOCAGE grid and from the uncertainty when sorting the UT data from the LS data using a monthly mean modelled PV field. More generally, the comparison between IAGOS-HR and IAGOS-DM for O3 and CO clearly shows that the processing applied for mapping the IAGOS high-resolution data set onto the MOCAGE coarse grid slightly modifies IAGOS characteristics. This processing, which enables a meaningful comparison between IAGOS long-term measurements and the REF-C1SD simulation, acts as a numerical filter. It is important to note that the seasonal cycles in IAGOS-DM generally show values ranging between the MOCAGE-M and the IAGOS-HR cycles, such as the yearly means in Table , especially in the LS where the mean O3 bias drops from 84 ppb with IAGOS-HR down to 19 ppb with IAGOS-DM. The correlation in time also tends to be enhanced by the use of the IAGOS-DM product. It confirms that the representation derived from IAGOS-HR cannot be reached by a model with the typical REF-C1SD resolution, especially for CO in the LS, but some main characteristics mentioned above can still be used as criteria. Last, the comparison synthesized in Table  also shows a better consistency between model and observations when our method is applied, mainly in the matter of biases in the LS. No significant change is observed in the UT.

We now assess the MOCAGE-M seasonal cycles by comparing them to IAGOS-DM. As complements to Fig. , statistical results are given in Table . Note that averages calculated over all represented regions have been computed only to synthesize the assessment and to provide a quantification that confirms some features seen in the figures. As they are similar to the zonal averages, they are not meant to have a geophysical signification. A qualitative summary is also provided in Table . In the UT, MOCAGE-M shows a springtime maximum and higher O3 concentrations (from ∼120 ppb up to 130 ppb), instead of the observed summertime maximum (the season in which O3 values range between ∼80 and 110 ppb). Adding the fact that simulated O3 levels are particularly strong in the northernmost regions (western North America and Siberia) where the stratosphere at the cruise levels is richer in O3, it is likely that the stratospheric influence on the UT is overestimated in the simulation. The inter-regional averages shown in Table  confirm the significant difference between the two data sets in the UT, both from the O3 mixing ratio (97±5 ppb in MOCAGE-M compared to 72±9 ppb in IAGOS-DM) and from the seasonality (r‾=0.35). In the LS, the simulation reproduces well the cycles including the seasonality (r‾=0.84 as shown in Table ), the magnitude, the amounts of ozone (203±23 ppb compared to 222±36 ppb from IAGOS-DM) and the inter-regional differences. The latter are characterized in both data sets by lower ozone levels in the two southernmost regions (western Mediterranean Basin and the Middle East) and higher ozone levels in the two northernmost regions (western North America and Siberia). Without the noisy signal characterizing western North America and the western Mediterranean Basin in IAGOS-DM, the interval representing the springtime interannual variabilities spreads from ∼200 ppb up to ∼400 ppb in both data sets, showing another feature well reproduced by the model. Though on a yearly basis, according to Table , the model tends to underestimate ozone IAV on average by a factor of 1.6.

The modelled CO seasonal cycles (Fig. ) in the UT show similarities with the observations (IAGOS-DM), including the higher concentrations in the two Pacific coast regions (western North America and northeastern Asia), the strong summertime concentrations in northeastern Asia and also comparable mixing ratios between the model and the IAGOS-DM observations in most regions, as confirmed by Table . However, the simulation overestimates the CO mixing ratios in the two Pacific coast regions, and the seasonal maxima generally take place during late winter–early spring in the simulation, earlier than the observed middle-of-spring maxima. The seasonal minima are in phase with the observations. In the LS, the seasonal cycles' magnitude is underestimated by the simulation but the overall bias remains relatively low, with a 73±5 ppb average for MOCAGE-M compared to 69±9 ppb for IAGOS-DM. In most regions, MOCAGE-M shows seasonal cycles in the LS in phase with the UT, thus contrasting with the observations and making the correlation drop from 0.64 in the UT to 0.31 in the LS. This suggests that the model simulation is affected in the LS by transport from the troposphere during springtime. Consistently with observations, MOCAGE-M shows a summertime maximum in northeastern Asia exclusively. Although part of this feature may originate from the positive bias in the UT, the fact that it only concerns the summer season, in contrast to the UT, suggests that summertime convection also plays a non-negligible role.