2.1 The assimilated MLS observations

The BRAM2 reanalysis is based on the assimilation of observations taken by the Microwave Limb Sounder (MLS; Waters et al., 2006) operating on NASA’s Aura satellite. MLS measures vertical profiles of around 15 chemical species. For BRAM2, the following species have been assimilated: O₃, H₂O, N₂O, HNO₃, HCl, ClO, CO and CH₃Cl. Other MLS species are not considered because, with the exception of OH, they either are available over only a limited vertical range or require substantial averaging prior to use in scientific studies. OH profiles have not been assimilated because modelled OH is more controlled by the atmospheric conditions (e.g. temperature) and the state of long-lived species (in particular H₂O) than by its initial conditions. While a similar situation holds for ClO in the middle stratosphere (here the long-lived species would be HCl), this is not the case in conditions of chlorine activation, such as in the lower-stratospheric polar vortex.

MLS was launched in July 2004 and provided its first profiles in August of that year. At the time of writing, the instrument is still in operation despite showing some ageing degradation. Around 3500 vertical profiles are delivered every day, measured during daytime and night-time. In this paper, we have used version 4.2 (v4) of MLS profiles as described in Livesey et al. (2015, denoted L2015 hereafter). Each MLS profile is checked before assimilation according to the recommendations given in L2015. Profiles are only assimilated in the vertical range of validity given in L2015 and reported in Table 4. Profiles, or part of them, are discarded if the “estimated precision”, “quality”, “convergence” and “status” are outside the ranges given in L2015. In particular, this screening discarded profiles contaminated by clouds, mainly for O₃, HNO₃ and CO. ClO profiles show biases at and below 68 hPa and have been corrected according to L2015.

MLS O₃ profiles exhibit vertical oscillations in the tropical tropopause layer (TTL; see L2015; Yan et al., 2016). Although improvements have been made in v4 compared to previous versions, this problem has not been eliminated (see Sect. 5.4). The BASCOE chemistry transport model (CTM) also suffers from an ozone deficit around 1 hPa that assimilation cannot correct. This led us to assimilate MLS O₃ observations only at altitudes below (i.e. pressure greater than) 4 hPa (see Sect. 3.1).

In MLS v4, the standard product for N₂O is derived from radiances measured by the 190 GHz radiometer. Previous MLS data versions used the 640 GHz radiometer, which provided slightly better quality, but this product ceased to be delivered after August 2013 because of instrumental degradation in the band used for that retrieval. For BRAM2, the 190 GHz N₂O product is assimilated for the whole period to avoid discontinuity when switching between different products.

CO profiles suffer from several artefacts as reported by L2015. They show a positive systematic error of 20 %–50 % in the mesosphere and a negative systematic error of 50 %–70 % near 30 hPa. Between 1 and 0.1 hPa, profiles are rather jagged. There is also a tendency toward negative values below levels where CO abundances are large, especially in the polar vortex when high concentrations of CO descend to the mid-stratosphere. No corrections have been applied to resolve these artefacts because none are recommended by the MLS team. Although BRAM2 has assimilated MLS CO within its recommended vertical range of validity (0.0046–215 hPa), BRAM2 CO will be evaluated only where CO is relevant for stratospheric dynamics, i.e. in the polar vortex above 10 hPa and in the upper troposphere–lower stratosphere (UTLS).

The error budget of each species has also been estimated by L2015. This information is given as uncertainty profiles of accuracy and precision, and it will be used in the validation of the BRAM2 products. Note that L2015 provides 2σ accuracy and 1σ precision. In this paper, we are using the 1σ uncertainties for both the accuracy and precision.

2.2 Independent observations used for validation

3.1 BASCOE

The BRAM2 reanalysis has been produced by the assimilation of MLS observations using the Belgian Assimilation System for Chemical ObsErvations (BASCOE; Errera et al., 2008; Errera and Ménard, 2012; Skachko et al., 2014, 2016). The system is based on a chemistry transport model (CTM) dedicated to stratospheric composition which includes 58 chemical species. For BRAM2, dynamical fields are taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis (Dee et al., 2011). The model horizontal resolution is 3.75^∘ longitude ×2.5^∘ latitude. The vertical grid is represented by 37 hybrid pressure levels going from the surface to 0.1 hPa, which are a subset of the ERA-Interim 60 levels. The vertical resolution is around 1 km at 100 hPa, 1.5 km in the middle stratosphere and increases to 5 km above 1 hPa. In the troposphere, the resolution is around 1.5 km. ERA-Interim is preprocessed to the BASCOE resolution ensuring mass flux conservation (Chabrillat et al., 2018). The model time step is 30 min. All species are advected by the flux-form semi-Lagrangian scheme (Lin and Rood, 1996). Around 200 chemical reactions (gas phase, photolysis and heterogeneous) are taken into account and the gas-phase and photolysis reaction rates have been updated according to Burkholder et al. (2011).

As for many other models (SPARC, 2010, see their Fig. 6.17), the BASCOE model suffers from an ozone deficit in the upper stratosphere–lower mesosphere. Skachko et al. (2016) showed that around 1 hPa, BASCOE underestimates MLS ozone by ∼20 %. They also pointed out that this deficit cannot be corrected by the assimilation of observations because the ozone lifetime is much shorter than the revisit time of MLS, typically 12 h between an ascending and a descending orbit of the Aura satellite. It turns out that assimilation of ozone in this altitude region introduces spatial discontinuities in the ozone fields around the locations of the most recent observations. For this reason, MLS O₃ observations at an altitude above 4 hPa have not been assimilated, and the BRAM2 ozone will not be discussed above that level.

The microphysics of polar stratospheric clouds (PSCs) and their impact on the chemistry is taken into account by a simple parameterization as described in Huijnen et al. (2016) but with several updates. In its original implementation, the BASCOE CTM overestimated the loss of HCl by heterogeneous chemistry (in contrast to other models which underestimate the loss of HCl; see Wohltmann et al., 2017; Grooß et al., 2018). Preliminary experiments prior to BRAM2 showed that data assimilation was not able to correct for this bias (not shown). The parameters of the Huijnen et al. (2016) formulation have been tuned by trial and error through CTM simulations of the Antarctic winter 2008. The best setup found includes the following updates: (1) nitric acid tri-hydrate (NAT) PSCs are assumed to exist when the ratio between HNO₃ vapour pressure and the equilibrium vapour pressure exceeds a supersaturation ratio set to 10 as in Considine et al. (2000), compared to 1 in the original setting; (2) the NAT surface area density has been reduced from $\mathrm{2}\times {\mathrm{10}}^{-\mathrm{7}}$ to 10⁻⁷ cm² cm⁻³; (3) the characteristic timescale of NAT sedimentation has been reduced from 20 to 10 d. BASCOE CTM results with this setup are discussed in Sect. 5.2.

Condensation of water vapour is approximated by capping its partial pressure to the vapour pressure of water ice (Murphy and Koop, 2005).

Two data assimilation methods have been implemented in BASCOE: 4D-Var (Errera and Ménard, 2012) and ensemble Kalman filter (EnKF) (Skachko et al., 2014, 2016). BRAM2 uses the EnKF method because this implementation offers a better scalability than 4D-Var on cluster computers. EnKF provides an estimation of the analysis uncertainty based on the standard deviation of the ensemble state. These values have not been evaluated here and will be the subject of a future study. For this reason, the standard deviation of the ensemble is not provided in the BRAM2 dataset. The EnKF implementation in BASCOE cycles through the following steps.

1. At the initial time, an ensemble of 20 members is generated based on 20 % Gaussian perturbations of a given model initial state.

2. Each ensemble member is propagated in time using the BASCOE CTM to the next model time step.

3. If MLS observations are available at the current model time step, add a perturbation to each ensemble member (see Sect. 3.2).

4. Save the ensemble mean and its variance (see Sect. 3.4).

5. If MLS observations are available, the EnKF equation is solved to compute the analysis for each ensemble member.

6. Steps 2 to 5 are repeated until the last model time step is reached.

3.2 EnKF setup

BRAM2 is the result of four streams (or runs) that have been produced in parallel to reduce the production time. The first stream starts on 1 August 2004, a few days before the first available MLS observations. The next three streams start on 1 April 2008, 2012 and 2016. Streams 1–3 end on 1 May 2008, 2012 and 2016, allowing 1 month of overlap between each stream. The fourth stream currently ends on 1 January 2018 and will be extended.

Initial conditions are taken from a 20-year BASCOE CTM simulation where boundary conditions for tropospheric source gases (e.g. CH₄, N₂O or chlorofluorocarbons – CFCs) vary as a function of latitude and time (Meinshausen et al., 2017). The 20 ensemble member states are calculated by adding spatially correlated perturbations to the initial conditions as described in Skachko et al. (2014).

The BASCOE setup used to produce BRAM2 is almost identical to the experiments performed by Skachko et al. (2016) since both studies assimilate the same observations with the same model. Horizontal and vertical localization length scales are defined as the L_h=2000 km and L_v=1.5 model levels, respectively. Note that correlations between the species are not taken into account in BASCOE EnKF. Except for HNO₃, BASCOE uses a background quality check (BgQC; Anderson and Järvinen, 1999; Skachko et al., 2016), which rejects any observation if its departure from the mean ensemble state and is 5 times the combined error of the observations and the background. For HNO₃, the BgQC was turned off because preliminary experiments prior to BRAM2 had shown better observation-minus-forecast statistics without this setup.

Figure 1Time series of the monthly mean χ² for MLS assimilated species (coloured lines – one colour for each stream, left y axes) and the corresponding number of assimilated observations m (grey bars, right y axes). The horizontal black lines show the expected theoretical value of χ²=1 and the vertical black lines show the dates of the transition between the different streams.

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The system includes two adjustable parameters that need to be calibrated: the model error parameter α and the observational error scaling factor s_o. The model error is calibrated using a χ² test. At a given model time step k, ${\mathit{\chi }}_k^{\mathrm{2}}$ measures the difference between the observations and the model forecast weighted by their combined error covariances. Ideally, if the covariances are correctly specified and if the model is un-biased, the average ${\mathit{\chi }}_k^{\mathrm{2}}$ should be close to the number of observations m_k, i.e. $〈{\mathit{\chi }}_k^{\mathrm{2}}/m_k〉\sim \mathrm{1}$, where 〈〉 denotes the mathematical expectation. Ménard and Chang (2000) have shown that the slope in the time series of $〈{\mathit{\chi }}_k^{\mathrm{2}}/m_k〉$ is sensitive to the model error parameter α while the time average of $〈{\mathit{\chi }}_k^{\mathrm{2}}/m_k〉$ is sensitive to the observational error. For MLS assimilation using BASCOE, Skachko et al. (2014, 2016) found a single value of α=2.5 % for each assimilated species and each model grid point, and the same value was used for BRAM2. For the observational error scaling factor, a vertical profile for each species has been calibrated using the Desroziers’ method (Desroziers et al., 2005) as implemented in Skachko et al. (2016).

Figure 1 shows the time series of the monthly mean $〈{\mathit{\chi }}_k^{\mathrm{2}}/m_k〉$ for the four streams. The total number of monthly assimilated observations is also shown. For all species, the χ² time series are stable, as expected. This validates the choice of α=2.5 %. For CH₃Cl, CO, HCl and N₂O, the values are very close to 1. For ClO and HNO₃, the values are slightly higher than 1 (around 1.1), and for H₂O and O₃, the values are slightly lower than 1 (around 0.95 and 0.9, respectively). The χ² time series for HNO₃ and O₃ also display seasonal variations of small amplitude (<0.1). Overall, these deviations are relatively small, e.g. when comparing with a χ² test obtained by the Tropospheric Chemical Reanalysis (Miyazaki et al., 2015). This validates the implementation of Desroziers’ method to adjust the observational error scaling factors. Note that the transition between the streams does not display visible discontinuities in the χ² time series, which validates the choice of a 1-month overlap between the streams. (Values for the first month of streams 2–4, which overlap with the last month of streams 1–3, are not shown.)

Figure 2Time series of monthly mean observational error scaling factors s_o for each assimilated species at five specific MLS levels: 1, 3.1, 10, 31 and 100 hPa. The vertical black lines show the dates of the transition between the different streams.

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Figure 2 shows the time series of the observational error scaling factors estimated by Desroziers’ method for all species at five selected pressure levels. Values higher (lower) than 1 indicate that the MLS error has been increased (decreased) by the Desroziers’ method. Values are usually between 0.5 and 1.5 except for O₃ at 100 hPa, which has a value between 2 and 3, this being likely due to vertical oscillation in MLS O₃ profiles in the TTL (see Sect. 5.4). The time series display seasonal variations for some species and/or levels usually with a 6-month period attributed to both polar winter seasons. As for the χ² test, no discontinuities are visible at the transition time between the streams. For some species and/or levels, the time series show a small positive drift. The cause of this drift has not been identified but is unlikely due to an issue in the BASCOE system. In such a case, this would have resulted in discontinuities at the dates of transition between the streams in the time series of the observational error scaling factors. This issue has not been investigated further in this paper.

3.3 BASCOE observation operator

The observation operator of BASCOE consists of a linear interpolation of the model state to the geolocation of the observed profile points available at the model time ±15 min, i.e. half of the model time step. It has been used to save the BRAM2 state in the space of MLS as well as in the space of the independent observations, except for NDACC ozonesondes, during the BRAM2 production. For NDACC ozonesondes, the BRAM2 state has been interpolated to the NDACC station from the 6-hourly BRAM2 gridded outputs. The error introduced by this method is negligible for O₃ below 10 hPa where ozonesondes are used (Geer et al., 2006). Note that no averaging kernels of any satellite dataset have been used in the BASCOE observation operator because the BASCOE EnKF is not ready for their use. The vertical resolution of these observations is sufficiently high – and similar to the model vertical resolution – that their use is typically considered unnecessary. We will see, however, that this is not always the case (see Sect. 5).

3.4 BRAM2 outputs

BRAM2 gridded outputs are the 6-hourly mean of the ensemble state. A second type of output is given in the space of the observations (see previous section) and will be referred to below as model-at-observation or, in short, ModAtObs. All outputs (gridded and ModAtObs) are taken at step 4 of the assimilation cycle (see Sect. 3.1), i.e. corresponding to the background state. For the gridded outputs, this allows the last model forecast to smooth any discontinuities at the edge of regions influenced by observations. For ModAtObs outputs, this means that all comparisons between BRAM2 and observations shown in this paper are using the background state.

3.5 Control run

For this publication, a control run has been produced, labelled CTRL. It is a BASCOE CTM simulation using the same configuration as BRAM2, covering the period May 2009–November 2010, and initialized by the BRAM2 analysis. CTRL is used to evaluate the added value of the assimilation compared to a pure model run where an 18-month simulation is sufficiently long. It will also indicate model processes that need to be improved in the future.

5.1 Middle stratosphere (MS)

The evaluation of BRAM2 in the middle stratosphere is based on two figures and one table: one figure showing vertical profiles of the FmO (Fig. 5), the other showing time series of the FmO at selected pressure levels (Fig. 6) and the table providing the FmO statistics at three pressure levels (Table 1). The first figure shows the mean and standard deviation of the FmO between BRAM2 and observations from MLS, ACE-FTS, MIPAS, MLS_N2O_640, SMILES ClO and ozonesondes. These statistics are calculated between 30 and 60^∘ N and between 0.1 and 100 hPa for the 2005–2017 period for MLS and ACE-FTS (FmO profiles between 60 and 30^∘ S and between 30^∘ S and 30^∘ N are given in the Supplement). For MIPAS and MLS_N2O_640, the datasets end in March 2012 and July 2013, respectively. For SMILES, the period is October 2009–April 2010. Note that comparison with MIPAS ClO is only performed in the polar winter conditions (see Sect. 2.2.2). CO is not shown in the figure because it is chemically irrelevant in the middle stratosphere – CO will be discussed in the USPV and the UTLS subsections. The figure also shows two types of error: first, the MLS accuracy and precision which are compared, respectively, to the mean and the standard deviation of the differences, and, second, the mean and standard deviation of the differences between MLS and ACE-FTS (see Sect. 4).

Figure 5Mean (top row) and standard deviation (bottom row) of the differences between BRAM2 and observations from MLS (red lines), ACE-FTS (blue lines), MIPAS (green lines), MLS_N2O_640 (purple lines), NDACC ozonesondes (orange lines) and SMILES (cyan lines). The statistics are in percent, normalized by the mean of BRAM2, and are taken between 30 and 60^∘ N and between 0.1 and 100 hPa. The period is 2005–2017 except for MIPAS (2005–2012), MLS_N2O_640 (2005–2013) and SMILES (October 2009–April 2010). The statistics are calculated for, from left to right, O₃, H₂O, N₂O, HNO₃, HCl, daytime ClO and CH₃Cl. The approximate numbers of observed profiles used in the FmO statistics are given in the upper left corner of the top row plots using the instrument colour code. The grey shaded area in the mean and standard deviation plots corresponds, respectively, to the MLS accuracy and precision, as provided in the MLS data quality document (see L2015). The thin black profiles represent the mean (top row) and standard deviations (bottom row) between MLS and ACE-FTS found in validation publications (see text for details). The horizontal black lines denote levels where time series are shown in Fig. 6.

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The second figure (Fig. 6) shows time series of monthly FmO for the 2005–2017 period corresponding to the 30–60^∘ N latitude band at three pressure levels in the high, middle and lower stratosphere: 0.68, 4.6 and 46 hPa. Statistics shown are the bias against the different instruments and the standard deviation against ACE-FTS (FmO time series between 60 and 30^∘ S and between 30^∘ S and 30^∘ N are given in the Supplement). For ClO, which is not retrieved in ACE-FTS v3.6, the standard deviation against SMILES is shown. The time series are in percent except for ClO, which is in parts per billion by volume. For this species, only daytime observations are taken into account in Figs. 5 and 6.

In general, BRAM2 represents a good proxy for MLS. The biases against MLS are smaller than the MLS accuracy so that they are not significant (Fig. 5). Moreover, the standard deviations against MLS and the MLS precision are usually in good agreement, except for O₃. Time series of the bias against MLS is in general very stable with negligible amplitude in the seasonal variations, except for N₂O at 0.68 hPa, HNO₃ at 4.6 hPa, ClO at 4.6, and 46 hPa and CH₃Cl at 4.6 hPa (see Fig. 6).

The standard deviation against ACE-FTS is usually smaller than the standard deviation against MLS, which indicates that the variability in MLS observations is larger than that in ACE-FTS (Fig. 5). Also, the standard deviation against ACE-FTS is usually stable in time (Fig. 6).

The biases against ACE-FTS are in general similar to the differences between MLS and ACE-FTS calculated in published validation studies (see Sect. 4), except for HCl, N₂O above 3 hPa, HNO₃ above 10 hPa and CH₃Cl above 20 hPa. This means that most of the differences between BRAM2 and the independent observations are due to the difference between these datasets and MLS. Also, the standard deviations against ACE-FTS are as good as or better than those from direct comparisons between MLS and ACE-FTS (except for O₃ below 40 hPa). This suggests that a significant part of the standard deviations of (MLS − ACE-FTS) calculated in validation studies are due to sampling error introduced by the collocation approach. In our case, the sampling error is replaced by the representativeness error arising from the limited spatial and temporal resolution of the data assimilation system. We thus conclude that the representativeness errors within BRAM2 are smaller than the sampling errors inherent in validation studies based on collocation of profiles.

Thus, in general, BRAM2 mean values and their variability agree well with the observations. Let us now discuss these statistics from species to species (see Sect. 5.5 for a summary of BRAM2 evaluation in the different regions). This discussion is mainly qualitative and the reader should refer to Table 1 for quantitative results.

Figure 6Time series of the monthly mean differences between BRAM2 and the different observational datasets in the Northern Hemisphere mid-latitudes (30–60^∘ N) at three pressure levels (from left to right: 0.68, 4.6 and 46 hPa) and for (top to bottom) O₃, H₂O, N₂O, HNO₃, HCl, daytime ClO and CH₃Cl. Values are in percent except for ClO, which is shown in parts per billion by volume. The grey shaded area represents the standard deviation of the differences between BRAM2 and ACE-FTS except for ClO, where SMILES data are used.

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Table 1Mean difference and standard deviation between BRAM2 and observations for the 2005–2017 period at three specific levels in the middle stratosphere: 0.68, 4.6 and 46 hPa. Values are in percent, normalized by BRAM2. Abbreviations: not assimilated (N.A.) and not observed (N.O.).

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O₃. We recall that ozone is not assimilated at altitudes higher (i.e. pressure lower) than 4 hPa due to a BASCOE model ozone deficit (see Sect. 3.1), and comparisons above that level are not shown. Below that level, BRAM2 agrees well with all instruments (see Fig. 5 and Table 1). The bias against MLS is almost negligible and is negative against ACE-FTS and MIPAS. The standard deviation profiles against MLS, ACE-FTS and MIPAS are similar, and the standard deviation against ozonesondes is usually larger by around 5 %, likely due to the higher representativeness of ozonesondes against the model.

Time series of the bias against all instruments at 4.6 and 46 hPa are stable from year to year, with small seasonal variations (substantially higher against MIPAS at 4.6 hPa; see Fig. 6). The standard deviations against ACE-FTS are small with small seasonal variations at 4.6 and 46 hPa. Similar statistics are found in the southern hemispheric mid-latitudes and in the tropics above 50 hPa (see Figs. S1–S4 in the Supplement). Given the good agreement between BRAM2 and the observations, we recommend BRAM2 O₃ in the MS for scientific use between 4 and 100 hPa.

H₂O. The general agreement with MLS, ACE-FTS and MIPAS is very good below 1 hPa (see Fig. 5 and Table 1). Above that level, the bias against MIPAS increases, as well as the standard deviation against MLS and MIPAS, although to a reasonable extent in all cases. Bias against ACE-FTS is positive below 0.5 hPa.

Biases are stable over time, with small seasonal variation between 0.68 and 46 hPa (Fig. 6). Standard deviations against ACE-FTS are also stable, displaying negligible seasonal variations. As for ozone, similar statistics are found in the Southern Hemisphere and in the tropics above 50 hPa (see Figs. S1–S4), and we recommend BRAM2 H₂O for scientific use in these regions.

N₂O. At altitudes below 10 hPa, BRAM2 agrees well with MLS and independent observations from MLS_N2O_640, ACE-FTS and MIPAS (Fig. 5 and Table 1). From 10 hPa to higher altitudes (i.e. lower pressure), the standard deviations increase with larger values against MLS and MLS_N2O_640 than against ACE-FTS and MIPAS.

Above 3 hPa, BRAM2 is poorly characterized by comparison against observations. The standard deviations against MLS are large (Fig. 5), and the time series of the FmO are noisy, with large peak-to-peak variations. In the upper stratosphere, the MLS precision degrades to around 65 % at 2 hPa, which limits the constraint of the assimilated observations on the reanalysis.

Bias between BRAM2 and MLS is stable (and small) over time at 4.6 hPa (Fig. 6), which is not the case for the bias between BRAM2 and the independent observations. At 46 hPa, the bias with independent observations is small but increases over the time, suggesting a positive drift in BRAM2. Analyses of the deseasonalized time series of the biases reveal a significant drift of −5, −7 and −5 % per decade against ACE-FTS, MIPAS and MLS_N2O_640 for the period 2005–2012 and −10 % against ACE-FTS for 2005–2017 (not shown). This drift has been mentioned in Froidevaux et al. (2019) and is under investigation by the MLS team (Livesey et al., 2019).

Similar agreement is found in other latitude regions (see Figs. S1–S4). Therefore at altitudes below 3 hPa and excluding trend analysis, we recommend BRAM2 N₂O for scientific use in the middle stratosphere. At altitudes above 3 hPa, BRAM2 should not be used without consulting the BASCOE team.

HNO₃. At altitudes below 10 hPa, BRAM2 agrees well with MLS, ACE-FTS and MIPAS (see Fig. 5 and Table 1). From 10 to 3 hPa, the bias against ACE-FTS and MIPAS increases to relatively large values (with a negative sign), as well as the standard deviation against MLS. On the other hand, the standard deviations against ACE-FTS and MIPAS remain relatively small. Above 3 hPa, BRAM2 HNO₃ is poorly characterized by comparison against observations. The biases against the three instruments are large and disagree in sign and size (Fig. 5). Above that level, MLS precision degrades (reaching 0.6 ppbv, i.e. 6 times the typical amount of HNO₃ at that level), and the constraint by the assimilated observations on BASCOE is weak.

These values are stable over the years at 4.6 and 46 hPa, while displaying significant seasonal oscillations at 4.6 hPa (Fig. 6). These seasonal variations may be due to including observations belonging to the polar vortex or the tropical region, the 30–60^∘ N region not being a strict definition of the mid-latitudes of the Northern Hemisphere. At 4.6 hPa, the standard deviation against ACE-FTS displays significant seasonal oscillation, from ∼10 % during summer to ∼20 % during winter, likely due to the polar influence during the winter.

Similar statistics are found in the Southern Hemisphere and in the tropics (see Figs. S1–S4), and we recommend BRAM2 HNO₃ for scientific use between 3 and 100 hPa. The use of BRAM2 HNO₃ above 3 hPa should be carried out in consultation with the BASCOE team.

HCl. BRAM2 agrees well with MIPAS and ACE-FTS between 0.4 and 70 hPa (see Fig. 5 and Table 1, this pressure range being reduced to 0.4–50 hPa in the tropics; see Fig. S2). At 100 hPa, the bias increases (positive values) with larger values in the tropics than at mid-latitudes. The difference (BRAM2 − ACE-FTS) is larger by around 5 % than the difference (MLS − ACE-FTS) from Froidevaux et al. (2008b). This is due to the different versions of MLS and ACE-FTS used here (v4 and v3.6, respectively) and in Froidevaux et al. (2008b, using v2 and v2.2) and the fact that the HCl amount has been reduced by around 5 % in the latest version of ACE-FTS data (Waymark et al., 2013). Our comparison is thus an update of Froidevaux et al. (2008b). Also note the lower standard deviation against ACE-FTS than against MLS, suggesting higher precision of ACE-FTS compared to MLS.

Bias time series are very stable against MLS with negligible seasonal variations (Fig. 6). On the other hand, a small drift is noticeable in the bias time series against ACE-FTS at 0.68 and 4.6 hPa. The MLS HCl v4 standard product assimilated for BRAM2 is retrieved from band 14 of the 640 GHz radiometer, while band 13 – more sensitive to HCl – was originally planned. This change of strategy by the MLS retrieval team was due to the deterioration of band 13, which was turned off in 2006 (see L2015). For this reason, the MLS HCl values from band 14 (and BRAM2) are not suited for detailed trend studies in the USLM.

Based on these comparisons, we recommend BRAM2 HCl for scientific use between 0.4 and 100 hPa (50 hPa in the tropics), but it cannot be used for trend studies.

ClO. For ClO, the analysis increments are very small in the middle stratosphere and the bias (BRAM2 − MLS) and (CTRL − MLS) are similar, as suggested by Fig. 3. There is a relatively good agreement with MLS and SMILES between 1.5 and 30 hPa (see Fig. 5 and Table 1). Note the lower standard deviations against SMILES in this altitude range, suggesting higher precision of SMILES compared to MLS. Comparison against MIPAS ClO is not shown because MIPAS V5_ClO_22[0_or_1] is only valid under conditions of chlorine activation in the polar winter (see Sect. 2.2.2).

Time series of the bias against MLS show small seasonal variations at 4.6 and 46 hPa (Fig. 6). Similar values are found in the southern mid-latitudes and in the tropics (see Figs. S1–S4). We conclude that BRAM2 ClO in the middle stratosphere is more a CTM product than a data assimilation product. Nevertheless, BRAM2 ClO in the middle stratosphere can be recommended for scientific use between 1 and 70 hPa and should be used in consultation with the BASCOE team outside this vertical range.

CH₃Cl. Below 30 hPa, BRAM2 agrees relatively well with MLS (see Fig. 5 and Table 1). The FmO values are usually larger than for other species due to the higher MLS uncertainties for this species. The bias against ACE-FTS is larger, being negative at 100 hPa to positive at 10 hPa. The standard deviations against both instruments are similar below 30 hPa. At 46 hPa, the time series of the biases display negligible seasonal variations against MLS and small seasonal variations against ACE-FTS (see Fig. 6). (Spikes in the ACE-FTS time series around 2010/2011 are due to (1) the small number of ACE-FTS profiles in October 2010 and February 2011 and (2) the fact that some of them display unphysical vertical oscillations – not shown.) The agreement with MLS and ACE-FTS is better in the tropics (see Figs. S2 and S4).

Above 30 hPa at mid-latitudes (10 hPa in the tropics), the agreement between BRAM2 and MLS degrades. More worrying is that CTRL agrees better with MLS observations than BRAM2 (see Fig. 3), indicating that MLS observations are not properly assimilated. The reason for this issue is probably twofold. First, there is a relatively large number of negative MLS CH₃Cl observations above 10 hPa, and, second, the MLS averaging kernels are not used in the BASCOE observation operator. Assimilating negative data is not an issue as long as the overall analysis is positive, which is not always the case with CH₃Cl. In BASCOE, negative analyses are clipped to nearly zero (10⁻²⁵), which in the case of CH₃Cl introduces a positive bias in the analysis. Since significant information in the retrieved profiles comes from the a priori profiles above 10 hPa (see L2015, their Fig. 3.3.2), the use of the averaging kernels would help to ensure positiveness of the analysis. Unfortunately, this issue was not considered before starting the production of BRAM2. Consequently, we recommend the use of BRAM2 CH₃Cl only below 30 hPa at mid-latitudes and 10 hPa in the tropics.

5.2 Lower-stratospheric polar vortex (LSPV)

Figure 7 shows the evolution of the southern hemispheric (SH) polar vortex composition from MLS observations, BRAM2 and CTRL in 2009. Values correspond to daily means in the inner vortex, i.e. between 90 and 75^∘ S of equivalent latitude. The vertical domain is between 320 and 700 K potential temperature, approximately between 100 and 10 hPa. The SH inner polar vortex was chosen because it is the region where CTRL differs most from the reanalysis. Evaluating BRAM2 in these conditions is thus a stronger test for the quality of the reanalysis. The species shown in Fig. 7 and discussed throughout this section are O₃, H₂O, N₂O, HNO₃, HCl and ClO. For ClO, only daytime values are taken into account.

Figure 7Time series of (from top to bottom) daily mean MLS profiles, the corresponding values of BRAM2, the corresponding values of CTRL, the relative mean difference (%) between BRAM2 and MLS and the associated standard deviation (%). Values are shown between May and November 2009 in the lower-stratospheric inner vortex (i.e. within 90–75^∘ S equivalent latitude and within 320–700 K) for (from left to right) O₃, H₂O, N₂O, HNO₃, HCl and ClO. Only daytime values of ClO are considered in the mean calculations. White areas correspond to locations and dates without valid observations.

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Qualitatively, there is a very good agreement between BRAM2 and MLS, as expected, for the patterns associated with both chemical and dynamical processes. For the chemistry, the loss of HCl by heterogeneous chemistry and the activation of ClO are reproduced well by BRAM2, as is the loss of HNO₃ and H₂O by denitrification and dehydration. The ozone depletion in BRAM2 that occurs in September–October is also in very good agreement with MLS. Dynamical patterns are also in good agreement. The descent of air from above 700 K that starts in May and ends in October, exhibited by the decrease in N₂O and the increase in H₂O and O₃, is reproduced well by BRAM2. Dynamical patterns of shorter timescales are also reproduced well by BRAM2, e.g. the increase in N₂O in late July and late August.

Comparison between the CTRL and BRAM2 shows the regions where MLS observations correct the bias in the BASCOE CTM. For chemical patterns, the loss of HCl is relatively well represented in the model, between 450 and 650 K. Below 450 K, modelled HCl overestimates the observations. Above 400 K, the loss of HNO₃ is also reproduced well by the model while the model has a negative bias below that level. The model also slightly underestimates the ClO activation and the loss of H₂O by dehydration. The good performance of CTRL, especially for HCl, contrasts with recent studies showing the difficulties of CTMs (Lagrangian and Eulerian) to simulate the loss of HCl by heterogeneous chemistry (Wohltmann et al., 2017; Grooß et al., 2018). Note that the BASCOE CTM is based on a relatively simple PSC parameterization and that its parameters have been tuned to improve the model representation (see Sect. 3.1). In other words, it does not include the state of the art of heterogeneous chemistry treatment as in these other studies, and this setup is justified to create a reanalysis in good agreement with observations.

For dynamical patterns, CTRL shows a more pronounced bias. Descent of air (exhibited by high values of H₂O and low values of N₂O between 600 and 700 K), which is correctly reproduced from the beginning of the simulation, is abruptly interrupted in July, probably due to a weakening of the polar vortex at that time. This bias can be attributed to the coarse horizontal resolution of the model (Strahan and Polansky, 2006) and is successfully corrected in BRAM2.

Figure 7 also displays the daily mean and standard deviation of the differences between BRAM2 and MLS. The differences are normalized by the daily mean of BRAM2. The differences between CTRL and MLS are shown in Fig. S5. In general, large biases (around 50 %) correspond to conditions with very low absolute values, i.e. for (1) HNO₃ and HCl between July and October and below 650 K, (2) ClO outside conditions of chlorine activation, and (3) N₂O between 600 and 700 K during the descent of upper-stratospheric air. Large bias also occurs for H₂O below 400 K, i.e. in the UTLS. When chlorine is activated (i.e. when ClO abundance is greater than 1 ppbv), the mean differences between BRAM2 and MLS are below 10 %, well within the MLS accuracy (0.1 ppbv; see L2015). Bias also increases for O₃ in late September, during the development of the ozone hole, but to a reasonable magnitude (10 %).

The standard deviations of the differences also increase when the concentration of the species is very low. In particular, the standard deviation can be higher than 100 % for N₂O between 600 and 700 K during the descent of upper-stratospheric air and for HNO₃ and HCl between July and October and below 650 K. In these cases, standard deviations are more relevant when unnormalized (i.e. in vmr units) and the corresponding values for these three species are, respectively, 10, 0.2 and 0.1 ppbv (not shown). The standard deviation for O₃ also increases and is at a maximum (between 25 % and 50 %) in late September between 400 and 500 K.

Figure 8Time series of the monthly mean differences between BRAM2 and the different observational datasets between 90 and 75^∘ S of equivalent latitude at two potential temperature levels (from left to right: 650 and 450 K) and for (top to bottom) O₃, H₂O, N₂O, HNO₃, HCl and daytime ClO. Values are in percent except for ClO, which is shown in parts per billion by volume. The grey shaded area represents the standard deviation of the differences between BRAM2 and ACE-FTS except for ClO where MIPAS data are used.

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Table 2Mean difference and standard deviation between BRAM2 and observations in the Antarctic inner vortex (between 90 and 75^∘ of equivalent latitude) at 650 and 450 K of potential temperature and between June and November of the 2005–2017 period. Values in percent are relative to BRAM2. Abbreviations: not assimilated (N.A.) and not observed (N.O.).

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Comparison of BRAM2 in LSPV conditions with independent observations and for years other than 2009 is shown in Fig. 8 and summarized in Table 2. It shows time series of monthly FmO between 90 and 75^∘ S of equivalent latitude for the 2005–2017 period and at two potential temperature levels: 650 and 450 K (∼15 and ∼50 hPa, similar figures for the outer vortex and the Arctic winters are shown in the Supplement). Statistics shown are the bias against the different instruments and the standard deviation against ACE-FTS. For ClO, which is not retrieved in ACE-FTS v3.6, the standard deviation against MIPAS is shown. The time series are in percent except for ClO, which is shown in parts per billion by volume. For this species, only daytime observations are taken into account. As expected, comparisons against MLS provide lower biases than against independent observations. Let us discuss Fig. 8 for each species individually.

O₃. BRAM2 agrees well with MLS, ACE-FTS and MIPAS. Although also in good agreement, larger differences occur against ozonesondes, likely due to their higher representativeness. Compared to intercomparison between instrument climatologies, performed in SPARC (2017, Figs. 4.1.19 and 4.1.20), the comparison of BRAM2 against independent data displays lower biases. The standard deviations against ACE-FTS are generally smaller than 10 % with larger values (around 15 %) in the lower stratosphere (450 K) during Antarctic springs. We recommend the use of BRAM2 O₃ for scientific use in the LSPV.

H₂O. BRAM2 agrees well with MLS and ACE-FTS although displaying larger seasonal variations against the latter. The biases against MIPAS are larger with larger seasonal variations, in particular at 650 K where the descent of upper-atmospheric air is significant. In cold conditions (i.e. during polar winter at 650 K), the MIPAS averaging kernels (AKs) are smoother than in warm conditions (i.e. during polar summer). These AKs were applied to a few BRAM2 ModAtObs profiles at MIPAS during summer and winter polar conditions, resulting in a better agreement with MIPAS (not shown). We recommend the use of BRAM2 H₂O for scientific use in the lower-stratospheric polar vortex.

N₂O. The amount of N₂O decreases during polar winter, in particular at 650 K in the beginning of Antarctic spring. While the normalized differences against MLS and the independent observations can be greater than 50 %, the unnormalized differences are always <20 ppbv and typically <10 ppbv, which is small (see typical values in Fig. 7). At 650 K, the standard deviation against ACE-FTS displays larger seasonal variations (up to 20 %–50 % in early spring). At 450 K, where the abundance of N₂O is larger, the agreement between BRAM2 and the observations is better. As discussed in Sect. 5.1, one can see a drift between BRAM2 and the independent datasets, likely due to a drift in the MLS N₂O standard product. Overall, BRAM2 N₂O is reliable in the LSPV and is recommended for scientific use except for trend studies.

HNO₃. At 650 K, BRAM2 agrees very well with MLS and has a relatively small positive bias against ACE-FTS and MIPAS. This bias is of opposite sign compared to those in the middle stratosphere, in agreement with observation intercomparison (SPARC, 2017, their Fig. 4.13.3). At 450 K, where HNO₃ is lost by PSC uptake and denitrification, the agreement of BRAM2 with those instruments is slightly less good. For example, BRAM2 overestimates MLS during late winter–early spring by around 5 %, which is, nevertheless, within the MLS accuracy. The standard deviation of the differences against ACE-FTS is larger during the denitrification period (reaching 20 % to 50 % depending on the year). This means that data assimilation can correct the model bias due to the BASCOE PSC parameterization but does little to improve its lack of precision. Overall, BRAM2 HNO₃ in LSPV is a reliable product and is recommended for scientific use even though it is affected by a large positive relative bias (but small when unnormalized) in regions completely denitrified by PSC sedimentation.

HCl. Similar conclusions hold for HCl. BRAM2 agrees relatively well with MLS and ACE-FTS at 650 K, at the upper limit of PSC activity (see Fig. 7). At 450 K, larger relative differences occur, where BRAM2 usually overestimates the observations, even though the unnormalized differences remain small (e.g. 0.2±0.2 ppbv against ACE-FTS, not shown). Again, as for HNO₃, BRAM2 HCl in the LSPV is a reliable product and is recommended for scientific use even though it is affected by large positive relative bias (but small when unnormalized) in regions where HCl has been completely destroyed by heterogeneous reactions on the surface of PSCs.

ClO. Because the abundance of ClO can change by 1 to 2 orders of magnitude from “quiet” periods to periods of chlorine activation, FmO values for ClO are shown in parts per billion by volume in Fig. 8 and Table 2. In general, the agreement with MLS and MIPAS is relatively good during the period of activation. For example, the bias is between 0.02 and 0.04 ppbv, which is small compared to a total abundance between 0.5 and 2.5 ppbv (see Fig. 7). Comparison of BRAM2 with SMILES ClO has been carried out for the chlorine activation period in the Arctic winter 2009–2010 (not shown). Around 500 K (i.e. the level where ClO reaches a maximum during chlorine activation), BRAM2 overestimates SMILES by around 10 % with a standard deviation around 50 %. Again, BRAM2 ClO in the LSPV is a reliable product when ClO is enhanced by chlorine activation and is recommended for scientific use in these conditions.

5.3 Upper-stratosphere–lower-mesosphere polar vortex (USPV)

Upper-stratosphere–lower-mesosphere polar winters are influenced by the descent of mesospheric and thermospheric air into the stratosphere, in particular by the descent of NO_x (i.e. NO+NO₂), CO and H₂O (Lahoz et al., 1996; Funke et al., 2005, 2009). Enhanced stratospheric NO_x induces production of HNO₃ by ion cluster chemistry (e.g. Kvissel et al., 2012). In the Arctic, all these processes may be affected by stratospheric major warmings that displace or split the polar vortex (Charlton and Polvani, 2007). The BASCOE CTM does not account for mesospheric or thermospheric sources, nor for ion chemistry. Nevertheless, Lahoz et al. (2011) have shown that the BASCOE system constrained by MLS H₂O observations was able to describe the Arctic vortex split of January 2009. In this section, we will evaluate how the results of Lahoz et al. (2011) could be extended to BRAM2 for years other than 2009, in both hemispheres and for N₂O, HNO₃ and CO.

Figure 9Time series of (from top to bottom) daily mean MLS profiles, the corresponding BRAM2 values, the relative mean differences (%) between BRAM2 and MLS, and the associated standard deviation (%). Values are shown between October 2016 and May 2017, between 90 and 60^∘ N, and between 0.1 and 10 hPa for (from left to right) H₂O, N₂O, HNO₃ and CO.

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Note that CTRL is not shown in this section. It displays large disagreement with MLS and/or BRAM2, which only highlights the CTM limitations explained above.

Figure 9 shows the time series of MLS and BRAM2 during the USPV Arctic winter 2016–2017, between 0.1 and 10 hPa and averaged between 60 and 90^∘ N. Note that the figure is not given in the equivalent latitude–theta view as in the LSPV in order to keep the upper model levels in the discussion. This winter was subject to intense dynamical activity with two strong warmings – although not major – where the vortex almost split at the end of January and February (not shown). Around these dates, discontinuities in the time series of MLS H₂O and CO are clearly visible and reproduced well by BRAM2. Time series of MLS HNO₃ show enhanced values in January around 3 hPa most likely due to ion chemistry. While this process is not included in the BASCOE CTM, BRAM2 agrees well with MLS. The average BRAM2 N₂O also agrees well with MLS below 1 hPa.

Figure 9 also displays the relative mean difference and standard deviation between BRAM2 and MLS. As for previous comparisons shown in this paper, the mean and standard deviation are large when the abundance of the species is relatively low, i.e. in the upper parts of the plots for N₂O and HNO₃, or for CO outside the period of enhancement by descent of mesospheric air. Otherwise, the agreement is relatively good for CO during descent of mesospheric air, during production of HNO₃ by ion chemistry or at lower altitude for N₂O when its abundance exceeds ∼20 ppbv. In those cases, the bias is around ±10 %, while the standard deviations of the differences are relatively large but still acceptable (<50 %). For H₂O the agreement is very good below 0.2 hPa, where bias and standard deviation are usually lower than ±2 % and 20 %, respectively.

Figure 10Mean (top row) and standard deviation (bottom row) of the differences between BRAM2 and observations from MLS (red lines), ACE-FTS (blue lines), MIPAS (green lines) and MLS_N2O_640 (purple lines). The statistics are in percent, normalized by the mean of BRAM2, and are taken between 60 and 90^∘ N, between 0.1 and 10 hPa, and between the months January and February (i.e. during months observed by ACE-FTS) of the period 2005–2017. The statistics are calculated for, from left to right, H₂O, N₂O, HNO₃ and CO. The approximate numbers of observed profiles used in the FmO statistics are given in the upper left corner of the top row plots using the instrument colour code. The grey shaded area in the mean and standard deviation plots corresponds, respectively, to the MLS accuracy and precision, as provided in the MLS data quality document (see L2015). The thin black profiles represent the mean (top row) and standard deviations (bottom row) between MLS and ACE-FTS found in validation publications (see text for details).

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Figure 10 shows the FmO statistics between BRAM2 and observations from MLS, ACE-FTS, MIPAS and MLS_N2O_640 in the USPV. These statistics are calculated between 60 and 90^∘ N and between 0.1 and 10 hPa for the January–February 2005–2017 periods, these months being observed by ACE-FTS (for MIPAS and MLS_N2O_640, the datasets end in 2012 and 2013, respectively). The figure also shows the MLS accuracy and precision, and the mean and standard deviation of the differences (MLS − ACE-FTS) estimated by S2017. A similar figure for the southern polar winter is provided in the Supplement (Fig. S9).

The FmO statistics are similar to those found in the middle stratosphere (see Fig. 5, Sect. 5.1) for H₂O and N₂O. FmO statistics for HNO₃ are also somewhat similar to those found in the middle stratosphere. The major difference is the smaller bias found in the USPV between BRAM2 and MLS (<5 %) at altitude below 3 hPa, approximately the upper level where HNO₃ is produced by ion chemistry, well within the MLS accuracy. The FmO statistics of these three species are also stable from year to year (not shown) and similar values are found in the Southern Hemisphere (see Fig. S9).

For CO, bias against MLS is small ($<\pm \mathrm{5}$ %) and well within the MLS accuracy. The biases against ACE-FTS or MIPAS are similar, usually within ±10 % with a maximum positive bias of +15 % at 1 hPa. The bias against ACE-FTS agrees well with the direct comparison between MLS and ACE-FTS which suggests that the BRAM2 bias comes from the differences between the two instruments.

The standard deviations of CO against MLS (∼35 %) agree well with the MLS precision. BRAM2 provides similar standard deviations against ACE-FTS which are significantly lower than in the direct comparison between MLS and ACE-FTS. Against MIPAS, the standard deviation is relatively large, between 50 % and 80 %.

In the Southern Hemisphere, the biases against ACE-FTS and MIPAS are slightly larger by ∼5 % and ∼10 %, respectively (see Fig. S9). On the other hand, the standard deviations are smaller by around 10 % against all datasets, with very good agreement with the difference (MLS − ACE-FTS). These statistics are relatively stable over the years. BRAM2 CO in the USPV agrees well with observations and is recommended for scientific use.

5.4 Upper troposphere–lower stratosphere (UTLS))

In the UTLS, the evaluation of BRAM2 must take into account several limitations of the BASCOE CTM and the satellite observations. The BASCOE CTM does not include tropospheric processes, in particular the convection which is necessary to represent correctly vertical transport from the lower to the upper troposphere (Pickering et al., 1996; Folkins et al., 2002). Moreover, the BASCOE spatial resolution used for BRAM2 is relatively coarse to represent vertical and horizontal gradients in this region. Additionally, satellite observations are less reliable in the UTLS because large dynamical variability and steep gradients across the tropopause limit instruments with low temporal (occultation sounders such as ACE-FTS) or vertical (emission sounders such as MLS and MIPAS) resolution. Also, cloud interference and saturation of the measured radiances pose challenges to the instruments, depending on the measurement mode applied (SPARC, 2017).

Figure 11The filled contour maps (between 60^∘ S and 60^∘ N) show the BRAM2 (left column) and CTRL (right column) distribution of, from top to bottom, O₃, H₂O, CO and CH₃Cl on 14 July 2009 at 12:00 UT at 390 K (O₃) or 100 hPa (H₂O, CO and CH₃Cl). Coloured squares correspond to the MLS values between 06:00 and 18:00 UT on that date at the same levels. To improve the readability, only one in two MLS observations is shown. Scatter plots in the lower right corner of each map show the correlation between MLS and BASCOE (BRAM2 or CTRL, MLS on the x axis, BASCOE on the y axis) where all MLS data for that day are used, where BASCOE values are taken from the ModAtObs files (see Sect. 3.4) and where the black lines show the perfect correlation.

In this section, the following BRAM2 species will be evaluated: O₃, H₂O, CO and CH₃Cl. Figure 11 shows the horizontal distribution of these species in the lower stratosphere from BRAM2 and CTRL on 14 July 2009 at 12:00 UT. To highlight the added value of the assimilation, MLS data between 06:00 and 18:00 UT on the same date are overplotted on each map. Finally, the qualitative agreement between MLS and the BASCOE values – BRAM2 or CTRL – corresponding to the selected situation is plotted in the lower right corner of each map. Ozone is shown at 390 K (∼80 hPa in the tropics, ∼150 hPa in mid-latitudes) while the other species are shown at 100 hPa. Despite the BASCOE model limitations, BRAM2 and MLS are in good agreement for O₃ (also confirmed by the high correlation between MLS and BRAM2 shown in the figure). For H₂O, CH₃Cl and CO, the agreement is also generally good although the correlation between MLS and BRAM2 is less compact compared to O₃.

During boreal summer, the lower stratosphere is influenced by the anticyclonic circulation located above Asia, which is associated with the Asian summer monsoon (e.g. Randel and Jensen, 2013). In BRAM2 (see Fig. 11), which agrees relatively well with MLS in this region, the anticyclone is marked by low O₃ abundance and high abundances of H₂O, CO and CH₃Cl above Asia, indicating air of tropospheric origin. Also related to the Asian summer monsoon anticyclone is transport from mid-latitudes to the tropics. This transport is marked by the O₃ tongue (values ∼400 ppbv) in BRAM2 starting in the northeastern Pacific and ending above India, which denotes air of stratospheric origin around the eastern flank of the anticyclone (Randel and Jensen, 2013, their Fig. 4, although for a different year). Processes related to stratospheric chemistry are relatively well reproduced by CTRL, such as this ozone tongue. This is not the case for tropospheric processes where CTRL shows large disagreement against MLS in the Asian summer monsoon region or over the western Pacific in the tropics.

Figure 12Mean (top row) and standard deviation (bottom row) of the differences between BRAM2 and observations from MLS (red lines), ACE-FTS (blue lines), MIPAS (green lines) and NDACC ozonesondes (orange lines). The statistics are in percent, normalized by the mean of BRAM2, and are taken between 30^∘ S and 30^∘ N, between 50 and 300 hPa, and between 2005 and 2017. The statistics are calculated for, from left to right, O₃, H₂O, CO and CH₃Cl. The approximate numbers of observed profiles used in the FmO statistics are given in the upper left corner of the top row plots using the instrument colour code. The grey shaded area in the mean and standard deviation plots corresponds, respectively, to the MLS accuracy and precision, as provided in the MLS data quality document (see L2015). The horizontal black lines denote levels where time series are shown in Fig. 13.

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Figure 12 shows the FmO statistics between BRAM2 and observations from MLS, ACE-FTS, MIPAS and ozonesondes in the tropical UTLS, or the tropical tropopause layer (TTL), for the 2005–2017 period (2005–2012 for MIPAS). The figure also shows the MLS error budget (accuracy and precision). The differences between MLS and ACE-FTS derived from validation papers (S2017 for O₃ and H₂O; Santee et al. (2013) for CH₃Cl) are not shown as is the case in the MS and USPV sections, due to the lack of coincident profiles between the two instruments in the tropics. Figure 12 is complemented by Fig. 13 showing time series of the mean differences of the FmO and the standard deviation against ACE-FTS or ozonesondes (for O₃ ). Table 3 provides FmO statistics at three typical levels of the TTL for the period 2005–2017. The results for each species are discussed individually below with a focus on the TTL. FmO values in the extra-tropical UTLS (ex-UTLS) are provided in the Supplement (see Figs. S10–S13 and Table S1).

Figure 13Time series of the monthly mean differences between BRAM2 and the different observational datasets in the TTL (30^∘ S–30^∘ N) at three pressure levels (from left to right: 68, 100 and 147 hPa) and for (top to bottom) O₃, H₂O, CO and CH₃Cl. Values are in percent. The grey shaded area represents the standard deviation of the differences between BRAM2 and ACE-FTS except for O₃ where ozonesonde data are used.

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O₃. In the TTL, the bias profile against MLS oscillates (Fig. 12), due to vertical oscillations in the MLS profiles (Yan et al., 2016, see L2015). The vertical resolution of BASCOE and MLS being similar in the TTL for O₃, the system cannot find a state that simultaneously minimizes the difference against all values of the MLS profiles, and it delivers a vertically smoother reanalysis. Because of these oscillations, the bias against MLS is larger than the MLS accuracy. Nevertheless, the bias against independent observations is relatively small, in particular against ozonesondes. Note that satellite observations of O₃ in the TTL show a large climatological uncertainty and SPARC (2017) recommends the use of in situ observations. Note also the similar disagreement between BRAM2 and the independent observations from ACE-FTS, MIPAS and ozonesondes below 150 hPa. This suggests good agreement between these three observational datasets and a positive bias in MLS O₃. Standard deviation of the differences is small against all observations above 70 hPa (<20 %) and increases to values between 35 % and 80 % in the troposphere, depending on the instruments. Considering the limitations of BRAM2 and the satellite observations mentioned above, this level of agreement is satisfactory.

Time series of the biases against MLS are stable even though they show seasonal variations (see Fig. 13), with the highest amplitude being found at 100 hPa (∼15 %). The difference with independent observations is small at 68 hPa and increases at lower altitude. In all cases, the agreement against ozonesondes is better than ±15 %. This is satisfactory considering the higher representativeness of ozonesondes against BRAM2.

In the ex-UTLS, the agreement with MLS and independent observations is even better; in particular the agreement with MLS is within the MLS uncertainty (see Figs. S10–S13 and Table S1). Considering the low abundance of O₃ in the UTLS and the limitations of BASCOE and satellite measurements in this region, we found good agreement between BRAM2 and independent observations. Overall, we recommend BRAM2 O₃ in this region for scientific use.

H₂O. In the TTL and above the tropopause (approximately 100 hPa in the TTL), BRAM2 agrees well with MLS, ACE-FTS and MIPAS (see Fig. 12 and Table 3). Below that level, the FmO statistics are larger where BRAM2 underestimates ACE-FTS and MIPAS and overestimates MLS. FmO statistics against ACE-FTS and MIPAS are similar above 200 hPa, suggesting a high bias between MLS and these two instruments. On the other hand, comparisons between MLS and in situ cryogenic frost point hygrometer (CFH) observations display similar bias profiles to those between MLS and BRAM2 (Vömel et al., 2007; Yan et al., 2016, respectively their Figs. 6 and 8). This suggests that BRAM2 is closer to in situ CFH observations in the upper troposphere, with MLS being too dry and ACE-FTS and MIPAS being too wet.

Time series of the FmO statistics are relatively stable (no drifts) at 68 and 100 hPa in the TTL (see Fig. 13). At 147 hPa, the bias against MLS increases (from ∼10 % in 2005 to ∼15 % in 2018) with small seasonal variations (amplitude around 5 %). The bias against ACE-FTS shows larger variability, probably due to the low sampling of ACE-FTS in the tropics. Against MIPAS, the biases are large at each level, with seasonal variations around 20 %.

Similar FmO statistics are found in the ex-UTLS (see Figs. S10–S13 and Table S1). Overall, above the tropopause, we recommend BRAM2 H₂O for scientific use. In the upper troposphere BRAM2 overestimates MLS and underestimates ACE-FTS and MIPAS and seems to be in good agreement with CFH observations. Nevertheless, the standard deviations of the differences are large in the upper troposphere, and below the tropopause BRAM2 H₂O should not be used without consulting the BASCOE team.

CO. BRAM2 agrees well with MLS (Fig. 12 and Table 3) in the TTL, within the MLS accuracy and precision. The bias against ACE-FTS and MIPAS shows similar profiles where BRAM2 displays a negative bias in the upper troposphere and a positive bias in the lower stratosphere against these two instruments. Although showing similar profiles, ACE-FTS seems to measure more CO than MIPAS, between 5 % and 20 %, depending on the altitudes.

There are no drifts or seasonal variations in the time series of the bias against MLS (Fig. 13). For ACE-FTS, the time series are noisy, without clear seasonal variations, probably due to the poor sampling of this instrument in the TTL. For MIPAS, the time series are also noisy at 100 and 147 hPa, while at 68 hPa it displays a clear seasonal variation with a 25 % amplitude. Since the retrieval of MIPAS CO is carried out in log-space, their AKs are vmr-dependent such that their use in the comparison with BRAM2 would have reduced the apparent discrepancies. The impact of MIPAS AKs for CO has not been tested and is left for future comparison.

In the ex-UTLS, the agreement of BRAM2 with MLS is similar to that in the TTL while the biases against ACE-FTS and MIPAS are shifted by around +25 % and display larger seasonal variations (see Figs. S10–S13 and Table S1). Nevertheless, BRAM2 CO is well characterized in the UTLS and we recommend the product for scientific use.

Table 3Mean difference and standard deviation between BRAM2 and observations for the 2005–2017 period at three typical levels in the TTL: 68, 100 and 147 hPa. Values are in percent, normalized by BRAM2. Abbreviations: not assimilated (N.A.).

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CH₃Cl. In the TTL, BRAM2 agrees well with MLS (Fig. 12 and Table 3), within the MLS accuracy and precision. BRAM2 underestimates ACE-FTS, within −25 % to −15 %. There are no drifts or seasonal variations in the time series of the bias against MLS (Fig. 13), while comparisons with ACE-FTS are noisy with a small noticeable drift at 68 and 100 hPa (the origin of this drift, from MLS or ACE-FTS measurements, has not been identified). Similar conclusions hold in the ex-UTLS (see Figs. S10–S13 and Table S1). Excluding trend studies, we recommend BRAM2 CH₃Cl in the TTL for scientific use.

5.5 Summary of the evaluation

The evaluation of BRAM2 is based on the comparison against assimilated MLS data and independent observations from ACE-FTS, MIPAS, MLS_N2O_640, SMILES ClO and NDACC ozonesondes. The evaluation has been performed in four regions: the middle stratosphere (MS), the lower-stratospheric polar vortex (LSPV), the upper-stratospheric–lower-mesospheric polar vortex (USPV) and the tropical tropopause layer (TTL).

In general, the mean differences between BRAM2 and MLS are negligible and within the MLS accuracy. The standard deviations of the differences are also generally within the MLS precision, except for O₃ in the MS. This means that in general, BRAM2 can be considered a proxy for MLS. Each species is discussed individually below. The vertical range of validity of BRAM2 in the four evaluated regions is given in Table 4.

Table 4Vertical ranges of validity (hPa) of MLS v4 assimilated species and the corresponding BRAM2 products in the middle stratosphere (MS), the lower-stratospheric polar vortex (LSPV), the upper-stratospheric–lower-mesospheric polar vortex (USPV) and the tropical tropopause layer (TTL). Abbreviations: not evaluated (N.E.) and not assimilated (N.A.).

¹ O₃ has not been assimilated above 4 hPa (see text for details). Above that level, BRAM2 O₃ has not been evaluated and should not be used. ² In the tropics. At mid-latitudes, the vertical range is 30–100 hPa.

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O₃. Ozone has been evaluated in the MS, LSPV and UTLS. The BASCOE CTM has an ozone deficit around 1 hPa (−20 % vs. MLS; Skachko et al., 2016), which is also present in other models (SPARC, 2010, see their Fig. 6.17). For this reason, MLS O₃ has not been assimilated (and has not been evaluated) at altitude above (i.e. pressure lower than) 4 hPa. With the exception of the TTL, BRAM2 agrees well with MLS (±5 %), ACE-FTS (±5 %), MIPAS (−8 % to 5 %) and ozonesondes (±10 %), with substantial changes in the values depending on the region of interest. In the TTL, MLS O₃ profiles display unphysical oscillations that are smoothed in BRAM2, with an agreement against ozonesondes generally better than ±10 %. In the upper troposphere BRAM2 underestimates MLS by around 10 % at 260 hPa and overestimates ACE-FTS, MIPAS and ozonesondes by around 20 % at 260 hPa. This suggests that MLS has a positive bias against the three other instruments.

H₂O. Water vapour has been evaluated in all four regions. Between the tropopause and the model lid (0.1 hPa), BRAM2 H₂O agrees very well with MLS (±2 %) and ACE-FTS (±10 %). Except in the LSPV, the agreement with MIPAS is also good (better than ±10 %). In the LSPV, a larger bias against MIPAS is found that could be reduced by using the MIPAS averaging kernels in the comparison. Below the tropical tropopause, BRAM2 has a positive bias against MLS (around 25 %) and a negative bias against ACE-FTS and MIPAS (around −30 % at 178 hPa). On the other hand MLS underestimates in situ cryogenic frost point hygrometer (CFH; Vömel et al., 2007; Yan et al., 2016) observations by around 25 %, which suggests a good agreement between BRAM2 and CFH as well as a positive bias of ACE-FTS and MIPAS against CFH.

N₂O. Nitrous oxide has been evaluated in the MS, LSPV and USPV. BRAM2 is well characterized by comparison against independent observations at altitudes below (i.e. pressures larger than) 3 hPa. In the MS, BRAM2 agrees well with MLS (±1 %), ACE-FTS (±10 %), MIPAS (±15 %) and MLS_N2O_640 (±5 %). Above that level, BRAM2 is poorly characterized by comparison against observations where time series of mean differences are noisy. In conditions of low abundance of N₂O encountered during the subsidence of the polar vortex (LSPV and USPV), BRAM2 overestimates independent observations but unnormalized differences are generally small. This study reveals a negative drift between BRAM2 and the three independent observational datasets, which suggests a negative drift in the MLS N₂O standard product. This issue is under investigation by the MLS team (Livesey et al., 2019).

HNO₃. Nitric acid has been evaluated in the MS, LSPV and USPV. BRAM2 is well characterized by comparison against independent observations below 3 hPa. Below 10 hPa, BRAM2 agrees well against MLS (±2 %), ACE-FTS and MIPAS (−10 % to 1 % in both cases). From 10 to 3 hPa, the mean differences grow (±10 % against MLS) and exceed ±50 % above 2 hPa for the three datasets. Above that level, MLS precision degrades (to around 0.6 ppbv, i.e. 6 times the typical amount of HNO₃ at that level), and the constraint by the assimilated observations on BASCOE is weak. In the polar vortex after denitrification, BRAM2 has a small negative bias against independent observations (−5 %). Despite the lack of ion chemistry and sources of mesospheric NO_x in BASCOE, enhanced HNO₃ in the USPV is well represented in BRAM2.

HCl. Hydrogen chloride has been evaluated in the MS and LSPV. BRAM2 agrees well with ACE-FTS in MS between 0.4 and 100 hPa at mid-latitudes and between 0.4 and 50 hPa in the tropics. At altitude above 5 hPa, MLS HCl drifts (see Sect. 5.1 for details), which results in a positive drift in the comparison between BRAM2 and ACE-FTS. In the LSPV when HCl is completely depleted by heterogeneous chemistry on PSCs, BRAM2 has a positive bias against independent observations, which remains small when the unnormalized bias is considered (<0.2 ppbv).

ClO. Chlorine monoxide has been evaluated in the MS and the LSPV. In the MS, BRAM2 agrees well with independent observations even though it is poorly constrained by MLS observations. The constraint of MLS observations is stronger in LSPV under conditions of chlorine activation, when BRAM2 agrees well with independent observations.

CH₃Cl. Methyl chloride has been evaluated in the MS and the UTLS. At altitudes below 10 hPa in the tropics and 30 hPa in the mid-latitudes, BRAM2 agrees very well with MLS (±5 %). The agreement is good with ACE-FTS in the tropics (±20 %) and less good at mid-latitudes (−60 % to 20 %). Above these altitudes, BRAM2 has a positive bias against MLS, likely because the averaging kernels of MLS are not used. BRAM2 agrees well with MLS (±1 %) and ACE-FTS (−25 % to −15 %) in the UTLS.

CO. Carbon monoxide has been evaluated in the USPV and the UTLS. In the USPV, during descent of mesospheric CO, BRAM2 agrees well with MLS (±5 %) and independent observations (typically 10 % against ACE-FTS and MIPAS). In the UTLS, the bias between BRAM2 and MLS is negligible (±2 %). BRAM2 agrees reasonably well with ACE-FTS and MIPAS in the TTL: typical biases are, respectively, 25 % and 18 % around 70 hPa to −8 % and −30 % at 215 hPa. In the ex-UTLS, biases against ACE-FTS and MIPAS are shifted by around +20 % and display larger seasonal variations.

According to the evaluation of BRAM2 with MLS and independent observations, we recommend scientific use of BRAM2 with the following limitations. The use of BRAM2 species should be restricted to their evaluated regions (see Table 4). In the MS, O₃, N₂O and HNO₃ should be used at altitude below, respectively, 4, 3 and 3 hPa. BRAM2 N₂O and HCl should be excluded from any trend studies. Methyl chloride should be used below 10 hPa in the tropics and 30 hPa at mid-latitudes. In the tropics at altitudes below 50 hPa, BRAM2 HCl can be used with the caveat of a positive bias with respect to independent observations.

5.6 Note on the BRAM2 unobserved species

The above evaluation focuses on the eight species constrained by MLS observations while BRAM2 includes many others. Although it is beyond the scope of this paper, one can ask what is the value of BRAM2 unobserved species. For long-lived species (e.g. methane or the chlorofluorocarbons), small discontinuities appear in the troposphere at the stream transitions. This is due to the fact that each stream is initialized by a 20-year simulation with time-dependent emissions of tropospheric source gases (see Sect. 3.2) while emissions are kept constant during the stream productions. For short-lived species, the impact is mixed. For example, we found an improvement from CTRL to BRAM2 NO₂ when compared to MIPAS or ACE-FTS in the lower stratosphere but a degradation in the upper stratosphere. Except for Cl₂O₂, which is closely related to ClO during the chlorine activation period, BRAM2 unobserved species are not delivered.