This work is an extended evaluation of near-surface ozone as part of the global reanalysis of atmospheric composition, produced within the European-funded project MACC (Monitoring Atmospheric Composition and Climate). It includes an evaluation over the period 2003–2012 and provides an overall assessment of the modeling system performance with respect to near-surface ozone for specific European subregions. Measurements at rural locations from the European Monitoring and Evaluation Program (EMEP) and the European Air Quality Database (AirBase) were used for the evaluation assessment. The fractional gross error of near-surface ozone reanalysis is on average 24 % over Europe, the highest found over Scandinavia (27 %) and the lowest over the Mediterranean marine stations (21 %). Near-surface ozone shows mostly a negative bias in winter and a positive bias during warm months. Assimilation reduces the bias in near-surface ozone in most of the European subregions – with the exception of Britain and Ireland and the Iberian Peninsula and its impact is mostly notable in winter. With respect to the seasonal cycle, the MACC reanalysis reproduces the photochemically driven broad spring-summer maximum of surface ozone of central and south Europe. However, it does not capture adequately the early spring peak and the shape of the seasonality at northern and north-eastern Europe. The diurnal range of surface ozone, which is as an indication of the local photochemical production processes, is reproduced fairly well, with a tendency for a small overestimation during the warm months for most subregions (especially in central and southern Europe). Possible reasons leading to discrepancies between the MACC reanalysis and observations are discussed.
The European projects MACC (Monitoring Atmospheric Composition and Climate) and MACC-II (Interim Implementation) were established under the umbrella of the European Copernicus programme, formerly known as GMES (Global Monitoring for Environment and Security), to build and demonstrate a core capability for providing a comprehensive range of services related to the chemical and particulate composition of the atmosphere (Hollingsworth et al., 2008; Flemming et al., 2009; Inness et al., 2013). Within MACC operational forecasts of atmospheric composition on global (Stein et al., 2012) and regional scale are produced. Furthermore, the MACC reanalysis (Inness et al., 2013) provides global atmospheric composition fields which can be used to serve as boundary conditions for regional air quality models over Europe and world-wide.
The MACC global model used for both reanalysis and forecasts consists of the European Center for Medium-Range Weather Forecasts' (ECMWF) Integrated Forecast System (IFS) coupled to the MOZART-3 (Kinnison et al., 2007) chemistry transport model. The ECMWF modeling system makes use of its data-assimilation capabilities to combine observations of atmospheric composition with the numerical model in order to produce a reanalysis of atmospheric composition (Inness et al., 2009, 2015). ECMWF has many years of experience in producing reanalysis products, starting from ERA-40 (Dethof and Holm, 2004) and continuing with ERA-Interim (Dragani, 2010, 2011).
Evaluation of MACC data is being done on a regular basis (Eskes et al., 2015) and specifically for trace gases in the global troposphere (e.g. Stein et al., 2014) and the stratosphere (e.g. Lefever et al., 2014). The global reanalysis products are mostly used as a reference data set for specific case studies (e.g. Knowland et al., 2014) or as boundary conditions for international activities, like the Air Quality Modeling Evaluation International Initiative-AQMEII (Air Quality Modeling Evaluation International Initiative) starting from phase I (e.g. Schere et al., 2012) up to its current phase III. It is therefore useful to have a systematic analysis on a key atmospheric species of the global reanalysis product (i) as a reference for those wishing to use it in their studies and (ii) as a general assessment of the system performance, identifying potential issues needing further improvement.
In this work special emphasis is given on the evaluation of near-surface
ozone over Europe for the whole reanalysis period produced within MACC
(2003–2012). Near-surface ozone is one of the main pollutants affecting both
human health and vegetation (Fuhrer and Booker, 2003; Scebba et al., 2005;
Schlink et al., 2006). Sources of tropospheric ozone can be either the
stratosphere-troposphere transport or the photochemical production through
oxidation of VOCs (volatile organic compounds) and CO in the presence of
adequate NO
The issue of the short-term and long-term ozone variability is complex, being
related to changes of anthropogenic and natural emissions, meteorological
conditions, atmospheric boundary layer mixing processes and
stratosphere-troposphere exchange. Although a number of measures aimed at
reducing NO
Furthermore, although the current consensus view is that photochemistry is
the major contributor to the observed background ozone levels in the
troposphere, there is still no consensus as to the mechanisms that lead to
the formation of the spring ozone maximum observed in certain locations of
the Northern Hemisphere, distant from nearby pollution sources (Crutzen et
al., 1999; Lelieveld and Dentener, 2000; Monks, 2000; Zanis et al., 2007).
The spring ozone maximum observed in certain locations of the Northern
Hemisphere, distant from nearby pollution sources, has mainly two
contributions; (i) the stratosphere to troposphere transport (STT) (Stohl et
al., 2003 and references therein) and (ii) ozone production in the
troposphere on a hemispherical scale, related to photochemical processing of
precursor tropospheric trace gases (CO, NO
In this paper we evaluate near-surface ozone of the MACC reanalysis over Europe from 2003 to 2012. We provide an overall assessment of the model performance, putting special emphasis on the reproduction of annual and diurnal cycles. When possible, we provide potential explanations for model inabilities to reproduce specific observational characteristics of certain subregions and finally we suggest points of future work.
The IFS includes greenhouse gases (Engelen et al., 2009) and aerosols (Benedetti et al., 2009; Morcrette et al., 2009). In MACC, the MOZART-3 chemistry transport model has been coupled to the IFS to provide chemical tendencies for ozone, carbon monoxide, nitrogen oxides and formaldehyde (Flemming et al., 2009), while chemical data assimilation for these species takes place in IFS (Inness et al., 2009, 2015). MOZART-3 as used in the MACC reanalysis system is described in Stein et al. (2012, 2013).
Ozone satellite retrievals that were assimilated in the MACC reanalysis. PROF denotes profile data, TC total columns, PC partial columns, and SOE solar elevation. PC SBUV/2 data consist of six layers between the surface and 0.1 hPa. NRT (near-real time) data are available within a few hours after the observation is made, and are being used in operational forecast systems. For periods towards the end of the MACC reanalysis period, NRT data were used for some of the species when no offline products were available.
A data assimilation system for aerosol, greenhouse gases and reactive gases
is in place based on ECMWF's 4D-VAR data assimilation system. The fields of
MACC reanalysis (hereafter MRE) are available globally at a horizontal
resolution of
Since several satellite instruments are used to assimilate one parameter in
the data assimilation system, a bias correction method is applied to the
data to account for the instrumental inconsistencies. In MRE a variational
bias correction scheme for radiance data has been extended to atmospheric
composition data (Inness et al., 2013). In the variational scheme biases are
estimated during the analysis by including bias parameters in the control
vector. The bias corrections are continuously adjusted to optimize the
consistency with all information used in the analysis. The impact of
assimilation on near-surface ozone is only the residual of correcting
the stratospheric and total ozone column, plus the assimilation of other
relevant gases that impact ozone chemistry (CO, NO
To investigate the impact of assimilation on key atmospheric species, a control run was also performed (hereafter CTRL), using the same reanalysis settings without assimilation. As explained in Inness et al. (2013) (Sect. 2.5), it would have been computationally too expensive to produce a control analysis experiment that was identical to the MACC reanalysis, but did not actively assimilate observations of reactive gases. Instead, a MOZART-3 stand-alone run was carried out that applied the same settings (model code, resolution, emissions) as MOZART in the MACC reanalysis. The meteorological data for the stand-alone run were taken from the reanalysis, but the control run had free-running chemistry. The results from this control run can be used to detect the impact of the assimilation of greenhouse reactive gases observations in the MACC reanalysis. Since the meteorological input data were derived from interpolation of archived 6-hourly output from the MACC reanalysis, and not through an hourly exchange as in the reanalysis, the stand-alone run was not a completely clean control run. However, these differences would be small. The comparison between the MRE and the CTRL is confined to the time period 2003–2010, when both time series are available.
Measurements from ground based European stations were used for the evaluation of modeled surface ozone, from the European Monitoring and Evaluation Programme (EMEP) and the European Environment Agency databases (AirBase) covering the time period from 2003 to 2012. The observations used for this evaluation are independent from the assimilated ones. EMEP is appropriate to evaluate coarse resolution simulations, as it is fitted to catch background air pollution patterns with stations at a considerable distance from source areas in rural or remote regions (Schaap et al., 2015). Only background rural stations have been used from the AirBase database for comparisons with the coarse resolution model surface ozone. These include stations class 1–3 according to the Joly-Peuch classification methodology for surface ozone (Joly and Peuch, 2012). There is a total of 138 stations included in the current analysis, fulfilling the above-mentioned criteria. This selection ensures that all stations are adequate for comparisons with coarse resolution (80 km) model data.
Observed data from the EMEP and AirBase database were available in hourly resolution, while model values were available in 3-hourly intervals. The corresponding observational data were extracted with a 3-hourly interval, to be comparable with modeled time-series. The modeled data were extracted from the coupled system by means of interpolating surface ozone into each station location. Different model levels were used for comparison with ground-based stations. The rationale behind the selection of different model level selection instead of extracting time series from the first model level (surface) is that in coarse resolution grids, areas with anomalous terrain (e.g. mountainous areas) are represented with an average elevation, which is less than the actual station elevation. Based on the difference between the actual station altitude and the average grid-cell elevation, the corresponding model level is selected, using atmospheric pressure as the correction criterion. We have used only those stations that fulfil the criteria of 75 % data availability for near-surface ozone.
In order to acquire a more detailed view of model performance, eight European subregions have been defined as shown in Fig. 1. These regions fit data coverage and avoid overlapping between each subregion. The eight European subregions are: Britain and Ireland (BI), France (FR), Iberian Peninsula (IP), Eastern Europe (EA), middle Europe (ME), Mediterranean (MD), south-middle Europe (SME) and Scandinavia (SC). Furthermore, the Mediterranean region was further split into the continental part (MDc) and the marine part (MDm), according to their spatial location (coastal or interior continental), since each type of station has different characteristics.
The European subregions that were used in the analysis and the corresponding EMEP and AIRBASE stations. The numbers denote the number of stations taken into consideration for every subregion. The subregions are: Britain and Ireland (BI), France (FR), Iberian Peninsula (IP), Eastern Europe (EA), middle Europe (ME), Mediterranean (MD), south-middle Europe (SME) and Scandinavia (SC).
Additional NO and NO
We have also to take into consideration that the NO
Ozonesondes are used to validate ozone MRE profiles into the troposphere at
six European stations: Haute-Provence (43.9
For the current evaluation study we use statistical metrics to quantify the bias, gross error and temporal correlation of the model with regards to observational surface ozone. Comparisons of the diurnal ranges and cycles are also performed, as indices of photochemical processes. As is also discussed by Savage et al. (2013), spatial and temporal variations in chemical composition, including tropospheric ozone, can be large, while also differences between model and observed values are frequently much larger in magnitude than usual for meteorological variables. Therefore, mean error and root mean square error, even though being important metrics for estimating model errors, are not optimal when assessing model performance at different chemical regimes as found over Europe.
Based on the evaluation guidelines and previous work within GEMS/MACC
(Seigneur et al., 2010; Elguindi et al., 2010; Ordonez et al., 2010; Eskes
et al., 2015) we use the Modified Normalized Mean Bias (MNMB) as a measure
of the bias of modeled versus observed values. This metric treats over- and
underprediction in a symmetric manner ranging between
Furthermore, as a measure of the overall model error we use the Fractional
Gross Error (FGE) calculated from Eq. (2), with its values ranging
between 0 and 2. The advantage of this measure is the linear dependence on
the departure, which makes this measure less sensitive to outliers and tails
in the distribution as compared to the more standard root-mean square.
Average 2003–2012 seasonal FGE (top), MNMB (middle) and annual
In the following section we present a thorough evaluation of surface ozone covering the years from 2003 to 2012, including the three basic validation metrics, analysis of diurnal/annual cycles and diurnal ranges. Seasonal averages are calculated as: winter (DJF), spring (MAM), summer (JJA) and autumn (SON). Additionally, surface ozone data are discussed along with nitrogen oxides, wherever data allows comparisons, in order to characterize different chemistry regimes above Europe, with respect to photochemical production.
The annual statistics of surface ozone are shown in Table 2. The FGE for the whole reanalysis period (2003–2012) ranges mostly from 21 % in Mediterranean marine stations to 27 % in Scandinavia. Figure 2 shows the basic validation metrics on a seasonal basis for the MACC reanalysis. The Iberian Peninsula and mid-Europe have a more stable performance with respect to FGE, with an average 20 % for all seasons. All other regions have errors ranging from 10 to 30 % depending on season. A more thorough analysis on the seasonal behavior of surface ozone is provided in the following section.
Annual statistics of near-surface ozone for the MACC reanalysis (2003–2012) over the different European subregions. FGE and MNMB are expressed in %.
The seasonal MNMB in Fig. 2 (middle panel) is close to zero for most
subregions. The final MRE surface ozone product exhibits its highest MNMB
for Scandinavia and East Europe in winter (
Figure 2 (bottom panel) shows the temporal correlation of the 2003–2012
near-surface ozone time series, built upon mean monthly values, and therefore
providing a clue on the representation of ozone seasonality. The lowest
correlation is found over Scandinavia (0.26), followed by Britain and
Ireland (0.51) and the Mediterranean marine stations (0.54). All other regions have
correlations
To investigate the impact of assimilation on near-surface ozone we compare the MRE and CTRL simulations with the observations. Table 3 shows the annual statistics of the MRE and the CTRL simulation. The greatest improvement in the MACC reanalysis because of the assimilation is noted over Scandinavia, where the annual FGE is reduced from 40 to 27 %, Eastern Europe (FGE drops from 38 to 25 %), Mediterranean continental stations (from 43 to 29 %) and mid-Europe (from 31 to 24 %). In the same areas the MNMB is also reduced by up to 23 % (SC). In France and the Iberian Peninsula there seems to be a small increase in the FGE (6 and 8 % respectively) and a small change in the MNMB (reduced to zero in FR and increased by 5 % in IP). Over south-middle Europe and the Mediterranean marine stations the change in FGE and MNMB is negligible on an annual basis.
Annual statistics of near-surface ozone for the MACC reanalysis (MRE) and the control run (CTRL) over the different European subregions for the common period from 2003 to 2010. FGE and MNMB are expressed in %.
The temporal correlation of monthly mean time series from 2003 to 2010 is reduced in the MRE, especially over the Mediterranean marine stations (drops from 0.74 to 0.49) and Scandinavia (from 0.39 to 0.23). The temporal correlation over Scandinavia is very low, because the MRE cannot capture the spring maximum, as it will be shown in Sect. 3.2. Moreover, the issue of the MLS bias correction in the assimilation procedure has caused drifts in the tropospheric ozone concentrations between August 2004 and December 2007 (a detailed explanation of this issue can be found in Inness et al., 2013). The problem was tracked down and alleviated after year 2008 of the MRE. The deterioration of the temporal correlation in the MRE in comparison to the control simulation can be attributed to the assimilation procedure followed up to MRE year 2008. Calculation of temporal correlation coefficients before (2003–2007) and after (2008–2012) indicates that R increases in all subregions after removal of MLS bias correction. Figure 3 shows the comparison of the seasonal FGE, MNMB and R for the MRE and the CTRL near surface ozone over the different European subregions for the common time period 2003–2010. On a seasonal basis the greatest improvement due to assimilation is seen during the winter months, when the CTRL suffers from the largest negative bias. The impact on surface ozone is smaller in summer, eventually because near-surface ozone is largely controlled by photochemical processes.
Average 2003–2010 seasonal FGE (top), MNMB (middle) and annual
The average 2003–2012 observed and MRE annual cycle of near-surface ozone is shown in Fig. 4. With the only exception of the Mediterranean region (MDc and MDm), the modeled annual cycles of ozone have differences in the shape from the observed ones. The most striking disagreement is seen over Scandinavia (SC), where the MRE captures the annual range (13 ppb: the monthly maximum minus the monthly minimum of the year), but completely fails to reproduce surface ozone seasonality. While observations indicate a clear spring maximum (40 ppb), a characteristic ozone behavior in very clean and remote atmospheres in the Northern Hemisphere (Volz and Kley, 1988), no indication of spring ozone maximum is evident in the MRE surface ozone; on the contrary, a clear lower maximum (35 ppb) is found in late summer.
Mean 2003–2012 annual cycle of near-surface ozone for the different European subregions of the MACC reanalysis and observations. The shading areas denote 95 % confidence interval of the mean values.
Over Britain and Ireland (BI) we also note striking differences in the shape of the annual cycle. Specifically, there is disagreement (a) in the timeliness of the early spring maximum, which is seen in April for observed ozone and the late spring–early summer for the MRE, and (b) in the annual ozone range, which is overestimated by about 7 ppb. The overestimation occurs mainly during the summer/autumn season. We should note that, even though the MRE near-surface ozone at SC and BI does not capture the observed spring maximum peaking in April, this spring ozone maximum is better seen in the lower free troposphere at 850 and 700 hPa vertical levels of MRE (not shown here).
In mid-Europe (ME), the observational broad spring-summer maximum (April–July) is captured by the MRE, with a month's time-lag (May to August) causing an underestimation in MRE of 2–3 ppbv from January to April and an overestimation from May to November (Fig. 4). The highest overestimation (ranging from 5 to 9 ppbv) in MRE is seen during the warm months from June to September. This behavior results in an overestimated annual amplitude in MRE in comparison to observations.
Over the Iberian Peninsula (IP) there is an agreement in the seasonal cycle
of MRE near-surface ozone with observations, with a broad spring-summer
maximum but MRE misses the April peak shown in observations. The amplitude
of the MRE annual cycle is also overestimated by roughly 4 ppbv in
comparison to observations, mostly stemming from the MRE summer O
A similar pattern of differences between MRE and observations are found for France (FR), south-middle Europe (SME) and Eastern Europe (EA) although over EA the differences are smaller.
Overall, the annual cycles of the observed data reflect the specific subregional characteristics, namely the broad spring-summer maximum at Mediterranean (MDc and MDm) and south-middle Europe (SME), the broad spring-summer maximum peaking in April at Eastern Europe (EA), mid-Europe (ME), France (FR) and Iberian Peninsula (IP) and the early spring maximum over northern latitudes at Scandinavia (SC) and Britain and Ireland (BI). MRE near-surface ozone reproduces fairly well the photochemically driven broad spring-summer maximum of surface ozone of the sub-regions at central and south Europe, however, fails to capture the early spring peak in most of these subregions. This shortfall of MRE to capture the early spring peak has also been noted by Inness et al. (2013) and it is further discussed in the following sections. Furthermore, there is generally a tendency for overestimating the annual amplitude in MRE in comparison to observations.
Factors improving ozone seasonality could be emission strengths and temporal profiles and dry deposition (Val Martin et al., 2014). Ongoing work on the impact of dry deposition on surface ozone indicates that the new on-line dry depositions schemes currently tested in the C-IFS system improve the surface ozone positive bias, appearing mostly over southern Europe in summer, but cannot completely tackle the spring ozone maximum problem over northern Europe (J. Flemming, personal communication, 2015).
Figure 5 depicts the mean 2003–2012 diurnal cycle of near-surface ozone for each season for the selected European regions. All diurnal cycles have the expected behavior with sharply increasing ozone concentrations during the daytime hours (from 05:00–06:00 UTC in summer and 1–2 h later in winter to 15:00–16:00 UTC) and decreasing afterwards. The diurnal cycles are more pronounced in the summer season and south Europe due to the more intense photochemistry. The MRE reproduces the diurnal cycle but exhibits positive bias in summer (except for the Mediterranean marine region), which may be persisting during the whole day (BI, SME, IP, ME) or occur mostly during daytime (EA, FR, MDc). In winter there is a small negative bias in all regions, except for MDc (positive bias) and BI (zero bias). The transitional seasons have diurnal cycles that share both winter and summertime characteristics: the spring diurnal bias resembles winter with respect to bias, but has the enhanced photochemical diurnal cycle of summer, though not fully developed.
Mean 2003–2012 diurnal cycle of near-surface ozone for the different European subregions based on MRE (green line) and observations (black line) calculated for winter (DJF), spring (MAM), summer (JJA) and autumn (SON).
Figure 6 shows the annual cycle of the diurnal range of near-surface ozone over the different European subregions. The diurnal range of ozone is a good indication of the potential for the local diurnal ozone build up through photochemical production processes (Zanis et al., 2000). There is generally a good agreement with observations, suggesting that MRE reproduces adequately the observed diurnal ozone range with a tendency for a small overestimation during the warm months for the subregions of central and southern Europe. More specifically, over SME, FR and MDc the diurnal range is overestimated during the whole year but, to a lesser extent in colder months, while over EA, ME, BI and SC the overestimation is smaller and restricted during the summer. Hence the diurnal range is overestimated more at the southern regions (SME, FR and MDc) than at the northern regions (EA, ME, BI and SC) and more during the warm months than during the cold months.
Annual cycle of the diurnal range of near-surface ozone for observations (black line) and MRE (green line) averaged over the time period 2003–2012 for the different European subregions. Shading areas denote the 95 % confidence interval of the mean values. The 95 % confidence interval is not displayed for the Mediterranean subregions, which consist of a limited number of stations.
In this section we discuss possible reasons for the differences revealed in
the shape of the annual cycle of near-surface ozone between observations and
MRE and the failure in MRE to capture the early spring peak in most of the
subregions. We discuss possible contributions from the above-mentioned
processes based on the comparison of MRE ozone profiles with available
ozonesonde measurements, as well as on NO
Comparison with ozonesonde measurements at different locations (Fig. 7) indicate that MRE ozone profiles reproduce the basic structure of the profile, overestimating in most cases ozone below the 850 hPa. We note positive and negative biases depending on the location and the altitude, but there is a tendency for a larger positive bias during summer and autumn for most locations below 850 hPa, while the % biases in the middle and upper troposphere are generally smaller. This is in agreement with the study of Inness et al. (2013), who, analyzing MACC reanalysis over the time period 2003–2010, reported a negative bias with respect to ozonesondes above 650 hPa and the largest positive bias below 800 hPa. It should also be considered that the range of the % biases in the troposphere are comparable with the respective precision of electrochemical concentration cell ozonesonde measurements.
Mean 2003–2012 ozone profiles based on MRE near-surface ozone
(green line) and ozonesonde measurements (black line) at the stations of
Sodankyla (67.4
Mean 2003–2012 annual cycle of lower tropospheric ozone at 700 hPa
based on MRE (green line) and ozonesonde measurements (black line) at the
stations of Sodankyla (67.4
Furthermore, the shape of the observed ozone annual cycle (based on the ozonesondes) in lower free troposphere at 700 hPa is reproduced rather well by the MRE (Fig. 8). The course of the annual cycle is also reproduced for the middle troposphere at 500 hPa (not shown here). Despite the biases, the reasonable reproduction of the shape of the observed ozone seasonal cycle by MRE in the middle and lower free troposphere is consistent with transport processes from the lower stratosphere and the upper troposphere as well as long-range transport being resolved adequately by the MRE.
According to the analysis of ozone profiles (see Sect. 4.1) we may assume
that assimilation in MRE leads to a reasonable representation of the ozone
annual cycles at the middle and upper troposphere, thus mediating for a
realistic contribution of STT. Hence, it could be speculated that differences
in the shape of the seasonal cycle of near-surface ozone between observations
and the MRE could be also linked to the potential of photochemical ozone
production and the strength of the exchange between the lower free
troposphere and the atmospheric boundary layer (ABL). Two tentative
explanations could be provided on the mismatch between model and
observations: (a) inadequate seasonality/emission strengths in surface
emissions of precursor species (some issues discussed in Stein et al., 2014)
and (b) a loose coupling of the free troposphere to the ABL, which would be
responsible for the entrainment of the assimilated free tropospheric O
In global scales nitrogen oxides (NO
Here, in order to assess the potential of the photochemical ozone production
related to NO
Mean annual cycle of near-surface O
Figure 9 shows the annual cycle of O
Figure 10 shows the average diurnal cycle of O
Based on the diurnal amplitudes of O
Mean diurnal cycle of near-surface O
The amplitude of the diurnal cycle of NO
In the case of IP, the amplitude of the diurnal cycle of NO
In the current work we evaluate the MACC-II reanalysis (MRE) near-surface ozone for the time period 2003–2012 using rural stations of the EMEP and AirBase monitoring networks. Overall, the evaluation of MRE near-surface ozone with station based observations shows a negative bias in winter over northern Europe and generally positive bias during warm months. With respect to the seasonal cycle, MRE reproduces the photochemically driven broad spring-summer maximum of near-surface ozone at central and south Europe. However, it does not adequately capture the shape of the seasonality with a characteristic early spring maximum at northern and north-eastern Europe. The diurnal range of surface ozone, which is an indication of the local photochemical production processes, is reproduced fairly well in the MACC reanalysis, with a tendency for a small overestimation during the warm months for the subregions of central and south Europe. Comparison of MRE ozone profiles with ozonesonde profiles revealed reasonable reproduction of the shape of the observed ozone seasonal cycle in the middle and lower free troposphere, despite the biases. This suggests that transport processes from the lower stratosphere and the upper troposphere are resolved acceptably by MRE with the aid of the assimilation.
More specifically, the characteristics of near-surface ozone in the MACC
reanalysis 2003–2012 can be summarized as follows for the different
sub-regions:
At Britain and Ireland and Scandinavia, the observed near-surface spring
ozone maximum peaking in April is not reproduced by MRE. However, this spring
ozone maximum is better seen in the lower free troposphere (at 850 and
700 hPa) implying adequate vertical transport within the free troposphere,
as was also indicated by the good comparison with ozonesonde data. The
possibility of insufficient entrainment and mixing from the lower free
troposphere into the atmospheric boundary layer should be further
investigated. MRE diurnal range of near-surface ozone compares relatively
well with the observed diurnal range with a slight overestimation during
summer. Analysis of the average MRE diurnal cycle of O The ozone summer maximum of the Mediterranean area is captured by the
MRE, with a slight overestimation during summer and autumn for the
continental stations (MDc). The MRE near-surface ozone diurnal range compares
well with the observed one throughout the year for the marine stations (MDm)
and is slightly overestimated during the warm months for the continental
stations (MDc). This implies that part of the MRE overestimation of near
surface in summer and autumn for MDc may be associated with an overestimation
of local photochemical production. Zanis et al. (2014) also noted for the
Mediterranean an overestimation of near-surface ozone during summer by
another global chemistry–climate model, due to overestimated photochemical
ozone production within the atmospheric boundary layer. In Eastern Europe, mid-Europe, south-mid Europe and France, MRE
near-surface ozone reproduces the photochemically driven broad spring-summer
maximum, but fails to capture the early spring peak in April. Furthermore,
there is a slight shift of the seasonal cycle towards summer in MRE compared
to observations, with a tendency for an underestimation of ozone levels in
cold months (from January to April) and an overestimation in summer and
autumn. The diurnal range of near-surface ozone in the MRE is overestimated
during summer. This maybe implies an overestimated local photochemical ozone
production, which can partially account for the summer overestimated MRE near
surface ozone levels (similarly to MDc). Further analysis of the average
diurnal cycle of O At the Iberian Peninsula there is a positive bias throughout the
year and the MRE does not capture the April peak shown in the observed
seasonal cycle. The MRE diurnal range compares relatively well with the
observed diurnal range, maybe indicating that local photochemical production
is captured adequately throughout the year. This is also supported from the
analysis of the average diurnal cycle of O
Our analysis suggests that in order to understand better the behavior of
near-surface ozone, further analysis is needed for firm conclusions,
including model diagnostics for photochemical production and loss terms, as
well as the mixing between ABL and free troposphere. Improvement in the
dry-deposition scheme – which is fixed in the current implementation –
would also contribute to improvement of model performance (bias/seasonality)
with respect to near-surface ozone.
MACC II is funded by the European Union's Seventh Framework Programme (FP7)
under Grant Agreement no. 283576. We thank the European Environmental Agency
(AirBase) and the European Monitoring and Evaluation Programme (EMEP) for
providing access to European O