We derived global tropospheric ozone (O3) columns from GOME-2A (Global Ozone Monitoring Experiment) and OMI (Ozone Monitoring Instrument) O3 profiles, which were simultaneously assimilated into the TM5 (Tracer Model, version 5) global
chemistry transport model for the year 2008.
The horizontal model resolution has been increased by a factor of 6 for more accurate
results. To reduce computational cost, the number of model layers
has been reduced from 44 to 31. The model ozone fields are used to
derive tropospheric ozone, which is defined here as the partial column
between mean sea level and 6km altitude. Two methods for
calculating the tropospheric columns from the free model run and
assimilated O3 fields are compared. In the first method, we
calculate the residual between assimilated total columns and the partial
model column between 6km and the top of atmosphere. In the
second method, we perform a direct integration of the assimilated
O3 fields between the surface and 6km. The results
are validated against tropospheric columns derived from ozone sonde
measurements. Our results show that the residual method has too large a variation to be used reliably for the determination of tropospheric
ozone, so the direct integration method has been used instead. The
median global bias is smaller for the assimilated O3 fields than
for the free model run, but the large variation makes it difficult to
make definitive statements on a regional or local scale. The monthly
mean ozone fields show significant improvements and more detail when
comparing the assimilated O3 fields with the free model run,
especially for features such as biomass-burning-enhanced O3
concentrations and outflow of O3 rich air from Asia over the
Pacific.
Introduction
Tropospheric ozone has direct and detrimental effects on human health
. It mostly affects the respiratory
tract and the lungs, causing, for example, shortness of breath, coughing and a
reduced lung function. Respiratory illnesses such as asthma and
bronchitis are aggravated by exposure to ozone. Long-term exposure to
ozone might increase the mortality rate due to respiratory illnesses.
Ozone also negatively affects ecosystems and crop yield because it
reduces photosynthesis and plant growth . Because plants
react differently to exposure to ozone, the balance between species
in an ecosystem may shift as well. give an
extensive review on tropospheric ozone and its precursors in relation
to air quality and climate.
Apart from the direct and indirect effects on living
organisms, ozone is also a greenhouse gas. It strongly absorbs solar
radiation below 300nm, which is why the temperature of the
stratosphere is increasing with altitude. Therefore, understanding
the ozone distribution is important for understanding the thermal
structure of the atmosphere.
Ozone occurs naturally in the troposphere, but concentrations
have increased due to human activity. Locally, ozone is produced
primarily by reaction cycles involving carbon monoxide, NOx, methane and
other hydrocarbons. The most important source sectors of these
pollutants are transport and industry. Photodissociation of
tropospheric ozone is the main source of OH, which has a major
role in removing pollutants from the atmosphere. Ozone can also be
transported from the stratosphere down to the troposphere in
stratosphere–troposphere exchange events.
The tropospheric ozone column is defined as the total ozone
amount per unit area between the surface and the tropopause. However,
near the tropopause, stratosphere–troposphere exchange of air may
occur, which can lead to an under- or overestimation of the lower
tropospheric ozone column. Since the tropospheric ozone in the lower
layers has the highest impact on living organisms, we will focus on
the partial ozone column between the surface and 6km above
mean sea level.
Satellite measurements are not very sensitive close to the
surface, but in the altitude range chosen some information from the measurements
is still present (see Fig. ).
Because the top level is at a fixed altitude, it will be
referred to as the fixed altitude top level (FAT) hereafter. The
corresponding 0–6km ozone partial column will be referred
to as the FAT column.
Tropospheric ozone can be determined by a number of satellite-based methods.
In nadir–limb matching techniques, the integrated profile from a limb instrument
is subtracted from the total column for the same air mass. Limb profiles and
total columns can be obtained from the same instrument e.g. SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY);, but also from different
instruments on the same satellite e.g. OMI (Ozone Monitoring Instrument) total column and MLS (Microwave Limb Sounder) limb
profile;. In , the horizontal resolution
of the MLS limb profiles was increased by trajectory calculations before
subtracting them from the OMI total columns. Tropospheric ozone columns were
also derived from assimilated OMI total columns and MLS limb profiles by
. Using only nadir observations, and
combined Total Ozone Mapping Spectrometer (TOMS) total columns and
Solar Backscattered Ultraviolet (SBUV) stratospheric profiles and determined
tropospheric ozone with the empirically corrected tropospheric ozone residual
method. Assimilated GOME (Global Ozone Monitoring Experiment) profiles were subtracted from GOME–TOMS total columns
by .
The methods mentioned above all use the UV–visible range of the spectrum.
There are also a number of ozone emission lines in the
thermal infrared (i.e. the wavelength range where the atmosphere
emits radiation, instead of reflecting solar light), most notably near
9.6µm. This emission line can also be used by satellite
instruments (e.g. IASI, Infrared Atmospheric Sounding Interferometer) to measure ozone e.g..
In the tropics, the cloud top height is very stable at an
altitude of approximately 200hPa. Therefore, cloudy scenes can be
used to obtain the above-cloud ozone column, while the cloud-free
scenes can be used to obtain total ozone columns. The difference
between these two values is the ozone column below the cloud top.
This convective-cloud-differential method has
recently been applied to European satellite measurements to study the
trends in a 20-year time series and as a preparation for the TROPOMI (TROPOspheric Monitoring Instrument) mission .
Outside the tropics, the cloud top height varies too much to reliably
obtain ozone columns using the convective-cloud-differential method.
UV–visible retrievals are not very sensitive to the altitude where tropospheric
ozone is located, so
direct integration of UV–visible ozone profiles does
not provide a viable alternative either. The height information can
be restored by using data assimilation, where information from ozone
profiles, averaging kernels and the chemical transport model are combined.
The sensitivity and information content of UV–visible retrievals is
higher in the stratosphere; therefore, an alternative approach is to
subtract stratospheric columns, derived from assimilated ozone
profiles, from accurate total columns (for example, from DOAS – differential optical absorption spectroscopy – retrievals). The remainder is taken as the residual tropospheric
column .
The assimilation of ozone measurements from satellites is usually done by
either 4DVAR (4-D variational data assimilation) or (ensemble) Kalman filters. For example, 4DVAR data assimilation
has been used for the ERA-Interim data set and in the Belgian
Assimilation System for Chemical ObsErvations (BASCOE; ).
The stratospheric ozone analyses from the BASCOE system have been evaluated by
, and it has been coupled to the Integrated Forecast system of
the ECMWF . Ensemble Kalman filters
have been used for the assimilation of multiple trace gas measurements by
. In this research, we use the Kalman filter as described in
and for the assimilation of ozone profiles from the GOME-2 and OMI UV–visible satellite instruments.
The assimilated ozone fields
will be used to derive tropospheric columns in two
ways. One method is to integrate the assimilated ozone column up to
the FAT, hereafter called the FAT column
(i.e. the column between the surface and 6km altitude). The other method is to
take the difference between the integrated assimilated profile from
the FAT to the top of the atmosphere and the assimilated total ozone
columns from the Multi Sensor Reanalysis MSR;,
hereafter called the residual-FAT column.
The MSR is a long-term (1970–2017) data set of assimilated total
columns from all available satellite measurements.
Methodology
We use the ozone profiles from the UV–visible instruments GOME-2
and OMI that
are described in . The ozone profiles from both
instruments are retrieved with the optimal estimation technique. For
GOME-2 the algorithm is described in , while
the OMI algorithm is described in . The ozone
profiles are assimilated into the global chemistry transport model
TM5 Tracer Model, version 5; e.g.. Two major
changes with respect to the settings used in are
an increased model resolution and a change from operational to
ERA-Interim meteorological fields that drive TM5.
The ERA5 reanalysis data were not yet available for use in the TM5 version
used in the assimilation.
Above 230hPa, the TM5 version in this research uses the
parameterized ozone chemistry scheme version 2.1 of and .
Below the 230hPa, the ozone concentrations are nudged towards climatological values.
To obtain more accurate assimilated ozone fields, the horizontal
resolution of TM5 is increased from 3∘× 2∘ to
1∘× 1∘ (longitude × latitude).
At the same time, the vertical resolution is
decreased from 44 to 31 layers to reduce the computational cost.
The original 44 layers are a subset from the vertical grid used by the
European Centre for Medium-Range Weather Forecasts (ECWMF)
operational data stream, while the new 31 layers are a subset from
the vertical grid used for the ERA-Interim reanalysis.
Below about 73hPa (19km), the layers are between
0.8 and 1.5km thick, until about 54km every other
level is selected and the layer thickness increases from
3 to 5.5km, and the top four levels are all selected.
It is not expected that the reduction in vertical resolution affects
the accuracy of the outcome, since the thickness of the model layers is
still less than the estimated vertical sensitivity of the retrievals,
which is about 7–10km in the stratosphere .
The sensitivity of the retrieval to the true state of the atmosphere is given
by the averaging kernel (AK). The trace of this matrix equals the degree of freedom
for the signal (DFS). The rows of the AK give an indication of how the true profile is smoothed over the layers of the retrieval.
An extended discussion on the information content that can be
derived from AKs from GOME, SCIAMACHY, GOME-2 and OMI is presented by
. AKs are also an important factor in intercomparison of
different ozone retrieval algorithms such as in .
Like , we assume that
the spatial correlation between any two points in the 3-D ozone field
is constant in time and that changes over time occur in
the ozone standard deviation only. Therefore, the
model covariance matrix is parameterized into a time-independent correlation field and a time-dependent uncertainty field.
Due to the changes in resolution and meteorological fields, the
correlation field had to be derived again according the same
method as described in . No other changes have
been made to the assimilation algorithm.
Since the horizontal resolution of the chemical transport model has been
increased, the computational cost of the assimilation algorithm
did also increase. In order to limit the total processing time only ozone
profiles for the year 2008 were assimilated. TM5 was used
in two runs: a free
model run without assimilation of observations and an assimilation
model run with the simultaneous assimilation of both GOME-2 and OMI
ozone profiles. For each model run, the FAT
column was calculated by direct integration of the O3
fields, and the residual-FAT column was calculated using the MSR ) total columns.
The total columns are distributed over the layers of the model
proportionally to the subcolumn of that layer. The MSR model uses the
same parameterized ozone chemistry as the profile assimilation used
in this research , but with the more
up-to-date version 2.9 of the chemistry parameters.
The results are validated against ozone sondes downloaded from the
public World Ozone and Ultraviolet Radiation Data Center
database. Since the model produces O3 fields with
a 6h interval at 00:00, 06:00, 12:00 and 18:00 hUTC, the
maximum difference between sonde launch and model field time is set
to 3 h. The sonde profile is compared to the model profile
from the grid cell containing the sonde launch site, no interpolation
of the model field to the sonde launch location is performed. In
order for the ozone sondes to be used in the validation, it should
have reached a minimum altitude of 10hPa, and the integrated ozone
profile should be between 100 and 550DU (Dobson unit).
Results
Figure
shows the monthly mean FAT columns for the year 2008.
Monthly mean tropospheric O3 fields. Panels (a, d, g, j): free model run; (b, e, h, k): assimilated O3 fields; (c, f, i, l): the relative difference ((free-assim)/free×100).
From top to bottom: March (a, b, c), June (d, e, f), September (g, h, i) and
December (j, k, l) 2008.
In general, the free model shows higher ozone concentrations than the
assimilated ozone fields. The ozone chemistry parameterization used in TM5
is known to overestimate ozone concentrations ,
resulting in the higher ozone concentrations in the free model.
Note that since the FAT has a fixed altitude with respect to sea level,
elevated regions such as Antarctica or the Tibetan Plateau show a
small tropospheric column. The Northern Hemisphere has a higher FAT
column than the Southern Hemisphere, and a yearly cycle can be
clearly seen in the plots. The high ozone concentrations in the
Northern Hemisphere have various sources such as
stratosphere–troposphere exchanges and anthropogenic precursor emissions
. An increase in ozone concentration is seen in
the Southern Atlantic Ocean for September, and between Africa and
Australia in a zonal band around -25∘latitude. This increase can be
attributed to biomass burning and coincides with the month of maximum
NOx concentration (an ozone precursor) in
Africa . From March to September, transport
of ozone-rich air can be seen from Asia across the Pacific. Similar
features in the yearly cycle of ozone are also observed in the
tropospheric ozone climatology by . This
climatology is based on the residual of OMI total columns and MLS
stratospheric columns (using the thermal tropopause definition),
at a horizontal resolution of 5∘× 5∘. Two sharply defined, narrow
zonal features of elevated ozone concentrations can be seen at
10∘ and -20∘latitude. These zonal features are also present in the free
model run (left column in Fig. ),
so they are not caused by the observations. Since the monthly mean (surface) pressure
fields do not show a similar feature, it is unlikely that it is
caused by the meteorological data that are used to drive the model.
The most likely cause for these narrow zonal elevated ozone
concentrations is therefore a model artefact.
It should be noted that the difference is only a few DU, so these zonal features
are not easily observed in total column maps.
(a) Yearly mean FAT tropospheric column, derived from
assimilated ozone fields, and (b) the corresponding
relative error.
TM5 validation results with respect to sondes. Panel (a) shows
the locations of all sondes used in the validation of the model. The
colour coding of the sondes is the same as in Fig. . Panel (b): median absolute difference; (c): median
relative difference. The blue line is the model run without
assimilation; the red line is the model run with assimilation of
GOME-2 and OMI. The error bars indicate the range between the 25th
and 75th percentile.
In the assimilation, the spatial correlation is assumed to be constant. The
correlation is derived by the same method as described in .
The errors in the profile are part of the assimilation output, and combining the correlation matrix and error profiles in a post-processing step
allows the reconstruction of the covariance matrix in the vertical direction.
The tropospheric part of this covariance matrix can be used to derive the
relative error in the tropospheric column. In Fig.
the yearly mean assimilated tropospheric FAT column is given in panel (a), while the associated relative errors are shown in panel (b). The yearly mean
FAT columns vary between 3 and 23DU, while the relative error
varies between 11% and 18%, depending on the location on Earth.
Mean AKs calculated from all assimilated retrievals from GOME-2
(a) and OMI (b). The markers show the altitude of the
retrieval layer, which is listed in hPa in the legend
below each plot.
Scatterplots of tropospheric columns based on model output versus
sonde measurements. The plot symbols are the median values of
collocations grouped by station. The error bars indicate the
25th–75th percentiles of the distribution. Panel (a): free model run; (b): assimilated O3 fields; (c): residual-FAT column for the assimilated O3
fields. Colours indicate 30∘ latitude bands: SP – South Pole; SML – southern midlatitudes; STr – southern tropics; NTr – northern tropics; NML – northern midlatitudes; NP – North Pole. The grey dashed line is the 1:1 line, and the red
dashed line gives the best linear fit to the data. The fit
parameters are listed in the table at (d). The columns marked
a and b are the linear fit parameters of the line a+bx; r is
the linear Pearson correlation coefficient; rms is the root mean
square between the values on both axes. The number of stations included
in each plot is 48.
In order to estimate the impact of the upgraded TM5 resolution and
meteorological data used to drive TM5, we validate the resulting tropospheric
ozone columns with ozone sondes (from the surface up to approximately 30km).
Figure shows absolute and relative
biases for both the free model run and assimilated O3 fields. There is a
significant improvement of the assimilated O3 fields over the free
model run when compared to ozone sondes, with the exception of the
UTLS (upper troposphere and lower stratosphere, around 15km). The sharp ozone gradients in this altitude range
are not captured fully by the model and the satellite observations.
These results are comparable to the TM5 run used in
see their Fig. 13, where the same satellite data were
assimilated into TM5, running on a coarser model resolution and with operational
meteo data. In , the median bias for the tropospheric column
is between -5% and 0% for the period 2008–2011, while in the current
research it is between -2% and 3% for 2008 only.
The FAT rms (a) and mean (b) per station as a
function of latitude. The blue line gives the results for the free
model run compared to sondes. The red line gives the results for
the assimilated O3 fields compared to sondes. Panel (c):
green dots indicate stations where
rmsassim<rmsfree, and red dots where
rmsassim>rmsfree.
Panel (d): green dots indicate stations where
meanassim<meanfree, and red dots where
meanassim>meanfree.
Only results for stations with at least 10 collocations have been plotted.
Time series of monthly median global FAT columns
for
(a) the Southern Hemisphere (-90≤lat<-30),
(b) the tropics (-30≤lat<30) and
(c) the Northern Hemisphere (lat≥30). Blue line: free
model run; red line: assimilated O3 fields; green line: sonde data;
yellow line: Fortuin and Kelder climatology. The numbers
along the x axis indicate the number of collocations between model and sondes.
Note the different scale on the y axis in each panel.
The assimilation of the satellite data has the largest impact in the altitude
range between 100 and 5hPa. This is also the region where the
retrievals are most sensitive to the measurements. To illustrate this, we
averaged the AKs of all GOME-2 and OMI measurements that were assimilated into
the model. The resulting mean AKs are plotted in Fig. ,
with a marker at the altitude of the retrieval layer. The AKs have the largest
value in the altitude range where the improvement of the assimilated
O3 fields over the free model run is the largest. The oscillations in
the AKs near the top of the retrieval have a limited effect because the O3
concentration is small in that altitude range.
In Fig. ,
scatterplots of the FAT columns are shown for the free model run
and the assimilated O3 fields and of the residual-FAT column for
the assimilated O3 fields only. The data are grouped according to
ozone sonde station location. The free model run and assimilated O3 fields perform comparably, and both have a higher correlation coefficient than the
residual method (see Fig. d).
The residual method shows some negative columns, indicating that
the stratospheric part of the assimilated profiles is larger than
the total column from the MSR. Residual-FAT columns based on the
free model run show even more negative values, so they are not shown
in the figure. The residual method has a lower correlation coefficient
and a higher uncertainty than the FAT columns of the free model run and
assimilated O3 fields, and therefore will be omitted from the
subsequent analysis.
We can see from Fig.
that the bias with respect to sondes in the troposphere is smaller for the
assimilated O3 fields than for the free model run.
Figure shows that the root mean square (rms) and
correlation for the assimilated O3 fields slightly improve compared
to the free model run. To further investigate the variation between TM5 results and sonde measurements, the rms and mean differences between the model
and sonde FAT columns are plotted in
Fig. .
The figure gives the rms for all collocations (with a minimum of 10) per
station as a function of latitude in panel (a) and the mean difference
in panel (b). The green dots in the maps indicate stations where the
absolute value of the rms (or mean) from the assimilated O3 fields
is smaller than for the free model run
(rmsassim<rmsfree or
meanassim<meanfree).
The red dots indicate stations where the reverse is true
(rmsassim>rmsfree or
meanassim>meanfree
). In the Southern Hemisphere (lat<-30), the assimilated O3
fields show a smaller rms and a smaller absolute value of the mean
for four and five out of seven stations, respectively.
In the tropics (-30≤lat<30), the assimilated O3
fields show a smaller rms and a smaller absolute value of the mean
for 9 and 10 out of 14 stations, respectively. The assimilated O3 fields
perform better than the free model run for the majority of the tropical stations,
but note that the rms and the absolute value of the mean are
larger than at higher latitudes.
In the Northern Hemisphere (lat≥30), the assimilated O3
fields show a smaller rms and a smaller absolute value of the mean
for 9 and 13 out of 24 stations, respectively.
To study temporal variation, time series of monthly median FAT columns are shown in
Fig. for three different latitude bands.
For the Northern Hemisphere (Fig. a),
the free model run is closer to the sondes than the assimilated O3
fields for January until May. The assimilated O3 fields
are closer to the sonde measurements than the free model run
from June until December. For the lapse rate tropopause (not shown here), the
assimilated O3 fields are closer to the sonde data than the free
model run throughout the year. Since in the troposphere the model is
nudged towards an ozone climatology , the
climatological value for each collocation has been calculated and the
monthly median is also shown in
Fig. .
The free model run follows a similar pattern as the climatological values.
It should be noted that the free model run and assimilated O3 fields
start with the same ozone concentrations. Due to the assimilation of
observations they diverge quickly, and the monthly median values for January
are not the same.
For the tropics (Fig. b),
the ozone sonde FAT columns are lower than the assimilation model run, which in
turn is lower than the free model run throughout the year. This behaviour is
consistent with the plots shown in Fig. .
For the Southern Hemisphere (Fig. c),
both model runs, sonde measurements and climatological values are close together,
except for November and December, which might be a consequence of the ozone hole. Note that for all three latitude bands,
the differences are very small, of the order of 2–3DU,
and close to the uncertainty.
As an example of the FAT-column variability throughout the year,
Fig.
shows time series for the free model run and assimilated O3 fields and for the sonde measurements over three different stations: the
Antarctic station Neumayer (70.56∘S, 8.26∘W),
the tropical station Hilo (19.43∘N, 155.04∘W)
and the Northern Hemisphere station Lerwick (60.14∘N, 1.19∘W).
Time series for all sonde station locations used in this research with more
than 10 collocations with the model output are available in the Supplement.
For the Neumayer station, the free model run and
assimilated O3 fields give comparable results during the polar
night. The decrease in the tropospheric column that is visible from October
onward is caused by solar radiation and NOx-induced
O3 destruction, not by the halogen-induced destruction of the ozone
hole see, e.g.,. For the Hilo station, the assimilated O3 fields show systematically lower FAT columns than the free model run. The FAT columns
from the assimilated O3 fields are in better agreement with the
sonde FAT columns than the free model run. For the Lerwick station,
the free model run and assimilated O3 fields show similar FAT columns, and
the rms bias of the assimilated O3 fields is larger than for the
free model run. However, the absolute value of the mean bias is
larger for the free model run than for the assimilated O3 fields.
Three time series of collocated model output and ozone sonde
measurements. From top to bottom: Neumayer (a),
Hilo (b) and Lerwick (c).
The station coordinates are indicated in the plot titles.
Blue line: FAT column from model run without assimilation; red
line: FAT column from model run with assimilation of GOME-2 and
OMI measurements; green circles: ozone sonde measurements.
Discussion
Deriving tropospheric ozone from nadir-looking UV–visible instruments
is a big challenge due to the limited sensitivity of these instruments
in the troposphere. Since most of the radiation in the wavelength range
between 280 and 330nm is absorbed by the ozone layer, only a
small part reaches the surface. Typical values for the DFS (a measure of the number of independent pieces of information in
the retrieval) of the tropospheric column are between ∼0.5 at higher
latitudes to ∼1.2 in the tropics .
Both the DOAS total columns used in the MSR and the UV–visible
stratospheric partial columns from the retrievals used in this research are accurate measurements of the ozone
concentration. The large variation in the residual-FAT column was
therefore unexpected, and we discuss the differences between both
assimilation systems in some more detail. The MSR only assimilates
total columns, which are distributed over the layers of the model
proportionally to the subcolumn of that layer. The MSR model uses the
same parameterized ozone chemistry as the profile assimilation used
in this research , but with a more
up-to-date version of the chemistry parameters (2.9 for the MSR, 2.1 for this
research). However, since both assimilation systems are frequently
updated with observations, it seems unlikely that the difference in
parameterization version plays a major role in the observed residual-FAT-column variation. Also, data from all available total ozone satellite
sensors is assimilated into the MSR instead of only the profiles from the
two GOME-2 and OMI instruments that are assimilated into the current
system. The observations are both bias corrected: the total columns
with respect to Brewer–Dobson measurements and the profiles with
respect to sondes. The MSR model resolution is 0.5∘×0.5∘, while
the profile assimilation runs on 1∘×1∘. The most extreme
negative residuals are found for the Antarctic sonde stations, so
high solar zenith angles may have some effect. However, since
negative residuals are also found at lower latitudes, it cannot be the
only explanation.
Since the residual-FAT column cannot be used reliably for
determining the tropospheric ozone column, the directly integrated
FAT columns from the assimilated O3 fields might offer an
alternative. The global median difference with O3 sondes is clearly
lower for the assimilated O3 fields than for the free model run
(see Fig. ). However,
this is not so clear from the scatterplots of the FAT columns grouped by station
(see Fig. ). The spatial
distribution is also much better for the assimilated O3 fields than
for the free model run (see Fig. ).
This can be seen, for example, in the outflow of ozone-rich air from Asia over
the Pacific and biomass-burning-enhanced O3 concentrations.
There are several potential explanations for the small improvements
of the assimilation tropospheric ozone columns compared to the free model
run. The reduced sensitivity in the troposphere of GOME-2 and OMI is
compensated for by incorporating the averaging kernel into the
observation operator, and the tropospheric column is changed due to
the assimilation. However, the tropospheric uncertainties of the
observations might be too large to reduce the model uncertainties,
so the improvement due to the assimilation only becomes clear when
looking at the global median results.
The parameterized chemistry version that is being used is known to
overestimate low-latitude ozone in the troposphere .
Below 230hPa, however, the model is
nudged towards the climatology of .
Above 230hPa the full Cariolle chemistry scheme is used, but two
of the parameters in that scheme (i.e. the average volume mixing ratio and the
overhead ozone column) are set to the climatological values.
Other possible factors contributing to the large variation in
the FAT columns are the representation errors between the model and
sondes and between model and observations. Since TM5 is running on a
1∘× 1∘ horizontal grid, the model ozone concentrations are an
average over the grid cell, while the ozone sonde measurements are
point sources. In mountainous regions, the altitude of the model grid
cell might also not correspond to the altitude of the sonde station.
The ground pixel size and location of the satellite observations
might not coincide with the model grid cells either. For example, the
footprint size of the GOME-2 measurements used in this research is
about 160 km×160km, which is larger than the model grid cells. The
satellite instrument ground pixel centre determines in which model
grid cell the pixel is assimilated.
Throughout the year, the FAT column from the assimilated O3 fields
is smaller than the FAT column from the free model run
(Fig. ). This is
consistent with the validation results for the whole profile
(Fig. ) and with the rms values between model and sondes in the
scatterplots of Fig. .
The Northern Hemisphere sonde FAT columns are closer to the free model run from January until May but closer to the assimilated O3 fields from June until December.
The reason for the “smallest bias” shift from the free model run to the
assimilated O3 fields is unknown, but it should be stressed that
the differences are small (of the order of 2–3DU) and close to the
uncertainty. If, instead of the FAT column, the column based on the lapse rate
tropopause is used, such a smallest bias shift does not occur and the bias
with respect to the assimilated O3 fields run is always smaller than
for the free model run.
Conclusions
Ozone profiles retrieved from GOME-2A and OMI measurements were
assimilated simultaneously into the TM5 global chemistry transport
model for the year 2008. With respect to the model version used in
, the horizontal resolution of TM5 is increased from
3∘× 2∘ to 1∘× 1∘
(longitude × latitude). At the same time, the vertical resolution is
decreased from 44 to 31 layers to reduce the computational cost. The meteorological
data used to drive the model has also been upgraded from the operational data
stream from the ECMWF to the ERA-Interim data set.
Due to the large variation in the residual-FAT columns in the current model
setup, they cannot be used reliably, and the direct, integrated FAT
columns should be used instead. The median global bias with respect to
O3 sondes is smaller for the assimilated O3 fields than
for the free model run. When the tropospheric O3 columns are grouped
according to station, the root mean square of the median sonde columns and
model output is smaller for the assimilated O3 fields than for the
free model run. The rms for each station separately also shows an improvement
for the majority of stations on the Southern Hemisphere and in the tropics.
The absolute value of the bias is also smaller for the assimilated O3
fields than for the free model run for the majority of stations globally.
The monthly median global FAT columns show a small bias with respect to
ozone sonde measurements for the free model January until May, but from
June until December, the assimilated O3 fields have the smallest
biases with respect to ozone sondes. The monthly mean ozone fields show
significant improvements and more detail when comparing the assimilated
O3 fields with the free model run, especially for features such
as biomass-burning-enhanced ozone concentrations and outflow of ozone-rich
air from Asia over the Pacific.
Data availability
OMI ozone profiles are operationally retrieved and can be
obtained from NASA's Goddard Earth Sciences (GES) Data and Information Services
Center (DISC) online archive at https://aura.gesdisc.eosdis.nasa.gov/data/Aura_OMI_Level2/OMO3PR.003/ (last access: 1 December 2018; ).
GOME-2 ozone profiles are specifically retrieved for this research and can be obtained by
contacting the author. Although not used in this research, operationally
retrieved GOME-2 ozone profiles can be retrieved from EUMETSATs ACSAF (https://acsaf.org/index.html, last access: 1 December 2018; ),
but note that a registration is required.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-8297-2019-supplement.
Author contributions
JvP performed the calculations and analyses of the
research, and he wrote the paper with comments from RvdA. RvdA was involved in
the conceptualization of the paper and had a supervising role.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors acknowledge all scientists and institutes who contributed their
ozone sonde data to the World Ozone and Ultraviolet Radiation Data Center
and the Meteorological Service of Canada for hosting
this important public database. EUMETSAT is acknowledged for providing the
GOME-2 L1 data and Olaf Tuinder and Robert van Versendaal for their help in the
retrieval of the GOME-2 ozone profiles. The Dutch–Finnish OMI instrument is part
of the NASA EOS Aura satellite payload. The OMI ozone profiles (OMO3PR, v003)
were retrieved at the NASA Goddard Earth Sciences Data and Information Services
Center (GES DISC) and accessed from the local storage at the Royal Netherlands
Meteorological Institute (KNMI). This research was part of the Ozone_cci project
(http://www.esa-ozone-cci.org, last access: 1 December 2018), which is
part of the Climate Change Initiative (CCI) programme of the European
Space Agency (ESA).
Review statement
This paper was edited by Michel Van Roozendael and reviewed by two anonymous referees.
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