Introduction
Long-term records of the tropospheric composition of gases such as
ozone (O3), carbon monoxide (CO), and nitrogen oxides
(NOx) are important for understanding the changes in
tropospheric chemistry and human activity and consequences for the
atmospheric environment and climate change (HTAP, 2010; IPCC,
2013). Satellite instruments provide observations of the global
distributions of tropospheric composition. For example, measurements
of tropospheric O3 have been retrieved using the Tropospheric
Emission Spectrometer (TES) since 2004 (Beer, 2006) and by the
Infrared Atmospheric Sounding Interferometer (IASI) since 2007 (Coman
et al., 2012). Tropospheric NO2 column concentrations have
been retrieved by the Ozone Monitoring Instrument (OMI) since 2004
(Levelt et al., 2006), the Scanning Imaging Absorption Spectrometer for
Atmospheric Cartography (SCIAMACHY) from 2002 to 2012 (Bovensmann
et al., 1999), the Global Ozone Monitoring Experiment (GOME) from
1996 to 2003, and GOME-2 since 2007 (Callies et al., 2000). The
availability of satellite-derived measurements of various chemical
species has prompted increasing interest in developing methods for
combining these sources of satellite observational information for
studies of long-term variations within the atmospheric environment and
for improving estimates of emissions sources (Inness et al., 2013;
Streets et al., 2013).
Combining measurements of O3, CO and NOx in the
atmosphere puts constraints on the concentration of OH, the
main radical responsible for the removal of pollution from the
atmosphere and determining the lifetime of many chemicals (Levy, 1971;
Logan et al., 1981; Thompson, 1992). At the same time the combined use
provides constraints on different sources of surface emissions and
production of NOx by lightning (LNOx) (e.g. Martin
et al., 2007; Miyazaki et al., 2014). The information that may be
obtained from a combined use of multiple satellite data sets without
involving a model is limited, related to differing vertical
sensitivity profiles, different overpass times, and mismatches in
spatial and temporal coverage between the instruments, as well as
missing information on the chemical regime and origin of the air
masses.
Data assimilation is the technique for combining different
observational data sets with a model by considering the
characteristics of each measurement (e.g. Kalnay, 2003; Lahoz and
Schneider, 2014). Advanced data assimilation schemes like the Kalman
filter or the related 4D-Var technique use the information provided by
satellite-derived measurements and propagate it, in time and space,
from a limited number of observed species to a wide range of chemical
components to provide global fields that are physically and chemically
consistent and in agreement with the observations. Various studies
have demonstrated the capability of data assimilation techniques
regarding the analysis of chemical species in the troposphere and
stratosphere.
Assimilation of satellite limb measurements for O3 profiles
and nadir measurements for O3 columns has been used to study
O3 variations in the stratosphere and the upper troposphere
(e.g. Stajner and Wargan, 2004; Jackson, 2007; Stajner et al., 2008;
Wargan et al., 2010; Flemming et al., 2011; Barré et al., 2013; Emili
et al., 2014). Long-term integrated data sets of stratospheric
O3 have been produced by several studies by combining multiple
satellite retrieval data sets (e.g. Kiesewetter et al., 2010; van der
A et al., 2010). The assimilation of satellite observations has been
also applied to investigate global variations in the tropospheric
composition of gases such as O3 and CO (e.g.
Parrington et al., 2009; Coman et al., 2012; Miyazaki et al.,
2012b). For providing long-term integrated data of tropospheric
composition, as a pioneer study, Inness et al. (2013) performed an
8-year reanalysis of tropospheric chemistry for 2003–2010 using
an advanced data assimilation system. They included atmospheric
concentrations of O3, CO, NOx, and formaldehyde
(CH2O) as the forecast model variables in the integrated
forecasting system with modules for atmospheric composition (C-IFS),
and they demonstrated improved O3 and CO profiles for
the free troposphere. They also highlighted biases remaining in the
lower troposphere associated with fixed surface emissions, which are
not adjusted in the 4D-Var assimilation scheme presented by Inness
et al. (2013).
Currently available bottom-up inventories of emissions, produced based on statistical data such as emission-related activities and emissions factors, contain large
uncertainties, mainly because of inaccurate activity rates and
emission factors for each category and poor representation of their
seasonal and interannual variations (e.g. Jaeglé et al., 2005;
Xiao et al., 2010; Reuter et al., 2014). Top-down inverse approaches
using satellite retrievals have been applied to obtain optimised
emissions of CO (e.g. Kopacz et al., 2010; Hooghiemstra et al.,
2011) and NOx (e.g. Lamsal et al., 2010; Miyazaki et al.,
2012a; Mijling et al., 2013) by minimising the differences between
observed and simulated concentrations, as summarised by Streets
et al. (2013). In addition to surface emissions, the improved
representations of LNOx sources are important for a realistic
representation of O3 formation and chemical processes in the
upper troposphere (Schumann and Huntrieser, 2007; Miyazaki et al.,
2014).
The simultaneous adjustment of emissions and concentrations of various
species is a new development in tropospheric chemical reanalysis and
long-term emissions analysis. Miyazaki et al. (2012b) developed a data
assimilation system, called CHASER-DAS, for the simultaneous
optimisation of the atmospheric concentration of various trace gases,
together with an optimisation of the surface emissions of NOx
and CO, and the LNOx sources, while taking their
complex chemical interactions into account, as represented by the
CHASER chemistry-transport model. Within the simultaneous optimisation
framework, the analysis adjustment of atmospheric concentrations of
chemically related species has the potential to improve the emission
inversion (Miyazaki and Eskes, 2013; Miyazaki et al., 2014). This was
compared with an emission inversion based on measurements from one
species alone, where uncertainties in the model chemistry affect the
quality of the emission source estimates. In addition, the improved
estimates of emissions benefit the atmospheric concentration analysis
through a reduction in model forecast error. The simultaneous
adjustment of the emissions and the concentrations is therefore
a powerful approach to optimise all aspects of the chemical system
influencing tropospheric O3 (Miyazaki et al., 2012b).
In this study, we present a tropospheric chemistry reanalysis data set
for the 8-year period from 2005 to 2012 using CHASER-DAS. This
reanalysis is produced with the CHASER-DAS system introduced in
Miyazaki et al. (2012b). The system uses the ensemble Kalman filter
(EnKF) assimilation technique and assimilates Microwave Limb Sounder
(MLS), OMI, TES, and Measurement of Pollution in the Troposphere
(MOPITT) retrieved observations. The chemical concentrations and
emission sources are simultaneously optimised during the reanalysis,
and are expected to provide useful information for various research
topics related to the interannual variability of the atmospheric
environment and short-term trends.
The remainder of this paper is structured as follows. Section 2
describes the observations used for the assimilation and
validation. Section 3 introduces the data assimilation system and
Sect. 4 evaluates the reanalysis performance based on analyses of data
assimilation statistics. Section 5 presents comparisons against
independent observations. Section 6 describes the emission source
estimation results. Section 7, which discusses possible errors in the
reanalysis data and offers thoughts on future developments, is
followed by the conclusions in Sect. 8.
Data assimilation system
The CHASER-DAS system (Miyazaki et al., 2012a, b, 2014; Miyazaki and
Eskes, 2013) has been developed based on an EnKF approach and a global
chemical transport model called CHASER. The data assimilation settings
used for the reanalysis calculation are mostly the same as in Miyazaki
et al. (2014), but the calculation was extended to cover the eight
years from 2005 to 2012, and several updates were applied to the
a priori and state vector settings. Brief descriptions of the forecast
model, data assimilation approach, and experimental settings are
presented below.
Forecast model
The CHASER model (Sudo et al., 2002; Sudo and Akimoto, 2007) was used as a forecast
model. It has so-called T42 horizontal resolution (2.8∘ for longitude and the T42 Gaussian grid for latitude) and
32 vertical levels from the surface to 4 hPa. It is coupled to
the atmospheric general circulation model (AGCM) version 5.7b of the
Center for Climate System Research and Japanese National Institute for
Environmental Studies (CCSR/NIES). Meteorological fields are provided
by the AGCM at every time step of CHASER (i.e. every
20 min). The AGCM fields were nudged toward the National
Centers for Environmental Prediction/Department of Energy Atmospheric
Model Intercomparison Project II (NCEP-DOE/AMIP-II) reanalysis
(Kanamitsu et al., 2002) at every time step of the AGCM to reproduce
past meteorological fields. The nudged AGCM enabled us to perform
CHASER calculations that included short-term atmospheric variations
and parameterised transport processes by sub-grid-scale convection and
boundary layer mixing.
The a priori value for surface emissions of NOx and CO
were obtained from bottom-up emission inventories. Anthropogenic
NOx and CO emissions were obtained from the Emission
Database for Global Atmospheric Research (EDGAR) version 4.2. Emissions from biomass burning are based on the monthly Global
Fire Emissions Database (GFED) version 3.1 (van der Werf et al.,
2010). Emissions from soils are based on monthly mean Global Emissions
Inventory Activity (GEIA) (Graedel et al., 1993). EDGAR version 4.2
was not available after 2008 at the time the reanalysis was started;
therefore, the emissions for 2008 were used in the calculations for
2009–2012. GFED 3.1 was not available for 2012, and thus the
emissions averaged over 2005–2011 were used in the calculation for
2012. For surface NOx emissions, a diurnal variability scheme
developed by Miyazaki et al. (2012a, b) was applied depending on the
dominant category for each area: anthropogenic, biogenic, and soil
emissions.
For the calculation of a priori LNOx emissions, the global
distribution of the flash rate was parameterised in CHASER for
convective clouds based on the relation between lightning activity and
cloud top height (Price and Rind, 1992). To obtain a realistic
estimate of the global annual total flash occurrence, a tuning factor
was applied for the global total frequency, which is independent of
the lightning adjustment in the assimilation. The global distribution
of the total flash rate is generally reproduced well by the model in
comparison with the observations, except for overestimations over
northern South America and underestimations over both Central Africa
and most of the oceanic Intertropical Convergence Zone (Miyazaki
et al., 2014).
Data assimilation technique
The data assimilation technique employed is an EnKF approach, i.e.
a local ensemble transform Kalman filter (LETKF; Hunt et al., 2007)
based on the ensemble square root filter (SRF) method, which uses an
ensemble forecast to estimate the background error covariance
matrix. The covariance matrices of the observation error and
background error determine the relative weights given to the
observation and the background in the analysis. The LETKF has conceptual and computational advantages
over the original EnKF. First, the analysis is performed locally in
space and time, which reduces sampling errors caused by limited
ensemble size. Second, performing the analysis independently for
different grid points allow parallel computations to be performed that
reduce the computational cost. These advantages are important in the
chemical reanalysis calculation because of the many analysis steps
included in the 8-year reanalysis run and the large state vector
size used for the multiple-states optimisation (cf. Sect. 2.3 and 2.7).
The assimilation step transforms a background ensemble
(xib;i=1,…,k) into an analysis ensemble
(xia;i=1,…,k) and updates the analysis mean,
where x represents the model variable, b the background
state, a the analysis state, and k the ensemble size. The forecast
and analysis steps are described briefly below.
The forecast step
In the forecast step, the background ensemble mean xb‾ and its
perturbation Xb are obtained from the evolution of each
ensemble member using the forecast model at every model grid,
xb‾=1k∑i=1kxib;Xib=xib-xb‾.
Xib is the ith column of an N×k matrix Xb, where N indicates the
system dimension (the state vector size times the physical system dimension). Based on the
assumption that background ensemble perturbations Xb
sample the forecast errors, the background error covariance is
estimated as follows:
Pb=Xb(Xb)T,
where the background error covariance Pb varies with time and space, reflecting dominant atmospheric processes and locations of the observations.
An ensemble of background vectors yib and an ensemble of
background perturbations in the observation space Yb
are estimated using the observation operator H (cf. Sect. 2.5):
yib=Hxib;Yib=yib-yb‾.
The analysis step
The analysis ensemble mean is obtained by updating the background
ensemble mean:
xa‾=xb‾+XbP̃a(Yb)TR-1(yo-yb‾),
where yo represents the observation vector, R
is the p×p observation error covariance, and p indicates
the number of observations. The observation error information is
obtained for each retrieval (cf. Sect. 2.6), where
P̃a is the k×k local analysis
error covariance in the ensemble space:
P̃a=(k-1)I1+Δ+(Yb)TR-1Yb-1.
A covariance inflation factor (Δ=6 %) was applied to
inflate the forecast error covariance at each analysis step. The
inflation is used to prevent an underestimation of background error
covariance and resultant filter divergence caused by model errors and
sampling errors. The estimation of the
P̃a matrix does not require any
calculation of large vectors or matrices with N dimensions in the
LETKF algorithm.
The new analysis ensemble perturbation matrix in the model space
(Xa) is obtained by transforming the background
ensemble Xb with P̃a:
Xa=Xb(k-1)P̃a1/2.
The new ensemble members xib after the next forecast step are
then obtained from model simulations starting from the analysis ensemble xia.
State vector
The state vector for the reanalysis calculation is chosen to optimise
the tropospheric chemical system and to improve the reanalysis
performance. The state vector used in the reanalysis includes several
emission sources (surface emissions of NOx and CO, and
LNOx sources) as well as the predicted concentrations of 35
chemical species. The chemical concentrations in the state vector are expressed in the form of volume mixing ratio, while the emissions are represented by scaling factors for each surface
grid cell for the total NOx and CO emissions at the
surface (not for individual sectors), and for each production rate
profile of the LNOx sources. Perturbations obtained by adding
these model parameters into the state vector introduced an ensemble
spread of chemical concentrations and emissions in the forecast
step. The background error correlations, estimated from the ensemble
model simulations at each analysis step, determine the relationship
between the concentrations and emissions of related species, which can
reflect daily, seasonal, interannual, and geographical variations in
transport and chemical reactions. The emission sources were optimised
at every analysis step throughout the reanalysis period, which reduced the initial
bias in the a priori emissions during the
data assimilation cycle.
Covariance localisation
The EnKF approach always has the problem of introducing unrealistic
long-distance error correlations because of the limited number of
ensemble members. During the reanalysis calculation, such spurious
correlations lead to errors in the fields that may accumulate and will
influence the reanalysis quality in a negative way. In order to
improve the filter performance, the covariance among non- or weakly
related variables in the state vector is set to zero based on
sensitivity calculation results, as in Miyazaki et al. (2012b). The
analysis of surface emissions of NOx and CO allowed for
error correlations with OMI NO2 and MOPITT CO data,
while those with other data were neglected. For the LNOx
sources, covariances with MOPITT CO data were
neglected. Concentrations of NOy species and O3 were
optimised from TES O3, OMI NO2, and MLS O3 and
HNO3 observations. One difference to the study of Miyazaki
et al. (2012b) is that concentrations of non-methane hydrocarbons (NMHCs) were
not optimised in the reanalysis. The assimilation of MOPITT CO
data led to concentrations of NMHCs that increased to unrealistic
values during the reanalysis, likely associated with too much chemical
destruction of CO (cf. Sect. 7.4.2).
Covariance localisation was also applied to avoid the influence of
remote observations, which is described in Sect. 2.7.
Observation operator
The observation operator (H) includes the spatial interpolation
operator (S), a priori profile (xa priori), and
averaging kernel (A), which maps the model fields
(xib) into retrieval space (yib), thereby
accounting for the vertical averaging implicit in the observations, as
follows:
yib=H(xib)=xa priori+A(S(xib)-xa priori),
where xib is the N-dimensional state vector and
yib is the p-dimensional model equivalent of the
observational vector. The averaging kernel A defines the vertical
sensitivity profile of the satellite observation. Even though the retrieval
yo and the model equivalent yib both
depend on the a priori, the use of the kernel removes the dependence of the
analysis or the relative model–retrieval comparison
(yib-yo)/yib on
the retrieval a priori profile (Eskes and Boersma, 2003; Migliorini, 2012).
Observation error
The observation error provided in the retrieval data products includes
contributions from the smoothing errors, model parameter errors,
forward model errors, geophysical noise, and instrument errors. In
addition, a representativeness error was added for the OMI NO2
and MOPITT CO observations to account for the spatial
resolution differences between the model and the observation using
a super-observation approach following Miyazaki et al. (2012a). The
super-observation error was estimated by considering an error
correlation of 15 % among the individual satellite observations
within a model grid cell.
Reanalysis settings
Because a single continuous data assimilation calculation for 8 years requires
a long computational time, we parallelised the reanalysis
calculation. Eight series of 1-year calculations from 1 January of each year in 2005–2012 with a 2-month spin-up starting from 1 November of
the previous year were conducted to produce the 8-year reanalysis data set. Each 1-year run was parallelised on 16
processors. The 2-month spin-up removed the differences in the
analysis between the different time series, providing a continuous
8-year data set. Because of distinct diurnal variations in the
tropospheric chemical system, the data assimilation cycle was set to
be short (i.e. 120 min) to reduce sampling errors. The
emission and concentration fields were analysed and updated at every
analysis step.
In the reanalysis calculation the ensemble size was set to 30, which is
somewhat smaller than the 48 members used in our previous studies. A smaller
ensemble size reduces computational cost but slightly degrades analysis
performance, as quantified in Miyazaki et al. (2012b). The horizontal
localisation scale L was set to 450 km for NOx
emissions and to 600 km for CO emissions, LNOx,
and for the concentrations. The physical vertical localisation length was set
to ln(P1/P2) [hPa] = 0.2. These choices are based on sensitivity
experiments (Miyazaki et al., 2012b), for which the influence of an
observation was set to zero when the horizontal distance between the
observation and analysis point was larger than 2L×10/3 (the cut-off radius is set to 2191 km for L=600 km). We also account
for the influence of the averaging kernels of the instruments, which captures
the vertical sensitivity profiles of the retrievals. The ensemble members and
ensemble spread (error covariance) do vary from one location to the next, and
from one species to the next, thereby representing the large number of
degrees of freedom contained in the model and the way these are constrained
by the observations.
The a priori error was set to 40 % for surface emissions of
NOx and CO and 60 % for LNOx sources, but
a model error term was not implemented for emissions during the
forecast. To prevent covariance underestimation and maintain emission
variability during the long-term reanalysis calculation, we applied
covariance inflation to the emission source factors in the analysis
step – i.e. model error is implemented through a covariance inflation
term. The standard deviation was artificially inflated to a minimum
predefined value (30 % of the initial standard deviation) at each
analysis step. This was found to be important for representing
realistic seasonal and interannual variability in the emission
estimates, as confirmed by the improved agreements between the
predicted concentrations and independent observations when this
emission covariance inflation setting is used.
In addition to the standard reanalysis run, we conducted a control run
for the 8-year period from 2005 to 2012 and several sensitivity
calculations for 2005 and 2010 by changing the data assimilation
settings. The control run was performed without any data assimilation,
but using the same model settings as used in the reanalysis run. The
settings and results of sensitivity calculations are presented in
Sect. 7.
Data assimilation statistics
χ2 diagnosis
The long-term stability of the data assimilation performance is
important in evaluating the reanalysis. The χ2 test can be used
to evaluate the data assimilation balance (e.g. Ménard and Chang,
2000), which is estimated from the ratio of the actual
observation minus forecast (OmF: yo-Hxb) to the sum of the estimated model
and observation error covariances in the observational space
(HPbHT+R), as
follows:
Y=1m(HPbHT+R)-1/2(yo-Hxb),χ2=traceYYT,
where m is the number of observations. χ2 becomes 1 if the
background error covariances (Pb) are properly
determined to match with the observed OmF (yo-Hxb) under the presence of the
prescribed observation error (R).
Figure 1 shows the temporal evolution of the number of assimilated
observations (m) and χ2 for each assimilated measurement
type. The number of super-observations is shown for the OMI NO2
and MOPITT CO. For most cases, the mean values of χ2 are
generally within 50 % difference from the ideal value of 1, which
suggests that the forecast error covariance is reasonably well
specified in the data assimilation throughout the reanalysis. Note
that the covariance inflation factors for the concentrations and
emissions were optimised to approach to the ideal value based on
sensitivity experiments (Miyazaki et al., 2012b). For the OMI
NO2 assimilation, the χ2 is >1, which indicates
overconfidence in the model or underestimation of the super-observation error
(computed as a combination of the measurement error and the representativeness error). The χ2 for the OMI NO2 was
less sensitive to the choice of the inflation factor compared to that
for other assimilated measurements. Lower tropospheric NO2 is
controlled by fast chemical reactions restricted by biased chemical
equilibrium states, leading to an underestimation of the background
error covariance during the forecast. Although the emission analysis
introduces spread to the concentration ensemble, the perturbations are
present primarily near the surface and tend to be removed in the free
troposphere because of the short chemical lifetime of NOx.
Time series of the monthly mean chi-square value and its
standard deviation (black lines) and the number of assimilated
observations per month (blue bars) for OMI NO2, TES
O3, MOPITT CO, MLS O3, and MLS
HNO3. A super-observation approach is employed to the OMI
and MOPITT measurements (the number of super-observations is
shown), whereas individual observations are used in the analysis of
the others.
Before 2010, the annual mean χ2 is roughly constant, which
confirms the good stability of the performance. Seasonal and
interannual variations, especially after 2010, in χ2 can be attributed to variations
in the coverage and quality of satellite retrievals as well as changes
in atmospheric conditions (e.g. chemical lifetime and dominant
transport type). The increased χ2 for OMI NO2 after 2010
is associated with a decrease in the number of the assimilated
measurements and changes in the super-observation error. Both the mean
measurement error and the representativeness error (a function of the
number of OMI observations) are typically larger in 2010–2012 than in
2005–2009; the mean measurement error and the total super-observation
error (a sum of the measurement error and the representativeness
error) averaged over 30–55∘ N in January are about 7 and
9 % larger in 2010–2012 than in 2005–2009, respectively. After
2010, the excessive χ2 indicates underestimations in the
analysis spread, while the increased OmF indicates smaller corrections
by the assimilation (cf. Sect. 4.2). To correct the concentrations
and emission from OMI super-observations that have larger super-observation errors, the forecast error needs to be further
inflated. A technique to adaptively inflate the forecast error
covariance for the concentrations and emissions of NO and
NO2 is required to better represent the data assimilation
balance throughout the reanalysis.
Time–latitude cross section of the monthly and zonal mean
OmF obtained without assimilation (left panels) and with
assimilation (centre panels). The positive and negative OmF values
are shown in red and blue, respectively. Positive OmF represents
negative model bias compared with observations. Right panels show
latitudinal distributions of the 8-year mean OmF bias (black
line) and RMSE (red line) obtained with assimilation (solid line)
and without assimilation (dotted line). The first row is the OmF for
OMI NO2 data (in 1015 moleccm-2), second row is
for TES O3 data between 500 and 300 hPa (in
ppb), third row is for MOPITT CO data between 700 and
500 hPa (in ppb), fourth row is for MLS O3 data
between 216 and 100 hPa (in ppm), and fifth row is for MLS
HNO3 data between 150 and 80 hPa (in
ppb). A super-observation approach is employed to the OMI and
MOPITT measurements, whereas individual observations are used in the
analysis of the others.
OmF
OmF statistics are computed in observation space to investigate the
structure of model–observation differences and to measure
improvements in the reanalysis (Fig. 2). Model biases, as measured
from the OmF in the control run, are persistent throughout the
reanalysis period and vary considerably with season. The figure shows
an underestimation (i.e. positive OmF) of tropospheric NO2
columns compared with the OMI NO2 data from the Southern Hemisphere (SH) subtropics
to NH mid-latitudes, an underestimation of tropospheric CO
compared with MOPITT CO data in the NH, an overestimation
(i.e. negative OmF) of middle and upper-tropospheric O3 in
the extratropics compared with TES and MLS O3 data, and
underestimation of middle-tropospheric O3 in the tropics
compared with TES. The underestimation of tropospheric CO by CHASER was found to be very similar to that in most of the other chemistry-transport models (CTMs) (Shindell et al., 2006).
After 2010, the positive OmF for MOPITT CO in the control run
decreases in the NH, and the positive OmF for OMI NO2
increases in the NH mid-latitudes. As the quality of these retrievals
is considered constant in the reanalysis period (e.g. Worden et al.,
2013), the interannual variations in OmF are probably attributed to
long-term changes in the model bias. The anthropogenic emission
inventories for 2008 were used in the model simulation for 2009–2012,
which could be partly responsible for the absence of a concentration
trend in the model.
In the reanalysis run, the OmF bias and root-mean-square error (RMSE) for MLS O3
becomes nearly zero globally because of the assimilation. The
systematic reductions of the OmF confirm the continuous corrections
for model errors by the assimilation. The remaining error is almost
equal to the mean observational error. The OmF reduction is relatively
smaller for MLS HNO3 than for MLS O3 because of the
larger observational errors.
The mean OmF bias against TES O3 data in the middle
troposphere is almost completely removed because of the assimilation,
and the mean OmF RMSE is reduced by about 40 % in the SH
extratropics and by up to 15 % from the tropics to the NH. The
error reduction is weaker in the lower troposphere (figure not shown)
because of the reduced sensitivity of the TES retrievals to lower-tropospheric O3. The analysed OmF becomes larger after 2010
corresponding to the decreased number of assimilated measurements.
Data assimilation removes most of the OmF bias against MOPITT
CO data with a mean bias (RMSE) reduction of about 85 %
(60 %) in the NH extratropics and about 80 % (30 %) in the
tropics, respectively. The annual mean OmF becomes almost constant
through the reanalysis, suggesting that the a posteriori emissions
realistically represent the interannual variations.
The mean OmF bias against OMI NO2 is reduced with a mean
reduction of about 30–60 % at the NH mid-latitudes and about
50–60 % in the tropics. The remaining errors could be associated
with the short chemical lifetime of NOx in the boundary layer
as compared to the OMI revisit time of roughly 1 day, biases in the
simulated chemical equilibrium state, and the underestimation of the
emission spread. The OmF is relatively larger in 2010–2012 than in
other years, corresponding to about half the reduction in the OMI
NO2 observation. The number of assimilated measurements is
important for reducing model errors, even when global coverage is
provided. The mean observation-minus-analysis (OmA) bias is about
10–15 %; it is smaller in the NH mid-latitudes and almost the
same in the tropics and SH compared with the mean OmF in the
reanalysis (figure not shown).
Time–latitude cross section of the analysis increment (upper
panels, in ppb per analysis step) and the analysis spread (lower panels, in ppb/analysis step)
obtained for O3 at 700 hPa (left), 400 hPa
(centre), and 200 hPa (right).
Analysis increment
The analysis increment information, estimated from the differences between
the forecast and the analysis both in the reanalysis run, is a measure of the
adjustment made in the analysis step. The analysis increment for O3
is mostly positive at 700 hPa and negative at 400 hPa at mid-latitudes (Fig. 3). The positive (negative) increments imply that the
short-term model forecast underestimates (overestimates) the O3
concentrations. As the increments are introduced by the TES assimilation,
these vertical structures suggest that the tropospheric TES O3 data
have independent information for the lower- and upper-tropospheric O3.
Jourdain et al. (2007) showed that the TES retrievals have 1–2 DOFs in the
troposphere, with the largest DOFs for clear-sky scenes occurring at low
latitudes where TES can distinguish between lower- and upper-tropospheric
O3. The obtained analysis increments correspond well to the OmF in
the control run at the same altitude (figure not shown), confirming that the
data assimilation effectively reduced the model errors through the analysis
steps. Assimilation of other measurement generally provides much smaller
increments on the tropospheric O3. The analysis increment varies
largely with season and year, reflecting variations in short-term systematic
model errors and observational constraints. After 2010 the availability of
TES observations is strongly reduced, which explains the small increments in
the later years.
The mean analysis increment for NO2 varies largely with space
and time in the troposphere (not shown). For some regions with strong
surface emissions, especially at NH mid-latitudes, the NO2
increment becomes negative in the free troposphere because of the
assimilation of non-NO2 measurements, compensating for the
tropospheric NO2 column changes caused by the (positive)
surface emissions adjustment. This demonstrates that simultaneous data
assimilation provides independent constraints on the surface emissions
and free-tropospheric NO2 concentration, because of the use of
observations from multiple species with different measurement
sensitivities. Large adjustments are introduced to the NO2
concentration in the upper troposphere–lower stratosphere (UTLS), because the MLS O3 and HNO3
assimilation effectively corrects the model NO2 bias as
a result of the correlations between species in the error covariance
matrix.
Model minus observation comparisons of the mean O3 concentrations between the
analysis or control run (in brackets) and the observations. The units of
the root-mean-square error (RMSE) and bias are ppb. Results are provided for
WOUDC ozonesonde observations during 2005–2012, MOZAIC/IAGOS aircraft measurements during 2005–2012, and HIPPO aircraft measurements during 2009–2011.
90–55∘ S
55–15∘ S
15S–15∘ N
15–55∘ N
55–90∘ N
Bias
RMSE
Bias
RMSE
Bias
RMSE
Bias
RMSE
Bias
RMSE
850–
-1.7
4.0
-1.0
5.6
2.8
7.4
-0.9
6.9
-3.9
6.0
500
(-1.6)
(4.2)
(-1.2)
(6.0)
(0.6)
(7.4)
(-2.4)
(7.3)
(-5.4)
(6.5)
WOUDC
500–
5.0
19.6
-1.9
14.6
1.0
9.4
-1.3
17.7
-8.0
29.0
sonde
200
(32.5)
(32.7)
(11.5)
(21.5)
(-2.6)
(10.0)
(-0.2)
(19.1)
(-12.3)
(31.7)
200–
46.3
88.8
7.6
48.7
-1.6
19.7
4.0
67.1
2.7
95.2
90
(240.4)
(202.8)
(103.7)
(100.6)
(4.0)
(25.3)
(44.3)
(84.1)
(34.8)
(125.4)
850–
–
–
–
–
4.1
11.2
2.7
10.3
-1.7
8.1
500
–
–
–
–
(1.6)
(11.0)
(1.0)
(10.3)
(-3.9)
(8.8)
MOZAIC/IAGOS
500–
–
–
–
–
4.2
11.4
4.8
16.3
-2.7
36.5
aircraft
300
–
–
–
–
(0.6)
(11.8)
(4.8)
(16.9)
(-2.8)
(37.1)
300–
–
–
–
–
6.8
14.2
6.1
34.1
7.3
64.0
200
–
–
–
–
(-0.3)
(13.9)
(7.2)
(36.7)
(-17.6)
(69.4)
850–
0.1
6.1
1.0
6.9
2.3
8.4
-0.9
10.0
-3.1
7.5
HIPPO
500
(0.9)
(6.6)
(1.4)
(7.4)
(1.3)
(8.3)
(-2.6)
(10.3)
(-5.3)
(8.1)
aircraft
500–
-3.5
28.1
4.2
15.2
4.2
10.2
3.5
20.8
-2.2
42.9
200
(33.8)
(46.4)
(15.3)
(23.3)
(3.1)
(10.7)
(4.0)
(22.8)
(-4.1)
(46.3)
Evaluation using independent observations
O3
Ozonesonde
The validation of the reanalysis and control run with global
ozonesonde observations is summarised in Table 1. As depicted in
Figs. 4 and 5, the CHASER simulation reproduced the observed main
features of global O3 distributions in the troposphere and
lower stratosphere. However, there are systematic differences such as
a negative bias in the NH high-latitude troposphere and a positive
bias from the middle troposphere to the lower stratosphere in the SH.
The reanalysis shows improved agreements with the ozonesonde
observations. The mean negative bias in the NH high latitudes is
reduced in the troposphere. In the NH mid-latitudes, the model's
positive bias in the UTLS and negative bias in the lower troposphere
is mostly removed. The large reduction of the mean lower-tropospheric
bias in the NH mid-latitudes is attributed primarily to increased
O3 concentrations in boreal spring–summer (Fig. 5). The RMSEs
compared with the ozonesonde observations are also reduced throughout
the troposphere. The remaining errors, especially near the surface,
are associated with low retrieval sensitivities in the lower
troposphere and gaps in the spatial representation between the model
and observations.
In the tropics, the data assimilation generally increases the
O3 concentration, reducing the negative bias in the upper
troposphere but increasing the positive bias in the lower
troposphere. The increased positive bias could be attributed to the
positive bias in the TES measurements (Sect. 7.2).
In the SH, the model's positive bias from the middle troposphere to
the lower stratosphere is attributed largely to a positive bias in the
prescribed O3 concentrations above 70 hPa in CHASER,
which is mostly removed in the reanalysis. The observed seasonal and
interannual variations are captured well in the reanalysis.
The observed tropospheric O3 concentration shows variations
from year to year during the reanalysis period (Fig. 5). As summarised
in Table 2, the reanalysis reveals better agreements with the observed
linear slope in most cases. The observed linear slope during the
reanalysis period is positive (+2.9±2.8 ppb(8years)-1) at the NH mid-latitudes between 850
and 500 hPa, but the significance of this trend is not very
high. The slope over the 8-year period at the same region is also
positive in the reanalysis data (+1.2±2.1 ppb(8years)-1), whereas it is negative in the
control run (-1.2±2.1 ppb(8years)-1). At the NH
mid-latitudes in the lower stratosphere (200–90 hPa), the
observed slope is negative (-17.7±41.9 ppb(8years)-1), whereas the reanalysis (-25.7±38.8 ppb(8years)-1) shows better agreement with the
observed slope than the control run (-35.8±46.3 ppb(8years)-1). The seasonal and year-to-year
variations are generally well reproduced in the control run in the NH
troposphere (r=0.73–0.93), whereas the reanalysis further improves
the temporal correlation by 0.07 between 850 and 500 hPa and
by 0.04 between 500 and 200 hPa at the NH mid-latitudes.
Comparison of the vertical O3 profiles between
ozonesondes (black), control run (blue), and reanalysis (red)
averaged for the period 2005–2012. The left column shows the mean
profile; centre and right columns show the mean difference and the
RMSE between the control run and the observations (blue) and between
the reanalysis and the observations (red). From top to bottom,
results are shown for the NH high latitudes (55–90∘ N), NH
mid-latitudes (15–55∘ N), tropics
(15∘ S–15∘ N), SH mid-latitudes
(15–55∘ S), and SH high latitudes (55–90∘ S).
The observed time series show obvious year-to-year variations in the
tropics associated with variations such as in the El
Niño–Southern Oscillation (ENSO), including their influences on
the biomass-burning activity. The tropical O3 variations are
better represented in the reanalysis (r=0.80 between 850 and
500 hPa and r=0.72 between 500 and 200 hPa)
than in the control run (r=0.74 and r=0.59). In the
tropics and SH, annual and zonal mean O3 concentration does
not show clear linear trends during the reanalysis period either in
the observations or reanalysis. However, local O3
concentrations might have significant trends. For instance, Thompson
et al. (2014) showed wintertime free-tropospheric O3 increases
over Irene and Réunion probably due to long-range transport
of growing pollution in the SH. Further analyses will be required to
investigate the detailed characteristics of O3 variation.
The ozonesonde–analysis difference is slightly larger in 2010–2012
than in 2005–2009 (Table 3 and Fig. 6). The large positive bias
throughout the troposphere in winter and negative bias below
500 hPa in spring–autumn remain in 2010–2012 (Fig. 6). This
is associated with the decreased number of assimilation measurements
(TES and OMI); this is discussed further in Sect. 7.3. In contrast,
during 2005–2009 the mean O3 bias does not change
significantly with year in the reanalysis, which confirms the stable
performance of the O3 reanalysis field. Verstraeten
et al. (2013) highlighted that the time series of the TES–sonde
O3 biases do not change over time, which suggests that TES is
an appropriate instrument for long-term analysis of free-tropospheric
O3.
Aircraft
Both the model and the reanalysis generally capture well the observed
horizontal, vertical, and seasonal variations in O3
concentration compared with the MOZAIC/IAGOS aircraft measurements
(Figs. 7 and 8). However, the model mostly overestimates O3
concentration from the northern tropics to the mid-latitudes and
underestimates it at the NH high latitudes in the middle and upper
troposphere (between 850 and 300 hPa in Table 1), as
consistently revealed by comparison with ozonesonde observations.
Although the improvement is not large in the upper troposphere
(500–300 hPa, Fig. 7), an improved agreement with the MOZAIC/IAGOS
measurements is found in the reanalysis run in the middle troposphere
(850–500 hPa) and at the aircraft cruising altitude
(300–200 hPa), as summarised in Table 1. Most of the negative bias
of the model in the troposphere of the NH high latitudes is reduced
throughout the reanalysis period. A substantial improvement is observed at
the aircraft cruising altitude around the tropopause (between 300 and
200 hPa) at the NH high latitudes; the mean positive bias is reduced
from +8 % in the control run to +3 % in the reanalysis. By separately
assimilating individual measurements through the observing system experiments
(OSEs), we confirmed that the improvement is mainly attributed to the MLS
assimilation (not shown).
From
the NH subtropics to the mid-latitudes, the mean positive bias of the
model at the aircraft cruising altitude (300–200 hPa) is
reduced, whereas the positive bias of low concentration in
autumn–winter in the middle troposphere (850–500 hPa) is
increased. In the tropics, the MOZAIC/IAGOS measurements were mostly
collected near large biomass-burning areas (Fig. 7: e.g. Central
Africa and Southeast Asia), where O3 concentration in the
troposphere becomes too high in the reanalysis probably attributed to
a positive bias in the TES O3 observations (cf.
Sect. 7.2). Note that more substantial improvements in comparison with
the aircraft measurements are found in 2005–2009 than in the later
years.
HIPPO measurements provide information on the vertical O3
profiles over the Pacific. The observed tropospheric O3
concentration is higher in the extratropics than the tropics, with
higher concentrations in the NH than the SH (Fig. 9). The observed
tropospheric O3 concentration displays a maximum in the NH
subtropics in March (HIPPO3) because of the strong influence of
stratospheric inflows along the westerly jet stream. The observed
latitudinal–vertical distributions are generally captured well by both
the model and the reanalysis for all the HIPPO campaigns.
Time series of the monthly mean O3 concentration
obtained from ozonesondes (black), control run (blue), and
reanalysis (red) averaged between 850 and 500 hPa (left
column), 500 and 200 hPa (centre column), and
200 and 90 hPa (right column). From top to bottom the results
are shown for the NH high latitudes (55–90∘ N), NH
mid-latitudes (15–55∘ N), tropics
(15∘ S–15∘ N), SH mid-latitudes
(15–55∘ S), and SH high latitudes (55–90∘ S).
The model shows negative biases in the NH extratropics and positive
biases from the tropics to the SH compared with the HIPPO measurements
(Table 1). These characteristics of the bias are commonly found in
comparisons with global ozonesonde observations in this study (cf. Sect. 5.1.1) and are reduced
effectively in the reanalysis. A considerable bias reduction can be
found in the lower- and middle-tropospheric O3 at the NH
mid-latitudes where O3 variations could be influenced by
long-range transport from the Eurasian continent. Direct concentration
adjustment by TES measurements in the troposphere and by MLS
measurements in the UTLS played important roles in correcting
tropospheric O3 profiles. In addition, corrections made to the
O3 precursors emissions over the Eurasian continent by OMI,
especially over East Asia, were important in influencing tropospheric
O3 concentration over the North Pacific around
35–60∘ N, especially in boreal spring. This demonstrates
that the assimilation of multiple-species data sets is a powerful
means by which to correct the global tropospheric O3 profiles,
including those over remote oceans. In contrast, the positive bias in
the tropics is further increased in the reanalysis (from +5 % in
the control run to +8 % in the reanalysis between 850 and
500 hPa and from +10 to +15 % between 500 and
300 hPa), as mostly commonly found in comparisons against the
MOZAIC/IAGOS and ozonesonde measurements (cf. Sect. 5.1.1 and 5.1.2).
Vertical profiles obtained during the NASA aircraft campaigns were
also used to validate the O3 profile (Fig. 10). The
comparisons show improved agreements in the reanalysis in the middle
and upper troposphere during INTEX-B over Mexico and during the ARCTAS
campaign over the Arctic, but the model's positive bias near the surface is further increased for the INTEX-B profile. For the DISCOVER-AQ profile, the model's
negative bias in the free troposphere is mostly removed in the
reanalysis. For the DC3 profiles, the model captures the observed
tropospheric O3 profiles well, whereas the assimilation leads
to small overestimations.
CO
Surface
Surface CO concentrations are compared with the WDCGG surface
observations from 59 stations, as summarised in Table 4 and depicted
for 12 selected stations in Fig. 11. The control run underestimates
CO concentration by up to about 60 ppb in the NH
extratropics, with the largest negative bias in winter and smallest
bias in summer. The model underestimation has been commonly found in
most of the CTMs (Shindell et al., 2006; Kopacz et al., 2010;
Fortems-Cheiney et al., 2011; Stein et al., 2014). The model's
negative bias is also found in most tropical sites, but not in the SH.
Linear trend (slope in ppb(8years)-1) and standard
deviation (in ppb) of O3 derived from the WMO ozonesonde
observations, the control run, and the reanalysis during 2005–2012.
90–55∘ S
55–15∘ S
15S–15∘ N
15–55∘ N
55–90∘ N
Obs.
Reanalysis
Obs.
Reanalysis
Obs.
Reanalysis
Obs.
Reanalysis
Obs.
Reanalysis
(control)
(control)
(control)
(control)
(control)
850–
-0.2±2.9
-0.8±2.7
+2.4±2.9
-0.5±3.0
-6.3±2.3
-1.6±1.7
+2.9±2.8
+1.2±2.1
+1.1±2.4
+1.8±1.8
500
(+0.2±2.6)
(-0.7±3.1)
(-1.8±1.4)
(-1.2±2.1)
(+0.2±1.9)
500–
-2.5±5.5
+8.2±4.1
+7.7±5.7
+7.2±5.3
-1.8±3.0
-0.9±2.0
+1.1±7.9
-0.3±7.1
-7.1±17.1
-4.2±15.4
200
(-1.4±6.9)
(+0.9±6.4)
(-1.7±1.5)
(-3.8±7.1)
(-3.1±14.9)
200–
-13.6±36.7
-1.2±36.7
+7.2±29.5
+3.3±29.7
+3.8±7.5
+0.7±6.9
-17.7±41.9
-25.7±38.8
-67.7±78.4
-72.7±74.9
90
(-4.4±36.4)
(-6.5±33.1)
(-1.7±6.4)
(-35.8±46.3)
(-68.0±85.6)
Comparisons of the mean O3 concentrations between the
reanalysis run and the WOUDC ozonesonde observations in the Southern Hemisphere
(SH)
(90–30∘ S), troposphere (TR) (30∘ S–30∘ N) and Northern Hemisphere
(NH)
(30–90∘ N). The mean differences are shown for each year of the
reanalysis period and for mean concentrations during 2005–2009 and during
2010–2012. The latter includes results for the control run given in
brackets.
850–500 hPa
500–200 hPa
200–90 hPa
SH
TR
NH
SH
TR
NH
SH
TR
NH
2005
-2.3
0.9
-2.3
3.0
0.4
0.9
27.3
4.6
13.3
2006
-0.2
1.1
-2.6
0.2
-0.3
-4.9
27.3
-2.7
3.9
2007
0.2
0.8
-2.5
-2.1
-0.6
-5.4
23.8
-1.9
-1.3
2008
1.4
1.9
-1.8
0.7
1.2
-5.2
30.9
1.2
10.8
2009
0.2
2.3
-2.0
2.7
0.3
-8.4
33.6
-3.1
3.2
2010
-2.5
3.5
-2.8
7.1
0.7
-6.6
42.8
1.4
-5.3
2011
-2.3
1.8
-2.7
7.1
0.3
-3.7
30.8
-6.4
-2.5
2012
-1.9
2.0
-3.6
6.8
-1.6
2.9
31.5
-5.1
10.1
2005–2009
-0.1
1.4
-2.2
0.9
0.2
-4.6
28.6
-0.4
6.0
(-0.3)
(-0.3)
(-3.5)
(27.6)
(-2.2)
(-3.0)
(193.8)
(11.0)
(52.0)
2010–2012
-2.3
2.4
-3.0
7.0
-0.2
-2.5
35.0
-3.4
0.8
(-1.2)
(-0.6)
(-5.6)
(26.5)
(-3.2)
(-4.7)
(191.0)
(6.5)
(45.9)
Most of the negative bias in the NH extratropics and in the tropics is
removed in the reanalysis run, due to the increased surface CO
emissions in the analysis (cf. Sect. 6). The MOPITT assimilation
dominates the negative bias reduction through the surface CO
emission optimisation, whereas the assimilation of other data has only
a small influence on the CO concentration analysis through
changes in the OH field. The annual and regional mean surface
bias becomes positive after assimilation at NH mid- and
high latitudes, which is illustrated at locations such as Midway and
Bermuda (32∘ N, 65∘ W; figure not shown). The
observed negative trends at most NH sites are captured well in the
reanalysis.
Tropical CO concentrations show district interannual variations
associated with variations in tropical biomass-burning activities and
meteorological conditions. The temporal correlations with the
observations are about 0.1–0.2 higher in the reanalysis compared with
the control run in the tropics at Christmas Island and Barbados.
In the SH, the model generally shows good agreement with the surface
observations. However, assimilation increases the CO
concentration and leads to overestimations in some places (e.g.
Showa). The mean negative bias at the SH mid-latitudes changed from
-10 % in the control run to +7 % in the reanalysis.
Vertical profiles of the time series of the monthly mean
O3 concentration difference (in %) between the control
run and ozonesondes (top) and between the reanalysis and ozonesondes
(bottom) averaged over the NH mid-latitudes (15–55∘ N).
Aircraft
The model underestimates the CO concentration in the tropics
and the NH compared with the MOZAIC/IAGOS aircraft measurements
throughout the troposphere (below 300 hPa) and around the
tropopause at the aircraft cruising altitude (between 300 and
200 hPa), as depicted in Fig. 12. The model's negative bias is
mostly removed in the reanalysis, with a mean improvement of
50–90 % throughout the troposphere, as summarised in
Table 4. This confirms that the constraints provided for the surface emissions are propagated well into the concentrations of the entire troposphere with
a delay in the peak timing and decay in the amplitude. Note that the CO concentrations were not directly adjusted in the data assimilation. The spatial
distribution in the upper troposphere is also captured well in the
reanalysis (Fig. 7). Despite the overall improvement, the low
concentrations in the NH lower and middle troposphere in summer and
autumn remain underestimated, whereas the analysed concentration
becomes too high in the NH high latitudes at the aircraft cruising
altitude (Fig. 12). A decreasing trend is observed in both the lower
and upper troposphere in the NH, which is represented realistically in
the reanalysis. The EDGAR 4.2 for 2008 was used for the model
simulation for 2009–2012. The analysis and the comparison with the
independent observations show that this caused unrealistic
interannual CO variations and an underestimate of the
decreasing trend in the control run.
Spatial distributions of O3 (left column) and
CO (right column) averaged between 500 and 300 hPa
and during 2005–2012 obtained from the MOZAIC/IAGOS aircraft
measurements (first row), control run (second row), and reanalysis (third
row). Differences between the control run and observations (fourth row)
and between the reanalysis and observations (fifth row) are also
plotted. Units are ppb.
The distinct interannual variations in the tropics (over Southeast
Asia and around Central and North Africa) observed from the
MOZAIC/IAGOS aircraft measurements mainly reflect variations in
biomass-burning emissions. The temporal variations of CO are
captured better by the reanalysis between 850 and 500 hPa (r=0.67 in the control run and 0.78 in the reanalysis).
The HIPPO observations exhibit large latitudinal CO gradients
around 15–25∘ N over the Pacific for all campaigns
(Fig. 13). Tropospheric air can be distinguished between the tropics
and extratropics because of the transport barrier around the
subtropical jet (Bowman and Carrie, 2002; Miyazaki et al., 2008). The transport
barrier produces the large CO gradient in the subtropics and
acts to accumulate high levels of CO in the NH extratropics. In
the SH, CO concentration increases with height in the free
troposphere, because of the strong poleward transport in the upper
troposphere from the tropics to the SH high latitudes.
Same as Table 1, but for mean CO concentrations. Units are
ppb. Observations used are the WDCGG observations during 2005–2012,
MOZAIC/IAGOS aircraft measurements during 2005–2012, and HIPPO aircraft
measurements during 2009–2011.
90–55∘ S
55–15∘ S
15S–15∘ N
15–55∘ N
55–90∘ N
Bias
RMSE
Bias
RMSE
Bias
RMSE
Bias
RMSE
Bias
RMSE
WDCGG
-0.6
7.3
4.3
19.8
-13.6
27.4
27.2
62.8
11.1
40.0
surface
(-4.6)
(8.0)
(-5.8)
(15.8)
(-18.9)
(33.4)
(-41.7)
(60.4)
(-51.1)
(57.9)
850–
–
–
–
–
-19.8
34.6
-15.1
29.3
-10.5
15.6
500
–
–
–
–
(-37.7)
(45.6)
(-48.3)
(53.1)
(-51.1)
(51.5)
MOZAIC/IAGOS
500–
–
–
–
–
-10.3
18.1
-8.6
18.9
-3.4
19.7
aircraft
300
–
–
–
–
(-21.3)
(25.4)
(-30.0)
(33.6)
(-30.9)
(35.3)
300–
–
–
–
–
-9.9
24.4
0.0
18.2
10.2
23.5
200
–
–
–
–
(-21.5)
(30.6)
(-16.8)
(24.7)
(-10.0)
(24.8)
850–
2.1
2.8
-0.6
5.1
-3.6
6.9
-11.8
17.1
-11.5
16.4
HIPPO
500
(-1.6)
(2.4)
(-4.8)
(5.9)
(-8.8)
(10.6)
(-35.3)
(37.0)
(-49.5)
(50.0)
aircraft
500–
6.2
7.4
-1.2
6.7
-2.0
6.7
-7.2
17.0
-4.3
23.7
200
(2.6)
(6.5)
(-5.0)
(7.8)
(-7.0)
(9.0)
(-23.9)
(28.4)
(-27.9)
(38.1)
The assimilation increases CO concentration and reduces the
mean model negative bias by about 60–80 % in the NH extratropics
against the HIPPO measurements. The remaining negative bias could be
attributed to overemphasised chemical destruction while air is
transported from the Eurasian continent to the HIPPO locations over
the central Pacific. For instance, the negative bias of the surface
CO concentration is mostly removed in the reanalysis over
Yonaguni at the ground surface, located near (downwind of) large
sources of Chinese emissions (Fig. 11). This suggests that the
emission sources are realistically represented in the reanalysis.
Errors in stratospheric CO might also cause the negative bias
through stratosphere–troposphere exchange (STE).
Time series of the monthly mean O3 concentration
obtained from the MOZAIC/IAGOS aircraft measurements (black),
control run (blue), and reanalysis (red) averaged between
850 and 500 hPa (left column), 500 and 300 hPa (centre
column), and 300 and 200 hPa (right column). From top to bottom
the results are shown for the NH high latitudes
(55–90∘ N), NH mid-latitudes (15–55∘ N), and
tropics (15∘ S–15∘ N).
Latitude–pressure cross section of mean O3
concentration (in ppb) obtained from HIPPO aircraft
measurements (first row), control run (second row), and reanalysis (third
row). The relative difference (in %) between the control run and
the observation (fourth row) and between the reanalysis and the observation (fifth row) is also shown.
Results are shown for all HIPPO campaigns (from left to right: HIPPO I, 8–30 January 2009; HIPPO II, 31 October to 22 November 2009; HIPPO III,
24 March to 16 April 2010; HIPPO IV, 14 June to 11 July 2011; and
HIPPO V, 9 August to 9 September 2011).
Reductions in the negative model bias of tropospheric CO can be
found in comparisons against the NASA aircraft campaign profiles from
INTEX-B, ARCTAS-A, and DC3 (Fig. 10), although the bias reduction is
small for the ARCTAS-B profile. Bian et al. (2013) demonstrated that
most of the enhanced CO concentrations observed during the
ARCTAS-A originate from Asian anthropogenic emissions. This suggests
that the reanalysis realistically represents the Asian anthropogenic
emissions and their influences on the western Arctic CO
level. Bian et al. (2013) also suggested a lower fraction of CO
from Asian anthropogenic emissions during the ARCTAS-B than during the
ARCTAS-A and showed that the along-track measurements are not
representative of the concentrations within the large domain of the
western Arctic during the ARCTAS-B, which may explain the small bias
reduction for the ARCTAS-B profile in our comparison. MOPITT data are
assimilated equatorward of 65∘, and only the CO
emissions are optimised in the reanalysis. Direct adjustment of
CO concentration using high-latitude retrievals could be
expected to improve the representation of CO in the ARCTAS
profiles, as demonstrated by Klonecki et al. (2012) using IASI
measurements.
NO2
Tropospheric column
Compared with the satellite retrievals, the model generally
underestimates the NO2 concentration over most industrial
areas (e.g. East China, Europe, eastern USA, and South Africa) and
over large biomass-burning areas (e.g. Central Africa), as shown by
Fig. 14. The model underestimations are commonly found in comparisons
against three different retrievals. The three products are produced
using the same retrieval approach (Boersma et al., 2011). Therefore,
the overpass time difference and diurnal variations in chemical
processes and emissions dominate the differences between these
retrievals. The negative bias over these regions is greatly reduced in
the reanalysis, decreasing the 8-year global mean negative bias by
about 65, 45, and 30 % as compared with OMI, SCIAMACHY, and
GOME-2, respectively (Table 5). The improvement can be also seen in
the increased spatial correlation of 0.03–0.05 and in the reduced
RMSE of 15–30 %.
Mean vertical profiles of O3 (ppb), CO
(ppb), NO2 (ppb), OH (ppt),
HO2 (ppb), HNO3 (ppt), and
CH2O (ppt) obtained from aircraft measurements
(black), control run (blue), and reanalysis (red), for the INTEX-B
profile (first row), ARCTAS-A profile (second row), ARCTAS-B profile (third
row), DISCOVER-AQ profile (fourth row), DC3-DC8 profile (fifth row), and
DC3-GV profile (sixth row). Error bars represent the standard
deviation of all data within one bin (with an interval of
30 hPa).
Over East China, the model's negative bias is large in winter, whereas
the assimilation reduces the wintertime bias by about 40 %
compared with OMI retrievals. The observed low concentration in 2009
and high concentration in 2010–2012 are captured in the reanalysis,
whereas the control run mostly failed to reproduce the interannual
variability. The reanalysis shows larger positive trends than the
control run, but the observed trend is even higher. The
underestimation in the mean concentration and positive trend remain
large in the reanalysis, especially when compared with the SCIAMACHY
and GOME-2 retrievals. Note that over polluted areas, realistic
concentration pathways of NO2 do not follow simple linear
trends but reflect a combination of effects of environmental policies
and economic activities. For instance, NOx emissions in China
have been increasing because of the rapid economic growth, although an
economic slowdown affected the growth rate in 2009 (Gu et al., 2013).
Over Europe, the model's negative bias in summertime is reduced by
about 10–30 % in the reanalysis. The observed wintertime
concentration is high in 2011–2012 and relatively low in 2010 because
of the global economic recession and emission controls (Castellanos
and Boersma, 2012). The assimilation increases the wintertime
NO2 concentration in 2011–2012 and captures the observed
interannual variations better.
Over the eastern USA, the observed NO2 concentration is high
in 2005–2007 and low after 2008. The control run failed to reproduce
these variations. In the reanalysis run, the model's negative bias is
reduced in 2005–2007 compared with the OMI retrievals, showing
a negative trend in the reanalysis period. The improvement is smaller
for the SCIAMACHY and GOME-2 retrievals.
Time series of monthly mean CO concentration obtained
from the WDCGG ground measurements (black), control run (blue), and
reanalysis (red). Model simulation results with the HTAP emissions
are also plotted (green).
Comparisons of global tropospheric NO2 columns between the control
run and the satellite retrievals in brackets, and between the reanalysis run and the
satellite retrievals: OMI for 2005–2012, SCIAMACHY for 2005–2011, and GOME-2
for 2007–2012. S-Corr is the global spatial correlation coefficient. The bias represents the control run or reanalysis minus the
retrievals. The averaging kernel of each retrieval is applied to the control run and the
reanalysis. The units for the RMSE and bias are 1015 moleccm-2.
OMI
SCIAMACHY
GOME-2
S-Corr
0.970
0.916
0.924
(0.931)
(0.862)
(0.881)
Bias
-0.048
-0.091
-0.185
(-0.122)
(-0.162)
(-0.256)
RMSE
0.383
0.946
0.847
(0.533)
(1.102)
(0.990)
Despite the general improvement, the reanalysis still has large negative
biases compared with the satellite retrievals over the polluted regions.
There may be several reasons for the remaining underestimation of NO2
concentrations. The analysis increment can partly be lost after the forecast
because of the short lifetime of NOx (Miyazaki and Eskes, 2013),
especially when concentrations are adjusted. Other model processes, such as
the diurnal cycle, boundary layer mixing and venting, and the chemical
equilibrium at overpass, may not be described well. Also, the averaging
kernels show a relatively small sensitivity close to the surface, resulting
in relatively smaller adjustments in the assimilation. The remaining bias
varied considerably with season (e.g. the bias is mostly absent during
summer over East China and the eastern USA), whereas the eight series of
1-year calculations were conducted separately. Therefore, the remaining
underestimation of NO2 concentrations did not cause (spurious)
gradual intra-annual and year-to-year increases in the estimated surface
NOx emissions during the reanalysis period (cf. Sect. 6.1). The
larger discrepancies with respect to the SCIAMACHY and GOME-2 retrievals may
be attributed to the errors in the simulated diurnal NO2 variations
and a bias between OMI and these retrievals. Both the emission factors and
the tropospheric concentrations of NOx are constrained primarily
in the early afternoon by OMI, whereas no direct observational constraint on
tropospheric NOx is available in the morning (i.e. during the
SCIAMACHY and GOME-2 overpass time).
Over North and Central Africa, the data assimilation removes
most of the negative bias throughout the year because of the
increased biomass-burning emissions. The remaining negative bias in
the reanalysis is relatively large when compared with the GOME-2 over
North Africa and with SCIAMACHY and GOME-2 over Central Africa. The
observed concentration is relatively small in 2010–2012 over North
Africa, and the reanalysis captures the observed interannual
variations better compared with the control run.
Same as in Fig. 8, but for CO concentration obtained
from MOZAIC/IAGOS aircraft measurements.
The control run fails to reproduce the observed distinct seasonal and
interannual variations over Southeast Asia (r=0.74–0.79 in
the control run and r=0.89–0.98 in the reanalysis compared
with the three retrievals). The control run underestimates the
concentration throughout the year with the largest biases in boreal
spring in 2008–2009. The negative bias is greatly reduced in the
reanalysis throughout the year, and the interannual variations are
represented realistically. The remaining negative bias is large,
especially when compared with the GOME-2 retrievals.
Aircraft
Compared with the vertical NO2 profiles from the aircraft
measurements, the simulated NO2 concentration in the
troposphere is generally too low (Fig. 10). For the ARCTAS profiles,
the data assimilation has less impact in the troposphere. At high
latitudes, the surface NOx emissions have only a small effect on
the tropospheric NO2 profiles, and the observational error of
the OMI measurements is large in comparison with the observed low
concentration. Compared with the two DC3 profiles, the model is too
high in the lower troposphere and too low in the middle/upper
troposphere. Data assimilation further increases the positive bias in
the lower troposphere. The relatively coarse resolution of the model
could cause large differences near the surface for comparisons at
urban sites such as the DC3 profiles. Compared with the DISCOVER-AQ
profile, the rapid change in NO2 concentration in the lower
troposphere is captured well by both the model and the reanalysis. The
MLS O3 and HNO3 data assimilation effectively corrects
the amount of NO2 in the lower stratosphere, especially for
the ARCTAS-A profile, because of the use of the interspecies
correlation in the analysis step and by influencing the
NOx / NOy species in the forecast step.
Other reactive species
The observed main features of the HNO3 profiles are captured
by both the control and reanalysis runs. The increase in HNO3
toward the surface is driven mainly by oxidation of NOx in
polluted areas, which is visible in the INTEX-B, ARCTAS-B, DC3-DC8,
DC3-GV, and DISCOVER-AQ profiles. The positive corrections by
assimilation, primarily attributable to the increased NO2
concentration and NOx emissions, reduce the model's
underestimation for the DC3-GV profile, but led to concentrations that
are too high for the INTEX-B, DC3-DC8, DC3-GV, and DISCOVER-AQ
profiles. The assimilation only slightly influences the tropospheric
HNO3 concentration for the ARCTAS profiles because of the
negligible impact of surface NOx emissions at NH
high latitudes and because of the absence of HNO3 measurements
for the troposphere. To further improve the lower-tropospheric
HNO3 concentrations, corrections for its removal processes
including depositions might be important. In the middle and upper
troposphere, both the control and reanalysis runs generally
underestimate HNO3 concentration. The assimilation partly
reduces the negative bias for the DC3 profiles. Additional positive
increments of NO2 appear to be required in order to compensate for the
negative bias in HNO3. In the UTLS, the model HNO3
negative bias is reduced globally in the reanalysis because of the MLS
assimilation. For the ARCTAS profiles, Liang et al. (2011) and Wespes
et al. (2012) found that an adequate representation of stratospheric
NOy inputs is important for the accurate simulation of
tropospheric Arctic O3 and NOx at pressures <400 hPa.
Same as in Fig. 9, but for CO concentration (in
ppb, from first to third row) and its absolute difference (in ppb, from fourth to fifth row) obtained from HIPPO aircraft measurements.
The vertical HO2 profile mainly reflects variations in water
vapour concentrations in the troposphere, which decrease with
latitude. The control run overestimates the tropospheric HO2
concentration for the INTEX-B and ARCTAS-A profiles but
underestimates it for the ARCTAS-B, DC3-DC8, and DC3-GV profiles. The
reanalysis generally increases HO2 concentrations, while
it decreases OH concentration. The reaction of OH with
CO converts OH into HO2. Because of the
increased CO concentration, the assimilation increases the
production of HO2 in the NH. On the other hand, the
HO2 / OH ratio should decrease because of NOx
increases, which enhances the NO + HO2 and
NO2 + HO2 reactions. Further increase in NOx
concentration is expected to reduce the HO2 overestimation for
the INTEX-B and ARCTAS-A profiles. Errors in the removal of
HO2 by wet deposition processes might also cause biased
concentrations.
Both the control and reanalysis runs overestimate the OH
concentration in the troposphere for the INTEX-B profile, but they
underestimate it for the ARCTAS and DC3 profiles. Data assimilation
generally decreases the OH concentration in the NH extratropics
for the ARCTAS and DC3 profiles, corresponding to the increased
concentration of CO. For the INTEX-B profile, the data
assimilation increases OH and O3 in the lower part of
the troposphere because of the increased NOx emissions
compensating for the decrease due to CO. Errors in the simulated
H2O could also influence the performance of the simulation of
OH and HOx. Furthermore, large uncertainty in observed
OH concentrations also remains an important issue (e.g. Heard
and Pilling, 2003; Stone et al., 2012).
Time series of regional monthly mean tropospheric NO2
columns (in 1015 moleccm-2) averaged over eastern
China (110–123∘ E, 30–40∘ N), Europe
(10∘ W–30∘ E, 35–60∘ N), the eastern
United States (71–95∘ W, 32–43∘ N), North
Africa (20∘ W–40∘ E, Equator–20∘ N),
Central Africa (10–40∘ E, Equator–20∘ S),
Southeast Asia (96–105∘ E, 10–20∘ N) obtained
from the satellite retrievals (black), control run (blue), and
reanalysis (red). Results are shown for the OMI retrievals (left
columns), SCIAMACHY retrievals (centre columns), and GOME-2
retrievals (right columns).
The model captures the observed CH2O profiles in the
troposphere well, but it generally underestimates the
concentration. The reanalysis generally increases the CH2O
concentration and reduces the negative bias of the model. However, its
influence on the concentrations is small because of the lack of any
direct measurement and the neglect of any interspecies correlation
with CH2O in the reanalysis framework. Therefore, additional
constraints from satellite measurements are required. Optimising
isoprene emissions from CH2O measurements will be an important
development (cf. Sect. 7.7).
Generally, these results reveal the positive benefit of the
assimilation of multiple-species data with different sensitivities on
the analysis of unobserved species profiles in the troposphere and
lower stratosphere. In particular, constraints obtained for the
OH profiles have a large potential to influence the chemistry
of the entire troposphere (cf. Sect. 7.4.2). However, many factors
determine the overall analysis performance, such as chemical reaction
rates, deposition rates, and atmospheric transports, which are hardly
optimised by the currently available measurements.
Estimated emissions
In previous publications (Miyazaki and Eskes, 2013; Miyazaki et al.,
2014) we demonstrated that the simultaneous analysis of chemical
concentrations and emissions improves the estimate of surface
NOx emissions and LNOx sources, with differences of up
to 58 % in regional surface NOx emissions. The analysis
increment produced directly via the chemical concentrations plays an
important role in reducing the model–observation mismatches that
arise from model errors other than those related to emissions. Here we
describe the estimated emissions briefly. Further detailed analyses of
the 8-year variations in the estimated emission sources will be
discussed in a separate paper.
Surface NOx emissions
The time series and global distributions of the analysed emission
sources obtained during the reanalysis period are depicted in Figs. 15
and 16, respectively. The data assimilation increases the 8-year
mean of global total surface NOx emissions from 38.4 to
42.2 Tg N. The approximate 10 % increase in global total
emissions is attributable to an approximately 7 % increase in the NH
(20–90∘ N) and a 14 % increase in the tropics
(20∘ S–20∘ N). The large increase in the NH
emissions is associated with positive corrections over industrial
areas such as China and India, and with corrections in Europe and the
USA. Meanwhile, the increased emissions over Central Africa indicate
larger emissions from biomass burning than shown by the
inventories. These needed adjustments were commonly revealed by
referring to our previous estimates for 2007 (Miyazaki and Eskes,
2013). The seasonal and interannual variability is also modified
considerably in many regions. The emission inventories exhibit
considerable uncertainties in representing seasonal and interannual
emission variabilities associated with uncertain input information,
such as economic conditions, biomass-burning activity, and emission
factors (e.g. Jaeglé et al., 2005; Xiao et al., 2010; Reuter
et al., 2014). For instance, the anthropogenic emissions were reported
on a yearly basis, and thus seasonal variability in anthropogenic
emissions such as from wintertime heating of buildings (e.g. Streets
et al., 2003) was not considered in the a priori emissions. Wang
et al. (2007) also suggested that the emission inventories largely
underestimate soil emissions by a factor of 2–3 at NH mid-latitudes
during summer. The assumptions applied to the a priori emissions (cf.
Sect. 2.1; for example, the anthropogenic emissions for 2008 are used in the
estimations for 2009–2012) also cause an unrealistic lack of
interannual variability in the a priori emissions and lead to
significant differences between the a priori and a posteriori
emissions.
Time series of monthly total global and regional surface
NOx emissions (in TgNyr-1, top), LNOx
emissions (in TgNyr-1, centre), and surface CO
emissions (in TgCOyr-1, bottom) obtained from the
reanalysis (solid lines) and the emission inventories or the control
run (dashed lines) over the globe (90∘ S–90∘ N),
NH (20–90∘ N), tropics (TR,
20∘ S–20∘ N), and SH (90–20∘ S). The
8-year mean emissions values obtained from the reanalysis run
and the emission inventories (in bracket) are shown on the
right-hand side.
Global distributions of surface NOx emissions (in 10-13kgm-2s-1) (left
column), LNOx sources (in 10-14kgm-2s-1) (centre column), and surface
CO emissions (in 10-10kgm-2s-1) (right column) averaged over 2005–2012. The
a priori emissions (upper row), a posteriori emissions (middle
row), and analysis increment (lower row), i.e. the difference
between the a posteriori and the a priori emissions, are shown for
each panel.
LNOx sources
The average yearly global flash rate obtained for the reanalysis
period 2005–2012 was 45.3 flashess-1, which is
comparable with climatological estimates of 46 flashess-1
derived from Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) measurements
(Cecil et al., 2014). The LNOx shows large discrepancies
between the control and reanalysis runs. The mean annual global total
LNOx source in the reanalysis run is estimated at 6.4 Tg N
for 2005–2012 and 6.0 Tg N for 2005–2009, which is about 24 and
18 % higher than estimated from the parameterisation (5.1 Tg N
for both 2005–2009 and 2005–2012), respectively. The analysed
LNOx sources show a positive slope during 2005–2012
(+3.8 % ± 4.2 year-1) and enhanced sources during
2010–2012. From a sensitivity reanalysis calculation that was
performed by removing the TES measurements for 2005, we conclude that
the large increase in 2010–2012 is at least partly introduced
artificially because of the lack of constraints from the TES
measurements. The TES data assimilation generally tends to decrease
the global LNOx amount in the simultaneous assimilation
framework (the global total LNOx source in 2005 is 5.8 and
6.6 Tg N when estimated with and without the TES measurements,
respectively). For the period 2005–2009, when the assimilated
measurement density is nearly constant, the analysed LNOx
variability is considered to be induced by variations in convective
activity, thunderstorm type, and cloud distributions. The positive
slope (+3.1 % ± 4.2 year-1) obtained for the
period 2005–2009 in the reanalysis implies that variations in such
processes led to the LNOx sources increase. The increase in
the global LNOx sources for the period 2005–2009 is
attributed to large increases over North Africa
(+5.7 % ± 26.8 year-1), South America
(+3.2 % ± 22.0 year-1), and the Atlantic Ocean
(+7.4 % ± 11.5 year-1). Further detailed analyses
are required in order to understand the possible causal mechanisms.
The global LNOx amount in the reanalysis (6.15 Tg N) for
2007 is in agreement with our previous estimate (6.31 Tg N) for the
same year (Miyazaki et al., 2014). However, because the tuning factor
applied for the global total flash frequency is about 10 % larger
than in the previous estimate based on the recent climatological
estimates (Cecil et al., 2014), the analysis increment can be
different between the two estimates. For instance, the positive
increment for 2007 is smaller or becomes negative over Siberia,
Southeast Asia, and South America in the reanalysis. Note that the
global structure of the analysis increment is generally similar
between 2007 (figure not shown) and the 8-year reanalysis
mean. Meanwhile, the seasonal variation in the tropical LNOx
sources is modified more significantly in the reanalysis than in the
previous estimate. In the reanalysis, the observational information is
accumulated during the consequent 1-year calculation after
a 2-month spin-up, while continuously correcting the LNOx
source factors. In the previous estimate (Miyazaki et al., 2014), the
LNOx sources were estimated from shorter data assimilation
calculations (i.e. twelve 1-month calculations were conducted after
a 15-day spin-up).
Model-minus-observation comparison of mean O3 concentrations
(in ppb) between the control/reanalysis calculations and the ozonesonde observations
for 2005 in the SH (90–30∘ S), TR (30∘ S–30∘ N),
and NH (30–90∘ N). Sensitivity reanalysis calculations were conducted
by excluding the emission factors from the state vector (w/o emission), with TES
O3 bias correction (TES-bias), without assimilation of TES measurements
(w/o TES), and with HTAP-v2 emission inventories for 2008 as the a priori surface emissions (HTAP).
850–500
500–200
200–90
SH
TR
NH
SH
TR
NH
SH
TR
NH
Control
-0.8
-0.6
-3.5
27.9
-2.3
-1.4
195.9
18.0
72.1
Reanalysis
-2.3
1.0
-2.3
3.0
0.4
0.9
27.3
4.6
13.2
w/o emission
-3.2
-0.5
-3.2
2.3
-1.7
-0.7
28.5
3.6
14.3
TES-bias
-4.4
-0.1
-4.9
0.7
-0.4
-2.7
25.4
4.8
13.2
w/o TES
-1.1
1.7
-1.0
9.3
1.6
5.6
27.5
5.7
14.6
HTAP
-1.9
1.9
0.1
3.1
0.7
2.1
28.4
8.3
16.3
Surface CO emissions
The 8-year mean of global total emissions of CO is
increased by 36 % by data assimilation (1298 Tg CO vs. 820 Tg CO),
attributable mainly to an approximately 110 % increase in the
NH. The increase in the total CO emission in the NH is large in
the boreal late winter–spring period, especially over China and
Europe. Stein et al. (2014) commonly found it necessary to adjust
emissions seasonally, using regionally varying scaling factors with
large corrections during winter–spring for industrialised
countries. A similar seasonality in the adjustments is found in
Fig. 15, whereas the seasonality in the NH is mostly absent in the
a priori emissions. The positive increments for surface CO
emissions are introduced by assimilation of MOPITT CO
observations, whereas the assimilation of non-CO observations
also affects the CO emission estimation via changes in
OH concentrations. For instance, changes in surface NOx
emissions decreased tropospheric OH concentrations at NH
mid-latitudes, and this in turn acted to increase the tropospheric
CO concentrations; this is discussed further in Sect. 7.4.2.
Discussion
Impact of emission analysis
The impact of the emission optimisation on the tropospheric O3
analysis is evaluated based on comparison between the reanalysis run and
a sensitivity calculation that excludes the emission factors for the surface
emissions and LNOx sources from the state vector. The emission
optimisation influences the O3 concentrations with mean changes of
about 15 % in the tropics and 10 % in the NH mid-latitudes in the
lower troposphere. These changes improve the agreement with ozonesonde
observations in the lower troposphere in both the NH and SH (reanalysis vs. w/o emission in Table 6), but not in the tropics. At the NH mid-latitudes the
changes introduced by optimising the emission factors improve the agreement
with the ozonesonde observation from April to August below about
500 hPa (Fig. 17) associated with the pronounced O3
production caused by NOx increases; the monthly mean positive
bias below about 900 hPa is reduced by 10–15 % in the summer and the
negative bias between 900 and 500 hPa is reduced by 30–50 % in spring and
summer. Vertical transport of O3 and its precursors propagate the
variations in surface emissions into the free troposphere, whereas the
LNOx source optimisation improves the performance of the upper-tropospheric O3 simulation directly. The impact of the emission
optimisation on the free troposphere is large throughout the year in the
tropics.
Month–pressure cross section of the zonal mean bias of
O3 concentration (in %) compared with the ozonesonde
observations averaged over 30–60∘ for the reanalysis run
(top) and the sensitivity experiment that excludes the emission
factors from the state vector (w/o emission, bottom).
The observed O3 concentration in the NH mid-latitude between
850 and 500 hPa increased from 2005 to 2010
(+2.3 ppb(5years)-1); the positive slope is represented
in the reanalysis run (+1.0 ppb(5years)-1), whereas
a case without emission source optimisation (w/o emission) shows
a negative slope (-1.1 ppb(5years)-1). These results
imply that the simultaneous optimisation approach improves the
concentrations and emissions in the model and produces high-quality
multiple-year reanalysis data for tropospheric O3 profiles.
Biases in the observations
TES O3 retrievals are known to have positive bias compared
with ozonesonde observations in the troposphere (e.g. Herman and
Osterman, 2012; Verstraeten et al., 2013). Based on systematic
comparisons with ozonesonde observations, Verstraeten et al. (2013)
determined that the upper and lower troposphere mean biases range from
-0.4 to +13.3 and +3.9 to +6.0 ppb, respectively. In the
reanalysis described in this paper we did not apply a bias correction
to TES because of the difficulty in estimating the bias structure that
possibly varies temporally and spatially in the reanalysis period. We
tested a bias correction scheme with a linear concentration–bias
relationship, in which the slope and intercept estimated by
Verstraeten et al. (2013) for five latitudinal bands of the upper
troposphere (above 464 hPa), at 464 hPa, and for the
lower troposphere (below 464 hPa) were interpolated in
log pressure to the model's vertical layers. For the Arctic lower
troposphere, a constant bias of 1.1 ppb was assumed because of
the very small correlation found by Verstraeten
et al. (2013). A sensitivity calculation for the year 2005 with the
TES bias correction (TES-bias in Table 6) shows reductions in the
positive O3 bias in the tropical lower and middle troposphere
against the ozonesonde observations. Conversely, in the NH mid- and
high latitudes, the mean negative O3 bias in the lower and
middle troposphere increases. Because the bias was assumed constant
with time, the representation of the interannual O3 variation
between 2005 and 2010 was not improved by applying the TES bias
correction.
In the CHASER-DAS data assimilation approach, the O3 analysis
bias is not solely determined by bias in the assimilated O3
measurements. A sensitivity experiment without the assimilation of TES
measurements (w/o TES in Table 6) shows improvements in the lower and
middle-tropospheric O3 in the NH extratropics compared with
the control run, demonstrating that the use of measurements other than
TES measurements led to corrections in the lower- and middle-tropospheric O3. The additional use of the TES O3
measurements further improved the O3 analysis in most cases
(see Table 6).
Satellite data availability
Any discontinuities in the availability and coverage of the
assimilated measurement will affect the quality of the reanalysis and
estimated interannual variability. In particular, the number of
assimilated TES O3 retrievals decreases after 2010 through
2012, while approximately half of the OMI retrieval pixels per orbit
are compromised since December 2009. Correspondingly, the data
assimilation performance, as measured from the data assimilation
statistics (Sect. 4) and comparisons against the independent
observations (Sect. 5), became worse after 2010 in the NH. The lack of
direct O3 measurements and the reduced constraints from the
precursor (i.e. NO2) measurements will degrade the O3
analysis in the NH after 2010, and will also limit the evaluation of
the analysis uncertainties (cf. Sect. 7.6) and may cause spurious
interannual changes and trends. Changes in the observing system thus
limit the usability of the reanalysis for long-term variability
studies.
Model bias
A priori emissions
The choice of the a priori emissions will influence the reanalysis
result. To study the sensitivity of the reanalysis to the a priori
settings, emissions obtained from EDGAR-HTAP v2
(http://edgar.jrc.ec.europa.eu/htap_v2/index.php?SECURE=123) for
the years 2008 and 2010 were alternatively used as a priori
anthropogenic NOx and CO emissions in the calculation
for 2005 and 2010, respectively (the inventory was not provided for
2005 at the time of this study). EDGAR-HTAP v2 was produced using
nationally reported emissions combined with regional scientific
inventories from the European Monitoring and Evaluation Programme (EMEP),
Environmental Protection Agency (EPA), Greenhouse gas-Air Pollution
Interactions and Synergies (GAINS), and Regional Emission Inventory in
Asia (REAS). The model simulation using the a priori emissions,
constructed based on the EDGAR v4 and GFED v3 emissions, shows
significant underestimations in tropospheric CO concentrations,
as in most of the CTMs (e.g. Stein et al., 2014), and this
underestimation is large over urban sites in the NH (Sect. 5.2). The
global CO emissions of EDGAR-HTAP v2 inventory are about
20 % higher than the a priori emissions. Using the EDGAR-HTAP v2
emissions instead of the a priori emissions means that the negative
bias in the simulated surface CO concentration could be reduced
by about 20–40 % in the tropics and the NH extratropics as is
shown by the green lines in Fig. 11. The error reduction is large in
winter–spring and small in summer in the NH, whereas it is mostly
negligible in the SH.
Despite the large differences in the simulated concentration, the
choice of a priori emissions has only slight influence on the
a posteriori CO concentrations and emissions. The annual global
total emission is 1398 Tg CO in the case with the EDGAR v4 and GFED v3
emissions and 1360 Tg CO with the HTAP v2 emissions in 2005.
Latitude–pressure cross section of the 8-year mean
OH concentration (right panels) and time–latitude
cross section of the monthly mean OH concentration averaged
between 1000 and 300 hPa (left panels). The OH
concentration obtained from the reanalysis (top panels) and the
difference between the reanalysis and the control run (bottom
panels) are also shown. Units are ppt.
The O3 analysis is only slightly influenced by the choice of a priori
emissions (reanalysis vs. HTAP in Table 6), except that the agreement against
the ozonesonde observation is improved in the NH extratropics between 850 and
500 hPa through use of the EDGAR HTAP v2 emissions. The changes are
attributable to the slightly different a posteriori surface CO and
NOx emission (annual NH (20–90∘ N) total emission of
26.5 Tg N in the case of the EDGAR v4 and GFED v3 emissions, and
29.4 Tg N with the HTAP v2 emissions in 2005). The spatial distribution of
the estimated LNOx sources is also somewhat influenced by the
choice of a priori surface emissions in the NH mid-latitudes (not shown),
which led to differences in the agreement with the ozonesonde observation in
the upper troposphere at 200 hPa.
OH distribution
OH is a key driver of the tropospheric chemical system as the
processes leading to the removal of hydrocarbons from the atmosphere
start with the reaction with OH. However, its distribution is
represented poorly in CTMs. Patra et al. (2014) estimated an
NH / SH OH ratio of 0.97±0.12 with the help
of methyl chloroform observations (a proxy for OH
concentrations), whereas the ratio was estimated at 1.26 in the CHASER
control run. The simulated ratio from this study falls within the
range 1.28±0.10 in the ACCMIP (the Atmospheric Chemistry and
Climate Model Intercomparison Project) (Naik et al., 2013). The
concentration of OH is directly linked to the concentrations of
species determining the primary production (O3 and
H2O), removal (CO, CH4), and regeneration of
OH (NOx). Because the CHASER-DAS system constrains
O3, CO, and NOx, this holds the promise of
a positive impact on the modelled OH concentration, given that
the reactions are reasonably well described by the model. The impact
of the assimilation on OH is shown in Fig. 18.
The tropospheric OH concentration is decreased by the
assimilation in the NH and increased in the SH tropics; these changes
are primarily attributable to the increased concentration of CO
and O3, respectively. From a sensitivity experiment in which
the state vector was modified (either the emission factors or the
concentrations were excluded from the state vector), we confirmed that
the emission optimisation solely decreases the OH concentration
in the NH troposphere, whereas both the concentration assimilation
(mainly TES O3) and the emission optimisation (mainly
NOx emissions) increase the OH concentration in the
tropics. The decrease in the tropospheric OH concentration in
the NH is found throughout the reanalysis period, with the largest
reductions of about 10 % during boreal spring–summer, leading to
about 2 % decrease in the global annual mean OH
concentration linked to CO increases in the NH. Changes in
surface NOx emissions tend to decrease the annual mean
tropospheric OH concentration in the NH mid-latitudes by about
3 % and increase it in the tropics by about 5 %. The
8-year mean NH / SH OH ratio is 1.18 in the
reanalysis, which is smaller than the values of 1.26 in the control
run and 1.28 in the ACCMIP; the value of 1.18 is closer to the
observational estimate (0.97) of Patra et al. (2014). Because the
chemical lifetimes of NOx and CO are affected by the
amount of OH, these changes once more suggest the importance of
the simultaneous optimisation of the concentration and emissions on
the entire tropospheric chemical system and the emission estimates.
Although the methyl chloroform analysis in Patra et al. (2014) has
considerable uncertainties, the large discrepancy between the analysis
of Patra et al. (2014) and our estimate suggests that possible errors
in the modelled OH could have had a negative influence on the
reanalysis quality. If it is assumed that OH is overestimated
in the NH, then top-down emission estimates of reactive species such
as CO in the NH could also be overestimated. Sensitivity
calculations were conducted to investigate the influence of the
remaining possible OH positive bias on the reanalysis
results. In the sensitivity reanalysis calculations, a factor of 0.8
was applied to the chemical reaction rate in the calculation of the
chemical reaction CO + OH → CO2 + HO2
for the NH, in consideration of the obtained difference
(1.18 vs. 0.97). Other chemical reaction rates were not adjusted so as to
simplify interpretation of the calculations. In the sensitivity model
calculation with reduced OH, the model's CO negative
bias is reduced by about 30–50 % in the NH. After assimilation
with reduced OH, the a posteriori annual total CO
emissions become smaller by 15 % in the NH, whereas the
a posteriori CO concentration at the surface does not change so
obviously. Conversely, in the free troposphere, the a posteriori
CO concentration becomes higher by about 5–10 % with the
reduced OH, which shows better agreement with the MOZAIC/IAGOS
aircraft measurements. Thus, a possible overestimation of the
simulated OH might lead to overestimations in the estimated
CO emissions and underestimations in the analysed CO
concentration in the free troposphere. The large positive adjustment
needed for the CO concentrations in the NH may therefore be
related to deficiencies in the modelling of OH, instead of too
low emissions.
Note that CO is produced by the oxidation of methane and
biogenic NMHCs, a process that contributes about
half of the background CO (Duncan et al., 2007). This component
can also account for part of the missing CO
concentrations. Stein et al. (2014) considered that anthropogenic
CO and VOC emissions in their inventory are too low for
industrialised countries during winter and spring.
Other error sources
The emissions of O3 precursors other than NOx and
CO, such as VOCs, have a pronounced influence on tropospheric
chemistry. Further constraints are required to improve the O3
analysis. Optimising isoprene emissions from satellite CH2O
measurements in the reanalysis framework have the potential to improve
the O3 analysis; this will be investigated in a future study.
Incorrect model processes in atmospheric transport and chemistry lead
to model forecast errors and degrade the reanalysis
performance. Improving the forecast model is important for properly
propagating observational information in space and among different
species.
Meteorological fields used as inputs to the chemical reanalysis
calculation were produced using an AGCM simulation nudged toward the
meteorological reanalysis in order to reproduce past meteorological
variations while simulating the influence of sub-grid transport
processes. Simultaneous assimilation of meteorological and chemical
observations using an advanced data assimilation technique with
consideration of radiative feedbacks and the covariances between the
meteorological and chemical fields is expected to reduce systematic
model errors and improve the chemical reanalysis performance.
Data assimilation setting
To improve the data assimilation analysis with the limited ensemble
size, covariance localisation was applied to neglect the error
correlation among non- or weakly related variables in the background
error covariance matrix. The inclusion of correlations between
a larger number of variables allows the propagation of observational
information among various fields, but it requires a large ensemble
size to represent the multivariate relationships properly. For
instance, Zoogman et al. (2014) demonstrated the possibility of
substantial benefit from joint O3–CO data assimilation
in analysing near-surface O3, if the instrument sensitivity
for CO in the boundary layer is larger than that for
O3. Such covariances were not considered in our reanalysis
calculation.
Uncertainty estimation
Important information regarding the reanalysis product is provided by
the error covariance. The analysis ensemble spread, which is estimated
as the standard deviation of the simulated concentrations across the
ensemble, in combination with the χ2 test can be used as
a measure of the uncertainty of the reanalysis product within the EnKF
assimilation framework (Miyazaki et al., 2012b). The analysis spread
is caused by errors in the model input data, model processes, and
errors in the assimilated measurements, and it is reduced if the
analysis converges to a true state.
The analysis spread for O3 is about 8–12 % relative to
the analysed concentration in the tropical upper troposphere at
200 hPa (lower panels in Fig. 3), which is mostly determined
by the assimilation of TES and MLS O3 retrievals. The analysis
spread is relatively small in the extratropical lower stratosphere
(4–7 %) except at the polar regions, because of the high accuracy
of the MLS measurements. At 700 and 400 hPa, the O3
analysis spread is generally smaller in the tropics than the
extratropics because of the higher sensitivities in the TES O3
retrievals. The simultaneous emission and concentration optimisation
is important in producing proper ensemble perturbations, especially in
the lower troposphere.
The global analysis spread for O3 at 700 and 400 hPa
is small in 2010–2012 (lower panels in Fig. 3). Considering the
smaller level of agreement with the ozonesonde observations in
2010–2012 than in 2005–2009 (Table 3), the small analysis spread
cannot be regarded as an error reduction caused by the analysis
converging to a true state. The small analysis spread is likely
associated with the lack of effective observations for measuring the
analysis uncertainties and with the stiff chemical system. The
obtained results indicate the requirements for additional
observational information and/or stronger covariance inflation to the
forecast error covariance for measuring the long-term analysis spread
corresponding to actual analysis uncertainty. The too large χ2
for OMI NO2 and TES O3 (Fig. 1) also suggested
underestimations in the forecast error covariance in comparison with
the actual OmF in 2010–2012 (cf. Sect. 4.1).
Applications and future developments
The chemical reanalysis data set has great potential to contribute in
a number of ways to studies of the atmospheric environment and
climate:
The concentration and emission data, which are produced
consistently from a single analysis system, provide comprehensive
information on atmospheric composition variability in order to improve the
understanding of the processes controlling the atmospheric
environment, including OH, and their roles in changing
climate.
The reanalysis data provide initial and boundary conditions for
climate and chemical simulations. They can also be used as an input
to meteorological reanalyses for radiation calculations (Dragani and
McNally, 2013).
The obtained emission data can be used to study emission
variabilities and to evaluate bottom-up emission inventories.
The statistical information obtained during the reanalysis
calculation can be used to suggest developments of models and
observations. The large spread can be regarded as an indicator for
the requirement for further constraints, whereas the analysis
increment identifies sources of model error.
Several further developments have been identified as necessary to
improve the quality and value of the reanalysis data set:
Discontinuities in the assimilated measurements lead to changes
in the reanalysis quality. The O3 analysis performance was
degraded in 2010–2012, corresponding to the decreased number of
assimilated measurements. The influence of data discontinuities must
be considered or removed when studying interannual variability and
trends using products from reanalyses. Including more data sets such
as from IASI and GOME-2 measurements could improve the reanalysis
quality.
Application of a bias correction procedure for multiple
measurements could improve the reanalysis quality but should be
carefully checked (Inness et al., 2013). Observations taken from
aircraft and ozonesonde measurements or independent satellite
data sets can be used as anchors in the bias
correction. Alternatively, these data could be assimilated to
provide additional unbiased constraints, as has been demonstrated by
Baier et al. (2013).
Additional constraints are required to improve the lower
troposphere and boundary layer concentrations and
emissions. Recently developed retrievals with high sensitivity to
the lower troposphere would be helpful (e.g. Deeter et al., 2013;
Cuesta et al., 2013). Moreover, the optimisation of additional
precursors emissions could be important for improving the lower
tropospheric analysis, including the representation of long-term
variability.
Extension of the forecast model to the entire stratosphere with
detailed stratospheric chemistry is expected to reduce forecast
errors in both the stratosphere and the troposphere. We plan to
replace the forecast model with one that has an updated chemical
scheme and a model top extended to the stratosphere (Watanabe
et al., 2011). This would also allow the assimilation of total
column measurements, in which the combined assimilation of limb
profiles with nadir column measurements could benefit the reanalysis
performance, especially in the UTLS (Barré et al., 2013; Inness
et al., 2013; Emili et al., 2014).
Conclusions
We conducted a chemical reanalysis calculation for the 8 years
from 2005 to 2012 based on an assimilation of multiple satellite data
sets obtained from OMI, MLS, TES, and MOPITT. The simultaneous
optimisation of the chemical concentrations and the precursors
emissions provides a comprehensive data set that can be used for
various applications in air-quality and climate research. By analysing
simultaneously concentrations and emissions, the improved atmospheric
concentrations of chemically related species have the potential to
improve the emission inversion, whereas the improved representations
of the seasonal, interannual, and geographical variability of the
emissions benefit the atmospheric concentration reanalysis through
a reduction in model forecast error.
Data assimilation statistics were analysed to evaluate the long-term
stability of the chemical reanalysis. The analysis confirmed that the
forecast error covariance was specified reasonably well. The OmFs
without assimilation varied with year, which suggested an unrealistic
lack of interannual variations in the precursor's emissions. The OmFs
after assimilation became almost constant and decreased in the
reanalysis, implying persistent reduction of model error and improved
representation of emission variability. The information on the
analysis uncertainty obtained during the assimilation adds value to
the chemical reanalysis data set, in which the observed large analysis
spreads indicated a requirement for further constraints from
additional observations. However, the discontinuity in the assimilated
measurements limited the usability of the reanalysis product. The
number of available TES measurements decreased significantly after
2010, which produced unrealistically small analysis spreads and
degraded the quality of the tropospheric O3 analysis.
The analysed O3, CO, and NO2 concentrations in the
troposphere showed good agreement with independent observations on
both regional and global scales, for seasonal and interannual
variations from the lower troposphere to the lower stratosphere. The
linear ozone slopes observed during the reanalysis period were
positive at NH mid-latitudes in the lower troposphere and negative in
the NH UTLS; these interannual variations were captured well in the
reanalysis. The model simulation without any assimilation mostly
failed to reproduce the observed variations. The simultaneous
assimilation of multiple-species data with optimisation of both the
concentrations and emission fields was shown to be effective in
correcting the profiles for the entire troposphere, including the
long-term variations in O3, CO, NO2. The global
distribution of OH was modified considerably, decreasing the
difference between NH and SH because of the simultaneous assimilation
throughout the reanalysis period, which played an important role in
propagating observational information among various species and in
modifying the chemical lifetimes of reactive gases. To conclude, the
combined analysis of concentrations and emissions is considered an
important development in tropospheric chemistry reanalysis.
To produce better chemical reanalysis data, it will be necessary to have
additional constraints, a better forecast model, and bias correction.
Although the assimilation of multi-species data influences the representation
of the entire chemical system, the influence of persistent model errors
remains a concern. For instance, the reanalysis still has large negative
biases in NO2 concentrations over the polluted regions, which may be
associated with errors in, for instance, the model chemical equilibrium states,
planetary boundary layer mixing, and diurnal variations in chemical
processes and emissions. Adjusting additional model parameters such as VOC
emissions, deposition, and/or chemical reactions rates by adding
observational constraints will help to reduce model errors. An extension of
the forecast model to the entire stratosphere and incorporating detailed
stratospheric chemistry is expected to reduce forecast errors in both the
stratosphere and troposphere and allow the assimilation of total column
measurements (Inness et al., 2013). Techniques to reduce the influence of
discontinuities in the assimilated measurements and to use sparse
observations efficiently (van der A et al., 2010) on the quality of the
reanalysis are also required.