We introduce a Multi-mOdel Multi-cOnstituent Chemical
data assimilation (MOMO-Chem) framework that directly accounts for model
error in transport and chemistry, and we integrate a portfolio of data
assimilation analyses obtained using multiple forward chemical transport
models in a state-of-the-art ensemble Kalman filter data assimilation
system. The data assimilation simultaneously optimizes both concentrations
and emissions of multiple species through ingestion of a suite of
measurements (ozone, NO2, CO, HNO3) from multiple satellite
sensors. In spite of substantial model differences, the observational
density and accuracy was sufficient for the assimilation to reduce the
multi-model spread by 20 %–85 % for ozone and annual mean bias by
39 %–97 % for ozone in the middle troposphere, while simultaneously
reducing the tropospheric NO2 column biases by more than 40 % and
the negative biases of surface CO in the Northern Hemisphere by 41 %–94 %.
For tropospheric mean OH, the multi-model mean meridional hemispheric
gradient was reduced from 1.32±0.03 to 1.19±0.03, while the
multi-model spread was reduced by 24 %–58 % over polluted areas. The
uncertainty ranges in the a posteriori emissions due to model errors were
quantified in 4 %–31 % for NOx and 13 %–35 % for CO regional emissions.
Harnessing assimilation increments in both NOx and ozone, we show that the
sensitivity of ozone and NO2 surface concentrations to NOx emissions
varied by a factor of 2 for end-member models, revealing fundamental
differences in the representation of fast chemical and dynamical processes.
A systematic investigation of model ozone response and analysis increment in
MOMO-Chem could benefit evaluation of future prediction of the chemistry–climate
system as a hierarchical emergent constraint.
Introduction
Data assimilation is a technique for combining different observational data
sets with a model, taking into consideration of the characteristics of
individual measurements and model dynamics (e.g., Kalnay, 2003; Lahoz and
Schneider, 2014). Atmospheric composition and chemical data assimilation
using advanced data assimilation techniques such as four-dimensional
variational data assimilation (4D-Var) and ensemble Kalman filter (EnKF)
allows the propagation of observational information in time and space from a
limited number of observed species to a wide range of chemical components
(e.g., Lahoz et al., 2007; Sandu and Chai, 2011;
Bocquet et al., 2015). Data
assimilation provides global fields that are statistically consistent with
individual observations. Various studies have demonstrated the capabilities
of chemical data assimilation systems in the analysis of chemical species in
the troposphere and stratosphere (e.g., Parrington et al., 2009; Kiesewetter
et al., 2010; Flemming et al., 2011; Coman et al., 2012; Emili et al., 2014;
Miyazaki et al., 2012a, b, 2015, 2019; van der A et al., 2015),
emissions optimization (e.g., Miyazaki et al., 2012a;
2014; Miyazaki and Eskes, 2013; Stavrakou
et al., 2013; Streets et al., 2013; Inness et al.,
2015; Jiang et al., 2018),
and chemical reanalyses to provide long-term data assimilation products
(Inness et al., 2013; Gaubert et al., 2016; Miyazaki et al., 2015; Flemming
et al., 2017). Chemical data assimilation frameworks have also been used to
evaluate observing system impacts through observation system simulation
experiments (OSSEs) (Yumimoto, 2013; Lahoz and Schneider, 2014; Bocquet et
al., 2015; Abida et al., 2017; Liu et al., 2017) and evaluate
chemistry–climate model simulations (Miyazaki and Bowman, 2017; Kuai et al.,
2020).
Developments of advanced data assimilation techniques and satellite
retrievals have contributed to improving data assimilation analysis and
prediction of atmospheric composition (e.g., Skachko et
al., 2016; Boersma et al., 2018a). However, a limiting factor in the accuracy
of these systems is the performance of forecast models, which have limited
fidelity in the representation of atmospheric dynamics and chemistry. For
example, intercomparison studies of the Atmospheric Chemistry and Climate
Model Intercomparison Project (ACCMIP) (Bowman et al., 2013; Young et al.,
2013; Stevenson et al., 2013) and the Chemistry-Climate Model Initiative
(CCMI) (Morgenstern et al., 2017;
Kuai et al., 2020) revealed a large
diversity in simulations of tropospheric composition owing to differences in
model processes and input data. The choice of forecast model, thus, largely
influences the a priori uncertainty in chemical data assimilation and the a
posteriori data assimilation analysis.
As opposed to 4D-variational techniques that require a model adjoint, EnKF
systems are independent from forecast model code and therefore can readily
integrate multiple models into a multi-model data assimilation framework
(Houtekamer and Zhang, 2016). EnKF techniques have been successfully applied
to multiple different chemical transport models (CTMs) in our previous
studies (e.g., Miyazaki et al., 2012b, 2015, 2017, 2019), which have been
used to assimilate multi-constituent composition measurements from multiple
sensors where both the chemical states and emissions of various species were
simultaneously optimized. However, the sensitivity of concentrations to
emissions, such as ozone response to NOx emissions, is strongly model
dependent and therefore has a first-order impact on the performance in a
multi-constituent data assimilation framework. Consequently, quantification
of this impact is important not only for analysis but also for Observing
System Simulation Experiments (OSSEs) used to assess and design new
observing systems. Nevertheless, the importance of forecast model
performance on chemical data assimilation has not been demonstrated using a
common data assimilation framework for tropospheric chemistry analysis. A
multi-model framework can also be used to provide multi-model integrated
analysis fields, which are less dependent on individual model performance.
Data assimilation that relies on a single model may lead to biased
estimation and underestimate model uncertainty by under-sampling the
relevant model space. The limitations with a single model could be overcome
by integrating multi-model information in data assimilation in various ways.
First, ensembles of models can be used to construct a flow-dependent
analysis system. For instance, Xue and Zhang (2014) extended data
assimilation to the multi-model Bayesian model averaging analysis framework,
in which the posterior model weight for each model is determined through
Bayes' theorem reflecting the prior probability of each model and the
analysis consistency with the observations. This approach requires a
framework to execute and update multiple-model states continuously, which is
difficult with multiple state-of-the-art CTMs that have been optimized using
different platforms. Another way to integrate multiple-model information is
to apply a common data assimilation framework with multiple models. By
assimilating the same sets of observations, this framework can be used to
demonstrate the importance of forecast model performance on data
assimilation analysis, while uncertainty information of individual analyses
can be evaluated consistently by using a same data assimilation framework.
Uncertainty-weighed multi-model integrated analysis fields would provide
unique information that is less dependent on individual model performance
and is fundamentally different from averages of individual data
assimilation analyses. Quantifying model performance with a multi-model
integration is difficult when using different data assimilation frameworks.
This study demonstrates, for the first time, the importance of forecast
model performance on data assimilation analysis of tropospheric composition
and emissions, by utilizing four different CTM frameworks and applying a
common EnKF approach. As illustrated in Fig. 1, an EnKF data assimilation
system based on the GEOS-Chem model is newly developed in this study. Using
the same data assimilation settings and assimilating almost the same
multi-constituent observations from multiple satellite sensors, we examine
how model bias affects tropospheric chemistry data assimilation performance,
including emission estimation, and provide integrated data assimilation
analysis fields from an ensemble of analyses that ingested multiple models
and multi-constituent measurements.
Schematic diagram of the MOMO-Chem framework. The MOMO-Chem
utilizes four different CTMs and applies a common EnKF approach to
investigate the importance of forecast model performance and model
sensitivities for data assimilation analysis. This framework also provides
multi-model integrated analysis fields and its uncertainty ranges.
MethodologyData assimilation module
The data assimilation technique is based on a local ensemble transform
Kalman filter (LETKF) approach developed by Hunt et al. (2007). The LETKF
uses an ensemble forecast to estimate the background error covariance matrix
and generates an analysis ensemble mean and covariance that satisfy the
Kalman filter equations for linear models. In the forecast step, a
background ensemble, xib (i=1,…,k), is obtained from
the evolution of an ensemble model forecast. Here, x represents the
model variable, b indicates the background state, and k is the ensemble size
(32 in this study). The background ensemble mean xb‾ and its
perturbation Xb are then estimated as follows:
1xb‾=1k∑i=1kxib,2Xib=xib-xb‾.
The background error covariance is then estimated at each time step at each
grid point as follows:
Pb=XbXbT.
The background ensemble is converted into the observation space,
yib=Hxib, using the
observation operator H, which is the composite of a spatial interpolation
operator and a satellite retrieval operator (see Sect. 2.3). An ensemble
of background perturbation is defined as
Yib=yib-yb‾.
Using the covariance matrices of observation and background error, the data
assimilation determines the relative weights of the observation and
background and subsequently transforms a background ensemble into an
analysis ensemble, xia (i=1,…,k). The analysis ensemble
mean xa‾ is obtained by updating the background ensemble mean as
follows:
4xa‾=xb‾+XbP̃aYbTR-1yo-yb‾,5P̃a=k-11+ΔI+YbTR-1Yb-1,
where P̃a is the k×k local analysis error covariance in
the ensemble space, yo is the observation vector, and
R is the observation error covariance. A covariance inflation
factor (Δ, 6 % in this study for all the models,
following the setting in Miyazaki et al., 2015) is applied to inflate the
forecast error covariance.
The observation-minus-forecast (OmF), that is known as the observational
increment, is defined as
yo-yb‾.
The analysis increment is defined as the correction made by data
assimilation as follows:
xa‾-xb‾.
The analysis ensemble perturbation matrix in the model space
(Xa) is obtained by transforming the background ensemble as
follows and is used in the subsequent forecast step as the initial condition:
Xa=Xbk-1P̃a1/2.
In the data assimilation analysis, covariance localization is applied so
that the covariance among unrelated or weakly related variables is
neglected. This removes the influence of spurious correlations resulting
from the limited size of the ensemble. Further, it removes the influence of
remote observations that may cause sampling errors. The data assimilation
settings such as localization length used in this study are given in Sect. 2.6. Estimation of emissions is based on a state augmentation technique that
uses the background error correlations for each grid point to determine the
relationship between the concentrations and emissions of various species
(Miyazaki et al., 2012a). A more detailed description of the basic data
assimilation framework is available in Miyazaki et al. (2017).
Forecast models
We applied the same data assimilation system to four CTM frameworks:
GEOS-Chem, AGCM-CHASER, MIROC-Chem, and MIROC-Chem-H. The specifications of
these systems are summarized in Table 1. The major differences among the
models are the meteorological input data, the complexity of the chemical
mechanisms (simplest in AGCM-CHASER for the troposphere), emission
inventories (oldest in GEOS-Chem), vertical coordinate (sigma in AGCM-CHASER
only), and spatial resolution (highest in MIROC-Chem-H).
Summary of the forecast models used in this study.
GEOS-ChemAGCM-CHASER (TRC-1)MIROC-ChemMIROC-Chem-H (TCR-2)Horizontal resolution2∘×2.5∘2.8∘×2.8∘2.8∘×2.8∘1.1∘×1.1∘Vertical resolution47 layers to 0.1 hPa (hybrid)32 layers to 4 hPa (sigma)32 layers to 4 hPa (hybrid)32 layers to 4 hPa (hybrid)Forecast modelGEOS-Chem v9 (adjoint v35)CCSR/NIES/FRCGC AGCM-CHASERMIROC-ChemMIROC-ChemChemistry43 species, 318 reactions47 species, 88 reactions92 species, 262 reactions92 species, 262 reactionsMeteorological dataGEOS-5Nudged to NCEP-DOE/AMIP-2Nudged to ERA-InterimNudged to ERA-InterimAssimilated dataOMI NO2 (DOMINO2), SCIAMACHY NO2 (DOMINO2), TES ozone (v5) MOPITT CO (v6 NIR) MLS ozone & HNO3 (v3.3)OMI NO2 (DOMINO2), SCIAMACHY NO2 (DOMINO2), TES ozone (v5) MOPITT CO (v6 NIR) MLS ozone & HNO3 (v3.3)OMI NO2 (DOMINO2), SCIAMACHY NO2 (DOMINO2), TES ozone (v5) MOPITT CO (v6 NIR) MLS ozone & HNO3 (v3.3)OMI NO2 (QA4ECV), SCIAMACHY NO2 (QA4ECV), TES ozone (v6) MOPITT CO (v7J) MLS ozone & HNO3 (v3.3)A priori emissionsEDGAR 3, NEI2008, RETRO, GFED2EDGAR 4.2, GFED 3.1, GEIAEDGAR 4.2, GFED 3.1, GEIAHTAP v2, GFED 4, GEIAState vectorConcentrations of 43 species + emissions (NOx, CO, LNOx)Concentrations of 35 species + emissions (NOx, diurnal variability, CO, LNOx)Concentrations of 35 species + emissions (NOx, diurnal variability, CO, LNOx)Concentrations of 35 species + emissions (NOx, diurnal variability, CO, SO2, LNOx)Reference (forecast model)Henze et al. (2007)Sudo et al. (2002)Watanabe et al. (2011)Sekiya et al. (2018)Reference (data assimilation)This studyMiyazaki et al. (2012a, b, 2014, 2015)Miyazaki et al. (2017)Miyazaki et al. (2019)GEOS-Chem
The GEOS-Chem model is driven by assimilated meteorological data from the
Goddard Earth Observing System (GEOS-5) of the NASA Global Modeling and
Assimilation Office (GMAO). The adjoint model version 35 (Henze et al.,
2007), which corresponds to version 9 of the forward model, with a
horizontal resolution of 2∘×2.5∘ and 47 vertical
levels extending from the surface to 0.1 hPa, was used as a forward forecast
model (i.e., without adjoint calculations) in this study. Although newer and
improved versions of the forward model are available, we chose this version
(the latest version of the adjoint model) so that an intercomparison study
of 4D-Var and EnKF using the same modeling system can be conducted in a
separate study. The core of GEOS-Chem computes the local changes in
atmospheric concentrations due to emissions, chemical reactions, and
deposition. Further, it can simulate coupled aerosol–oxidant chemistry in
the troposphere and stratosphere. This model uses the advection algorithm
developed by Lin and Rood (1996) on the rectilinear grid. Convective
transport is computed from the convective mass fluxes available in the
meteorological archive. The application of the EnKF chemical data
assimilation system based on the GEOS-Chem model is newly developed in this
manuscript.
The a priori emission data for NOx and CO were obtained from the Emission
Database for Global Atmospheric Research (EDGAR) version 3 inventory
(Olivier and Berdowski, 2001) for global anthropogenic emissions and from the
monthly the Global Fire Emissions Database (GFED) version 2 inventory (van der
Werf, 2006) for biomass burning emissions. Volatile organic compound (VOC)
emission data were obtained from the RETRO inventory (Schultz et al., 2008).
Emission data for North America were replaced with the 2008 National
Emissions Inventory (NEI).
AGCM-CHASER
The chemical atmospheric general circulation model for the study of
atmospheric environment and radiative forcing (CHASER; Sudo et al., 2002)
simulates tracer transport, wet and dry deposition, and emissions. It has a
horizontal resolution of T42 (2.8∘×2.8∘) and 32σ
levels from the surface to 4 hPa. This model is coupled to the Center for
Climate System Research/National Institute for Environmental Studies
(CCSR/NIES) atmospheric general circulation model (AGCM) version 5.7b. The
AGCM fields in this model are nudged towards the National Centers for
Environmental Prediction Department of Energy Atmospheric Model
Intercomparison Project II (NCEP-DOE/AMIP-II) reanalyses (Kanamitsu et al.,
2002) at each time step of the AGCM (i.e., every 20 min) to reproduce past
meteorological conditions. The data assimilation system based on the
AGCM-CHASER model (Miyazaki et al., 2012a, b; Miyazaki and Eskes, 2013)
was used to conduct our first chemical reanalysis calculation for 2005–2012
(TCR-1; Miyazaki et al., 2015) and elucidate the 3-D structures of
lightning-induced NOx (LNOx) sources (Miyazaki et al., 2014).
The anthropogenic NOx and CO emissions were obtained from EDGAR version
4.2. Emissions from biomass burning are based on the GFED version 3.1 (van
der Werf et al., 2010), while those from soils are based on the monthly
Global Emissions Inventory Activity (GEIA) (Graedel et al., 1993). Using the
settings reported by LOTOS-EUROS (Schaap et al., 2008) and Boersma et al. (2008), a diurnal variability scheme
developed by Miyazaki et al. (2012a)
was applied for surface NOx emissions depending on the dominant category for
each area (anthropogenic, biogenic, and soil emissions). LNOx sources were
determined based on the relationship between lightning activity and cloud-top height (Price and Rind, 1992) and using the convection scheme of the
AGCM. Biogenic emissions from vegetation are considered for non-methane
hydrocarbons (NMHCs) based on Guenther et al. (2006). Oxidations of ethane,
propane, ethene, propene, isoprene, and terpenes were included explicitly.
MIROC-Chem
MIROC-Chem is the chemistry component of the MIROC Earth system model (ESM)
and is coupled to the MIROC-AGCM version 4 (Watanabe et al., 2011). It has a
horizontal resolution of T42 (2.8∘×2.8∘) and 32 hybrid
vertical levels from the surface to 4.4 hPa. Its tropospheric chemistry was
developed based on the CHASER model with updates related to chemical
reactions and emissions. MIROC-Chem considers the fundamental chemical cycle
of Ox–NOx–HOx–CH4–CO along with
oxidation of non-methane VOCs (NMVOCs)
to accurately represent ozone chemistry in the troposphere. Its
stratospheric chemistry simulates chlorine- and bromine-containing compounds,
chlorofluorocarbons (CFCs), hydrofluorocarbons (HFCs), carbonyl sulfide
(OCS) and N2O. Further, it simulates the formation of polar
stratospheric clouds (PSCs) and the associated heterogeneous reactions on
their surfaces. The simulated meteorological fields were nudged towards the
6-hourly ERA-Interim reanalysis (Dee et al., 2011). An EnKF system that is
based on MIROC-Chem has been used to study decadal changes in NOx emissions
(Miyazaki et al., 2017; Jiang et al., 2018). The emission data and LNOx
scheme for this model are the same as in the AGCM-CHASER.
MIROC-Chem-H
A high-resolution (1.1∘×1.1∘) version of the
MIROC-Chem model, MIROC-Chem-H (Sekiya et al., 2018), was also used. This
model utilizes the same chemical and transport module as MIROC-Chem
(see Sect. 2.2.3) and has been used to study processes controlling air
quality in east Asia during the KORUS-AQ aircraft campaign (Miyazaki et al.,
2019; Thompson et al., 2019) and conduct the second Tropospheric Chemistry
Reanalysis (TCR-2; Jet Propulsion Laboratory, 2019) for 2005–2018.
Kanaya et al. (2019) demonstrated the overall good performance of the ozone and CO
analyses in TCR-2 over remote oceans using observations from research
vessels.
Data for anthropogenic emissions of NOx and CO were obtained from the HTAP
version 2 inventory for 2010 (Janssens-Maenhout et al., 2015). This
inventory combines nationally reported emissions data with data from
regional scientific inventories of 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). Emissions from biomass burning were based on the
monthly GFED version 4.2 inventory (Randerson et al., 2018) for NOx and CO,
while those from soils were based on the monthly GEIA inventory (Graedel et
al., 1993) for NOx. Emission data for other compounds were taken from the
HTAP version 2 and GFED version 4 inventories.
As summarized in Table 1 and described in Sect. 2.3, the satellite
products used in MIROC-Chem-H were more recent than those used in the other
three models. Diversity among the data assimilation systems was enhanced by
the use of different assimilated data. Although the effects of varying
assimilated measurements need careful evaluation, the recently developed
retrieval products reveal rather similar characteristics in general. We
thus expect that the forecast model performance has a greater influence on
data assimilation analysis.
Assimilated measurements
To assimilate satellite measurements, we have developed an observation
operator (H) for individual assimilated measurements. This operator
includes the spatial interpolation operator (S), a priori profile
for the satellite retrievals (xapriori), and
averaging kernel (A), which maps the model fields
(xib) into the retrieval space (yb),
as follows:
yib=Hxib=xapriori+ASxib-xapriori.
The averaging kernel captures the vertical sensitivity profiles of the
retrievals (e.g., Eskes and Boersam, 2003; Jones et al.,
2003; Migliorini et al., 2008). Even though the retrieval
yo and the model equivalent
yib depend on the a priori profile, using the averaging
kernel removes the dependence of the analysis on model–retrieval
comparison.
Biases in the assimilated satellite retrievals can degrade data assimilation
performance. The ozone analysis bias is not solely determined by bias in the
assimilated ozone measurements in the multi-constituent data assimilation
approach. Miyazaki et al. (2015) demonstrated that the assimilation of
measurements other than TES measurements led to corrections in the lower and
middle tropospheric ozone. Application of a bias correction procedure for
multiple measurements could improve the data assimilation analysis quality.
However, we did not apply any bias correction because of the difficulty in
estimating the bias structure that could vary temporally and spatially.
Meanwhile, since the data are the same for all comparisons with different
models, the differences with respect to independent observations are
relatively independent of those biases.
OMI and SCIAMACHY NO2
The tropospheric NO2 column retrievals from the DOMINO version 2 for
Ozone Monitoring Instrument (OMI) and Scanning Imaging Absorption
Spectrometer for Atmospheric Chartography (SCIAMACHY) (Boersma et al.,
2011), obtained from the Tropospheric Emission Monitoring Internet Service
(TEMIS) website (http://www.temis.nl, last access: 1 June 2019) were used for the GEOS-Chem,
AGCM-CHASER, and MIROC-Chem systems. For MIROC-Chem-H, retrievals from the
QA4ECV version 1.1 level 2 (L2) product for OMI (Boersma et al., 2017a) and
SCIAMACHY (Boersma et al., 2017b) were used. Low-quality data were excluded
following the published recommendations (Boersma et al., 2011, 2018b).
We employed a super-observation approach to produce representative data with
the horizontal resolution of each forecast model, following the approach of
Miyazaki et al. (2012a). Super-observation error was estimated using the
provided retrieval uncertainty and considering an error correlation of 15 % among the individual satellite observations within a model grid cell
and representativeness errors in all the systems.
TES ozone
The Tropospheric Emission Spectrometer (TES) ozone retrievals used are the
version 5 level 2 nadir data obtained from the global survey mode (Bowman et al., 2006;
Herman and Kulawik, 2013) for the GEOS-Chem, AGCM-CHASER, and
MIROC-Chem systems. The version 6 level 2 nadir data were used for the
MIROC-Chem-H system. This data set consists of 16 daily orbits with a
spatial resolution of 5–8 km along the orbit track. The standard quality
flags were used to exclude low-quality data. The data assimilation of the
TES ozone retrievals was performed based on the logarithm of the mixing
ratio following the retrieval product specification (Bowman et al., 2006).
MLS ozone and HNO3
The Microwave Limb Sounder (MLS) data used were the version 3.3 ozone and
HNO3 L2 products (Livesey et al., 2011) for all models except
MIROC-Chem-H, which used the version 4.2 data. We used MLS data for
pressures of less than 215 hPa for ozone and less than 150 hPa for
HNO3, while tropical-cloud-induced outliers were excluded. The provided
accuracy and precision of the measurement error were included as the
diagonal element of the observation error covariance matrix.
MOPITT CO
The version 6 level 2 thermal infrared (TIR) products (Deeter et al., 2013)
of the Measurement of Pollution in the Troposphere (MOPITT) were used for
all models except the MIROC-Chem-H, for which the version 7 level 2 TIR–near-infrared (NIR) total column CO data were used (Deeter et al., 2017). The
version 7 products have been improved from the version 6 products with
respect to overall retrieval biases, bias variability and bias drift
uncertainty (Deeter et al., 2017). Owing to data quality problems, we
excluded data poleward of 65∘ and nighttime data. For the version
6 TIR products, data at 700 hPa were used for constraining surface CO
emissions. For the version 7 TIR–NIR products, the total column averaging
kernel was used in the observation operator to estimate simulated total
columns. The uncertainty information provided in the retrievals was used in
the observation error. Like in the case of NO2 measurements, the
super-observation approach was applied for MOPITT measurements as well.
Validation dataWOUDC ozonesonde data
All available ozonesonde observations taken from the World Ozone and
Ultraviolet Radiation Data Center (WOUDC) database (available at http://www.woudc.org, last access: 1 June 2019) were used as validation data. All ozonesonde
profiles have been interpolated to a common vertical pressure grid, with a
bin of 25 hPa. The ozone fields from the control and data assimilation
calculations were linearly interpolated to the time and location of each
measurement, with a bin of 25 hPa, and then compared with the measurements
at 4∘×4∘ grid points. The observation error is 5 %–10 % between the surface and 30 km (Smit et al., 2007).
WDCGG surface carbon monoxide
Surface CO concentration observations were obtained from the World Data
Centre for Greenhouse Gases (WDCGG) operated by the World Meteorological
Organization (WMO) Global Atmospheric Watch program (http://ds.data.jma.go.jp/gmd/wdcgg/, last access: 1 June 2019). Hourly and event observations from 59 stations were used
to validate surface CO concentrations from the control and data assimilation
runs at 5∘×5∘ grid points.
Multi-model analysis
We construct integrated data assimilation analysis using multiple models
combined with multiple-species measurements (Fig. 1). The multi-model
integrated analysis xm‾(xmultimodel‾) is
obtained by combining data assimilation analyses (xja)
weighted by analysis uncertainties (σj2) of individual models (j=1–4) as follows:
xm‾=∑xja/σj2∑1/σj2.
The analysis uncertainties (σj2)
are estimated from the root mean square of the analysis ensemble
perturbation matrix (Xa; see Eq. 8) that is obtained by
transforming the background ensemble using the local analysis error
covariance (see Eq. 5). The integrated analysis (xm‾) provides unique information on
atmospheric states, which are less dependent on the characteristics of
individual models used for data assimilation, and considers the uncertainty
of individual data assimilation analyses. The uncertainty of the integrated
analysis (σ‾m2) is defined as follows:
σ‾m2=1∑1/σj2.
We apply this approach for estimating multi-model mean ozone fields in this
study. Because of the predefined minimum values of the standard deviations
applied to surface emissions of CO and NO2 to prevent covariance
underestimation during data assimilation (see Sect. 2.6), the analysis
spreads of near-surface NOx and CO concentrations tend to be similar among
the models due to the artificial adjustments and are not fully meaningful.
Therefore, for the concentrations and emissions of CO and NOx their
multi-model mean and uncertainty were estimated as a standard ensemble mean
and spread, without using the analysis uncertainty of individual models. The
multi-model integrated analysis fields were produced at the highest
horizontal resolution of the models (1.1∘×1.1∘) after
linearly interpolation. Given the small number of models (j=1–4) used in
this study, the multi-model integration would suffer from sampling biases.
With an increase in the number of models in future studies, this approach would
provide more robust statistics.
Experimental setting
We conducted 1 year of data assimilation calculations and forward model
simulations (i.e., control run) from 1 January 2007, with a 2-month spin
up from 1 November 2006, using the four systems. This assimilation period
was chosen to provide comprehensive constraints by OMI measurements while
avoiding the influences of OMI row anomalies (December 2009 onwards)
(Schenkeveld et al., 2017) and reduced numbers of the TES measurements (2010
onwards). A control run was performed in each system using the same model
settings as the data assimilation run but without performing data
assimilation. The validation results for the control and data assimilation
runs were compared to measure the improvements achieved through data
assimilation in each system.
Almost the same data assimilation settings were used for the four systems as
follows. The state vector includes the chemical concentrations of various
species as well as the surface sources of NOx and CO and LNOx sources. The
LNOx source optimization is based on the scheme developed by Miyazaki et al. (2014). For the MIROC-Chem-H system, the state vector also includes surface
SO2 emissions, as implemented in Miyazaki et al. (2019). The state
vectors for the MIROC-Chem and MIROC-Chem-H systems include a correction
factor for emission diurnal variability to improve the representation of
diurnal emission variability using the OMI and SCIAMACHY retrievals obtained
at different overpass times, based on the scheme developed by Miyazaki et al. (2017).
Covariance inflation was applied to analyses of both concentrations and
emissions to prevent underestimation of background error covariance and
filter divergence caused by sampling errors associated with the limited
ensemble size and by model errors, following the settings used by Miyazaki
et al. (2015). Further, localization was applied to avoid the influence of
remote observations that may cause sampling errors, with a cutoff radius of
approximately 1650 km for NOx emissions and 2000 km for CO emissions, LNOx
sources, and chemical concentrations, as in Miyazaki et al. (2015). We also
applied covariance localization for different variables in the state vector
(Kang et al., 2011), by setting the covariance among unrelated or weakly related
variables to zero. The analysis of surface emissions of NOx and CO
allowed for error correlations with NO2 and CO concentrations only,
respectively. For LNOx sources, covariances with CO data were neglected.
Assimilation of MOPITT CO data was used to constrain surface CO emissions
only. Concentrations of NOy species and ozone were optimized from TES ozone,
OMI and SCIAMACHY NO2, and MLS ozone and HNO3 observations.
The a priori error was set to 40 % for surface emissions of NOx and CO and
60 % for LNOx sources, which are comparable to the reported emission
uncertainty (e.g., Schumann and Huntrieser, 2007; Kaiser et al., 2012; Li et
al., 2017). To prevent covariance underestimation and maintain emission
variability during the long-term assimilation calculation, we applied
covariance inflation to the emission source factors in the analysis step.
The standard deviation of the emission source factors was artificially
inflated to a minimum predefined value (30 % of the initial standard
deviation) at each analysis step.
The data assimilation cycle was set to be 6 h for the AGCM-CHASER,
MIROC-Chem, and MIROC-Chem-H systems and 6 h for the GEOS-Chem system
because of the limitation associated with meteorological data input in
GEOS-Chem. The emission and concentration fields were analyzed and updated
at each analysis step in all the systems. We have confirmed that the results
of data assimilation can differ when the data assimilation cycle is changed
from 2 h to 6 h using the AGCM-CHASER system. This occurs, in
particular, for the analysis of short-lived species with strong diurnal
variability and NOx emission estimates. The performance of the GEOS-Chem
data assimilation can thus be expected to differ with the use of a 2 h
data assimilation cycle and meteorological data inputs with higher temporal
frequency for short-lived species.
In summary, there are differences in the assimilated measurements (updated
retrievals were used in MIROC-Chem-H), diurnal emission variability (data
assimilation corrections were made in the MIROC-Chem and MIROC-Chem-H
systems only) and data assimilation cycle (6 h in GEOS-Chem) of the
four systems. These differences will lead to discrepancies in the data
assimilation analyses of the four systems attributable to assimilation
system configuration rather than the forward models themselves. While impact
of these configurations can be further refined in future studies, the major
discrepancies in the data assimilation analyses are still primarily
attributable to the models themselves.
Data assimilation statisticsAnalysis increment
The analysis increment (Eq. 7) information is a measure of the adjustment
made in the analysis step, which is estimated from the differences between
the forecast and the analysis after each analysis step. As shown in Fig. 2a,
the annual mean analysis increments are largely different among the models,
reflecting different systematic model biases. For individual systems, the
analysis increments are in good agreement with the OmF (Eq. 6). This
confirms that the model errors were effectively reduced using data
assimilation.
Spatial distributions of (a) analysis increment (ppbv d-1) and
(b) analysis uncertainty (ppb) of ozone at 500 hPa averaged over 2007
from the four systems.
In the ozone concentration field at 500 hPa, the AGCM-CHASER system gives
large positive increments in the extratropics of both hemispheres, with
annual mean values in the range of 1–3 ppb d-1, whereas the increments are
negative at low latitudes (up to -1.5 ppbv d-1). The standard deviations of
the analysis increment are 0.8–1.7 ppb d-1 in the extratropics and 0.2–0.4 ppb d-1 at low latitudes. The analysis increments are relatively low in
GEOS-Chem (up to -1.8 ppbv d-1) and MIROC-Chem (up to 1.4 ppbv d-1) in the
Northern Hemisphere (NH) extratropics; in GEOS-Chem (-0.5–1.5 ppbv d-1) and MIROC-Chem-H (up to
-1.0 ppbv d-1) in the tropics; and in MIROC-Chem (up to 1.4 ppbv d-1) in the
Southern Hemisphere (SH) extratropics. GEOS-Chem exhibits negative increments except over central
Africa and northern South America, with large negative increments
(up to 2 ppbv d-1) over the Southern Ocean and the US west coast in the strong
westerlies and Aleutian Low regions. The positive increments
over central Africa and northern South America could imply underestimated
ozone productions due to biomass burning or VOC emissions.
The analysis increments differed significantly between the lower and upper
troposphere as well as among seasons in all the systems (figure not shown).
GEOS-Chem shows large positive increments (0.5–2.2 ppbv d-1) in the
extratropics at 700 hPa, in contrast to negative increments (up to -2.0 ppbv d-1) at low latitudes and midlatitudes at 350 hPa. In AGCM-CHASER and
MIROC-Chem, the increments changed from positive at 700 hPa (up to 2.2 ppb d-1 in AGCM-CHASER and 0.5 ppb d-1 in MIROC-Chem) to negative at 350 hPa
(up to -2.5 and -1.2 ppb d-1) in the extratropics of
both hemispheres. The positive increments in MIROC-Chem-H decreased with
height in the extratropical troposphere. As the increments in the
troposphere are mainly introduced by the TES assimilation, the vertical
structures suggest that the assimilated TES ozone measurements have
independent information regarding the lower- and upper-tropospheric ozone.
Using observing system experiments (OSEs), our previous studies (Miyazaki et
al., 2012b, 2015, 2019) revealed that the TES ozone data assimilation
dominates the corrections in the tropospheric ozone analysis, whereas the use
of measurements other than TES measurements (mainly NO2 measurements)
led to corrections in the lower- and middle-tropospheric ozone during the
forecast. Jourdain et al. (2007) showed that the TES retrievals have 1–2 DOFs (degrees of freedom) in the troposphere, with the highest number of DOFs for the clear-sky
tropics and subtropics. The seasonal changes in the analysis increment
reflect variations in the short-term systematic model errors and
observational constraints, which also differed significantly among the
models.
Analysis uncertainty
The analysis uncertainty, which is estimated as the standard deviation of
the analyzed concentrations across the ensemble (Eq. 8) in individual
systems, can be used as a measure of the uncertainty of each data
assimilation analysis. The analysis uncertainty is due to errors in the
model input data, model processes, and assimilated measurements and is
reduced as the analyses converge to the true state. Because the model input
data and assimilated measurements are almost the same among the models,
differences in model processes such as response of ozone to perturbed
emissions and chemical lifetimes should be primarily responsible for the
analysis spreads among the models through the forecast step. Detailed
investigation on the impact of different model processes for each region and
season would be helpful to interpret the results but is beyond the scope of
this paper. The simultaneous emissions and concentration optimization were
important in producing appropriate ensemble perturbations in ozone,
especially in the lower and middle troposphere.
The ozone analysis uncertainty at 500 hPa shown in Fig. 2b is generally
smaller in the tropics than in the extratropics, likely a consequence of the
higher sensitivities in the TES ozone retrievals in the tropics. Because
common settings were applied to the ensemble size and covariance inflation,
the obtained inter-model differences in the spread reflect different
systematic model errors related to the assimilation window size. The annual
mean analysis uncertainty is generally larger in AGCM-CHASER and MIROC-Chem
than in GEOS-Chem and MIROC-Chem-H. In the tropics, the analysis uncertainty
is approximately 2–5 ppb in GEOS-Chem and MIROC-Chem-H and approximately
5–11 ppb in AGCM-CHASER and MIROC-Chem. In the extratropics, the analysis
spread is approximately 6–10 ppb in GEOS-Chem and MIROC-Chem-H and 10–16 ppb in AGCM-CHASER and MIROC-Chem. The analysis increments are generally
similar among the models (see Fig. 2a). These results suggest that the
model forecasts tended to diverge more quickly in AGCM-CHASER and
MIROC-Chem, likely as a result of larger differences in the equilibrium
state between the model and assimilation. In the upper troposphere–lower stratosphere (UTLS) region, the analysis
uncertainty is relatively smaller in the extratropics than in the tropics
because of the high accuracy of the MLS measurements. The spatial patterns in
GEOS-Chem and MIROC-Chem-H are remarkably similar, but the CHASER and
MIROC patterns are much more similar.
The multi-model standard deviation of the ozone analyses (typically
<5 ppb for the globe, Fig. 3c) is significantly lower than the
analysis uncertainty in AGCM-CHASER and MIROC-Chem (Fig. 2b). As will be
discussed in Sect. 4.1, mean errors against independent observations are
also significantly smaller than the analysis uncertainty in these models.
These results indicate that the analysis uncertainty depends on the choice
of forward model and was possibly overestimated in AGCM-CHASER and
MIROC-Chem because of a large diversity in forecast trajectories. The
overestimated analysis error covariance was also confirmed by smaller chi
squares (e.g., Ménard and Chang, 2000) in these models (not shown). To
measure the analysis spread corresponding to the actual analysis
uncertainties, additional observational information and optimizing the
covariance inflation to the forecast error covariance would be required.
Spatial distributions of the multi-model mean values of (a) data
assimilation analysis and (b) its uncertainty of annual mean ozone at 500 hPa estimated using Eqs. (10) and (11), respectively. Panel (c) shows the standard
deviation (i.e., multi-model spread) of the annual mean ozone analysis among
the four models.
Multi-model integrations
Figure 3a shows the integrated ozone analysis fields, xm‾ defined in Eq. (10), that were created using MOMO-Chem. The annual and
multi-model mean ozone concentrations at 500 hPa are high in the NH
extratropics (55–70 ppbv) and low over the Maritime Continent and the
tropical western Pacific (22–35 ppbv). Because the analyses from the
GEOS-Chem and MIROC-Chem-H systems exhibit smaller analysis spreads (see
Sect. 3.2), they exert a strong control on the integrated fields. At 500 hPa, the estimated uncertainty of the integrated fields, σm2‾ defined in Eq. (11), is 2–4.5 ppbv in the NH, 0.5–2 ppbv in the
tropics and 3–5.5 ppbv in the SH (Fig. 3b). These values are smaller than
the uncertainties of the individual model analyses (Fig. 2b), demonstrating
that the integrated fields can provide more reliable and unique information.
The multi-model spread of individual data assimilation analysis (Fig. 3c) is
typically smaller than the multi-model mean integrated uncertainty (Fig. 3b). Again, with the multi-model spread (Fig. 3c) and the differences with
the ozonesonde measurements (Sect. 4.1) being smaller than the multi-model
mean uncertainty (Fig. 3b), the comparisons suggest that the analysis
uncertainty might be overestimated in some of the analyses.
Over northern South America, the larger multi-model spread compared to
the multi-model mean uncertainty suggests that the background errors might
have been underestimated, as rapid error growths due to deep convection and
biomass burning might not have been accounted for properly. Differences in
isoprene emissions and chemistry could also enhance the multi-model spread
over the region (Archibald et al., 2010). Techniques such as adaptive
inflation for background error covariance (e.g., Anderson, 2007) would be
helpful to represent rapid changes in background errors in the individual
models.
Validation resultsOzone profilesComparisons against TES observations
Figure 4 compares the annual zonal mean ozone from the lower to upper
troposphere. In comparison with the TES measurements, at 750 hPa, all the
control runs underestimate the mean ozone in the NH extratropics (by -4.4 to
-3.2 ppb at 50∘ N). At low latitudes, the mean ozone in
MIROC-Chem-H is underestimated by -6 to -3 ppbv. In the SH extratropics, all
the models reproduced the lower tropospheric ozone well. At 510 hPa, the
zonal mean biases differ obviously among the models, with multi-model
standard deviations of 1.5–4 ppv in the SH, 3.2–5 ppb in the tropics, and
3–6.6 ppb in the NH. The biases are largely negative in GEOS-Chem (-7.4 ppb
at 50∘ N) and AGCM-CHASER (-5.3 ppb) in the NH extratropics; they
are negative in MIROC-Chem-H (-11 to -6 ppb) at low latitudes and positive
in the models except GEOS-Chem (3.2 to 5.1 ppb at 50∘ S) in the SH
extratropics. Similarly, at 316 hPa, the biases obtained using the models
are quite different, with large positive biases in MIROC-Chem and
MIROC-Chem-H in the extratropics of both hemispheres and large negative
biases in MIROC-Chem-H in the tropics. Global total budgets and the
production rates of tropospheric ozone can also differ, as suggested by
multi-model intercomparison studies including GEOS-Chem and MIROC-Chem
(Young et al., 2013, 2018; Hu et al., 2017). Sekiya et al. (2018)
demonstrated that the ozone chemical productions are smaller in MIROC-Chem-H
(4647 Tg yr-1 for 2008) than in MIROC-Chem (4809 Tg yr-1).
Comparisons of latitudinal distributions of annual and zonal mean
ozone concentrations between the TES measurements (black line), control runs
(blue line), and data assimilation analyses (red line) at 316 hPa (upper
panels), 510 hPa (middle panels), and 750 hPa (lower panels) in 2007 for the
four systems.
After the data assimilation, all the models are in good agreement with the
assimilated TES measurements as expected and demonstrate improved
inter-model consistency. In the NH, the mean bias at 750 hPa is reduced by
19 %–73 % to between -4.1 and -0.4 ppb (at 50∘ N) in all the
models. At 510 hPa, the large negative model biases in GEOS-Chem and
AGCM-CHASER are reduced by 76 % and 92 % at 50∘ N,
respectively. In the SH, most of the large model biases in MIROC-Chem-H are
removed throughout the troposphere.
Figure 5 shows the spatial distributions of the annual mean ozone
concentrations at 510 hPa. The general structure of tropospheric ozone is
well reproduced by the control runs, such as the low ozone concentrations
over the tropical western Pacific and the high over the Middle East. The
annual and zonal mean model biases are negative in the tropics in all the
models, with large negative biases over the southern Atlantic; the bias is
largest in MIROC-Chem-H (by up to 20 ppbv). After data assimilation, most of
the model biases are removed for the globe. In the extratropical UTLS
(figure not shown), the remaining mean bias was close to the mean
observational error of the MLS ozone measurements in all the systems.
Comparisons of the spatial distributions of annual mean ozone
concentrations between the TES measurements, control runs, and data
assimilation analyses at 510 hPa in 2007. Unit is parts per billion by volume.
As shown in Fig. 6a, the multi-model standard deviation of the annual mean
ozone at 510 hPa obtained from the control runs, with applying the TES
averaging kernels (AKs), is typically 5–10 ppb from the tropics to the NH
high latitudes and 1–5 ppb in the SH extratropics. After the data
assimilation, the standard deviation mostly becomes smaller than 5 and 3 ppb for these regions, respectively, with reductions for the zonal mean
values by 20 %–60 % in the NH and 30 %–85 % in the SH. The results
demonstrate that the assimilation framework provides highly consistent
analysis fields among the systems, less dependent on the performance of the
individual models. The obtained multi-model standard deviation after data
assimilation (Fig. 6b) is comparable to the mean model errors relative to
the TES measurements for most regions, which could thus be used as an
estimate of the mean data assimilation uncertainty. The mean retrieval
uncertainty of the TES measurements is typically between 5 and 10 ppb in the SH
and between 10 and 15 ppb in the NH, which is larger than the multi-model spread and
the mean model errors after data assimilation.
(a) Standard deviation among the models for the data assimilation
analysis with application of the TES AK at 510 hPa. (b) Spatial distributions of
multi-model mean (root mean square) values of error against TES measurements
for the control runs (left) and data assimilation analyses (right) at 510 hPa.
Comparisons against ozonesonde observations
The current ozonesonde network is heterogeneously distributed globally with
a sampling intervals of typically a week or longer. Model errors are also
expected to vary greatly in time and space at various scales. As a
consequence, the ozonesonde measurements suffer from significant sampling
bias. Miyazaki and Bowman (2017) demonstrated that this ozonesonde sampling
bias in the evaluated model bias for the seasonal mean concentration
relative to global coverage reaches 80 % for the global tropics.
Nevertheless, the ozonesonde network provides a critical independent
validation of the data assimilation products, while the data assimilation
products are advantageous for evaluating actual regionally and seasonally
representative model performance, which are required for model improvements.
The synergy of the two provides a mechanism to characterize chemical
reanalysis evaluation of chemistry–climate models (Miyazaki and Bowman,
2017).
Figure 7 compares the seasonal variation in ozone with the WOUDC global
ozonesonde measurements from the lower troposphere to the lower
stratosphere. In the lower troposphere (850–500 hPa), all the models mostly
underestimate ozone at NH midlatitudes and high latitudes, except for GEOS-Chem at NH
midlatitudes in boreal summer. The negative model biases are large at NH
high latitudes in boreal spring, with an annual mean bias of -4.7 to -2.6 ppbv (as summarized in Table 2) and large multi-model spreads. In the
tropical lower troposphere, the models, other than MIROC-Chem-H, mostly
overestimate ozone except in September–October, whereas MIROC-Chem-H
underestimates the annual mean ozone by 5.8 ppbv. In the SH, all the models
underestimate ozone throughout the year, with an annual mean bias of -6.2 to
-0.7 ppbv at midlatitudes and -4.6 to -2.2 ppbv at high latitudes. The
negative model biases in the SH have been found in most of the
chemistry–climate models in the ACCMIP project (Bowman et al., 2013; Young
et al., 2013).
Comparison of seasonal variation in ozone concentration between
the ozonesonde observations (black solid line), model simulations (colored
dotted lines), and data assimilation (colored solid lines) averaged between
90–55∘ S, 55–15∘ S,
15∘ S–15∘ N, 15–55∘ N, and
55–90∘ N for 2007. From top to bottom, results are
shown for concentrations averaged over 80–200, 200–500, and
500–850 hPa. The ±1σ deviation among the four models (i.e.,
model spread) is shown in gray for the control runs and in light blue for
the data assimilation results.
Annual mean bias of the mean ozone concentrations (ppbv) between
the data assimilation or control run (in brackets) and the ozonesonde
observations from the WOUDC network for 850–500 and 500–200 hPa in parts per billion
for five latitudinal bands, SH high latitudes (55–90∘ S), SH midlatitudes (15–55∘ S),
tropics (15∘ S–15∘ N), NH midlatitudes (15–55∘ N), and NH high latitudes (55–90∘ N). The results are shown for individual models, multi-model mean
(mean ±1σ), and integrated analysis (xm‾±σm2‾).
In the middle and upper troposphere (500–200 hPa), the model biases reveal
a large diversity at NH high latitudes. The enhanced multi-model spread in
spring could be associated with the different representations of the
stratosphere–troposphere exchange (STE) processes. At NH midlatitudes,
MIROC-Chem and MIROC-Chem-H overestimate annual mean ozone by 16.1 and 4.1 ppbv, respectively. In the tropics, the models, other than MIROC-Chem-H,
overestimate ozone in boreal winter and underestimate it in boreal autumn,
thus underestimating the seasonal amplitudes. In the SH, all the models
overestimate ozone with an annual mean bias of 2.8–20.5 ppbv at midlatitudes and 8.7–29.9 ppbv at high latitudes. In the upper troposphere and
lower stratosphere (UTLS, 200–80 hPa), the large multi-model spread can
primarily be due to the different representations of the stratospheric
chemistry, STE, and convective transport in the tropics. Large
positive model biases exist in MIROC-Chem and MIROC-Chem-H in the NH
extratropics, MIROC-Chem and GEOS-Chem in the tropics, and all the models in
the SH extratropics.
Because of data assimilation, the large negative model biases in the lower
troposphere are largely reduced in the NH lower troposphere in boreal
spring. Nevertheless, the annual mean concentrations in all the systems
become too high in the NH lower troposphere, with an annual mean bias
from 1.7 to 4.3 ppb at high latitudes and from 1.9 to 5.2 ppb at midlatitudes, while the underestimation in the seasonal amplitude is reduced in
all the models. The weak sensitivity of the assimilated measurements and the
changes made to the precursor emissions (see Sect. 6) could be
responsible for the overestimations. In the tropics, the negative model bias
in boreal autumn is reduced via data assimilation, thus enhancing the
seasonal amplitudes in the whole system, whereas the analyzed concentrations
become too high in AGCM-CHASER and MIROC-Chem-H in boreal summer. In the SH,
the data assimilation reduced the negative model biases of MIROC-Chem-H at
midlatitudes (from -6.2 ppb to 1.0 ppbv annual mean bias) and MIROC-Chem
and MIROC-Chem-H at high latitudes (from -4.6 to -4.5 ppbv to 0.9 to 3.6 ppbv). The observed rapid increases during August–October at SH midlatitudes are reproduced well after data assimilation in all the systems. At
high latitudes of both hemispheres, some of the models exhibit too high
concentrations after data assimilation. An inaccurate balance between the
midlatitudes and high latitudes in model transport and the lack of direct
observational constraints could limit the effectiveness of data assimilation
at high latitudes. Conducting observational impact analysis would help
suggesting a framework to obtain a better global tropospheric ozone
analysis.
Both the agreements against the observation and the multi-model consistency
are greatly improved via data assimilation from the middle troposphere to
the lower stratosphere for the globe, with annual mean bias reductions from
-29.9 to 29.7 ppbv to -9.2 to -6.2 ppbv (i.e., by 53 %–81 %) at NH high
latitudes, from -4.0 to 16.1 ppbv to -2.5 to -0.2 ppbv (by 39 %–76 % except
for AGCM-CHASER) at NH midlatitudes, from -11.8 to 1.3 ppbv to -0.3 to 3.0 ppbv (by 50 %–91 %) in the tropics, from 2.8 to 20.5 ppbv to -1.9 to 3.0 ppbv (by 71 %–94 %) at SH midlatitudes, and from 8.7 to 29.9 ppbv to 1.0
to 4.7 ppbv (by 46 %–97 %) at SH high latitudes for 500–200 hPa. The
estimated RMSEs (2.5–9.0 ppbv at the SH high latitudes, 3.0–4.3 ppbv at the
SH midlatitudes, 2.5–5.3 ppbv in the tropics, 0.7–3.8 ppbv at the NH midlatitudes, and 2.6–6.3 ppbv at the NH high latitudes for 850–500 hPa) are
significantly smaller than the analysis uncertainty (Fig. 2b) in AGCM-CHASER
and MIROC-Chem (10–16 ppb) and are comparable to that in GEOS-Chem and
MIROC-Chem-H. These results suggest overestimated analysis uncertainty in
AGCM-CHASER and MIROC-Chem.
The uncertainty-weighted multi-model integrated fields (Eq. 10) show a closer
agreement with the ozonesonde observations than the (non-weighted)
multi-model means for the lower troposphere, except at SH high latitudes, as
summarized in Table 2. The annual and regional mean bias is smaller by 15 %–40 % in the uncertainty-weighted fields from the SH midlatitudes to NH high latitudes,
reflecting the larger analysis biases and larger analysis uncertainties in
AGCM-CHASER and MIROC-Chem for most cases. The closer agreements suggest
improved estimates of ozone in the multi-model integrated fields. In the
extratropical middle and upper troposphere, GEOS-Chem revealed the smallest
analysis uncertainty and largest analysis errors against the ozonesonde
observations and dominated the uncertainty-weighted integrated fields,
likely associated with the less complex stratospheric chemistry (i.e.,
smaller spread growth). This model dominated the uncertainty-weighted
integrated fields and led to a degradation of the integrated fields. These
results suggest a requirement to optimize the analysis uncertainty in some
of the systems, considering the fundamental differences in the model
framework such as model complexity and resolution, as discussed above.
Increasing the number of models would also help to provide more robust
statistics.
Figure 8 shows that the data assimilation introduces similar changes to the
seasonal amplitudes of ozone (defined as the difference between the maximum
and minimum concentrations) in the four models, such as the increases in the
lower and middle troposphere and the decreases in the extratropical upper
troposphere and lower stratosphere. Between 850 and 500 hPa, the control
runs underestimated the seasonal amplitude in the extratropics of both
hemispheres compared with the ozonesonde measurements (e.g., by up to -29 % at the NH midlatitudes). The model underestimates are largely reduced
by data assimilation in all the models. Between 500 and 200 hPa, data
assimilation mostly removed the negative bias in GEOS-Chem (-8 %) and
AGCM-CHASER (-5 %) and the positive bias of the seasonal amplitude in
MIROC-Chem-H (47 %) against the ozonesonde measurements in the NH and
the large positive bias in MIROC-Chem (22 %) in the SH. Between 200 and 90 hPa, positive biases are reduced in all the models globally. In the NH, the
range in the bias from 13 % to 40 % is reduced to a range from -12 % to 3 %,
with the largest reduction observed in MIROC-Chem-H (from 40 % to 2 %). In
the tropics, the range in the bias is reduced from 20 % to 148 % to 10 %
to 25 %, with the largest reduction observed in GEOS-Chem (from 148 % to
10 %). In the SH, the range in bias is reduced from 15 % to 92 % to
-1 % to 19 %, with the largest reduction observed in MIROC-Chem (from 92 % to
10 %).
Latitude–pressure cross section of changes in seasonal amplitude
of zonal mean ozone concentrations (ppbv) due to data assimilation (data
assimilation minus control runs), as estimated from the maximum minus
minimum values of the monthly mean ozone concentrations.
Tropospheric NO2 columns
For the comparisons with the OMI NO2 retrievals, the OMI NO2 AKs
from the DOMINO2 products were applied to GEOS-Chem, AGCM-CHASER, and
MIROC-Chem, whereas those from QA4ECV were applied to MIROC-Chem-H,
corresponding to the assimilated measurements for each system. In Fig. 9,
the converted tropospheric NO2 columns from the control and
assimilation runs are then compared with the assimilated OMI retrievals: the
DOMINO2 product for GEOS-Chem, AGCM-CHASER, and MIROC-Chem (black line vs.
blue, red, and green lines in Fig. 9) and the QA4ECV product for
MIROC-Chem-H (gray line vs. yellow line).
Time series of regional monthly mean tropospheric NO2 columns
(1015 molecules cm-2) from the satellite retrievals (black for
OMI QA4ECV and gray for OMI DOMINO v2), control runs (colored dotted lines),
and data assimilation analysis (colored solid lines) for 2007. The model
simulation and data assimilation results are obtained at the local overpass
time of the retrievals by applying the averaging kernel of OMI DOMINO v2 for
GEOS-Chem, CHASER, and MIROC, and of OMI QA4ECV for MIROC-H, respectively,
corresponding to the assimilated measurements. The multi-model standard
deviations are not shown because of the use of different assimilated
measurements in the individual systems.
As summarized in Table 3, the model bias in tropospheric NO2 column
differed largely among the models because of the different model
configurations (e.g., chemical lifetime of NOx) and input data (e.g., NOx
emissions). The models, other than GEOS-Chem, mostly underestimate
tropospheric NO2 columns over polluted areas, same as in most other
CTMs (van Noije et al., 2006), with an annual mean bias ranging from -2.07
to -0.37×1015 molecules cm-2 over eastern China, -0.51
to -0.26×1015 molecules cm-2 over the United States,
and -0.82 to -0.32×1015 molecules cm-2 over Europe.
GEOS-Chem overestimates tropospheric NO2 columns over some parts of
China (with annual and regional mean bias of 0.13×1015 molecules cm-2 over eastern China), Europe (0.60×1015 molecules cm-2), and the United States (0.29×1015 molecules cm-2). The model biases in tropospheric NO2 columns can
vary with changing the model configurations. For instance, important NOx
sink pathways determining NO2 simulation uncertainties include the
NO2+OH reaction and the formation of HNO3 in the NO+HO2
reaction (Lin et al., 2012; Stavrakou et al., 2013), which are represented
differently in the models. The columns simulated from MIROC-Chem-H are
higher than those from MIROC-Chem, with the same AKs applied over some parts
of the polluted areas such as eastern China; these differences are
attributable to the increased model resolution, which suppresses the
dilution effects (Sekiya et al., 2018).
Annual mean bias and temporal correlation of regional mean
tropospheric NO2 columns: the data assimilation minus the satellite
retrievals from OMI in 1015 molecules cm-2. The results of the
control run are also shown in brackets. The results are shown for eastern
China (30–40∘ N, 110–123∘ E), Europe (35–60∘ N, 10∘ W–30∘ E), the USA (28–50∘ N, 70–125∘ W), South America (20∘ S–Equator, 50–70∘ W), northern Africa (Equator–20∘ N, 20∘ W–40∘ E), central Africa (Equator–20∘ S, 10–40∘ E), and southern Africa (22–31∘ S, 25–34∘ E). The results are shown for individual models and
multi-model mean (mean ±1σ).
E ChinaE USAEuropeIndiaSE AsiaS AmericaN AfricaC AfricaS AfricaBiasGEOS-Chem(0.13)(0.29)(0.60)(-0.20)(-0.15)(-0.02)(-0.11)(-0.11)(-0.89)-0.160.110.21-0.050.050.020.000.00-0.46AGCM-CHASER(-2.07)(-0.51)(-0.82)(-0.27)(-0.44)(-0.06)(-0.32)(-0.39)(-1.05)-0.69-0.18-0.41-0.12-0.18-0.02-0.08-0.09-0.45MIROC-Chem(-1.59)(-0.26)(-0.32)(-0.15)(-0.30)(-0.11)(-0.30)(-0.39)(-0.93)-0.60-0.14-0.35-0.09-0.18-0.03-0.07-0.09-0.34MIROC-Chem-H(-0.37)(-0.31)(-0.56)(-0.14)(-0.56)(-0.19)(-0.23)(-0.23)(-0.96)-0.39-0.13-0.24-0.02-0.130.010.030.01-0.47Multi-model(-1.04±0.94)(-0.34±0.11)(-0.58±0.20)(-0.18±0.06)(-0.36±0.18)(-0.10±0.07)(-0.24±0.09)(-0.28±-0.14)(-0.96±0.07)-0.46±0.23-0.09±0.13-0.20±0.28-0.07±0.04-0.11±0.11-0.01±0.02-0.03±0.05-0.04±0.05-0.43±0.06T. corr.GEOS-Chem(0.97)(0.70)(0.95)(0.87)(0.96)(0.97)(0.90)(0.96)(0.38)1.000.860.970.940.991.000.980.990.92AGCM-CHASER(0.95)(0.83)(0.95)(0.66)(0.95)(0.94)(0.94)(0.96)(0.96)1.000.970.970.981.001.000.991.001.00MIROC-Chem(0.96)(0.80)(0.94)(0.20)(0.92)(0.91)(0.96)(0.96)(0.98)0.990.970.970.991.000.990.991.001.00MIROC-Chem-H(0.98)(0.58)(0.90)(0.86)(0.86)(0.98)(0.99)(0.99)(0.98)1.000.940.980.980.970.990.991.000.98Multi-model(0.97±0.01)(0.73±0.11)(0.94±0.02)(0.64±0.31)(0.92±0.05)(0.95±0.03)(0.95±0.04)(0.97±0.02)(0.83±0.30)1.00±0.010.94±0.050.97±0.010.97±0.020.99±0.011.00±0.010.99±0.011.00±0.010.98±0.04
Figure 9 compares the seasonal variation in tropospheric NO2. The models,
other than GEOS-Chem, underestimate tropospheric NO2 columns throughout
the year over eastern China, the United States, and Europe, with the largest
negative biases in boreal winter. Over India, GEOS-Chem reproduced the peak
observed in April and the rapid decrease from May to July, whereas the other
models underpredicted the seasonal variations. The retrieved tropospheric
NO2 columns are generally lower in the QA4ECV products than in the
DOMINO-2 products over most of major polluted areas. The different retrieved
columns can largely be attributed to differences in the a priori profiles
and do not directly influence the model–observation differences after
applying the AKs (Boersma et al., 2018a). Over Southeast Asia, the models,
except for GEOS-Chem, underestimate the peak observed in March, which is
associated with biomass burning, by 11 %–55 %, whereas the models
overestimate the peak over South America in September by 18 %–31 %.
Over northern, central, and southern Africa, all the models underestimate
tropospheric NO2 columns throughout the year, with an annual mean bias
ranging from -0.32 to -0.11×1015, -0.39
to -0.11×1015, and -1.05 to -0.89×1015 molecules cm-2, respectively. Over southern
Africa, the negative model bias is maximized in austral winter (by 43 %–63 %), with MIROC-Chem-H giving the smallest bias. The higher spatial
resolution of MIROC-Chem-H is considered essential in resolving individual
polluted areas in the Highveld region and in accurately simulating the
nonlinear effects on NO2 loss rate.
The tropospheric NO2 column retrievals from OMI and SCIAMACHY were
assimilated to optimize NOx emissions, and the assimilation of non-NO2
measurements influence the chemical lifetime of NOx through changes made to
OH. Data assimilation reduced the negative model biases in the models, other
than GEOS-Chem, over eastern China (from -2.07–0.37×1015 to -0.69–0.39×1015 molecules cm-2), the United States (from -0.51 to -0.26×1015 molecules cm-2 to -0.18 to -0.13×1015 molecules cm-2), and western Europe (from -0.82 to -0.32×1015 molecules cm-2 to -0.41 to -0.24×1015 molecules cm-2). The annual mean positive model biases in GEOS-Chem are
reduced by 72 % over the United States and by 65 % over Europe. The
temporal correlations are also improved in all the models.
Over India, the data assimilation increases tropospheric NO2 columns in
boreal winter–spring and reproduced the observed local maximum in May and
minimum in July in all the models. Consequently, the seasonal amplitude is
enhanced, leading to improved temporal correlations (from 0.20–0.87 to
0.94–0.99) while reducing the annual mean bias (from -0.27 to
-0.14×1015 molecules cm-2 to 0.12 to -0.02×1015 molecules cm-2, by 40 %–86 %). Over Southeast Asia, the
persistent model negative biases are reduced (from -0.56 to -0.15×1015 molecules cm-2 to -0.18 to 0.05×1015 molecules cm-2, by 40 %–77 %) with improved temporal correlations
(from 0.86–0.96 to 0.97–1.00) in all the models. Over South America, data
assimilation decreases tropospheric NO2 columns by up to 25 % in the
biomass burning season in all the models, while the negative model biases in
the biomass burning off-season are mostly removed. The OMI NO2
super-observation error is typically about 20 %–50 % of the tropospheric
NO2 columns over polluted areas, which are comparable to or larger than
the analysis error.
Over Africa, the annual mean negative model biases are reduced from -0.32 to
-0.11×1015 molecules cm-2 to -0.08 to 0.03×1015 molecules cm-2 (by 75 %–100 %) over northern Africa, from
-0.20 to -0.02×1015 molecules cm-2 to -0.03 to
0.02×1015 molecules cm-2 (by 77 %–100 %) over central
Africa, and from -1.05 to -0.89×1015 molecules cm-2 to
-0.47 to 0.45×1015 molecules cm-2 (by 48 %–63 %) over
southern Africa. The bias reductions over central and southern Africa are
large in austral winter–spring. Some of the model negative biases (14 %–50 %, with a standard deviation of 12 %) remain over southern Africa in
austral winter. The inadequate corrections of tropospheric NO2 columns
could be attributed to the insufficient model resolution, short chemical
lifetime of NOx, and biases in the simulated chemical equilibrium state.
Spatial resolutions higher than the MIROC-Chem-H resolution (1.1∘×1.1∘) would be useful to represent emissions and pollutants
over individual sources.
CO
Figure 10 compares the latitudinal variations in surface CO concentration
against the WDCGG observations from 59 stations. All the models
underestimate the zonal and annual mean CO concentrations by 25–70 ppb in
the NH extratropics and by 10–60 ppb in the tropics (expect for
MIROC-Chem and MIROC-Chem-H), as in most other CTMs (Shindell et al., 2006). In the
SH extratropics, GEOS-Chem underestimates surface CO by 8–15 ppb, whereas
MIROC-Chem and MIROC-Chem-H overestimate it by 5–20 ppb. Data assimilation
reduced most of the model biases for the globe, except for the remaining
negative model biases in the tropics in GEOS-Chem and AGCM-CHASER. The
different analysis results of CO at high latitudes could mainly reflect
differences in atmospheric transport from midlatitudes among the model. The
effect of data assimilation is limited because of the lack of measurements
at high latitudes.
Latitudinal distributions of zonal mean surface CO concentrations
(ppbv) averaged over the WDCGG surface measurement sites from the
observations (black), control runs (blue), and data assimilation analyses
(red).
Figure 11 compares the seasonal variation in surface CO for the selected
stations. All the models captured the observed seasonal variations well,
except for relatively low temporal correlations (Table 4) over Barbados
(r=0.58–0.85) and Ascension Island (r=0.72–0.94). In the NH extratropics, for
most stations, all the models reveal too low CO throughout the year, with
larger biases in boreal winter than in summer in the models. The summertime
negative biases are largest in GEOS-Chem. In the tropics, the rapid
increases in CO associated with biomass burning, e.g., in October over
Barbados and in September over Ascension Island, are underestimated by all the
models. In the SH extratropics, a large multi-model spread in the simulated
CO exists in austral winter–spring, likely due to the different
representation of poleward transport.
Time series of monthly mean surface CO concentrations (ppbv)
from the WDCGG observations (black solid line), control runs (colored dotted
lines), and data assimilation analyses (colored solid lines). The ±1σ deviation among the four models (i.e., model spread) is shown in
gray for the control runs and in light blue for the data assimilation
results.
Annual mean bias and temporal correlation of surface CO. Units are
parts per billion. The observations used are the WDCGG observations. The results of the
control run are also shown in brackets. The results are shown for individual
models and the multi-model mean (mean ±1σ).
The reductions in the model negative bias in the NH owing to data
assimilation can be found throughout the year, with annual mean bias
reductions of 65 %–76 % for Utqiaġvik, 41 %–74 % for Cold Bay, and 57 %–94 %
for Iceland, with MIROC-Chem-H exhibiting smaller reductions. The
insufficient corrections in MIROC-Chem-H suggest the need to optimize the
settings for the assimilation of total column retrievals for the higher-resolution system. Further efforts are clearly needed for improving the CO
analyses in MIROC-Chem-H. The negative model biases are also reduced at NH
low latitudes, i.e., by 68 %–94 % for Bermuda, 48 %–97 % for Midway, and
22 %–63 % for Mauna Loa. Over Barbados, data assimilation corrects the
timing of the maximum (in March) and minimum (in August) concentrations and
improved the temporal correlation from 0.58–0.85 to 0.75–0.85 in all the
models, whereas the observed peak in October is not represented by all the
systems. In the SH, the multi-model spread is greatly reduced by data
assimilation, while showing improved agreements with the observations except
for excessively high concentrations over Ascension Island in June–July in
MIROC-Chem-H.
The vertical gradients of CO differ largely among the models (Fig. 12a),
with the largest decrease in the annual tropical mean concentrations with
height in GEOS-Chem from the lower to upper troposphere. The sharp decrease
could be associated with weaker deep convection. In addition, the larger OH
concentrations in GEOS-Chem (Fig. 12b) suggest stronger chemical destruction
in the middle and upper troposphere. In models other than GEOS-Chem, the
tropical mean concentrations of CO show a clear maximum around 200 hPa.
After data assimilation, the CO gradient became even larger in GEOS-Chem, in
association with a large increase in OH in the upper troposphere. In other
models, the data assimilation introduced sharper decreases in CO from about
850 to 600 hPa, as a consequence of the enhanced chemical destructions
(i.e., the increased OH) at those levels. The increase in OH by data
assimilation is larger in MIROC-Chem-H than in other models in the middle
and upper troposphere, which have influenced the vertical profile of CO
substantially.
Vertical profiles of (a) annual mean CO concentrations and (b) annual mean OH concentrations averaged between 15∘ S and 15∘ N, obtained from the control runs (dotted lines) and data
assimilation analysis (solid line). For CO (a), the relative ratio to the
mean surface concentrations is shown. For OH (b), the unit is 106 molecules cm-3.
In summary, the tropical annual mean CO gradient between the surface and 400 hPa is decreased by 1 %–7 %, whereas the annual mean OH concentration is
increased by 7 %–20 % in the lower troposphere and 15 %–120 % in the
middle and upper troposphere in all the models. Therefore, the
multi-constituent data assimilation provides strong constraints on the
vertical profiles of CO and other species mainly through substantial changes
in OH. Changes in OH are further discussed in Sect. 4.4. It is also
suggested that, even after the multi-constituent data assimilation, the
representations of the vertical profiles can differ among the models,
reflecting both the different model configurations, e.g., in terms of deep
convection and chemical reaction rates, and the lack of direct observational
constraints on the vertical profiles.
OH
Because of the simultaneous assimilation of multiple-species data, the
global distribution of various species, including OH, is modified
considerably in the assimilation systems. The concentration of OH is
directly related to the concentrations of species determining the primary
production (ozone and H2O), removal (CO and CH4), and regeneration
of OH (NOx). Figure 13 compares the global distributions of annual and
tropospheric mean OH concentrations (averaged between the surface and 300 hPa). The multi-model comparisons reveal common characteristics such as
higher concentrations in the tropics than in the extratropics and enhanced
concentrations over central Africa, Indian Ocean, south and Southeast Asia,
and tropical Atlantic. As summarized in Table 5, the simulated OH is higher
in GEOS-Chem and AGCM-CHASER than other models over most of the major
polluted areas such as eastern China, India, western Europe, India,
Southeast Asia, and Africa. The multi-model standard deviation of OH is
large over central Africa, northern India, and around the Himalayas, Malay
peninsula, western United States, Brazil, and over the southern tropics such
as the eastern Pacific and northern Australia (right-top figure in Fig. 13). The zonal mean OH from the tropics to subtropics is lower in
MIROC-Chem-H than in other models by approximately 30 %–45 % (Fig. 14). The
zonal mean OH shows a strong latitudinal gradient around the subtropics. The
ratio of OH in the NH tropics–subtropics (Equator–30∘ N) to the
NH midlatitudes (30–60∘ N) ranges from 1.42
(GEOS-Chem) to 1.71 (MIROC-Chem).
Annual and regional mean OH concentration at 700 hPa. Units are
106 molecules cm-3. The results of the control run are also shown in
brackets. The results are shown for individual models, the multi-model mean
(mean ±1σ), and integrated analysis (xm‾±σm2‾). Changes in the multi-model spread due to
data assimilation (Δspread, %) are also shown.
E ChinaE USAEuropeIndiaSE AsiaS AmericaN AfricaC AfricaS AfricaGEOS-Chem1.91.51.22.52.31.22.12.12.2(2.2)(1.8)(1.5)(2.5)(2.2)(1.1)(2.1)(2.1)(2.1)AGCM-CHASER2.41.71.42.62.21.22.31.92.4(2.5)(1.7)(1.4)(2.6)(2.0)(1.2)(2.1)(1.6)(2.1)MIROC-Chem2.11.51.32.42.11.32.41.92.1(1.9)(1.3)(1.1)(2.1)(1.8)(1.1)(2.0)(1.5)(1.7)MIROC-Chem-H1.61.31.11.91.61.11.81.71.7(1.4)(1.1)(1.0)(1.7)(1.3)(0.7)(1.4)(1.3)(1.1)Multi-model mean2.0±0.31.5±0.21.3±0.12.3±0.32.0±0.31.2±0.12.1±0.21.9±0.22.1±0.3(2.0±0.4)(1.5±0.3)(1.3±0.2)(2.2±0.3)(1.8±0.3)(1.0±0.2)(1.9±0.3)(1.6±0.3)(1.8±0.4)Δspread (%)-31-41-44-25-27-58-16-50-24Integrate analysis2.0±0.21.5±0.11.3±0.12.5±0.22.2±0.21.2±0.12.1±0.12.0±0.22.3±0.2
Global distributions of annual mean OH concentrations (106 molecules cm-3) averaged over the troposphere (from the surface to
300 hPa) from the control runs (upper panels), data assimilation analyses
(middle panels), and differences between the data assimilation analyses and
the control runs (bottom panels, %). The figures on the right show
the standard deviation among the four systems for the control runs (right
top) and data assimilation analyses (right middle). The right bottom figure
shows the difference in the multi-model standard deviation between the data
assimilation analyses and the control runs (106 molecules cm-3).
Data assimilation largely modified global OH distributions in all the
systems. The analyzed OH fields and data assimilation increments are often
regionally localized, which demonstrates the importance of accurately
representing different chemical regimes and local emissions for each region,
for estimation of both regional and global OH distributions. The annual mean
OH is increased in the SH extratropics by 10 %–25 % in GEOS-Chem and
AGCM-CHASER and by 30 %–50 % in MIROC-Chem and MIROC-Chem-H, probably
because of the increased ozone. MIROC-Chem-H shows large increases in OH by
20 %–40 % over Africa, Southeast Asia, the tropical Pacific, and central and
South America, associated with the increased ozone and decreased CO. The NH
exhibits large inter-model differences in OH increments, decreasing in
GEOS-Chem by 10 %–40 % with large increments over east Asia, the United
States, and Europe and increasing in MIROC-Chem and MIROC-Chem-H by 15 %–30 % and by 20 %–40 % over the continents, respectively. The negative
increments in GEOS-Chem are likely associated with the increased CO and
decreased NOx, whereas the positive increments in MIROC-Chem and
MIROC-Chem-H could be attributed to the increased ozone and increased NOx.
The NH ratio of OH of the tropics–subtropics (Equator–30∘ N) to midlatitudes
(30–60∘ N) is increased in all the
models by 1 %–15 %, with the largest increase in GEOS-Chem (from 1.42 to
1.64).
Interhemispheric gradient (NH / SH) and global mean (with area
weight) concentration (106 molecules cm-3) of tropospheric mean OH
(averaged between the surface and 300 hPa) from the control and data
assimilation runs. The results are shown for individual models and
the multi-model mean (mean ±1σ).
GEOS-ChemAGCM-CHASERMIROC-ChemMIROC-Chem-HMulti-modelNH / SH ratioModel1.301.361.291.311.29±0.03assimilation1.171.231.181.211.18±0.03Global meanModel1.311.231.130.821.12±0.18Assimilation1.311.311.341.091.26±0.10
Because of the data assimilation, the multi-model spread of OH is reduced by
24 %–58 % over the major polluted areas of the globe such as over Europe
(44 %), China (31 %), the United States (41 %), central Africa
(50 %), and South America (58 %). At the local scale, the multi-model spread
is reduced largely over central eastern Africa (up to 55 %), associated
with adjustments made to biomass burning plumes, and over Indonesia (up to
40 %) and the western US (up to 55 %), corresponding to large changes in
local NOx and CO emissions and consequently in ozone production. The
improved multi-model consistency suggests that the multi-constituent data
assimilation provides a more similar representation of the tropospheric
chemistry system, by removing model errors in the relevant species in the
individual systems. The obtained OH fields, which are less dependent on
individual model performance due to reduced model errors in relevant
species, demonstrate the potential of the multi-constituent (ozone, CO, and
NO2) data assimilation for various atmospheric chemistry studies
including emission inversion and methane budget analyses. 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 optimization of
the concentration and emissions on the entire tropospheric chemical system
and the emission estimates.
The integrated analysis xm‾ shows slightly higher OH concentrations than the multi-model means for
most regions, mainly reflecting the largest OH spreads and smallest OH
concentrations in MIROC-Chem-H among the models. The analysis spread of OH
is determined by analysis spreads in various species such as ozone (see
Sect. 3.2) during model forecasts. Because of the different chemical
mechanisms and model responses to given perturbations (see Sect. 5), OH
spreads differed by factor of up to 2.5 among the models for the regional
means. The integrated uncertainty σm2‾ is smaller
than the multi-model spreads by 20 %–50 % for most regions.
As summarized in Table 6, the north-to-south gradient of the tropospheric OH
(averaged below 300 hPa) decreased owing to data assimilation in all the
models, i.e., from 1.29–1.36 (1.32±0.03) to 1.17–1.23 (1.19±0.03), as similarly suggested by our previous analysis (Miyazaki et
al., 2015). The NH / SH ratio of OH simulated from the four models
is in the range of 1.28±0.10 in the ACCMIP multi-model estimates
(Naik et al., 2013), whereas the values from the data assimilation runs are
significantly lower. The data assimilation estimates are in better
agreement with an observational estimate (0.97±0.12) obtained using
methyl chloroform observations (Patra et al., 2014). The significant changes
in the global OH distributions, which are common to all the models, are
important in propagating the observational information between various
species and modulating the chemical lifetimes of many species, thus
improving emission inversion. The simultaneous optimization of emissions and
concentrations was essential to modify the global OH distributions. The
increases (by 1 %–32 %) in the global mean OH concentrations by data
assimilation in all the models, with the multi-model mean values of
1.12±0.18×106 molecules cm-3 in the control runs and
1.26±0.10×106 molecules cm-3 in the data assimilation
as summarized in Table 6, suggest overestimated CH4 lifetimes in the
model simulations.
Ozone and NO2 response to NOx emissionsMulti-model comparisons
From a system analysis perspective, one of the fundamental questions in
atmospheric chemistry is the sensitivity of a constituent like ozone to
changes in surface emissions such as NOx emissions. With recent advances in
estimating preindustrial ozone (Yeung et al., 2019), model sensitivities are
the primary drivers of chemistry–climate estimates of quantities such as
ozone radiative forcing (Bowman et al., 2013, Myhre et al., 2013). While
these simulations describe relatively slow, equilibrium responses, data
assimilation incremental updates provide statistics on “fast” responses
within the short data assimilation windows. By simultaneously updating ozone
and NOx emissions, multi-constituent data assimilation can yield insight
into this fundamental quantity. We explore this potential by regressing both
the ozone and NO2 increments with respect to the NOx emission analysis
increments using the daily mean data assimilation outputs at each grid
point.
Linear regression of changes in surface NOx emissions (10-11 kg N m-2 s-1) and surface
concentrations of ozone and NO2 (ppb) by data
assimilation in May 2007 over areas with NOx emission changes greater than
5×10-13 kg N m-2 s-1 in the four models.
The results for regions without strong NOx emission changes (greater than
3×10-11 kg N m-2 s-1 that could suffer
from dilution effects) are shown in brackets.
SlopeInterceptCorrelation(ppb per (10-11 kg N m-2 s-1))(ppb)OzoneGEOS-Chem1.2 (1.8)2.2 (2.2)0.34 (0.34)AGCM-CHASER1.5 (3.6)3.6 (3.0)0.42 (0.45)MIROC-Chem1.0 (2.5)3.0 (2.7)0.35 (0.42)MIROC-Chem-H0.6 (1.3)4.1 (3.9)0.25 (0.42)NO2GEOS-Chem0.800.010.93AGCM-CHASER0.560.010.96MIROC-Chem0.540.010.94MIROC-Chem-H0.440.040.94
As summarized in Table 7, the response of ozone and NO2 analysis to
emission perturbations (i.e., data assimilation increments) is largely
different among the models. The NO2 surface response to NOx emissions
is well correlated (correlation >=0.93 for all models) but
the response differs by almost a factor of 2 between GEOS-Chem and
MIROC-Chem-H. Globally, this diversity holds between surface ozone
concentration and NOx emission increments (ΔO3ΔENOx) for these two models. However, the AGCM-CHASER ozone–NOx emissions
response (1.5 ppb per (10-11 kg N m-2 s-1)) is the largest among all
the models. On the other hand, the correlation between surface ozone and NOx
emissions is relatively weak (correlation <0.43), reflecting the
much more complicated chemical and dynamical relationship. For polluted
areas (greater than 3×10-11 kg N m-2 s-1, as shown in
the brackets in Table 7), the largest response is AGCM-CHASER and
MIROC-Chem, which is greater than the other two models by 40 %–180 %, with
similar intercepts and correlations. The multi-model diversity reflects the
different representation of NOx and VOC as well as dynamics, leading to
different ozone production efficiencies. In the case of GEOS-Chem and
MIROC-Chem-H, there appears to be a clearer relationship between
ΔNO2ΔENOx and ΔO3ΔENOx,
suggesting that NOx chemistry plays a more dominant role in ozone formation
than other factors. By separating these two responses, MOMO-Chem is able to
quantify the responses of forward models with unique diagnostics, without
making any sensitivity calculations.
Different model responses would directly impact the Kalman gain in Eq. (4),
leading to a more efficient model error reduction. Given the same predefined
minimum values for the surface NOx emission perturbation (see Sect. 2.6), a larger ozone analysis uncertainty (through a larger forecast model
spread) would be obtained in models with a stronger ozone response to NOx
emissions. In fact, stronger ozone response (Table 7) and larger analysis
uncertainty (Fig. 2b) are consistently found in AGCM-CHASER and MIROC-Chem.
Meanwhile, the ozone response to a given perturbation is dependent on the
background condition because of the nonlinear O3–NOx chemistry (e.g.,
Zaveri et al., 2003). The multi-constituent framework allows us to evaluate
model ozone response in a realistic condition while considering possible
error ranges in precursor emissions using emissions analysis increments
(see Sect. 6). The ozone analysis increments became substantially
smaller in all the models for most cases by including the emission
optimization, and the increments could be regarded as inherent and
persistent model biases of individual models. Therefore, a systematic
investigation of model ozone response and analysis increment in the
multi-constituent data assimilation framework could benefit evaluation of
future prediction of the chemistry–climate system as a hierarchical emergent
constraint that uses relationships between future and current climate states
to constrain projections of climate response with observations (Bowman et
al., 2018). They could also be useful for making effective ozone control
strategies.
Latitudinal distributions of annual and zonal mean OH
concentrations (106 molecules cm-3) averaged over the troposphere
obtained from the control runs (dotted lines) and data assimilation analyses
(solid lines) for the four systems.
In addition, tropospheric ozone shows strong correlations with other species
such as CO (Zhang et al., 2006) over regions such as continental outflow
regions. The relationship can be included in the state vector to improve the
tropospheric ozone analysis. The uncertainty information in the CO response
to ozone obtained from the MOMO-Chem can be expected to provide useful
information on model diagnostics and future predictions.
Implications for chemistry model predictions
By applying linear regressions to the multi-model integrated fields (see
Sect. 3.3), we evaluated model responses of surface ozone and NO2
concentrations to NOx emissions. We first produced the daily multi-model
integrated fields at 1.1∘×1.1∘ resolution and then
applied them to linear regressions. As shown by Fig. 15, the estimated model
responses from the MOMO-Chem integrated fields provide unique information on
fast responses to NOx emissions. The surface NO2 response exhibits a
large seasonal variation in the NH, with a maximum value of about 2 ppb per (10-11 kg N m-2 s-1) in January, reflecting the longer
chemical lifetime of NOx in winter. The rapid increases from September to
December and decreases from January to March can be associated primarily
with variations in temperature, OH, and NO2 photolysis. The annual mean
slope is about 40 % smaller in the tropics than in the NH (0.70 vs. 1.15 ppb per (10-11 kg N m-2 s-1)) because of the shorter chemical
lifetime of NOx in the tropics. The surface NO2 and NOx emissions in
the integrated fields are well correlated (coefficients >0.9)
throughout the year in both the NH and the tropics. The inter-model
differences (red shading) increase in winter in the NH, with a maximum
standard deviation of 35 % in January, implying strong model dependence
of surface NO2 given the same NOx emissions.
Time series of model response of surface ozone and NO2
concentrations to NOx emissions estimated from linear regressions using the
multi-model integrated fields in 2007 over areas with NOx emission changes
greater than 5×10-13 kg N m-2 s-1 for the Northern
Hemisphere (20–60∘ N, red line) and the tropics
(20∘ S–20∘ N, blue line). The ±1σ
deviation among the four models (i.e., model spread) is shown in light red
for the NH and in light blue for the tropics. The multi-model mean value
(i.e., an average of individual estimates) is shown by white lines. The
correlation is shown by dashed lines.
The ozone response shows a seasonal cycle opposite to the NO2 response
in the NH. It gradually increased from January to August by about 0.4 ppb per (10-11 kg N m-2 s-1) per month. It reaches 2.4 ppb per (10-11 kg N m-2 s-1) in August with relatively large coefficients (0.3–0.6)
in May–September. The large ozone response implies substantial
photochemical productions of surface ozone over polluted areas in summer.
Then, the slope decreases rapidly from August to October by about 1.1 ppb per (10-11 kg N m-2 s-1) per month, and it becomes negative in
winter but with low coefficients (-0.3–0.2). The negative slopes, with a
minimum value of -0.6 ppb per (10-11 kg N m-2 s-1) in January, could
be driven by the dilution effects over highly polluted areas.
In the tropics, the ozone response is stronger than in the NH (3.1 vs. 0.6 ppb per (10-11 kg N m-2 s-1) for annual mean), with the strongest
responses of about 4.3 ppb per (10-11 kg N m-2 s-1) in March and
October. The different ozone production efficiency implies that any
latitudinal shifts in NOx emissions from the extratropics to the tropics
would lead to increases in global tropospheric ozone, as suggested by Zhang
et al. (2016), while showing strong seasonality. Our analysis indicates that
the mean ozone response is comparable between the NH and the tropics in
August and September. The seasonal variation in the tropics is likely
associated with biomass burning events (e.g., Bowman et al., 2009; Jones et
al., 2009; Parrington et al., 2012), with enhanced ozone responses over
Southeast Asia during February–June (2.3–3.7 ppb per (10-11 kg N m-2 s-1)), over central America and tropical South America
during April–July (2.8–5.3 ppb per (10-11 kg N m-2 s-1)), over
central Africa in March (5.5 ppb per (10-11 kg N m-2 s-1)) and
October (7.1 ppb per (10-11 kg N m-2 s-1)), and over India in March
and October (5.1 ppb per (10-11 kg N m-2 s-1)). Although the surface
ozone and NOx emissions are well correlated in the multi-model integrated
analysis throughout the year (coefficients >0.5), the large
multi-model spreads (25 %–55 %) suggest that individual models have large
uncertainty in representing strong ozone productions, for instance,
associated with VOC emissions and chemistry that could result in different
chemical regimes. The correlations of ΔENOx and ΔO3 among
the models estimated at each point at each day were strongly dependent on
season and location (not shown), which also provide information on the
robustness (i.e., multi-model diversity) of the estimated ozone and NO2
responses for each location and season.
Finally, the model responses differ significantly between the MOMO-Chem
integrated fields (solid blue and red lines) and the mean of the individual
model estimates obtained by averaging the model responses from individual
model fields (solid white lines), especially when the model responses are
strong. The multi-model integrated fields exhibit about a 20 % larger
NO2 response in December and about a 70 % larger ozone response in
August than the mean of the individual model estimates in the NH. In the
tropics the monthly ozone response is up to about 60 % larger in the
multi-model integrated analysis. The different responses reflect
non-Gaussian distributions of the individual model fields. The results imply
that the observationally constrained, multi-model integrated fields provide
fundamentally different fast chemical processes than those in the individual
models. Meanwhile, the uncertainty-weighted multi-model integrated ozone
fields showed closer agreements with independent observations than the
multi-model averages in the lower troposphere (see Sect. 4.1.2). This
suggests that the MOMO-Chem framework provides improved estimates of the
atmospheric states for many cases. With further investigations of the
chemical relationships in the integrated fields, the MOMO-Chem framework
would provide insights into ozone production processes to inform chemical
predictions through relationships such as emergent constraints (Bowman et
al., 2018). This example demonstrates the unique capability of the MOMO-Chem
framework for various applications.
Estimated emissionsNOx emissions
As summarized in Table 8 and shown in Fig. 16, the global total NOx
emissions are increased by 12 % in GEOS-Chem, 40 % in AGCM-CHASER,
25 % in MIROC-Chem, and 30 % in MIROC-Chem-H due to data assimilation.
The a posteriori global total emissions vary from 39.1 TgN (GEOS-Chem) to
51.9 TgN (AGCM-CHASER) with the multi-model mean of 47.6±5.8 TgN, in
contrast to the a posteriori global total emissions varying from 37.1 TgN
(GEOS-Chem) to 42.4 TgN (MIROC-Chem-H). The regional NOx emissions are
increased in the models other than GEOS-Chem over the United States (with
annual regional total emission increases of 10 %–22 %), eastern China
(2 %–34 %), and western Europe (7 %–23 %). The a posteriori emissions
over eastern China in these models (5.8–6.4 TgN) are closer to the HTAP-v2
2010 inventory (5.7 TgN) than those from EDGAR v4.2 (4.2 TgN). The emissions
over Europe are largely increased in MIROC-Chem-H (by 23 %) and
AGCM-CHASER (by 24 %). In GEOS-Chem, the emissions are decreased over most
parts of eastern China (by 21 % for regional total emissions), the United
States (by 9 %), and western Europe (by 21 %), where the a posteriori
emissions are obviously lower than the other estimates. As shown in Fig. 17,
the multi-model mean of the a posteriori emissions shows strong NOx
emissions over major polluted areas, while the multi-model spread is large
for eastern China, the eastern United States, Mexico City, western
Europe, and South Africa. The multi-model spread of the a posteriori
regional NOx emissions is smaller than the assumed a priori emission
uncertainty (i.e., by 40 %) for all the polluted areas (Table 8), while
the a priori emission spreads could influence the obtained a posteriori
emission spreads. From sensitivity calculations, we confirmed that the daily
emission updates greatly reduce the dependence of the a priori emissions for
many regions (now shown).
Annual regional total NOx emissions of NOx (Tg N yr-1) and CO
(Tg CO yr-1) obtained from the a priori emissions (in brackets) and a
posteriori emissions for 2007. The multi-model mean and standard deviation
(“spread”) of the a posteriori emissions among the four systems (%)
is also shown.
Global distributions of annual mean surface NOx emissions (10-11 kg N m-2 s-1) and surface CO emissions (10-10 kg CO m-2 s-1)
for 2007. The a posteriori emissions and analysis
increment (a posteriori minus a priori emissions) are shown.
For biomass burning areas, the emissions are increased in all the models
by 17 %–25 % over Southeast Asia, by 13 %–30 % over northern Africa, and
by 4 %–39 % over central Africa. The positive increments over northern and
central Africa are smallest in MIROC-Chem-H, likely due to the use of
updated biomass burning emission inventories (GFED v4) as well as updated
NO2 retrievals. The a posteriori emissions for the biomass burning
areas are similar between the four systems: 0.6–0.8 TgN (16 % standard
deviation) for Southeast Asia, 2.9–3.2 TgN (4 %) for northern Africa, and
2.2–2.8 TgN (10 %) for central Africa. Over South Africa, the emissions
are increased by 29 %–50 % in all the systems, with a large multi-model
spread of the a posteriori emissions (0.4–0.9 TgN, 31 % standard
deviation).
Same as Fig. 16, but for the multi-model mean and spread.
The seasonal variations in NOx emissions are largely modified by data
assimilation for many regions, with common features for all four
systems (Fig. 18). Over eastern China, the emissions in early summer (June)
and winter (November–January) are enhanced in all the systems, which could
be associated with emissions from soils and the use of wintertime heating,
respectively. The magnitude of the summertime enhancement differs among the
models, which could reflect the different chemical lifetime of NOx under
strong photolysis conditions. Over the United States and Europe, large
enhancements in late spring and early summer and subsequently in the
seasonal amplitude are commonly found in all the systems. Also, the timing
of maximum emissions in summer moves forward by a few months (from 1 to
2 months over eastern China and Europe and from 2 to 3 months over
the United States) due to data assimilation in all the systems, likely due
to underestimated soil emissions in early summer, which has also been
suggested by Oikawa et al. (2015).
Time series of monthly total regional surface NOx emissions (Tg N yr-1) obtained from the a priori emissions (dotted lines) and the a
posteriori emissions (solid lines) for 2007 for the four systems.
AGCM-CHASER and MIROC-Chem use the same a priori emissions. The ±1σ deviation among the four models (i.e., model spread) is shown in
gray for the control runs and in light blue for the data assimilation
results.
Over India, the a posteriori emissions reveal strong increases from April to
June in all the systems, which is likely associated with open biomass
burning that is not represented by the bottom-up inventories (Venkataraman
et al., 2006). Over Southeast Asia, the emissions are mostly increased
throughout the year in all the systems, with large increases in the biomass
burning season (boreal spring), except in MIROC-Chem-H. Over South America,
the emissions in the biomass burning season (August–September) are
decreased by 30 %–50 % due to data assimilation in all the systems. The
negative increments suggest an overestimation of emissions by forest fires
in dry conditions in the GFED v2, v3, and v4 inventories, as similarly
suggested by Castellanos et al. (2014) for the GFED v3 inventory. In
contrast, the emissions are increased in the biomass burning off-season by
30 %–60 % in all the systems.
Over northern Africa, in the biomass burning season (boreal winter),
fire-related emission factors in the GFED v3 inventory (AGCM-CHASER,
MIROC-Chem) are suggested to be too low by 20 %–30 %, whereas those in the
GFED v2 (GEOS-Chem) and v4 (MIROC-Chem-H) inventories are too high by 50 %
and by 10 %, respectively. The multi-model consistency is high throughout
the year over northern Africa. Over central Africa, the emissions in the
biomass burning season (July–September) are increased by 30 %–45 % from
the GFED v2 and v3 inventories and decreased by 20 % from the GFED v4
inventory.
The differences in the a posteriori emissions could be explained by the
different model configurations, such as the chemical lifetime of NOx,
vertical mixing, lightning NOx sources, and model resolutions for many
regions. The obtained inter-model differences are generally larger for
industrialized areas (12 %–31 %) than biomass burning areas (4 %–21 %),
suggesting substantial influences of different urban chemistry
configurations and/or model settings for anthropogenic NOx emissions (e.g.,
NO2:NO ratio). Large uncertainties in chemical NOx loss have strong
effects on the simulated NOx lifetime and the accuracy of top-down NOx
source inversion (Lin et al., 2012; Stavrakou et al., 2013).
As discussed in Sect. 5, the NO2 response to NOx emissions
(ΔNO2ΔENOx) is stronger in GEOS-Chem than in other
models probably associated with a weaker chemical NOx loss. This suggests
that the same levels of tropospheric NO2 columns can be explained by
smaller amounts of NOx emissions, and this could explain the lower a
posterior NOx emissions evaluated in this model with respect to other
models. The multi-model differences in simulated NOx levels could also
explain parts of the diversity in model ozone response to NOx emissions
(Sect. 5). In addition, processes such as vertical mixing and lightning
NOx production are strongly model–dependent and influence the responses of
NO2 to NOx emissions. Meanwhile, the updated NO2 retrievals were
assimilated only in MIROC-Chem-H, whereas the diurnal emission variability
was optimized from data assimilation in MIROC-Chem and MIROC-Chem-H. These
differences could also lead to model dependence on emission estimates and
model responses to the updated emissions. To fully understand the
inter-model differences of the a posteriori emissions, their influence needs
to be explored.
CO emissions
The global total CO emissions are increased by 50 % in GEOS-Chem, 51 %
in AGCM-CHASER, 12 % in MIROC-Chem, and 25 % in MIROC-Chem-H due to data
assimilation, with a large diversity in the estimated global total emissions
(943.3–1376.9 TgCO, with 16 % multi-model standard deviation), as
summarized in Table 8. The CO emissions are increased by 18 %–119 % over
eastern China, 9 %–122 % over the United States, and 37 %–146 % over
Europe in all the models, suggesting significant underestimations of
anthropogenic CO emissions in the bottom-up inventories used as a priori
emissions. Using the same a priori emission data sets, the positive
increments are larger in AGCM-CHASER than in MIROC-Chem over eastern China,
western Europe, and the United States, likely associated with underestimated
(or overestimated) chemical production (destruction), as similarly discussed
by Jiang et al. (2015). In fact, AGCM-CHASER reveals relatively high OH
concentrations corresponding to large CO emissions (see Table 5) over
these regions. The multi-model spread of the a posteriori emissions is large
over these industrialized regions (13 %–32 %), with the largest spreads over
central eastern China (Fig. 17).
The a posteriori emissions exhibit a wintertime peak over eastern China in
the models other than MIROC-Chem and over Europe other than GEOS-Chem (Fig. 19). Stein et al. (2014) commonly found that large corrections are needed
for CO emissions in winter–spring seasons for industrialized areas. Because
the chemical destructions are weak in these seasons, the results suggest
underestimations in the bottom-up inventories rather than model errors in
OH. Meanwhile, the distinct differences in the seasonality as well as mean
strength of the a posteriori emissions highlight strong model dependence of
CO emission estimations for the anthropogenic emission regions.
Same as in Fig. 18, but for monthly total regional surface CO
emissions (Tg CO yr-1).
Over India, a pronounced peak in boreal spring is commonly introduced, and
the a posteriori emissions show similar seasonality between NOx and CO in
all the systems. Over Southeast Asia, the annual total emissions are
decreased by 3 %–11 % in all the models, with an enhanced multi-model
discrepancy in the biomass burning season. Over South America, the annual
total emissions are decreased by 27 %–41 % in the models except for
MIROC-Chem-H, with large reductions in the biomass burning season. The
results suggest a common overestimation problem in fire-related emission
factors for both CO and NOx (see Sect. 6.1) in GFED v2 and v3 over South
America. In African regions, although the analyzed seasonal variations are
similar, the annual total emissions reveal large discrepancies among the
models (20 %–35 %). In comparison with the averaged values in other models,
the estimated emissions are larger by 28 % in GEOS-Chem over northern
Africa, by 27 % in MIROC-Chem-H over central Africa, and by 40 % in
GEOS-Chem over southern Africa. The multi-model spread of the a posteriori
emissions is large over major biomass burning regions, such as eastern
central Africa, northern Thailand, and the Amazon (Fig. 17). The substantial
inter-model differences highlight the importance of chemistry and dynamics
in understanding the carbon budget over these regions.
The inter-model differences in data assimilation adjustments and a
posteriori emissions are generally larger for CO than for NOx, which can be
associated with different representations of atmospheric transports such as
convective transport and vertical mixing (e.g., Jiang et al., 2015) because
of the longer chemical lifetime of CO. Also, differences in the chemical
production of CO from the oxidation of NMHCs and the chemical lifetime of
CO, which were not optimized by the data assimilation, could lead to large
multi-model discrepancies in CO simulations and emission estimates, as
similarly discussed by Gaubert et al. (2016). Thus, the differences in
various factors can enhance the multi-model discrepancies in the a
posteriori CO emissions.
Our results suggest requirements for further development of the CO emission
optimization framework to obtain more consistent estimates, for instance, by
using a longer assimilation window and a larger ensemble size. The data
assimilation windows employed (2–6 h) are clearly insufficient to
optimize surface CO emissions using remote measurements while considering the
influence of atmospheric transports. The estimated CO emissions were also
sensitive to the choice of other parameters such as localization length and
covariance inflation factor, while optimal values of these parameters are
expected to differ among the models mainly associated with different
representations of atmospheric transport among the models. Optimizing these
parameters for individual models would thus also be important. Meanwhile,
adding observational constraints, for instance on NMHC emissions from
formaldehyde measurements (e.g., Stavrakou et al., 2009), and considering
interspecies correlations (e.g., between NOx and CO) would help to improve
the data assimilation analysis and multi-model consistency. Some of the
increments seem to be inadequate in MIROC-Chem-H, which could suggest
different optimal settings requirements for the assimilation of total column
retrievals and for higher-resolution models.
Conclusions and discussion
We developed the MOMO-Chem framework to integrate a portfolio of data
assimilation analyses obtained using forward CTMs (GEOS-Chem, AGCM-CHASER,
MIROC-Chem, MIROC-Chem-H) in a state-of-the-art ensemble Kalman filter data
assimilation system. The data assimilation was used to simultaneously
optimize both chemical concentrations and emissions of multiple species
through ingestion of a suite of measurements (ozone, NO2, CO,
HNO3) from multiple satellite sensors. The framework was used to
demonstrate the importance of the performance of forecast models for
tropospheric chemistry data assimilation and to provide multi-model
integrated information on the tropospheric chemistry system.
The forecast performance of the models differed for many species because of
the different model configurations. In the absence of data assimilation, the
multi-model discrepancies and forecast model errors for ozone against the
ozonesonde observations were obvious, with annual mean biases ranging from
-5.1 to 1.4 ppbv (from -6.2 to -0.7 ppbv) in the lower troposphere and from
-4.0 to 16.1 ppbv (from 2.8 to 20.5 ppbv) in the middle and upper
troposphere at NH (SH) midlatitudes. Tropospheric NO2 columns are
largely underestimated by the models other than GEOS-Chem over major
polluted areas, whereas the simulated column peaks in biomass burning
areas are largely biased. For CO, all the models underestimated surface
concentrations in the NH by 20–80 ppb.
Multi-constituent assimilation greatly improved the multi-model consistency
and the level of agreements with independent measurements. In comparison
with the ozonesonde measurements, the annual mean bias is reduced by about
40 %–80 % in the NH, by 50 %–90 % in the tropics, and 45 %–95 % in the SH
in the middle and upper troposphere, while reducing the multi-model spread
of annual mean ozone by 20 %–60 % in the NH and 30 %–85 % in the SH. Data
assimilation also reduced the model biases in tropospheric NO2 columns
by more than 40 % for both major industrialized and biomass burning areas
while improving the seasonal variations. The model negative biases of CO in
the NH are also reduced by about 40 %–95 % in all the models. These results
demonstrate that harnessing the current observing system provides sufficient
constraints to greatly reduce the influences of model errors and to provide consistent concentration analysis.
The multi-model comparisons of tropospheric OH reveal common features of
global distributions but with obvious differences in mean concentration
levels among the models. Data assimilation increments for OH differ largely
among the models, decreasing in GEOS-Chem by 10 %–40 % over east Asia, the
United States, and Europe and increasing in MIROC-Chem and MIROC-Chem-H over
most parts of the NH by 15 %–40 %. In spite of the different increments,
the multi-constituent data assimilation reduced the multi-model spread by
about 25 %–60 % over major polluted areas, while the north-to-south
hemispheric ratio is reduced in all the models from 1.32±0.03 to
1.19±0.03. These results suggests that the multi-constituent data
assimilation framework can be used to provide a common representation of the
tropospheric chemistry system that is less dependent on individual model
performance.
The MOMO-Chem framework provides possible uncertainty ranges in the a
posteriori emissions in the current data assimilation framework due to model
errors, which are quantified in 4 %–31 % for NOx and 13 %–35 % for CO
regional emissions from a multi-model spread of the a posteriori emissions.
Meanwhile, the multi-model analysis commonly suggests potential problems in
the bottom-up emission inventories, such as underestimation of soil NOx
emissions in early summer at NH midlatitudes, underestimations of open
biomass burning emissions in spring over India, and overestimation of
emissions by forest fires in dry conditions over South America. For NOx
emissions, the large inter-model discrepancies are attributable to the
chemical lifetime of NOx, vertical mixing, lightning NOx sources, and model
resolution. For CO emissions, the a posteriori emission differences are
largely attributable to different representations of atmospheric transport,
such as convective transport and vertical mixing, as well as chemical
destruction and production and the use of a short assimilation window. The
larger discrepancy for CO emissions than for NOx emissions suggest the need
to further develop the CO emission optimization framework, for instance, by
using a longer assimilation window and a larger ensemble size.
The response of surface NO2 and ozone concentrations to NOx emission
perturbations is largely different among the models. A stronger ozone
response could help to reduce model errors more efficiently through changes
in the model ozone equilibrium state from the emission optimization. The
multi-constituent framework allows us to evaluate model ozone responses in
realistic conditions while considering possible error ranges in precursor
emissions. The ozone and emission analysis increment information obtained
using the optimized emissions can be used as a diagnostic to quantify model
sensitivities related to chemistry and transport. Thus, a systematic
investigation of model ozone response and analysis increments in the
multi-constituent data assimilation framework could benefit evaluation of
future prediction of the chemistry–climate system as a hierarchical emergent
constraint (Bowman et al., 2018). By using the multi-model integrated fields
from MOMO-Chem and applying the linear regressions, we estimated the surface
concentration responses to NOx emissions in the NH to be largest in January
for NO2 (2.0 ppb per (10-11 kg N m-2 s-1) and in August for
ozone (2.4 ppb per (10-11 kg N m-2 s-1)). The estimated ozone
response was larger in the tropics than in the NH, implying that any
latitudinal shifts in NOx emissions from the extratropics to the tropics
would lead to increases in global tropospheric ozone. The obtained results
also suggest that the multi-model integrated fields could provide
fundamentally different chemical relationships than those in the individual
models, which would inform chemical predictions through relationships such
as emergent constraints. Meanwhile, more research is needed to comprehend
detailed chemical mechanisms. This example demonstrates the unique
capability of MOMO-Chem for various applications.
In summary, the MOMO-Chem framework can be used to generate an ensemble of
data assimilation analyses and to provide integrated unique information on
the tropospheric chemistry system including precursor emissions while
directly accounting for structural uncertainty. Meanwhile, the framework
provides uncertainty ranges in data assimilation analyses including the a
posteriori emissions due to model errors. The information on the uncertainty
obtained from the multi-model framework could be used to suggest
requirements for the development of the individual models and observations.
To obtain highly consistent data assimilation fields, increasing
observational constraints and/or optimization of model parameters, such as
VOC emissions, would be needed. Also, improving background error
information (e.g., by using multi-model ensembles), considering
interspecies emission correlations, and increasing the ensemble size would
be useful to improve the performance of the individual data assimilation
systems. Comparing different data assimilation methods, such as EnKF vs.
4D-Var, would also be important to investigate whether we are able to
produce a consistent data assimilation analysis that is independent of both
the data assimilation scheme and forecast model performance.
Data availability
The Tropospheric Chemical Reanalysis (TCR-2) data are available at https://tes.jpl.nasa.gov/chemical-reanalysis/ (Jet Propulsion Laboratory, 2019).
Author contributions
KM and KWB designed the study. KM, TW, and KY developed the data
assimilation code and set up the data assimilation experiments. KS developed
the model code. KM performed the model simulations and data assimilation
experiments. KM and KWB prepared the manuscript with contributions from all
co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We acknowledge the use of data products from the NASA Aura and EOS Terra and Aqua satellite missions. We also acknowledge the free use of the tropospheric NO2 column data from the SCIAMACHY, GOME-2, and OMI sensors from http://www.qa4ecv.eu (last access: 1 June 2019) and http://www.temis.nl. Part of this work was conducted as the “Post-K computer project Priority Issue 4 – Advancement of meteorological and global environmental predictions utilizing observational Big Data”. The Earth Simulator was used for model simulations under the “Strategic Project with Special Support” of the Japan Agency for Marine-Earth Science and Technology. Part of this work was conducted at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration (NASA). We would also like to thank the editor and the two anonymous reviewers for their valuable comments.
Financial support
This research has been supported by the JSPS KAKENHI (grant nos. 15K05296, 26220101, 26287117, 16H02946, and 18H01285) and the Environment Research and Technology Development Fund of the Ministry of the Environment, Japan (grant no. 2-1803).
Review statement
This paper was edited by Bryan N. Duncan and reviewed by two anonymous referees.
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