ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-20-4493-2020Evaluation of the CAMS global atmospheric trace gas reanalysis 2003–2016 using aircraft campaign observationsEvaluation of the CAMS global atmospheric trace gas reanalysis 2003–2016WangYutinghttps://orcid.org/0000-0002-5024-034XMaYong-Fenghttps://orcid.org/0000-0001-7102-2707EskesHenkhttps://orcid.org/0000-0002-8743-4455InnessAntjeFlemmingJohanneshttps://orcid.org/0000-0003-4880-5329BrasseurGuy P.guy.brasseur@mpimet.mpg.deMax Planck Institute for Meteorology, 20146 Hamburg, GermanyDepartment of Mechanics & Aerospace Engineering, Southern University of Science and Technology, Shenzhen, ChinaRoyal Netherlands Meteorological Institute, De Bilt, the NetherlandsECMWF, Shinfield Park, Reading, RG2 9AX, UKNational Center for Atmospheric Research, Boulder, CO, USAnow at: Department of Civil and Environmental Engineering, the Hong
Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
The Copernicus Atmosphere Monitoring Service (CAMS)
operated by the European Centre for Medium-Range Weather Forecasts (ECMWF)
has produced a global reanalysis of aerosol and reactive gases (called
CAMSRA) for the period 2003–2016. Space observations of ozone, carbon
monoxide, NO2 and aerosol optical depth are assimilated by a 4D-Var
method in the 60-layer ECMWF global atmospheric model, which for the
reanalysis is operated at a horizontal resolution of about 80 km. As a
contribution to the evaluation of the reanalysis, we compare atmospheric
concentrations of different reactive species provided by the CAMS reanalysis
with independent observational data gathered by airborne instrumentation
during the field campaigns INTEX-A, INTEX-B, NEAQS-ITCT, ITOP, AMMA, ARCTAS,
VOCALS, YAK-AEROSIB, HIPPO and KORUS-AQ. We show that the reanalysis
rather successfully reproduces the observed concentrations of chemical
species that are assimilated in the system, including O3 and CO with
biases generally less than 20 %, but generally underestimates the
concentrations of the primary hydrocarbons and secondary organic species. In
some cases, large discrepancies also exist for fast-reacting radicals such
as OH and HO2.
Introduction
Global reanalyses of the chemical composition of the atmosphere are intended
to provide a detailed and realistic view of the three-dimensional
distribution and evolution of the concentrations of the chemical species
over a period of several years. Information provided by advanced models in
which different observational data are assimilated is provided at rather
high spatial and temporal resolutions (typically 80–110 km and 3–6 h,
respectively). The Copernicus Atmosphere Monitoring Service
(http://atmosphere.copernicus.eu, last access: 1 March 2020; CAMS), operated by the European Centre for
Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission,
is currently producing a new global reanalysis of aerosols and reactive
trace gases (referred to as CAMSRA). The currently released reanalysis of
aerosols and reactive gases covers the period 2003–2016 (Inness et al.,
2019) and has recently been extended to 2017 and 2018
(Christophe et al., 2019); this reanalysis run will be continued close
to real time. The ECMWF has produced several other atmospheric composition (AC)
reanalyses. The earlier Monitoring Atmospheric Composition and Climate
(MACC) project produced the MACC reanalysis (MACCRA) for the period of
2003–2012 (Inness et al., 2013; Stein et al., 2012). The CAMS interim
reanalysis (CIRA) is a test product implemented after the retirement of the
coupled Integrated Forecast System (IFS-MOZART; Flemming et al., 2009) and
its replacement by the IFS with online integrated chemistry and aerosol
schemes (Flemming et al., 2015). The CIRA is available from 2003 to 2018
(Flemming et al., 2017). The CAMSRA is built on the experience gained during
the production of these two previous versions of the reanalysis, MACCRA and
CIRA.
The validation of the CAMSRA is routinely performed by the CAMS validation
team through the CAMS-84 contract coordinated by KNMI (Christophe et al.,
2019; Eskes et al., 2015, 2018). The validation uses various measurements,
including satellite observations, ground-based remote sensing and in situ
measurements, ozone soundings, and commercial aircraft measurements, to
assess the performance of the model versions and the reanalysis. The
validation results for CAMSRA 2003–2016 using these operational measurements
are shown by Eskes et al. (2018). The purpose of
our paper is to report on the validation of the CAMSRA by using aircraft
measurements performed during past field campaigns in different parts of the
world.
In contrast to long-term operational monitoring, aircraft campaigns are
designed to address specific scientific questions and perform intensive
measurements in a specific region during a limited period of time. Aircraft
campaigns are therefore valuable supplements to evaluate the models and in
particular the reanalyses. Another advantage of intensive campaigns is that
they provide the opportunity to measure the concentrations of the chemical
species that are not operationally monitored. The observations of these
additional species can be used to better investigate the performance of the
models, in particular their ability to represent some complex physical
and chemical processes (Emmons et al., 2000).
Ozone (O3) and carbon monoxide (CO) are two of the main chemical
species that are simulated in the three reanalyses (MACCRA, CIRA and
CAMSRA). Satellite measurements of these species are assimilated in these
three reanalyses, resulting in analyzed concentrations forced by observations
(Inness et al., 2019) but with constraints that differ from species to
species: these are strong in the case of CO and stratospheric ozone but
weaker in the case of tropospheric ozone and NO2 (due to the short
lifetime of this last species; Inness et al., 2015). The weaker constraint
in tropospheric ozone also results from the fact that the observed ozone amount
in this lower region of the atmosphere is provided by the difference between
the total and stratospheric ozone columns. Knowledge of the distribution of
ozone and CO is key for understanding the role of the chemical and transport
processes in the atmosphere. Ozone is a key indicator of photochemical
pollution. This molecule is produced in the atmosphere by reactions
between nitrogen oxides (NOx=NO+NO2), CO and volatile
organic compounds (VOCs) in the presence of sunlight. Hydrogen radicals
(HOx=OH+HO2) play an important role in this nonlinear
process (Jacob, 2000; Lelieveld and Dentener, 2000). The photolysis of ozone
followed by the reaction of the resulting electronically excited oxygen atom
with water vapor (H2O) represents the main sink of tropospheric O3
(Sheel et al., 2016). Carbon monoxide, either emitted at the surface by
incomplete combustion of fossil fuels and biomass burning or produced in the
atmosphere as a result of the oxidation of hydrocarbons (Khalil and
Rasmussen 1984, 1990; Fortems-Cheiney et al., 2011), is destroyed mainly by
reaction with the OH radical (Pressman and Warneck, 1970). In this paper, we
mainly evaluate the concentration of O3 and CO produced by all the
three reanalyses by comparing them with atmospheric observations made along
flight tracks during past field campaigns (see Table 2). These
comparisons are performed in different regions of the world.
Other chemical species (NOx, HOx, organics) produced by the CAMSRA
are also evaluated at selected locations. The hydrocarbons considered are
ethene (C2H4), ethane (C2H6) and propane
(C3H8). Secondary organic compounds, including methanol
(CH3OH), acetone (CH3COCH3), ethanol (C2H5OH) and
methyl hydroperoxide (CH3OOH), are the products of hydrocarbons and CO
oxidation. Peroxyacetyl nitrate (PAN) and nitric acid (HNO3) are
produced by photochemical reactions involving NOx (Emmons et al.,
2000). Hydrogen peroxide (H2O2) represents a major tropospheric
sink for HOx radicals. Formaldehyde (HCHO) is mainly produced by the
oxidation of hydrocarbons but is also directly emitted to the atmosphere from
industry sources; it has a substantial impact on the HOx concentration.
By comparing these species, the underlying processes in the model can be
further evaluated.
Model description
Three versions of the global reanalysis are evaluated by conducting a
comparison of the calculated fields with available measurements made from
aircraft during selected field campaigns. Some of the key setups of these
three reanalyses are listed in Table 1. The chemical schemes adopted for the
reanalysis models are the MOZART-3 mechanism (Kinnison et al., 2007) in the
case of MACCRA and a modified version of the Carbon Bond 2005 chemistry
mechanism (Huijnen et al., 2010) in the case of CIRA and CAMSRA. Surface
boundary conditions for the reactive gases are generally expressed as
emissions and deposition, and the account for biogenic, anthropogenic and
pyrogenic effects. Methane, carbon monoxide and OH are calculated
interactively with, in the case of methane, specified surface concentrations.
More details can be found in Inness et al. (2019). MACCRA covers the period
2003 to 2012, while CIRA and CAMSRA provide three-dimensional global fields
from 2003 to 2016. Thus, in our analysis, the campaigns that took place
after 2012 are excluded when compared to MACCRA. The model resolution for
MACCRA and CAMSRA is 80 km, while it is 110 km in the case of
CIRA. All three reanalyses are made with a 60-vertical-level model and
extend from the surface to the altitude pressure of 0.1 hPa. Each reanalysis
provides two different outputs: an analysis and a 0–24 h forecast. These two
fields were compared in the case of CAMSRA, and they appear to be very
similar (not shown here). The time resolution for the analysis fields is
6 h for MACCRA and CIRA and 3 h for CAMSRA. For the forecast
fields, the time resolution is 3 h for all the reanalysis versions.
To use same time resolution for the three reanalyses, the forecast fields
are used in this present study. The satellite datasets that are assimilated
in CAMSRA are summarized in Table 2. O3, CO and NO2 are
assimilated in CAMSRA, and each species is assimilated independently from
the others (Inness et al., 2019). O3 total-column, stratospheric
partial-column and profile retrievals from several satellites are used to
constrain mainly the stratospheric O3. As indicated above, the
tropospheric forcing is weaker because the information is provided by the
residual between the total and stratospheric columns (Inness et al., 2015). The
MOPITT total-column CO retrievals are assimilated in CAMSRA, and the
retrievals are mostly sensitive in the middle and upper troposphere (Deeter et
al., 2013), leading to the strongest constraint in that region. MOPITT data
used in the CAMS assimilation cover only the latitudes between 65∘ N
and 65∘ S, so the constraints are weak at high latitudes. For
NO2, the impact of the assimilation is small because the lifetime of
NO2 is short (Inness et al., 2015). An additional control run for
CAMSRA without data assimilation is also evaluated to separate the impact of
the assimilation from the other model-related factors.
Key setups of the three reanalyses.
ReanalysisMACCRACIRACAMSRAPeriod2003–20122003–20182003–presentSpatial resolution80 km110 km80 kmVertical resolution60 levels60 levels60 levelsTemporal resolution6-hourly analysis fields6-hourly analysis fields3-hourly analysis fields3-hourly forecast fields from 00:00 UTC up to 24 h3-hourly forecast fields from 06:00 and 18:00 UTC up to 12 h3-hourly forecast fields from 00:00 UTC up to 48 hAssimilation systemIFS Cycle 36r1 4D-VarIFS Cycle 40r2 4D-Var (2003–2015) and IFS Cycle 41r1 4D-Var (2016–2018)IFS Cycle 42r1 4D-VarChemistry moduleMOZART3 (Kinnison et al.,2007)CB05 and Cariolle ozone parameterization in the stratosphere (Huijnen et al., 2010)CB05 with updates and Cariolle ozone parameterization in the stratosphere (Huijnen et al., 2010)Anthropogenic emissionsMACCity (Granier et al., 2011)MACCity and CO emission upgrade (Stein et al., 2014)MACCity and CO emission upgrade (Stein et al., 2014)Biogenic emissionsMonthly mean VOC emissions by MEGAN2.1 (Guenther et al., 2006) for the year 2003Monthly mean VOC emissions by MEGAN2.1 using MERRA reanalysis meteorology for 2003–2010; a climatology dataset of the MEGAN-MACC for 2011–2017Monthly mean VOC emissions by MEGAN2.1 using MERRA reanalysis meteorology for the whole periodBiomass burningemissionsGFED (2003–2008) & GFAS v0 (2009–2012)GFAS v1.2GFAS v1.2
When comparing the concentrations calculated in the reanalyses with the
campaign data, the 4D model grid points (space and time) that are considered
are those that are closest to the measurement locations (latitude,
longitude and pressure layer) and times.
Aircraft measurements
Several aircraft campaigns are used to validate the three CAMSRA presented
above. These campaigns are briefly described below and in Table 2.
INTEX-A (Intercontinental Chemical Transport Experiment – North America
Phase A) was an integrated atmospheric field experiment performed over the
east coast of the United States organized by NASA during July and August
2004 (Singh et al., 2006). It has contributed to a large ICARTT program (International
Consortium for Atmospheric Research on Transport and Transformation;
Fehsenfeld et al., 2006). During this campaign, chemical species were measured by different
instruments onboard a DC-8 airplane. The measurement methodology for the
trace gases can be found in Singh et al. (2006).
The satellite datasets of trace gases assimilated in
CAMSRA.
SpeciesO3 (stratosphere)O3 (UTLS)O3 (free troposphere)CO (free troposphere)CO (surface and PBL)NO2 (free troposphere)SatellitesMIPAS, MLS, SCIAMACHY, GOME-2A, GOME-2B, OMI, SBUV-2Indirectly constrained by limb and nadir soundersIndirectly constrained by limb and nadir soundersMOPITTIndirectly constrained by satellite IR soundersSCIAMACHY, OMI, GOME-2
Note: indirectly constrained means that there are no data in this layer assimilated
for this species, but there is some impact coming from the residual of
combining the datasets from the other layers.
NEAQS-ITCT (New England Air Quality Study – Intercontinental Transport and
Chemical Transformation) was the NOAA component to the ICARTT program. The
instruments were set up on a WP-3D aircraft, and the details can be found in
Fehsenfeld et al. (2006).
ITOP (Intercontinental Transport of Pollution) was the European (UK,
Germany, and France) contribution to the ICARTT project. In the present study,
we collect the measurements made onboard the UK FAAM BAE-146 aircraft.
The instrument information is provided by Cook et al. (2007).
INTEX-B (Intercontinental Chemical Transport Experiment – Phase B) was the
second phase of the INTEX-NA experiment led by NASA. In March of 2006,
INTEX-B operated in support of the multi-agency MIRAGE/MILAGRO (The Megacity
Initiative: Local and Global Research Observations;
Molina et al., 2010)
project with a focus on observations in and around Mexico City. In its
second phase, INTEX-B focused on the east coast of the US and on the Pacific
Ocean during the spring of 2006 (Singh et al.,
2009). The NCAR component of MILAGRO was MIRAGE-Mex (Megacities Impact on
Regional and Global Environment), and NCAR also contributed to INTEX-B. The
NASA measurement platform was the DC-8 research aircraft. The measurement
approaches for the selected species were the same as those adopted for
INTEX-A. The NCAR measurements were made from the NSF/NCAR C-130 airplane.
The measurement method is described by Singh et al. (2009).
AMMA (African Monsoon Multidisciplinary Analysis) was an international
project to improve our knowledge and understanding of the West African
monsoon (Lebel et al., 2010).
Measurements to investigate the chemical composition of the middle and upper
troposphere in West Africa during the July to August 2006 campaign were
performed by the UK FAAM BAE-146 aircraft, and the details are described
by Saunois et al. (2009).
ARCTAS (Arctic Research of the Composition of the Troposphere from Aircraft
and Satellites) was conducted during April and July 2008 by NASA (Jacob et
al., 2010). ARCTAS was part of the international POLARCAT program during the
2007–2008 International Polar Year (IPY). In the present study, we use the
measurements made onboard NASA DC-8 research aircraft. The species
measured during ARCTAS were the same as during INTEX-A.
VOCALS (VAMOS Ocean–Cloud–Atmosphere–Land Study) was an international
program that is part of the CLIVAR VAMOS (Variability of the American
Monsoon Systems) project. The VOCALS experiment was conducted from 15 October to 15 November 2008 in the southeast Pacific region (Allen et al.,
2011). The NSF C-130 aircraft was used during the campaign.
YAK-AEROSIB (Airborne Extensive Regional Observations in Siberia) was a
bilateral cooperation activity coordinated by researchers from LSCE in
France and IAO in Russia. It aims to establish systematic airborne
observations of the atmospheric composition over Siberia. In the present
study, we used the O3 and CO measurements during 2006–2008 and in
2014. The program used a Tupolev Tu-134 aircraft. The detailed measurement
techniques can be found in Paris et al. (2008, 2010).
HIPPO (HIAPER Pole-to-Pole Observations), supported by the NSF and operated
NCAR, used the NSF/NCAR G-V aircraft. During five missions from 2009 to
2011 in different seasons, a large number of chemical species were observed
between the Arctic and the Antarctic over the Pacific Ocean. The details can
be found in Wofsy et al. (2012).
KORUS-AQ (Korea–US Air Quality Study) was a joint Korea and US campaign
that took place in South Korea from April to June 2016. The US
contribution was led by NASA, and the aircraft platform was the NASA DC-8.
The species were measured as during the INTEX-A campaign. A further
description of this field campaign can be found in the KORUS-AQ White Paper
(https://espo.nasa.gov/korus-aq/content/KORUS-AQ_Science_Overview_0, last access: 10 July 2019).
Since the goal of the present study is to evaluate the different ECMWF
reanalyses by comparing the calculated fields with observations conducted
during different campaigns and using different instruments, it is important
to state that the measurements of the major species are comparable. The
different instruments deployed during these campaigns were all carefully
calibrated, and in the case of ozone and carbon monoxide, for example, the
quoted uncertainties in the measurements are 3–5 and 2–5 ppb,
respectively, depending on the instrument. When, for a given campaign, more
than one instrument was used, the quantitative values were comparable and
were averaged before being used in our analysis. This was the case, for
example, for the HIPPO campaign during which ozone was measured by two
different instruments and carbon monoxide by three instruments.
Information on the aircraft campaigns is summarized in Table 3. The flight
tracks are shown in Fig. 1.
Flight tracks of the campaigns with the altitude of the
corresponding flight.
Evaluation of spatial distributions of chemical species
In the present section, we first evaluate the CAMSRA by comparing the
calculated (reanalyzed) and observed concentrations of ozone, carbon
monoxide and other chemical species in different regions of the world during
the selected field campaigns. Carbon monoxide and ozone were measured in all
the field campaigns considered in the present study. Data are available in
both hemispheres but principally in the regions of North America, eastern
Asia, Australia and across the Pacific Ocean. In the case of nitrogen
oxides, hydroxyl and peroxyl radicals, and formaldehyde, only the
measurements provided in North America, the northern Pacific and eastern
Asia are considered here.
Ozone
For the spatial evaluation, all the aircraft measurements and the extracted
model data points are combined regardless of the time of the measurement;
observations and models are separated into three altitude layers: the low
troposphere layer (0–3 km), the middle troposphere layer (3–9 km) and the
upper troposphere–lower stratospheric layer (9–14 km).
The comparison of O3 between the observation and the reanalyses is
shown in Figs. 2, 3 and 4. The tropospheric ozone concentration is higher in
the Northern Hemisphere than the Southern Hemisphere because of higher
anthropogenic emissions of ozone precursors (air pollution). In the 9–14 km
layer, the polar ozone concentrations are very high because the height of
the tropopause in that region is lower than at lower latitudes, and as a
result, the aircraft penetrated the ozone-rich stratosphere. The
comparison between the aircraft observations and the reanalysis values from
MACCRA is generally good. In the low troposphere, the biases of the averaged
grids are mostly within 20 %. MACCRA underestimates the O3
concentrations in the Arctic region and in the Southern Hemisphere, while it
overestimates the O3 concentrations in the northern low and
middle latitudes, especially over the western Pacific Ocean (over 50 %), the
eastern coast of US and the North Atlantic (about 40 %). The biases of
MACCRA in the middle troposphere are smaller than those in the low
troposphere. The positive biases in the lower layer become smaller with
increasing altitude everywhere except in the Arctic, where the negative
biases turn to positive values. In the upper troposphere, the agreement is
worse than in the lower layers. The biases are mostly positive over the
Pacific Ocean and negative in North America.
Campaign observations of O3(a). (b) The relative difference in percent between MACCRA and the observations
(MACCRA – observation). (c) The difference between CIRA and
the observations (CIRA – observation). (d) The difference
between CAMSRA and the observations (CAMSRA – observation), and (e) the difference between the control run and the observations
(control – observation). The data are averaged to
5∘×5∘ (latitude × longitude) and to the altitude bin of 0–3 km.
Note that MACCRA only includes campaigns between 2003 and 2012.
Same as Fig. 2, but for the altitude bin of 3–9 km.
Same as Fig. 2, but for the altitude bin of 9–14 km.
The agreement of CIRA with the aircraft measurements is similar to the
agreement of MACCRA when using the same measurements before 2013. In the
lowest layer, however, the mean bias of CIRA is slight smaller than that of
MACCRA. The CIRA reanalysis overestimates the observation in the middle of
the Pacific Ocean and northwest of the Atlantic Ocean, which is similar to
the values derived from MACCRA but with smaller biases; CIRA underestimates
ozone concentrations in the rest of the region with biases of less than 20 %. Above the Pacific Ocean, the positive bias, which is small in the
lower layers of the atmosphere, increases with height and becomes
substantial in the upper troposphere. The patterns of the biases in the CIRA
reanalysis in the upper troposphere are similar to those in the middle
troposphere layer but with larger values.
In the low troposphere, CAMSRA generally overestimates the O3
concentration relative to the observation, which is different from the
MACCRA and CIRA cases. The biases of CAMSRA are usually less than 15 %,
and the relatively larger biases are found in the tropics and Arctic, where
the reanalysis overestimates the measurements by about 30 %. In the free
troposphere, the biases of the reanalysis become larger than in the low
troposphere, especially over the tropical ocean, while the differences are
smaller in the western African region. For the comparison above 9 km, the
positive biases over the Pacific Ocean are even larger and reach 50 %.
The mean bias of the CAMS control simulation (model run performed without
assimilation of observed data) is similar to the bias associated with
CAMSRA, but the patterns are different. The bias of CAMSRA is more uniform
over the globe, which shows that data assimilation improves the global
distribution of the O3 concentration. In the low troposphere, the bias
of the control run is of the order of 15 %. The control run
underestimates the measurements on the west coast of US and in the south
of the Pacific Ocean, where the ozone concentrations provided by the CAMSRA
are higher than the observation. In the polar free troposphere, the control
simulation provides concentration values that are lower than suggested by
the observations with a bias of about 20 %; in contrast to this, in the
tropical region, the control simulation overestimates ozone, which is
similar to the corresponding estimates by CAMSRA. In the upper layer, the
bias pattern is similar to that in the free troposphere, but the bias values
are larger.
Overall, for ozone, the level of agreement between the observations and the
three reanalyses and between the observations and the control run are
similar, but the biases associated with CAMSRA are more uniform in space. A
linear regression was performed between all observed ozone data points and
ozone concentrations extracted from the three reanalyses and from the
control simulation. Table 4 lists the corresponding linear regression
parameters. Fewer data are available when considering MACCRA because MACCRA
includes information only until the year 2012. To more directly compare with
MACCRA, the regression parameters for the other models runs before 2013 are
also given in the table. The correlations of all three reanalysis cases
are high, with squared correlation coefficients larger than 0.9. The highest
correlation is achieved with CAMSRA. The squared correlation coefficient
R2 derived for the control simulation (0.89) is not substantially
smaller than in the three cases with assimilation (0.93). This suggests that
the CAMS model in its control mode has good predictive capability but that,
as expected, data assimilation slightly improves the calculated ozone
fields. To exclude the contribution of stratospheric ozone values in the
statistical analysis, the stratospheric data were filtered out and the
statistical parameters recalculated. The squared correlation coefficients
decreased from about 0.9–0.95 to about 0.6–0.7.
Linear regression of ozone between observations and models.
All data Troposphere data (> 350 hPa) NMBMAER2SlopeRMSENMBMAER2SlopeRMSEMACCRA19 5220.5913.010.92911.0226.06416 0090.219.130.61450.7111.705CIRA22 308-1.8712.710.92980.9422.47218 782-2.959.580.64980.6711.232CIRA (2003–2012)19 522-1.1812.670.93410.9423.22516 009-2.298.990.61110.6610.823CAMSRA22 3081.9211.900.93750.9421.17418 7821.018.770.69270.7210.996CAMSRA (2003–2012)19 5222.4911.970.94120.9421.88916 0091.558.320.66080.7210.608Control22 308-3.8913.460.89350.8425.39818 782-1.689.180.66870.6610.611Control (2003–2012)19 522-3.7113.720.89660.8526.66216 009-1.088.760.62290.6310.155
Note: N is the number of points considered for the calculation of the correlation,
MB the mean bias (ppb), MAE the mean absolute error (ppb), R the correlation coefficient and RMSE the root mean square error
(ppb).
Carbon monoxide
The comparison of carbon monoxide between the observation and the reanalyses
is shown in Figs. 5, 6 and 7. MACCRA underestimates the CO concentrations
in the Arctic region and Canada (about 30 %), West Africa (about 20 %), and the Southern Ocean (about 10 %). It overestimates the
concentrations in the other regions covered by the campaigns, with most of
the biases within 15 %. In the middle troposphere, the bias pattern is
similar to that of the low troposphere, but the biases are smaller than in
the lowest layer, especially in the Arctic. In the upper layer, often
located in the stratosphere, the biases become larger at high latitudes
(positive in the Arctic and negative in the Southern Ocean), with biases
larger than 50 %. In this layer, the patterns of the biases over the
Pacific Ocean are different than in the lower layers.
Campaign observations of CO (a). (b) The relative
difference in percent between MACCRA and the observations (MACCRA –
observation). (c) The difference between CIRA and the
observations (CIRA – observation). (d) The difference between
CAMSRA and the observations (CAMSRA – observation),
and (e) the difference between the control run and the observation (control –
observation). The data are averaged to 5∘×5∘ (latitude × longitude) and to the altitude bin of 0–3 km.
Same as Fig. 5, but for the altitude bin of 3–9 km.
Same as Fig. 5, but for the altitude bin of 9–14 km.
CIRA agrees better with the observations than MACCRA. In the low
troposphere, the biases are smaller than those derived with MACCRA, and the
large negative biases in the Arctic found in MACCRA disappear with CIRA. The
mean bias of CIRA is only of the order of 10 %. CIRA underestimates the
CO observation in the region of the northern Pacific Ocean, but MACCRA
overestimates the concentrations there. In the middle troposphere, CIRA
underestimates CO in most regions in the Northern Hemisphere, while it
overestimates CO in the Southern Hemisphere. In the upper layer, the biases
of CIRA are also large at high latitudes, but the biases are positive in the
both polar regions.
The agreement between the CO measurements and the CAMSRA is generally good,
with biases generally smaller than 15 %. In the low and middle
troposphere, the CAMSRA behaves similarly to CIRA; however, in the upper
layer, the biases are different. The biases in CAMSRA become smaller in the
polar region. CAMSRA underestimates CO concentrations in most regions of the
low and middle latitudes, with biases less than 20 %.
The bias between the control run and the CO observations is larger than for
the CAMSRA. The bias pattern of the control run in the lowest layer is
similar to that of CAMSRA, but the positive biases in the Southern
Hemisphere are larger (about 30 %). In the free troposphere, the control
run underestimates the CO concentration at latitudes north of 40∘ N, similar to the CAMSRA, but overestimates the CO elsewhere. The positive
model biases in the Southern Hemisphere and tropics are efficiently removed
by the assimilation of CAMSRA. In the upper layer, the biases are positive
in most regions except west of North America. The biases are large in the
polar stratosphere, where they reach about 50 %.
When confronted with CO data collected by airborne instrumentation, all
three reanalyses provide good results in the low and middle troposphere;
however, the two early reanalyses are not successful when considering the
field observations made in the polar region, specifically in the upper
troposphere–lower stratosphere. The situation is improved with the new
CAMSRA reanalysis. The control simulations performed without assimilation
overestimate the CO concentration in the Southern Hemisphere. The linear
regression parameters of CO are shown in Table 5. For all the data points,
the correlations are weak due to the extreme values appearing in localized
pollution plumes, which are not captured by coarse-resolution global models. After
filtering out these extreme values (values larger than 300 ppb), the
correlations of CO between the observations and models improve
substantially. The correlation calculated using CAMSRA is the highest, with a
correlation coefficient of 0.71 and a slope of 0.78. The mean bias of CAMSRA
is reduced with the assimilation resulting from the correction of the positive
bias in the Southern Hemisphere.
Linear regression of CO between the aircraft campaign
observations and the reanalyses.
All data Data < 300 ppb NMBMAER2SlopeRMSENMBMAER2SlopeRMSEMACCRA18 376-10.4027.130.20050.3054.92117 972-5.9618.690.59920.6323.346CIRA21 353-6.5528.560.39900.4960.91220 254-2.4817.720.67750.7225.052CIRA (2003–2012)18 376-4.8623.710.35730.4251.39717 894-1.6516.250.65880.7022.900CAMSRA21 353-6.8529.230.35590.4966.70620 233-3.8217.340.70610.7825.284CAMSRA (2003–2012)18 376-5.4225.260.27160.4060.38717 869-3.2116.040.68630.7723.553Control21 353-0.1131.780.35650.5068.48920 1872.4520.150.65880.7527.013Control (2003–2012)18 376-0.2527.740.27460.4160.12317 8812.0319.110.62340.7124.920
Note: N is the number of points considered for the calculation of the correlation,
MB the mean bias (ppb), MAE the mean absolute error (ppb), R the correlation coefficient and RMSE the root mean square error
(ppb).
Qualitative summary of the overestimation and underestimation by
the CAMSRA for several observed chemicals at four geographic locations and at
two altitudes (6 km and the surface).
At 6 km surface ArcticBangorHawaiiMexico CityArcticBangorHawaiiMexico CityO3GGGOOOOGOCOGUGUGOUONOxUUUUGUOOOOOHUUOGGUUOOGHO2OOGOUOGOH2O2UUUGGUUGOOOHNO3UUUUUGUOUOPANUUOOOOOOOOOOC2H4UGUUUUUOOOGC2H6UUUUUUUUUUUUUUC3H8UUUUUUUUUUUUUUHCHOUUUUUUUUUGOCH3OHUUUUGOOGOOCH3COCH3UUUUUUUUUUGUUUC2H5OHUUUUUUUUOUCH3OOHUUUUGUOO
Spatial distributions of nitrogen oxides (NOx=NO+NO2),
the hydroxyl radical (OH), the hydroperoxyl radical (HO2) and formaldehyde
(HCHO) for CAMSRA in the Northern Hemisphere are provided in the Appendix.
The CAMSRA reanalysis values are compared with observations from aircraft
for three different layers of the atmosphere. Because the measurements of
NOx, OH, HO2 and HCHO used in the work are only in North America,
the Arctic and Korea, the analyses below are for these regions.
In the case of NOx, the CAMSRA reanalysis underestimates the values
measured in the middle and upper troposphere but overestimates the observed
values in the lowest layer. There are several possible reasons: (1) the
model overestimates the effect of regional pollution sources; (2) the model
underestimates the local production (e.g., lightning); (3) the model
underestimates the convective transport; (4) the model underestimates the
lifetime of the surface emissions. We also compared the NOx fields
produced by CAMSRA and the control run in order to assess the benefit of
NO2 assimilation. Both fields are very similar, which suggests that the
assimilation does not significantly improve the reanalysis of NOx. This
is explained by the fact that NO2 has a short lifetime. Most of the
impact of the data assimilation is therefore lost between analysis cycles
(Inness et al., 2015). In the case of HCHO, the reanalysis underestimates
the observed concentrations at all levels. The negative biases in the low
troposphere are between 20 % and 40 %, while those in the higher levels are
about 50 %. In the case of OH, the calculated values are overestimated at
middle and low latitudes, which may lead to a shorter lifetime of NO2,
consistent with the vertical distribution of NOx discussed above.
CAMSRA underestimates OH concentrations in the Arctic region, which may be
related to the overestimation of CO in that region. Finally, no clear
pattern is found in the difference between model-simulated values and
observations of HO2.
Evaluation of vertical profiles at selected locations
The CAMS reanalysis provides the global distribution of a large number of
chemical species that are not directly assimilated by the CAMS system but
whose concentrations are calculated consistently with the assimilated
species, ozone, carbon monoxide and nitrogen dioxide. We evaluate several
key species calculated by CAMSRA at four selected locations with
observations from NASA campaigns (INTEX-A in 2004, INTEX-B in 2006 and
ARCTAS in 2008) that took place with the DC-8 research aircraft (Fig. 8).
These campaigns provide information on the atmospheric abundance of several
reactive gases related to ozone and CO chemistry. The vertical profiles at
the chosen locations are averaged based on the ARCTAS campaign in the case of
the Arctic region (measurements north to 60∘ N), on the INTEX-B
campaign in the case of Hawaii and Mexico, and on INTEX-A in the case of the
Bangor data. Since only O3, CO and NO2 are assimilated in CAMSRA
reanalysis, the control simulation without assimilation is shown only for
O3, CO and NOx. A comparison between the reanalysis and the
control simulations for species other than O3, CO and NOx is not
shown because the differences between the two runs are very small. The
vertical profiles of ozone, carbon monoxide, nitrogen oxides (NOx),
the hydroxyl (OH) and hydroperoxyl (HO2) radical, formaldehyde (HCHO),
hydrogen peroxide (H2O2), nitric acid (HNO3), peroxyacetyl
nitrate (PAN), ethene (C2H4), ethane (C2H6), propane
(C3H8), methanol (CH3OH), acetone (CH3COCH3),
methyl hydroperoxide (CH3OOH), and ethanol (C2H5OH) are shown
in Figs. 9, 10, 11 and 12.
The location of the four selected regions. The red, green,
blue and magenta rectangles show the Arctic (ARCTAS, April–July 2008), Hawaii
(INTEX-B, March–May 2006), Mexico (INTEX-B, March–May 2006) and Bangor (INTEX-A,
July–August 2004), respectively.
We first examine the case of the three assimilated species. In general, the
profiles calculated with assimilated observations are in good agreement with
the profiles observed by airborne instruments. There are some interesting
points to note, however.
Ozone
In the case of ozone in the Arctic (Fig. 9), where the vertical profile is
strongly affected by stratospheric processes, the control run underestimates
the O3 concentration above 1 km, particularly above 6 km of altitude.
The assimilation brings the profile much closer to the aircraft data. The
concentrations calculated by the control and the reanalysis runs in the
surface layer below 1 km are almost twice as large as those derived from the
observations, which may be affected by the halogen chemical removal in
Arctic spring. In the free troposphere and low stratosphere, the agreement
is best for CAMSRA. In Bangor (Fig. 10), the control and reanalysis
simulations underestimate the aircraft observations in the upper
troposphere, while they overestimate the measurements near the surface.
Averaged profiles of the trace constituents over the
Arctic during the ARCTAS campaign from April to July 2008. The black lines are
the observations, the red lines correspond to the CAMSRA reanalysis, and the
blue lines are the control run (only shown for O3, CO
and NOx). The error bars represent the standard
deviation of the data and model.
The low-latitude ozone profiles (Figs. 11 and 12) are well reproduced by
the reanalysis. However, the control run tends to overestimate ozone in
Mexico City and to a lesser extent in Hawaii. In this last region, the
agreement of O3 between the observations and models is quite good below
7 km: the biases are positive and smaller than 10 %, which is opposite to
what is found in the Arctic. The reanalysis provides slightly better results
than the control run. At higher altitudes the positive biases get larger and
the CAMSRA data become worse than in the control run, which is surprising
since the model with assimilated ozone should be better constrained. This
result may be due to the fact that the constraint on tropospheric ozone is
weak and the bias correction may be distributed incorrectly in the vertical.
In Mexico City, the model represents the ozone bulge that is detected
by the airborne instruments at 2 to 3 km and is observed for most chemical
species. At higher altitudes, the control model overestimates the ozone
concentration; however, the bias is reduced by the CAMSRA assimilation.
Carbon monoxide
In the case of Arctic CO (Fig. 9), the general agreement between the
control and reanalysis runs and the observed profile is very good. The
control run, however, slightly underestimates the CO concentration in the
troposphere but overestimates it in the stratosphere. The assimilation does
not change the simulation significantly as MOPITT observations with
latitudes higher than 65∘ were excluded in the CAMS assimilation. It
increases the biases in troposphere CO in CAMSRA but decreases the positive
biases in the stratosphere. In Bangor (Fig. 10), both the control and the
reanalysis simulations underestimate the observed concentrations by
typically 10 ppb above 3 km of altitude but underestimate the surface
concentrations. At low latitudes (Hawaii and Mexico; Figs. 11 and 12), the
control simulation overestimates the concentrations by about 10 ppb in the
free troposphere, while CAMSRA underestimates the values observed from the
DC-8 by 10 ppb. In Hawaii in the first 2 km above the surface, the control
run provides concentrations that are about 10 % lower than the aircraft
observation. In Mexico, the control model provides surface values that are
30 % higher than the observation. The bulge observed at 2–3 km of altitude
is not reproduced by the model.
Averaged profiles of the trace constituents over Bangor
during the INTEX-A campaign from July to August 2004. The black lines are the
observations, the red lines correspond to the CAMSRA reanalysis, and the
blue lines are the control run (only shown for O3, CO
and NOx). The error bars represent the standard
deviation of the data and model.
Nitrogen oxides
In the Arctic (Fig. 9) the control run underestimates NOx,
especially above 8 km, i.e., in the layers strongly influenced by the
injection of stratospheric air. The assimilation process does not
substantially reduce the discrepancy, since the CAMS model does not include
a detailed representation of stratospheric chemistry and NOx in the
stratosphere is strongly underestimated because of this. In Bangor (Fig. 10), the models underestimate NOx above 2 km as in the Arctic but
overestimate NOx below 2 km. In the low-latitude regions (Mexico
and Hawaii; Figs. 11 and 12), the calculated profiles are in rather good
agreement with the observations, except below 2 km, where the influence from
local air pollution is not well captured by the control and reanalysis
simulations. In Hawaii, the model tends to slightly underestimate the
observation. As in the Arctic, this underestimation is larger in the case of
the reanalysis. In all regions except the Arctic, the models provide higher
surface concentrations than suggested by the measurements.
Averaged profiles of the trace constituents over Hawaii
during the INTEX-B campaign from March to May 2006. The black lines are the
observations, the red lines correspond to the CAMSRA reanalysis, and the
blue lines are the control run (only shown for O3, CO
and NOx). The error bars represent the standard
deviation of the data and model.
Averaged profiles of the trace constituents over Mexico
during the INTEX-B campaign from March to May 2006. The black lines are the
observations, the red lines correspond to the CAMSRA reanalysis, and the
blue lines are the control run (only shown for O3, CO
and NOx). The error bars represent the standard
deviation of the data and model.
Hydroxyl and hydroperoxyl radicals
In the Arctic (Fig. 9), the model underestimates OH concentrations by
about 0.02 ppt at all altitudes (of the order of 50 %), which may be
linked to the slight overestimation of calculated stratospheric CO. In the
reanalysis, the concentrations of HO2 are overestimated by about 1 pptv
between 4 and 8 km of altitude. In Bangor (Fig. 10), the reanalysis
overestimates OH by about 0.2 pptv, which is coincident with the
underestimation of the CO concentration at this location. The HO2
concentrations are overestimated by 3–5 pptv. In Hawaii, the simulations
made for the reanalysis overestimate the OH concentrations below 6 km but
underestimate them above 8 km, which is consistent with the overestimation of
high-altitude CO in the control run. In Mexico City, the simulated OH
concentrations are larger than the measurements below 8 km but smaller
above 8 km. The reanalysis overestimates HO2 by about 4 pptv or 20 %.
Hydrogen peroxide
In the Arctic (Fig. 9), where the calculated concentrations of HO2
are too high in CAMSRA, the concentration of hydrogen peroxide is
overestimated by typically a factor of 2. In Bangor (Fig. 10), the
overestimation is of the order of 20 %. The agreement between the reanalysis
and observations is generally good in Hawaii (Fig. 11) and Mexico City
(Fig. 12), except in the lower levels of the atmosphere, where the model
overestimates the concentrations.
Nitric acid
Nitric acid concentrations are strongly affected by wet scavenging in the
troposphere and, at high latitudes, by the downward flux of stratospheric
air (Murphy and Fahey, 1994; Wespes et
al., 2007). The reanalysis generally underestimates the concentration of
HNO3 above 2 km of altitude. This is the case in the Arctic (Fig. 9),
Bangor (Fig. 10) and Hawaii (Fig. 11). The discrepancy is
particularly large in the upper levels of the Arctic, which implies that (1) scavenging of HNO3 is too strong, and (2) the reactive nitrogen (e.g.,
NOx) in the stratosphere is too low due to missing stratospheric
chemistry. The model accounts for the high concentrations observed in the
lowest levels of the atmosphere, specifically in Mexico City (Fig. 12) and
to a lesser extent in Bangor and Hawaii.
Peroxyacetyl nitrate (PAN)
The agreement between the calculated and observed PAN vertical profile is
good in the Arctic (Fig. 9), even though the concentrations are slightly
underestimated between 2 and 8 km of altitude. The agreement is also good in
Hawaii (Fig. 11) below 5 km of altitude, but a discrepancy of about 50 %
is found above this height. In Bangor (Fig. 10), PAN concentrations are
overestimated by about 25 % in the free troposphere and by as much as a
factor of 2 below 3 km of altitude. The calculated concentrations are slightly too
high in Mexico City (Fig. 12). The model shows the presence of a peak in
the PAN concentration at 3 km, but the calculated concentration values are
somewhat too low.
Primary organic compounds: ethene (C2H4), ethane
(C2H6) and propane (C3H8)
In most cases, the model underestimates the measured concentrations of the
primary hydrocarbons, which indicates that the emissions are too low. The
discrepancy is substantial at all altitudes, for example for C2H4
in Hawaii (Fig. 11), as well as C3H8 in the Arctic (Fig. 9)
and in Bangor (Fig. 10). Calculated C2H6 is substantially lower
than suggested by the observations at all four locations. In Mexico City
(Fig. 12), the model rather successfully reproduces the vertical profile
of C2H4 but underestimates C3H8 below 5 km of altitude.
This last compound is well represented in Hawaii in the upper troposphere
but is underestimated by the model below 7 km.
As should be expected from the underestimation by the reanalysis of the
atmospheric concentration of the primary hydrocarbons, the model also
underestimates the abundance of oxygenated organic species in the
troposphere. This is the case in the Arctic (Fig. 9), where the abundances
of formaldehyde, acetone and ethanol are underestimated by typically factors
of 3 to 8. Methanol is too low by about 30 %. Large discrepancies are also
found in Bangor (Fig. 10) where methanol and acetone are underestimated by
a factor of 2 and methyl peroxide by a factor of 5. In Hawaii (Fig. 11), the
concentration of formaldehyde is slightly underestimated in the middle and
upper troposphere, but the discrepancy reaches a factor of 2 at 2 km of altitude.
Methanol is underestimated by 30 %, but acetone and ethanol are
underestimated by a factor of 2. The model is in better agreement with the
observations in Mexico City (Fig. 12): this is the case for formaldehyde
(except below 4 km where the calculated concentrations are a factor of 3 too
low), methanol (except at the surface) and methyl hydroperoxide
except below 4 km. Ethanol is underestimated by a factor of 2.
To summarize the discussion, we have qualified the degree of success of the
reanalysis model versus the observational vertical profiles in the four regions
of the world that are considered in the present study. The results, based on
a subjective comparison between the vertical profiles derived from the
CAMSRA and the profiles measured independently by airborne instruments, are
presented in Table 6 for the altitudes of 6 km above the ground
and at the Earth's surface, respectively. The symbols used in this table are
the following: G for good agreement (bias < 10 %), O for
overestimation by the reanalysis model (10 % < bias < 40 %) and U for underestimation (-40 % < bias < -10 %). Double symbols (i.e., OO or UU) indicate from a subjective analysis
that the disagreement is large (bias > 40 %).
Concentration ratios
In order to analyze the performance of the reanalysis and to reproduce the
observed relationships between different reacting species, we present and
discuss the vertical distribution of the concentration ratio between
photochemically coupled chemical compounds. In order to avoid the chemically
and dynamically complex situation encountered in the boundary layer, we
limit this analysis to results (models and observations) obtained above 4 km
of altitude. We focus here on the NO/NO2, PAN/NO2, HNO3/NO2
and HO2/OH concentration ratios (Fig. 13).
Concentration ratios of NO/NO2,
HO2/OH,
HNO3/NO2 and
PAN/NO2 derived from aircraft measurements (black
curves), control runs (blue curves), reanalysis (red curves) and simple
equilibrium relations (except in the case of the PAN/NO2
ratio; green curves). The values are shown in the Arctic, Bangor, Hawaii
and Mexico. Note that, in most cases, the blue and red curves cannot be
distinguished.
We first examine for the four locations considered in the present study
(Arctic, Bangor, Hawaii and Mexico) the NO/NO2 concentration ratios
derived from the aircraft observations of NO and NO2, respectively, as
well as the similar ratios produced by the control case (blue curves),
reanalysis models (red curves) or derived from an approximative expression
based on the photochemical theory of the troposphere (green curves). We note
at all locations that the value derived from the reanalysis (with a detailed
chemical scheme included) is in good agreement with the value derived from
the simple photostationary expression:
NONO2=JNO2k1O3+k2[HO2}+X,
where JNO2 (about 10-2 s-1 in the entire troposphere for a
solar zenith angle of 45∘) represents the photolysis coefficient of
NO2, and k1 and k2 are the rate constants of the reaction of NO with
ozone and the hydroperoxy radical (HO2), respectively (Burkholder et
al., 2015). The symbol X accounts for the effects of additional conversion
mechanisms of NO to NO2. Note that, as the temperature and the ozone
number density decrease with height in the troposphere, the NO/NO2
ratio tends to increase with altitude. In the lower stratosphere, the ratio
is expected to decrease as the ozone concentration rapidly increases with
height above the tropopause.
In the Arctic, the ratio derived from observations (typically equal to 1;
see the black curve in Fig. 13) is about a factor of 2 smaller than the calculated ratio
between 6 and 10 km of altitude. In Bangor, its value (about 2 to 3) is higher
than the model calculations. Perhaps the most interesting point is the
substantial discrepancy between the models and the observations in the upper
troposphere of the tropics (Hawaii and Mexico). One notes, for example, that
the observed ratio does not increase as expected from theory, and at 11 km,
for example, the calculated ratio of close to 1 when derived from the
observations reaches a value of the order of 4 or 5. Among possible causes
for this discrepancy is an underestimation of the correction factor X due to
reactions not considered in the models. Possible mechanisms include the
reactions of NO with the methyl peroxy radical (CH3O2) and with
BrO (Sasha Madronich, personal communication, 2019). CH3O2 plays a significant
role in the NO to NO2 conversion. The BrO radical is expected to affect
the NO to NO2 ratio if the BrO concentration becomes larger than 2–5 pptv. Another point to stress is the large uncertainty that results from
dividing two mean concentration values to which substantial uncertainties
are attached so that the stated ratio derived from mean observations may be
subject to a large error.
Figure 13 also shows the concentration ratio between PAN and NO2 and
between HNO3 and NO2. In the first case, the ratios derived from
the models (control run and reanalysis) are in fair agreement with the
ratios derived from the measurements of NO2 and PAN concentrations. The
ratio decreases with height in the Arctic and Bangor but is
relatively constant with height (typically 10–20), with some elevated values
at some specific altitudes. In the case of the HNO2 to NO2 ratio,
the differences between ratios derived from the models and the aircraft
observations can be substantial. The control and reanalysis runs (blue and
red curves) underestimate the ratio in the Arctic and in Bangor. The
agreement is somewhat better in Hawaii and in Mexico, even though large
differences exist at specific altitudes. These discrepancies can probably be
explained by the role played by the heterogeneous conversion of nitrogen
oxides to nitric acid, which depends on the chaotic behavior of clouds and
aerosols in the troposphere. The green curve provides an estimate of the
ratio derived from the following expression (assuming equilibrium) that
ignores any heterogeneous conversion but is calculated using the observed
values of OH:
HNO3NO2=k3[OH]JHNO3+k4OH.
Here, JHNO3 (about 6×10-7 s) is the photolysis coefficient
for nitric acid, while k3 and k4 are the kinetic coefficients for
the reactions between NO2 and OH and between HNO3 and OH,
respectively (Burkholder et al., 2015).
Finally, we show in Fig. 13 the concentration ratio between HO2 and
OH, which is influenced by carbon monoxide, nitric oxide and ozone that, to
a good approximation, can be expressed as
[HO2][OH]=k5CO+k6O3k7NO+k8O3.
Here k5 and k6 refer to the reactions of OH with carbon monoxide and
ozone, respectively, and k7 and k8 to the reactions of HO2 with
nitric oxide and ozone, respectively (Burkholder et al., 2015). The ratio
derived from observations (black curves) follows the vertical distribution
of the ratios derived by the model (reanalysis, red curve) and calculated by
the above equilibrium relation with observed values of CO, NO and ozone
(green curve). The value of the ratio decreases from about 100±25 at
4 km (all sites except the Arctic) to about 30–40 at 12 km in the tropics
(Hawaii, Mexico) and to 10–20 at high latitudes (Bangor and the Arctic).
Summary
Overall, the reanalysis of assimilated tropospheric chemical species such as
ozone and carbon monoxide by the CAMSRA system rather
satisfactorily reproduces the observations made independently from aircraft platforms
during the analyzed campaigns that took place between 2004 and 2016.
In the case of ozone, the R2 coefficient is close to 0.9 and the RMSE
ranges between 21 and 26 ppbv, depending on the reanalysis case that is
being considered. The values of the same coefficient in the control case (no
assimilation) are 0.89 and 25.4 ppbv, respectively. When only tropospheric
ozone data are considered, the R2 coefficient is of the order of 0.61 to
0.69 and the RMSE is close to 11 ppbv. The corresponding values for the
control case are 0.67 and 10.6 ppbv, respectively. In other words, the
assimilation procedure improves, but only slightly, the value of the
statistical coefficients that are derived. Note that the RMSE is reduced by
a factor of 2 when only the tropospheric data are used, and the R2
coefficients are reduced by 20 %–30 %.
In the case of carbon monoxide, the R2 coefficient varies from 0.2 to
0.4 depending on the adopted reanalysis, and the RMSE ranges from 55 to 67 ppbv. When plumes are removed from the observational data, the R2
coefficient increases to 0.6–0.7 and the RMSE is reduced to 23–25 ppbv.
These values are not substantially different from the coefficients obtained
when the observations are compared with the control runs. But the
assimilation brought the simulated CO concentrations to a more uniform global
distribution, which is a success of the reanalysis system.
The CAMSRA reproduced the vertical profiles of O3 and CO quite well at
four selected locations. For the species largely affected by the local plume
(e.g., CO and NOx), the CAMSRA underestimated the peak values. The
simulation of OH and HO2 in CAMSRA is generally satisfactory, but in
some cases the disagreement is big. The CAMSRA generally underestimated the
primary hydrocarbons and the secondary organic compounds at all locations,
implying the emissions are too low in the inventory used by the CAMS system. It
will be important in the future to improve in the reanalysis simulations
the surface emissions of hydrocarbons and, if possible, assimilate organic
species other than formaldehyde.
Campaign observations of NOx(a, c, e) and the difference between the CAMSRA and the observations
(CAMSRA – observation; b, d, f). The data are
averaged to 5∘×5∘ (latitude × longitude) and to three altitude bins: 0–3,
3–9 and 9–14 km.
Same as Fig. A1, but for HCHO.
Same as Fig. A1, but for OH.
Same as Fig. A1, but for HO2.
Data availability
The data used for the analysis are available online (see Table 3). The results of the analysis are available on request from the first author.
Author contributions
YW and YFM contributed equally to this paper. They performed the analysis of the data produced by the different field campaigns. GPB developed the project idea. HE, AI and JF provided ideas and comments on the study and are directly involved in the CAMS project supported by the European Commission. YW and GPB wrote the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The present work was funded through the CAMS-84 (CAMS validation) contract,
coordinated by the Royal Netherlands Meteorological Organization (KNMI, Henk
Eskes). The Copernicus Atmosphere Monitoring Service (CAMS) is operated by
the European Centre for Medium-Range Weather Forecasts on behalf of the
European Commission as part of the Copernicus Programme. We acknowledge all
the investigators who have made the measurements and made them available
online. We thank Jean-Daniel Paris (Laboratoire des Sciences du Climat et de
l'Environnement, Gif sur Yvette, France) and Philippe Nédélec
(Laboratoire d'Aérologie, Toulouse, France) for providing the
YAK-AEROSIB campaign data. The National Center for Atmospheric Research is
sponsored by the US National Science Foundation.
Financial support
The article processing charges for this open-access publication were covered by the Max Planck Society.
Review statement
This paper was edited by Bryan N. Duncan and reviewed by two anonymous referees.
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