ECMWF COPERNICUS REPORT

Copernicus Atmosphere Monitoring Service

Upgrade verification note for the
CAMS near-real time global atmospheric composition service
Evaluation of the e-suite for the
CAMS Cy48R1 upgrade of 27
June 2023

Issued by: KNMI
Date: 9 June 2023
Ref: CAMS2_82_2022SC1_D82.3.2.1-2023Q2

This document has been produced in the context of the Copernicus Atmosphere Monitoring Service (CAMS).
The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts,
operator of CAMS on behalf of the European Union (Delegation Agreement signed on 11/11/2014). All information in thisdocument is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose.
The user thereof uses the information at its sole risk and liability. For the avoidance of all doubts, the European Commission
and the European Centre for Medium-Range Weather Forecasts has no liability in respect of this document, which is merely
representing the authors view.

Copernicus Atmosphere Monitoring Service

Upgrade verification note for the CAMS near-real time global
atmospheric composition service:Evaluation of the e-suite for the CAMS Cy48R1 upgrade of
27 June 2023
Authors:
H.J. Eskes (KNMI), A. Tsikerdekis (KNMI), A. Benedictow (MET-Norway), Y. Bennouna(CNRS-LAERO), L. Blake (MET-Norway), Q. Errera (BIRA-IASB), J. Griesfeller (MET-Norway),
L. Ilic (BSC), J. Kapsomenakis (AA), B. Langerock (BIRA-IASB), A. Mortier (MET-Norway),
M.R.A. Pitkänen (FMI), A. Richter (IUP-UB), A. Schoenhardt (IUP-UB), M. Schulz (MetNorway), V. Thouret (CNRS-LAERO), T. Warneke (UBC), C. Zerefos (AA)Reviewers:
I. Bouarar (MPI-M), I. Pison (LSCE)
Available at:https://atmosphere.copernicus.eu/eqa-reports-global-services
Citation:
H.J. Eskes, A. Tsikerdekis, A. Benedictow, Y. Bennouna, L. Blake, I. Bouarar, Q. Errera, J.
Griesfeller, L. Ilic, J. Kapsomenakis, B. Langerock, A. Mortier, I. Pison, M.R.A. Pitkänen, A.
Richter, A. Schoenhardt, M. Schulz, V. Thouret, T. Warneke, C. Zerefos, Upgrade verificationnote for the CAMS near-real time global atmospheric composition service: Evaluation of the esuite for the CAMS Cy48R1 upgrade of 27 June 2023, Copernicus Atmosphere Monitoring Service (CAMS) report, CAMS2_82_2022SC1_D82.3.2.1-2023Q2_Cy48R1_upgrade_evaluation.pdf,
June 2023, doi:10.24380/rzg1-8f3l
Status:Version 1.0, final
Date:
9 June 2023

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 3 of 106

Copernicus Atmosphere Monitoring Service

Executive Summary
The Copernicus Atmosphere Monitoring Service (CAMS, http://atmosphere.copernicus.eu) is a component of the European Earth Observation programme Copernicus. The CAMS global near-real time (NRT)
service provides daily analyses and forecasts of reactive trace gases, greenhouse gases and aerosol concentrations.
The CAMS service includes an activity (CAMS2_82) dedicated to the validation of the service products.
The latest validation results for the CAMS-global near-real time service (the o-suite) products can befound in Schoenhardt et al. (2023) and the activity is described in Eskes et al. (2015). The observational
datasets used for this validation are described in Eskes et al. (2023). The evaluation of the previous
upgrade of the CAMS global system to Cy47R3 is described in Eskes et al. (2021). These validation reports and the verification websites can be found at http://atmosphere.copernicus.eu/user-support/
validation/verification-global-services.
Before each upgrade, the new model and assimilation configuration is operated in parallel (the e-suite)
to the operational near-real time service (the o-suite, e.g., Cy47R3) for about half a year. For the 27
June 2023 upgrade to Cy48R1, the e-suite run is available from 1 October 2022 onwards, so there are
only a limited number of months of data in the Autumn and Winter available to the evaluation. The
corresponding e-suite control run is identical to the e-suite, but with the assimilation of the atmosphericcomposition satellite data disabled. The e-suite control run allows us to distinguish changes due to themodel from changes due to the analysis of the observations.
Hereafter, a set of quick-look validation results are presented from comparisons of the performance ofthe e-suite runs with the operational run (o-suite) and independent observations. The main conclusions
are summarized below, and the evidence for these findings is presented in Sections 2 and 3 in the form ofa series of plots comparing e-suite, o-suite and independent observations. The o-suite and e-suite systemdescriptions are presented in Section 1.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 4 of 106

Copernicus Atmosphere Monitoring Service

Table S.1: Scorecard for the relative performance of the e-suite versus the performance of the o-suite against observations.
Meaning of the “relative score” symbols: (++) e-suite performs significantly better than the o-suite (+) e-suite
shows small improvements, (n) (neutral) no significant difference between o-suite and e-suite, (-) score is somewhat
degraded in the e-suite, (–) e-suite performs significantly worse than the o-suite. Remote: Remote Sensing fromsurface station, AOD: Aerosol Optical Depth at 550nm, Ångstrøm exponent between 440nm and 870nm, PM10:
Particulate matter less than 10 microns, PM2.5: Particulate matter less than 2.5 microns. 1 Based on the average
statistics of ACE-FTS, SAGE-III/ISS, OMPS-LP, Aura MLS and ozonesondes. 2 Based on data from a network ofstations that were collected from FMI.
Species
AOD
AOD
AOD
AOD
AOD (Dust)
AE
AE
AE
AE
AE (Dust)
PM10
PM10
PM10
PM2.5
PM2.5
PM2.5
O3
O3
O3
O3
O3
O3
O3
O3
O3
CO
CO
CO
CO
CO
CO
CO
CO
NO2
NO2
NO2
NO2
NO2
SO2
SO2
HCHO
UV

Reference
AERONETAERONETAERONETAERONETAERONET SDA
AERONETAERONETAERONETAERONETAERONET
AIRBASECNEMC
AIRNOW
AIRBASECNEMC
AIRNOW
GAW, ESRL, IASOA
AIRBASECNEMC
AIRNOW
IAGOS
Sondes
REF1
REF1
IASI
GAW
AIRBASE
CNEMC
NDACC FTIRNDACC FTIR
IAGOS
IASI
MOPITT
AIRBASECNEMC
AIRNOW
TROPOMITROPOMIAIRBASECNEMC
TROPOMIREF2Class
RemoteRemoteRemoteRemoteRemoteRemoteRemoteRemoteRemoteRemoteIn-situIn-situIn-situIn-situIn-situIn-situIn-situIn-situIn-situIn-situAircraftSondesSatellite
Satellite
Satellite
In-situIn-situIn-situ
RemoteRemoteAircraft
Satellite
Satellite
In-situIn-situIn-situ
Satellite
Satellite
In-situIn-situ
Satellite
In-situ

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Type
ColumnColumnColumnColumnColumnColumnColumnColumnColumnColumnSurface
Surface
Surface
Surface
Surface
Surface
Surface
Surface
Surface
Surface
Tropospheric Profiles
Tropospehric Profiles
Stratospheric Profiles
Stratospheric Profiles
ColumnSurface
Surface
Surface
Tropospheric Profiles
Stratospheric Profiles
Tropospheric Profiles
ColumnColumnSurface
Surface
Surface
Tropospheric Column
Stratospheric Column
Surface
Surface
Tropospheric Column
Surface

RegionGlobalEuropeNorth America
East Asia
SaharaGlobalEuropeNorth America
East Asia
SaharaEuropeEast Asia
North America
EuropeEast Asia
North America
GlobalEuropeEast Asia
North America
GlobalGlobalGlobalTropics
GlobalGlobalEuropeEast Asia
GlobalGlobalGlobalGlobalGlobalEuropeEast Asia
North America
GlobalGlobalEuropeEast Asia
GlobalGlobalRelative Score

+
n
+
+
+
+
+
n
n
-n
n
n
n
n
+
n
n
n
+
++
+
n
+
+
+
+
+
+
+
+
n
+
+
+
+
n

Page 5 of 106

Copernicus Atmosphere Monitoring Service

Main conclusions on the performance of the e-suite
The upgrade of the CAMS global system of June 2023 (to Cy48R1) involves system changes in thecomposition modelling which are listed in Section 1.5. The Cy48R1 represent a major upgrade and is theresult of two years of model and data assimilation development. The upgrade introduces, among manyother changes, a stratospheric chemistry with the addition of 63 gas species, contains important emissions
updates, implements changes to the modelling of dust aerosol resulting in a redistribution of aerosol
particles towards larger sizes and adds two new aerosol species (anthropogenic and biogenic secondaryorganic aerosol) as well as four new tropospheric chemical species. In Cy48R1 the assimilation of
TROPOMI CO and VIIRS AOD is introduced.
The validation results are summarized in Table S.1. The aerosol optical depth evaluation shows improvements for most regions. The Ångström exponent, which is a measure representative of the aerosol size
distribution, shows some improvement on the global scale but over the Sahara the performance deteriorates showing a too large fraction of coarse particles. Particulate matter (PM10 and PM2.5) at the surface
is reduced in e-suite, which led to worse performance in East Asia and similar performance over North
America and Europe. Surface ozone and tropospheric ozone shows similar performance. Stratosphericozone and total column ozone have improved against observations, especially in the Tropics. The performance for CO has generally improved against all observations, except MOPITT. NO2 and SO2 bothshow improved validation results against multiple observations. The overestimation of HCHO in the
Tropics is more pronounced in the e-suite compare to TROPOMI. Finally, the UV evaluation shows only
minor changes.
Note of caution: The e-suite data is available for a relatively short period, for most evaluation plots about
5 months with a focus on late Autumn and Winter. Therefore some results may not be representative forthe full year. The assimilation of TROPOMI CO only began in the e-suite at the end of April 2023, andit’s impact has not been evaluated.
In summary, 84% of the evaluation datasets show a neutral or improved performance, while 16% indicate
a degradation. Based on these findings we support the upgrade to Cy48R1.

Ozone (O3 )Surface ozone form the e-suite and o-suite was compared to WMO-GAW, ESRL/GMD and Arctic IASOA surface station observations (Fig. 2.1.1). During October 2022 to February 2023 there was no
significant change in the ozone bias between CAMS e-suite and o-suite, with relative biases of less than
-40% for most of the stations. Exception is the SPO station over the Antarctica, where e-suite shows ahigher negative bias (-50%) than in o-suite (-40%). Notable is the improvement in the bias of the e-suite
control run over the NH mid-latitudes and Tropical stations, as well as the stronger negative offset over
Antarctica. In terms of correlations e-suite and o-suite have an almost equal performance.
The comparison against surface observations over Europe (EEA-Airbase), Fig. 2.2.1, shows that themodified normalised mean bias (MNMB) of the e-suite is higher than for the o-suite by up to 15%
especially over Central and Northern Europe. On the other hand, the e-suite correlation has improved
compared to the o-suite and the e-suite control run bias (Fig. 2.2.2) is slightly lower and improved
compared to the o-suite control run, in agreement with comparisons with WMO-GAW and ESRL/GMD
observations.
The surface ozone validation in China, based on observations from the China National Environmental
Monitoring Center, shows that the e-suite/e-suite control run significantly reduces the o-suite/o-suite
control run negative offset over China Megacities (Figs. 2.2.3, 2.2.4) indicating less titration of ozone
CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 6 of 106

Copernicus Atmosphere Monitoring Service

due to improved NO2 in the e-suite/e-suite control run as discussed below. The correlation on average isabout 0.62, similar for all experiments.
The evaluation of surface ozone using the AirNow ground-based stations over North America showsthat MNMB in e-suite overestimation is higher than o-suite (from +13% to +22%) while the temporalcorrelation (R) remains unchanged (0.56 for all experiments), see Fig. 2.2.5 and 2.2.6. Higher ozone
MNMB is probably related to a stronger underestimation of NO2 in e-suite compare to o-suite.
The e-suite and o-suite have been evaluated with IAGOS aircraft measurements of ozone. The time seriesof the daily profiles at the Frankfurt airport show that the e-suite presents similar results to the o-suite(Fig. 2.3.1). However in the mid-troposphere it can be seen that the e-suite control run presents mostof the time a smaller bias than the other runs from both the e-suite and the o-suite (Fig. 2.3.1). This is
more clearly depicted in the time series of the monthly scores by layers at Frankfurt (Fig. 2.3.2) withthe best performance (MNMB/FGE) from e-suite control run in the free troposphere. At most airports
worldwide the bias (MNMB/FGE) in the low troposphere is slightly larger for the e-suite than for theo-suite, and in particular over airports of Western Africa and Eastern Asia (not shown). Conversely
in the free troposphere, the bias (MNMB/FGE) is smaller for the e-suite than for the o-suite for mostvisited airports (Fig. 2.3.3). Moreover, as regards Western Africa the bias is for most airports negative
for the e-suite runs while it is positive for the o-suite runs (Fig. 2.3.3). In the upper troposphere, ozone
is overestimated and the results from the e-suite and the o-suite are very similar for all runs (Fig. 2.3.4)
except for o-suite control run which presents slightly larger biases than the other runs over the Northern
Atlantic and North America.
Tropospheric ozone profiles have been compared to ozone sonde observations, see Fig. 2.4.1 and 2.4.2.
The northern hemisphere profile difference can be considered statistically robust and little difference isseen between the o-suite and e-suite in the troposphere. The e-suite control run shows a large negative
bias in the upper troposphere and lower stratosphere (below 100hPa), while almost no bias is found in
the lower troposphere, in agreement with the IAGOS comparisons . For the other regions, the profile
count is much lower and differences between the o-suite and e-suite are mixed: for the Tropics and theAntarctic, the lower troposphere bias is smaller for the e-suite, while the reverse is seen for the Arctic
and southern hemisphere midlatitudes. The dispersion in the differences is similar for both runs.
The global maps of monthly mean total column ozone for October 2022 is compared with satellite observations from IASI, see Fig. 2.5.1. Overall, the comparison is somewhat improved for the e-suite
compared to the o-suite. A prominent bias feature over the Pacific ocean has disappeared in the e-suiteand the positive bias at low latitudes is smaller than in the o-suite.

Carbon Monoxide (CO)Compared to surface CO observations from the WMO-GAW five stations there was no significant change
in the bias between CAMS e-suite and o-suite runs, which does not exceed ±10% in general (Fig. 2.6.1).
The correlation for the e-suite and e-suite control run has slightly improved compared to the o-suite ando-suite control run over Europe and the Cape Verde station in the Tropics.
The comparison of the CO mixing ratios against EEA-Airbase observations over Europe shows that esuite/e-suite control run performs slightly better than o-suite/o-suite control run in terms of both biases
and correlations over most stations (Fig. 2.7.1 and 2.7.2).
Over China, the comparison with surface CO observations megacities indicates that the e-suite and esuite control run reduce the positive bias of the o-suite and o-suite control run in stations located in
the megacities especially in north-eastern China (Fig. 2.7.3 and 2.7.4). The e-suite and e-suite control

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 7 of 106

Copernicus Atmosphere Monitoring Service

run show similar correlations to the o-suite and o-suite control run with values exceeding 0.6 over moststations.
The e-suite has been evaluated with IAGOS measurements of CO (Level 1 data) for the period 1st October 2003 – 19 April 2023. Monthly and multi-month statistics and scores are only calculated for thecomplete months. The time series of the daily profiles (curtain plots) of the Modified Normalized mean
bias (MNMB) at the Frankfurt airport are presented in Fig. 2.8.1. The e-suite shows a smaller bias thanthe o-suite at all altitudes and this is the case for the analysis, o-suite control run, as well as the 1-day
forecast. This is also shown in the time series of the monthly scores by layers at Frankfurt (Fig. 2.8.2),
with an e-suite improvement for both the Lower Troposphere (LT) and the Free Troposphere (FT). CO
is underestimated by both the e-suite and the o-suite with a larger bias in the LT than in FT. According
to the fractional gross error (FGE) monthly values, the e-suite improvement is about -0.05 for most of
the months in the LT against about -0.02 in the FT. Regarding correlation results, no notable difference
is found. The control runs from the e-suite and o-suite show a different seasonal pattern of the bias. For
all other airports worldwide the bias (MNMB) in the low troposphere is very similar for the e-suite andthe o-suite (not shown). However and like at Frankfurt, at most visited airports the absolute differences(FGE) are better in the free troposphere for the e-suite compared with the o-suite as shown in Fig. 2.8.3.
In the upper troposphere, at cruise altitude, CO is underestimated by both the e-suite and the o-suite runs.
Like in the free troposphere there is a clear improvement of the bias in the upper troposphere over all
regions in the e-suite compared to the o-suite and in particular for the analysis (Fig. 2.8.4).
Figure 2.9.1 shows results of the CO comparison for October 2022 using satellite CO column retrievalsfrom the IASI and MOPITT instruments. The IASI total column CO observations are well reproduced by
the CAMS e-suite in terms of absolute amounts and spatial distribution. The relative bias stays mostly
within 20%, with wide areas below 5% and some negative bias above the Southern Africa biomassburning area up to 20% and positive biases south of 60°S. The e-suite analysis performs much better thanthe e-suite control run which shows negative biases for the major part of the globe up to 20% regularly
and peak amounts above South Africa exceeding 40%. The o-suite has mostly negative biases comparedto IASI, up to 20% regularly and up to 30% over the Pacific. For MOPITT the situation looks very
different, as overall biases are smaller for the o-suite than for the e-suite. The e-suite shows widespread
positive biases up to 20% which is not present in the o-suite. Only the negative biases of the o-suite up to
20% above Northern Africa improved to remain mostly below 10% in the e-suite. These results for CO
total columns demonstrate changes in the bias correction implemented in the e-suite, which is now using
IASI-C and TROPOMI as reference. Note, that TROPOMI CO data were not assimilated in the e-suite
until 28 April, so the impact of TROPOMI has not been evaluated here.
Comparisons against surface remote sensing CO partial columns and profiles from the FTIR instruments,
part of the NDACC network, have been preformed for the period October 2022 - February 2023. For
tropospheric CO columns (Fig. 2.10.1), the bias for the e-suite is reduced compared to the o-suite foralmost all sites. Although correlations are similar for the o-suite and e-suite, the ratio of the standard
deviation in the tropospheric columns for the e-suite and the FTIR time series is higher compared tothe o-suite. For the tropical sites the bias has switched sign and is now positive. For stratospheric CO
columns (Fig. 2.10.2), the bias for the e-suite is reduced significantly for the southern hemispheric sites
and is now of the order of the measurement uncertainty. The southern hemisphere correlations are also
higher (in particular for the Antarctic site Arrival Heights). For the northern hemisphere the e-suite ando-suite perform similarly. The o-suite has a degraded performance during this period in the southernhemisphere (Wollongong and Lauder stations, see Fig. 2.10.1), where the tropospheric columns show
an increased negative bias compared to the months before October 2022 and the stratospheric columns
show a strong (+20%) positive bias. A direct comparison between the o-suite and e-suite should therefore
be interpreted with care. Figure 2.10.3 shows that for the e-suite a reduced positive bias remains in thestratosphere for this period. This can be considered an improvement compared to the o-suite performance
CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 8 of 106

Copernicus Atmosphere Monitoring Service

prior to October 2022 (negative bias < −20%) and past October 2022 (positive bias > +20%).
As of 22 May 2023, wildfires have been raging across Canada’s Alberta province for three weeks. IAGOS
aircraft profiles of CO over Montreal and Calgary picked up the pollution plumes from these events, andhave been compared with the CAMS results (Fig. 2.19.1). For most profiles studied during this event,the e-suite is performing better than the o-suite with values of the CO mixing ratios higher than those
provided by the o-suite.

Nitrogen dioxide (NO2 )The comparison with surface NO2 observations in Europe obtained from EEA-Airbase shows that thee-suite performs better than o-suite in terms of bias for a majority of the stations especially in Central andNorthern Europe (Fig. 2.11.1 and 2.11.2). Interestingly, this coincides with the regions where a slightly
higher bias was found for ozone in the e-suite (Fig. 2.2.1) indicating chemical regime changes in themodel. In terms of correlations the e-suite and o-suite performed almost equally.
Over China, the e-suite and e-suite control run significantly reduce the o-suite/osuite control run positive
offset in surface NO2 over Megacities in eastern China (Fig. 2.11.3 and 2.11.4), which also led to reduced
bias in surface ozone as discussed previously. The differences between the e-suite and o-suite explained
NO2 daily variance vary between ±20% and the correlation of the e-suite/e-suite control run is slightly
reduced compared to the o-suite/o-suite control run. The mean correlation coefficient averaged over all
stations is 0.49 for e-suite and 0.55 for o-suite.
The evaluation of surface NO2 using the AirNow ground-based stations over North America shows thatthe underestimation in e-suite/e-suite control run is higher than o-suite/o-suite control run (from about-30% to -40%) while the temporal correlation (R) slightly improves (from 0.49 to 0.54), see Fig. 2.11.5
and 2.11.6.
The CAMS e-suite Cy48R1 tropospheric NO2 column data is compared to the TROPOMI scientific IUP
Bremen tropospheric NO2 product, see Figures 2.12.1 and 2.12.2. Compared to the o-suite, the e-suite
performs better compared to TROPOMI above the regions with major emissions such as Eastern China.
Here, the positive bias in the o-suite is reduced in the e-suite as well as above other hotspot regions.
Changes in applied emissions apparently improve the model results. No noteworthy degradation in
performance is seen when moving from the current o-suite to the e-suite. This is also true concerning
the background regions, both e-suite and o-suite perform well in representing background values close
to zero. Some typical biomass burning areas such as Southern Africa or South America show negative
biases around 40% as well as parts of the US, the Northern Atlantic, Northern Pacific and parts of Asia.
Locations strongly affected by anthropogenic pollution such as hotspots in China show strong positive
biases party above 100%. The positive bias above these hotspots is, however, noticeably reduced whencomparing the e-suite with the o-suite performance. Most areas with strong positive biases are smaller inspatial extent and smaller in their bias values. Negative biases above the Northern Atlantic and Pacific,
however, are larger in the e-suite. In addition, there are a few rather confined regions, where the e-suite
shows new positive biases not present in the o-suite comparison such as above the North Sea. Overall,
the performance of the e-suite is slightly better than the o-suite.

Sulfur dioxide (SO2 )The comparison of the SO2 mixing ratio against EEA-Airbase observations over Europe shows that thee-suite/e-suite control run performs better than the o-suite/o-suite control run in terms of bias for moststations (Fig. 2.13.1 and 2.13.2). The correlations are almost equal.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 9 of 106

Copernicus Atmosphere Monitoring Service

The comparison against SO2 surface observations from the China National Environmental Monitoring
Center shows that e-suite/e-suite control run improves the positive bias observed for the o-suite/o-suite
control run over Chinese megacities, though a high overestimation (>50%) is still observed in e-suite inmany of the stations indicating possible overestimation of SO2 emissions. Note that temporal correlation(R) is slighly reduced in o-suite compare to e-suite. (Fig. 2.13.3 and 2.13.4).

Formaldehyde (HCHO)The tropospheric formaldehyde (HCHO) column from the CAMS e-suite is compared to the TROPOMI
scientific IUP Bremen tropospheric HCHO product. Monthly mean global maps and differences areshown in Fig. 2.14.1 and relative differences in 2.14.2.
The e-suite overestimates the tropospheric HCHO signals over South America, and Northern Australia.
The allocation of the HCHO signals over Northern and Southern Africa in the two months is not correctly
reproduced by the model visible in the overestimation North of the equator and an underestimation in
the South.
In comparison to the o-suite, the overestimation in the e-suite above Southern America and Northern
Australia is stronger in the e-suite. Above Northern Africa, the HCHO values show a larger positive
bias, while over Southern Africa the negative bias is slightly less pronounced in the e-suite. Overall, theperformance is similar, but slightly worse for the e-suite.

Stratospheric ozone (O3 )Stratospheric ozone from CAMS e-suite Cy48R1 and o-suite have been compared against satellite limb
profiles and ozonesondes, considering the analyses, the 5th day forecasts and the o-suite control run(Fig. 2.15.1, 2.15.2 and 2.15.3). The period considered is Oct 2022 – Feb 2023. The main conclusions
are:• In the upper stratosphere (at pressure lower than 10 hPa), the e-suite displays a larger bias than theo-suite while the correlations are improved in the e-suite in the tropics.
• In the lower stratosphere, the e-suite agrees better with the observations than the o-suite. In particular, the e-suite agrees much better than the o-suite in the tropics. Also, the e-suite 5th day forecast
agrees better with independent observations than the o-suite during ozone hole conditions.
• The difference between the o-suite and the e-suite are due to the model update, as shown by thedifference between the control runs of the o-suite and the e-suite.
• All the scores of stratospheric ozone are improved with the new e-suite.
• The control run of the e-suite shows a higher negative bias than the o-suite control run in the upper
stratosphere above the 10 hPa level.
Stratospheric ozone has also been compared to lidar profile observations from the NDACC network,
see Fig. 2.16.1. Above 10hPa the e-suite mean profile deviates from the o-suite, showing a negative
bias compared to the lidar observations, increasing with altitude. Between 50hPa and 10hPa the e-suite
overestimates the ozone concentration. The negative bias feature observed around 20 hPa in the o-suite
is no longer present in the e-suite. The dispersion (std) in the differences is slightly, but systematically,
improved for the e-suite.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 10 of 106

Copernicus Atmosphere Monitoring Service

Other stratospheric trace gases
Other stratospheric species in the e-suite have been compared with satellite limb profiles considering
3 hourly first day forecast. The new CAMS cycle 48R1 introduces full stratospheric chemistry, andmeaningful comparisons with the o-suite is not possible. But reasonable concentration of these species
are important because of their impact on ozone. Species considered are: CCl4 (Fig. 2.15.5), CFC-11(Fig. 2.15.6), CFC-12 (Fig. 2.15.7), CH4 (Fig. 2.15.8), ClO (Fig. 2.15.9), ClONO2 (Fig. 2.15.10), H2 O
(Fig. 2.15.11), HCFC-22 (Fig. 2.15.12), HCl (Fig. 2.15.13), HNO3 (Fig. 2.15.14), N2 O (Fig. 2.15.15),
N2 O5 (Fig. 2.15.16), NO2 (Fig. 2.15.17), NOx (NO+NO2 ) (Fig. 2.15.18) and O3 (Fig. 2.15.4).
There is a relatively good agreement between the e-suite and observations for long-lived species (CCl4 ,CFC-11, CFC-12, CH4 , HCFC-22 and N2 O) and HCl. The CAMS e-suite H2 O is too dry. Even though
not perfect, CAMS e-suite NO2 (and NOx) agree relatively well with observations, which was not thecase in the previous o-suite cycles. For ClO, the agreement is good against MLS, less good with ACEFTS, but this dataset is supposed to be less reliable than MLS for this species.
Stratospheric NO2 has been compared with TROPOMI retrievals of the stratospheric column, see Fig.
2.17.1. The observational NO2 stratospheric column is represented well by the e-suite in terms of absolute amounts, latitudinal variation and temporal changes. Over the largest part of the globe, column
amounts agree to within 2x101 5 molec/cm2 , deviating by less than 10%. The e-suite performs well in
reproducing the general strong increase in stratospheric NO2 above the Antarctic region from November
to December.

UV radiation
Hourly UV index values from the CAMS e-suite were evaluated against ground based UV measurementsfrom 38 stations located in Europe, Israel, Thailand, Australia, New Zealand, and the Antarctic between
2022-10-01 and 2023-03-31. Fig. 2.18.1 indicates that the e-suite increases daily maximum UV index
compared to o-suite in the midlatitudes and tropics, except in small regional areas in Africa, Asia andSouth America, where the change was slightly negative. Fig. 2.18.2 quantifies the increase to range
between 0 % to +4 % outside northern high latitudes and in the north the change was between 0 % and-2.3 %, although UV levels in the north were very low due to low sun winter conditions. Fig. 2.18.3
summarizes the CAMS UVI e-suite and o-suite in terms of MNMB, R, and FGE compared to groundbased measurements. The MNMB, R, and FGE for e-suite were 0.04, 0.69, and 0.34, respectively, andtheir difference to corresponding statistics for o-suite are small and not significant, indicating that good
performance continues in CAMS e-suite UV forecasts.

Aerosol
The Cy48R1 introduces a redistribution of aerosol optical depth per species (Fig. 3.1.1 and 3.1.2).
Starting from Cy48R1, the SOA optical depth is now provided separately, whereas prior to this update,the SOA optical depth was included as part of the organic matter optical depth. For this report, theorganic matter optical depth in the e-suite includes the SOA, in order to ensure comparability with theorganic matter optical depth in the o-suite. Compared to o-suite there is less overall total AOD forthe e-suite particularly due to reduction in sulphate optical depth. For the e-suite there is less black
carbon optical depth, particularly over Central Africa, and less organic aerosol optical depth, over SouthAmerica. Nitrates optical depth slightly decrease over southeast Asia and Europe. Contrary, dust, sea
salt and ammonium optical depth increased. These changes are related to (1) the introduction of two new
secondary organic aerosol tracers (anthropogenic and biogenic) along with their respective precursor gas
tracers (2) modifications to dust emissions and removal simulation that increased the global dust mass
CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 11 of 106

Copernicus Atmosphere Monitoring Service

burden by a factor of 2, (3) a review of aerosol optical properties for dust and brown carbon, as well as(4) improvements on secondary inorganic aerosol simulation. The respective control runs for e-suite ando-suite are shown in Fig 3.1.3 and 3.1.4.
Daily Aerosol Optical Depth (AOD) and Ångström Exponent (AE) were evaluated against the groundbased network of Aeronet version 3 level 1.5 stations from December 2022 to February 2023, showingthat e-suite overestimates less compared to o-suite for both parameters (Fig. 3.2.1). Fig 3.2.2 and 3.2.3
indicates that e-suite improves in terms AOD MNMB globally (from +17% in o-suite to 0.5% in esuite), especially over North America, East Asia and Middle East. Over Europe and Sahara the MNMB
performance remains almost unchanged. Notably for the eastern Europe and Indian subcontinents (andalso South Asia) a deterioration in performance (higher underestimation) is observed compared to theo-suite. The temporal correlation (R) is improving globally to a lesser extent (from 0.83 in o-suite to0.85 in e-suite).
The AE, a parameter which is indicative of aerosol size distribution, improves in terms of global bias
(MNMB from +33% in o-suite to +19% in e-suite), with small improvements in Europe, North America
as well as East China (Fig. 3.2.5). The e-suite updated dust emissions and deposition fluxes, resulting
in a higher aerosol size distribution (smaller AE) and dust mass burden over the deserts in the e-suite
control run. Consequently, this leads to a high underestimation of the AE compared to Aeronet version
3 level 1.5 stations over North Africa (Fig. 3.2.4). Although AOD data assimilation improves the AE
underestimation over Sahara by more than 50%, the e-suite AE performance is worse compared to osuite (from +11% in o-suite to -46% in e-suite) over desert areas. The temporal correlation (R) of global
AE remains almost unchanged (from 0.55 in o-suite to 0.54 in e-suite), with little to no change in North
America, Europe and Sahara, better performance over the Middle East and lower performance in East
Asia.
Global PM10 and PM2.5 daily near real time data from surface observations, AirNow (North America,
EPA), EEA-NRT-rural (Europe, European Environmental Agency), and MEP (China, Ministry of Ecology and Environment of People’s Republic of China)) are used to evaluate the surface PM concentrations
for the period December 2022 to February 2023. The e-suite underestimates PM2.5 and PM10 more thanthe o-suite. Fig 3.3.2 show the overall change in MNMB in PM10 from -16% in o-suite to -40% in esuite and for PM2.5 from -8% in o-suite to -35% in e-suite. The intercomparison of MNMB over regions
reveal that performance degradation is largest over eastern Asia (Fig. 3.3.3).

Dust and aerosol over North Africa, the Middle East and Europe
The new dust emission update clearly increased the Dust Optical Depth (DOD) over the Sahara (Fig.
3.4.1). DOD of e-suite and o-suite were evaluated against the AERONET AOD coarse product of the
SDA (Spectral Deconvolution Algorithm) over North Africa and Europe (Fig. 3.4.1 and Table 3.4.2).
Overall, the e-suite performs better than o-suite in terms of MNMB and temporal correlation (R) over
North Africa and displays a similar performance over Europe and Middle East.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 12 of 106

Copernicus Atmosphere Monitoring Service

Table of Contents
Executive Summary ........................................................................................................
1.

2.

4

Description of the o-suite and e-suite ............................................................................ 14
1.1

The CAMS o-suite: model and data assimilation aspects ......................................................... 14

1.2

Short description of the CAMS upgrades in 2020/2021 (47R1/R2/R3) ........................................ 16

1.3

Control run .................................................................................................................... 17

1.4

High-resolution CO2 , CH4 forecasts and delayed-mode analyses ............................................... 17

1.5

CAMS Cy48R1 e-suite..................................................................................................... 17

Upgrade verification results: reactive gases .................................................................... 20
2.1

Ozone: Verification with ESRL-GMD and WMO-GAW surface observations .............................. 20

2.2

Ozone: Verification with surface observations in Europe, China and North America ...................... 21

2.3

Ozone: Verification with IAGOS aircraft observations ............................................................ 27

2.4

Ozone: Verification with sonde observations ......................................................................... 30

2.5

Ozone: Verification with IASI satellite observations ............................................................... 32

2.6

Carbon Monoxide: Verification with WMO-GAW surface observations ...................................... 33

2.7

Carbon Monoxide: Verification with surface observations in Europe and China ............................ 34

2.8

Carbon Monoxide: Verification with IAGOS aircraft observations ............................................. 38

2.9

Carbon Monoxide: Verification with MOPITTv8 and IASI satellite observations .......................... 41

2.10 Carbon Monoxide: Verification with NDACC remote-sensing observations ................................. 42
2.11 Nitrogen Dioxide: Verification with surface observations in Europe, China and North America........ 47
2.12 Nitrogen Dioxide: Verification with TROPOMI satellite observations......................................... 53
2.13 Sulfur Dioxide: Verification with Airbase and MEP surface observations .................................... 55
2.14 Formaldehyde: Verification with TROPOMI observations ........................................................ 59
2.15 Stratospheric Ozone: Verification with satellite observations .................................................... 61
2.16 Stratospheric Ozone: Verification with NDACC remote-sensing observations............................... 78
2.17 Stratospheric NO2 : Verification with TROPOMI.................................................................... 79
2.18 UV index and UV radiation ............................................................................................... 80
2.19 Event: Wildfires over Canada ............................................................................................ 82

3.

Upgrade verification results: aerosol ............................................................................. 83
3.1

Aerosol: Global aerosol speciation...................................................................................... 83

3.2

Aerosol: Verification of global AOD ................................................................................... 87

3.3

Aerosol: Verification of surface PM .................................................................................... 92

3.4

Dust and aerosol verification over North Africa, the Middle East and Europe ............................... 94

Acknowledgements......................................................................................................... 99

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 13 of 106

Copernicus Atmosphere Monitoring Service

1

Description of the o-suite and e-suite

Below a short model description is given for both the CAMS o-suite operational global data-assimilationand forecast system and the new e-suite.

1.1

The CAMS o-suite: model and data assimilation aspects

This section provides information on the CAMS global data-assimilation and forecast operational configuration (the CAMS o-suite). Table 1.1.1 provides information on the satellite data used in the o-suite.
Further details on the different model runs and their data usage can be found at
http://atmosphere.copernicus.eu/documentation-global-systems.
The last operational system version is Cy47R3, see
https://confluence.ecmwf.int/display/COPSRV/Current+global+production+suites.
For a history of the upgrades of the CAMS system, the data products, the satellite data assimilated (andmonitored) and relevant references, see
https://confluence.ecmwf.int/display/CKB/CAMS%3A+Global+atmospheric+composition+forecast+data+
documentation.
The results of the operational monitoring of the satellite data are available athttps://atmosphere.copernicus.eu/charts/cams_monitoring/.
The o-suite consists of the IFS-CB05 chemistry combined with the CAMS bulk aerosol model. The
chemistry is described in Flemming et al. (2015) and Flemming et al. (2017), aerosol is described in
Morcrette et al. (2009). The forecast length is 120 h. The o-suite data is stored under expver ‘0001’ ofclass ‘MC’. On 21 June 2016 the model resolution has seen an upgrade from T255 (approx. 80 km) toT511 (approx. 40 km), and forecasts are produced twice per day (at 00:00 and 12:00 UTC).
A short summary of the main model specifications:• The modified CB05 tropospheric chemistry is used (Williams et al., 2013), originally taken fromthe TM5 chemistry transport model (Huijnen et al., 2010).
• Stratospheric ozone during the forecast is computed from the Cariolle scheme (Cariolle & Teyssedre, 2007) as already available in IFS, while stratospheric NOx is constrained through a climatological ratio of HNO3 /O3 at 10 hPa.
• Monthly mean dry deposition velocities are based on the SUMO model provided by the MOCAGE
team.
• Data assimilation is described in Inness et al. (2015) and Benedetti et al. (2009) for chemical tracegases and aerosol, respectively. Satellite data assimilated is listed in Table 1.1.1.
• Anthropogenic and biogenic emissions are based on MACCity (Granier et al., 2011) and a climatology of the MEGAN-MACC emission inventories (Sindelarova et al., 2014). Anthropogenic
emissions changed to CAMS-GLOB v2.1 with the July 2019 update.
• NRT fire emissions are taken from GFASv1.4 (Kaiser et al., 2012).

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 14 of 106

Copernicus Atmosphere Monitoring Service

Table 1.1.1: Satellite retrievals of reactive gases and aerosol optical depth that are actively assimilated in the o-suite up to
Cy47R3.
Instrument Satellite
MLS
AURA

ProviderNASA

OMI
GOME-2
GOME-2
GOME-2
SBUV-2OMPS

AURA
Metop-A
Metop-B
Metop-CNOAA-19
Suomi-NPPNASA
EUMETSAT
EUMETSAT
EUMETSATNOAANOAA / EUMETSATOMPSTROPOMINOAA-20
Sentinel-5P

NOAA / EUMETSAT
ESA

IASI

MetOp-ALATMOS/ULB EUMETSAT

CO total column

IASI

MetOp-B

LATMOS/ULB EUMETSAT

CO total column

IASI
MOPITT

MetOp-CTERRA

EUMETSATNCAROMI
GOME-2
GOME-2
GOME-2TROPOMIAURA
MetOp-A
MetOp-B
MetOp-C
Sentinel-5P

KNMI
EUMETSAT
EUMETSAT
EUMETSAT
ESA

GOME-2
GOME-2
GOME-2TROPOMI

MetOp-A
MetOp-B
MetOp-C
Sentinel-5P

EUMETSAT
EUMETSAT
EUMETSAT
ESA

MODIS

AQUA
TERRA

NASA

PMAp

METOP-A
METOP-B
METOP-C
Suomi-NPPNOAA-20

EUMETSAT

VIIRS

NASA

Version
V4
V5
V883
GDP 4.8
GDP 4.8
GDP 4.9
V8

V8Pro
V8Pro
v1.1.5v1.1.6-v1.1.8
v2.1.3/v2.1.4
v2.2.1/v2.3.0
v2.4.1

V5-TIR
V7-TIR
V7-TIR Lance
V8-TIR
V9-TIRDOMINO V2.0
GDP 4.8
GDP 4.8
GDP 4.9v2.2.0 (v2.3.1)
v2.4.0GDP 4.8
GDP 4.8
GDP 4.9v2.1.3/v2.1.4
v2.2.1v2.3.0v2.4.1
Col. 5 Deep Blue
Col. 6, 6.1

Type
O3 profiles
O3 total column
O3 total column
O3 total column
O3 total column
O3 21-layer profiles
O3 13-layer profiles

O3 13-layer profiles
O3 column

CO total column
CO total column

NO2 tropospheric column
NO2 tropospheric column
NO2 tropospheric column
NO2 tropospheric column
NO2 tropospheric column
SO2 total column
SO2 total column
SO2 total column
Volcanic SO2 total column

Aerosol optical depth
Fire radiative power
Aerosol Optical Depth

V3r0

AOD

Status20130107 - 20220207
20220207 20090901 20131007 - 20181231
20140512 20200505 20121007 - 20201005
20170124 - 20190409
20201006 - 20220601
20220602 20230201 20181205 - 20190404
20190404 - 20200716
20200716 - 20210705
20210705 - 20220720
20220720 20090901 - 20180621
20180622 - 20191118
20140918 - 20180621
20180622 20191119 20130129 20160124 - 20180626
20180626 20190702 - 20211009
20211010 20120705 - 20210331
20180626 - 20200504
20180626 20200505 20211013 - 20220720
20220720 20150902 - 20191210
20150902 20200505 20201006 - 20210705
20210705 - 20220309
20220309 - 20220720
20220720 20090901 20150902 20170124 20170124 - 20210719
20170926 20210719 20230201 -The aerosol model in Cy47R3 includes 14 prognostic variables (Rémy et al., 2019):• 3 size bins each for sea-salt and desert dust
• 2 bins (hydrophibic and hydrophobic) each for organic matter and black carbon• 1 bin for sulphate• 2 bins (fine and coarse) for nitrate (since 46R1)• 1 bin for ammonium (since 46R1)
The SO2 precursor for sulfate aerosol no longer exists as a separate prognostic in the aerosol scheme,
which since 46R1 couples directly to the SO2 in the chemistry scheme instead. Likewise, the precursors
CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 15 of 106

Copernicus Atmosphere Monitoring Service

for the new nitrate and ammonium aerosol (nitric acid and ammonia) are also part of the chemistry
scheme rather than the aerosol scheme.
Aerosol total mass is constrained by the assimilation of MODIS and PMAp AOD at 550 nm (Benedetti
et al., 2009). A variational bias correction is currently applied for the PMAp AOD based on the approach
used also elsewhere in the IFS (Dee & Uppala, 2009).
The CAMS o-suite system is upgraded regularly, following updates to the ECMWF meteorologicalmodel as well as CAMS-specific updates such as changes in chemical data assimilation. These changes
are documented in the e-suite validation reports, as can be found from the links above.

1.2

Short description of the CAMS upgrades in 2020/2021 (47R1/R2/R3)

These three most recent past upgrades were implemented in the period of about one year. For the aerosol
and reactive trace gas components the upgrade to Cy47R1 took place on 6 October 2020. The upgrade
to Cy47R3_CAMS took place on 13 October 2021. Cy47R2 was a minor upgrade with little impact on
the analysis results. The latest system configuration is described in
https://confluence.ecmwf.int/display/COPSRV/Current+global+production+suites.
The evaluation of the latest Cy47R3 upgrade is described in Eskes et al. (2021).
The meteorological changes can be found on the ECMWF-IFS CY47R3
https://confluence.ecmwf.int/display/COPSRV/Implementation+of+IFS+cycle+47r3.
Developments implemented in cycles 47R1 and 47R3 include:
Assimilation:• Introduction of linear chemistry for NOx in minimization (tangent linear and adjoint code) (47R3).
• Updated background error statistics based on a more recent version of the model (47R3).
Observations:• TROPOMI volcanic SO2 : Activation on 6 Oct 2020 (for SO2 > 5DU) (47R1).
• OMPS O3 layers from NOAA-20 and NPP activated on 2020/10/06 (47R1).
• SBUV/2 NOAA-19 O3 layers retired on 2020/10/05 (47R1).
• Assimilation of Sentinel-5p NO2 (47R3).
Emissions:• Updated emissions inventories: CAMS_GLOB_ANT v4.2 (anthropogenic) and volcanic outgassing(based on Carn et al., 2017) (47R1).
• Updated to GFASv1.4 biomass-burning emissions (47R1).
• Excluded agricultural waste burning from CAMS_GLOB_ANT, avoiding double-counting withGFAS (47R1).
• Improved diurnal cycle (CO, NO, SO2 , NH3 ) and vertical profile for anthropogenic emissions(SO2 , all over sea) (47R1).
• Spatial smoothing applied to NO emission field in a limited area of Central Asia to eliminate
numerical artefacts (47R3).
Other model changes:
CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 16 of 106

Copernicus Atmosphere Monitoring Service• Hybrid Linear Ozone (HLO) scheme (a Cariolle-type linear parameterisation of stratosphericozone chemistry using the multi-year mean of the CAMS reanalysis as mean state) (47R1).
• New sea-salt emission scheme based on Albert et al. (2016), providing better agreement withmeasured sea-salt size distribution (47R1).
• Updated dust source function, reducing excess dust in the Sahara, Middle East and other regions,and restoring missing dust over Australia (47R1).
• Revised coefficients in UV processor, based on ATLAS3 spectrum (47R1).
• Updated aerosol dry deposition based on Zhang and He (2014) (47R3).
• Updated aerosol wet deposition based on Luo et al. (2019) (47R3).
Evaluation and Quality Assurance reports
The global forecasting system is continually being evaluated to ensure the output meets the expected
requirements. Comprehensive Evaluation and Quality Assurance (EQA) reports are provided on a quarterly basis. All reports, including reports for previous system upgrades, are available here:
https://atmosphere.copernicus.eu/node/325
Quality monitoring graphics
CAMS uses a multitude of independent data sets to routinely monitor its global forecasts. It works withvarious data providers, acquiring the observations with appropriate timelines and generating graphics
that show the differences between the forecasts and the independent observations. See
https://atmosphere.copernicus.eu/global-services.
The two main websites are the global evaluation server for near-real time analysis and forecast products
https://global-evaluation.atmosphere.copernicus.eu
and the AeroVal evaluation server https://cams2-82.aeroval.met.no.

1.3

Control run

The control run applies the same settings as the respective o-suite (or e-suite), based on the coupled IFSCB05 system with CAMS aerosol, except that data assimilation is not switched on. The meteorology inthe control run is initialised with the meteorological fields from the o-suite. A control run is produced
both for the e-suite (e-suite control run) and o-suite (o-suite control run). These runs are useful todistinguish data assimilation from modelling changes.

1.4

High-resolution CO2 , CH4 forecasts and delayed-mode analyses

The pre-operational analyses and forecasts of CO2 and CH4 use an independent setup of the IFS. The
upgrade of the greenhouse gas system to Cy48R1 is foreseen to take place in fall 2023, and will be
discussed in a separate upgrade evaluation report.

1.5

CAMS Cy48R1 e-suite

The CAMS IFS cycle 48R1 is based on the ECMWF’s IFS cycle 48R1. This upgrade encompasses
several significant scientific advancements. Notably, it incorporates comprehensive stratospheric chemistry and an improved modeling for dust aerosol that redistribute particles towards larger sizes, resulting
in a substantial increase in global dust mass burden. Furthermore, two new aerosol species have been
included, namely anthropogenic and biogenic secondary organic aerosol. In addition, the tropospheric
CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 17 of 106

Copernicus Atmosphere Monitoring Service

chemistry scheme now incorporates four additional species and the prescribed emissions have been updated to a more recent versions.
Recent developments of the CAMS system are described in Williams et al. (2022) for tropospheric chemistry modelling, Rémy et al. (2022) for aerosol modelling, the report by Chabrillat et al. (2023) on stratospheric chemistry modelling, the paper by Garrigues et al. (2023) on the assimilation of VIIRS AOD andthe paper by Inness et al. (2022) on the assimilation of TROPOMI CO in the CAMS system. Documentation on the CAMS Cy48R1 upgrade for aerosols and reactive gases is in preparation and will become
available soon.
More information regarding the aerosol/chemistry changes and the meteorological changes are provided
in the following links:
https://confluence.ecmwf.int/display/COPSRV/Implementation+of+IFS+cycle+48r1+for+CAMS
https://confluence.ecmwf.int/display/FCST/Implementation+of+IFS+cycle+48R1
A list of some important updates in Cy48R1:
Assimilation• Instrument specific bias correction for AOD• Data assimilation for separate volcanic SO2 tracer (optional)• Nitrate and Ammonium aerosols can be modified by data assimilation (bugfix)
Observations• Activation of assimilation of Sentinel-5p/TROPOMI CO retrievals from 27 April 2022 onward• The bias correction for CO now uses IASI-C and TROPOMI as anchor• AOD from VIIRS (activated already in Cy47R3 1.2.2023)• Passive monitoring of IASI SO2 retrievals
Emissions• CAMS_GLOB_ANT v5.3 with sector-specific emission input to the IFS and application of sector
specific injection height ranges and diurnal cycle profiles• CAMS_GLOB_BIO v3.1 climatology• CAMS_GLOB_OCE v.3.1 climatology DMS• CAMS_GLOB_AIR v3.1 CO2 /NOx (aircraft)• Using IS4FIRES injection heights (from GFAS version 1.4)
• Natural emissions as used previously
Model changes• Activation of stratosphere chemistry in IFS-COMPO based on the BASCOE chemistry scheme adding 63 reactive gas species (Chabrillat et al., 2023)• New tropospheric species: Glyoxal, xylenes, toluene, HONO, HCN, CH3CN, and their related
intermediates• Improved isoprene oxidation simulation

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 18 of 106

Copernicus Atmosphere Monitoring Service• Update of dry deposition formulation for reactive gases• Two new secondary organic aerosol tracers (anthropogenic and biogenic) and respective precursor
gas tracers• Major update of dust emissions and removal simulation, resulting in a redistribution of dust aerosol
towards larger particles with an increase in the global dust mass burden by a factor of 2.
• Review of aerosol optical properties (dust, brown carbon)• Improved secondary inorganic aerosol simulation• Use of COMADH advection for aerosols, reactive gases and GHGs
• Change of the bias correction for CO. Now IASI-C and TROPOMI are used as anchors withoutbias correction and bias correct IASI-B and MOPITT, producing a noticeable change in the CO
fields compared to Cy47R3. This was done to ’future-proof’ the system.
The e-suite run has experiment id "0078", with class=mc, and is available from 1 October 2022 onwards.
The corresponding e-suite control run, without the assimilation of the atmospheric composition satellitedata, has experiment id "hylz", class =rd.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 19 of 106

Copernicus Atmosphere Monitoring Service

2

Upgrade verification results: reactive gases

2.1

Ozone: Verification with ESRL-GMD and WMO-GAW surface observations

Figure 2.1.1: Ozone normalised bias (MNMB, top) and correlation coefficients (bottom), in comparison to WMO-GAW,
ESRL/GMD and Arctic IASOA surface stations during the period 1 October 2022 to 28 February 2028. No
significant change in the ozone bias between CAMS e-suite and o-suite is observed for most of the globe, except
for Antarctica. The bias of the e-suite control run over the NH mid-latitudes and Tropical stations has improved.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 20 of 106

Copernicus Atmosphere Monitoring Service

2.2

Ozone: Verification with surface observations in Europe, China
and North America

Figure 2.2.1: CAMS surface ozone compared to observations in Europe from EEA-Airbase. Spatial distribution of the O3
normalised bias (MNMB in %, left) and correlation coefficient (right) for the e-suite (1st row), o-suite (2nd row)and difference (3rd row) during the period 1 October 2022 to 28 February 2023. The e-suite MNMB is higher
than the corresponding o-suite up to 15% especially over Central and Noerthern Europe. The e-suite correlation
has improved compared to the o-suite.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 21 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.2.2: Control run surface ozone compared to observations in Europe from EEA-Airbase. Spatial distribution of the O3
normalised bias (MNMB in %, left) and correlation coefficient (right) for the e-suite control run (1st row), o-suite
control run (2nd row) and difference (3rd row) during the period from 1 October 2022 to 28 February 2023. The
e-suite control run bias is slightly lower and improved compared to the o-suite control run, while correlations are
similar.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 22 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.2.3: CAMS surface ozone compared to observations in China from the China National Environmental Monitoring
Center. Spatial distribution of the O3 MNMB in % (left) and correlation coefficient (right) for e-suite (1st row),o-suite (2nd row) and its difference (3rd row) during the period from 1 October 2022 to 28 February 2023 over
China. The e-suite significantly reduces the negative offset over the China Megacities observed in the o-suite.
The correlation is similar for the e-suite and o-suite.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 23 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.2.4: Surface ozone from the control runs compared to observations in China from the China National Environmental
Monitoring Center. Spatial distribution of the O3 normalised bias (MNMB in %, left) and correlation coefficient(right) for e-suite control run (1st row), o-suite control run (2nd row) and its difference (3rd row) during theperiod from 1 October 2022 to 28 February 2023 over China. The e-suite control run significantly reduces thenegative offset over China Megacities observed in the o-suite control run. The correlation is similar.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 24 of 106

Copernicus Atmosphere Monitoring Service

e-suite MNMB

e-suite R

o-suite MNMB

o-suite R

e-suite control run MNMB

e-suite control run R

o-suite control run MNMB

o-suite control run R

Figure 2.2.5: MNMB (left column) and R (right column) evalaution of surface O3 against the AirNow network for the period
1st of December to 15th of February.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 25 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.2.6: Evalaution of surface O3 against the AirNow network. Timeseries of global O3 mean in daily and monthly
temporal resolution (top), scatterplot of models against observatios for daily (small symbols) and monthly (bigsymbols) data (middle) as well as MNMB and R averaged over subregions in North America. The middle andbottom panel are for the period 1st of December to 15th of February.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 26 of 106

Copernicus Atmosphere Monitoring Service

2.3

Ozone: Verification with IAGOS aircraft observations(a) e-suite analysis normalized differences(b) o-suite analysis normalized differences(c) e-suite control run normalized differences(d) o-suite control run normalized differences(e) e-suite 1-d forecast normalized differences(f) o-suite 1-d forecast normalized differences

Figure 2.3.1: Ozone comparisons using aircraft profile observations from IAGOS (http://www.iagos.org). Time series of modified normalized differences CAMS-IAGOS in the daily profiles of ozone at Frankfurt between October 2022
and April 2023 from the e-suite (left) and the o-suite (right). Panels are provided for the analysis (top), control
run (middle) and 1-day forecast (bottom). The e-suite shows similar bias results as the o-suite. However in the
mid-troposphere it can be seen that the e-suite control run shows most of the time a smaller bias than the otherruns.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 27 of 106

Copernicus Atmosphere Monitoring Service(a) Lower Troposphere(b) Free Troposphere(c) MNMB LT(d) MNMB FT(e) FGE LT(f) FGE FT(g) Correlation LT(h) Correlation FTFigure 2.3.2: Ozone comparisons using aircraft profile observations from IAGOS (http://www.iagos.org). Monthly time seriesof ozone from IAGOS aircraft observations, from e-suite (solid) and the o-suite (dashed) for analysis (red), 1-day
forecast (orange) and control run (blue) as well as associated scores CAMS-IAGOS (top: MNMB, middle: FGE,
bottom: Pearson Correlation) at Frankfurt in two different atmospheric layers (left: Low Troposphere LT, right:
Free Troposphere FT) during the period October 2022 - April 2023. The histogram bars indicate the number of
profiles (i.e. layer values) based on available observations. The control run from the e-suite presents most of thetime a smaller bias than the other runs for these months, especially in the middle troposphere.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 28 of 106

Copernicus Atmosphere Monitoring Service(a) MNMB e-suite analysis FT(b) MNMB e-suite control run FT(c) MNMB e-suite 1-d forecast FT(d) MNMB o-suite analysis FT(e) MNMB o-suite control run FT(f) MNMB o-suite 1-d forecast FTFigure 2.3.3: Ozone comparisons using aircraft profile observations from IAGOS (http://www.iagos.org). Map of CAMSIAGOS MNMB from the e-suite on the top row (left: analysis, middle: control run: right: 1-day forecast) ando-suite on the bottom row for ozone in the free troposphere (FT) based on profile observations at the different
airports visited during the period October 2022 – March 2023. In the free troposphere, the bias (MNMB) issmaller for the e-suite than for the o-suite for most visited airports.

(a) MNMB e-suite analysis UT(b) MNMB e-suite control run UT(c) MNMB e-suite 1-day UT(d) MNMB o-suite analysis UT(e) MNMB o-suite control run UT(f) MNMB o-suite 1-day UT

Figure 2.3.4: Ozone comparisons using aircraft observations from IAGOS (http://www.iagos.org). Global maps of CAMSIAGOS MNMB (gridded to 5x5 degrees) from the e-suite (top panels, from left to right : analysis, control run
and 1-day forecast) and o-suite (bottom panels, from left to right : analysis, control run and 1-day forecast) forozone in the Upper Troposphere (UT) based on IAGOS-cruise data (filtered observations with PV values below
2) during the period October 2022 - March 2023. In the upper troposphere, ozone is overestimated and the resultsfrom the e-suite and the o-suite are very similar except for the control run which is providing smaller bias for thee-suite than for the o-suite.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 29 of 106

Copernicus Atmosphere Monitoring Service

2.4

Ozone: Verification with sonde observations

Figure 2.4.1: Mean ozone profile relative differences for the o-suite analysis (red), the e-suite analysis (blue), the o-suite control(dark blue) and the e-suite control run (dark green) compared to ozone sonde observations. The corresponding
dispersion around the mean (std) is shown in light colours. The northern hemisphere profile difference can beconsidered statistically robust and little difference is seen between the o-suite and e-suite in the troposphere.
The e-suite control run show a large negative bias in the lower stratosphere (below 100hPa) in the northernhemisphere, while the o-suite control shows positive biases. For the other regions, the profile count is much lower
and differences between the o-suite and e-suite are mixed: for the tropics and the Antarctic, the lower tropospherebias is smaller for the e-suite, while the reverse is seen for the arctic and southern hemisphere midlatitudes. The
dispersion in the differences is similar for both runs.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 30 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.4.2: Curtain plot of ozone profile MNMB for the e-suite analysis (top), the o-suite analysis (second panel), the esuite control run (third) and o-suite control run (bottom) against sonde profiles for the northern hemispheremidlatitudes. The horizontal black line represents the tropopause. The vertical black line indicates the latest
change in the background error covariance. The e-suite and o-suite show similar biases in the troposphere, anda positive bias of about 10% at the tropopause. The control runs show a large negative (e-control) or positive(o-control) bias in the lower stratosphere (below 100hPa).

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 31 of 106

Copernicus Atmosphere Monitoring Service

2.5

Ozone: Verification with IASI satellite observations

Figure 2.5.1: Global maps of monthly mean total column ozone (Dobson Unit) for October 2022 compared with satellite
observations from IASI. The plot shows the relative bias of the e-suite (top) and o-suite (bottom) with respect to
IASI (%). Overall, the comparison is somewhat improved for the o-suite compared to the e-suite. A prominentbias feature over the Pacific ocean has disappeared in the e-suite.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 32 of 106

Copernicus Atmosphere Monitoring Service

2.6

Carbon Monoxide: Verification with WMO-GAW surface observations

Figure 2.6.1: Carbon Monoxide comparisons using WMO-GAW surface observations for the period 1 October 2022 to 28
February 2028. The stations included are ... The plots show the normalised bias (MNMB, top) and correlation
coefficients (bottom), for the e-suite (red, solid), o-suite (red dashed) and corresponding control runs (blue).
Biases are small and are similar for the e-suite and o-suite. The correlation shows an improvement for stations
ZUG, SNB and JFJ.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 33 of 106

Copernicus Atmosphere Monitoring Service

2.7

Carbon Monoxide: Verification with surface observations in Europe
and China

Figure 2.7.1: Comparison with surface CO observations in Europe obtained from EEA-Airbase. Spatial distribution of the CO
normalised bias (MNMB in %, left) and correlation coefficient (right) for e-suite (1st row), o-suite (2nd row) anddifference (3rd row) during the period from 1 October 2022 to 28 February 2023. The e-suite performs better
than the o-suite for both bias and correlations over most stations.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 34 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.7.2: Comparison with surface CO observations in Europe obtained from EEA-Airbase. Spatial distribution of the CO
normalised bias (MNMB in %. left) and correlation coefficient (right) for e-suite control run (1st row), o-suite
control run (2nd row) and difference (3rd row) during the period from 1 October 2022 to 28 February 2023. The
e-suite control run performs better than the o-suite control run for both bias and correlations over most stations.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 35 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.7.3: CAMS CO compared to observations in China from the China National Environmental Monitoring Center. Spatial distribution of the CO normaised bias (MNMB in %, left) and correlation coefficient (right) for e-suite (1strow), o-suite (2nd row) and difference (3rd row) during the period from 1 October 2022 to 28 February 2023 over
China. The e-suite reduces the positive bias observed for the o-suite over megacities. Correlations are similar on
average.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 36 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.7.4: CAMS CO compared to observations in China from the China National Environmental Monitoring Center. Spatial distribution of the CO normalised bias (MNMB in %, left) and correlation coefficient (right) for e-suite
control run (1st row), o-suite control run (2nd row) and its difference (3rd row) during the period from 1 October
2022 to 28 February 2023 over China. The e-suite run control reduces the positive bias observed for the o-suite
control run over megacities. Correlations are similar on average.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 37 of 106

Copernicus Atmosphere Monitoring Service

2.8

Carbon Monoxide: Verification with IAGOS aircraft observations(a) e-suite analysis normalized differences(b) o-suite analysis normalized differences(c) e-suite control run normalized differences(d) o-suite control run normalized differences(e) e-suite 1-d forecast normalized differences(f) o-suite 1-d forecast normalized differences

Figure 2.8.1: CO comparisons using aircraft observations from IAGOS (http://www.iagos.org). Time series of modified normalized differences CAMS-IAGOS in the daily profiles of CO at Frankfurt between October 2022 and April
2023 from the e-suite (left) and the o-suite (right). Panels are provided for the analysis (top), control run (middle)
and 1-day forecast (bottom). The e-suite shows a smaller negative bias than the o-suite at all altitudes and this is
the case for the analysis, control run, as well as the 1-day forecast.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 38 of 106

Copernicus Atmosphere Monitoring Service(a) Lower Troposphere(b) Free Troposphere(c) MNMB LT(d) MNMB FT(e) FGE LT(f) FGE FT(g) Correlation LT(h) Correlation FTFigure 2.8.2: CO comparisons using aircraft observations from IAGOS (http://www.iagos.org). Monthly time series for COfrom IAGOS aircraft observations, from e-suite (solid) and the o-suite (dashed) for analysis (red), 1-day forecast(orange) and control run (blue) as well as associated scores CAMS-IAGOS (top: MNMB, middle: FGE, bottom:
Pearson Correlation) at Frankfurt in two different atmospheric layers (left: Low Troposphere LT, right: Free
Troposphere FT) during the period October 2022 - March 2023. The histogram bars indicate the number of
profiles (i.e. layer values) based on available observations. The e-suite shows a smaller negative bias than theo-suite for both the lower and free tropospere layers and this is the case for the analysis, control run, as well as
the 1-day forecast, except for the months January to March for control run from the e-suite.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 39 of 106

Copernicus Atmosphere Monitoring Service(a) FGE e-suite analysis FT(b) FGE e-suite control run FT(c) FGE e-suite 1-d forecast FT(d) FGE o-suite analysis FT(e) FGE o-suite control run FT(f) FGE o-suite 1-d forecast FTFigure 2.8.3: CO comparisons using aircraft observations from IAGOS (http://www.iagos.org). Map of CAMS-IAGOS FGEfrom the e-suite on the top row (left: analysis, middle: control run, right: 1-day forecast) and o-suite on thebottom row for CO in the free troposphere (FT) based on profile observations at the different airports visited
during the period October 2022 – March 2023. At most visited airports the absolute differences (FGE) are betterin the free troposphere for the e-suite compared with the o-suite.

(a) MNMB e-suite analysis UT(b) MNMB e-suite control run UT(c) MNMB e-suite 1-day UT(d) MNMB o-suite analysis UT(e) MNMB o-suite control run UT(f) MNMB o-suite 1-day UT

Figure 2.8.4: Global maps of CAMS-IAGOS MNMB (gridded to 5x5 degrees) from the e-suite (top panels, from left to right :analysis, control run and 1-day forecast) and o-suite (bottom panels, from left to right : analysis, control run and1-day forecast) for CO in the Upper Troposphere (UT) based on IAGOS-cruise data (filtered observations withPV values below 2) during the period October 2022 - March 2023. CO is underestimated by both the e-suite andthe o-suite runs. Like in the free troposphere there is a clear improvement of the bias in the upper troposphere
over all regions in the e-suite compared to the o-suite and in particular for the analysis.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 40 of 106

Copernicus Atmosphere Monitoring Service

2.9

Carbon Monoxide: Verification with MOPITTv8 and IASI satellite
observations

Figure 2.9.1: Global maps of monthly mean total column CO for November 2022 comparing satellite observations from IASI
(left column) with MOPITT (right column) validation including the satellite observation (top), the e-suite result(second row), the relative bias of the e-suite with reference to the satellite data (third row) as well as the relative
bias of the current o-suite analysis (bottom) for direct comparison. The second row shows twice the results forthe e-suite, but sampled at the IASI (left) and MOPITT (right) footprint and using the averaging kernels of thetwo instruments. The CO analysis with respect to IASI and MOPITT are so different because MOPITT is used
as anchor for the bias correction in CY47R3, while IASI-C is used as anchor in CY48R1. The plots show thatthe implementation of this anchoring is effective.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 41 of 106

Copernicus Atmosphere Monitoring Service

2.10

Carbon Monoxide: Verification with NDACC remote-sensing observations

Figure 2.10.1: Curtain plots of the CO profile difference of the o-suite analysis compared to FTIR observations from the
NDACC network for the two southern hemispheric stations Wollongong and Lauder. Model profiles aresmoothed with the FTIR averaging kernels. The horizontal black line is the tropopause. The vertical blackline indicates the latest change in the background error covariance. An increased negative bias is observed in
the o-suite for this evaluation period after October 2022.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 42 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.10.2: similar as Figure 2.10.1 but for the o-suite control run.

Table 2.10.1: Overview statistics for tropospheric CO column differences at 13 NDACC FTIR stations (rows). Statistics areshown for the e-suite (left) and o-suite (right) analysis and controls. Shown are the number of matched observations (count), the ratio of the standard deviation in the tropospheric columns for the o/e-suite and the FTIR timeseries (ratio std), the Pearson correlation coefficient, the normalised mean bias (NMB) and standard deviation of
the bias (std NMB). The bias for the e-suite is reduced for almost all sites. Although correlations are similar forthe o-suite and e-suite, the ratio of the standard deviation in the tropospheric columns for the esuite and the FTIR
time series is higher compared to the osuite. For the tropical sites the bias has switched sign and is now positive.
FTIR
THULE
ST.PETERSBURG
BREMENZUGSPITZEJUNGFRAUJOCH
TORONTO.TAO
BOULDER.CO
MAUNA.LOA.HI
PARAMARIBO
LA.REUNION.MAIDO
WOLLONGONGLAUDERARRIVAL.HEIGHTS
mean

e-suiteAN
Pearson mean NMB[1][%]latitude[degrees-north]count

ratio std
[1]

76.52
59.88
53.10
47.42
46.55
43.60
40.04
19.54
5.81
−21.08−34.41−45.04−77.82

39
24
8
70
11
125
155
34
2
22
165
149
61

0.92
1.32
1.29
0.76
1.06
0.85
0.97
0.86
0.59
0.99
0.94
0.99
0.91

0.89***
0.92***
0.73**
0.72***
0.76***
0.90***
0.90***
0.98***
1.00
0.97***
0.81***
0.96***
0.98***

66

0.96

0.87

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

o-suiteAN
Pearson mean NMB[1][%]

std NMB[%]count

ratio std[1]

std NMB[%]−18.54
−4.71
−5.59
−4.22
−11.39−6.58−10.66
6.63
2.90
9.67
0.99
0.48
6.38

3.45
4.75
5.29
5.75
6.47
5.59
6.10
2.62
5.43
3.65
9.66
5.24
5.22

39
24
8
70
11
125
155
34
2
22
165
149
61

0.95
1.05
0.90
0.71
0.73
0.91
1.03
0.86
0.53
0.93
0.98
0.97
1.10

0.86***
0.95***
0.75**
0.75***
0.60*
0.90***
0.92***
0.97***
1.00
0.97***
0.79***
0.98***
0.98***−17.16−2.94−9.36−12.37−16.41−8.28−14.31−11.23−7.11−4.95−11.38
−8.33
6.20

3.52
3.61
5.53
5.91
7.68
5.02
5.44
3.68
5.84
4.55
9.14
4.14
4.62

−2.67

5.32

66

0.90

0.87

−9.05

5.28

Page 43 of 106

Copernicus Atmosphere Monitoring Service

Table 2.10.1 – continued from previous page
FTIR
THULE
ST.PETERSBURG
BREMENZUGSPITZEJUNGFRAUJOCH
TORONTO.TAO
BOULDER.CO
MAUNA.LOA.HI
PARAMARIBO
LA.REUNION.MAIDO
WOLLONGONGLAUDERARRIVAL.HEIGHTS

e-control
Pearson mean NMB[1][%]latitude[degrees-north]count

ratio std
[1]

76.52
59.88
53.10
47.42
46.55
43.60
40.04
19.54
5.81
−21.08−34.41−45.04−77.82

25
24
7
70
11
95
142
34
2
22
156
143
61

1.30
1.59
0.80
1.27
1.08
1.02
1.09
0.83
0.83
2.33
0.94
1.22
1.18

0.83***
0.82***
0.68*
0.87***
0.55*
0.70***
0.64***
0.97***
1.00
0.73***
0.66***
0.87***
0.99***

60

1.19

0.78

mean

control
Pearson mean NMB[1][%]

std NMB[%]count

ratio std[1]

std NMB[%]−24.03−13.92−12.50−7.55−10.76−12.72−7.32−3.99−17.44
−7.09
1.22
2.03
−5.81

1.29
6.54
5.21
2.88
6.00
6.31
8.28
3.50
3.13
9.66
11.98
8.16
3.64

36
24
7
70
11
95
145
35
2
22
158
149
62

0.97
0.85
0.50
0.95
0.86
0.76
0.89
1.01
0.85
1.90
1.13
1.30
1.17

0.99***
0.92***
0.75**
0.82***
0.86***
0.74***
0.79***
0.93***
1.00
0.63***
0.71***
0.91***
0.99***−19.95−12.92−16.42−14.58−10.17−13.88−10.01−8.23−22.86−11.59
−1.10
1.65
−1.51

4.10
6.66
8.45
3.76
3.82
7.76
7.56
3.65
3.32
9.79
10.33
6.83
3.68

−9.22

5.89

62

1.01

0.84

−10.89

6.13

Table 2.10.2: Overview statistics for stratospheric CO column differences at 13 NDACC FTIR stations (rows). Statistics areshown for the e-suite (left) and o-suite (right) analysis and controls. Shown are the number of matched observations (count), the ratio of the standard deviation in the tropospheric columns for the o/e-suite and the FTIR timeseries (ratio std), the Pearson correlation coefficient, the normalised mean bias (NMB) and standard deviation of
the bias (std NMB). The bias for the e-suite is reduced significantly for the southern hemispheric sites and is nowof the order of the measurement uncertainty. The SH correlations are also higher (in particular for the Antarctic
site Arrival Heights). In the northern hemisphere the e-suite and o-suite perform similar.
FTIR
THULE
ST.PETERSBURG
BREMENZUGSPITZEJUNGFRAUJOCH
TORONTO.TAO
BOULDER.CO
MAUNA.LOA.HI
PARAMARIBO
LA.REUNION.MAIDO
WOLLONGONGLAUDERARRIVAL.HEIGHTS

count

ratio std
[1]

76.52
59.88
53.10
47.42
46.55
43.60
40.04
19.54
5.81
−21.08−34.41−45.04−77.82

39
24
8
70
11
125
155
34
2
22
165
149
61

0.90
1.59
0.96
1.12
0.86
0.88
1.05
0.77
1.43
0.79
0.95
0.95
0.80

0.74***
0.34
0.98***
0.93***
0.97***
0.96***
0.86***
0.99***
1.00
0.99***
0.98***
0.92***
0.91***

66

1.00

latitude[degrees-north]count

ratio std
[1]

0.93
1.54
e-control
Pearson mean NMB
[1]
[%]

76.52
59.88
53.10
47.42
46.55
43.60
40.04
19.54
5.81
−21.08−34.41−45.04−77.82

25
24
7
70
11
95
142
34
2
22
156
143
61

1.01
1.51
0.99
1.10
0.83
0.90
0.95
0.77
1.39
0.93
0.89
0.99
0.68

0.50**
0.67***
0.98***
0.95***
0.96***
0.98***
0.95***
0.95***
1.00
1.00***
0.97***
0.90***
0.83***

60

1.00

0.89

mean
FTIR
THULE
ST.PETERSBURG
BREMENZUGSPITZEJUNGFRAUJOCH
TORONTO.TAO
BOULDER.CO
MAUNA.LOA.HI
PARAMARIBO
LA.REUNION.MAIDO
WOLLONGONGLAUDERARRIVAL.HEIGHTS
mean

e-suiteAN
Pearson mean NMB[1][%]latitude[degrees-north]

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

o-suiteAN
Pearson mean NMB[1][%]

std NMB[%]count

ratio std
[1]

11.11
26.56
8.55
10.56
9.42
9.83
12.88
8.24
5.78
6.63
9.14
10.66
12.74

39
24
8
70
11
125
155
34
2
22
165
149
61

0.79
1.57
0.80
1.03
0.77
0.83
1.11
0.85
0.59
0.95
0.98
0.82
0.40

0.78***
0.23
0.98***
0.94***
0.94***
0.97***
0.84***
0.97***
1.00
0.99***
0.94***
0.86***
0.55***

10.93

66

0.88

std NMB[%]count

ratio std
[1]

0.89
0.63
control
Pearson mean NMB[1][%]−20.57−16.49
−8.75
0.14
−2.46
5.55
−0.18
16.31
−1.44
5.99
13.96
26.00
14.95

9.38
18.49
6.81
8.27
8.01
7.60
9.13
8.87
5.20
7.83
9.85
13.17
18.93

36
24
7
70
11
95
145
35
2
22
158
149
62

1.19
1.50
1.11
1.05
0.89
0.93
1.07
0.89
1.67
1.08
0.94
0.95
0.62

0.40**
0.56***
0.97***
0.91***
0.97***
0.98***
0.93***
0.94***
1.00
0.99***
0.97***
0.89***
0.69***−26.09−31.13−16.35−10.73−11.56−1.79−15.85
−0.84
−9.28
−0.04
4.88
11.30
−8.08

11.96
18.12
6.94
8.95
6.87
7.38
9.56
12.49
5.72
6.30
9.30
11.29
25.60

2.54

10.12

62

1.07

0.85

−8.89

10.81

−26.55−15.93
−6.57
3.08
−3.93
7.95
−7.35
18.98
3.88
12.18
4.90
12.10
17.34

−25.02−18.11
−6.82
0.34
−7.56
5.09
−14.10
−7.56
17.54
12.14
5.10
20.05
27.06

std NMB
[%]
11.73
28.20
12.42
9.75
12.11
10.79
12.77
11.45
17.19
9.82
15.38
17.36
40.46
16.11
std NMB[%]

Page 44 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.10.3: Curtain plots of the CO profile difference of the e-suite analysis compared to FTIR observations from the
NDACC network for the two southern hemispheric stations Wollongong and Lauder. Model profiles aresmoothed with the FTIR averaging kernels. Period: 1 October 2022 - 28 February 2023. The black line is
the tropopause. A positive bias in the stratosphere is observed, but is reduced compared to the o-suite.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 45 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.10.4: Similar as Figure 2.10.3 but for the e-suite control run. Compared to the o-control, the e-control shows anincreased positive bias in the stratosphere.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 46 of 106

Copernicus Atmosphere Monitoring Service

2.11

Nitrogen Dioxide: Verification with surface observations in Europe, China and North America

Figure 2.11.1: Comparison with surface NO2 observations in Europe obtained from EEA-Airbase. Spatial distribution of theNO2 normalised bias (MNMB in %, left) and correlation coefficient (right) for e-suite (1st row), o-suite (2ndrow) and its difference (3rd row) during the period from 1 October 2022 to 28 February 2023. The e-suite
performs better than o-suite in terms of bias for a majority of the stations. The correlations are similar fore-suite and o-suite.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 47 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.11.2: Comparison with surface NO2 observations in Europe obtained from EEA-Airbase. Spatial distribution of theNO2 normalised bias (MNMB in %, left) and correlation coefficient (right) for e-suite control run (1st row),o-suite control run (2nd row) and its difference (3rd row) during the period from 1 October 2022 to 28 February
2023.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 48 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.11.3: CAMS NO2 compared to surface observations in China from the China National Environmental Monitoring
Center. Spatial distribution of the NO2 normalised bias (MNMB in %, left) and correlation coefficient (right)
for e-suite (1st row), o-suite (2nd row) and its difference (3rd row) during the period from 1 October 2022 to 28
February 2023 over China. The e-suite significantly reduce the o-suite positive bias in surface NO2 over China
Megacities. The correlation of the e-suite is slightly reduced compared to the o-suite. The mean correlation
coefficient averaged over all stations is 0.49 for e-suite and 0.55 for o-suite.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 49 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.11.4: CAMS NO2 compared to surface observations in China from the China National Environmental Monitoring
Center. Spatial distribution of the NO2 normalised bias (MNMB in %, left) and correlation coefficient (right)
for e-suite control run (1st row), o-suite control run (2nd row) and its difference (3rd row) during the period
from 1 October 2022 to 28 February 2023 over China. The e-suite control run significantly reduce the o-suite
control run positive offset in surface NO2 over China Megacities. The correlation of the e-suite control run isslightly reduced compared to the o-suite control run. The mean correlation coefficient averaged over all stations
is 0.49 for e-suite and 0.55 for o-suite (0.47 for e-suite control and 0.53 for the o-suite control run).

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 50 of 106

Copernicus Atmosphere Monitoring Service

e-suite MNMB

e-suite R

o-suite MNMB

o-suite R

e-suite control run MNMB

e-suite control run R

o-suite control run MNMB

o-suite control run R

Figure 2.11.5: MNMB (left column) and R (right column) evalaution of surface NO2 against the AirNow network for theperiod December 2022 to February 2023.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 51 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.11.6: Evalaution of surface NO2 against the AirNow network for the period of December 2022 to February 2023.
Timeseries of global NO2 mean in daily and monthly temporal resolution (top), scatterplot of models against
observatios for daily (small symbols) and monthly (big symbols) data (middle) as well as MNMB and R averaged over subregions in North America.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 52 of 106

Copernicus Atmosphere Monitoring Service

2.12

Nitrogen Dioxide: Verification with TROPOMI satellite observations

Figure 2.12.1: Monthly mean global maps of tropospheric NO2 column densities for TROPOMI satellite observations (top),
the e-suite results (second row), the difference between e-suite and satellite (3rd row) as well as the difference
between the current o-suite and satellite (bottom) for October 2022 (left column) and November 2022 (rightcolumn). Unit: 1e15 molecules cm−2 . The e-suite in general shows a reduction of the positive bias over theemission hotspots.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 53 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.12.2: Monthly mean maps of relative bias values for tropospheric NO2 comparing the e-suite (left) and the o-suite(right) with the IUP-UB TROPOMI product for the month of October 2022. Regions with background values
below 5x1014 molec. cm−2 are not included in the analysis.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 54 of 106

Copernicus Atmosphere Monitoring Service

2.13

Sulfur Dioxide: Verification with Airbase and MEP surface observations

Figure 2.13.1: Comparison with surface SO2 observations in Europe obtained from EEA-Airbase. Spatial distribution of the
SO2 normalised bias (MNMB in %, left) and correlation coefficient (right) for e-suite (1st row), o-suite (2ndrow) and difference (3rd row) during the period from 1 October 2022 to 28 February 2023. The e-suite performs
better than o-suite in terms of biases over most stations. The correlations are almost equal.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 55 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.13.2: Comparison with surface SO2 observations in Europe obtained from EEA-Airbase. Spatial distribution of the
SO2 normalised bias (MNMB in %, left) and correlation coefficient (right) for e-suite control run (1st row),o-suite control run (2nd row) and difference (3rd row) during the period from 1 October 2022 to 28 February
2023. The e-suite control run performs better than the o-suite control run in terms of biases over most stations.
The correlations are almost equal.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 56 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.13.3: CAMS SO2 compared to surface observations in China from the China National Environmental Monitoring
Center. Spatial distribution of the SO2 normalised bias (MNMB in %, left) and correlation coefficient (right)
for e-suite (1st row), o-suite (2nd row) and difference (3rd row) during the period from 1 October 2022 to
28 February 2023 over China. The e-suite improves the positive bias observed for the o-suite over Chinese
megacities. The correlations are similar or slightly lower.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 57 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.13.4: CAMS SO2 compared to surface observations in China from the China National Environmental Monitoring
Center. Spatial distribution of the SO2 normalised bias (MNMB in %, left) and correlation coefficient (right)
for e-suite control run (1st row), o-suite control run (2nd row) and the difference (3rd row) during the period
from 1 October 2022 to 28 February 2023 over China. The e-suite control run improves the positive bias
observed for the o-suite control run over Chinese megacities. The correlations are similar or slightly lower.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 58 of 106

Copernicus Atmosphere Monitoring Service

2.14

Formaldehyde: Verification with TROPOMI observations

Figure 2.14.1: Monthly mean global maps of IUP-UB tropospheric HCHO column retrievals from Sentinel-5P TROPOMI
satellite observations (top), the e-suite results (second row), the difference between e-suite and satellite (3rdrow) as well as the difference between the current o-suite and satellite (bottom) for October 2022 (left column)
and November 2022 (right column). The e-suite overestimates the tropospheric HCHO signals over SouthAmerica, and Northern Australia. The allocation of the HCHO signals over Northern and Southern Africa
in the two months is not quite correctly reproduced by the model visible in the overestimation in the Northand underestimation south of the equator. In comparison to the o-suite, the overestimation in the e-suite above
Southern America and Northern Australia is stronger. Above Africa, the Northern HCHO values show a stronger
positive bias, while the negative bias for Southern Africa is slightly less pronounced in the e-suite. Overall, theperformance is similar, but slightly worse for the e-suite.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 59 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.14.2: Monthly mean maps of relative bias values for tropospheric HCHO comparing the e-suite (left) and the o-suite(right) with the IUP-UB TROPOMI product for the month of October 2022. Regions with mean backgroundvalues below 2x1015 molec/cm2 are excluded from the analysis (white areas).

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 60 of 106

Copernicus Atmosphere Monitoring Service

2.15

Stratospheric Ozone: Verification with satellite observations

Figure 2.15.1: CAMS analyses compared to a Multi-Instrument-Mean (MIM), consisting of observed ozone profiles from
ACE-FTS v4.1, Aura-MLS v4.2, OMPS-LP v2.5, SAGE-III/ISS v5.2 and ozonesondes. Top row: normalisedmean difference between the MIM ozone observed profiles and (i) the o-suite analyses (solid red line), (ii) theo-suite 5th day forecast (solid green line), (iii) the o-suite control run (solid blue line), (iv) the e-suite analyses(dashed red line), (v) the e-suite 5th day forecast (dashed green line) and (vi) the e-suite control run (dashedblue line). The period is October 2022, i.e. during Antarctic ozone hole conditions. Five latitude bands are
considered, from left to right: 90°S-60°S, 60°S-30°S, 30°S-30°N, 30°N-60°N and 60°N-90°N. Middle row:
The corresponding standard deviation of the differences showed in the first row. Bottom row: The correlation
coefficient between observed profiles and CAMS.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 61 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.15.2: As previous figure, but for the period Oct 2022-Feb 2023.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 62 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.15.3: Monthly scores of the o-suite (red line) and e-suite (green line) 5th day forecast calculated above Antarctica(left column), the middle stratosphere (centre column) and in the tropical tropause layer (right column) for theperiod Oct 2022-Feb 2023 calculated using satellite observations from ACE-FTS v4.1, Aura-MLS v4.2, OMPSLP v2.5 and SAGE-III/ISS v5.2.

Figure 2.15.4: Comparison of the e-suite 1st day forecast of ozone against profiles from limb instruments (see legend for colorcode) between Oct 2022-Jan 2023 for the same five latitude bands as in figure 2.15.1. Statistical values shown
are, from top to bottom: mean bias normalized by the mean of the model profile (NMB), the standard deviationof the mean bias (STD) and the correlation between models and observations (Correl).

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 63 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.15.5: Comparison of CCL4 from the e-suite with ACE-FTS observations. Shown are the e-suite 1st day forecast
against profiles from the satellite instrument between Oct 2022 - Feb 2023. Five latitude bands are considered,from left to right: 90°S-60°S, 60°S-30°S, 30°S-30°N, 30°N-60°N and 60°N-90°N. Statistical values shown are,from top to bottom: mean bias normalized by the mean of the model profile (NMB), the standard deviation of
the mean bias (STD) and the correlation between models and observations (Correl).

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 64 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.15.6: Comparison of CFC-11 from the e-suite with ACE-FTS observations. Shown are the e-suite 1st day forecast
against profiles from the satellite instrument between Oct 2022 - Feb 2023. Five latitude bands are considered,from left to right: 90°S-60°S, 60°S-30°S, 30°S-30°N, 30°N-60°N and 60°N-90°N. Statistical values shown are,from top to bottom: mean bias normalized by the mean of the model profile (NMB), the standard deviation of
the mean bias (STD) and the correlation between models and observations (Correl).

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 65 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.15.7: Comparison of CFC-12 from the e-suite with ACE-FTS observations. Shown are the e-suite 1st day forecast
against profiles from the satellite instrument between Oct 2022 - Feb 2023. Five latitude bands are considered,from left to right: 90°S-60°S, 60°S-30°S, 30°S-30°N, 30°N-60°N and 60°N-90°N. Statistical values shown are,from top to bottom: mean bias normalized by the mean of the model profile (NMB), the standard deviation of
the mean bias (STD) and the correlation between models and observations (Correl).

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 66 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.15.8: Comparison of CH4 from the e-suite with ACE-FTS observations. Shown are the e-suite 1st day forecast against
profiles from the satellite instrument between Oct 2022 - Feb 2023. Five latitude bands are considered, fromleft to right: 90°S-60°S, 60°S-30°S, 30°S-30°N, 30°N-60°N and 60°N-90°N. Statistical values shown are, from
top to bottom: mean bias normalized by the mean of the model profile (NMB), the standard deviation of themean bias (STD) and the correlation between models and observations (Correl).

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 67 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.15.9: Comparison of ClO from the e-suite with MLS (red) and ACE-FTS (blue) profile observations. Shown are
the e-suite 1st day forecast against profiles from the satellite instrument between Oct 2022 - Feb 2023. Fivelatitude bands are considered, from left to right: 90°S-60°S, 60°S-30°S, 30°S-30°N, 30°N-60°N and 60°N90°N. Statistical values shown are, from top to bottom: mean bias normalized by the mean of the model profile(NMB), the standard deviation of the mean bias (STD) and the correlation between models and observations(Correl). The number of profiles used in the comparison is indicated in the plot.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 68 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.15.10: Comparison of ClONO2 from the e-suite with ACE-FTS observations. Shown are the e-suite 1st day forecast
against profiles from the satellite instrument between Oct 2022 - Feb 2023. Five latitude bands are considered,from left to right: 90°S-60°S, 60°S-30°S, 30°S-30°N, 30°N-60°N and 60°N-90°N. Statistical values shown are,from top to bottom: mean bias normalized by the mean of the model profile (NMB), the standard deviation of
the mean bias (STD) and the correlation between models and observations (Correl).

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 69 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.15.11: Comparison of H2 O from the e-suite with MLS (red) and ACE-FTS (blue) profile observations. Shown are
the e-suite 1st day forecast against profiles from the satellite instrument between Oct 2022 - Feb 2023. Fivelatitude bands are considered, from left to right: 90°S-60°S, 60°S-30°S, 30°S-30°N, 30°N-60°N and 60°N90°N. Statistical values shown are, from top to bottom: mean bias normalized by the mean of the model profile(NMB), the standard deviation of the mean bias (STD) and the correlation between models and observations(Correl). The number of profiles used in the comparison is indicated in the plot.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 70 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.15.12: Comparison of HCFC-22 from the e-suite with ACE-FTS observations. Shown are the e-suite 1st day forecast
against profiles from the satellite instrument between Oct 2022 - Feb 2023. Five latitude bands are considered,from left to right: 90°S-60°S, 60°S-30°S, 30°S-30°N, 30°N-60°N and 60°N-90°N. Statistical values shown are,from top to bottom: mean bias normalized by the mean of the model profile (NMB), the standard deviation of
the mean bias (STD) and the correlation between models and observations (Correl).

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 71 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.15.13: Comparison of HCl from the e-suite with MLS (red) and ACE-FTS (blue) profile observations. Shown are
the e-suite 1st day forecast against profiles from the satellite instrument between Oct 2022 - Feb 2023. Fivelatitude bands are considered, from left to right: 90°S-60°S, 60°S-30°S, 30°S-30°N, 30°N-60°N and 60°N90°N. Statistical values shown are, from top to bottom: mean bias normalized by the mean of the model profile(NMB), the standard deviation of the mean bias (STD) and the correlation between models and observations(Correl). The number of profiles used in the comparison is indicated in the plot.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 72 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.15.14: Comparison of HNO3 from the e-suite with MLS (red) and ACE-FTS (blue) profile observations. Shown are
the e-suite 1st day forecast against profiles from the satellite instrument between Oct 2022 - Feb 2023. Fivelatitude bands are considered, from left to right: 90°S-60°S, 60°S-30°S, 30°S-30°N, 30°N-60°N and 60°N90°N. Statistical values shown are, from top to bottom: mean bias normalized by the mean of the model profile(NMB), the standard deviation of the mean bias (STD) and the correlation between models and observations(Correl). The number of profiles used in the comparison is indicated in the plot.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 73 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.15.15: Comparison of N2 O from the e-suite with MLS (red) and ACE-FTS (blue) profile observations. Shown are
the e-suite 1st day forecast against profiles from the satellite instrument between Oct 2022 - Feb 2023. Fivelatitude bands are considered, from left to right: 90°S-60°S, 60°S-30°S, 30°S-30°N, 30°N-60°N and 60°N90°N. Statistical values shown are, from top to bottom: mean bias normalized by the mean of the model profile(NMB), the standard deviation of the mean bias (STD) and the correlation between models and observations(Correl). The number of profiles used in the comparison is indicated in the plot.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 74 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.15.16: Comparison of N2 O5 from the e-suite with ACE-FTS observations. Shown are the e-suite 1st day forecast
against profiles from the satellite instrument between Oct 2022 - Feb 2023. Five latitude bands are considered,from left to right: 90°S-60°S, 60°S-30°S, 30°S-30°N, 30°N-60°N and 60°N-90°N. Statistical values shown are,from top to bottom: mean bias normalized by the mean of the model profile (NMB), the standard deviation of
the mean bias (STD) and the correlation between models and observations (Correl).

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 75 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.15.17: Comparison of NO2 from the e-suite with ACE-FTS observations. Shown are the e-suite 1st day forecast
against profiles from the satellite instrument between Oct 2022 - Feb 2023. Five latitude bands are considered,from left to right: 90°S-60°S, 60°S-30°S, 30°S-30°N, 30°N-60°N and 60°N-90°N. Statistical values shown are,from top to bottom: mean bias normalized by the mean of the model profile (NMB), the standard deviation of
the mean bias (STD) and the correlation between models and observations (Correl).

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 76 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.15.18: Comparison of NOx (NO + NO2 ) from the e-suite with ACE-FTS observations. Shown are the e-suite 1st
day forecast against profiles from the satellite instrument between Oct 2022 - Feb 2023. Five latitude bands
are considered, from left to right: 90°S-60°S, 60°S-30°S, 30°S-30°N, 30°N-60°N and 60°N-90°N. Statisticalvalues shown are, from top to bottom: mean bias normalized by the mean of the model profile (NMB), thestandard deviation of the mean bias (STD) and the correlation between models and observations (Correl).

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 77 of 106

Copernicus Atmosphere Monitoring Service

2.16

Stratospheric Ozone: Verification with NDACC remote-sensing
observations

Figure 2.16.1: Mean ozone profile differences for the o-suite (red, standard deviation in orange), the e-suite (blue, standard
deviation in light blue), e-suite control run (green, standard deviation in yellow) and control run (blue, standard deviation in purple) compared to stratospheric ozone lidar observations from the NDACC network. Above
10hPa the e-suite mean profile deviates from the o-suite, showing a negative bias compared to the lidar observations, increasing with altitude. Between 50hPa and 10hPa the e-suite overestimates the ozone concentration.
The negative bias feature observed around 20 hPa in the o-suite is no longer present in the e-suite. The dispersion (std) in the differences is slightly, but systematically, improved for the e-suite.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 78 of 106

Copernicus Atmosphere Monitoring Service

2.17

Stratospheric NO2 : Verification with TROPOMI

Figure 2.17.1: Monthly mean NO2 stratospheric column amounts retrieved using TROPOMI observations (top) compared tothe e-suite results (second row). The third row shows the differences between the e-suite and TROPOMI (thirdrow). Results are shown for October 2022 (left column), November 2022 (second column), and December
2022 (right column). The STREAM-B algorithm was used to estimate the stratospheric contribution to the total
retrieved column. The o-suite is not shown: because of the missing stratospheric chemistry the simulated NO2
in the stratosphere was not meaningful. The zonal distribution as well as the increase of stratospheric NO2 over
the Antarctic from October to December agree very well with the TROPOMI observations.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 79 of 106

Copernicus Atmosphere Monitoring Service

2.18

UV index and UV radiation

Figure 2.18.1: Global average of daily maximum all sky UV difference between o-suite and e-suite during 2022-10-01 and2023-03-31. The e-suite shows higher daily maximum UV index compared to the o-suite in the midlatitudes
and tropics, except in regional areas in Africa, Asia and South America, where the change was slightly negative.

Figure 2.18.2: Left: zonal mean of global daily maximum all sky UVI for e-suite (red) and o-suite (blue). Right: zonal meanfor daily max UVI relative difference between e-suite and o-suite. The time range is from 2022-10-01 to 202203-31.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 80 of 106

Copernicus Atmosphere Monitoring Service

Figure 2.18.3: Hourly UV index values from the CAMS e-suite and o-suite evaluated against ground based UV measurementsfrom 38 stations located in Europe, Israel, Thailand, Australia, New Zealand, and the Antarctic. Scatterplots of
MNMB (top), R (centre) and FGE (bottom) between measurements, and o-suite (Cy47R3, x-axis) and measurements, and e-suite (Cy48R1, y-axis). Each dot represents a single measurement station, either in the northernlatitudes (red) or southern latitudes (blue). The time range is from 2022-10-01 to 2022-03-31. Despite a subtle
increase in UV levels outside northern high latitudes, there was no significant change in the statistical measures
between the CAMS e-suite and o-suite. The evaluation indicates that the good CAMS UV performance continues in the e-suite.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 81 of 106

Copernicus Atmosphere Monitoring Service

2.19

Event: Wildfires over Canada

Figure 2.19.1: Selection of IAGOS aircraft CO profiles from May 2023 at Montreal and Calgary airports sampled by aircraft operated by Air Canada, showing the transport of plumes from forest wildfires occurring in the Canadianprovince of Alberta. IAGOS observations are shown in black, and colored curves show the CAMS modeled
profiles from the e-suite (top panels) and the o-suite (bottom panels) analysis (red), 1-day forecast (orange),
4-day forecast yellow and e-suite and o-suite control run (blue). Units (ppbv). For most profiles studied during
this event, the e-suite is performing better than the o-suite with values of the CO mixing ratios higher than those
provided by the o-suite.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 82 of 106

Copernicus Atmosphere Monitoring Service

3

Upgrade verification results: aerosol

3.1

Aerosol: Global aerosol speciation
AOD e-suite

AOD o-suite

Dust AOD e-suite

Dust AOD o-suite

Sea Salt AOD e-suite

Sea Salt AOD o-suite

Organic Matter AOD e-suite

Organic Matter AOD o-suite

Figure 3.1.1: January 2023 average fields of AOD (first row), dust optical depth (second row), sea salt optical depth (third row)and organic matter optical depth (fourth row) for e-suite (left) and o-suite (right). The organic matter opticaldepth for this comparison includes also Secondary Organic Aerosol (SOA) for both experiments. Starting from
Cy48R1 the SOA optical depth is provided distinctively, while before this update SOA optical depth was part ofthe organic matter optical depth. The AOD and organic matter optical depth decreases while dust and sea salt
optical depth increases in e-suite compared to o-suite.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 83 of 106

Copernicus Atmosphere Monitoring Service

Black Carbon AOD e-suiteBlack Carbon AOD o-suite

Sulphate AOD e-suite

Sulphate AOD o-suite

Nitrates AOD e-suite

Nitrates AOD o-suite

Ammonium AOD e-suite

Ammonium AOD o-suite

Figure 3.1.2: January 2023 average fields of black carbon optical depth (first row), sulphate optical depth (second row) nitrates
optical depth (third row) and ammonium optical depth (fourth row) for e-suite (left) and o-suite (right). The black
carbon and sulphate optical depth mostly decreases in e-suite compared to o-suite.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 84 of 106

Copernicus Atmosphere Monitoring Service

AOD e-suite control run

AOD o-suite control run

Dust AOD e-suite control run

Dust AOD o-suite control run

Sea Salt AOD e-suite control run

Sea Salt AOD o-suite control run

Organic Matter AOD e-suite control run

Organic Matter AOD o-suite control run

Figure 3.1.3: January 2023 average fields of AOD (first row), dust optical depth (second row), sea salt optical depth (thirdrow) and organic matter optical depth (fourth row) for e-suite control run (left) and o-suite control run (right).
The organic matter optical depth for this comparison includes also Secondary Organic Aerosol (SOA) for bothexperiments. Starting from Cy48R1 the SOA optical depth is provided distinctively, while before this update
SOA optical depth was part of the organic matter optical depth. The AOD and organic matter optical depth
decreases while dust and sea salt optical depth increases in e-suite control run compared to o-suite control run.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 85 of 106

Copernicus Atmosphere Monitoring Service

Black Carbon AOD e-suite control run

Black Carbon AOD o-suite control run

Sulphate AOD e-suite control run

Sulphate AOD o-suite control run

Nitrates AOD e-suite control run

Nitrates AOD o-suite control run

Ammonium AOD e-suite control run

Ammonium AOD o-suite control run

Figure 3.1.4: January 2023 average fields of black carbon optical depth (first row), sulphate optical depth (second row), nitrates
optical depth (third row) and ammonium optical depth (fourth row) for e-suite control run (left) and o-suite control
run (right). The black carbon and sulphate optical depth mostly decreases in e-suite control run compared to osuite control run.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 86 of 106

Copernicus Atmosphere Monitoring Service

3.2

Aerosol: Verification of global AOD

Figure 3.2.1: MNMB (left) and Correlation (right) statistics for daily data comparison for the period of December 2022 toFebruary 2023 of e-suite (first column), e-suite_cntrl (second column), o-suite (third column) and o-suite_cntrl(fourth column).

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 87 of 106

Copernicus Atmosphere Monitoring Service

e-suite MNMB

e-suite R

o-suite MNMB

o-suite R

e-suite control run MNMB

e-suite control run R

o-suite control run MNMB

o-suite control run R

Figure 3.2.2: MNMB (left column) and R (right column) evaluation of Aerosol Optical Depth (AOD) at 550nm against Aeronet
v3 level1.5 daily data for the period of December 2022 through February 2023.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 88 of 106

Copernicus Atmosphere Monitoring Service

Figure 3.2.3: Evaluation of Aerosol Optical Depth (AOD) at 550nm against Aeronet v3 level1.5 daily data for the period
December 2022 to February 2023. Timeseries of global AOD mean in daily and monthly temporal resolution(top), scatterplot of models against observations for daily (small symbols) and monthly (large symbols) data
(middle) as well as MNMB and R (bottom) averaged over subregions. AOD in e-suite is generally lower in all
subregions compare to o-suite.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 89 of 106

Copernicus Atmosphere Monitoring Service

e-suite MNMB

e-suite R

o-suite MNMB

o-suite R

e-suite control run MNMB

e-suite control run R

o-suite control run MNMB

o-suite control run R

Figure 3.2.4: MNMB (left column) and R (right column) evaluation of Ångström Exponent (AE) between 440nm and 870nm
against Aeronet v3 level1.5 daily data for the period of December 2022 to February 2023.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 90 of 106

Copernicus Atmosphere Monitoring Service

Figure 3.2.5: Evaluation of Ångström exponent (AE) between 440nm and 870nm against the Aeronet v3 level1.5 daily data
for the period of December 2022 to February 2023. Timeseries of global AE mean in daily and monthly temporal
resolution (top), scatterplot of models against observations for daily (small symbols) and monthly (big symbols)
data (middle) as well as MNMB and R (bottom) averaged over subregions. AE in e-suite is generally lower in all
regions compared to o-suite. Note that e-suite has considerable worse performance in desert areas compared too-suite.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 91 of 106

Copernicus Atmosphere Monitoring Service

3.3

Aerosol: Verification of surface PM
PM10 e-suite

PM10 o-suite

PM10 e-suite control run

PM10 o-suite control run

PM2.5 e-suite

PM2.5 o-suite

PM2.5 e-suite control run

PM2.5 o-suite control run

Figure 3.3.1: January 2023 average fields of PM10 (first and second row) and PM2.5 (third and fourth row)for e-suite (left)
and o-suite (right) in µg m−3 . White pixels indicate areas with PM10 and PM2.5 above the maximum value ofthe legend (80µg m−3 and 45µg m−3 respectively). Note the change from o-suite to e-suite for PM10 and PM2.5
in northern China, northern Africa and Australia in particular the contribution from dust (new dust emissions),
see lowest row in Figure 3.1.1.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 92 of 106

Copernicus Atmosphere Monitoring Service

Figure 3.3.2: Modified Normal Mean Bias of PM10 (left) and PM2.5 (right) for o-suite, o-suite control run, e-suite and e-suite
control run at AirNow (North America), EEA-NRT-rural (Europe) and MEP (East Asia) monitoring stations.

Figure 3.3.3: Modified Normal Mean Bias of PM10 (upper row) and PM2.5 (lower row) for o-suite (left) and e-suite (right) at
MEP (China) monitoring stations.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 93 of 106

Copernicus Atmosphere Monitoring Service

3.4

Dust and aerosol verification over North Africa, the Middle Eastand Europe

Figure 3.4.1: Seasonal o-suite AOD averaged over the first 24h forecast (top left) along with the corresponding e-suite (topright). Dust Optical Depth (DOD) from o-suite (bottom left) and e-suite (bottom right). DOD is considerably
higher in e-suite compare to o-suite.

o-suite Forecasts 0-24 hours
COR MB
Mediterranean 0.65 -0.02
NAfrica
0.48 -0.11
Europe0.65 -0.02
MiddleEast
0.51 -0.04
e-suite Forecasts 0-24 hours
COR MB
Mediterranean 0.68 -0.02
NAfrica
0.75 -0.03
Europe0.65 -0.02
MiddleEast
0.50 -0.01

RMSE
0.05
0.24
0.03
0.07

MNMB
-1.12
-0.77
-1.84
-0.79

RMSE
0.05
0.16
0.03
0.08

MNMB
-1.04
-0.34
-1.50
-0.46

Table 3.4.1: Skill scores (MB, MNMB, RMSE, and r) of 0-24 hours forecasts (on a 3-hourly basis) for CAMS o-suite ande-suite for DJF-2023. DOD (AODcoarse SDA product) from AERONET is the reference. The e-suite performs
better that the o-suite for DOD over North Africa.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 94 of 106

Copernicus Atmosphere Monitoring Service

Figure 3.4.2: Skill scores (correlation coefficient, MB, MNMB and RMSE) for 24-hour forecasts of e-suite (first column),
e-suite control run (second column), for the DJF-2023. AOD-coarse from AERONET SDA is the reference.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 95 of 106

Copernicus Atmosphere Monitoring Service

References
Albert, M. F., Anguelova, M. D., Manders, A. M., Schaap, M., & De Leeuw, G. (2016). Parameterization of oceanic whitecap fraction based on satellite observations. Atmospheric Chemistry andPhysics, 16(21), 13725–13751. https://doi.org/10.5194/acp-16-13725-2016
Benedetti, A., Morcrette, J.-J., Boucher, O., Dethof, A., Engelen, R., Fisher, M., Flentje, H., Huneeus,N., Jones, L., Kaiser, J., et al. (2009). Aerosol analysis and forecast in the european centre
for medium-range weather forecasts integrated forecast system: 2. data assimilation. Journalof Geophysical Research: Atmospheres, 114(D13). https://doi.org/10.1029/2008JD011235
Cariolle, D., & Teyssedre, H. (2007). A revised linear ozone photochemistry parameterization for use intransport and general circulation models: Multi-annual simulations. Atmospheric chemistry andphysics, 7(9), 2183–2196. https://doi.org/10.5194/acp-7-2183-2007
Carn, S., Fioletov, V., McLinden, C., Li, C., & Krotkov, N. (2017). A decade of global volcanic so2 emissions measured from space. Scientific reports, 7(1), 1–12. https://doi.org/10.1038/srep44095
Chabrillat, S., Minganti, D., Huijnen, V., & Remy, S. (2023). Report documenting the changes in psc
parameterization and improvements of ozone hole forecasts. https://doi.org/10.24380/2NXVH84F
Dee, D. P., & Uppala, S. (2009). Variational bias correction of satellite radiance data in the era-interim
reanalysis. Quarterly Journal of the Royal Meteorological Society: A journal of the atmosphericsciences, applied meteorology and physical oceanography, 135(644), 1830–1841. https://doi.
org/10.1002/qj.493
Eskes, H., Huijnen, V., Arola, A., Benedictow, A., Blechschmidt, A.-M., Botek, E., Boucher, O., Bouarar,
I., Chabrillat, S., Cuevas, E., et al. (2015). Validation of reactive gases and aerosols in the macc
global analysis and forecast system. Geoscientific Model Development, 8(11), 3523–3543. https://doi.org/10.5194/gmd-8-3523-2015
Eskes, H., Basart, S., Benedictow, A., Bennouna, Y., Blechschmidt, A.-M., Errera, Q., Hansen, K., Kapsomenakis, J., Langerock, B., Richter, A., Sudarchikova, N., Schulz, M., & Zerefos, C. (2021).
Upgrade verification note for the cams real-time global atmospheric composition service: Evaluation of the e-suite for the cams 47r3 upgrade of 12 october 2021. https://doi.org/10.24380/
HFVP-FQ98
Eskes, H., Basart, S., Benedictow, A., Bennouna, Y., Cuevas, E., Errera, Q., Kapsomenakis, J., Langerock, B., Pitkänen, M., Ramonet, M., Richter, A., Schönhardt, A., Schulz, M., Warneke, T.,& Zerefos, C. (2023). Observation characterisation and validation methods document. https ://doi.org/10.24380/P5DE-SSQC
Flemming, J., Huijnen, V., Arteta, J., Bechtold, P., Beljaars, A., Blechschmidt, A.-M., Diamantakis, M.,
Engelen, R., Gaudel, A., Inness, A., et al. (2015). Tropospheric chemistry in the integrated forecasting system of ecmwf. Geoscientific model development, 8(4), 975–1003. https://doi.org/10.
5194/gmd-8-975-2015
Flemming, J., Benedetti, A., Inness, A., Engelen, R. J., Jones, L., Huijnen, V., Remy, S., Parrington, M.,
Suttie, M., Bozzo, A., et al. (2017). The cams interim reanalysis of carbon monoxide, ozone andCAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 96 of 106

Copernicus Atmosphere Monitoring Service

aerosol for 2003–2015. Atmospheric Chemistry and Physics, 17(3), 1945–1983. https://doi.org/
10.5194/acp-17-1945-2017
Garrigues, S., Ades, M., Remy, S., Flemming, J., Kipling, Z., laszlo, I., Parrington, M., Inness, A., Ribas,
R., Jones, L., Engelen, R., & Peuch, V.-H. (2023). Impact of assimilating NOAA VIIRS aerosol
optical depth (AOD) observations on global AOD analysis from the copernicus atmosphere monitoring service (CAMS). https://doi.org/10.5194/egusphere-2023-398
Granier, C., Bessagnet, B., Bond, T., D’Angiola, A., Denier van der Gon, H., Frost, G. J., Heil, A.,
Kaiser, J. W., Kinne, S., Klimont, Z., Kloster, S., Lamarque, J.-F., Liousse, C., Masui, T., Meleux,F., Mieville, A., Ohara, T., Raut, J.-C., Riahi, K., . . . van Vuuren, D. P. (2011). Evolution ofanthropogenic and biomass burning emissions of air pollutants at global and regional scales
during the 1980–2010 period. Climatic Change, 109(1), 163. https://doi.org/10.1007/s10584011-0154-1
Huijnen, V., Williams, J., van Weele, M., van Noije, T., Krol, M., Dentener, F., Segers, A., Houweling, S.,
Peters, W., de Laat, J., et al. (2010). The global chemistry transport model tm5: Description andevaluation of the tropospheric chemistry version 3.0. Geoscientific Model Development, 3(2),445–473. https://doi.org/10.5194/gmd-3-445-2010
Inness, A., Aben, I., Ades, M., Borsdorff, T., Flemming, J., Jones, L., Landgraf, J., Langerock, B., Nedelec, P., Parrington, M., & Ribas, R. (2022). Assimilation of s5p/tropomi carbon monoxide datawith the global cams near-real-time system. Atmospheric Chemistry and Physics, 22(21), 14355–
14376. https://doi.org/10.5194/acp-22-14355-2022
Inness, A., Blechschmidt, A.-M., Bouarar, I., Chabrillat, S., Crepulja, M., Engelen, R., Eskes, H., Flemming, J., Gaudel, A., Hendrick, F., et al. (2015). Data assimilation of satellite-retrieved ozone,
carbon monoxide and nitrogen dioxide with ecmwf’s composition-ifs. Atmospheric chemistry
and physics, 15(9), 5275–5303. https://doi.org/10.5194/acp-15-5275-2015
Kaiser, J., Heil, A., Andreae, M., Benedetti, A., Chubarova, N., Jones, L., Morcrette, J.-J., Razinger,
M., Schultz, M., Suttie, M., et al. (2012). Biomass burning emissions estimated with a global
fire assimilation system based on observed fire radiative power. Biogeosciences, 9(1), 527–554.
https://doi.org/10.5194/bg-9-527-2012
Luo, G., Yu, F., & Schwab, J. (2019). Revised treatment of wet scavenging processes dramatically improves geos-chem 12.0. 0 simulations of surface nitric acid, nitrate, and ammonium over theunited states. Geoscientific Model Development, 12(8), 3439–3447. https://doi.org/10.5194/
gmd-12-3439-2019
Morcrette, J.-J., Boucher, O., Jones, L., Salmond, D., Bechtold, P., Beljaars, A., Benedetti, A., Bonet,
A., Kaiser, J., Razinger, M., et al. (2009). Aerosol analysis and forecast in the european centre
for medium-range weather forecasts integrated forecast system: Forward modeling. Journal ofGeophysical Research: Atmospheres, 114(D6). https://doi.org/10.21957/jxqm3uq6j
Rémy, S., Kipling, Z., Huijnen, V., Flemming, J., Nabat, P., Michou, M., Ades, M., Engelen, R., & Peuch,
V.-H. (2022). Description and evaluation of the tropospheric aerosol scheme in the integrated
forecasting system (ifs-aer, cycle 47r1) of ecmwf. Geoscientific Model Development, 15(12),4881–4912. https://doi.org/10.5194/gmd-15-4881-2022
Rémy, S., Kipling, Z., Flemming, J., Boucher, O., Nabat, P., Michou, M., Bozzo, A., Ades, M., Huijnen,
V., Benedetti, A., et al. (2019). Description and evaluation of the tropospheric aerosol scheme inthe european centre for medium-range weather forecasts (ecmwf) integrated forecasting system(ifs-aer, cycle 45r1). Geoscientific Model Development, 12(11), 4627–4659. https://doi.org/10.
5194/gmd-12-4627-2019
Schoenhardt, A., Arola, A., Benedictow, A., Bennouna, Y., Bouarar, I., Cuevas, E., Errera, Q., Eskes, H.,
Griesfeller, J., Basart, S., Kapsomenakis, J., Langerock, B., Mortier, A., Pison, I., Pitkänen, M.,
Ramonet, M., Richter, A., Schulz, M., Tarniewicz, J., . . . Zerefos, C. (2023). Validation report ofCAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 97 of 106

Copernicus Atmosphere Monitoring Service

the cams near-real-time global atmospheric composition service period september – november
2022. https://doi.org/10.24380/RRMJ-AJ0K
Sindelarova, K., Granier, C., Bouarar, I., Guenther, A., Tilmes, S., Stavrakou, T., M"uller, J.-F., Kuhn,
U., Stefani, P., & Knorr, W. (2014). Global data set of biogenic voc emissions calculated by themegan model over the last 30 years. Atmospheric Chemistry and Physics, 14(17), 9317–9341.
https://doi.org/10.5194/acp-14-9317-2014
Williams, J. E., Huijnen, V., Bouarar, I., Meziane, M., Schreurs, T., Pelletier, S., Marécal, V., Josse, B., &Flemming, J. (2022). Regional evaluation of the performance of the global cams chemical modeling system over the united states (ifs cycle 47r1). Geoscientific Model Development, 15(12),4657–4687. https://doi.org/10.5194/gmd-15-4657-2022
Williams, J., Van Velthoven, P., & Brenninkmeijer, C. (2013). Quantifying the uncertainty in simulating
global tropospheric composition due to the variability in global emission estimates of biogenic
volatile organic compounds. Atmospheric Chemistry and Physics, 13(5), 2857–2891. https://doi.
org/10.5194/acp-13-2857-2013
Zhang, L., & He, Z. (2014). An empirical algorithm estimating dry deposition velocity of fine, coarse
and giant particles. Atmospheric Chemistry and Physics, 14(7), 3729–3737. https://doi.org/10.
5194/acp-14-3729-2014

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 98 of 106

Copernicus Atmosphere Monitoring Service

Acknowledgements
Listed below are the authors contributing to the sections in this report. The people contributing to
the model description and reviewers are also provided, as well as acknowledgements to the validation
datasets.
Tropospheric reactive gases
Yasmine Bennouna, Valerie Thouret, CNRS-LAERO
Anja Schönhardt and Andreas Richter, IUP-UBNatalia Sudarchikova and Idir Bouarar, MPI-M
John Kapsomenakis, Christos Zerefos, AA
Bavo Langerock, BIRA-IASB
Tropospheric aerosol
Michael Schulz, MET Norway
Anna Benedictow, Jan Griesfeller, Augustin Mortier, MET Norway
Luka Ilic, Oriol Jorba, Dene Bowdalo, Carlos Pérez García-Pando, María Gonçalves BSC
Emilio Cuevas, AEMET
Stratospheric reactive gases and UVQuentin Errera, BIRA-IASB
Bavo Langerock, BIRA-IASB
Mikko Pitkänen, FMI
Antti Arola, FMI
Internal reviewers
Isabelle Pison, LSCE
Idir Bouarar, MPI-M
Henk Eskes, KNMI
Athanasios Tsikerdekis, KNMI
Reactive gas, aerosol and greenhouse gas modeling
Antje Inness, ECMWF
Johannes Flemming, ECMWF
Anna Agusti-Panareda, ECMWF
Samuel Remy, HYGEOS
Vincent Huijnen, KNMI
Richard Engelen, ECMWFCAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 99 of 106

Copernicus Atmosphere Monitoring Service

Acknowledgements for the validation datasets used
We wish to acknowledge the provision of NRT GAW observational data by: Institute of Atmospheric
Sciences and Climate (ISAC) of the Italian National Research Council (CNR), South African Weather
Service, National Centre for Atmospheric Science (NCAS, Cape Verde), National Air Pollution Monitoring Network (NABEL) (Federal Office for the Environment FOEN and Swiss Federal Laboratories
for Materials Testing and Research EMPA), Atmospheric Environment Division Global Environment
and Marine Department Japan Meteorological Agency, Chinese Academy of Meteorological Sciences(CAMS), Alfred Wegener Institut, Umweltbundesamt (Austria), National Meteorological Service (Argentina), Umweltbundesamt (UBA, Germany)
We are grateful to the numerous operators of the AERONET network and to the central data processing
facility at NASA Goddard Space Flight Center for providing the NRT sun photometer data, especially
Ilya Slutker and Brent Holben for sending the data.
The authors thank to all researchers, data providers and collaborators of the World Meteorological Organization’s Sand and Dust Storm Warning Advisory and Assessment System (WMO SDS-WAS) forNorthern Africa, Middle East and Europe (NAMEE) Regional Node. Also special thank to Canary
Government as well as AERONET, MODIS, MSG Eumetsat, EOSDIS World Viewer, the International
Cooperative for Aerosol Prediction (ICAP) principal investigators and scientists for establishing andmaintaining data used in the activities of the WMO Barcelona Dust Regional Center (i.e. the WMO
SDS-WAS Regional Center for Northern Africa, the Middle East and Europe).
We wish to acknowledge the provision of ozone sonde data by the World Ozone and Ultraviolet Radiation
Data Centre established at EC in Toronto (http://woudc.org), by the Data Host Facility of the Network
for the Detection of Atmospheric Composition Change established at NOAA (http://ndacc.org), by the
Norwegian Institute for Air Research, by the National Aeronautics and Space Administration (NASA)and the European Validation Data Centre (EVDC, https://evdc.esa.int).
Sonde data, FTIR, MWR, LIDAR, DOBSON and UVVIS DOAS data used in this publication wereobtained from the Network for the Detection of Atmospheric Composition Change (NDACC) and areavailable through the NDACC website www.ndacc.org.
The authors acknowledge the NOAA Earth System Research Laboratory (ESRL) Global Monitoring
Division (GMD) for the provision of ground-based ozone concentrations.
The MOPITT CO data were obtained from the NASA Langley Research Center ASDC. We acknowledge
the LATMOS IASI group for providing IASI CO data.
SCIAMACHY lv1 radiances were provided to IUP-UB by ESA through DLR/DFD.
GOME-2 lv1 radiances were provided to IUP-UB by EUMETSAT.
Sentinel-5 Precursor is a European Space Agency (ESA) mission on behalf of the European Commission (EC). The TROPOMI payload is a joint development by ESA and the Netherlands Space Office(NSO). The Sentinel-5 Precursor ground segment development has been funded by ESA and with national contributions from the Netherlands, Germany, and Belgium. This work contains modified Copernicus Sentinel-5P TROPOMI data (2018–2023).
S5P lv1 radiances and NO2 operational were provided by EU Copernicus.
The authors acknowledge Environment and Climate Change Canada for the provision of Alert ozone
data and Sara Crepinsek – NOAA for the provision of Tiksi ozone data. Surface ozone data from the
Zeppelin Mountain, Svalbard are from www.luftkvalitet.info. Surface ozone data from the Villum Research Station, Station Nord (VRS) were financially supported by “The Danish Environmental Protection
CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 100 of 106

Copernicus Atmosphere Monitoring Service

Agency” with means from the MIKA/DANCEA funds for Environmental Support to the Arctic Region.
The Villum Foundation is acknowledged for the large grant making it possible to build VRS in North
Greenland.
We thank the several institutes, their experts, and partners for providing us with UV index and spectral UV data from their ground based observation stations: Australian Radiation Protection and Nuclear
Safety Agency (ARPANSA, Australia), Department for Environment Food & Rural Affaird (DEFRA,UK), Finnish Meteorological Institute (FMI, Finland), Israel Meteorological Service (IMS, Israel), Laboratory of Atmospheric Physics of the Aristotle University of Thessaloniki (Greece), National Instituteof Water and Atmospheric Research (NIWA, New Zealand), Silpakorn University (Thailand)
We acknowledge the National Aeronautics and Space Administration (NASA), USA for providing the
OMPS limb sounder data (http://npp.gsfc.nasa.gov/omps.html), the SAGE III-ISS ozone data https://
eosweb.larc.nasa.gov/project/sageiii-iss/sageiii-iss_table and the Aura-MLS offline data (http://mls.jpl.
nasa.gov/index-eos-mls.php).
We thank the Canadian Space Agency and ACE science team for providing level 2 data retrieved from
ACE-FTS on the Canadian satellite SCISAT-1.
The European Environment Information and Observation Network (EIONET) Air Quality portal provides details relevant for the reporting of air quality information from EU Member States and other EEA
member and co-operating countries. This information is submitted according to Directives 2004/107/EC
and 2008/50/EC of the European Parliament and of the Council.
We are grateful to the IAGOS operators from the various institutes which are members of IAGOS-AISBL(http://www.iagos.org). The authors also acknowledge the strong support of the European Commission,
Airbus, and the airlines (Lufthansa, Air France, Austrian, Air Namibia, Cathay Pacific, Iberia, China
Airlines and Hawaiian Airlines so far) which have carried the MOZAIC or IAGOS equipment andundertaken maintenance since 1994. In the last 10 years of operation, MOZAIC has been funded by
INSU-CNRS (France), Météo-France, Université Paul Sabatier (Toulouse, France) and Research Center
Jülich (FZJ, Jülich, Germany). IAGOS has been additionally funded by the EU projects IAGOS-DS andIAGOS-ERI. The MOZAIC–IAGOS database (http://www.iagos-data.fr) is supported by AERIS (CNES
and INSU-CNRS). Data are also available via AERIS web site http://www.aeris-data.fr.
We acknowledge the contribution of the ICOS Atmospheric Thematic Center (Lynn Hazan, Amara Abbaris, and Leonard Rivier) for the near real time data processing of surface CO2 and CH4 concentrations.
The ICOS monitoring sites are maintained by the national networks: ICOS-Belgium (Martine De Mazière , Mahesh Kumar Sha , Nicolas Kumps), ICOS-Czech Rep. (Michal Marek, Katerina Komínková,
Gabriela Vítková), ICOS-Finland (Olli Peltola, Janne Levula, Tuomas Laurila, Juha Hatakka, Ari Leskinen, Ivan Mammarella, Petri Keronen), ICOS-France (Michel Ramonet, Marc Delmotte, Sebastien
Conil, Laurent Langrene , Morgan Lopez, Victor Kazan, Aurélie Colomb, Jean Marc Pichon, Olivier
Laurent, Camille Yver-Kwok, Zineb Mandrick, Jean-Marc Metzger), ICOS Germany (Matthias Lindauer, Dagmar Kubistin, Christian Plass-Duelmer, Dietmar Weyrauch, Jennifer Müller-Williams), ICOSItaly (Paolo Cristofanelli, Michela Maione, Francesco Apadula, Pamela Trisolino), ICOS-Netherlands(Huilin Chen, Bert Scheeren), ICOS-Norway (Cathrine Lund Myhre, Ove Hermansen, Chris Lunder),ICOS-Sweden (Jutta Holst, Michal Heliasz, Meelis Molder, Mikaell Ottosson Lofvenius, Irene Lehner,
Per Marklund, Paul Smith), ICOS-Switzerland (Martin Steinbacher, Markus Leuenberger), European
Commission, Joint Research Centre, Directorate for Energy, Transport and Climate (Peter Bergamaschi,
Giovanni Manca).
We acknowledge the MAP-IO (Marion Dufresne Atmospheric Program – Indian Ocean, www.mapio.re/)for the provision of CO2 and CH4 measurements in Indian South Ocean.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 101 of 106

Copernicus Atmosphere Monitoring Service

The TCCON site at Orleans is operated by the University of Bremen and the RAMCES team at LSCE
(Gif-sur-Yvette, France).
The TCCON site at Bialystok is operated by the University of Bremen. Funding for the two sites wasprovided by the EU-project ICOS-INWIRE and the University of Bremen. The TCCON site at Réunionis operated by BIRA-IASB, in cooperation with UReunion and is funded by BELSPO in the frameworkof the Belgian ICOS program.

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 102 of 106

Copernicus Atmosphere Monitoring Service

Abbreviations
AA
ACE
AEMET
AERONETAOD
AQMEIIBASCOE
BIRA-IASB
BSC
CAF
CAMS
CNRS
DJF
DOAS
DOD
DWD
ECMWF
EDGAR
EEA
EMEP
EPA
ESA
ESRL
EUMETSATFGE
FLEXPART
FMI
FTIR
FTS
GAW
GFAS
GHG
GMD
GOME-2
GOSAT
IAGOS
IASI
IASOA
ICAP
ICOS
IFS
IPSL
ISS
IUP-UB
JJA
KNMI
KPI

Academy of Athens
Atmospheric Chemistry ExperimentState Meteorological Agency
Aerosol Robotic Network
Aerosol Optical Depth
Air Quality Modelling Evaluation International Initiative
Belgian Assimilation System for Chemical ObsErvations
Belgian Institute for Space Aeronomy
Barcelona Supercomputing Center
Common Area Fraction
Copernicus Atmosphere Monitoring Service
French National Center for Scientific Research
December January February
Differential Optical Absorption Spectroscopy
Dust Optical Depth
German Meteorological Service
European Centre for Medium-Range Weather Forecasts
Emissions Database for Global Atmospheric Research
European Environment Agency
European Monitoring and Evaluation Programme
Environmental Protection Agency (USA)European Space Agency
Earth System Research Laboratory
The European Organization for the Exploitation of Meteorological Satellites
Fractional Gross Error
FLEXible PARTicle dispersion model
Finnish Meteorological InstituteFourier Transform Infrared Spectroscopy
Fourier Transform Spectrometer
Global Atmosphere Watch Programme
Global Fire Assimilation System
Greenhouse Gas
Global Monitoring Laboratory
Global Ozone Monitoring Experiment 2
Greenhouse Gases Observing Satellite
In-service Aircraft for a Global Observing System
Infrared Atmospheric Sounding Interferometer
International Arctic Systems for Observing the Atmosphere
International Cooperative for Aerosol Prediction
Integrated Carbon Observation System
Integrated Forecast System
Climate Modeling Center - Institut Pierre-Simon Laplace
International Space Station
Institute of Environmental Physics, University of Bremen
June July AugustRoyal Netherlands Meteorological Institute
Key Performance Indicators

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 103 of 106

Copernicus Atmosphere Monitoring Service

LAERO
LIDAR
LMD
LSCE
MACC
MET-Norway
MISR
MLS
MNB
MNMB
MODIS
MOPITT
MOZART
MPI-M
MWR
NA-ME-E
NASA
NCAR
NDACC
NILU
NMB
NOAA
NRT
OMI
OMPS
PM10
PM2.5
PMAp
PV
QA4EO
QI
RMSE
rRMSE
SAGE-III
SBUV/2SCIAMACHY
SDA
SDS-WASSHADOZ
Skynet
SLSTR
SON
SPARC
TCCON
TROPOMI
UBC
UTC
UVI
VIIRS

Laboratoire d’aérologie
Laser Imaging, Detection, And Ranging
Laboratoire de Météorologie Dynamique
Climate and Environment Sciences Laboratory
Monitoring Atmospheric Composition and Climate
Norwegian Meteorological InstituteMulti-angle Imaging SpectroRadiometer
Microwave Limb Sounder
Modified Normalized Bias
Modified Normalised Mean Bias
Moderate Resolution Imaging Spectroradiometer
Measurement of Pollution in the Troposphere
Model for OZone And Related chemical Tracers
Max Planck Institute for Meteorology
Microwave RadiometerNorthern Africa, Middle East and Europe Regional Center
National Aeronautics and Space Administration
National Center for Atmospheric Research
Network for the Detection of Atmospheric Composition Change
Norwegian Institute for Air Research
Normalized Mean Bias
National Oceanic and Atmospheric Administration
Near Real Time
Ozone Monitoring Instrument
Ozone Mapping and Profiler Suite
Particulate Matter with a diameter up to 10 micrometers (µm)
Particulate Matter with a diameter up to 2.5 micrometers (µm)
Polar Multi-sensor Aerosol properties
Potential Vorticity
Quality Assurance Framework for Earth Observation
Quality Indicator
Root Mean Squared Error
Relative Root Mean Squared Error
Stratospheric Aerosol and Gas Experiment III
Solar Backscatter Ultraviolet Radiometer
SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY
Spectral Deconvolution Algorithm
Sand and Dust Storm Warning Advisory and Assessment System
Southern Hemisphere ADditional OZonesondes
International research network dedicated to aerosol, cloud and radiation
Sea and Land Surface Temperature Radiometer
September October November
Stratosphere-troposphere Processes And their Role in Climate
Total Carbon Column Observing Network
TROPOspheric Monitoring Instrument
University of British Columbia
Coordinated Universal TimeUltraviolet index
Visible Infrared Imaging Radiometer Suite

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 104 of 106

Copernicus Atmosphere Monitoring Service

WMO
WOUDC

World Meteorological Organization
World Ozone and Ultraviolet Radiation Data Centre

CAMS2_82_2022SC1_D82.3.2.1-2023Q2

Page 105 of 106

Copernicus Atmosphere Monitoring Service

ECMWF - Shinfield Park, Reading RG2 9AX, UK
Contact: info@copernicus-atmosphere.eu
atmosphere.copernicus.eu

copernicus.eu

ecmwf.int