Introduction
The impact of reactive gases on climate, human health and the environment has
gained increasing public and scientific interest in the last decade (Bell et
al., 2006; Cape 2008; Mohnen et al., 1993; Seinfeld
and Pandis 2006; Selin et al., 2009) as air pollutants such as carbon
monoxide (CO), nitrogen oxides (NOx) and ozone (O3) are known to
have acute and chronic effects on human health, ranging from minor upper
respiratory irritation to chronic respiratory and heart disease, lung cancer,
acute respiratory infections in children and chronic bronchitis in adults
(Bell et al., 2006; Kampa and Castanas, 2006). Tropospheric ozone, even in
small concentrations, is also known to cause plant damage through reducing plant
primary productivity as well as crop yields (e.g. Ashmore, 2005). It also
contributes to global warming by direct and indirect radiative forcing
(Forster et al., 2007; Sitch et al., 2007). Pollution events can be caused by
local sources and processes but are also influenced by continental and
intercontinental transport of air masses. Global models can provide the
transport patterns of air masses and deliver the boundary conditions for
regional models, facilitating the forecast and investigation of air
pollutants.
The European Union (EU)-funded research project Monitoring Atmospheric Composition and Climate (MACC) (consisting of a series
of European projects, MACC to MACC-III), provides the preparatory work that
will form the basis of the European Union's Copernicus Atmosphere Monitoring Service (CAMS). This
service was established by the EU to provide a range of products of societal
and environmental value with the aim to help European governments respond to
climate change and air quality problems (more information about this service
can be found on CAMS website
http://www.copernicus.eu/main/atmosphere-monitoring). The MACC project
provides reanalyses, monitoring products of atmospheric key constituents
(e.g. Inness et al., 2013), as well as operational daily forecasting of
greenhouse gases, aerosols and reactive gases (Benedetti et al., 2011; Stein
et al., 2012) on a global and on European-scale level, and derived products
such as solar radiation. An important aim of the MACC system is to describe
the occurrence, magnitude and transport pathways of disruptive events, e.g.,
volcanoes (Flemming and Inness, 2013), major fires (Huijnen et al., 2012;
Kaiser et al., 2012) and dust storms (Cuevas et al., 2015). The product
catalogue can be found on the MACC website:
http://copernicus-atmosphere.eu. For the generation of atmospheric
products, state-of-the-art atmospheric modelling is combined with assimilated
satellite data (Hollingsworth et al., 2008; Inness et al., 2013, 2015; more
general information about data assimilation can be found in, e.g.,
Ballabrera-Poy et al., 2009 or Kalnay, 2003). Within the MACC project there
is a dedicated validation activity to provide up-to-date information on the
quality of the reanalysis, daily analyses and forecasts. Validation reports
are updated regularly and are available on the MACC websites.
The MACC global near-real-time (NRT) production model for reactive gases and
aerosol has operated with data assimilation from September 2009 onwards,
providing boundary conditions for the MACC regional air quality (RAQ) products, and other downstream users. The model simulations also provide input
for the stratospheric ozone analyses delivered in near-real-time by the MACC
stratospheric ozone system (Lefever et al., 2014).
In this paper we describe the investigation of the potential and challenges
of near-real-time modelling with the MACC analysis system between 2009 and
2012. We concentrate on this period because of the availability of validated
independent observations, namely surface observations from the Global
Atmosphere Watch (GAW) Programme, the European Monitoring and Evaluation
Programme (EMEP), as well as total column/tropospheric column satellite data
from the MOPITT (Measurement Of Pollution In The Troposphere), SCIAMACHY
(SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) and
GOME-2 (Global Ozone Monitoring Experiment-2) sensors. In particular, we
study the model's ability to reproduce the seasonality and absolute values of
CO and NO2 in the troposphere as well as NO2, O3 and CO at the
surface. The impact of changes in model version, data assimilation and
emission inventories on the model performance is examined and discussed. The
paper is structured in the following way: Sect. 2 contains a description of
the model and the validation data sets as well as the applied validation
metrics. Section 3 presents the validation results for CO, NO2 and
O3. Section 4 provides the discussion and Sect. 5 the conclusions of the
paper.
Data and methods
The MACC model system in the 2009–2012 period
The MACC global products for reactive gases consist of a reanalysis performed
for the years 2003–2012 (Inness et al., 2013) and the near-real-time
analysis and forecast, largely based on the same assimilation and forecasting
system, but targeting different user groups (operational air quality
forecasting and regional climate modelling, respectively). The Model for
OZone And Related chemical Tracers (MOZART) chemical transport model (CTM) is
coupled to the integrated forecast system (IFS) of the European Centre for
Medium-Range Weather Forecast (ECMWF), which together represent the
MOZART–IFS model system (Flemming et al., 2009; Stein et al., 2012). An
alternative analysis system has been set up based on the global chemistry
transport model version 5 (TM5; see also Huijnen et al., 2010). Details of
the MOZART version used in the MACC global products can be found in Kinnison
et al. (2007) and Stein et al. (2011, 2012). In our simulation, the IFS and
the MOZART model run in parallel and exchange several two- and
three-dimensional fields every model hour using the Ocean Atmosphere Sea Ice
Soil version 4 (OASIS4) coupling software (Valcke and Redler, 2006), thereby
producing three-dimensional IFS fields for O3, CO, SO2, NOx,
HCHO, sea salt aerosol, desert dust, black carbon, organic matter, and total
aerosol. The IFS provides meteorological data to MOZART. Data assimilation
and transport of the MACC species takes place in the IFS, while the whole
chemical reaction system is calculated in the MOZART model.
The MACC_osuite (operational suite) is the global near-real-time MACC
model production run for aerosol and reactive gases. Here, we have
investigated only the MACC analysis. In contrast to the reanalysis, the
MACC_osuite is a near-real-time run, which implies that it is only run
once in near-real-time and may thus contain inconsistencies in, e.g., the
assimilated data. The MACC_osuite was based on the IFS cycle CY36R1 with
IFS model resolution of approximately 100 km by 100 km at 60 levels
(T159L60) from September 2009 to July 2012. The gas-phase chemistry module
in this cycle is based on MOZART version 3.0 (Kinnison et al., 2007). The
model has been upgraded, following updates of the ECMWF meteorological model
and MACC-specific updates, i.e. in chemical data assimilation and with
respect to the chemical model itself. Thus, from July 2012 onwards, the
MACC_osuite has run with a change of the meteorological model to a new IFS
cycle (version CY37R3), with an IFS model resolution of approximately 80 km
at 60 levels (T255L60) and an upgrade of the MOZART version 3.5 (Kinnison et
al., 2007; Emmons et al., 2011; Stein et al., 2013). This includes, amongst others, updated velocity
fields for the dry deposition of O3 over ice, as described in Stein et
al. (2013). A detailed documentation of system changes can be found at
http://atmosphere.copernicus.eu/user-support/operational-info.
Emission inventories and assimilated data sets
In the MACC_osuite, anthropogenic emissions are based on emissions from
the EU project REanalysis of the TRopospheric chemical composition Over (RETRO) the
past 40 years merged with updated emissions for East Asia from the
Regional Emission inventory in ASia (REAS) inventory (Schultz et al., 2007)
–
in the following referred to as RETRO–REAS. The horizontal resolution is
0.5∘ in latitude and longitude and it contains a monthly temporal
resolution. Biogenic emissions are taken from Global Emissions InitiAtive
(GEIA), fire emissions are based on a climatology derived from Global Fire
Emissions Database version 2 (GFEDv2; van der Werf et al., 2006) until April
2010, when fire emissions change to global fire assimilation system (GFAS)
emissions (Kaiser et al., 2012). Between January and October 2011 there has
been a fire emission reading error in the model where, instead of adjusting
emissions to the appropriate month, the same set of emissions have been read
throughout this period.
List of assimilated data in the MACC_osuite.
Instrument
Satellite
Provider
Version
Type
Status (YYYYMMDD)
MLS
AURA
NASA
V02
O3 Profiles
20090901–20121231
OMI
AURA
NASA
V883
O3 Total column
20090901–20121231
SBUV-2
NOAA
NOAA
V8
O3 6 layer profiles
20090901–20121231
SCIAMACHY
Envisat
KNMI
O3 total column
20090916–20120408
IASI
MetOp-A
LATMOS/ULB
V20100815
CO Total column
20090901–20121231
MOPITT
TERRA
NCAR
V4
CO Total column
20120705–20121231
OMI
AURA
KNMI
DOMINO V2.0
NO2 Tropospheric column
20120705–20121231
OMI
AURA
NASA
v003
SO2 Tropospheric column
20120705–20121231
MODIS
AQUA/TERRA
NASA
Col. 5
Aerosol total optical depth
20090901–20121231
Description of the set-up of the MACC_osuite between
September 2009 and December 2012. Details on the assimilated data are
provided in Table 1. A description of the emissions is given in Sect. “Emission inventories and assimilated data
sets”
in the text.
Model cycle
CTM
Assimilated data
Emissions
CY36R1
MOZART v3.0
O3 (MLS, OMI, SBUV-2 SCIAMACHY), CO (IASI)
RETRO/REAS/GEIA/GFEDv2/GFAS
CY37R3
MOZART v3.5
O3 (MLS, OMI, SBUV-2), CO (IASI, MOPITT), NO2 (OMI), SO2 (OMI)
MACCity / MEGAN / GFASv1.0 daily
After the model upgrade to the new cycle version CY37R3, in July 2012, the
emission inventories changed from the merged RETRO–REAS and GEIA inventories,
used in the previous cycle, to the MACCity (MACC/CityZEN EU projects) anthropogenic and biogenic
emissions (Granier et al., 2011) and (climatological) Model of Emissions of
Gases and Aerosols from Nature version 2 (MEGAN-v2; see Guenther et al.,
2006) emission inventories. Wintertime anthropogenic CO emissions are scaled
up over Europe and North America (see Stein et al., 2014). Near-real-time
fire emissions are taken from GFASv1.0 (Kaiser et al., 2012), for both
gas-phase and aerosol.
In the MACC_osuite, the initial conditions for some of the chemical
species are provided by data assimilation of atmospheric composition
observations from satellites (see Benedetti et al., 2008; Inness et al., 2009, 2013; Massart et al.,
2014). Table 1 lists the assimilated data products. From September 2009 to
June 2012, O3 total columns from the microwave limb sounder (MLS) and
solar backscatter ultraviolet (SBUV-2) instruments are assimilated, as well
as ozone monitoring instrument (OMI) and SCIAMACHY total columns (the latter
only until March 2012, when the European Space Agency lost contact with the
ENVIronmental SATellite – ENVISAT). The CO total columns are assimilated from
the Infrared Atmospheric Sounding Interferometer (IASI) sensor and aerosol
total optical depth is assimilated from the Moderate Resolution Imaging
Spectroradiometer (MODIS) instrument. After the model cycle update in
July 2012, data assimilation also includes OMI tropospheric columns of
NO2 and SO2, as well as CO MOPITT total columns.
Tables 1 and 2 summarise the data assimilation and set-up of the
MACC_osuite.
Validation data and methodology
In this study, we have tended to use the same evaluation data sets as during
the MACC near-real-time validation exercise. This implies some
discontinuities in the evaluations, e.g. the substitution of SCIAMACHY data
with GOME-2 data after the loss of the Envisat sensor or an exclusion of MOPITT satellite data after the start of
its assimilation into the model. The continuous process of updating and
complementation of data sets in databases requires the selection and
definition of a validation data set at some point. The comparatively small
inconsistencies between our data sets are considered to have a negligible
impact on the overall evaluation results.
GAW surface O3, CO and
NO2 observations
The GAW programme of the World Meteorological Organisation (WMO) has been
established to provide reliable long-term observations of the chemical
composition and physical properties of the atmosphere, which are relevant for
understanding atmospheric chemistry and climate change (WMO, 2013). The GAW
tropospheric O3 measurements are performed in a way to be suited for the
detection of long-term regional and global changes. Furthermore, the GAW
measurement programme focusses on observations that are regionally
representative and should be free from influence of significant local
pollution sources and suited for the validation of global chemistry climate
models (WMO, 2007). Detailed information on GAW- and GAW-related O3, CO
and NO2 measurements can be found in WMO (2010, 2011, 2013) and Penkett
(2011).
List of GAW and EMEP stations used in the evaluation (GAW listed by
label, EMEP listed by region: northern Europe NE; central Europe CE; and
southern Europe, SE). The numbers by the station name provide the type of
gas: a = O3, b = CO, c = NO2. Positive latitude values
refer to the Northern Hemisphere, negative latitude values to the Southern
Hemisphere.
Station
Label/region
Programme
Lat [∘]
Long [∘]
Alt [m a.s.l.]
Station
Label/region
Programme
Lat [∘]
Long [∘]
Alt [m a.s.l.]
Ähtäri IIa
NE
EMEP
62.58
24.18
180
Masenberga
CE
EMEP
47.35
15.88
1170
Alertb
ALT
GAW
82.45
-62.52
210
Mauna Loaa
MAU
GAW
19.54
-155.58
3397
Arrival Heightsa
ARH
GAW
-77.80
166.67
184
Minamitorishimaa,b
MNM
GAW
24.29
153.98
8
Aspvretena
NE
EMEP
58.80
17.38
20
Montandona
CE
EMEP
47.30
6.83
836
Assekrema
ASS
GAW
23.27
5.63
2710
Monte Cimonea,b
MCI
GAW
44.18
10.70
2165
Aston Hilla
NE
EMEP
52.50
-3.03
370
Monte Velhoa
SE
EMEP
38.08
-8.80
43
Auchencortha
NE
EMEP
55.79
-3.24
260
Montelibrettia
CE
EMEP
42.10
12.63
48
Ayia Marinaa
SE
EMEP
35.04
33.06
532
Montfranca
CE
EMEP
45.80
2.07
810
Barcarrolaa
SE
EMEP
38.47
-6.92
393
Morvana
CE
EMEP
47.27
4.08
620
Baring Heada
BAH
GAW
-41.41
174.87
85
Narbertha
NE
EMEP
51.23
-4.70
160
Barrowa
BAR
GAW
71.32
-156.60
11
Neuglobsowa,b
NGW/NE
GAW/EMEP
53.17
13.03
62
BEO Moussalaa,b
BEO
GAW
42.18
23.59
2925
Neumayera
NEU
GAW
-70.65
-8.25
42
Birkenesa
NE
EMEP
58.38
8.25
190
Niembroa
CE
EMEP
43.44
-4.85
134
Bredkälena
NE
EMEP
63.85
15.33
404
Norra-Kvilla
NE
EMEP
57.81
15.56
261
Busha
NE
EMEP
55.86
-3.21
180
O Saviñaoa
CE
EMEP
43.23
-7.70
506
Cabauwa
NE
EMEP
51.97
4.92
60
Offagnea
CE
EMEP
49.88
5.20
430
Cabo de Creusa
CE
EMEP
42.32
3.32
23
Oulankaa
NE
EMEP
66.32
29.40
310
Cairoa
CAI
GAW
30.08
31.28
35
Pallasa
NE
EMEP
68.00
24.15
340
Campisabalosa
CE
EMEP
41.28
-3.14
1360
Payernea,b
PAY/CE
GAW/EMEP
46.81
6.94
510
Cape Grima
CAG
GAW
-40.68
144.68
94
Penausendea
CE
EMEP
41.28
-5.86
985
Cape Pointa,b
CAP
GAW
-34.35
18.48
230
Peyrusse Vieillea
CE
EMEP
43.62
0.18
200
Cape Verdea,b
CVO
GAW
16.85
-24.87
10
Pic du Midia,b
PIC/CE
GAW/EMEP
42.94
0.14
2877
Charlton Mackrella
NE
EMEP
51.06
-2.68
54
Pillersdora
CE
EMEP
48.72
15.94
315
Chaumonta
CE
EMEP
47.05
6.98
1130
Preilaa
NE
EMEP
55.35
21.07
5
Chibougamaub
CHI
GAW
49.68
-74.34
393
Prestebakkea
NE
EMEP
59.00
11.53
160
Chopoka
CE
EMEP
48.93
19.58
2008
Puy de Dômea,b
PUY/CE
GAW/EMEP
45.77
2.95
1465
Concordiaa
CON
GAW
-75.10
123.33
3233
Ragged Pointa
RAG
GAW
13.17
-59.43
45
De Zilka
NE
EMEP
52.30
4.50
4
Raoa
NE
EMEP
57.39
11.91
10
Diabla Goraa
NE
EMEP
54.15
22.07
157
Revina
CE
EMEP
49.90
4.63
390
Dobelea
DOB
GAW
56.37
23.19
42
Rigia,b,c
RIG/CE
GAW/EMEP
47.07
8.46
1030
Doñanaa
SE
EMEP
37.03
-6.33
5
Rojen Peaka
CE
EMEP
41.70
24.74
1750
Donona
CE
EMEP
48.50
7.13
775
Rucavaa
RUC/NE
GAW/EMEP
56.10
21.10
18
Dunkelsteinerwalda
CE
EMEP
48.37
15.55
320
Ryoria,b
RYO
GAW
39.03
141.82
260
East Trout Lakeb
ETL
GAW
54.35
-104.98
492
Sable Islandb
SAB
GAW
43.93
-60.02
5
Egbertb
EGB
GAW
44.23
-79.78
253
San Pablo de los Montesa
SE
EMEP
39.55
-4.35
917
Eibergena
NE
EMEP
52.08
6.57
20
Sandvea
NE
EMEP
59.20
5.20
15
Els Tormsa
CE
EMEP
41.40
0.72
470
Schauinslanda,b,c
SCH/CE
GAW/EMEP
47.92
7.92
1205
Eskdalemuira
NE
EMEP
55.31
-3.20
243
Schmückea
NE
EMEP
50.65
10.77
937
Esrangea
NE
EMEP
67.88
21.07
475
Sibtona
NE
EMEP
52.29
1.46
46
Estevan Pointa,b
ESP
GAW
49.38
-126.55
39
Śnieżkaa
NE
EMEP
50.73
15.73
1603
Eupena
NE
EMEP
51.46
6.00
295
Sonnblicka,b,c
SBL/CE
GAW/EMEP
47.05
12.96
3106
Continued.
Station
Label/region
Programme
Lat [∘]
Long [∘]
Alt [m a.s.l.]
Station
Label/region
Programme
Lat [∘]
Long [∘]
Alt [m a.s.l.]
Everest – Pyramida
EVP
GAW
27.96
86.82
5079
South Polea
SPO
GAW
-89.98
-24.80
2810
Finokaliaa
SE
EMEP
35.32
25.67
250
Spitsbergena
NE
EMEP
78.90
11.88
474
Forsthofa
CE
EMEP
48.10
15.91
581
St. Osytha
NE
EMEP
51.78
1.08
8
Fraserdaleb
FRA
GAW
49.88
-81.57
210
Stará Lesnáa
CE
EMEP
49.15
20.28
808
Gänserndorfa
CE
EMEP
48.33
16.73
161
Starinaa
CE
EMEP
49.05
22.27
345
Gerlitzena
CE
EMEP
46.69
13.92
1895
Stixneusiedla
CE
EMEP
48.05
16.68
240
Graz Plattea
CE
EMEP
47.11
15.47
651
Strath Vaich Dama
NE
EMEP
57.73
-4.77
270
Great Dun Fella
NE
EMEP
54.68
-2.45
847
Summita
SUM
GAW
72.58
-38.48
3238
Grebenzena
CE
EMEP
47.04
14.33
1648
Svratoucha
CE
EMEP
49.73
16.05
737
Grimsoea
NE
EMEP
59.73
15.47
132
Syowa Stationa
SYO
GAW
-69.00
39.58
16
Harwella
NE
EMEP
51.57
-1.32
137
Tänikona
CE
EMEP
47.48
8.90
540
Haunsberga
CE
EMEP
47.97
13.02
730
Topolnikya
CE
EMEP
47.96
17.86
113
Heidenreichsteina
CE
EMEP
48.88
15.05
570
Trinidad Heada
TRI
GAW
41.05
-124.15
120
High Mufflesa
NE
EMEP
54.33
-0.80
267
Tsukubaa
TSU
GAW
36.05
140.13
25
Hurdala
NE
EMEP
60.37
11.08
300
Tudor Hilla
TUD
GAW
32.27
-64.87
30
Illmitza
CE
EMEP
47.77
16.77
117
Tustervatna
NE
EMEP
65.83
13.92
439
Iskrbaa
ISK/CE
GAW/EMEP
45.56
14.86
520
Tutuilaa
TUT
GAW
-14.24
-170.57
42
Izaña (Tenerife)a,b
IZO
GAW
28.30
-16.50
2367
Ushuaiaa,b
USH
GAW
-54.85
-68.32
18
Jarczewa
NE
EMEP
51.82
21.98
180
Utöa
NE
EMEP
59.78
21.38
7
Jungfraujocha,b,c
JFJ/CE
GAW/EMEP
46.55
7.99
3578
Vavihilla
NE
EMEP
56.01
13.15
175
Karasjoka
NE
EMEP
69.47
25.22
333
Vezina
NE
EMEP
50.50
4.99
160
Keldsnora
NE
EMEP
54.73
10.73
10
Vilsandia
NE
EMEP
58.38
21.82
6
Kollumerwaarda,b,c
KOW/NE
GAW/EMEP
53.33
6.28
1
Vindelna
VIN/NE
GAW/EMEP
64.25
19.77
225
Koŝeticea,b,c
KOS/CE
GAW/EMEP
49.58
15.08
534
Virolahti IIa
NE
EMEP
60.53
27.69
4
Kovka
KOV/CE
GAW/EMEP
46.12
15.11
600
Vorhegga
CE
EMEP
46.68
12.97
1020
K-pusztaa
CE
EMEP
46.97
19.58
125
Vredepeela
NE
EMEP
51.54
5.85
28
Krvaveca,b
KRV/CE
GAW/EMEP
46.30
14.54
1740
Waldhofa
WAL/NE
GAW/EMEP
52.80
10.77
74
La Coulonchea
CE
EMEP
48.63
-0.45
309
Westerlanda
WES/NE
GAW/EMEP
54.93
8.32
12
La Tardièrea
CE
EMEP
46.65
-0.75
143
Weybournea
NE
EMEP
52.95
1.12
16
Lac La Bicheb
LAC
GAW
54.95
-112.45
540
Wicken Fena
NE
EMEP
52.30
-0.29
5
Ladybower Res.a
NE
EMEP
53.40
-1.75
420
Yarner Wooda
NE
EMEP
50.59
-3.71
119
Lahemaaa
NE
EMEP
59.50
25.90
32
Yonagunijimaa,b
YON
GAW
24.47
123.02
30
Laudera
LAU
GAW
-45.03
169.67
370
Zarodnjea
CE
EMEP
46.42
15.00
770
Le Casseta
CE
EMEP
45.00
6.47
750
Zarraa
SE
EMEP
39.09
-1.10
885
Lebaa
NE
EMEP
54.75
17.53
2
Zavodnjea
ZAV
GAW
46.43
15.00
770
Lerwicka
NE
EMEP
60.13
-1.18
85
Zillertaler Alpena
CE
EMEP
47.14
11.87
1970
Lille Valbya
NE
EMEP
55.69
12.13
10
Zingsta
ZIN/NE
GAW/EMEP
54.43
12.73
1
Lough Navara
NE
EMEP
54.44
-7.87
126
Zoebelbodena
CE
EMEP
47.83
14.44
899
Lullington Heatha
NE
EMEP
50.79
0.17
120
Zosenia
ZOS/NE
GAW/EMEP
57.13
25.90
188
Mace Heada
NE
EMEP
53.17
-9.50
15
Zugspitzea,b
SFH
GAW
47.42
10.98
2656
Market Harborougha
NE
EMEP
52.55
-0.77
145
Hourly O3,CO and NO2 data have been downloaded from the WMO/GAW
World Data Centre for Greenhouse Gases (WDCGG) for the period between
September 2009 and December 2012 (the download was carried out in July 2013).
Our validation includes 6 stations with surface observations for NO2,
29 stations for CO and 50 stations with surface observations for O3.
Table 3 lists the geographic coordinates and altitudes of the individual
stations. Being a long-term data network, the data in the database are
provided with a temporal delay of approximately 2 years. As the data in the
database become sparse towards the end of the validation period,
near-real-time observations, as used in the MACC-project for near-real-time
validation, presented on the MACC website, have been included to complement
the validation data sets. For the detection of long-term trends and
year-to-year variability, the data quality objectives (DQOs) for CO in GAW
measurements are set to a maximum uncertainty of ±2 ppb and to
±5 ppb for marine boundary layer sites and continental sites that are
influenced by regional pollution, and to ±1 ppb for ozone (WMO, 2012,
2013) and 0.08 ppb for NO2 (WMO, 2011).
For the validation with GAW station data, 6-hourly values (00:00, 06:00,
12:00, 18:00 UTC) of the analysis mode have been extracted from the model
and are matched with hourly observational GAW station data. Model mixing
ratios at the stations' locations have been linearly interpolated from the
model data in the horizontal. In the vertical, modelled gas mixing ratios
have been extracted at the model level, which is closest to the GAW stations'
altitude. Validation scores (see Sect. 2.3) have been calculated for each
station between the 6-hourly model analysis data and the corresponding
observational data for the entire period
(September 2009–December 2012) and as
monthly averages.
EMEP surface O3 observations
The EMEP is a scientifically
based and policy driven programme under the Convention on Long-Range Transboundary Air Pollution (CLRTAP) for international co-operation to solve
transboundary air pollution problems. Measurements of air quality in Europe
have been carried out under the EMEP programme since 1977.
A detailed description of the EMEP measurement programme can be found in
Tørseth et al. (2012). The surface hourly ozone data between
September 2009 and December 2012 have been downloaded from the EMEP data
web page (http://www.nilu.no/projects/ccc/emepdata.html). For the
validation, only stations meeting the 75 % availability threshold per day
and per month are taken into account. The precision is close to 1.5 ppb for
a 10 s measurement. More information about the ozone data quality,
calibration and maintenance procedures can be found in Aas et al. (2000).
For comparison with EMEP data, 3-hourly model values (00:00, 03:00, 06:00,
12:00, 15:00, 18:00, 21:00 UTC) of the analysis mode have been chosen. We
used this data set to test the dependency of the biases on a daytime and
night-time basis, separately. Gas mixing ratios have been extracted from the
model and are matched with hourly observational surface ozone data at 124
EMEP stations in the same way as for the GAW station data. The EMEP surface
ozone values and the interpolated surface modelled values are compared on a
monthly basis for the latitude bands of 30–40∘ N (southern Europe),
40–50∘ N (central Europe) and 50–70∘ N (northern Europe).
For the identification of differences in the MACC_osuite performance
between day and night-time, the MACC_osuite simulations and the EMEP
observations for the three latitude bands have been additionally separated
into daytime (12:00–15:00 local time, LT) and night-time (00:00–03:00 LT)
intervals.
MOPITT CO total column retrievals
The MOPITT instrument is
mounted on board the NASA EOS Terra satellite and provides CO distributions
at the global scale (Deeter et al., 2004). The MOPITT instrument has a
horizontal resolution of 22 km × 22 km and allows for global coverage
within 3 days. The data used in this study correspond to CO total columns
from version 5 (V5) of the MOPITT thermal infrared (TIR) product level 3.
This product is available via the following web server:
http://www2.acd.ucar.edu/mopitt/products. Validation of the MOPITT V5
product against in situ CO observations shows a mean bias of
0.06 × 1018 molecules cm-2 (Deeter et al., 2013).
Following the recommendation in the users' guide
(www.acd.ucar.edu/mopitt/v5_users_guide_beta.pdf), the MOPITT data are
averaged by taking into account their relative errors provided by the
observation quality index (OQI).
Also, to achieve better data quality, we use only daytime CO data since
retrieval sensitivity is greater for daytime rather than night-time
overpasses. A further description of the V5 data is presented in Deeter et
al. (2013) and Worden et al. (2014).
For the validation, the model CO profiles (X) are transformed by applying
the MOPITT averaging kernels (A) and the a priori CO profile (Xa)
according to the following equation (Rodgers, 2000) to derive the smoothed
profiles X* appropriate for comparison with MOPITT data:
X*=Xa+A(X-Xa).
Details on the method of calculation are referred to in Deeter et al. (2004)
and Rodgers (2000). The averaging kernels indicate the sensitivity of the
MOPITT measurement and retrieval system to the true CO profile, with the
remainder of the information set by the a priori profile and retrieval
constraints (Emmons, 2009; Deeter et al., 2010). The CO data X* (derived
using the above equation) have the same vertical resolution and a priori
dependence as the MOPITT retrievals and have been used to calculate averaging
kernel smoothed model CO total columns, which are compared to the MOPITT CO
total columns. For the validation, eight regions are defined (see Fig. 1):
Europe, Alaska, Siberia, North Africa, southern Africa, South Asia, East Asia
and the United States.
Regions used for regional data stratification in the troposphere
for the comparison with satellite data. The following regions are defined:
1: Europe (15∘ W–35∘ E, 35–70∘ N); 2: Alaska (150–105∘ W,
55–70∘ N); 3: Siberia (100–140∘ E, 40–65∘ N); 4: North
Africa (15∘ W–45∘ E, 0–20∘ N); 5: southern Africa (15–45∘ E,
20–0∘ S);
6: South Asia (50–95∘ E, 5–35∘ N); 7: East Asia (100–142∘ E,
20–45∘ N); 8: United States (120–65∘ W, 30–45∘ N).
The model update in July 2012 includes an integration of MOPITT CO total
columns in the model's data assimilation system. With this, the MOPITT
validation data have lost their independency for the rest of the validation
period and MOPITT validation data have thus only been used until June 2012
for validation purposes.
SCIAMACHY and GOME-2 NO2 satellite
observations
The SCIAMACHY (Bovensmann et al., 1999) onboard the
Envisat and the GOME-2 (Callies et
al., 2000) onboard the Meteorological Operational Satellite-A (MetOp-A)
comprise UV–VIS (ultraviolet–visible) and NIR
(near-infrared) sensors designed to
provide global observations of atmospheric trace gases.
In this study, the tropospheric NO2 column data set described in Hilboll
et al. (2013a) has been used. The measured radiances are analysed using
differential optical absorption spectroscopy (DOAS) (Platt and Stutz, 2008)
in the 425–450 nm wavelength window (Richter and Burrows, 2002; Richter et
al., 2011). The influence of stratospheric NO2 air masses has been
accounted for using the algorithm detailed by Hilboll et al. (2013b), using
stratospheric NO2 fields from the Bremen 3D chemistry and transport
model (B3dCTM; see also Sinnhuber et al., 2003a, b; Winkler et al., 2008).
Tropospheric air mass factors have been calculated with the radiative
transfer model SCIATRAN 2.0 (Rozanov et al., 2005). Only measurements with Fast
REtrieval Scheme for Cloud from Oxygen A band (FRESCO+) algorithm (Wang et
al., 2008) cloud fractions of less than 20 % are used.
Tropospheric NO2 vertical column density (VCD) from the MACC_osuite
is compared to tropospheric NO2 VCD from GOME-2 and SCIAMACHY. As the
European Space Agency lost contact with Envisat in April 2012, GOME-2 data are used for model validation
from 1 April 2012 onwards, while SCIAMACHY data are used for the remaining
time period (September 2009 to March 2012). Satellite observations are
gridded to the horizontal model resolution, i.e. 1.875∘ for IFS cycle
CY36R1 (September 2009–June 2012) and 1.125∘ for cycle CY37R3
(July–December 2012).
A few processing steps are applied to the MACC_osuite data to account for
differences with the satellite data such as observation time. First,
tropospheric NO2 VCDs are calculated from the model data by vertical
integration from the ground up to the height of the tropopause. The latter
is derived based on National
Centers for Environmental Prediction (NCEP) reanalysis (Kalnay et al., 1996)
climatological tropopause pressure shown in Fig. 1 of Santer et al. (2003).
Second, simulations are interpolated linearly in time to the SCIAMACHY
Equator crossing time (roughly 10:00 LT). This most likely leads to some
minor overestimation of model NO2 VCDs compared to GOME-2 data, as the
Equator crossing time for GOME-2 is about 09:30 LT. Moreover, only model
data for which corresponding satellite observations exist are considered. For
the validation, the same regions have been used as for MOPITT (Fig. 1),
except for Siberia and Alaska. In contrast to comparisons of MOPITT and model
data of CO, no averaging kernels were applied to the model NO2 data.
Satellite observations of tropospheric NO2 columns have relatively large
uncertainties, mainly linked to errors in the stratospheric correction
method, i.e. in stratospheric NO2 columns (important over clean regions
and at high latitudes in winter and spring) and to uncertainties in air mass
factors (mainly over polluted regions) (e.g. Boersma et al., 2004; Richter et
al., 2005). The uncertainty varies with geolocation and time but in first
approximation can be separated into an absolute error of 5×1014 molec cm-2 and a relative error of about 30 %. As some of the
contributions to this uncertainty can have systematic causes (e.g. a
systematic error in the assumed aerosol load can lead to seemingly random
errors in the retrieved NO2 columns due to the complexities of
atmospheric radiative transfer, i.e. relative positions of absorber and
aerosol layers), averaging over longer time periods does not reduce the
errors as much as one would expect for purely random errors. Over polluted
regions, the uncertainty from random noise in the spectra is small in
comparison to other error sources, in particular for monthly averages.
Validation metrics
A comprehensive model validation requires the selection of validation
metrics that provide complementary aspects of model performance. The
following metrics have been used in the validation:
Modifiednormalisedmeanbias(MNMB)MNMB=2N∑ifi-oifi+oiRootmeansquareerror(RMSE)RMSE=1N∑i(fi-oi)2
CorrelationcoefficientR=1N∑ifi-f¯oi-o¯σfσo,
where N is the number of observations, f the modelled analysis and
o the observed values, f¯ and o¯ are the mean values of the
analysis and observed values and σf and σo the
corresponding standard deviations.
The validation metrics above have been chosen to provide complementary
aspects of model performance. The modified normalised mean bias is a
normalisation based on the mean of the observed and forecast value (e.g.
Elguindi et al., 2010). It ranges between -2 and 2 and when multiplied by
100 %, it can be interpreted as a percentage bias.
We chose to use the modified normalised mean bias
(MNMB) in our evaluations because
verifying chemical species concentration values significantly differs from
verifying standard meteorological fields. For example, spatial or temporal
variations can be much greater and the differences between model and observed
values (“model errors”) are frequently much larger in magnitude. Most
importantly, typical concentrations can vary quite widely between different
pollutant types (e.g. O3 and CO) and regions (e.g. Europe vs.
Antarctica), and a given bias or error value can have a quite different
significance. It is useful, therefore, to consider bias and error metrics
that are normalised with respect to observed concentrations and hence can
provide a consistent scale regardless of pollutant type (see e.g. Elguindi et
al., 2010, or Savage et al., 2013). Moreover, the MNMB is robust to outliers
and converges to the normal bias for biases approaching zero, while taking
into account the representativeness issue when comparing coarse-resolved
global models versus site-specific station observations. Though GAW stations
prove regionally representative in general, the experience is that local
effects cannot always be ruled out reliably in long worldwide data sets,
because each of the different species has its individual scale of transport
and chemical processes, which in one case may exceed and in the other case
fall bellow the model resolution. Referencing to the model/observation mean
again constitutes a pragmatic solution to avoid misleading bias tendencies,
particularly in sensitive regions with sparse data coverage. Within MACC, the
MNMB is used as an important standard score. It is used in the MACC quarterly
validation reports and it appears in many recent publications, e.g. Cuevas et
al. (2015), Eskes et al. (2015), Sheel et al. (2014).
Modified normalised mean biases (MNMBs) [%] derived from the
evaluation of the MACC_osuite with GAW O3 surface observations during
the period September 2009 to December 2012 globally (top), and for Europe
(bottom). Blue colours represent large negative values and red/brown colours
represent large positive values.
The MNMB varies symmetrically with respect to under- and overestimation.
However, when calculated over longer time periods, a balance in model error,
with model over- and underestimation compensating each other, can lead to a
small MNMB for the overall period. For this reason, it is important to
additionally consider an absolute measure, such as the root mean square error (RMSE). However, it has
to be noted that the RMSE is strongly influenced by larger values and
outliers, due to squaring. The correlation coefficient R can vary between 1
(perfect correlation) and -1 (perfect negative correlation) and is an
important measure for checking the linearity between model and observations.
Time series plots of the MACC_osuite 6-hourly O3 mixing
ratios (red) and GAW surface observations (black) for South Pole, SPO
(Antarctica); Neumayer, NEU (Antarctica); Summit, SUM (Denmark); Tsukuba, TSU
(Japan); Ragged Point, RAG (Barbados); Cape Verde Observatory, CVO (Cape
Verde); Monte Cimone, MCI (Italy); Kosetice, KOS (Czech Republic); and Kovk, KOV (Slovenia) during the period September 2009 to December 2012. Unit: ppb.
Results
Validation of ozone
The evaluation of the MACC_osuite run with O3 from GAW surface
observations (described in Sect. 2.2.1) demonstrates good agreement in
absolute values and seasonality for most regions. Figure 2 shows maps with
MNMB (see Sect. 2.3) evaluations for 50 GAW
stations globally (top) and in Europe (bottom). Figure 3 presents selected
time series plots representing the results for high latitudes, low latitudes
and Europe. Large negative MNMBs over the whole period September 2009 to
December 2012 (-30 to -82 %) are observed for stations located in
Antarctica (Neumayer (NEU), South Pole (SPO), Syowa (SYO) and Concordia (CON))
whereby O3 surface mixing ratios are strongly underestimated by the
model. For stations located at high latitudes in the Northern Hemisphere
(Barrow (BAR), Alaska, and Summit (SUM), Denmark), the MACC_osuite exhibits
similar underestimated values of up to -35 % for the whole evaluation
period. The time series plots for Arctic and Antarctic stations (e.g.
SUM, NEU and SPO) in Fig. 3 show that an
underestimation seen in these regions appears to be remedied and model
performance improved with an updated dry deposition parameterisation over
ice, which has been introduced with the new model cycle in July 2012 (see
Sect. 2.1).
Large positive MNMBs (up to 50 to 70 %, Fig. 2) are observed for stations
that are located in or nearby cities and thus exposed to regional sources of
contamination (Iskrba (ISK), Slovenia; Tsukuba (TSU), Japan; Cairo (CAI), Egypt). In
tropical and subtropical regions, O3 surface mixing ratios are
systematically overestimated (by about 20 % on average) during the
evaluation period. The time series plots for tropical and subtropical
stations (e.g. for Ragged Point (RAG), Barbados, and Cape Verde Observatory,
Cape Verde (CVO), Fig. 3) reveal a slight systematic positive offset throughout
the year, however with high correlation coefficients (0.6 on average).
Modified normalised mean bias (MNMB) [%], correlation coefficient
(R), and root mean square error (RMSE) [ppb] derived from the evaluation of
the MACC_osuite with Global Atmosphere Watch (GAW) O3 surface
observations during the period September 2009 to December 2012. The
conventional station names are listed in Table 3.
Station
ARH
ASS
BAH
BAR
BEO
CAI
CAG
CAP
CVO
CON
DOB
EVP
ISK
IZO
JFJ
KOW
KOS
MNMB
-39.8
-6.3
-8.6
-35.1
-21.4
70.1
-12.7
13.7
15.2
-81.6
6.3
18.4
67.2
10.4
1.9
5.8
-5.9
R
0.6
0.7
0.5
0.3
0.4
-0.1
0.4
0.6
0.6
0.3
0.3
0.7
0.1
0.5
0.7
0.6
0.6
RMSE
10.6
6.5
8.0
13.8
20.4
29.2
8.9
7.6
8.0
17.2
14.3
12.0
34.5
10.8
7.4
12.0
16.3
Station
KOV
KRV
LAU
MAU
MNM
MCI
NGW
NEU
PAY
PIC
PUY
RAG
RIG
RUC
RYO
SCH
SBL
MNMB
21.2
9.5
-5.5
13.7
38.6
2.3
-11.4
-45.2
-28.8
5.5
12.8
38.6
-80.3
-0.1
10.5
8.5
8.1
R
0.6
0.6
0.5
0.6
0.8
0.7
0.5
0.5
0.7
0.6
0.6
0.6
0.3
0.3
0.1
0.7
0.6
RMSE
19.5
11.1
9.0
11.5
13.0
8.2
14.3
11.4
15.6
7.7
10.6
10.6
28.4
15.0
14.4
12.2
9.3
Station
SFH
SPO
SUM
SYO
TRI
TSU
TUD
TUT
USH
VIN
WAL
WES
YON
ZAV
ZIN
ZOS
MNMB
10.1
-70.6
-24.4
-31.2
3.2
55.1
45.3
40.2
-7.0
4.6
-18.0
-12.3
22.0
19.7
-17.5
22.3
R
0.6
0.4
0.5
0.7
0.3
0.0
0.5
0.8
0.5
0.4
0.6
0.6
0.7
0.6
0.4
0.2
RMSE
9.3
16.3
11.7
8.9
13.3
27.6
18.2
8.0
7.6
11.2
13.6
11.6
13.6
18.6
13.9
17.0
For GAW stations in Europe, the evaluation of the MACC_osuite for the
whole period shows MNMBs between -80 and 67 %. Large biases appear only
for two GAW stations located in Europe: Rigi (RIG), Switzerland
(-80 %),
located near mountainous terrain and ISK, Slovenia (67 %). For the
rest of the stations MNMBs lie between 22 and -30 %. RMSEs (see
Sect. 2.3) range between 7 and 35 ppb (15 ppb on average). Again, results
for ISK and RIG show the largest errors. All other stations show
RMSEs between 7 and 20 ppb. Correlation coefficients here range between 0.1
and 0.7 (with 0.5 on average). Table 4 summarises the results for all
stations individually.
Monthly MNMBs (see Fig. 4) show a seasonally varying bias, with positive
MNMBs occurring during the northern summer months (with global average
ranging between 5 and 29 % during the months June and October), and
negative MNMBs during the northern winter months (between -2 and
-33 % during the months December to March). These deviations partly
cancel each other out in MNMB for the whole evaluation period. For the RMSEs
(Fig. 5) maximum values also occur during the northern summer months with the
global average ranging between 11 and 16 ppb for June to September. The
smallest errors appear during the northern hemispheric winter months (global
average falling between 8 and 10 ppb for December and January). The
correlation does not show a distinct seasonal behaviour (see Fig. 6).
Modified normalised mean bias (MNMB) in % derived from the
evaluation of the MACC_osuite with GAW O3 surface observations during
the period September 2009 to December 2012 (black line: global average of 50
GAW stations. Multi-coloured lines: individual station results; see legend to
the right).
Root mean square error (RMSE) in ppb derived from the evaluation of
the MACC_osuite with GAW O3 surface observations during the period
September 2009 to December 2012 (black line: global average of 50 GAW
stations. Multi-coloured lines: individual station results; see legend to the
right).
Correlation coefficient (R), derived from the evaluation of the
MACC_osuite with GAW O3 surface observations during the period
September 2009 to December 2012 (black line: global average of 50 GAW
stations. Multi-coloured lines: individual station results; see legend to the
right).
The time series plots in Fig. 3 show that the seasonal cycle of O3
mixing ratios with maximum concentrations during the summer months and
minimum values occurring during winter times for European stations (e.g.
Monte Cimone (MCI), Italy; Kosetice (KOS), Czech Republic; and Kovk (KOV),
Slovenia), could be well reproduced by the model, although there is some
overestimation in summer resulting mostly from observed minimum
concentrations that are not captured correctly by the MACC_ osuite
(KOS, Czech Republic, and KOV, Slovenia).
Modified normalised mean bias (MNMB) [%], correlation coefficient
(R), and root mean square error (RMSE) [ppb] derived from the evaluation of
the MACC_osuite with Global Atmospheric Watch (GAW) CO surface
observations during the period September 2009 to December 2012. The
conventional station names are listed in Table 3.
Station
ALT
BEO
CAP
CHI
CVO
EGB
ESP
ETL
FRA
IZO
JFJ
KOS
KOW
KRV
LAC
MCI
MNM
MNMB
-6.9
-36.1
29.7
-7.3
-0.6
4.5
-1.7
-19.9
-12.0
-6.8
-15.1
-50.1
-5.9
-30.4
-24.2
-19.0
6.4
R
0.5
0.0
0.6
0.4
0.7
0.3
0.5
0.1
0.3
0.7
0.6
0.2
0.4
0.4
0.0
0.6
0.8
RMSE
23.4
90.3
20.4
31.1
14.2
60.1
25.7
53.9
35.9
15.3
25.8
131.1
70.1
49.1
58.5
32.0
22.0
Station
NGW
PAY
PIC
PUY
RIG
RYO
SAB
SBL
SCH
SFH
USH
YON
MNMB
-1.7
-7.3
-9.3
-10.4
28.2
-4.8
-8.1
-25.1
-15.8
-25.7
-9.1
-1.6
R
0.4
0.3
0.7
0.6
0.0
0.4
0.4
0.5
0.5
0.4
0.6
0.7
RMSE
61.6
99.2
18.4
30.6
143.5
44.5
31.6
36.8
39.8
45.0
12.3
62.3
The validation with EMEP surface ozone observations (described in
Sect. 2.2.2) in three different regions in Europe for the period
September 2009 to December 2012 likewise confirms the behaviour of the model
to overestimate O3 mixing ratios during the warm period and
underestimate O3 concentrations during the cold period of the year (see
Fig. 7). The mostly positive bias (May–November) is between -9 and
56 % for northern Europe and central Europe and between 8 and 48 %
for southern Europe. Negative MNMBs appear, in accordance with GAW validation
results, during the winter–spring period (December–April) ranging between
-48 and -7 % for EMEP stations in northern Europe (exception:
December 2012 with 25 %), between -1 and -39 % in central Europe
(exception: December 2012 with 31 %), whereas in southern Europe,
deviations are smaller and remain mostly positive (between -8 and 9 %)
in winter (exception: December 2012 with 37 %). The different behaviour
for December 2012 likely results from the limited availability of
observations towards the end of the validation period. The separate
evaluation of day and night-time O3 mixing ratios (Fig. 8) shows that
for northern Europe night-time biases exceed daytime biases during all
seasons. For central Europe and southern Europe night-time biases are larger
(negative MNMBs) during cold periods (December–April), whereas during warm
periods (May–November) larger biases (positive MNMBs) appear during daytime.
Validation of carbon monoxide
The validation of the MACC_osuite with surface observations of 29 GAW
stations (described in Sect. 2.2.1) shows that over the whole period
September 2009 to December 2012, CO mixing ratios could be reproduced with an
average MNMB of -10 %. The MNMBs for all stations range between -50
and +30 %. Results are listed in Table 5; a selection of time series
plots shows the results for stations in Europe, Asia and Canada in Fig. 9.
MNMBs exceeding ±30 % appear for stations that are either located in
or nearby cities and thus exposed to regional sources of contamination
(KOS, Czech Republic) or are located in or near complex mountainous
terrain (RIG, Switzerland, and BEO Moussala (BEO), Bulgaria) which is not
resolved by the topography of the global model. The RMSEs fall between 12 and
143 ppb (on average 48 ppb) for all stations during the validation period,
but for only four stations (RIG, KOS, Payerne (PAY), Switzerland
and BEO, all located in Europe) do the RMSEs exceed 70 ppb.
Correlation coefficients from the comparison with GAW station data calculated
over the whole time period range between 0 and 0.8 (on average 0.4), with
only four stations showing values smaller than 0.2 (RIG, BEO,
East Trout Lake (ETL) and Lac la Biche (LAC); the latter two located in Canada).
Modified normalised mean biases (MNMBs in %) derived from the
evaluation of the MACC_osuite with EMEP O3 surface observations in
three different parts in Europe (blue: northern Europe, orange: central
Europe, red: southern Europe) during the period September 2009 to December 2012.
Modified normalised mean biases (MNMBs in %) derived from the
evaluation of the MACC_osuite with EMEP O3 surface observations
during daytime (yellow colour), and night-time (blue colour) over northern
Europe (a), central Europe (b) and southern Europe (c) during the period
September 2009 to December 2012.
Time series plots of the MACC_osuite 6-hourly CO mixing ratios
(red) and GAW surface observations (black) for Jungfraujoch, JFJ
(Switzerland); Sonnblick, SBL (Austria); Izana Observatory, IZO (Tenerife);
Minamitorishima, MNM (Japan); Yonagunijima, YON (Japan); and Estevan Point, EVP
(Canada) during the period September 2009 to December 2012. Unit: ppb.
Modified normalised mean bias (MNMB) [%] derived from CO
satellite observations (MOPITT) and the MACC_osuite simulations of CO
total columns from October 2009 until June 2012 averaged over different
regions.
9 Oct
9 Nov
9 Dec
10 Jan
10 Feb
10 Mar
10 Apr
10 May
10 Jun
10 Jul
10 Aug
Europe
4.17
1.35
-7.02
-7.17
-7.84
-8.56
-5.20
-2.15
-2.96
0.75
-2.88
Alaska
0.31
-3.16
-6.71
-8.85
-6.39
-3.13
-4.49
-3.85
-8.69
-6.18
-3.94
Siberia
2.02
1.62
-1.44
-2.75
-1.36
-2.27
-3.58
-2.93
-5.30
4.21
-8.43
N. Africa
6.53
9.17
5.82
7.05
3.45
-2.96
-3.53
-1.75
-3.40
-1.21
-3.58
S. Africa
-12.45
-9.44
3.10
6.53
8.27
6.63
3.57
2.33
7.34
0.57
-2.75
S. Asia
9.20
13.73
6.95
6.41
6.69
1.12
3.18
1.26
-3.01
1.98
2.15
E. Asia
8.04
12.33
-5.86
-9.18
-6.64
-4.49
-5.12
-5.61
-7.72
-4.34
-2.80
US
9.73
6.71
-5.42
-7.75
-10.88
-6.26
-3.80
-2.04
1.58
2.54
2.98
10 Sep
10 Oct
10 Nov
10 Dec
11 Jan
11 Feb
11 Mar
11 Apr
11 May
11 Jun
11 Jul
Europe
-1.97
-0.92
-2.94
-7.78
-15.41
-17.22
-18.78
-17.34
-13.34
-6.62
-3.91
Alaska
-5.00
-1.89
-4.87
-7.51
-14.54
-9.90
-9.29
-12.54
-11.95
-10.04
-4.73
Siberia
-2.94
-1.93
-1.73
-3.02
-7.71
-7.78
-12.09
-21.99
-17.23
-11.59
-4.97
N. Africa
-1.22
3.33
5.98
7.03
-0.53
4.31
2.66
1.37
4.23
4.71
4.37
S. Africa
-5.13
2.84
7.39
4.37
1.41
3.39
3.80
0.99
5.71
3.45
-2.75
S. Asia
5.05
6.72
9.63
10.30
2.19
2.91
1.48
-1.76
1.68
1.62
2.90
E. Asia
6.13
6.93
2.44
3.23
-11.25
-9.18
-9.63
-8.58
-4.73
-1.62
5.00
US
0.08
-0.71
1.20
-8.06
-18.30
-16.98
-14.33
-13.52
-8.10
-4.72
-0.64
11 Aug
11 Sep
11 Oct
11 Nov
11 Dec
12 Jan
12 Feb
12 Mar
12 Apr
12 May
12 Jun
Europe
-2.57
-7.28
-10.80
-11.85
-14.79
-13.50
-14.16
-15.30
-11.49
-7.00
-3.65
Alaska
-5.69
-11.86
-18.05
-14.33
-12.29
-11.50
-11.24
-11.92
-9.42
-8.71
-4.74
Siberia
-6.05
-15.16
-16.50
-10.32
-11.59
-10.15
-8.45
-13.14
-12.18
-11.08
-4.45
N. Africa
6.15
5.35
6.27
-0.93
3.37
2.04
1.11
-5.90
-3.40
-3.59
-0.95
S. Africa
-6.70
-4.43
-0.58
3.64
4.66
4.25
2.91
0.91
3.41
1.33
-1.23
S. Asia
3.80
2.27
4.24
4.76
7.00
3.24
1.72
-1.23
-0.90
0.49
-0.61
E. Asia
3.05
1.60
-2.60
-2.48
-5.15
-5.56
-4.63
-0.85
-0.36
-2.63
0.68
US
-1.17
-2.40
-4.23
-6.14
-10.84
-13.30
-14.87
-9.19
-6.94
-2.88
-2.55
Considering the global monthly MNMBs and RMSEs, it can be seen that during
the northern hemispheric summer months, June to September, both are small
(absolute differences less than 5 %); see Figs. 10 and 11. Negative MNMBs
(up to -35 %) and larger RMSEs (up to 72 ppb) appear during the
northern hemispheric winter months, November to March, when anthropogenic
emissions are at a highest level,
especially for the US, northern latitudes and Europe. Monthly correlation
coefficients are between 0.1 and 0.5 and do not show a distinct seasonal
behaviour (see Fig. 12), the low values of 0.1 during the period January to
October 2011 result from the reading error in the fire emissions (see
Sect. “Emission inventories and assimilated data sets”). The generally only
moderate correlation coefficient is related to mismatches in the strong
short-term variability seen in both the model and the measurements.
The time series plots for stations in Europe, Asia and Canada in Fig. 9
demonstrate that the annual CO cycle could to a large degree be reproduced
correctly by the model with maximum values occurring during the winter period
and minimum values appearing during the summer season. However, the model
shows a negative offset during the winter period. Seasonal air mass transport
patterns that lead to regular annual re-occurring CO variations could be
reproduced for GAW stations in East Asia: the time series plots for
Yonagunijima (YON) and Minamitorishima (MNM) station, Japan (Fig. 9), show that
the drop of CO, associated with the air mass change from continental to
cleaner marine air masses after the onset of the monsoon season during the
early summer months, is captured by the MACC_osuite. Deterioration in all
scores is visible during December 2010 in the time series plots of several
stations (e.g. Jungfraujoch (JFJ), and Sonnblick (SBL), Fig. 9). This is likely a
result of changes in the processing of the L2 IASI data and a temporary
blacklisting of IASI data (to avoid model failure) in the assimilation.
The comparison with MOPITT satellite CO total columns between October 2009
and June 2012 (described in Sect. 2.2.3) shows a good qualitative agreement
of spatial patterns and seasonality; see Table 6. The MNMBs for eight regions
are listed in Fig. 13 and range between -22 and 14 %. The seasonality
of the satellite observations is captured well by the MACC_osuite over
Asia and Africa, with MNMBs between -6 and 9 % (North Africa), -12
and 8 % (southern Africa), -11 and 12 % (East Asia) and -3 and
14 % (South Asia). The largest negative MNMBs appear during the winter
periods, especially from December 2010 to May 2011 and from September 2011 to
April 2012, for Alaska and Siberia and for the US and Europe (MNMBs up to
-22 %), which coincides with large differences between MOPITT and IASI
satellite data (see Fig. 14). On the global scale the average difference
between the IASI and MOPITT total columns is less than 10 % (George et
al., 2009), and there is a close agreement of MOPITT and IASI for South Asia
and Africa (see Fig. 14). However, larger differences between MOPITT and IASI
data appear during the northern winter months over Alaska, Siberia, Europe
and the US, which result in lower CO concentrations in the model, due to the
assimilation of IASI CO data in the MACC_osuite. The differences between
MOPITT and IASI data can be mainly explained by the use of different a priori
assumptions in the IASI and MOPITT retrieval algorithms (George et al.,
2015). The Fast Optimal Retrievals on Layers for IASI (FORLI; Hurtmans et
al., 2012) software uses a single a priori CO profile (with an
associated variance-covariance matrix) whereas the MOPITT retrieval algorithm
uses a variable a priori, depending on time and location. George et
al. (2015) showed that differences above Europe and the US in January and
December (for a 6 year study) decrease by a factor of 2 when comparing IASI
with a modified MOPITT product using the IASI single a priori. Between
January and October 2011 there has also been a reading error in the fire
emissions that contributes to larger MNMBs during this period (see
Sect. “Emission inventories and assimilated data sets”).
Modified normalised mean bias (MNMB) in % derived from the
evaluation of the MACC_osuite with GAW CO surface observations over the
period September 2009 to December 2012 (black line: global average of 29 GAW
stations. Multi-coloured lines: individual station results; see legend to the
right).
Root mean square error (RMSE) in ppb derived from the evaluation of
the MACC_osuite with GAW CO surface observations over the period
September 2009 to December 2012 (black line: global average of 29 GAW
stations multi-coloured lines: individual station results; see legend to the
right).
Correlation coefficient (R), derived from the evaluation of the
MACC_osuite with GAW CO surface observations over the period
September 2009 to December 2012 (black line: global average of 29 GAW
stations. Multi-coloured lines: individual station results; see legend to the
right).
Monthly average of modified normalised mean biases (MNMBs in %)
derived from the comparison of the MACC_osuite with MOPITT CO total
columns for eight different regions during the period September 2009 to June 2012
(see legend on the right).
Time series plots of MOPITT CO total columns (black line) compared
to IASI CO total columns (black dashed line) and the MACC_osuite CO total
columns (red line) for eight different regions (defined in Fig. 1) during the
period September 2009 to June 2012. Top: Siberia (left), Alaska (right),
second row: United States (left), Europe (right), third row: South Asia
(left), East Asia (right) bottom: southern Africa (left), North Africa (right).
Long-term average of daily tropospheric NO2 VCD
[1015 molec cm-2] from September 2009 to March 2012 for (left)
MACC_osuite simulations and (right) SCIAMACHY satellite observations. Blue
colours represent relatively low values; red/brown colours represent
relatively high values.
Validation of tropospheric nitrogen dioxide
Figure 15 shows global maps of daily tropospheric NO2 VCD averaged from
September 2009 to March 2012. Overall, the spatial distribution and magnitude
of tropospheric NO2 observed by SCIAMACHY are well reproduced by the
model. This indicates that emission patterns and NOx photochemistry are
reasonably well represented by the model. However, the model underestimates
tropospheric NO2 VCDs over industrial areas in Europe, East China,
Russia and Southeast Africa compared to satellite data. This could imply that
anthropogenic emissions from RETRO–REAS are too low in these regions, or
that the lifetime in the model is too short. The model simulates larger
NO2 VCD maxima over central Africa, which mainly originate from wild
fires. It remains unclear if GFEDv2/GFAS fire emissions are too high here or if NO2 fire plumes closer to the ground cannot be seen by SCIAMACHY due to light scattering by biomass burning aerosols (Leitão et al., 2010). In the Northern
Hemisphere, background values of NO2 VCD over the ocean are lower in the
simulations than in the satellite data. The same is true for the South
Atlantic Ocean to the west of Africa (see Fig. 15). This might suggest a
model underestimation of NO2 export from continental sources towards the
ocean or too rapid conversion of NO2 into its reservoirs. However, as
the NO2 columns over the oceans are close to the uncertainties in the
satellite data, care needs to be taken when interpreting these differences.
Statistics derived from satellite observations (SCIAMACHY from
September 2009 until March 2012, GOME-2 from April 2012 to December 2012) and
the MACC_osuite simulations of daily tropospheric NO2 VCD [1015
molec cm-2] averaged over different regions for September 2009 to
December 2012.
Region
United States
Europe
South Asia
East Asia
Southern Africa
North Africa
Model mean NO2 VCD [1015 molec cm-2]
2.6
2.1
1.0
2.4
0.8
0.9
Satellite mean NO2 VCD [1015 molec cm-2]
3.1
3.6
1.2
6.2
1.1
0.9
Modified normalised meanbias (MNMB) [%]
-17.3
-49.0
-13.4
-70.7
-36.8
-0.4
Root mean square error(RMSE) [1015 molec cm2]
1.2
2.0
0.3
6.0
0.5
0.3
Correlation coefficient (R) [dimensionless]
0.6
0.8
0.8
0.8
0.6
0.5
Time series of daily tropospheric NO2 VCD
[1015 molec cm-2] averaged over different regions. Top: United
States (left), Europe (right); second row: South Asia (left), East Asia
(right); bottom: southern Africa (left), North Africa (right). Black lines show
satellite observations (SCIAMACHY up to March 2012, GOME-2 from April 2012 to
December 2012), red lines correspond to the MACC_osuite simulations.
Time series of daily tropospheric NO2 VCD averaged over different
regions and corresponding monthly means are presented in Figs. 16 and 17,
respectively. Time series of the MNMB and RMSE are shown in Figs. 18 and 19,
respectively. Table 7 summarises the statistical values derived over the
whole time period. High anthropogenic emissions occur over the US, Europe,
South Asia and East Asia compared to other regions on the globe (e.g.
Richter et al., 2005). In principle, the MACC_osuite catches the pattern
of satellite NO2 VCD over these regions. However, the model tends to
underestimate NO2 VCDs throughout the whole time period investigated
here. The negative bias is most pronounced over East Asia with a modelled
mean NO2 VCD for September 2009 to December 2012 of about
3.8 × 1015 molec cm-2 lower than that derived from
satellite measurements (see Table 7).
As in Fig. 16 but for monthly means of daily tropospheric NO2
VCD [1015 molec cm-2] averaged over different regions. Top: United States (left), Europe (right); second row: South Asia (left), East
Asia (right); bottom: southern Africa (left), North Africa (right).
Modified normalised mean bias [%] for monthly means of daily
tropospheric NO2 VCD averaged over different regions (see Fig. 1 for
latitudinal and longitudinal boundaries) derived from the MACC_osuite
simulations and satellite observations (SCIAMACHY up to March 2012, GOME-2
from April 2012 to December 2012). Top: United States (left), Europe (right),
second row: South Asia (left), East Asia (right); bottom: southern Africa
(left), North Africa (right). Values have been calculated separately for each
month.
As in Fig. 18 but for the root mean square error
[1015 molec cm-2].
Considering monthly values, the MACC_osuite strongly underestimates
magnitude and seasonal variation of satellite NO2 VCD over East Asia
(MNMBs between about -40 and -110 % and RMSE between 1×1015 and 14×1015 molec cm-2 throughout the whole time
period). A change in the modelled NO2 values is apparent in July 2012
when the emission inventories changed and the agreement with the satellite
data improved for South and East Asia but deteriorated for the US and Europe.
This results in a drop of MNMBs (Fig. 18) for Europe and the US with values
approaching around -70 % by the end of 2012. Nevertheless, correlations
between daily satellite and model data derived for the whole time period (see
Table 7) are high for East Asia (0.8), South Asia (0.8), Europe (0.8), and
lower, but still rather high, for the US (0.6).
The North African and southern African regions are strongly affected by biomass
burning (Schreier et al., 2014). Magnitude and seasonality of daily and monthly tropospheric
NO2 VCDs (Figs. 16 and 17, respectively) are rather well represented by
the model, apart from January to October 2011, due to difficulties in reading
fire emissions for this time period (see Sect. “Emission inventories and assimilated data sets”). The latter results in
large absolute values of the MNMB (Fig. 18) and large RMSEs (Fig. 19) between
January and October 2011 compared to the rest of the time period. As for
other regions investigated in this section, mean values of simulated daily
tropospheric NO2 VCDs over North Africa and southern Africa between
September 2009 and December 2012 tend to be lower than the corresponding
satellite mean values (see Table 7). The correlation between daily model and
satellite data over the whole time period is about 0.6 for southern Africa and
0.5 for North Africa. Whether this difference in model performance for the
African regions is due to meteorology, chemistry or emissions needs to be
investigated, but this is outside the scope of this paper.
Modified normalised mean bias (MNMB) [%], correlation coefficient
(R), and root mean square error (RMSE) [ppb] derived from the evaluation of
the MACC_osuite with Global Atmospheric Watch (GAW) NO2 surface
observations during the period September 2009 to December 2012. The
conventional station names are listed in Table 3.
Station
JFJ
KOW
KOS
RIG
SCH
SBL
MNMB
-44.7
-28.7
-38.5
68.0
-25.7
-160.6
R
0.2
0.6
0.4
0.2
0.4
0.1
RMSE
0.3
5.2
5.4
8.9
2.2
0.9
The evaluation of modelled NO2 with GAW surface data for six European
stations accordingly shows that NO2 is generally underestimated at the
surface. The MNMBs are typically in the range of -26 and -45 %,
larger MNMBs appear only for two stations in complex mountainous terrain (RIG
68 % and SBL -160 %). The RMSEs are between 0.3 and 9 ppb, and the
correlation coefficients between 0.1 and 0.6 for the period between
September 2009 and December 2012 (Table 8). The annual cycle of NO2 with
maximum concentrations during the winter period is in principle captured by
the model, shown in the time series plots in Fig. 20. As is observed for the
satellite VCDs, NO2 surface concentrations decrease in the model with
the introduction of the updated model version and emission inventories. For
stations located in complex terrain (e.g. Rigi, Fig. 20), results improve
after the model update, likely also due to the higher model resolution.
Monthly values of MNMB, R and correlation coefficient are shown in Figs. 21
to 23.
Time series plots of the MACC_osuite 6-hourly NO2 mixing
ratios (red) and GAW surface observations (black) for Kollumerwaard, KOW
(Netherlands); Kosetice, KOS (Czech Republic); Jungfraujoch, JFJ
(Switzerland);
Schauinsland, SCH (Germany); Sonnblick, SBL (Austria) and Rigi, RIG
(Switzerland) during the period September 2009 to December 2012. Unit: ppb.
Modified normalised mean bias (MNMB) in % derived from the
evaluation of the MACC_osuite with GAW NO2 surface observations over
the period September 2009 to December 2012 (black line: global average of
six GAW stations, multi-coloured lines: individual station results; see legend
to the right).
Root mean square error (RMSE) in ppb derived from the evaluation of
the MACC_osuite with GAW NO2 surface observations over the period
September 2009 to December 2012 (black line: global average of six GAW
stations,
multi-coloured lines: individual station results; see legend to the right).
Correlation coefficient (R), derived from the evaluation of the
MACC_osuite with GAW NO2 surface observations over the period
September 2009 to December 2012 (black line: global average of 6 GAW
stations, multi-coloured lines: individual station results; see legend to the
right).
Discussion
The validation of global O3 mixing ratios with GAW observations at the
surface levels showed that the MACC_osuite could generally reproduce the
observed annual cycle of ozone mixing ratios. Model validation with surface
data shows global average monthly MNMBs between -30 and 30 % (GAW) and
for Europe between -50 and 60 % (EMEP). For stations located in the
northern mid-latitudes, the evaluation reveals a seasonally dependent bias,
with an underestimation of the observed O3 mixing ratios by the
MACC_osuite during the winter season and an overestimation during the
summer months. The validation of daytime versus night-time concentrations
for northern and central Europe shows larger negative MNMBs in the winter
months during night-time than daytime (Fig. 8), so that the negative bias in
winter could be attributed to the simulation of vertical mixing at night,
also described by Ordoñez et al. (2010) and Schaap et
al. (2008), which
remains a challenge for the model. The systematic underestimation of O3
mixing ratios throughout the year for high latitude northern regions and
Antarctica has its origin in an overestimation of the O3 dry deposition
velocities over ice. With the implementation of the new model cycle and the
updated MOZART model version, which includes updated velocity fields for the
dry deposition of O3, as described in Stein et al. (2013), the negative
offset in the MACC_osuite model has been remedied for high latitude
regions from July 2012 onwards (see the time series plots for the SPO and NEU in Fig. 3). The overestimation of O3 mixing
ratios during the summer months is a well-known issue and has been described
by various model validation studies (e.g. Brunner et al., 2003; Schaap et
al., 2008; Ordoñez et al., 2010; Val Martin et al., 2014). Inadequate
ozone precursor concentrations and aerosol induced radiative effects
(photolysis) have been frequently identified as being the main factors. The
time series plots in Fig. 3, however, demonstrate that the minimum
concentrations in particular are not captured by the model during summer.
Possible explanations include a general underestimation of NO titration, which
especially applies to stations with urban surroundings and strong sub-grid-scale emissions (e.g. TSU Fig. 3), including difficulties by the
global model to resolve NO titration in urban plumes. It also seems likely
that dry deposition at wet surfaces in combination with the large surface
sink gradient due to nocturnal stability cannot be resolved with the model's
relatively coarse vertical resolution. In regions such as central and
southern Europe (Fig. 8) where daytime biases exceed night-time biases, the
overestimation of O3 might be related to an underestimation of daytime
dry deposition velocities. Val Martin et al. (2014) described a reduction of
the summertime O3 model bias for surface ozone after the implementation
of adjustments in stomatal resistances in the MOZART model's dry deposition
parameterisation.
The MACC_osuite model realistically reproduces CO total columns over most
of the evaluated regions with monthly MNMBs falling between 10 and
-20 % (Table 6). There is close agreement of modelled CO total columns
and satellite observations for Africa and South Asia throughout the
evaluation period. However, there is a negative offset compared to the
observational CO data over Europe and North America. The largest deviations
occur during the winter season when the observed CO concentrations are at a
highest level. The evaluation with GAW
surface CO data accordingly shows a wintertime negative bias of up to
-35 % in magnitude at the surface for stations in Europe and the US. A
general underestimation of CO from global models in the Northern Hemisphere
has been described by various authors (e.g. Shindell et al., 2006; Naik et
al., 2013). According to Stein et al. (2014) this underestimation likely
results from a combination of errors in the dry deposition parameterisation
and certain limitations in the current emission inventories. The latter
include too low anthropogenic CO emissions from traffic or other combustion
processes and missing anthropogenic VOCs (Volatile Organic Compounds) emissions in the
inventories together with an insufficiently established seasonality in the
emissions. An additional reason for the apparent underestimation of emissions
in MACCity may be an exaggerated downward trend in the RCP8.5 (Representative
Concentration Pathways) scenario in North America and Europe between 2000 and
2010, as this scenario was used to extrapolate the MACCity emissions from
their bench mark year, i.e. 2000. For CO, uncertainties in the evaluation
also include the retrieved amount of CO total columns between IASI and
MOPITT. These vary with region, with IASI showing lower CO concentrations in
several regions (Alaska, Siberia, Europe and the US) during the northern
winter months, which possibly contribute to the deviations observed between
the modelled data and MOPITT satellite data, as only IASI data have been
assimilated in the model. The differences can primarily be explained by the
use of different a priori assumptions in the IASI and MOPITT retrieval
algorithms (George et al., 2015). On a global scale, however, the average
difference between the IASI and MOPITT total columns is less than 10 %
(George et al., 2009).
Modelled NO2 tropospheric columns agree well with satellite observations
over the US, South Asia and North Africa. However, there is also a negative
offset for NO2 over East Asia and Europe. For the latter, these findings
are supported by the evaluation with GAW surface data. Again, the largest
deviations occur during the winter season. The quality of the emission
inventory is even more crucial for short-lived reactive species such as
NO2, where model results depend to a large extent on emission
inventories incorporated in the simulations. This is highlighted by the
deterioration of agreement between model results and satellite data for the
US in July 2012 when anthropogenic emissions were changed from RETRO–REAS to
MACCity. This change led to an increasing negative bias in NO2 over
Europe and North America and to an improvement for South and East Asia (see
Fig. 18). A deterioration in MNMBs associated with the fire emissions is
visible between January and October 2011 over regions with heavy fire
activity (Africa and East Asia), and goes back to a temporary error in the
model regarding the reading of fire emissions (see Figs. 16 to 18).
Particular challenges for an operational forecast system are regions with
rapid changes in emissions such as China, where emission inventories need to
be extrapolated to analysis times of the MACC_osuite to obtain reasonable
trends. The latter is done as emission inventories usually refer to times
prior to MACC_osuite analysis times. A large underestimation of NO2
in China, especially in winter, has been reported for other CTMs in previous
publications (He et al., 2007; Itahashi et al., 2014). The latter has been
linked to an underestimation of NOx and VOC emissions, unresolved
seasonality in the emissions and expected non-linearity of NOx
chemistry. The change in validation data sets from SCIAMACHY to GOME-2 has
been shown to have negligible impact on the validation results and
conclusions.
Conclusion
The MACC_osuite is the global near-real-time MACC model analysis run for
aerosol and reactive gases. The model has been evaluated with surface
observations and satellite data concerning its ability to simulate reactive
gases in the troposphere. Results showed that the model proved capable of a
realistic reproduction of the observed annual cycle for CO, NO2 and
O3 mixing ratios at the surface, however, with seasonally dependent
biases. For ozone, these seasonal biases likely result from difficulties in
the simulation of vertical mixing at night and deficiencies in the model's
dry deposition parameterisation. For CO, a negative offset in the model
during the winter season is attributed to limitations in the emission
inventories together with an insufficiently established seasonality in the
emissions.
The NO2 total columns derived from satellite sensors and surface
NO2 observed by European GAW stations could be reproduced reasonably
well over most of the evaluated regions, but showed a negative offset
compared to the observational data, especially over Europe and East Asia. It has become clear, that the emission inventories play a crucial
role in the quality of model results and remain a challenge for
near-real-time modelling, especially over regions with rapid changes in
emissions. Inconsistencies in the assimilated satellite data and fire
emissions showed only a temporary impact on the quality of model results. The
implementation of a model update improved the results especially at high
latitudes (surface ozone) and over South and East Asia.
The MACC NRT forecast system is constantly evolving. A promising step in
model development is the on-line
integration of modules for atmospheric chemistry in the IFS, currently being
tested for implementation in the MACC_osuite (Flemming et al., 2015). In contrast to the coupled
model configuration as used in this paper, the on-line integration in the
Composition IFS (C-IFS) provides major advantages; apart from an enhanced
computational efficiency, C-IFS promises an optimisation of the
implementation of feedback processes between gas-phase/aerosol chemical
processes and atmospheric composition and meteorology, which is expected to
improve the modelling results for reactive gases. Additionally, C-IFS will be
available in combination with different CTMs (MOZART and TM5), which will
help to explain whether deviations between model and observations go back to
deficiencies in the chemistry scheme of a model.