Post on 20-Jan-2016
TitleEMEP Unified model
Importance of observations for model evaluation
Svetlana Tsyro
MSC-W / EMEP
TFMM workshop, Lillestrøm, 19 October 2010
Meteorologisk Institutt met.no
Emission input (anthropogenic):
gaseous - SOx, NOx, NH3, NMVOC, CO,
particles - PM2.5, PM10
EMEP emission database (CEIP)
Yearly totals per country and per SNAP-1 sector (11)
Gridding is based on the reported data or by MSC-W using auxiliary data
The Unified EMEP model – Eulerian 3D;
describes the emissions, chemical transformations, transport and dry and wet removal of gaseous and particulate air pollutants (70 species, about 140 reactions)
Emissions natural: sea salt and wind blown dust - modelled
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The Unified EMEP model
Horizontal resolution: 50 x 50 km2 (25x25, 10x10 km2))
Vertical resolution: 20 layers ( up to 100 hPa)
Off-line meteorology: 3-h HIRLAM (EC MWF)
Calculation domains
Black – “old” 50x50 km2
Red – extended 50x50 km2 25x25 km2
Blue – 10x10 km2
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Model structure
Transport - Physics - Chemistry
Initialisation, Boundary & Initial conditions
Emissions – temporal variation
Meteorology (3-hourly)
Time-step 20 min
Daily/Monthly/Yearly output
Advection + turbulent mixing
Chemistry
Dry Deposition
Wet Deposition
Sea salt Windblown dust
Hourly output
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SIASO4, NO3, NH4
Primary PM
(EC, POC, dust)Mineral dust
water
Anthropogenic
emissions
Natural sources
Sea salt
SO2
NOx
NH3
PM2.5
PM10
Atmospheric particle
biogenic SOA
Anthr. SOA
bio
aero
sols
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SO4 NO3 NH4
ECPOC Min. dust+
Sea salt
PM10 PM2.5
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SR calculations
-15%
15%
All countries
15% reduction in country A
Pollution due to 15% emissions from country A
x 100/15
Pollution due to A
x Area_B Pollution in B due to A
Reference run
RUNS:RUNS: for all countries,reduction of SOx, NH3, NOx+PM, VOC
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PM2.5 in Russia due to (15%) Russian emissions TB contribution
Sources of PM2.5 in RF:
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TRENDS
Consistent observation datasets are essential!!
Why should we trust the model?
How can we know if calculations reproduce reality?
We use observations (making an assumption they
represent the reality)
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Annual mean concentrations of PM in 2001
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PM2.5 PM10
Largest in Spain
Unified model
EMEP obs
General underestimation
► EMEP: aerosol components in 2001
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AT CH CZ DE DK ES FI FR HU IE IS IT LV NL NO PL RU SE SI SK TR total
TSP 1 10 1 4 16
PM10 3 4 8 10 1 2 28
PM2.5 1 2 3 10 1 2 19
SO4 1 3 2 5 3 10 4 8 1 3 1 2 2 2 7 4 3 4 1 5 1 72
NO3 1 2 2 2 7 3 3 5 1 26
NH4 1 2 2 2 7 3 3 1 21
Na 3 7 10
Cl 7 7
Al 1 1
Ca 1 7 8
Mg 7 7
► AIRBASE (rural), PM10:
- 49 sites in 2000, over 300 in 2001 (temporal coverage? chemical composition?)
► 4 Austrian stations: PM10, PM2.5, chemical composition, particle number (during June 99 - Oct 01) - urban / rural
► 3 Spanish sites: PM10, PM2.5, chemical composition (varying sampling periods and frequency, from 1999 to Aug. 2002)
What is available of measurements
Annual mean PM10 in 2000
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EMEP: 13 sites AIRBASE: 49 sites
• Underestimation of PM10 by the model
• Smaller PM10 horizontal gradients (PPM? missing dust?)
Bias= -31%
Corr= 0.52
Bias= -44%
Corr= 0.74
elevated
• PM10 – complex pollutant. To explain the discrepancies between calculated and measured PM10 verification of the individual components is needed.
Annual mean PM2.5 and PM10 (2001)
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Spain: bias= - 67%, corr= 0.44
N=17
Bias= -46%
Corr= 0.61
N=25
Bias= -51%
Corr= 0.15
Bias= -41%
Corr= 0.59
Small modelled PM10 gradients
Better prediction of PM2.5 regional gradients
Annual mean SIA (2001, EMEP)
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Bias= -19%
Corr= 0.81
Bias= 15%
Corr= 0.89
Sites without PM10 measurements
These results alone cannot explain the model
underestimation of PM10 and PM2.5 and too small
PM10 gradients
Monthly series in 1999-2000 (all available EMEP)
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Better PM10 results for wrong reasons
N=78
N=27
N=20
N=13
What are measured PM10 and PM2.5
made of?
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PM10 (PM2.5) chemical composition in Spain
0
5
10
15
20
25
Obs Bemantes
Mod Bemantes
Obs Monagrega
Mod Monagrega
Obs Montseny
Mod Montseny
ug/m3
ND
other
dust
SS
OC+EC
NH4
NO3
SO4
PM108.01-27.12 /2001
PM101.01.99 - 31.07.00
PM2.522.03-29.08 /2002
Averaged chemical composition of PM (UNI-AERO): Spain, rural background
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????
Largest discrepancies:
underestimation of (OC+EC) and mineral dust
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NO01
0
2
4
6
8
10
12
PM10_o PM10_m PM25_o PM25_m
ug/m
3
DE44
0
5
10
15
20
PM10_o PM10_m PM25_o PM25_m
ug/m
3
IT01
0
10
20
30
40
PM10_o PM10_m PM25_o PM25_m
ug
/m3
ND
dust
SS
OM
EC
NH4
NO3
SO4
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Justified use of the Unified model for aerosol massJustified use of the Unified model for aerosol mass
The Unified model underestimates PM10 by 30-60% The Unified model underestimates PM10 by 30-60% and PM2.5 byand PM2.5 by 25-50%25-50%, , correlation ≈ 0.4-0.7 correlation ≈ 0.4-0.7
Identified reasons: Identified reasons:
underestimation of OC (EC) and mineral dustunderestimation of OC (EC) and mineral dust
unaccounted fraction in PM mass (residual water ?)unaccounted fraction in PM mass (residual water ?)
Actions: further model development Actions: further model development (implementation of SOA, wind blown dust,..)(implementation of SOA, wind blown dust,..)
Identified needs:
more PM measurements including chemical composition
co-locate and concurrent aerosol measurements
adequate information on PM emissions and their chemicaladequate information on PM emissions and their chemical compositioncomposition
Summary:
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2008
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NO01 PM10
0
3
6
9
12
Obs06 Mod06 Obs07 Mod07
ug/m3
NDDUClNaOMECNH4NO3SO4
NO01 PM2.5
0
3
6
9
12
Obs06 Mod06 Obs07 Mod07
ug/m3
NDDUClNaOMECNH4NO3SO4
DE44 PM10
0
5
10
15
20
Obs06 Mod06 Obs07 Mod07
ug/m3
NDDUClNaOMECNH4NO3SO4
DE44 PM2.5
0
5
10
15
20
Obs06 Mod06 Obs07 Mod07
ug/m3
NDDUClNaOMECNH4NO3SO4
IT01 PM10
-10
0
10
20
30
40
Obs06 Mod06 Obs07 Mod07
ug/m3
NDDUClNaOMECNH4NO3SO4
IT01 PM2.5
-10
0
10
20
30
40
Obs06 Mod06 Obs07 Mod07
ug/m3
NDDUClNaOMECNH4NO3SO4
Intensive measurement periods
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Melpitz DE44: PM10 ECMWF – red HIRLAM - blue
Model underestimation (exc. NO3, Na), more so using HIRLAM
ECMWF – better correlations, slightly higher concentrations
Most of episodes are reproduce with both met, models
0.7/0.57 0.71/0.49 0.68/0.52
0.51/0.34 0.42/0.36 0.71/0.71
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NO01
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
1 2 3 4 5 6samples
ugC/m3 IE31
0
0.1
0.2
0.3
0.4
1 2 3 4 5 6samples
ugC/m3
DK41
0
0.1
0.2
0.3
0.4
0.5
0.6
1 2 3 4samples
ugC/m3
obs
modN
modO
DE44
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
1 2 3 4samples
ugC/m3
obs
modN
modO
CZ01
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1 2 3 4samples
ugC/m3
obs
modN
modO
HU02
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
1 2 3 4samples
ugC/m3
obs
modN
modO
CH02
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1 2 3 4samples
ugC/m3
obs
modN
modO
IT04
0
0.5
1
1.5
2
2.5
1 2 3 4samples
ugC/m3
obs
modN
modO
IT01
0
0.5
1
1.5
2
1 2 3 4samples
ugC/m3
obs
modN
modO
EC (17 Sept – 17 Oct 2008) –
Using obs + model to test for primary PM emissions
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MINERAL DUST (Si, Mg, Al, Ca, K)
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Still ….MINERAL DUST
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In Summary….
It’s impossible to overestimate the importance on observation data of good quality for evaluation, improvement and development of models